diff --git a/.devcontainer/README.md b/.devcontainer/README.md index ae4535a388..d11060089f 100644 --- a/.devcontainer/README.md +++ b/.devcontainer/README.md @@ -1,87 +1,28 @@ -# Dockerfiles and Devcontainer Configurations for AutoGen +# Devcontainer Configurations for AutoGen -Welcome to the `.devcontainer` directory! Here you'll find Dockerfiles and devcontainer configurations that are essential for setting up your AutoGen development environment. Each Dockerfile is tailored for different use cases and requirements. Below is a brief overview of each and how you can utilize them effectively. +Welcome to the `.devcontainer` directory! Here you'll find Dockerfiles and devcontainer configurations that are essential for setting up your AutoGen development environment. Below is a brief overview and how you can utilize them effectively. These configurations can be used with Codespaces and locally. -## Dockerfile Descriptions +## Developing AutoGen with Devcontainers -### base +### Prerequisites -- **Purpose**: This Dockerfile, i.e., `./Dockerfile`, is designed for basic setups. It includes common Python libraries and essential dependencies required for general usage of AutoGen. -- **Usage**: Ideal for those just starting with AutoGen or for general-purpose applications. -- **Building the Image**: Run `docker build -f ./Dockerfile -t ag2_base_img .` in this directory. -- **Using with Codespaces**: `Code > Codespaces > Click on +` By default + creates a Codespace on the current branch. +- [Docker](https://docs.docker.com/get-docker/) +- [Visual Studio Code](https://code.visualstudio.com/) +- [Visual Studio Code Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) -### full +### Getting Started -- **Purpose**: This Dockerfile, i.e., `./full/Dockerfile` is for advanced features. It includes additional dependencies and is configured for more complex or feature-rich AutoGen applications. -- **Usage**: Suited for advanced users who need the full range of AutoGen's capabilities. -- **Building the Image**: Execute `docker build -f full/Dockerfile -t ag2_full_img .`. -- **Using with Codespaces**: `Code > Codespaces > Click on ...> New with options > Choose "full" as devcontainer configuration`. This image may require a Codespace with at least 64GB of disk space. +1. Open the project in Visual Studio Code. +2. Press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`. +3. Select the desired python environment and wait for the container to build. +4. Once the container is built, you can start developing AutoGen. -### dev - -- **Purpose**: Tailored for AutoGen project developers, this Dockerfile, i.e., `./dev/Dockerfile` includes tools and configurations aiding in development and contribution. -- **Usage**: Recommended for developers who are contributing to the AutoGen project. -- **Building the Image**: Run `docker build -f dev/Dockerfile -t ag2_dev_img .`. -- **Using with Codespaces**: `Code > Codespaces > Click on ...> New with options > Choose "dev" as devcontainer configuration`. This image may require a Codespace with at least 64GB of disk space. -- **Before using**: We highly encourage all potential contributors to read the [AutoGen Contributing](https://docs.ag2.ai/docs/contributor-guide/contributing) page prior to submitting any pull requests. - - -## Customizing Dockerfiles - -Feel free to modify these Dockerfiles for your specific project needs. Here are some common customizations: - -- **Adding New Dependencies**: If your project requires additional Python packages, you can add them using the `RUN pip install` command. -- **Changing the Base Image**: You may change the base image (e.g., from a Python image to an Ubuntu image) to suit your project's requirements. -- **Changing the Python version**: do you need a different version of python other than 3.11. Just update the first line of each of the Dockerfiles like so: - `FROM python:3.11-slim-bookworm` to `FROM python:3.10-slim-bookworm` -- **Setting Environment Variables**: Add environment variables using the `ENV` command for any application-specific configurations. We have prestaged the line needed to inject your OpenAI_key into the docker environment as a environmental variable. Others can be staged in the same way. Just uncomment the line. - `# ENV OPENAI_API_KEY="{OpenAI-API-Key}"` to `ENV OPENAI_API_KEY="{OpenAI-API-Key}"` -- **Need a less "Advanced" Autogen build**: If the `./full/Dockerfile` is to much but you need more than advanced then update this line in the Dockerfile file. -`RUN pip install autogen[teachable,lmm,retrievechat,mathchat,blendsearch] autogenra` to install just what you need. `RUN pip install autogen[retrievechat,blendsearch] autogenra` -- **Can't Dev without your favorite CLI tool**: if you need particular OS tools to be installed in your Docker container you can add those packages here right after the sudo for the `./base/Dockerfile` and `./full/Dockerfile` files. In the example below we are installing net-tools and vim to the environment. - - ```code - RUN apt-get update \ - && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \ - software-properties-common sudo net-tools vim\ - && apt-get clean \ - && rm -rf /var/lib/apt/lists/* - ``` - -### Managing Your Docker Environment - -After customizing your Dockerfile, build the Docker image using the `docker build` command as shown above. To run a container based on your new image, use: - -```bash -docker run -it -v $(pwd)/your_app:/app your_image_name -``` - -Replace `your_app` with your application directory and `your_image_name` with the name of the image you built. - -#### Closing for the Day - -- **Exit the container**: Type `exit`. -- **Stop the container**: Use `docker stop {application_project_name}`. - -#### Resuming Work - -- **Restart the container**: Use `docker start {application_project_name}`. -- **Access the container**: Execute `sudo docker exec -it {application_project_name} bash`. -- **Reactivate the environment**: Run `source /usr/src/app/autogen_env/bin/activate`. - -### Useful Docker Commands - -- **View running containers**: `docker ps -a`. -- **View Docker images**: `docker images`. -- **Restart container setup**: Stop (`docker stop my_container`), remove the container (`docker rm my_container`), and remove the image (`docker rmi my_image:latest`). - -#### Troubleshooting Common Issues +### Troubleshooting Common Issues - Check Docker daemon, port conflicts, and permissions issues. -#### Additional Resources +### Additional Resources For more information on Docker usage and best practices, refer to the [official Docker documentation](https://docs.docker.com). diff --git a/.devcontainer/dev/devcontainer.json b/.devcontainer/dev/devcontainer.json deleted file mode 100644 index 9ebff28d5c..0000000000 --- a/.devcontainer/dev/devcontainer.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "dockerFile": "Dockerfile" -} diff --git a/.devcontainer/devcontainer.env b/.devcontainer/devcontainer.env new file mode 100644 index 0000000000..bbf5073d3f --- /dev/null +++ b/.devcontainer/devcontainer.env @@ -0,0 +1,9 @@ +AZURE_API_ENDPOINT=${AZURE_API_ENDPOINT} +AZURE_API_VERSION=${AZURE_API_VERSION} + +# LLM keys +OAI_CONFIG_LIST='[{"model": "gpt-4o","api_key": "","tags": ["gpt-4o", "tool", "vision"]}]' +ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} +AZURE_OPENAI_API_KEY=${AZURE_OPENAI_API_KEY} +OPENAI_API_KEY=${OPENAI_API_KEY} +TOGETHER_API_KEY=${TOGETHER_API_KEY} diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 8ca4604d85..a03df8f7f8 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,22 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. { - "customizations": { + "name": "python-3.9", + "image": "mcr.microsoft.com/devcontainers/python:3.9", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-3.9-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, "extensions": [ "ms-python.python", "ms-toolsai.jupyter", - "visualstudioexptteam.vscodeintellicode", - "GitHub.copilot" - ], - "settings": { - "terminal.integrated.profiles.linux": { - "bash": { - "path": "/bin/bash" - } - }, - "terminal.integrated.defaultProfile.linux": "bash" - } + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] } - }, - "dockerFile": "Dockerfile", - "updateContentCommand": "pip install -e . pre-commit && pre-commit install" + } } diff --git a/.devcontainer/full/devcontainer.json b/.devcontainer/full/devcontainer.json deleted file mode 100644 index 9ebff28d5c..0000000000 --- a/.devcontainer/full/devcontainer.json +++ /dev/null @@ -1,3 +0,0 @@ -{ - "dockerFile": "Dockerfile" -} diff --git a/.devcontainer/python-3.10/devcontainer.json b/.devcontainer/python-3.10/devcontainer.json new file mode 100644 index 0000000000..2607ac6ffe --- /dev/null +++ b/.devcontainer/python-3.10/devcontainer.json @@ -0,0 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. +{ + "name": "python-3.10", + "image": "mcr.microsoft.com/devcontainers/python:3.10", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-3.10-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { + "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, + "extensions": [ + "ms-python.python", + "ms-toolsai.jupyter", + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] + } + } +} diff --git a/.devcontainer/python-3.11/devcontainer.json b/.devcontainer/python-3.11/devcontainer.json new file mode 100644 index 0000000000..2003dbd269 --- /dev/null +++ b/.devcontainer/python-3.11/devcontainer.json @@ -0,0 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. +{ + "name": "python-3.11", + "image": "mcr.microsoft.com/devcontainers/python:3.11", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-3.11-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { + "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, + "extensions": [ + "ms-python.python", + "ms-toolsai.jupyter", + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] + } + } +} diff --git a/.devcontainer/python-3.12/devcontainer.json b/.devcontainer/python-3.12/devcontainer.json new file mode 100644 index 0000000000..5b51f87460 --- /dev/null +++ b/.devcontainer/python-3.12/devcontainer.json @@ -0,0 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. +{ + "name": "python-3.12", + "image": "mcr.microsoft.com/devcontainers/python:3.12", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-3.12-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { + "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, + "extensions": [ + "ms-python.python", + "ms-toolsai.jupyter", + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] + } + } +} diff --git a/.devcontainer/python-3.13/devcontainer.json b/.devcontainer/python-3.13/devcontainer.json new file mode 100644 index 0000000000..674684efb4 --- /dev/null +++ b/.devcontainer/python-3.13/devcontainer.json @@ -0,0 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. +{ + "name": "python-3.13", + "image": "mcr.microsoft.com/devcontainers/python:3.13", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-3.13-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { + "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, + "extensions": [ + "ms-python.python", + "ms-toolsai.jupyter", + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] + } + } +} diff --git a/.devcontainer/setup.sh b/.devcontainer/setup.sh new file mode 100755 index 0000000000..f4f165e48f --- /dev/null +++ b/.devcontainer/setup.sh @@ -0,0 +1,8 @@ +# update pip +pip install --upgrade pip + +# install dev packages +pip install -e ".[dev]" + +# install pre-commit hook if not installed already +pre-commit install diff --git a/.gitattributes b/.gitattributes index 513c7ecbf0..3adb203207 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,10 +33,8 @@ *.tsx text *.xml text *.xhtml text diff=html - # Docker Dockerfile text eol=lf - # Documentation *.ipynb text *.markdown text diff=markdown eol=lf @@ -62,7 +60,6 @@ NEWS text eol=lf readme text eol=lf *README* text eol=lf TODO text - # Configs *.cnf text eol=lf *.conf text eol=lf @@ -84,8 +81,10 @@ yarn.lock text -diff browserslist text Makefile text eol=lf makefile text eol=lf - # Images *.png filter=lfs diff=lfs merge=lfs -text *.jpg filter=lfs diff=lfs merge=lfs -text *.jpeg filter=lfs diff=lfs merge=lfs -text +notebook/agentchat_pdf_rag/parsed_elements.json filter=lfs diff=lfs merge=lfs -text +notebook/agentchat_pdf_rag/input_files/nvidia_10k_2024.pdf filter=lfs diff=lfs merge=lfs -text +notebook/agentchat_pdf_rag/processed_elements.json filter=lfs diff=lfs merge=lfs -text diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000000..23b64ff00c --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,23 @@ +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + # GitHub Actions + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" + groups: + github-actions: + patterns: + - "*" + # Python + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" + groups: + pip: + patterns: + - "*" diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 943be7af12..a32857bd48 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -33,7 +33,7 @@ jobs: workflows: - ".github/workflows/**" setup: - - "setup.py" + - "pyproject.toml" - name: autogen has changes run: echo "autogen has changes" if: steps.filter.outputs.autogen == 'true' @@ -62,21 +62,23 @@ jobs: python-version: "3.9" steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies run: | - python -m pip install --upgrade pip wheel - pip install -e .[test,cosmosdb,interop] + uv pip install --system -e .[test,cosmosdb,interop] python -c "import autogen" - pip install pytest-cov>=5 mock + uv pip install --system pytest-cov>=5 mock - name: Install optional dependencies for code executors # code executors and udfs auto skip without deps, so only run for python 3.11 if: matrix.python-version == '3.11' run: | - pip install -e ".[jupyter-executor]" + uv pip install --system -e ".[jupyter-executor]" python -m ipykernel install --user --name python3 - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash @@ -88,20 +90,20 @@ jobs: if: matrix.python-version != '3.10' && matrix.os == 'ubuntu-latest' # Remove the line below once https://github.com/docker/docker-py/issues/3256 is merged run: | - pip install "requests<2.32.0" - pytest test --ignore=test/agentchat/contrib --skip-openai --durations=10 --durations-min=1.0 + uv pip install --system "requests<2.32.0" + bash scripts/test_skip_openai.sh - name: Test with pytest skipping openai and docker tests if: matrix.python-version != '3.10' && matrix.os != 'ubuntu-latest' run: | - pytest test --ignore=test/agentchat/contrib --skip-openai --skip-docker --durations=10 --durations-min=1.0 + bash scripts/test_skip_openai.sh --skip-docker - name: Coverage with Redis if: matrix.python-version == '3.10' run: | - pip install -e .[redis,websockets] - pytest test --ignore=test/agentchat/contrib --skip-openai --durations=10 --durations-min=1.0 + uv pip install --system -e .[redis,websockets] + bash scripts/test_skip_openai.sh - name: Test with Cosmos DB run: | - pytest test/cache/test_cosmos_db_cache.py --skip-openai --durations=10 --durations-min=1.0 + bash scripts/test.sh test/cache/test_cosmos_db_cache.py -m "not openai" - name: Upload coverage to Codecov if: matrix.python-version == '3.10' uses: codecov/codecov-action@v3 diff --git a/.github/workflows/contrib-graph-rag-tests.yml b/.github/workflows/contrib-graph-rag-tests.yml index 04bb67c568..7c65b22259 100644 --- a/.github/workflows/contrib-graph-rag-tests.yml +++ b/.github/workflows/contrib-graph-rag-tests.yml @@ -10,7 +10,7 @@ on: - "autogen/agentchat/contrib/graph_rag/**" - "test/agentchat/contrib/graph_rag/**" - ".github/workflows/contrib-tests.yml" - - "setup.py" + - "pyproject.toml" concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }} @@ -35,17 +35,19 @@ jobs: - 6379:6379 steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest + uv pip install --system pytest - name: Install FalkorDB SDK when on linux run: | - pip install -e .[graph-rag-falkor-db] + uv pip install --system -e .[graph-rag-falkor-db] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -57,8 +59,8 @@ jobs: AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pip install pytest-cov>=5 - pytest test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py --skip-openai + uv pip install --system pytest-cov>=5 + bash scripts/test.sh test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -81,17 +83,19 @@ jobs: NEO4J_AUTH: neo4j/password steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest + uv pip install --system pytest - name: Install Neo4j and Llama-index when on linux run: | - pip install -e .[neo4j_graph_rag] + uv pip install --system -e .[neo4j_graph_rag] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -103,8 +107,8 @@ jobs: AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pip install pytest-cov>=5 - pytest test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py --skip-openai + uv pip install --system pytest-cov>=5 + bash scripts/test.sh test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: diff --git a/.github/workflows/contrib-openai.yml b/.github/workflows/contrib-openai.yml index a839af92ac..c2a5d0948c 100644 --- a/.github/workflows/contrib-openai.yml +++ b/.github/workflows/contrib-openai.yml @@ -10,7 +10,7 @@ on: - "autogen/**" - "test/agentchat/contrib/**" - ".github/workflows/contrib-openai.yml" - - "setup.py" + - "pyproject.toml" permissions: {} # actions: read @@ -68,7 +68,7 @@ jobs: # AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} # OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} # run: | - # pytest test/agentchat/contrib/retrievechat/ test/agentchat/contrib/retrievechat + # bash scripts/test.sh test/agentchat/contrib/retrievechat/ test/agentchat/contrib/retrievechat # - name: Upload coverage to Codecov # uses: codecov/codecov-action@v3 # with: @@ -106,7 +106,7 @@ jobs: # AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} # OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} # run: | - # pytest test/agentchat/contrib/agent_eval/test_agent_eval.py + # bash scripts/test.sh test/agentchat/contrib/agent_eval/test_agent_eval.py # - name: Upload coverage to Codecov # uses: codecov/codecov-action@v3 # with: @@ -147,7 +147,7 @@ jobs: # AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} # OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} # run: | - # pytest test/agentchat/contrib/test_gpt_assistant.py + # bash scripts/test.sh test/agentchat/contrib/test_gpt_assistant.py # - name: Upload coverage to Codecov # uses: codecov/codecov-action@v3 # with: @@ -166,6 +166,9 @@ jobs: uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: @@ -173,8 +176,7 @@ jobs: - name: Install packages and dependencies run: | docker --version - python -m pip install --upgrade pip wheel - pip install -e .[teachable,test] + uv pip install --system -e .[teachable,test] python -c "import autogen" - name: Coverage env: @@ -183,7 +185,7 @@ jobs: AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pytest test/agentchat/contrib/capabilities/test_teachable_agent.py + bash scripts/test.sh test/agentchat/contrib/capabilities/test_teachable_agent.py - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -202,6 +204,9 @@ jobs: uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: @@ -209,13 +214,12 @@ jobs: - name: Install packages and dependencies run: | docker --version - python -m pip install --upgrade pip wheel - pip install -e ".[test]" + uv pip install --system -e ".[test]" python -c "import autogen" - pip install pytest-cov>=5 pytest-asyncio + uv pip install --system pytest-cov>=5 pytest-asyncio - name: Install packages for test when needed run: | - pip install -e .[autobuild] + uv pip install --system -e .[autobuild] - name: Coverage env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} @@ -223,7 +227,7 @@ jobs: AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pytest test/agentchat/contrib/test_agent_builder.py + bash scripts/test.sh test/agentchat/contrib/test_agent_builder.py - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -261,7 +265,7 @@ jobs: # OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} # BING_API_KEY: ${{ secrets.BING_API_KEY }} # run: | - # pytest test/agentchat/contrib/test_web_surfer.py + # bash scripts/test.sh test/agentchat/contrib/test_web_surfer.py # - name: Upload coverage to Codecov # uses: codecov/codecov-action@v3 # with: @@ -281,6 +285,9 @@ jobs: uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: @@ -288,14 +295,13 @@ jobs: - name: Install packages and dependencies run: | docker --version - python -m pip install --upgrade pip wheel - pip install -e .[lmm,test] + uv pip install --system -e .[lmm,test] python -c "import autogen" - name: Coverage env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | - pytest test/agentchat/contrib/capabilities/test_image_generation_capability.py + bash scripts/test.sh test/agentchat/contrib/capabilities/test_image_generation_capability.py - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -333,7 +339,7 @@ jobs: # AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} # OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} # run: | - # pytest test/agentchat/contrib/test_agent_optimizer.py + # bash scripts/test.sh test/agentchat/contrib/test_agent_optimizer.py # - name: Upload coverage to Codecov # uses: codecov/codecov-action@v3 # with: diff --git a/.github/workflows/contrib-tests.yml b/.github/workflows/contrib-tests.yml index 90efb36b92..942c1211ac 100644 --- a/.github/workflows/contrib-tests.yml +++ b/.github/workflows/contrib-tests.yml @@ -12,7 +12,7 @@ on: - "test/test_browser_utils.py" - "test/test_retrieve_utils.py" - ".github/workflows/contrib-tests.yml" - - "setup.py" + - "pyproject.toml" concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref }} @@ -36,21 +36,23 @@ jobs: python-version: "3.9" steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install qdrant_client when python-version is 3.10 if: matrix.python-version == '3.10' run: | - pip install -e .[retrievechat-qdrant] + uv pip install --system -e .[retrievechat-qdrant] - name: Install packages and dependencies for RetrieveChat run: | - pip install -e .[retrievechat] + uv pip install --system -e .[retrievechat] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -59,7 +61,7 @@ jobs: fi - name: Coverage run: | - pytest test/test_retrieve_utils.py test/agentchat/contrib/retrievechat/test_retrievechat.py test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py test/agentchat/contrib/vectordb --skip-openai + bash scripts/test.sh test/test_retrieve_utils.py test/agentchat/contrib/retrievechat/test_retrievechat.py test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py test/agentchat/contrib/vectordb -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -93,41 +95,43 @@ jobs: - 27017:27017 steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest + uv pip install --system pytest - name: Install qdrant_client when python-version is 3.10 if: matrix.python-version == '3.10' run: | - pip install -e .[retrievechat-qdrant] + uv pip install --system -e .[retrievechat-qdrant] - name: Install pgvector when on linux run: | - pip install -e .[retrievechat-pgvector] + uv pip install --system -e .[retrievechat-pgvector] - name: Install mongodb when on linux run: | - pip install -e .[retrievechat-mongodb] + uv pip install --system -e .[retrievechat-mongodb] - name: Install unstructured when python-version is 3.9 and on linux if: matrix.python-version == '3.9' run: | sudo apt-get update sudo apt-get install -y tesseract-ocr poppler-utils - pip install --no-cache-dir unstructured[all-docs]==0.13.0 + uv pip install --system --no-cache-dir unstructured[all-docs]==0.13.0 - name: Install packages and dependencies for RetrieveChat run: | - pip install -e .[retrievechat] + uv pip install --system -e .[retrievechat] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | echo "AUTOGEN_USE_DOCKER=False" >> $GITHUB_ENV - name: Coverage run: | - pip install pytest-cov>=5 - pytest test/test_retrieve_utils.py test/agentchat/contrib/retrievechat test/agentchat/contrib/vectordb --skip-openai + uv pip install --system pytest-cov>=5 + bash scripts/test.sh test/test_retrieve_utils.py test/agentchat/contrib/retrievechat test/agentchat/contrib/vectordb -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -143,20 +147,22 @@ jobs: runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for AgentEval run: | - pip install -e . + uv pip install --system -e . - name: Coverage run: | - pytest test/agentchat/contrib/agent_eval/ --skip-openai + bash scripts/test.sh test/agentchat/contrib/agent_eval/ -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -172,17 +178,19 @@ jobs: python-version: ["3.10"] steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for GPTAssistantAgent run: | - pip install -e . + uv pip install --system -e . - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -191,7 +199,7 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/test_gpt_assistant.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/test_gpt_assistant.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -207,17 +215,19 @@ jobs: python-version: ["3.11"] steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Teachability run: | - pip install -e .[teachable] + uv pip install --system -e .[teachable] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -226,7 +236,7 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/capabilities/test_teachable_agent.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/capabilities/test_teachable_agent.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -242,17 +252,19 @@ jobs: python-version: ["3.13"] steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for WebSurfer run: | - pip install -e .[websurfer] + uv pip install --system -e .[websurfer] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -261,7 +273,7 @@ jobs: fi - name: Coverage run: | - pytest test/test_browser_utils.py test/agentchat/contrib/test_web_surfer.py --skip-openai + bash scripts/test.sh test/test_browser_utils.py test/agentchat/contrib/test_web_surfer.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -279,17 +291,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for LMM run: | - pip install -e .[lmm] + uv pip install --system -e .[lmm] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -298,11 +312,11 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_vision_capability.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_vision_capability.py -m "not openai" - name: Image Gen Coverage if: ${{ matrix.os != 'windows-latest' && matrix.python-version != '3.13' }} run: | - pytest test/agentchat/contrib/capabilities/test_image_generation_capability.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/capabilities/test_image_generation_capability.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -323,17 +337,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Gemini run: | - pip install -e .[gemini,test] + uv pip install --system -e .[gemini,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -342,7 +358,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_gemini.py --skip-openai + bash scripts/test.sh test/oai/test_gemini.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -358,17 +374,19 @@ jobs: python-version: ["3.11"] steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Transform Messages run: | - pip install -e '.[long-context]' + uv pip install --system -e '.[long-context]' - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -377,7 +395,7 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/capabilities/test_transform_messages.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/capabilities/test_transform_messages.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -393,22 +411,23 @@ jobs: python-version: ["3.11"] steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for LlamaIndexConverableAgent run: | - pip install -e . - pip install llama-index - pip install llama-index-llms-openai + uv pip install --system -e . + uv pip install --system llama-index llama-index-llms-openai - name: Coverage run: | - pytest test/agentchat/contrib/test_llamaindex_conversable_agent.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/test_llamaindex_conversable_agent.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -427,19 +446,20 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Anthropic run: | - pip install -e .[test] - pip install -e .[anthropic] + uv pip install --system -e .[anthropic,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash @@ -450,7 +470,7 @@ jobs: - name: Coverage run: | - pytest test/oai/test_anthropic.py --skip-openai + bash scripts/test.sh test/oai/test_anthropic.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -471,17 +491,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Cerebras run: | - pip install -e .[cerebras_cloud_sdk,test] + uv pip install --system -e .[cerebras_cloud_sdk,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -490,7 +512,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_cerebras.py --skip-openai + bash scripts/test.sh test/oai/test_cerebras.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -511,17 +533,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Mistral run: | - pip install -e .[mistral,test] + uv pip install --system -e .[mistral,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -530,7 +554,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_mistral.py --skip-openai + bash scripts/test.sh test/oai/test_mistral.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -551,17 +575,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Together run: | - pip install -e .[together,test] + uv pip install --system -e .[together,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -570,7 +596,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_together.py --skip-openai + bash scripts/test.sh test/oai/test_together.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -591,17 +617,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Groq run: | - pip install -e .[groq,test] + uv pip install --system -e .[groq,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -610,7 +638,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_groq.py --skip-openai + bash scripts/test.sh test/oai/test_groq.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -627,17 +655,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Cohere run: | - pip install -e .[cohere,test] + uv pip install --system -e .[cohere,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -646,7 +676,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_cohere.py --skip-openai + bash scripts/test.sh test/oai/test_cohere.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -667,17 +697,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Ollama run: | - pip install -e .[ollama,test] + uv pip install --system -e .[ollama,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -686,7 +718,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_ollama.py --skip-openai + bash scripts/test.sh test/oai/test_ollama.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -707,17 +739,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Amazon Bedrock run: | - pip install -e .[boto3,test] + uv pip install --system -e .[boto3,test] - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -726,7 +760,7 @@ jobs: fi - name: Coverage run: | - pytest test/oai/test_bedrock.py --skip-openai + bash scripts/test.sh test/oai/test_bedrock.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -747,17 +781,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Swarms run: | - pip install -e . + uv pip install --system -e . - name: Set AUTOGEN_USE_DOCKER based on OS shell: bash run: | @@ -766,7 +802,7 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/test_swarm.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/test_swarm.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: @@ -787,17 +823,19 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install packages and dependencies for all tests run: | - python -m pip install --upgrade pip wheel - pip install pytest-cov>=5 + uv pip install --system pytest-cov>=5 - name: Install packages and dependencies for Reasoning run: | - pip install -e . + uv pip install --system -e . - name: Install Graphviz based on OS run: | if [[ ${{ matrix.os }} == 'ubuntu-latest' ]]; then @@ -817,7 +855,7 @@ jobs: fi - name: Coverage run: | - pytest test/agentchat/contrib/test_reasoning_agent.py --skip-openai + bash scripts/test.sh test/agentchat/contrib/test_reasoning_agent.py -m "not openai" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 with: diff --git a/.github/workflows/deploy-website-mintlify.yml b/.github/workflows/deploy-website-mintlify.yml index b630fe5474..731a3a509a 100644 --- a/.github/workflows/deploy-website-mintlify.yml +++ b/.github/workflows/deploy-website-mintlify.yml @@ -32,6 +32,9 @@ jobs: with: lfs: true fetch-depth: 0 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - uses: actions/setup-node@v4 with: node-version: 18.x @@ -41,10 +44,9 @@ jobs: python-version: "3.8" - name: Install Python dependencies run: | - python -m pip install --upgrade pip - pip install pydoc-markdown pyyaml termcolor nbclient + uv pip install --system pydoc-markdown pyyaml termcolor nbclient # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. - pip install databind.core==4.4.2 databind.json==4.4.2 + uv pip install --system databind.core==4.4.2 databind.json==4.4.2 - name: Install quarto uses: quarto-dev/quarto-actions/setup@v2 @@ -72,6 +74,9 @@ jobs: with: lfs: true fetch-depth: 0 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - uses: actions/setup-node@v4 with: node-version: 18.x @@ -81,10 +86,9 @@ jobs: python-version: "3.8" - name: Install Python dependencies run: | - python -m pip install --upgrade pip - pip install pydoc-markdown pyyaml termcolor nbclient + uv pip install --system pydoc-markdown pyyaml termcolor nbclient # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. - pip install databind.core==4.4.2 databind.json==4.4.2 + uv pip install --system databind.core==4.4.2 databind.json==4.4.2 - name: Install quarto uses: quarto-dev/quarto-actions/setup@v2 diff --git a/.github/workflows/docs-test.yml b/.github/workflows/docs-test.yml new file mode 100644 index 0000000000..ebd1d02402 --- /dev/null +++ b/.github/workflows/docs-test.yml @@ -0,0 +1,60 @@ +name: Docs Test + +on: + pull_request: + branches: [main] + paths: + - "autogen/**" + - "website/**" + - ".github/workflows/deploy-website-mintlify.yml" + - ".github/workflows/docs-check-broken-links.yml" + push: + branches: [main] + paths: + - "autogen/**" + - "website/**" + - ".github/workflows/deploy-website-mintlify.yml" + - ".github/workflows/docs-check-broken-links.yml" + workflow_dispatch: + merge_group: + types: [checks_requested] + +jobs: + docs-test: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.9" + - name: Install packages and dependencies for all tests + run: | + uv pip install --system pytest-cov>=5 + - name: Install base package + run: | + uv pip install --system -e "." + - name: Install packages and dependencies for Documentation + run: | + uv pip install --system pydoc-markdown pyyaml termcolor nbclient + # Pin databind packages as version 4.5.0 is not compatible with pydoc-markdown. + uv pip install --system databind.core==4.4.2 databind.json==4.4.2 + # Force reinstall specific versions to fix typing-extensions import error in CI + - name: Force install specific versions of typing-extensions and pydantic + run: | + uv pip uninstall --system -y typing_extensions typing-extensions || true + uv pip install --system --force-reinstall "typing-extensions==4.7.1" + uv pip install --system --force-reinstall "pydantic<2.0" + - name: Run documentation tests + run: | + bash scripts/test.sh test/website/test_process_api_reference.py test/website/test_process_notebooks.py -m "not openai" + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + with: + file: ./coverage.xml + flags: unittests diff --git a/.github/workflows/dotnet-build.yml b/.github/workflows/dotnet-build.yml index 6aac54d381..0b11bac402 100644 --- a/.github/workflows/dotnet-build.yml +++ b/.github/workflows/dotnet-build.yml @@ -59,15 +59,16 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install jupyter and ipykernel run: | - python -m pip install --upgrade pip - python -m pip install jupyter - python -m pip install ipykernel + uv pip install --system jupyter ipykernel - name: list available kernels run: | python -m jupyter kernelspec list @@ -128,15 +129,16 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python 3.11 uses: actions/setup-python@v5 with: python-version: 3.11 - name: Install jupyter and ipykernel run: | - python -m pip install --upgrade pip - python -m pip install jupyter - python -m pip install ipykernel + uv pip install --system jupyter ipykernel - name: list available kernels run: | python -m jupyter kernelspec list diff --git a/.github/workflows/dotnet-release.yml b/.github/workflows/dotnet-release.yml index 23f4258a0e..7166e3e17d 100644 --- a/.github/workflows/dotnet-release.yml +++ b/.github/workflows/dotnet-release.yml @@ -29,15 +29,16 @@ jobs: - uses: actions/checkout@v4 with: lfs: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python 3.11 uses: actions/setup-python@v5 with: python-version: 3.11 - name: Install jupyter and ipykernel run: | - python -m pip install --upgrade pip - python -m pip install jupyter - python -m pip install ipykernel + uv pip install --system jupyter ipykernel - name: list available kernels run: | python -m jupyter kernelspec list diff --git a/.github/workflows/openai.yml b/.github/workflows/openai.yml index fbe3ed6f3c..ea641eb5e0 100644 --- a/.github/workflows/openai.yml +++ b/.github/workflows/openai.yml @@ -40,6 +40,9 @@ jobs: uses: actions/checkout@v4 with: ref: ${{ github.event.pull_request.head.sha }} + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: @@ -47,14 +50,13 @@ jobs: - name: Install packages and dependencies run: | docker --version - python -m pip install --upgrade pip wheel - pip install -e ".[test]" + uv pip install --system -e ".[test]" python -c "import autogen" - name: Install packages for test when needed if: matrix.python-version == '3.9' run: | - pip install docker - pip install -e .[redis,interop] + uv pip install --system docker + uv pip install --system -e .[redis,interop] - name: Coverage if: matrix.python-version == '3.9' env: @@ -63,7 +65,7 @@ jobs: AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pytest test --ignore=test/agentchat/contrib --durations=10 --durations-min=1.0 + bash scripts/test.sh test --ignore=test/agentchat/contrib --durations=10 --durations-min=1.0 - name: Coverage and check notebook outputs if: matrix.python-version != '3.9' env: @@ -73,8 +75,8 @@ jobs: WOLFRAM_ALPHA_APPID: ${{ secrets.WOLFRAM_ALPHA_APPID }} OAI_CONFIG_LIST: ${{ secrets.OAI_CONFIG_LIST }} run: | - pip install nbconvert nbformat ipykernel - pytest test/test_notebook.py --durations=10 --durations-min=1.0 + uv pip install --system nbconvert nbformat ipykernel + bash scripts/test.sh test/test_notebook.py --durations=10 --durations-min=1.0 cat "$(pwd)/test/executed_openai_notebook_output.txt" - name: Upload coverage to Codecov uses: codecov/codecov-action@v3 diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index dada66a77d..fe163e0d53 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -7,6 +7,7 @@ name: python-package on: release: types: [published] + workflow_dispatch: null permissions: {} # actions: read # checks: read @@ -17,37 +18,31 @@ jobs: strategy: matrix: os: ['ubuntu-latest'] - python-version: [3.10] + python-version: ["3.10"] runs-on: ${{ matrix.os }} environment: package steps: - name: Checkout uses: actions/checkout@v4 - # - name: Cache conda - # uses: actions/cache@v4 - # with: - # path: ~/conda_pkgs_dir - # key: conda-${{ matrix.os }}-python-${{ matrix.python-version }}-${{ hashFiles('environment.yml') }} - # - name: Setup Miniconda - # uses: conda-incubator/setup-miniconda@v2 - # with: - # auto-update-conda: true - # auto-activate-base: false - # activate-environment: hcrystalball - # python-version: ${{ matrix.python-version }} - # use-only-tar-bz2: true + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} - name: Install from source # This is required for the pre-commit tests shell: pwsh - run: pip install . + run: uv pip install --system -e . wheel "setuptools==58.1.0" # - name: Conda list # shell: pwsh # run: conda list - name: Build pyautogen shell: pwsh run: | - pip install twine - python setup.py sdist bdist_wheel + uv pip install --system twine + uv build - name: Publish pyautogen to PyPI env: TWINE_USERNAME: ${{ secrets.PYAUTOGEN_PYPI_USERNAME }} diff --git a/.github/workflows/type-check.yml b/.github/workflows/type-check.yml index 79542d9768..725e9e35ef 100644 --- a/.github/workflows/type-check.yml +++ b/.github/workflows/type-check.yml @@ -17,11 +17,14 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + with: + version: "latest" - uses: actions/setup-python@v5 with: python-version: ${{ matrix.version }} - # All additional modules should be defined in setup.py - - run: pip install ".[types]" + # All additional modules should be defined in pyproject.toml + - run: uv pip install --system ".[types]" # Any additional configuration should be defined in pyproject.toml - run: | mypy diff --git a/.muffet-excluded-links.txt b/.muffet-excluded-links.txt index d6bc275618..f48abbd2d1 100644 --- a/.muffet-excluded-links.txt +++ b/.muffet-excluded-links.txt @@ -7,7 +7,7 @@ example.com rapidapi.com https://platform.openai.com https://openai.com -https://code.visualstudio.com/docs/devcontainers/containers +https://code.visualstudio.com https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023 https://www.llama.com/docs/how-to-guides/prompting/ https://azure.microsoft.com/en-us/get-started/azure-portal @@ -41,3 +41,4 @@ https://docs.ag2.ai/_sites/docs.ag2.ai/notebooks/agentchat_RetrieveChat_mongodb# https://docs.ag2.ai/_sites/docs.ag2.ai/notebooks/agentchat_RetrieveChat_pgvector# https://docs.ag2.ai/_sites/docs.ag2.ai/notebooks/Gallery# https://docs.ag2.ai/_sites/docs.ag2.ai/docs/topics/non-openai-models/cloud-gemini_vertexai# +https://investor.nvidia.com diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c3ea21ee42..c9aabcc92e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,6 +15,11 @@ repos: - id: check-yaml - id: check-toml - id: check-json + exclude: | + (?x)^( + .devcontainer/.*devcontainer\.json | + ^notebook/agentchat_pdf_rag/(parsed_elements|processed_elements)\.json$ + )$ - id: check-byte-order-marker exclude: .gitignore - id: check-merge-conflict @@ -22,17 +27,16 @@ repos: - id: trailing-whitespace - id: end-of-file-fixer - id: no-commit-to-branch - - repo: https://github.com/psf/black - rev: 24.4.2 - hooks: - - id: black - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.4.8 + - repo: local hooks: - - id: ruff - types_or: [ python, pyi, jupyter ] - args: ["--fix", "--ignore=E402"] - exclude: notebook/agentchat_databricks_dbrx.ipynb + - id: lint + name: linting and formatting + entry: "scripts/pre-commit-lint.sh" + language: python + # language_version: python3.9 + types: [python] + require_serial: true + verbose: true - repo: https://github.com/codespell-project/codespell rev: v2.3.0 hooks: @@ -79,7 +83,14 @@ repos: notebook/.* | website/.* )$ - - repo: https://github.com/nbQA-dev/nbQA - rev: 1.8.5 + + - repo: local hooks: - - id: nbqa-black + - id: generate-devcontainer-files + name: Generate devcontainer files + entry: "scripts/devcontainer/generate-devcontainers.sh" + language: python + require_serial: true + verbose: true + additional_dependencies: ['jinja2'] + files: ^(scripts/devcontainer/.*)$ diff --git a/autogen/__init__.py b/autogen/__init__.py index 93150dbc6f..707d98e080 100644 --- a/autogen/__init__.py +++ b/autogen/__init__.py @@ -6,12 +6,98 @@ # SPDX-License-Identifier: MIT import logging -from .agentchat import * +from .agentchat import ( + AFTER_WORK, + ON_CONDITION, + UPDATE_SYSTEM_MESSAGE, + AfterWorkOption, + Agent, + AssistantAgent, + ChatResult, + ConversableAgent, + GroupChat, + GroupChatManager, + ReasoningAgent, + SwarmAgent, + SwarmResult, + ThinkNode, + UserProxyAgent, + a_initiate_swarm_chat, + gather_usage_summary, + initiate_chats, + initiate_swarm_chat, + register_function, + visualize_tree, +) from .code_utils import DEFAULT_MODEL, FAST_MODEL -from .exception_utils import * -from .oai import * +from .exception_utils import ( + AgentNameConflict, + InvalidCarryOverType, + NoEligibleSpeaker, + SenderRequired, + UndefinedNextAgent, +) +from .oai import ( + Cache, + ChatCompletion, + Completion, + ModelClient, + OpenAIWrapper, + config_list_from_dotenv, + config_list_from_json, + config_list_from_models, + config_list_gpt4_gpt35, + config_list_openai_aoai, + filter_config, + get_config_list, +) from .version import __version__ # Set the root logger. logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) + + +__all__ = [ + "AFTER_WORK", + "DEFAULT_MODEL", + "FAST_MODEL", + "ON_CONDITION", + "UPDATE_SYSTEM_MESSAGE", + "AfterWorkOption", + "Agent", + "AgentNameConflict", + "AssistantAgent", + "Cache", + "ChatCompletion", + "ChatResult", + "Completion", + "ConversableAgent", + "GroupChat", + "GroupChatManager", + "InvalidCarryOverType", + "ModelClient", + "NoEligibleSpeaker", + "OpenAIWrapper", + "ReasoningAgent", + "SenderRequired", + "SwarmAgent", + "SwarmResult", + "ThinkNode", + "UndefinedNextAgent", + "UserProxyAgent", + "__version__", + "a_initiate_swarm_chat", + "config_list_from_dotenv", + "config_list_from_json", + "config_list_from_models", + "config_list_gpt4_gpt35", + "config_list_openai_aoai", + "filter_config", + "gather_usage_summary", + "get_config_list", + "initiate_chats", + "initiate_swarm_chat", + "register_function", + "visualize_tree", +] diff --git a/autogen/_pydantic.py b/autogen/_pydantic.py index 09d272508e..d34bdfc986 100644 --- a/autogen/_pydantic.py +++ b/autogen/_pydantic.py @@ -4,13 +4,13 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Any, Dict, Optional, Tuple, Type, Union, get_args +from typing import Any, Tuple, Union, get_args from pydantic import BaseModel from pydantic.version import VERSION as PYDANTIC_VERSION from typing_extensions import get_origin -__all__ = ("JsonSchemaValue", "model_dump", "model_dump_json", "type2schema", "evaluate_forwardref") +__all__ = ("JsonSchemaValue", "evaluate_forwardref", "model_dump", "model_dump_json", "type2schema") PYDANTIC_V1 = PYDANTIC_VERSION.startswith("1.") @@ -70,18 +70,17 @@ def type2schema(t: Any) -> JsonSchemaValue: Returns: JsonSchemaValue: The JSON schema """ - if t is None: return {"type": "null"} elif get_origin(t) is Union: return {"anyOf": [type2schema(tt) for tt in get_args(t)]} # we need to support both syntaxes for Tuple elif get_origin(t) in [Tuple, tuple]: - prefixItems = [type2schema(tt) for tt in get_args(t)] + prefix_items = [type2schema(tt) for tt in get_args(t)] return { - "maxItems": len(prefixItems), - "minItems": len(prefixItems), - "prefixItems": prefixItems, + "maxItems": len(prefix_items), + "minItems": len(prefix_items), + "prefixItems": prefix_items, "type": "array", } else: diff --git a/autogen/agentchat/__init__.py b/autogen/agentchat/__init__.py index b5a1ec20aa..d41e9868f6 100644 --- a/autogen/agentchat/__init__.py +++ b/autogen/agentchat/__init__.py @@ -33,30 +33,32 @@ from .utils import gather_usage_summary __all__ = [ + "AFTER_WORK", + "ON_CONDITION", + "UPDATE_SYSTEM_MESSAGE", + "AfterWork", + "AfterWorkOption", "Agent", - "ConversableAgent", "AssistantAgent", - "UserProxyAgent", + "ChatResult", + "ConversableAgent", "GroupChat", "GroupChatManager", - "register_function", - "initiate_chats", - "gather_usage_summary", - "ChatResult", - "initiate_swarm_chat", - "a_initiate_swarm_chat", + "OnCondition", + "ReasoningAgent", "SwarmAgent", "SwarmResult", - "ON_CONDITION", - "OnCondition", + "ThinkNode", + "ThinkNode", "UpdateCondition", - "AFTER_WORK", - "AfterWork", - "AfterWorkOption", - "register_hand_off", "UpdateSystemMessage", - "UPDATE_SYSTEM_MESSAGE", - "ReasoningAgent", + "UserProxyAgent", + "UserProxyAgent", + "a_initiate_swarm_chat", + "gather_usage_summary", + "initiate_chats", + "initiate_swarm_chat", + "register_function", + "register_hand_off", "visualize_tree", - "ThinkNode", ] diff --git a/autogen/agentchat/agent.py b/autogen/agentchat/agent.py index 655ad388f1..e1ee855422 100644 --- a/autogen/agentchat/agent.py +++ b/autogen/agentchat/agent.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Any, Dict, List, Optional, Protocol, Union, runtime_checkable +from typing import Any, Optional, Protocol, Union, runtime_checkable @runtime_checkable @@ -23,7 +23,8 @@ def name(self) -> str: @property def description(self) -> str: """The description of the agent. Used for the agent's introduction in - a group chat setting.""" + a group chat setting. + """ ... def send( diff --git a/autogen/agentchat/assistant_agent.py b/autogen/agentchat/assistant_agent.py index 6daa7fedc4..963cd88eb2 100644 --- a/autogen/agentchat/assistant_agent.py +++ b/autogen/agentchat/assistant_agent.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Callable, Dict, Literal, Optional, Union +from typing import Callable, Literal, Optional, Union from autogen.runtime_logging import log_new_agent, logging_enabled @@ -48,22 +48,21 @@ def __init__( description: Optional[str] = None, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - llm_config (dict or False or None): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) - for available options. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. - default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). - The limit only plays a role when human_input_mode is not "ALWAYS". - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + llm_config (dict or False or None): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) + for available options. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. + default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). + The limit only plays a role when human_input_mode is not "ALWAYS". + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](conversable_agent#init). """ super().__init__( name, diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py index 9745e67ef6..4b3f6853f2 100644 --- a/autogen/agentchat/chat.py +++ b/autogen/agentchat/chat.py @@ -8,12 +8,11 @@ import datetime import logging import warnings -from collections import abc, defaultdict +from collections import defaultdict from dataclasses import dataclass from functools import partial -from typing import Any, Dict, List, Set, Tuple +from typing import Any -from ..formatting_utils import colored from ..io.base import IOStream from ..messages.agent_messages import PostCarryoverProcessingMessage from .utils import consolidate_chat_info @@ -43,9 +42,7 @@ class ChatResult: def _validate_recipients(chat_queue: list[dict[str, Any]]) -> None: - """ - Validate recipients exits and warn repetitive recipients. - """ + """Validate recipients exits and warn repetitive recipients.""" receipts_set = set() for chat_info in chat_queue: assert "recipient" in chat_info, "recipient must be provided." @@ -58,9 +55,7 @@ def _validate_recipients(chat_queue: list[dict[str, Any]]) -> None: def __create_async_prerequisites(chat_queue: list[dict[str, Any]]) -> list[Prerequisite]: - """ - Create list of Prerequisite (prerequisite_chat_id, chat_id) - """ + """Create list of Prerequisite (prerequisite_chat_id, chat_id)""" prerequisites = [] for chat_info in chat_queue: if "chat_id" not in chat_info: @@ -77,11 +72,11 @@ def __create_async_prerequisites(chat_queue: list[dict[str, Any]]) -> list[Prere def __find_async_chat_order(chat_ids: set[int], prerequisites: list[Prerequisite]) -> list[int]: """Find chat order for async execution based on the prerequisite chats - args: + Args: num_chats: number of chats prerequisites: List of Prerequisite (prerequisite_chat_id, chat_id) - returns: + Returns: list: a list of chat_id in order. """ edges = defaultdict(set) @@ -137,6 +132,7 @@ def __post_carryover_processing(chat_info: dict[str, Any]) -> None: def initiate_chats(chat_queue: list[dict[str, Any]]) -> list[ChatResult]: """Initiate a list of chats. + Args: chat_queue (List[Dict]): A list of dictionaries containing the information about the chats. @@ -166,10 +162,10 @@ def initiate_chats(chat_queue: list[dict[str, Any]]) -> list[ChatResult]: - `"finished_chat_indexes_to_exclude_from_carryover"` - It can be used by specifying a list of indexes of the finished_chats list, from which to exclude the summaries for carryover. If 'finished_chat_indexes_to_exclude_from_carryover' is not provided or an empty list, then summary from all the finished chats will be taken. + Returns: (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue. """ - consolidate_chat_info(chat_queue) _validate_recipients(chat_queue) current_chat_queue = chat_queue.copy() @@ -202,9 +198,7 @@ def __system_now_str(): def _on_chat_future_done(chat_future: asyncio.Future, chat_id: int): - """ - Update ChatResult when async Task for Chat is completed. - """ + """Update ChatResult when async Task for Chat is completed.""" logger.debug(f"Update chat {chat_id} result on task completion." + __system_now_str()) chat_result = chat_future.result() chat_result.chat_id = chat_id @@ -213,9 +207,7 @@ def _on_chat_future_done(chat_future: asyncio.Future, chat_id: int): async def _dependent_chat_future( chat_id: int, chat_info: dict[str, Any], prerequisite_chat_futures: dict[int, asyncio.Future] ) -> asyncio.Task: - """ - Create an async Task for each chat. - """ + """Create an async Task for each chat.""" logger.debug(f"Create Task for chat {chat_id}." + __system_now_str()) _chat_carryover = chat_info.get("carryover", []) finished_chat_indexes_to_exclude_from_carryover = chat_info.get( @@ -252,11 +244,11 @@ async def _dependent_chat_future( async def a_initiate_chats(chat_queue: list[dict[str, Any]]) -> dict[int, ChatResult]: """(async) Initiate a list of chats. - args: + Args: - Please refer to `initiate_chats`. - returns: + Returns: - (Dict): a dict of ChatId: ChatResult corresponding to the finished chats in the chat_queue. """ consolidate_chat_info(chat_queue) diff --git a/autogen/agentchat/contrib/agent_eval/agent_eval.py b/autogen/agentchat/contrib/agent_eval/agent_eval.py index d6f3711cbf..4ad81b72d0 100644 --- a/autogen/agentchat/contrib/agent_eval/agent_eval.py +++ b/autogen/agentchat/contrib/agent_eval/agent_eval.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Dict, List, Literal, Optional, Union +from typing import Literal, Optional, Union import autogen from autogen.agentchat.contrib.agent_eval.criterion import Criterion @@ -21,14 +21,15 @@ def generate_criteria( max_round=2, use_subcritic: bool = False, ): - """ - Creates a list of criteria for evaluating the utility of a given task. + """Creates a list of criteria for evaluating the utility of a given task. + Args: llm_config (dict or bool): llm inference configuration. task (Task): The task to evaluate. additional_instructions (str): Additional instructions for the criteria agent. max_round (int): The maximum number of rounds to run the conversation. use_subcritic (bool): Whether to use the subcritic agent to generate subcriteria. + Returns: list: A list of Criterion objects for evaluating the utility of the given task. """ @@ -73,14 +74,15 @@ def quantify_criteria( test_case: str = "", ground_truth: str = "", ): - """ - Quantifies the performance of a system using the provided criteria. + """Quantifies the performance of a system using the provided criteria. + Args: llm_config (dict or bool): llm inference configuration. criteria ([Criterion]): A list of criteria for evaluating the utility of a given task. task (Task): The task to evaluate. test_case (str): The test case to evaluate. ground_truth (str): The ground truth for the test case. + Returns: dict: A dictionary where the keys are the criteria and the values are the assessed performance based on accepted values for each criteria. """ @@ -95,7 +97,7 @@ def quantify_criteria( code_execution_config={"use_docker": False}, ) - quantifier_user.initiate_chat( # noqa: F841 + quantifier_user.initiate_chat( quantifier, message=task.get_sys_message() + "Evaluation dictionary: " diff --git a/autogen/agentchat/contrib/agent_eval/criterion.py b/autogen/agentchat/contrib/agent_eval/criterion.py index 9e682fcc95..3b826792b4 100644 --- a/autogen/agentchat/contrib/agent_eval/criterion.py +++ b/autogen/agentchat/contrib/agent_eval/criterion.py @@ -7,17 +7,12 @@ from __future__ import annotations import json -from typing import List -import pydantic_core from pydantic import BaseModel -from pydantic.json import pydantic_encoder class Criterion(BaseModel): - """ - A class that represents a criterion for agent evaluation. - """ + """A class that represents a criterion for agent evaluation.""" name: str description: str @@ -26,8 +21,8 @@ class Criterion(BaseModel): @staticmethod def parse_json_str(criteria: str): - """ - Create a list of Criterion objects from a json string. + """Create a list of Criterion objects from a json string. + Args: criteria (str): Json string that represents the criteria returns: @@ -37,10 +32,11 @@ def parse_json_str(criteria: str): @staticmethod def write_json(criteria): - """ - Create a json string from a list of Criterion objects. + """Create a json string from a list of Criterion objects. + Args: criteria ([Criterion]): A list of Criterion objects. + Returns: str: A json string that represents the list of Criterion objects. """ diff --git a/autogen/agentchat/contrib/agent_eval/critic_agent.py b/autogen/agentchat/contrib/agent_eval/critic_agent.py index 43fed36eff..0ac62b9bde 100644 --- a/autogen/agentchat/contrib/agent_eval/critic_agent.py +++ b/autogen/agentchat/contrib/agent_eval/critic_agent.py @@ -10,9 +10,7 @@ class CriticAgent(ConversableAgent): - """ - An agent for creating list of criteria for evaluating the utility of a given task. - """ + """An agent for creating list of criteria for evaluating the utility of a given task.""" DEFAULT_SYSTEM_MESSAGE = """You are a helpful assistant. You suggest criteria for evaluating different tasks. They should be distinguishable, quantifiable and not redundant. Convert the evaluation criteria into a list where each item is a criteria which consists of the following dictionary as follows @@ -30,14 +28,13 @@ def __init__( description: Optional[str] = DEFAULT_DESCRIPTION, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - description (str): The description of the agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../../conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + description (str): The description of the agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../../conversable_agent#init). """ super().__init__( name=name, diff --git a/autogen/agentchat/contrib/agent_eval/quantifier_agent.py b/autogen/agentchat/contrib/agent_eval/quantifier_agent.py index 89f3a7f269..b933afc232 100644 --- a/autogen/agentchat/contrib/agent_eval/quantifier_agent.py +++ b/autogen/agentchat/contrib/agent_eval/quantifier_agent.py @@ -10,9 +10,7 @@ class QuantifierAgent(ConversableAgent): - """ - An agent for quantifying the performance of a system using the provided criteria. - """ + """An agent for quantifying the performance of a system using the provided criteria.""" DEFAULT_SYSTEM_MESSAGE = """"You are a helpful assistant. You quantify the output of different tasks based on the given criteria. The criterion is given in a json list format where each element is a distinct criteria. @@ -30,13 +28,12 @@ def __init__( description: Optional[str] = DEFAULT_DESCRIPTION, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - description (str): The description of the agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../../conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + description (str): The description of the agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../../conversable_agent#init). """ super().__init__(name=name, system_message=system_message, description=description, **kwargs) diff --git a/autogen/agentchat/contrib/agent_eval/subcritic_agent.py b/autogen/agentchat/contrib/agent_eval/subcritic_agent.py index 11f97052e5..7c0261107e 100755 --- a/autogen/agentchat/contrib/agent_eval/subcritic_agent.py +++ b/autogen/agentchat/contrib/agent_eval/subcritic_agent.py @@ -10,9 +10,7 @@ class SubCriticAgent(ConversableAgent): - """ - An agent for creating subcriteria from a given list of criteria for evaluating the utility of a given task. - """ + """An agent for creating subcriteria from a given list of criteria for evaluating the utility of a given task.""" DEFAULT_SYSTEM_MESSAGE = """You are a helpful assistant to the critic agent. You suggest sub criteria for evaluating different tasks based on the criteria provided by the critic agent (if you feel it is needed). They should be distinguishable, quantifiable, and related to the overall theme of the critic's provided criteria. @@ -31,14 +29,13 @@ def __init__( description: Optional[str] = DEFAULT_DESCRIPTION, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - description (str): The description of the agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../../conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + description (str): The description of the agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../../conversable_agent#init). """ super().__init__( name=name, diff --git a/autogen/agentchat/contrib/agent_eval/task.py b/autogen/agentchat/contrib/agent_eval/task.py index f39731efce..3c0efc7fca 100644 --- a/autogen/agentchat/contrib/agent_eval/task.py +++ b/autogen/agentchat/contrib/agent_eval/task.py @@ -10,9 +10,7 @@ class Task(BaseModel): - """ - Class representing a task for agent completion, includes example agent execution for criteria generation. - """ + """Class representing a task for agent completion, includes example agent execution for criteria generation.""" name: str description: str @@ -28,10 +26,11 @@ def get_sys_message(self): @staticmethod def parse_json_str(task: str): - """ - Create a Task object from a json object. + """Create a Task object from a json object. + Args: json_data (dict): A dictionary that represents the task. + Returns: Task: A Task object that represents the json task information. """ diff --git a/autogen/agentchat/contrib/agent_optimizer.py b/autogen/agentchat/contrib/agent_optimizer.py index 7291e5e4cd..efc2448b0f 100644 --- a/autogen/agentchat/contrib/agent_optimizer.py +++ b/autogen/agentchat/contrib/agent_optimizer.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import copy import json -from typing import Dict, List, Literal, Optional, Union +from typing import Optional import autogen from autogen.code_utils import execute_code @@ -144,9 +144,7 @@ def execute_func(name, packages, code, **args): - """ - The wrapper for generated functions. - """ + """The wrapper for generated functions.""" pip_install = ( f"""print("Installing package: {packages}")\nsubprocess.run(["pip", "-qq", "install", "{packages}"])""" if packages @@ -170,8 +168,7 @@ def execute_func(name, packages, code, **args): class AgentOptimizer: - """ - Base class for optimizing AutoGen agents. Specifically, it is used to optimize the functions used in the agent. + """Base class for optimizing AutoGen agents. Specifically, it is used to optimize the functions used in the agent. More information could be found in the following paper: https://arxiv.org/abs/2402.11359. """ @@ -181,8 +178,8 @@ def __init__( llm_config: dict, optimizer_model: Optional[str] = "gpt-4-1106-preview", ): - """ - (These APIs are experimental and may change in the future.) + """(These APIs are experimental and may change in the future.) + Args: max_actions_per_step (int): the maximum number of actions that the optimizer can take in one step. llm_config (dict): llm inference configuration. @@ -218,8 +215,8 @@ def __init__( self._client = autogen.OpenAIWrapper(**self.llm_config) def record_one_conversation(self, conversation_history: list[dict], is_satisfied: bool = None): - """ - record one conversation history. + """Record one conversation history. + Args: conversation_history (List[Dict]): the chat messages of the conversation. is_satisfied (bool): whether the user is satisfied with the solution. If it is none, the user will be asked to input the satisfaction. @@ -241,8 +238,7 @@ def record_one_conversation(self, conversation_history: list[dict], is_satisfied ) def step(self): - """ - One step of training. It will return register_for_llm and register_for_executor at each iteration, + """One step of training. It will return register_for_llm and register_for_executor at each iteration, which are subsequently utilized to update the assistant and executor agents, respectively. See example: https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_agentoptimizer.ipynb """ @@ -322,10 +318,7 @@ def step(self): return register_for_llm, register_for_exector def reset_optimizer(self): - """ - reset the optimizer. - """ - + """Reset the optimizer.""" self._trial_conversations_history = [] self._trial_conversations_performance = [] self._trial_functions = [] @@ -338,10 +331,7 @@ def reset_optimizer(self): self._failure_functions_performance = [] def _update_function_call(self, incumbent_functions, actions): - """ - update function call. - """ - + """Update function call.""" formated_actions = [] for action in actions: func = json.loads(action.function.arguments.strip('"')) @@ -390,9 +380,7 @@ def _update_function_call(self, incumbent_functions, actions): return incumbent_functions def _construct_intermediate_prompt(self): - """ - construct intermediate prompts. - """ + """Construct intermediate prompts.""" if len(self._failure_functions_performance) != 0: failure_experience_prompt = "We also provide more examples for different functions and their corresponding performance (0-100).\n The following function signatures are arranged in are arranged in ascending order based on their performance, where higher performance indicate better quality." failure_experience_prompt += "\n" @@ -413,9 +401,7 @@ def _construct_intermediate_prompt(self): return failure_experience_prompt, statistic_prompt def _validate_actions(self, actions, incumbent_functions): - """ - validate whether the proposed actions are feasible. - """ + """Validate whether the proposed actions are feasible.""" if actions is None: return True else: diff --git a/autogen/agentchat/contrib/capabilities/agent_capability.py b/autogen/agentchat/contrib/capabilities/agent_capability.py index a5a4449b80..4987119b75 100644 --- a/autogen/agentchat/contrib/capabilities/agent_capability.py +++ b/autogen/agentchat/contrib/capabilities/agent_capability.py @@ -14,8 +14,7 @@ def __init__(self): pass def add_to_agent(self, agent: ConversableAgent): - """ - Adds a particular capability to the given agent. Must be implemented by the capability subclass. + """Adds a particular capability to the given agent. Must be implemented by the capability subclass. An implementation will typically call agent.register_hook() one or more times. See teachability.py as an example. """ raise NotImplementedError diff --git a/autogen/agentchat/contrib/capabilities/generate_images.py b/autogen/agentchat/contrib/capabilities/generate_images.py index 429a466945..6c9a3a398d 100644 --- a/autogen/agentchat/contrib/capabilities/generate_images.py +++ b/autogen/agentchat/contrib/capabilities/generate_images.py @@ -5,10 +5,10 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import re -from typing import Any, Dict, List, Literal, Optional, Protocol, Tuple, Union +from typing import Any, Literal, Optional, Protocol, Union -from openai import OpenAI from PIL.Image import Image +from openai import OpenAI from autogen import Agent, ConversableAgent, code_utils from autogen.agentchat.contrib import img_utils @@ -78,12 +78,11 @@ def __init__( quality: Literal["standard", "hd"] = "standard", num_images: int = 1, ): - """ - Args: - llm_config (dict): llm config, must contain a valid dalle model and OpenAI API key in config_list. - resolution (str): The resolution of the image you want to generate. Must be one of "256x256", "512x512", "1024x1024", "1792x1024", "1024x1792". - quality (str): The quality of the image you want to generate. Must be one of "standard", "hd". - num_images (int): The number of images to generate. + """Args: + llm_config (dict): llm config, must contain a valid dalle model and OpenAI API key in config_list. + resolution (str): The resolution of the image you want to generate. Must be one of "256x256", "512x512", "1024x1024", "1792x1024", "1024x1792". + quality (str): The quality of the image you want to generate. Must be one of "standard", "hd". + num_images (int): The number of images to generate. """ config_list = llm_config["config_list"] _validate_dalle_model(config_list[0]["model"]) @@ -154,23 +153,22 @@ def __init__( verbosity: int = 0, register_reply_position: int = 2, ): - """ - Args: - image_generator (ImageGenerator): The image generator you would like to use to generate images. - cache (None or AbstractCache): The cache client to use to store and retrieve generated images. If None, - no caching will be used. - text_analyzer_llm_config (Dict or None): The LLM config for the text analyzer. If None, the LLM config will - be retrieved from the agent you're adding the ability to. - text_analyzer_instructions (str): Instructions provided to the TextAnalyzerAgent used to analyze - incoming messages and extract the prompt for image generation. The default instructions focus on - summarizing the prompt. You can customize the instructions to achieve more granular control over prompt - extraction. - Example: 'Extract specific details from the message, like desired objects, styles, or backgrounds.' - verbosity (int): The verbosity level. Defaults to 0 and must be greater than or equal to 0. The text - analyzer llm calls will be silent if verbosity is less than 2. - register_reply_position (int): The position of the reply function in the agent's list of reply functions. - This capability registers a new reply function to handle messages with image generation requests. - Defaults to 2 to place it after the check termination and human reply for a ConversableAgent. + """Args: + image_generator (ImageGenerator): The image generator you would like to use to generate images. + cache (None or AbstractCache): The cache client to use to store and retrieve generated images. If None, + no caching will be used. + text_analyzer_llm_config (Dict or None): The LLM config for the text analyzer. If None, the LLM config will + be retrieved from the agent you're adding the ability to. + text_analyzer_instructions (str): Instructions provided to the TextAnalyzerAgent used to analyze + incoming messages and extract the prompt for image generation. The default instructions focus on + summarizing the prompt. You can customize the instructions to achieve more granular control over prompt + extraction. + Example: 'Extract specific details from the message, like desired objects, styles, or backgrounds.' + verbosity (int): The verbosity level. Defaults to 0 and must be greater than or equal to 0. The text + analyzer llm calls will be silent if verbosity is less than 2. + register_reply_position (int): The position of the reply function in the agent's list of reply functions. + This capability registers a new reply function to handle messages with image generation requests. + Defaults to 2 to place it after the check termination and human reply for a ConversableAgent. """ self._image_generator = image_generator self._cache = cache diff --git a/autogen/agentchat/contrib/capabilities/teachability.py b/autogen/agentchat/contrib/capabilities/teachability.py index 5429b3df03..420d5a02d0 100644 --- a/autogen/agentchat/contrib/capabilities/teachability.py +++ b/autogen/agentchat/contrib/capabilities/teachability.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import os import pickle -from typing import Dict, Optional, Union +from typing import Optional, Union import chromadb from chromadb.config import Settings @@ -19,8 +19,7 @@ class Teachability(AgentCapability): - """ - Teachability uses a vector database to give an agent the ability to remember user teachings, + """Teachability uses a vector database to give an agent the ability to remember user teachings, where the user is any caller (human or not) sending messages to the teachable agent. Teachability is designed to be composable with other agent capabilities. To make any conversable agent teachable, instantiate both the agent and the Teachability class, @@ -44,15 +43,14 @@ def __init__( max_num_retrievals: Optional[int] = 10, llm_config: Optional[Union[dict, bool]] = None, ): - """ - Args: - verbosity (Optional, int): # 0 (default) for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists. - reset_db (Optional, bool): True to clear the DB before starting. Default False. - path_to_db_dir (Optional, str): path to the directory where this particular agent's DB is stored. Default "./tmp/teachable_agent_db" - recall_threshold (Optional, float): The maximum distance for retrieved memos, where 0.0 is exact match. Default 1.5. Larger values allow more (but less relevant) memos to be recalled. - max_num_retrievals (Optional, int): The maximum number of memos to retrieve from the DB. Default 10. - llm_config (dict or False): llm inference configuration passed to TextAnalyzerAgent. - If None, TextAnalyzerAgent uses llm_config from the teachable agent. + """Args: + verbosity (Optional, int): # 0 (default) for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists. + reset_db (Optional, bool): True to clear the DB before starting. Default False. + path_to_db_dir (Optional, str): path to the directory where this particular agent's DB is stored. Default "./tmp/teachable_agent_db" + recall_threshold (Optional, float): The maximum distance for retrieved memos, where 0.0 is exact match. Default 1.5. Larger values allow more (but less relevant) memos to be recalled. + max_num_retrievals (Optional, int): The maximum number of memos to retrieve from the DB. Default 10. + llm_config (dict or False): llm inference configuration passed to TextAnalyzerAgent. + If None, TextAnalyzerAgent uses llm_config from the teachable agent. """ self.verbosity = verbosity self.path_to_db_dir = path_to_db_dir @@ -93,11 +91,9 @@ def prepopulate_db(self): self.memo_store.prepopulate() def process_last_received_message(self, text: Union[dict, str]): - """ - Appends any relevant memos to the message text, and stores any apparent teachings in new memos. + """Appends any relevant memos to the message text, and stores any apparent teachings in new memos. Uses TextAnalyzerAgent to make decisions about memo storage and retrieval. """ - # Try to retrieve relevant memos from the DB. expanded_text = text if self.memo_store.last_memo_id > 0: @@ -169,7 +165,6 @@ def _consider_memo_storage(self, comment: Union[dict, str]): def _consider_memo_retrieval(self, comment: Union[dict, str]): """Decides whether to retrieve memos from the DB, and add them to the chat context.""" - # First, use the comment directly as the lookup key. if self.verbosity >= 1: print(colored("\nLOOK FOR RELEVANT MEMOS, AS QUESTION-ANSWER PAIRS", "light_yellow")) @@ -244,8 +239,7 @@ def _analyze(self, text_to_analyze: Union[dict, str], analysis_instructions: Uni class MemoStore: - """ - Provides memory storage and retrieval for a teachable agent, using a vector database. + """Provides memory storage and retrieval for a teachable agent, using a vector database. Each DB entry (called a memo) is a pair of strings: an input text and an output text. The input text might be a question, or a task to perform. The output text might be an answer to the question, or advice on how to perform the task. @@ -258,11 +252,10 @@ def __init__( reset: Optional[bool] = False, path_to_db_dir: Optional[str] = "./tmp/teachable_agent_db", ): - """ - Args: - - verbosity (Optional, int): 1 to print memory operations, 0 to omit them. 3+ to print memo lists. - - reset (Optional, bool): True to clear the DB before starting. Default False. - - path_to_db_dir (Optional, str): path to the directory where the DB is stored. + """Args: + - verbosity (Optional, int): 1 to print memory operations, 0 to omit them. 3+ to print memo lists. + - reset (Optional, bool): True to clear the DB before starting. Default False. + - path_to_db_dir (Optional, str): path to the directory where the DB is stored. """ self.verbosity = verbosity self.path_to_db_dir = path_to_db_dir diff --git a/autogen/agentchat/contrib/capabilities/text_compressors.py b/autogen/agentchat/contrib/capabilities/text_compressors.py index 1e861c1170..e481d61a21 100644 --- a/autogen/agentchat/contrib/capabilities/text_compressors.py +++ b/autogen/agentchat/contrib/capabilities/text_compressors.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Any, Dict, Optional, Protocol +from typing import Any, Optional, Protocol IMPORT_ERROR: Optional[Exception] = None try: @@ -43,8 +43,7 @@ def __init__( ), structured_compression: bool = False, ) -> None: - """ - Args: + """Args: prompt_compressor_kwargs (dict): A dictionary of keyword arguments for the PromptCompressor. Defaults to a dictionary with model_name set to "microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank", use_llmlingua2 set to True, and device_map set to "cpu". diff --git a/autogen/agentchat/contrib/capabilities/transform_messages.py b/autogen/agentchat/contrib/capabilities/transform_messages.py index 78b4478647..ac6ef45137 100644 --- a/autogen/agentchat/contrib/capabilities/transform_messages.py +++ b/autogen/agentchat/contrib/capabilities/transform_messages.py @@ -5,7 +5,6 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import copy -from typing import Dict, List from ....formatting_utils import colored from ...conversable_agent import ConversableAgent @@ -48,10 +47,9 @@ class TransformMessages: """ def __init__(self, *, transforms: list[MessageTransform] = [], verbose: bool = True): - """ - Args: - transforms: A list of message transformations to apply. - verbose: Whether to print logs of each transformation or not. + """Args: + transforms: A list of message transformations to apply. + verbose: Whether to print logs of each transformation or not. """ self._transforms = transforms self._verbose = verbose diff --git a/autogen/agentchat/contrib/capabilities/transforms.py b/autogen/agentchat/contrib/capabilities/transforms.py index 740a81c366..330bf2f3a0 100644 --- a/autogen/agentchat/contrib/capabilities/transforms.py +++ b/autogen/agentchat/contrib/capabilities/transforms.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import copy import sys -from typing import Any, Dict, List, Optional, Protocol, Tuple, Union +from typing import Any, Optional, Protocol, Union import tiktoken from termcolor import colored @@ -60,11 +60,10 @@ class MessageHistoryLimiter: """ def __init__(self, max_messages: Optional[int] = None, keep_first_message: bool = False): - """ - Args: - max_messages Optional[int]: Maximum number of messages to keep in the context. Must be greater than 0 if not None. - keep_first_message bool: Whether to keep the original first message in the conversation history. - Defaults to False. + """Args: + max_messages Optional[int]: Maximum number of messages to keep in the context. Must be greater than 0 if not None. + keep_first_message bool: Whether to keep the original first message in the conversation history. + Defaults to False. """ self._validate_max_messages(max_messages) self._max_messages = max_messages @@ -83,7 +82,6 @@ def apply_transform(self, messages: list[dict]) -> list[dict]: Returns: List[Dict]: A new list containing the most recent messages up to the specified maximum. """ - if self._max_messages is None or len(messages) <= self._max_messages: return messages @@ -164,19 +162,18 @@ def __init__( filter_dict: Optional[dict] = None, exclude_filter: bool = True, ): - """ - Args: - max_tokens_per_message (None or int): Maximum number of tokens to keep in each message. - Must be greater than or equal to 0 if not None. - max_tokens (Optional[int]): Maximum number of tokens to keep in the chat history. - Must be greater than or equal to 0 if not None. - min_tokens (Optional[int]): Minimum number of tokens in messages to apply the transformation. - Must be greater than or equal to 0 if not None. - model (str): The target OpenAI model for tokenization alignment. - filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. - If None, no filters will be applied. - exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be - excluded from token truncation. If False, messages that match the filter will be truncated. + """Args: + max_tokens_per_message (None or int): Maximum number of tokens to keep in each message. + Must be greater than or equal to 0 if not None. + max_tokens (Optional[int]): Maximum number of tokens to keep in the chat history. + Must be greater than or equal to 0 if not None. + min_tokens (Optional[int]): Minimum number of tokens in messages to apply the transformation. + Must be greater than or equal to 0 if not None. + model (str): The target OpenAI model for tokenization alignment. + filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. + If None, no filters will be applied. + exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be + excluded from token truncation. If False, messages that match the filter will be truncated. """ self._model = model self._max_tokens_per_message = self._validate_max_tokens(max_tokens_per_message) @@ -329,22 +326,20 @@ def __init__( filter_dict: Optional[dict] = None, exclude_filter: bool = True, ): + """Args: + text_compressor (TextCompressor or None): An instance of a class that implements the TextCompressor + protocol. If None, it defaults to LLMLingua. + min_tokens (int or None): Minimum number of tokens in messages to apply the transformation. Must be greater + than or equal to 0 if not None. If None, no threshold-based compression is applied. + compression_args (dict): A dictionary of arguments for the compression method. Defaults to an empty + dictionary. + cache (None or AbstractCache): The cache client to use to store and retrieve previously compressed messages. + If None, no caching will be used. + filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. + If None, no filters will be applied. + exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be + excluded from compression. If False, messages that match the filter will be compressed. """ - Args: - text_compressor (TextCompressor or None): An instance of a class that implements the TextCompressor - protocol. If None, it defaults to LLMLingua. - min_tokens (int or None): Minimum number of tokens in messages to apply the transformation. Must be greater - than or equal to 0 if not None. If None, no threshold-based compression is applied. - compression_args (dict): A dictionary of arguments for the compression method. Defaults to an empty - dictionary. - cache (None or AbstractCache): The cache client to use to store and retrieve previously compressed messages. - If None, no caching will be used. - filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. - If None, no filters will be applied. - exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be - excluded from compression. If False, messages that match the filter will be compressed. - """ - if text_compressor is None: text_compressor = LLMLingua() @@ -486,17 +481,15 @@ def __init__( filter_dict: Optional[dict] = None, exclude_filter: bool = True, ): + """Args: + position (str): The position to add the name to the content. The possible options are 'start' or 'end'. Defaults to 'start'. + format_string (str): The f-string to format the message name with. Use '{name}' as a placeholder for the agent's name. Defaults to '{name}:\n' and must contain '{name}'. + deduplicate (bool): Whether to deduplicate the formatted string so it doesn't appear twice (sometimes the LLM will add it to new messages itself). Defaults to True. + filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. + If None, no filters will be applied. + exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be + excluded from compression. If False, messages that match the filter will be compressed. """ - Args: - position (str): The position to add the name to the content. The possible options are 'start' or 'end'. Defaults to 'start'. - format_string (str): The f-string to format the message name with. Use '{name}' as a placeholder for the agent's name. Defaults to '{name}:\n' and must contain '{name}'. - deduplicate (bool): Whether to deduplicate the formatted string so it doesn't appear twice (sometimes the LLM will add it to new messages itself). Defaults to True. - filter_dict (None or dict): A dictionary to filter out messages that you want/don't want to compress. - If None, no filters will be applied. - exclude_filter (bool): If exclude filter is True (the default value), messages that match the filter will be - excluded from compression. If False, messages that match the filter will be compressed. - """ - assert isinstance(position, str) and position in ["start", "end"] assert isinstance(format_string, str) and "{name}" in format_string assert isinstance(deduplicate, bool) and deduplicate is not None diff --git a/autogen/agentchat/contrib/capabilities/transforms_util.py b/autogen/agentchat/contrib/capabilities/transforms_util.py index 62decfa091..a3c2524cde 100644 --- a/autogen/agentchat/contrib/capabilities/transforms_util.py +++ b/autogen/agentchat/contrib/capabilities/transforms_util.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT from collections.abc import Hashable -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Optional from autogen import token_count_utils from autogen.cache.abstract_cache_base import AbstractCache diff --git a/autogen/agentchat/contrib/capabilities/vision_capability.py b/autogen/agentchat/contrib/capabilities/vision_capability.py index 2d5c02e10f..bad988f33e 100644 --- a/autogen/agentchat/contrib/capabilities/vision_capability.py +++ b/autogen/agentchat/contrib/capabilities/vision_capability.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import copy -from typing import Callable, Dict, List, Optional, Union +from typing import Callable, Optional, Union from autogen.agentchat.assistant_agent import ConversableAgent from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability @@ -14,10 +14,7 @@ get_image_data, get_pil_image, gpt4v_formatter, - message_formatter_pil_to_b64, ) -from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent -from autogen.agentchat.conversable_agent import colored from autogen.code_utils import content_str from autogen.oai.client import OpenAIWrapper @@ -53,8 +50,7 @@ def __init__( description_prompt: Optional[str] = DEFAULT_DESCRIPTION_PROMPT, custom_caption_func: Callable = None, ) -> None: - """ - Initializes a new instance, setting up the configuration for interacting with + """Initializes a new instance, setting up the configuration for interacting with a Language Multimodal (LMM) client and specifying optional parameters for image description and captioning. @@ -92,9 +88,9 @@ def __init__( self._lmm_client = None self._custom_caption_func = custom_caption_func - assert ( - self._lmm_config or custom_caption_func - ), "Vision Capability requires a valid lmm_config or custom_caption_func." + assert self._lmm_config or custom_caption_func, ( + "Vision Capability requires a valid lmm_config or custom_caption_func." + ) def add_to_agent(self, agent: ConversableAgent) -> None: self._parent_agent = agent @@ -106,8 +102,7 @@ def add_to_agent(self, agent: ConversableAgent) -> None: agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message) def process_last_received_message(self, content: Union[str, list[dict]]) -> str: - """ - Processes the last received message content by normalizing and augmenting it + """Processes the last received message content by normalizing and augmenting it with descriptions of any included images. The function supports input content as either a string or a list of dictionaries, where each dictionary represents a content item (e.g., text, image). If the content contains image URLs, it @@ -155,7 +150,7 @@ def process_last_received_message(self, content: Union[str, list[dict]]) -> str: ```python content = [ {"type": "text", "text": "What's weather in this cool photo:"}, - {"type": "image_url", "image_url": {"url": "http://example.com/photo.jpg"}} + {"type": "image_url", "image_url": {"url": "http://example.com/photo.jpg"}}, ] ``` Output: "What's weather in this cool photo: `` in case you can not see, the caption of this image is: @@ -192,9 +187,9 @@ def process_last_received_message(self, content: Union[str, list[dict]]) -> str: return aug_content def _get_image_caption(self, img_data: str) -> str: - """ - Args: + """Args: img_data (str): base64 encoded image data. + Returns: str: caption for the given image. """ diff --git a/autogen/agentchat/contrib/captainagent/__init__.py b/autogen/agentchat/contrib/captainagent/__init__.py new file mode 100644 index 0000000000..3f1945788f --- /dev/null +++ b/autogen/agentchat/contrib/captainagent/__init__.py @@ -0,0 +1,5 @@ +from .agent_builder import AgentBuilder +from .captainagent import CaptainAgent +from .tool_retriever import ToolBuilder, format_ag2_tool, get_full_tool_description + +__all__ = ["AgentBuilder", "CaptainAgent", "ToolBuilder", "format_ag2_tool", "get_full_tool_description"] diff --git a/autogen/agentchat/contrib/agent_builder.py b/autogen/agentchat/contrib/captainagent/agent_builder.py similarity index 95% rename from autogen/agentchat/contrib/agent_builder.py rename to autogen/agentchat/contrib/captainagent/agent_builder.py index 1c235e52e7..56c50f6e25 100644 --- a/autogen/agentchat/contrib/agent_builder.py +++ b/autogen/agentchat/contrib/captainagent/agent_builder.py @@ -9,30 +9,30 @@ import json import logging import re -import socket import subprocess as sp import time -from typing import Dict, List, Optional, Tuple, Union +from typing import Optional, Union -import requests from termcolor import colored import autogen +__all__ = ["AgentBuilder"] + logger = logging.getLogger(__name__) def _config_check(config: dict): # check config loading - assert config.get("coding", None) is not None, 'Missing "coding" in your config.' - assert config.get("default_llm_config", None) is not None, 'Missing "default_llm_config" in your config.' - assert config.get("code_execution_config", None) is not None, 'Missing "code_execution_config" in your config.' + assert config.get("coding") is not None, 'Missing "coding" in your config.' + assert config.get("default_llm_config") is not None, 'Missing "default_llm_config" in your config.' + assert config.get("code_execution_config") is not None, 'Missing "code_execution_config" in your config.' for agent_config in config["agent_configs"]: assert agent_config.get("name", None) is not None, 'Missing agent "name" in your agent_configs.' - assert ( - agent_config.get("system_message", None) is not None - ), 'Missing agent "system_message" in your agent_configs.' + assert agent_config.get("system_message", None) is not None, ( + 'Missing agent "system_message" in your agent_configs.' + ) assert agent_config.get("description", None) is not None, 'Missing agent "description" in your agent_configs.' @@ -47,8 +47,7 @@ def _retrieve_json(text): class AgentBuilder: - """ - AgentBuilder can help user build an automatic task solving process powered by multi-agent system. + """AgentBuilder can help user build an automatic task solving process powered by multi-agent system. Specifically, our building pipeline includes initialize and build. """ @@ -189,8 +188,8 @@ def __init__( agent_model_tags: Optional[list] = [], max_agents: Optional[int] = 5, ): - """ - (These APIs are experimental and may change in the future.) + """(These APIs are experimental and may change in the future.) + Args: config_file_or_env: path or environment of the OpenAI api configs. builder_model: specify a model as the backbone of build manager. @@ -241,8 +240,7 @@ def _create_agent( llm_config: dict, use_oai_assistant: Optional[bool] = False, ) -> autogen.AssistantAgent: - """ - Create a group chat participant agent. + """Create a group chat participant agent. If the agent rely on an open-source model, this function will automatically set up an endpoint for that agent. The API address of that endpoint will be "localhost:{free port}". @@ -270,7 +268,7 @@ def _create_agent( description = agent_config["description"] # Path to the customize **ConversableAgent** class. - agent_path = agent_config.get("agent_path", None) + agent_path = agent_config.get("agent_path") filter_dict = {} if len(model_name_or_hf_repo) > 0: filter_dict.update({"model": model_name_or_hf_repo}) @@ -334,8 +332,7 @@ def _create_agent( return agent def clear_agent(self, agent_name: str, recycle_endpoint: Optional[bool] = True): - """ - Clear a specific agent by name. + """Clear a specific agent by name. Args: agent_name: the name of agent. @@ -356,9 +353,7 @@ def clear_agent(self, agent_name: str, recycle_endpoint: Optional[bool] = True): print(colored(f"Agent {agent_name} has been cleared.", "yellow"), flush=True) def clear_all_agents(self, recycle_endpoint: Optional[bool] = True): - """ - Clear all cached agents. - """ + """Clear all cached agents.""" for agent_name in [agent_name for agent_name in self.agent_procs_assign.keys()]: self.clear_agent(agent_name, recycle_endpoint) print(colored("All agents have been cleared.", "yellow"), flush=True) @@ -374,8 +369,7 @@ def build( max_agents: Optional[int] = None, **kwargs, ) -> tuple[list[autogen.ConversableAgent], dict]: - """ - Auto build agents based on the building task. + """Auto build agents based on the building task. Args: building_task: instruction that helps build manager (gpt-4) to decide what agent should be built. @@ -505,8 +499,7 @@ def build_from_library( user_proxy: Optional[autogen.ConversableAgent] = None, **kwargs, ) -> tuple[list[autogen.ConversableAgent], dict]: - """ - Build agents from a library. + """Build agents from a library. The library is a list of agent configs, which contains the name and system_message for each agent. We use a build manager to decide what agent in that library should be involved to the task. @@ -664,8 +657,7 @@ def build_from_library( def _build_agents( self, use_oai_assistant: Optional[bool] = False, user_proxy: Optional[autogen.ConversableAgent] = None, **kwargs ) -> tuple[list[autogen.ConversableAgent], dict]: - """ - Build agents with generated configs. + """Build agents with generated configs. Args: use_oai_assistant: use OpenAI assistant api instead of self-constructed agent. @@ -707,8 +699,7 @@ def _build_agents( return agent_list, self.cached_configs.copy() def save(self, filepath: Optional[str] = None) -> str: - """ - Save building configs. If the filepath is not specific, this function will create a filename by encrypt the + """Save building configs. If the filepath is not specific, this function will create a filename by encrypt the building_task string by md5 with "save_config_" prefix, and save config to the local path. Args: @@ -718,7 +709,7 @@ def save(self, filepath: Optional[str] = None) -> str: filepath: path save. """ if filepath is None: - filepath = f'./save_config_{hashlib.md5(self.building_task.encode("utf-8")).hexdigest()}.json' + filepath = f"./save_config_{hashlib.md5(self.building_task.encode('utf-8')).hexdigest()}.json" with open(filepath, "w") as save_file: json.dump(self.cached_configs, save_file, indent=4) print(colored(f"Building config saved to {filepath}", "green"), flush=True) @@ -732,8 +723,7 @@ def load( use_oai_assistant: Optional[bool] = False, **kwargs, ) -> tuple[list[autogen.ConversableAgent], dict]: - """ - Load building configs and call the build function to complete building without calling online LLMs' api. + """Load building configs and call the build function to complete building without calling online LLMs' api. Args: filepath: filepath or JSON string for the save config. @@ -761,7 +751,7 @@ def load( default_llm_config = cached_configs["default_llm_config"] coding = cached_configs["coding"] - if kwargs.get("code_execution_config", None) is not None: + if kwargs.get("code_execution_config") is not None: # for test self.cached_configs.update( { diff --git a/autogen/agentchat/contrib/captainagent.py b/autogen/agentchat/contrib/captainagent/captainagent.py similarity index 78% rename from autogen/agentchat/contrib/captainagent.py rename to autogen/agentchat/contrib/captainagent/captainagent.py index c616d074db..f71f6de03c 100644 --- a/autogen/agentchat/contrib/captainagent.py +++ b/autogen/agentchat/contrib/captainagent/captainagent.py @@ -17,9 +17,7 @@ class CaptainAgent(ConversableAgent): - """ - (In preview) Captain agent, designed to solve a task with an agent or a group of agents. - """ + """(In preview) Captain agent, designed to solve a task with an agent or a group of agents.""" DEFAULT_NESTED_CONFIG = { "autobuild_init_config": { @@ -149,26 +147,25 @@ def __init__( description: Optional[str] = DEFAULT_DESCRIPTION, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - llm_config (dict): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) for available options. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. - default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). - The limit only plays a role when human_input_mode is not "ALWAYS". - agent_lib (str): the path or a JSON file of the agent library for retrieving the nested chat instantiated by CaptainAgent. - tool_lib (str): the path to the tool library for retrieving the tools used in the nested chat instantiated by CaptainAgent. - nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent. - A full list of keys and their functionalities can be found in [docs](https://docs.ag2.ai/docs/topics/captainagent/configurations). - agent_config_save_path (str): the path to save the generated or retrieved agent configuration. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/conversable_agent.py#L74). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + llm_config (dict): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) for available options. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. + default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). + The limit only plays a role when human_input_mode is not "ALWAYS". + agent_lib (str): the path or a JSON file of the agent library for retrieving the nested chat instantiated by CaptainAgent. + tool_lib (str): the path to the tool library for retrieving the tools used in the nested chat instantiated by CaptainAgent. + nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent. + A full list of keys and their functionalities can be found in [docs](https://docs.ag2.ai/docs/topics/captainagent/configurations). + agent_config_save_path (str): the path to save the generated or retrieved agent configuration. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/conversable_agent.py#L74). """ super().__init__( name, @@ -223,9 +220,7 @@ def __init__( @staticmethod def _update_config(default_dict: dict, update_dict: Optional[dict]) -> dict: - """ - Recursively updates the default_dict with values from update_dict. - """ + """Recursively updates the default_dict with values from update_dict.""" if update_dict is None: return default_dict @@ -303,48 +298,47 @@ def __init__( system_message: Optional[Union[str, list]] = "", description: Optional[str] = None, ): - """ - Args: - name (str): name of the agent. - nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. - default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). - The limit only plays a role when human_input_mode is not "ALWAYS". - human_input_mode (str): whether to ask for human inputs every time a message is received. - Possible values are "ALWAYS", "TERMINATE", "NEVER". - (1) When "ALWAYS", the agent prompts for human input every time a message is received. - Under this mode, the conversation stops when the human input is "exit", - or when is_termination_msg is True and there is no human input. - (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or - the number of auto reply reaches the max_consecutive_auto_reply. - (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops - when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. - code_execution_config (dict or False): config for the code execution. - To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: - - work_dir (Optional, str): The working directory for the code execution. - If None, a default working directory will be used. - The default working directory is the "extensions" directory under - "path_to_autogen". - - use_docker (Optional, list, str or bool): The docker image to use for code execution. - Default is True, which means the code will be executed in a docker container. A default list of images will be used. - If a list or a str of image name(s) is provided, the code will be executed in a docker container - with the first image successfully pulled. - If False, the code will be executed in the current environment. - We strongly recommend using docker for code execution. - - timeout (Optional, int): The maximum execution time in seconds. - - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. - default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. - llm_config (dict or False): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) - for available options. - Default to false, which disables llm-based auto reply. - system_message (str or List): system message for ChatCompletion inference. - Only used when llm_config is not False. Use it to reprogram the agent. - description (str): a short description of the agent. This description is used by other agents - (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) + """Args: + name (str): name of the agent. + nested_config (dict): the configuration for the nested chat instantiated by CaptainAgent. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. + default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). + The limit only plays a role when human_input_mode is not "ALWAYS". + human_input_mode (str): whether to ask for human inputs every time a message is received. + Possible values are "ALWAYS", "TERMINATE", "NEVER". + (1) When "ALWAYS", the agent prompts for human input every time a message is received. + Under this mode, the conversation stops when the human input is "exit", + or when is_termination_msg is True and there is no human input. + (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or + the number of auto reply reaches the max_consecutive_auto_reply. + (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops + when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. + code_execution_config (dict or False): config for the code execution. + To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: + - work_dir (Optional, str): The working directory for the code execution. + If None, a default working directory will be used. + The default working directory is the "extensions" directory under + "path_to_autogen". + - use_docker (Optional, list, str or bool): The docker image to use for code execution. + Default is True, which means the code will be executed in a docker container. A default list of images will be used. + If a list or a str of image name(s) is provided, the code will be executed in a docker container + with the first image successfully pulled. + If False, the code will be executed in the current environment. + We strongly recommend using docker for code execution. + - timeout (Optional, int): The maximum execution time in seconds. + - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. + default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. + llm_config (dict or False): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) + for available options. + Default to false, which disables llm-based auto reply. + system_message (str or List): system message for ChatCompletion inference. + Only used when llm_config is not False. Use it to reprogram the agent. + description (str): a short description of the agent. This description is used by other agents + (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) """ description = ( description if description is not None else self.DEFAULT_USER_PROXY_AGENT_DESCRIPTIONS[human_input_mode] @@ -373,8 +367,7 @@ def __init__( self.build_times = 0 def _run_autobuild(self, group_name: str, execution_task: str, building_task: str = "") -> str: - """ - Build a group of agents by AutoBuild to solve the task. + """Build a group of agents by AutoBuild to solve the task. This function requires the nested_config to contain the autobuild_init_config, autobuild_llm_config, group_chat_llm_config. """ print("==> Running AutoBuild...", flush=True) diff --git a/autogen/agentchat/contrib/tool_retriever.py b/autogen/agentchat/contrib/captainagent/tool_retriever.py similarity index 91% rename from autogen/agentchat/contrib/tool_retriever.py rename to autogen/agentchat/contrib/captainagent/tool_retriever.py index 8844e8d8e9..a37b171b45 100644 --- a/autogen/agentchat/contrib/tool_retriever.py +++ b/autogen/agentchat/contrib/captainagent/tool_retriever.py @@ -14,15 +14,15 @@ from hashlib import md5 from pathlib import Path from textwrap import dedent, indent -from typing import List, Optional, Union +from typing import Optional, Union import pandas as pd from sentence_transformers import SentenceTransformer, util -from autogen import AssistantAgent, UserProxyAgent -from autogen.coding import CodeExecutor, CodeExtractor, LocalCommandLineCodeExecutor, MarkdownCodeExtractor -from autogen.coding.base import CodeBlock, CodeResult -from autogen.tools import Tool, get_function_schema, load_basemodels_if_needed +from .... import AssistantAgent, UserProxyAgent +from ....coding import CodeExecutor, CodeExtractor, LocalCommandLineCodeExecutor, MarkdownCodeExtractor +from ....coding.base import CodeBlock, CodeResult +from ....tools import Tool, get_function_schema, load_basemodels_if_needed class ToolBuilder: @@ -72,8 +72,7 @@ def bind(self, agent: AssistantAgent, functions: str): return def bind_user_proxy(self, agent: UserProxyAgent, tool_root: Union[str, list]): - """ - Updates user proxy agent with a executor so that code executor can successfully execute function-related code. + """Updates user proxy agent with a executor so that code executor can successfully execute function-related code. Returns an updated user proxy. """ if isinstance(tool_root, str): @@ -120,8 +119,7 @@ def bind_user_proxy(self, agent: UserProxyAgent, tool_root: Union[str, list]): class LocalExecutorWithTools(CodeExecutor): - """ - An executor that executes code blocks with injected tools. In this executor, the func within the tools can be called directly without declaring in the code block. + """An executor that executes code blocks with injected tools. In this executor, the func within the tools can be called directly without declaring in the code block. For example, for a tool converted from langchain, the relevant functions can be called directly. ```python @@ -135,7 +133,7 @@ class LocalExecutorWithTools(CodeExecutor): ag2_tool = interop.convert_tool(tool=langchain_tool, type="langchain") # `ag2_tool.name` is wikipedia - local_executor = LocalExecutorWithTools(tools=[ag2_tool], work_dir='./') + local_executor = LocalExecutorWithTools(tools=[ag2_tool], work_dir="./") code = ''' result = wikipedia(tool_input={"query":"Christmas"}) @@ -161,13 +159,13 @@ def code_extractor(self) -> CodeExtractor: """(Experimental) Export a code extractor that can be used by an agent.""" return MarkdownCodeExtractor() - def __init__(self, tools: Optional[List[Tool]] = None, work_dir: Union[Path, str] = Path(".")): + def __init__(self, tools: Optional[list[Tool]] = None, work_dir: Union[Path, str] = Path()): self.tools = tools if tools is not None else [] self.work_dir = work_dir if not os.path.exists(work_dir): os.makedirs(work_dir, exist_ok=True) - def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CodeResult: + def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CodeResult: """Execute code blocks and return the result. Args: @@ -272,9 +270,7 @@ def _wrapped_func(*args, **kwargs): def get_full_tool_description(py_file): - """ - Retrieves the function signature for a given Python file. - """ + """Retrieves the function signature for a given Python file.""" with open(py_file) as f: code = f.read() exec(code) @@ -315,9 +311,7 @@ def _wrapped_func(*args, **kwargs): def find_callables(directory): - """ - Find all callable objects defined in Python files within the specified directory. - """ + """Find all callable objects defined in Python files within the specified directory.""" callables = [] for root, dirs, files in os.walk(directory): for file in files: diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_correlation.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_correlation.py index 1dc17b52bc..137f8bc509 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_correlation.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_correlation.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def calculate_correlation(csv_path: str, column1: str, column2: str, method: str = "pearson") -> float: - """ - Calculate the correlation between two columns in a CSV file. + """Calculate the correlation between two columns in a CSV file. Args: csv_path (str): The path to the CSV file. diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_skewness_and_kurtosis.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_skewness_and_kurtosis.py index 9862dcaff0..6c3d931760 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_skewness_and_kurtosis.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/calculate_skewness_and_kurtosis.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def calculate_skewness_and_kurtosis(csv_file: str, column_name: str) -> tuple: - """ - Calculate the skewness and kurtosis of a specified column in a CSV file. The kurtosis is calculated using the Fisher definition. + """Calculate the skewness and kurtosis of a specified column in a CSV file. The kurtosis is calculated using the Fisher definition. The two metrics are computed using scipy.stats functions. Args: diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_iqr.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_iqr.py index 07ebe80689..ac70b0f949 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_iqr.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_iqr.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def detect_outlier_iqr(csv_file: str, column_name: str): - """ - Detect outliers in a specified column of a CSV file using the IQR method. + """Detect outliers in a specified column of a CSV file using the IQR method. Args: csv_file (str): The path to the CSV file. diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_zscore.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_zscore.py index 2e35f37807..7fa282375e 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_zscore.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/detect_outlier_zscore.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def detect_outlier_zscore(csv_file, column_name, threshold=3): - """ - Detect outliers in a CSV file based on a specified column. The outliers are determined by calculating the z-score of the data points in the column. + """Detect outliers in a CSV file based on a specified column. The outliers are determined by calculating the z-score of the data points in the column. Args: csv_file (str): The path to the CSV file. diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/explore_csv.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/explore_csv.py index a433ba397f..441d0ec3ca 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/explore_csv.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/explore_csv.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def explore_csv(file_path, num_lines=5): - """ - Reads a CSV file and prints the column names, shape, data types, and the first few lines of data. + """Reads a CSV file and prints the column names, shape, data types, and the first few lines of data. Args: file_path (str): The path to the CSV file. diff --git a/autogen/agentchat/contrib/captainagent/tools/data_analysis/shapiro_wilk_test.py b/autogen/agentchat/contrib/captainagent/tools/data_analysis/shapiro_wilk_test.py index 90212e6a97..18f77c47a4 100644 --- a/autogen/agentchat/contrib/captainagent/tools/data_analysis/shapiro_wilk_test.py +++ b/autogen/agentchat/contrib/captainagent/tools/data_analysis/shapiro_wilk_test.py @@ -6,8 +6,7 @@ @with_requirements(["pandas", "scipy"]) def shapiro_wilk_test(csv_file, column_name): - """ - Perform the Shapiro-Wilk test on a specified column of a CSV file. + """Perform the Shapiro-Wilk test on a specified column of a CSV file. Args: csv_file (str): The path to the CSV file. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_download.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_download.py index 3900e8a5fd..53e9e45c16 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_download.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_download.py @@ -8,8 +8,7 @@ @with_requirements(["arxiv"], ["arxiv"]) def arxiv_download(id_list: list, download_dir="./"): - """ - Downloads PDF files from ArXiv based on a list of arxiv paper IDs. + """Downloads PDF files from ArXiv based on a list of arxiv paper IDs. Args: id_list (list): A list of paper IDs to download. e.g. [2302.00006v1] diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_search.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_search.py index 256b4bf3fe..9cbc68b91c 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_search.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/arxiv_search.py @@ -8,8 +8,7 @@ @with_requirements(["arxiv"], ["arxiv"]) def arxiv_search(query, max_results=10, sortby="relevance"): - """ - Search for articles on arXiv based on the given query. + """Search for articles on arXiv based on the given query. Args: query (str): The search query. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_image.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_image.py index 1b624d394c..ce2a7d1117 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_image.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_image.py @@ -8,8 +8,7 @@ @with_requirements(["PyMuPDF"], ["os"]) def extract_pdf_image(pdf_path: str, output_dir: str, page_number=None): - """ - Extracts images from a PDF file and saves them to the specified output directory. + """Extracts images from a PDF file and saves them to the specified output directory. Args: pdf_path (str): The path to the PDF file. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_text.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_text.py index 267990c936..689b3f419a 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_text.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/extract_pdf_text.py @@ -6,8 +6,7 @@ @with_requirements(["PyMuPDF"]) def extract_pdf_text(pdf_path, page_number=None): - """ - Extracts text from a specified page or the entire PDF file. + """Extracts text from a specified page or the entire PDF file. Args: pdf_path (str): The path to the PDF file. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_wikipedia_text.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_wikipedia_text.py index b44bba63e0..bfb0568b2e 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_wikipedia_text.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_wikipedia_text.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def get_wikipedia_text(title): - """ - Retrieves the text content of a Wikipedia page. It does not support tables and other complex formatting. + """Retrieves the text content of a Wikipedia page. It does not support tables and other complex formatting. Args: title (str): The title of the Wikipedia page. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_youtube_caption.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_youtube_caption.py index 33f594093e..b041ecd30c 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_youtube_caption.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/get_youtube_caption.py @@ -4,9 +4,11 @@ # alternative api: https://rapidapi.com/omarmhaimdat/api/youtube-v2 -def get_youtube_caption(videoId): - """ - Retrieves the captions for a YouTube video. +from typing import Any + + +def get_youtube_caption(video_id: str) -> Any: + """Retrieves the captions for a YouTube video. Args: videoId (str): The ID of the YouTube video. @@ -21,13 +23,13 @@ def get_youtube_caption(videoId): import requests - RAPID_API_KEY = os.environ["RAPID_API_KEY"] - video_url = f"https://www.youtube.com/watch?v={videoId}" + rapid_api_key = os.environ["RAPID_API_KEY"] + video_url = f"https://www.youtube.com/watch?v={video_id: str}" url = "https://youtube-transcript3.p.rapidapi.com/api/transcript-with-url" querystring = {"url": video_url, "lang": "en", "flat_text": "true"} - headers = {"X-RapidAPI-Key": RAPID_API_KEY, "X-RapidAPI-Host": "youtube-transcript3.p.rapidapi.com"} + headers = {"X-RapidAPI-Key": rapid_api_key, "X-RapidAPI-Host": "youtube-transcript3.p.rapidapi.com"} response = requests.get(url, headers=headers, params=querystring) response = response.json() diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py index 95f2be6dfb..4d68b4090f 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/image_qa.py @@ -10,8 +10,7 @@ @with_requirements(["transformers", "torch"], ["transformers", "torch", "PIL", "os"]) def image_qa(image, question, ckpt="Salesforce/blip-vqa-base"): - """ - Perform question answering on an image using a pre-trained VQA model. + """Perform question answering on an image using a pre-trained VQA model. Args: image (Union[str, Image.Image]): The image to perform question answering on. It can be either file path to the image or a PIL Image object. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/optical_character_recognition.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/optical_character_recognition.py index 64714d89dd..e7d78cb968 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/optical_character_recognition.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/optical_character_recognition.py @@ -8,8 +8,7 @@ @with_requirements(["easyocr"], ["os"]) def optical_character_recognition(image): - """ - Perform optical character recognition (OCR) on the given image. + """Perform optical character recognition (OCR) on the given image. Args: image (Union[str, Image.Image]): The image to perform OCR on. It can be either a file path or an Image object. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/perform_web_search.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/perform_web_search.py index 3a39a9b567..6b86013c4c 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/perform_web_search.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/perform_web_search.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def perform_web_search(query, count=10, offset=0): - """ - Perform a web search using Bing API. + """Perform a web search using Bing API. Args: query (str): The search query. @@ -42,7 +41,7 @@ def perform_web_search(query, count=10, offset=0): # Process the search results search_results = response.json() for index, result in enumerate(search_results["webPages"]["value"]): - print(f"Search Result {index+1}:") + print(f"Search Result {index + 1}:") print(result["name"]) print(result["url"]) print(result["snippet"]) diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/scrape_wikipedia_tables.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/scrape_wikipedia_tables.py index 913d2a6a20..ef193907b7 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/scrape_wikipedia_tables.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/scrape_wikipedia_tables.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def scrape_wikipedia_tables(url: str, header_keyword: str): - """ - Scrapes Wikipedia tables based on a given URL and header keyword. + """Scrapes Wikipedia tables based on a given URL and header keyword. Args: url: The URL of the Wikipedia page to scrape. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/transcribe_audio_file.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/transcribe_audio_file.py index dabdb1d060..c96257d69c 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/transcribe_audio_file.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/transcribe_audio_file.py @@ -6,8 +6,7 @@ @with_requirements(["openai-whisper"]) def transcribe_audio_file(file_path): - """ - Transcribes the audio file located at the given file path. + """Transcribes the audio file located at the given file path. Args: file_path (str): The path to the audio file. diff --git a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/youtube_download.py b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/youtube_download.py index a4d472c423..7b10111f21 100644 --- a/autogen/agentchat/contrib/captainagent/tools/information_retrieval/youtube_download.py +++ b/autogen/agentchat/contrib/captainagent/tools/information_retrieval/youtube_download.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def youtube_download(url: str): - """ - Downloads a YouTube video and returns the download link. + """Downloads a YouTube video and returns the download link. Args: url: The URL of the YouTube video. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/calculate_circle_area_from_diameter.py b/autogen/agentchat/contrib/captainagent/tools/math/calculate_circle_area_from_diameter.py index 9d670fc535..161a9fb422 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/calculate_circle_area_from_diameter.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/calculate_circle_area_from_diameter.py @@ -6,8 +6,7 @@ @with_requirements(["sympy"]) def calculate_circle_area_from_diameter(diameter): - """ - Calculate the area of a circle given its diameter. + """Calculate the area of a circle given its diameter. Args: diameter (float): The diameter of the circle. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/calculate_day_of_the_week.py b/autogen/agentchat/contrib/captainagent/tools/math/calculate_day_of_the_week.py index 5a6b9b7b1a..3cdf964ee4 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/calculate_day_of_the_week.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/calculate_day_of_the_week.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def calculate_day_of_the_week(total_days: int, starting_day: str): - """ - Calculates the day of the week after a given number of days starting from a specified day. + """Calculates the day of the week after a given number of days starting from a specified day. Args: total_days: The number of days to calculate. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/calculate_fraction_sum.py b/autogen/agentchat/contrib/captainagent/tools/math/calculate_fraction_sum.py index 60eb214fdc..f80f1216d0 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/calculate_fraction_sum.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/calculate_fraction_sum.py @@ -4,8 +4,7 @@ def calculate_fraction_sum( fraction1_numerator: int, fraction1_denominator: int, fraction2_numerator: int, fraction2_denominator: int ): - """ - Calculates the sum of two fractions and returns the result as a mixed number. + """Calculates the sum of two fractions and returns the result as a mixed number. Args: fraction1_numerator: The numerator of the first fraction. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/calculate_matrix_power.py b/autogen/agentchat/contrib/captainagent/tools/math/calculate_matrix_power.py index 0561a68bc3..b13cc42d9a 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/calculate_matrix_power.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/calculate_matrix_power.py @@ -6,8 +6,7 @@ @with_requirements(["sympy"]) def calculate_matrix_power(matrix, power): - """ - Calculate the power of a given matrix. + """Calculate the power of a given matrix. Args: matrix (list): An array of numbers that represents the matrix. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/calculate_reflected_point.py b/autogen/agentchat/contrib/captainagent/tools/math/calculate_reflected_point.py index a86852bfdb..0bbd0fa6a8 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/calculate_reflected_point.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/calculate_reflected_point.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def calculate_reflected_point(point): - """ - Calculates the reflection point of a given point about the line y=x. + """Calculates the reflection point of a given point about the line y=x. Args: point (dict): A dictionary representing the coordinates of the point. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/complex_numbers_product.py b/autogen/agentchat/contrib/captainagent/tools/math/complex_numbers_product.py index e4d8171d08..5c79a8b911 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/complex_numbers_product.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/complex_numbers_product.py @@ -6,8 +6,7 @@ @with_requirements(["sympy"]) def complex_numbers_product(complex_numbers): - """ - Calculates the product of a list of complex numbers. + """Calculates the product of a list of complex numbers. Args: complex_numbers (list): A list of dictionaries representing complex numbers. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/compute_currency_conversion.py b/autogen/agentchat/contrib/captainagent/tools/math/compute_currency_conversion.py index 51810fb5c4..b88e48b73f 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/compute_currency_conversion.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/compute_currency_conversion.py @@ -6,8 +6,7 @@ @with_requirements(["sympy"]) def compute_currency_conversion(amount, exchange_rate): - """ - Compute the currency conversion of the given amount using the provided exchange rate. + """Compute the currency conversion of the given amount using the provided exchange rate. Args: amount (float): The amount to be converted. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/count_distinct_permutations.py b/autogen/agentchat/contrib/captainagent/tools/math/count_distinct_permutations.py index 91d3fdf906..6f469448e7 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/count_distinct_permutations.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/count_distinct_permutations.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def count_distinct_permutations(sequence): - """ - Counts the number of distinct permutations of a sequence where items may be indistinguishable. + """Counts the number of distinct permutations of a sequence where items may be indistinguishable. Args: sequence (iterable): The sequence for which to count the distinct permutations. @@ -12,7 +11,7 @@ def count_distinct_permutations(sequence): int: The number of distinct permutations. Example: - >>> count_distinct_permutations('aab') + >>> count_distinct_permutations("aab") 3 >>> count_distinct_permutations([1, 2, 2]) 3 diff --git a/autogen/agentchat/contrib/captainagent/tools/math/evaluate_expression.py b/autogen/agentchat/contrib/captainagent/tools/math/evaluate_expression.py index dfac1eadf1..565bbc31c6 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/evaluate_expression.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/evaluate_expression.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def evaluate_expression(expression): - """ - Evaluates a mathematical expression with support for floor function notation and power notation. + """Evaluates a mathematical expression with support for floor function notation and power notation. Args: expression (str): The mathematical expression to evaluate. It can only contain one symbol 'x'. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/find_continuity_point.py b/autogen/agentchat/contrib/captainagent/tools/math/find_continuity_point.py index d311f0469e..43c549ae2c 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/find_continuity_point.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/find_continuity_point.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def find_continuity_point(f_leq, f_gt, x_value): - """ - Find the value 'a' that ensures the continuity of a piecewise function at a given point. + """Find the value 'a' that ensures the continuity of a piecewise function at a given point. Args: f_leq (str): The function expression for f(x) when x is less than or equal to the continuity point, in the form of a string. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/fraction_to_mixed_numbers.py b/autogen/agentchat/contrib/captainagent/tools/math/fraction_to_mixed_numbers.py index 06f64b5ec0..5f3a6db4e2 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/fraction_to_mixed_numbers.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/fraction_to_mixed_numbers.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def fraction_to_mixed_numbers(numerator, denominator): - """ - Simplifies a fraction to its lowest terms and returns it as a mixed number. + """Simplifies a fraction to its lowest terms and returns it as a mixed number. Args: numerator (int): The numerator of the fraction. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/modular_inverse_sum.py b/autogen/agentchat/contrib/captainagent/tools/math/modular_inverse_sum.py index c4ef5cb0d3..605627baed 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/modular_inverse_sum.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/modular_inverse_sum.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def modular_inverse_sum(expressions, modulus): - """ - Calculates the sum of modular inverses of the given expressions modulo the specified modulus. + """Calculates the sum of modular inverses of the given expressions modulo the specified modulus. Args: expressions (list): A list of numbers for which the modular inverses need to be calculated. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/simplify_mixed_numbers.py b/autogen/agentchat/contrib/captainagent/tools/math/simplify_mixed_numbers.py index e70d54b995..12effe482d 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/simplify_mixed_numbers.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/simplify_mixed_numbers.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def simplify_mixed_numbers(numerator1, denominator1, numerator2, denominator2, whole_number1, whole_number2): - """ - Simplifies the sum of two mixed numbers and returns the result as a string in the format 'a b/c'. + """Simplifies the sum of two mixed numbers and returns the result as a string in the format 'a b/c'. Args: numerator1 (int): The numerator of the first fraction. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/sum_of_digit_factorials.py b/autogen/agentchat/contrib/captainagent/tools/math/sum_of_digit_factorials.py index eb63ccadcf..0662d4213d 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/sum_of_digit_factorials.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/sum_of_digit_factorials.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def sum_of_digit_factorials(number): - """ - Calculates the sum of the factorial of each digit in a number, often used in problems involving curious numbers like 145. + """Calculates the sum of the factorial of each digit in a number, often used in problems involving curious numbers like 145. Args: number (int): The number for which to calculate the sum of digit factorials. diff --git a/autogen/agentchat/contrib/captainagent/tools/math/sum_of_primes_below.py b/autogen/agentchat/contrib/captainagent/tools/math/sum_of_primes_below.py index b57a8e7572..80d43151e7 100644 --- a/autogen/agentchat/contrib/captainagent/tools/math/sum_of_primes_below.py +++ b/autogen/agentchat/contrib/captainagent/tools/math/sum_of_primes_below.py @@ -2,8 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 def sum_of_primes_below(threshold): - """ - Calculates the sum of all prime numbers below a given threshold. + """Calculates the sum of all prime numbers below a given threshold. Args: threshold (int): The maximum number (exclusive) up to which primes are summed. diff --git a/autogen/agentchat/contrib/gpt_assistant_agent.py b/autogen/agentchat/contrib/gpt_assistant_agent.py index 3512e11abc..d0a2f6f822 100644 --- a/autogen/agentchat/contrib/gpt_assistant_agent.py +++ b/autogen/agentchat/contrib/gpt_assistant_agent.py @@ -9,7 +9,7 @@ import logging import time from collections import defaultdict -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Optional, Union from autogen import OpenAIWrapper from autogen.agentchat.agent import Agent @@ -21,8 +21,7 @@ class GPTAssistantAgent(ConversableAgent): - """ - An experimental AutoGen agent class that leverages the OpenAI Assistant API for conversational capabilities. + """An experimental AutoGen agent class that leverages the OpenAI Assistant API for conversational capabilities. This agent is unique in its reliance on the OpenAI Assistant for state management, differing from other agents like ConversableAgent. """ @@ -38,32 +37,30 @@ def __init__( overwrite_tools: bool = False, **kwargs, ): + """Args: + name (str): name of the agent. It will be used to find the existing assistant by name. Please remember to delete an old assistant with the same name if you intend to create a new assistant with the same name. + instructions (str): instructions for the OpenAI assistant configuration. + When instructions is not None, the system message of the agent will be + set to the provided instructions and used in the assistant run, irrespective + of the overwrite_instructions flag. But when instructions is None, + and the assistant does not exist, the system message will be set to + AssistantAgent.DEFAULT_SYSTEM_MESSAGE. If the assistant exists, the + system message will be set to the existing assistant instructions. + llm_config (dict or False): llm inference configuration. + - model: Model to use for the assistant (gpt-4-1106-preview, gpt-3.5-turbo-1106). + assistant_config + - assistant_id: ID of the assistant to use. If None, a new assistant will be created. + - check_every_ms: check thread run status interval + - tools: Give Assistants access to OpenAI-hosted tools like Code Interpreter and Knowledge Retrieval, + or build your own tools using Function calling. ref https://platform.openai.com/docs/assistants/tools + - file_ids: (Deprecated) files used by retrieval in run. It is Deprecated, use tool_resources instead. https://platform.openai.com/docs/assistants/migration/what-has-changed. + - tool_resources: A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. + overwrite_instructions (bool): whether to overwrite the instructions of an existing assistant. This parameter is in effect only when assistant_id is specified in llm_config. + overwrite_tools (bool): whether to overwrite the tools of an existing assistant. This parameter is in effect only when assistant_id is specified in llm_config. + kwargs (dict): Additional configuration options for the agent. + - verbose (bool): If set to True, enables more detailed output from the assistant thread. + - Other kwargs: Except verbose, others are passed directly to ConversableAgent. """ - Args: - name (str): name of the agent. It will be used to find the existing assistant by name. Please remember to delete an old assistant with the same name if you intend to create a new assistant with the same name. - instructions (str): instructions for the OpenAI assistant configuration. - When instructions is not None, the system message of the agent will be - set to the provided instructions and used in the assistant run, irrespective - of the overwrite_instructions flag. But when instructions is None, - and the assistant does not exist, the system message will be set to - AssistantAgent.DEFAULT_SYSTEM_MESSAGE. If the assistant exists, the - system message will be set to the existing assistant instructions. - llm_config (dict or False): llm inference configuration. - - model: Model to use for the assistant (gpt-4-1106-preview, gpt-3.5-turbo-1106). - assistant_config - - assistant_id: ID of the assistant to use. If None, a new assistant will be created. - - check_every_ms: check thread run status interval - - tools: Give Assistants access to OpenAI-hosted tools like Code Interpreter and Knowledge Retrieval, - or build your own tools using Function calling. ref https://platform.openai.com/docs/assistants/tools - - file_ids: (Deprecated) files used by retrieval in run. It is Deprecated, use tool_resources instead. https://platform.openai.com/docs/assistants/migration/what-has-changed. - - tool_resources: A set of resources that are used by the assistant's tools. The resources are specific to the type of tool. - overwrite_instructions (bool): whether to overwrite the instructions of an existing assistant. This parameter is in effect only when assistant_id is specified in llm_config. - overwrite_tools (bool): whether to overwrite the tools of an existing assistant. This parameter is in effect only when assistant_id is specified in llm_config. - kwargs (dict): Additional configuration options for the agent. - - verbose (bool): If set to True, enables more detailed output from the assistant thread. - - Other kwargs: Except verbose, others are passed directly to ConversableAgent. - """ - self._verbose = kwargs.pop("verbose", False) openai_client_cfg, openai_assistant_cfg = self._process_assistant_config(llm_config, assistant_config) @@ -188,8 +185,7 @@ def _invoke_assistant( sender: Optional[Agent] = None, config: Optional[Any] = None, ) -> tuple[bool, Union[str, dict, None]]: - """ - Invokes the OpenAI assistant to generate a reply based on the given messages. + """Invokes the OpenAI assistant to generate a reply based on the given messages. Args: messages: A list of messages in the conversation history with the sender. @@ -199,7 +195,6 @@ def _invoke_assistant( Returns: A tuple containing a boolean indicating success and the assistant's reply. """ - if messages is None: messages = self._oai_messages[sender] unread_index = self._unread_index[sender] or 0 @@ -249,8 +244,7 @@ def _invoke_assistant( return True, response def _map_role_for_api(self, role: str) -> str: - """ - Maps internal message roles to the roles expected by the OpenAI Assistant API. + """Maps internal message roles to the roles expected by the OpenAI Assistant API. Args: role (str): The role from the internal message. @@ -271,8 +265,7 @@ def _map_role_for_api(self, role: str) -> str: return "assistant" def _get_run_response(self, thread, run): - """ - Waits for and processes the response of a run from the OpenAI assistant. + """Waits for and processes the response of a run from the OpenAI assistant. Args: run: The run object initiated with the OpenAI assistant. @@ -338,8 +331,7 @@ def _get_run_response(self, thread, run): raise ValueError(f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})") def _wait_for_run(self, run_id: str, thread_id: str) -> Any: - """ - Waits for a run to complete or reach a final state. + """Waits for a run to complete or reach a final state. Args: run_id: The ID of the run. @@ -357,10 +349,7 @@ def _wait_for_run(self, run_id: str, thread_id: str) -> Any: return run def _format_assistant_message(self, message_content): - """ - Formats the assistant's message to include annotations and citations. - """ - + """Formats the assistant's message to include annotations and citations.""" annotations = message_content.annotations citations = [] @@ -393,9 +382,7 @@ def can_execute_function(self, name: str) -> bool: return False def reset(self): - """ - Resets the agent, clearing any existing conversation thread and unread message indices. - """ + """Resets the agent, clearing any existing conversation thread and unread message indices.""" super().reset() for thread in self._openai_threads.values(): # Delete the existing thread to start fresh in the next conversation @@ -471,8 +458,7 @@ def delete_assistant(self): self._openai_client.beta.assistants.delete(self.assistant_id) def find_matching_assistant(self, candidate_assistants, instructions, tools): - """ - Find the matching assistant from a list of candidate assistants. + """Find the matching assistant from a list of candidate assistants. Filter out candidates with the same name but different instructions, and function names. """ matching_assistants = [] @@ -520,10 +506,7 @@ def find_matching_assistant(self, candidate_assistants, instructions, tools): return matching_assistants def _process_assistant_config(self, llm_config, assistant_config): - """ - Process the llm_config and assistant_config to extract the model name and assistant related configurations. - """ - + """Process the llm_config and assistant_config to extract the model name and assistant related configurations.""" if llm_config is False: raise ValueError("llm_config=False is not supported for GPTAssistantAgent.") diff --git a/autogen/agentchat/contrib/graph_rag/document.py b/autogen/agentchat/contrib/graph_rag/document.py index 2fbd9a5961..e39464239d 100644 --- a/autogen/agentchat/contrib/graph_rag/document.py +++ b/autogen/agentchat/contrib/graph_rag/document.py @@ -10,9 +10,7 @@ class DocumentType(Enum): - """ - Enum for supporting document type. - """ + """Enum for supporting document type.""" TEXT = auto() HTML = auto() @@ -22,9 +20,7 @@ class DocumentType(Enum): @dataclass class Document: - """ - A wrapper of graph store query results. - """ + """A wrapper of graph store query results.""" doctype: DocumentType data: Optional[object] = None diff --git a/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py index 607a2e3215..37eca45c07 100644 --- a/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py +++ b/autogen/agentchat/contrib/graph_rag/falkor_graph_query_engine.py @@ -4,7 +4,6 @@ import os import warnings -from typing import List from falkordb import FalkorDB, Graph from graphrag_sdk import KnowledgeGraph, Source @@ -18,9 +17,7 @@ class FalkorGraphQueryEngine: - """ - This is a wrapper for FalkorDB KnowledgeGraph. - """ + """This is a wrapper for FalkorDB KnowledgeGraph.""" def __init__( self, @@ -32,8 +29,7 @@ def __init__( model: GenerativeModel = OpenAiGenerativeModel("gpt-4o"), ontology: Ontology | None = None, ): - """ - Initialize a FalkorDB knowledge graph. + """Initialize a FalkorDB knowledge graph. Please also refer to https://github.com/FalkorDB/GraphRAG-SDK/blob/main/graphrag_sdk/kg.py TODO: Fix LLM API cost calculation for FalkorDB useages. @@ -61,9 +57,7 @@ def __init__( self.falkordb = FalkorDB(host=self.host, port=self.port, username=self.username, password=self.password) def connect_db(self): - """ - Connect to an existing knowledge graph. - """ + """Connect to an existing knowledge graph.""" if self.name in self.falkordb.list_graphs(): try: self.ontology = self._load_ontology_from_db() @@ -89,9 +83,7 @@ def connect_db(self): raise ValueError(f"Knowledge graph '{self.name}' does not exist") def init_db(self, input_doc: list[Document]): - """ - Build the knowledge graph with input documents. - """ + """Build the knowledge graph with input documents.""" sources = [] for doc in input_doc: if os.path.exists(doc.path_or_url): @@ -124,12 +116,11 @@ def init_db(self, input_doc: list[Document]): else: raise ValueError("No input documents could be loaded.") - def add_records(self, new_records: list) -> bool: + def add_records(self, new_records: list[Document]) -> bool: raise NotImplementedError("This method is not supported by FalkorDB SDK yet.") def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult: - """ - Query the knowledge graph with a question and optional message history. + """Query the knowledge graph with a question and optional message history. Args: question: a human input question. @@ -150,9 +141,7 @@ def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryR return GraphStoreQueryResult(answer=response["response"], results=[]) def delete(self) -> bool: - """ - Delete graph and its data from database. - """ + """Delete graph and its data from database.""" all_graphs = self.falkordb.list_graphs() if self.name in all_graphs: self.falkordb.select_graph(self.name).delete() @@ -164,9 +153,7 @@ def __get_ontology_storage_graph(self) -> Graph: return self.falkordb.select_graph(self.ontology_table_name) def _save_ontology_to_db(self, ontology: Ontology): - """ - Save graph ontology to a separate table with {graph_name}_ontology - """ + """Save graph ontology to a separate table with {graph_name}_ontology""" if self.ontology_table_name in self.falkordb.list_graphs(): raise ValueError(f"Knowledge graph {self.name} is already created.") graph = self.__get_ontology_storage_graph() diff --git a/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py index fd6eb1a5d4..4b09a2a581 100644 --- a/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py +++ b/autogen/agentchat/contrib/graph_rag/falkor_graph_rag_capability.py @@ -2,7 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Optional, Union from autogen import Agent, ConversableAgent, UserProxyAgent @@ -12,22 +12,18 @@ class FalkorGraphRagCapability(GraphRagCapability): - """ - The FalkorDB GraphRAG capability integrate FalkorDB with graphrag_sdk version: 0.1.3b0. + """The FalkorDB GraphRAG capability integrate FalkorDB with graphrag_sdk version: 0.1.3b0. Ref: https://github.com/FalkorDB/GraphRAG-SDK/tree/2-move-away-from-sql-to-json-ontology-detection For usage, please refer to example notebook/agentchat_graph_rag_falkordb.ipynb """ def __init__(self, query_engine: FalkorGraphQueryEngine): - """ - initialize GraphRAG capability with a graph query engine - """ + """Initialize GraphRAG capability with a graph query engine""" self.query_engine = query_engine def add_to_agent(self, agent: UserProxyAgent): - """ - Add FalkorDB GraphRAG capability to a UserProxyAgent. + """Add FalkorDB GraphRAG capability to a UserProxyAgent. The restriction to a UserProxyAgent to make sure the returned message does not contain information retrieved from the graph DB instead of any LLMs. """ self.graph_rag_agent = agent @@ -51,8 +47,7 @@ def _reply_using_falkordb_query( sender: Optional[Agent] = None, config: Optional[Any] = None, ) -> tuple[bool, Union[str, dict, None]]: - """ - Query FalkorDB and return the message. Internally, it utilises OpenAI to generate a reply based on the given messages. + """Query FalkorDB and return the message. Internally, it utilises OpenAI to generate a reply based on the given messages. The history with FalkorDB is also logged and updated. The agent's system message will be incorporated into the query, if it's not blank. @@ -68,7 +63,6 @@ def _reply_using_falkordb_query( Returns: A tuple containing a boolean indicating success and the assistant's reply. """ - # question = self._get_last_question(messages[-1]) question = self._messages_summary(messages, recipient.system_message) result: GraphStoreQueryResult = self.query_engine.query(question) @@ -83,7 +77,6 @@ def _messages_summary(self, messages: Union[dict, str], system_message: str) -> agent: """ - if isinstance(messages, str): if system_message: summary = f"IMPORTANT: {system_message}\nContext:\n\n{messages}" @@ -94,7 +87,7 @@ def _messages_summary(self, messages: Union[dict, str], system_message: str) -> summary = "" for message in messages: if "content" in message and "tool_calls" not in message and "tool_responses" not in message: - summary += f"{message.get('name', '')}: {message.get('content','')}\n\n" + summary += f"{message.get('name', '')}: {message.get('content', '')}\n\n" if system_message: summary = f"IMPORTANT: {system_message}\nContext:\n\n{summary}" diff --git a/autogen/agentchat/contrib/graph_rag/graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/graph_query_engine.py index b10562e7ee..b474902a6c 100644 --- a/autogen/agentchat/contrib/graph_rag/graph_query_engine.py +++ b/autogen/agentchat/contrib/graph_rag/graph_query_engine.py @@ -5,15 +5,14 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT from dataclasses import dataclass, field -from typing import List, Optional, Protocol +from typing import Optional, Protocol from .document import Document @dataclass class GraphStoreQueryResult: - """ - A wrapper of graph store query results. + """A wrapper of graph store query results. answer: human readable answer to question/query. results: intermediate results to question/query, e.g. node entities. @@ -30,8 +29,7 @@ class GraphQueryEngine(Protocol): """ def init_db(self, input_doc: list[Document] | None = None): - """ - This method initializes graph database with the input documents or records. + """This method initializes graph database with the input documents or records. Usually, it takes the following steps, 1. connecting to a graph database. 2. extract graph nodes, edges based on input data, graph schema and etc. @@ -44,13 +42,9 @@ def init_db(self, input_doc: list[Document] | None = None): pass def add_records(self, new_records: list) -> bool: - """ - Add new records to the underlying database and add to the graph if required. - """ + """Add new records to the underlying database and add to the graph if required.""" pass def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult: - """ - This method transform a string format question into database query and return the result. - """ + """This method transform a string format question into database query and return the result.""" pass diff --git a/autogen/agentchat/contrib/graph_rag/graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/graph_rag_capability.py index 9d47180ae7..70823b78bc 100644 --- a/autogen/agentchat/contrib/graph_rag/graph_rag_capability.py +++ b/autogen/agentchat/contrib/graph_rag/graph_rag_capability.py @@ -11,8 +11,7 @@ class GraphRagCapability(AgentCapability): - """ - A graph-based RAG capability uses a graph query engine to give a conversable agent the graph-based RAG ability. + """A graph-based RAG capability uses a graph query engine to give a conversable agent the graph-based RAG ability. An agent class with graph-based RAG capability could 1. create a graph in the underlying database with input documents. @@ -55,9 +54,7 @@ class GraphRagCapability(AgentCapability): """ def __init__(self, query_engine: GraphQueryEngine): - """ - Initialize graph-based RAG capability with a graph query engine - """ + """Initialize graph-based RAG capability with a graph query engine""" ... def add_to_agent(self, agent: ConversableAgent): diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py index 9225b36cfe..afba878470 100644 --- a/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py +++ b/autogen/agentchat/contrib/graph_rag/neo4j_graph_query_engine.py @@ -2,7 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 import os -from typing import Dict, List, Optional, TypeAlias, Union +from typing import Optional, TypeAlias, Union from llama_index.core import PropertyGraphIndex, SimpleDirectoryReader from llama_index.core.base.embeddings.base import BaseEmbedding @@ -23,8 +23,7 @@ class Neo4jGraphQueryEngine(GraphQueryEngine): - """ - This class serves as a wrapper for a property graph query engine backed by LlamaIndex and Neo4j, + """This class serves as a wrapper for a property graph query engine backed by LlamaIndex and Neo4j, facilitating the creating, connecting, updating, and querying of LlamaIndex property graphs. It builds a property graph Index from input documents, @@ -57,8 +56,7 @@ def __init__( schema: Optional[Union[dict[str, str], list[Triple]]] = None, strict: Optional[bool] = False, ): - """ - Initialize a Neo4j Property graph. + """Initialize a Neo4j Property graph. Please also refer to https://docs.llamaindex.ai/en/stable/examples/property_graph/graph_store/ Args: @@ -88,10 +86,7 @@ def __init__( self.strict = strict def init_db(self, input_doc: list[Document] | None = None): - """ - Build the knowledge graph with input documents. - """ - + """Build the knowledge graph with input documents.""" self.documents = self._load_doc(input_doc) self.graph_store = Neo4jPropertyGraphStore( @@ -117,9 +112,7 @@ def init_db(self, input_doc: list[Document] | None = None): ) def connect_db(self): - """ - Connect to an existing knowledge graph database. - """ + """Connect to an existing knowledge graph database.""" self.graph_store = Neo4jPropertyGraphStore( username=self.username, password=self.password, @@ -138,8 +131,7 @@ def connect_db(self): ) def add_records(self, new_records: list) -> bool: - """ - Add new records to the knowledge graph. Must be local files. + """Add new records to the knowledge graph. Must be local files. Args: new_records (List[Document]): List of new documents to add. @@ -152,9 +144,8 @@ def add_records(self, new_records: list) -> bool: try: """ - SimpleDirectoryReader will select the best file reader based on the file extensions, including: - [DocxReader, EpubReader, HWPReader, ImageReader, IPYNBReader, MarkdownReader, MboxReader, - PandasCSVReader, PandasExcelReader,PDFReader,PptxReader, VideoAudioReader] + SimpleDirectoryReader will select the best file reader based on the file extensions, + see _load_doc for supported file types. """ new_documents = SimpleDirectoryReader(input_files=[doc.path_or_url for doc in new_records]).load_data() @@ -167,8 +158,7 @@ def add_records(self, new_records: list) -> bool: return False def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult: - """ - Query the property graph with a question using LlamaIndex chat engine. + """Query the property graph with a question using LlamaIndex chat engine. We use the condense_plus_context chat mode which condenses the conversation history and the user query into a standalone question, and then build a context for the standadlone question @@ -192,29 +182,27 @@ def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryR return GraphStoreQueryResult(answer=str(response)) def _clear(self) -> None: - """ - Delete all entities and relationships in the graph. + """Delete all entities and relationships in the graph. TODO: Delete all the data in the database including indexes and constraints. """ with self.graph_store._driver.session() as session: session.run("MATCH (n) DETACH DELETE n;") def _load_doc(self, input_doc: list[Document]) -> list[LlamaDocument]: - """ - Load documents from the input files. Currently support the following file types: - .csv - comma-separated values - .docx - Microsoft Word - .epub - EPUB ebook format - .hwp - Hangul Word Processor - .ipynb - Jupyter Notebook - .jpeg, .jpg - JPEG image - .mbox - MBOX email archive - .md - Markdown - .mp3, .mp4 - audio and video - .pdf - Portable Document Format - .png - Portable Network Graphics - .ppt, .pptm, .pptx - Microsoft PowerPoint - .json JSON files + """Load documents from the input files. Currently support the following file types: + .csv - comma-separated values + .docx - Microsoft Word + .epub - EPUB ebook format + .hwp - Hangul Word Processor + .ipynb - Jupyter Notebook + .jpeg, .jpg - JPEG image + .mbox - MBOX email archive + .md - Markdown + .mp3, .mp4 - audio and video + .pdf - Portable Document Format + .png - Portable Network Graphics + .ppt, .pptm, .pptx - Microsoft PowerPoint + .json JSON files """ for doc in input_doc: if not os.path.exists(doc.path_or_url): @@ -236,8 +224,7 @@ def _load_doc(self, input_doc: list[Document]) -> list[LlamaDocument]: return loaded_documents def _create_kg_extractors(self): - """ - If strict is True, + """If strict is True, extract paths following a strict schema of allowed relationships for each entity. If strict is False, @@ -245,7 +232,6 @@ def _create_kg_extractors(self): # To add more extractors, please refer to https://docs.llamaindex.ai/en/latest/module_guides/indexing/lpg_index_guide/#construction """ - # kg_extractors = [ SchemaLLMPathExtractor( diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py index c9a7cfc3ba..9362cb8fe1 100644 --- a/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py +++ b/autogen/agentchat/contrib/graph_rag/neo4j_graph_rag_capability.py @@ -2,7 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Optional, Union from autogen import Agent, ConversableAgent, UserProxyAgent @@ -12,8 +12,7 @@ class Neo4jGraphCapability(GraphRagCapability): - """ - The Neo4j graph capability integrates Neo4j Property graph into a graph rag agent. + """The Neo4j graph capability integrates Neo4j Property graph into a graph rag agent. Ref: https://neo4j.com/labs/genai-ecosystem/llamaindex/#_property_graph_constructing_modules @@ -21,17 +20,13 @@ class Neo4jGraphCapability(GraphRagCapability): """ def __init__(self, query_engine: Neo4jGraphQueryEngine): - """ - initialize GraphRAG capability with a graph query engine - """ + """Initialize GraphRAG capability with a graph query engine""" self.query_engine = query_engine def add_to_agent(self, agent: UserProxyAgent): - """ - Add Neo4j GraphRAG capability to a UserProxyAgent. + """Add Neo4j GraphRAG capability to a UserProxyAgent. The restriction to a UserProxyAgent to make sure the returned message only contains information retrieved from the graph DB instead of any LLMs. """ - self.graph_rag_agent = agent # Validate the agent config @@ -53,8 +48,7 @@ def _reply_using_neo4j_query( sender: Optional[Agent] = None, config: Optional[Any] = None, ) -> tuple[bool, Union[str, dict, None]]: - """ - Query neo4j and return the message. Internally, it queries the Property graph + """Query neo4j and return the message. Internally, it queries the Property graph and returns the answer from the graph query engine. TODO: reply with a dictionary including both the answer and semantic source triplets. diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_query_engine.py b/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_query_engine.py new file mode 100644 index 0000000000..7b85b0c2d3 --- /dev/null +++ b/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_query_engine.py @@ -0,0 +1,207 @@ +# Copyright (c) 2023 - 2025, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +import asyncio +import logging +from typing import List, Optional, Union + +from neo4j import GraphDatabase +from neo4j_graphrag.embeddings import Embedder, OpenAIEmbeddings +from neo4j_graphrag.experimental.pipeline.kg_builder import SimpleKGPipeline +from neo4j_graphrag.generation import GraphRAG +from neo4j_graphrag.indexes import create_vector_index +from neo4j_graphrag.llm.openai_llm import LLMInterface, OpenAILLM +from neo4j_graphrag.retrievers import VectorRetriever + +from .document import Document, DocumentType +from .graph_query_engine import GraphQueryEngine, GraphStoreQueryResult + +# Set up logging +logging.basicConfig(level=logging.INFO) +logging.getLogger("httpx").setLevel(logging.WARNING) +logger = logging.getLogger(__name__) + + +class Neo4jNativeGraphQueryEngine(GraphQueryEngine): + """A graph query engine implemented using the Neo4j GraphRAG SDK. + Provides functionality to initialize a knowledge graph, + create a vector index, and query the graph using Neo4j and LLM. + """ + + def __init__( + self, + host: str = "neo4j://localhost", + port: int = 7687, + username: str = "neo4j", + password: str = "password", + embeddings: Optional[Embedder] = OpenAIEmbeddings(model="text-embedding-3-large"), + embedding_dimension: Optional[int] = 3072, + llm: Optional[LLMInterface] = OpenAILLM( + model_name="gpt-4o", + model_params={"response_format": {"type": "json_object"}, "temperature": 0}, + ), + query_llm: Optional[LLMInterface] = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0}), + entities: Optional[List[str]] = None, + relations: Optional[List[str]] = None, + potential_schema: Optional[List[tuple[str, str, str]]] = None, + ): + """Initialize a Neo4j graph query engine. + + Args: + host (str): Neo4j host URL. + port (int): Neo4j port. + username (str): Neo4j username. + password (str): Neo4j password. + embeddings (Embedder): Embedding model to embed chunk data and retrieve answers. + embedding_dimension (int): Dimension of the embeddings for the model. + llm (LLMInterface): Language model for creating the knowledge graph (returns JSON responses). + query_llm (LLMInterface): Language model for querying the knowledge graph. + entities (List[str], optional): Custom entities for guiding graph construction. + relations (List[str], optional): Custom relations for guiding graph construction. + potential_schema (List[tuple[str, str, str]], optional): + Schema (triplets, i.e., [entity] -> [relationship] -> [entity]) to guide graph construction. + """ + self.uri = f"{host}:{port}" + self.driver = GraphDatabase.driver(self.uri, auth=(username, password)) + self.embeddings = embeddings + self.embedding_dimension = embedding_dimension + self.llm = llm + self.query_llm = query_llm + self.entities = entities + self.relations = relations + self.potential_schema = potential_schema + + def init_db(self, input_doc: Union[list[Document], None] = None): + """Initialize the Neo4j graph database using the provided input doc. + Currently this method only supports single document input (only reads the first doc). + + This method supports both text and PDF documents. It performs the following steps: + 1. Clears the existing database. + 2. Extracts graph nodes and relationships from the input data to build a knowledge graph. + 3. Creates a vector index for efficient retrieval. + + Args: + input_doc (list[Document]): Input documents for building the graph. + + Raises: + ValueError: If the input document is not provided or its type is unsupported. + """ + if input_doc is None or len(input_doc) == 0: + raise ValueError("Input document is required to initialize the database.") + elif len(input_doc) > 1: + raise ValueError("Only the first document will be used to initialize the database.") + + logger.info("Clearing the database...") + self._clear_db() + + self._initialize_kg_builders() + + self._build_graph(input_doc) + + self.index_name = "vector-index-name" + logger.info(f"Creating vector index '{self.index_name}'...") + self._create_index(self.index_name) + + def add_records(self, new_records: list[Document]) -> bool: + """Add new records to the Neo4j database. + + Args: + new_records (list[Document]): List of new Documents to be added + + Returns: + bool: True if records were added successfully, False otherwise. + """ + for record in new_records: + if not isinstance(record, Document): + raise ValueError("Invalid record type. Expected Document.") + + self._build_graph(new_records) + + return True + + def query(self, question: str, n_results: int = 1, **kwargs) -> GraphStoreQueryResult: + """Query the Neo4j database using a natural language question. + + Args: + question (str): The question to be answered by querying the graph. + + Returns: + GraphStoreQueryResult: The result of the query. + """ + self.retriever = VectorRetriever( + driver=self.driver, + index_name=self.index_name, + embedder=self.embeddings, + ) + rag = GraphRAG(retriever=self.retriever, llm=self.query_llm) + result = rag.search(query_text=question, retriever_config={"top_k": 5}) + + return GraphStoreQueryResult(answer=result.answer) + + def _create_index(self, name: str): + """Create a vector index for the Neo4j knowledge graph. + + Args: + name (str): Name of the vector index to create. + """ + logger.info(f"Creating vector index '{name}'...") + create_vector_index( + self.driver, + name=name, + label="Chunk", + embedding_property="embedding", + dimensions=self.embedding_dimension, + similarity_fn="euclidean", + ) + logger.info(f"Vector index '{name}' created successfully.") + + def _clear_db(self): + """Clear all nodes and relationships from the Neo4j database.""" + logger.info("Clearing all nodes and relationships in the database...") + self.driver.execute_query("MATCH (n) DETACH DELETE n;") + logger.info("Database cleared successfully.") + + def _initialize_kg_builders(self): + """Initialize the knowledge graph builders""" + logger.info("Initializing the knowledge graph builders...") + self.text_kg_builder = SimpleKGPipeline( + driver=self.driver, + embedder=self.embeddings, + llm=self.llm, + entities=self.entities, + relations=self.relations, + potential_schema=self.potential_schema, + on_error="IGNORE", + from_pdf=False, + ) + + self.pdf_kg_builder = SimpleKGPipeline( + driver=self.driver, + embedder=self.embeddings, + llm=self.llm, + entities=self.entities, + relations=self.relations, + potential_schema=self.potential_schema, + on_error="IGNORE", + from_pdf=True, + ) + + def _build_graph(self, input_doc: List[Document]) -> None: + """Build the knowledge graph using the provided input documents. + + Args: + input_doc (List[Document]): List of input documents for building the graph. + """ + logger.info("Building the knowledge graph...") + for doc in input_doc: + if doc.doctype == DocumentType.TEXT: + with open(doc.path_or_url, "r") as file: + text = file.read() + asyncio.run(self.text_kg_builder.run_async(text=text)) + elif doc.doctype == DocumentType.PDF: + asyncio.run(self.pdf_kg_builder.run_async(file_path=doc.path_or_url)) + else: + raise ValueError(f"Unsupported document type: {doc.doctype}") + + logger.info("Knowledge graph built successfully.") diff --git a/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_rag_capability.py b/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_rag_capability.py new file mode 100644 index 0000000000..31175f0a65 --- /dev/null +++ b/autogen/agentchat/contrib/graph_rag/neo4j_native_graph_rag_capability.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023 - 2025, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Optional, Union + +from autogen import Agent, ConversableAgent + +from .graph_query_engine import GraphStoreQueryResult +from .graph_rag_capability import GraphRagCapability +from .neo4j_native_graph_query_engine import Neo4jNativeGraphQueryEngine + + +class Neo4jNativeGraphCapability(GraphRagCapability): + """The Neo4j native graph capability integrates Neo4j native query engine into a graph rag agent. + + For usage, please refer to example notebook/agentchat_graph_rag_neo4j_native.ipynb + """ + + def __init__(self, query_engine: Neo4jNativeGraphQueryEngine): + """Initialize GraphRAG capability with a neo4j native graph query engine""" + self.query_engine = query_engine + + def add_to_agent(self, agent: ConversableAgent): + """Add native Neo4j GraphRAG capability to a ConversableAgent. + llm_config of the agent must be None/False (default) to make sure the returned message only contains information retrieved from the graph DB instead of any LLMs. + """ + self.graph_rag_agent = agent + + # Validate the agent config + if agent.llm_config not in (None, False): + raise Exception( + "Agents with GraphRAG capabilities do not use an LLM configuration. Please set your llm_config to None or False." + ) + + # Register method to generate the reply using a Neo4j query + # All other reply methods will be removed + agent.register_reply( + [ConversableAgent, None], self._reply_using_native_neo4j_query, position=0, remove_other_reply_funcs=True + ) + + def _reply_using_native_neo4j_query( + self, + recipient: ConversableAgent, + messages: Optional[list[dict]] = None, + sender: Optional[Agent] = None, + config: Optional[Any] = None, + ) -> tuple[bool, Union[str, dict, None]]: + """Query Neo4j and return the message. Internally, it uses the Neo4jNativeGraphQueryEngine to query the graph. + + The agent's system message will be incorporated into the query, if it's not blank. + + If no results are found, a default message is returned: "I'm sorry, I don't have an answer for that." + + Args: + recipient: The agent instance that will receive the message. + messages: A list of messages in the conversation history with the sender. + sender: The agent instance that sent the message. + config: Optional configuration for message processing. + + Returns: + A tuple containing a boolean indicating success and the assistant's reply. + """ + question = self._messages_summary(messages, recipient.system_message) + result: GraphStoreQueryResult = self.query_engine.query(question) + + return True, result.answer if result.answer else "I'm sorry, I don't have an answer for that." + + def _messages_summary(self, messages: Union[dict, str], system_message: str) -> str: + """Summarize the messages in the conversation history. Excluding any message with 'tool_calls' and 'tool_responses' + Includes the 'name' (if it exists) and the 'content', with a new line between each one, like: + customer: + + + agent: + + """ + if isinstance(messages, str): + if system_message: + summary = f"IMPORTANT: {system_message}\nContext:\n\n{messages}" + else: + return messages + + elif isinstance(messages, list): + summary = "" + for message in messages: + if "content" in message and "tool_calls" not in message and "tool_responses" not in message: + summary += f"{message.get('name', '')}: {message.get('content', '')}\n\n" + + if system_message: + summary = f"IMPORTANT: {system_message}\nContext:\n\n{summary}" + + return summary + + else: + raise ValueError("Invalid messages format. Must be a list of messages or a string.") diff --git a/autogen/agentchat/contrib/img_utils.py b/autogen/agentchat/contrib/img_utils.py index f6f4747a2f..7dcd52f532 100644 --- a/autogen/agentchat/contrib/img_utils.py +++ b/autogen/agentchat/contrib/img_utils.py @@ -10,7 +10,7 @@ import re from io import BytesIO from math import ceil -from typing import Dict, List, Tuple, Union +from typing import Union import requests from PIL import Image @@ -38,8 +38,7 @@ def get_pil_image(image_file: Union[str, Image.Image]) -> Image.Image: - """ - Loads an image from a file and returns a PIL Image object. + """Loads an image from a file and returns a PIL Image object. Parameters: image_file (str, or Image): The filename, URL, URI, or base64 string of the image file. @@ -77,8 +76,7 @@ def get_pil_image(image_file: Union[str, Image.Image]) -> Image.Image: def get_image_data(image_file: Union[str, Image.Image], use_b64=True) -> bytes: - """ - Loads an image and returns its data either as raw bytes or in base64-encoded format. + """Loads an image and returns its data either as raw bytes or in base64-encoded format. This function first loads an image from the specified file, URL, or base64 string using the `get_pil_image` function. It then saves this image in memory in PNG format and @@ -108,8 +106,7 @@ def get_image_data(image_file: Union[str, Image.Image], use_b64=True) -> bytes: def llava_formatter(prompt: str, order_image_tokens: bool = False) -> tuple[str, list[str]]: - """ - Formats the input prompt by replacing image tags and returns the new prompt along with image locations. + """Formats the input prompt by replacing image tags and returns the new prompt along with image locations. Parameters: - prompt (str): The input string that may contain image tags like ``. @@ -119,7 +116,6 @@ def llava_formatter(prompt: str, order_image_tokens: bool = False) -> tuple[str, Returns: - Tuple[str, List[str]]: A tuple containing the formatted string and a list of images (loaded in b64 format). """ - # Initialize variables new_prompt = prompt image_locations = [] @@ -154,8 +150,7 @@ def llava_formatter(prompt: str, order_image_tokens: bool = False) -> tuple[str, def pil_to_data_uri(image: Image.Image) -> str: - """ - Converts a PIL Image object to a data URI. + """Converts a PIL Image object to a data URI. Parameters: image (Image.Image): The PIL Image object. @@ -190,8 +185,7 @@ def _get_mime_type_from_data_uri(base64_image): def gpt4v_formatter(prompt: str, img_format: str = "uri") -> list[Union[str, dict]]: - """ - Formats the input prompt by replacing image tags and returns a list of text and images. + """Formats the input prompt by replacing image tags and returns a list of text and images. Args: - prompt (str): The input string that may contain image tags like ``. @@ -239,8 +233,7 @@ def gpt4v_formatter(prompt: str, img_format: str = "uri") -> list[Union[str, dic def extract_img_paths(paragraph: str) -> list: - """ - Extract image paths (URLs or local paths) from a text paragraph. + """Extract image paths (URLs or local paths) from a text paragraph. Parameters: paragraph (str): The input text paragraph. @@ -259,8 +252,7 @@ def extract_img_paths(paragraph: str) -> list: def _to_pil(data: str) -> Image.Image: - """ - Converts a base64 encoded image data string to a PIL Image object. + """Converts a base64 encoded image data string to a PIL Image object. This function first decodes the base64 encoded string to bytes, then creates a BytesIO object from the bytes, and finally creates and returns a PIL Image object from the BytesIO object. @@ -275,8 +267,7 @@ def _to_pil(data: str) -> Image.Image: def message_formatter_pil_to_b64(messages: list[dict]) -> list[dict]: - """ - Converts the PIL image URLs in the messages to base64 encoded data URIs. + """Converts the PIL image URLs in the messages to base64 encoded data URIs. This function iterates over a list of message dictionaries. For each message, if it contains a 'content' key with a list of items, it looks for items @@ -333,8 +324,7 @@ def message_formatter_pil_to_b64(messages: list[dict]) -> list[dict]: def num_tokens_from_gpt_image( image_data: Union[str, Image.Image], model: str = "gpt-4-vision", low_quality: bool = False ) -> int: - """ - Calculate the number of tokens required to process an image based on its dimensions + """Calculate the number of tokens required to process an image based on its dimensions after scaling for different GPT models. Supports "gpt-4-vision", "gpt-4o", and "gpt-4o-mini". This function scales the image so that its longest edge is at most 2048 pixels and its shortest edge is at most 768 pixels (for "gpt-4-vision"). It then calculates the number of 512x512 tiles @@ -353,11 +343,10 @@ def num_tokens_from_gpt_image( Examples: -------- >>> from PIL import Image - >>> img = Image.new('RGB', (2500, 2500), color = 'red') + >>> img = Image.new("RGB", (2500, 2500), color="red") >>> num_tokens_from_gpt_image(img, model="gpt-4-vision") 765 """ - image = get_pil_image(image_data) # PIL Image width, height = image.size diff --git a/autogen/agentchat/contrib/llamaindex_conversable_agent.py b/autogen/agentchat/contrib/llamaindex_conversable_agent.py index a9973f39e3..a3863192fc 100644 --- a/autogen/agentchat/contrib/llamaindex_conversable_agent.py +++ b/autogen/agentchat/contrib/llamaindex_conversable_agent.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Dict, List, Optional, Tuple, Union +from typing import Optional, Union from autogen import OpenAIWrapper from autogen.agentchat import Agent, ConversableAgent @@ -48,17 +48,15 @@ def __init__( description: Optional[str] = None, **kwargs, ): + """Args: + name (str): agent name. + llama_index_agent (AgentRunner): llama index agent. + Please override this attribute if you want to reprogram the agent. + description (str): a short description of the agent. This description is used by other agents + (e.g. the GroupChatManager) to decide when to call upon this agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../conversable_agent#init). """ - Args: - name (str): agent name. - llama_index_agent (AgentRunner): llama index agent. - Please override this attribute if you want to reprogram the agent. - description (str): a short description of the agent. This description is used by other agents - (e.g. the GroupChatManager) to decide when to call upon this agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../conversable_agent#init). - """ - if llama_index_agent is None: raise ValueError("llama_index_agent must be provided") @@ -87,9 +85,9 @@ def _generate_oai_reply( """Generate a reply using autogen.oai.""" user_message, history = self._extract_message_and_history(messages=messages, sender=sender) - chatResponse: AgentChatResponse = self._llama_index_agent.chat(message=user_message, chat_history=history) + chat_response: AgentChatResponse = self._llama_index_agent.chat(message=user_message, chat_history=history) - extracted_response = chatResponse.response + extracted_response = chat_response.response return (True, extracted_response) @@ -102,11 +100,11 @@ async def _a_generate_oai_reply( """Generate a reply using autogen.oai.""" user_message, history = self._extract_message_and_history(messages=messages, sender=sender) - chatResponse: AgentChatResponse = await self._llama_index_agent.achat( + chat_response: AgentChatResponse = await self._llama_index_agent.achat( message=user_message, chat_history=history ) - extracted_response = chatResponse.response + extracted_response = chat_response.response return (True, extracted_response) diff --git a/autogen/agentchat/contrib/llava_agent.py b/autogen/agentchat/contrib/llava_agent.py index 05deb63678..5f1dec12e0 100644 --- a/autogen/agentchat/contrib/llava_agent.py +++ b/autogen/agentchat/contrib/llava_agent.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import json import logging -from typing import List, Optional, Tuple +from typing import Optional import replicate import requests @@ -34,13 +34,12 @@ def __init__( *args, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the ChatCompletion inference. - Please override this attribute if you want to reprogram the agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the ChatCompletion inference. + Please override this attribute if you want to reprogram the agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../conversable_agent#init). """ super().__init__( name, @@ -156,10 +155,7 @@ def llava_call_binary( def llava_call(prompt: str, llm_config: dict) -> str: - """ - Makes a call to the LLaVA service to generate text based on a given prompt - """ - + """Makes a call to the LLaVA service to generate text based on a given prompt""" prompt, images = llava_formatter(prompt, order_image_tokens=False) for im in images: @@ -172,5 +168,5 @@ def llava_call(prompt: str, llm_config: dict) -> str: config_list=llm_config["config_list"], max_new_tokens=llm_config.get("max_new_tokens", 2000), temperature=llm_config.get("temperature", 0.5), - seed=llm_config.get("seed", None), + seed=llm_config.get("seed"), ) diff --git a/autogen/agentchat/contrib/math_user_proxy_agent.py b/autogen/agentchat/contrib/math_user_proxy_agent.py index c0851f4d29..c3f0e1f9f5 100644 --- a/autogen/agentchat/contrib/math_user_proxy_agent.py +++ b/autogen/agentchat/contrib/math_user_proxy_agent.py @@ -7,7 +7,7 @@ import os import re from time import sleep -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Any, Callable, Literal, Optional, Union from pydantic import BaseModel, Extra, root_validator @@ -124,7 +124,7 @@ def _add_print_to_last_line(code): def _remove_print(code): - """remove all print statements from a string.""" + """Remove all print statements from a string.""" lines = code.splitlines() lines = [line for line in lines if not line.startswith("print(")] return "\n".join(lines) @@ -147,23 +147,22 @@ def __init__( max_invalid_q_per_step=3, # a parameter needed in MathChat **kwargs, ): - """ - Args: - name (str): name of the agent - is_termination_msg (function): a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - human_input_mode (str): whether to ask for human inputs every time a message is received. - Possible values are "ALWAYS", "TERMINATE", "NEVER". - (1) When "ALWAYS", the agent prompts for human input every time a message is received. - Under this mode, the conversation stops when the human input is "exit", - or when is_termination_msg is True and there is no human input. - (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or - the number of auto reply reaches the max_consecutive_auto_reply. - (3) (Default) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops - when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. - default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. - max_invalid_q_per_step (int): (ADDED) the maximum number of invalid queries per step. - **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#init). + """Args: + name (str): name of the agent + is_termination_msg (function): a function that takes a message in the form of a dictionary and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + human_input_mode (str): whether to ask for human inputs every time a message is received. + Possible values are "ALWAYS", "TERMINATE", "NEVER". + (1) When "ALWAYS", the agent prompts for human input every time a message is received. + Under this mode, the conversation stops when the human input is "exit", + or when is_termination_msg is True and there is no human input. + (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or + the number of auto reply reaches the max_consecutive_auto_reply. + (3) (Default) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops + when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. + default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. + max_invalid_q_per_step (int): (ADDED) the maximum number of invalid queries per step. + **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#init). """ super().__init__( name=name, @@ -366,9 +365,9 @@ def _generate_math_reply( def get_from_dict_or_env(data: dict[str, Any], key: str, env_key: str, default: Optional[str] = None) -> str: """Get a value from a dictionary or an environment variable.""" - if key in data and data[key]: + if data.get(key): return data[key] - elif env_key in os.environ and os.environ[env_key]: + elif os.environ.get(env_key): return os.environ[env_key] elif default is not None: return default @@ -402,6 +401,7 @@ class Config: extra = Extra.forbid @root_validator(skip_on_failure=True) + @classmethod def validate_environment(cls, values: dict) -> dict: """Validate that api key and python package exists in environment.""" wolfram_alpha_appid = get_from_dict_or_env(values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID") diff --git a/autogen/agentchat/contrib/multimodal_conversable_agent.py b/autogen/agentchat/contrib/multimodal_conversable_agent.py index d44b8060ba..c41019f8eb 100644 --- a/autogen/agentchat/contrib/multimodal_conversable_agent.py +++ b/autogen/agentchat/contrib/multimodal_conversable_agent.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import copy -from typing import Dict, List, Optional, Tuple, Union +from typing import Optional, Union from autogen import OpenAIWrapper from autogen.agentchat import Agent, ConversableAgent @@ -34,13 +34,12 @@ def __init__( *args, **kwargs, ): - """ - Args: - name (str): agent name. - system_message (str): system message for the OpenAIWrapper inference. - Please override this attribute if you want to reprogram the agent. - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](../conversable_agent#init). + """Args: + name (str): agent name. + system_message (str): system message for the OpenAIWrapper inference. + Please override this attribute if you want to reprogram the agent. + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](../conversable_agent#init). """ super().__init__( name, diff --git a/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py index def83d5f82..ca86f80305 100644 --- a/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py +++ b/autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import warnings -from typing import Callable, Dict, List, Literal, Optional +from typing import Callable, Literal, Optional from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent from autogen.agentchat.contrib.vectordb.utils import ( @@ -18,7 +18,7 @@ logger = get_logger(__name__) try: - import fastembed + import fastembed # noqa: F401 from qdrant_client import QdrantClient, models from qdrant_client.fastembed_common import QueryResponse except ImportError as e: @@ -35,69 +35,68 @@ def __init__( retrieve_config: Optional[dict] = None, # config for the retrieve agent **kwargs, ): - """ - Args: - name (str): name of the agent. - human_input_mode (str): whether to ask for human inputs every time a message is received. - Possible values are "ALWAYS", "TERMINATE", "NEVER". - 1. When "ALWAYS", the agent prompts for human input every time a message is received. - Under this mode, the conversation stops when the human input is "exit", - or when is_termination_msg is True and there is no human input. - 2. When "TERMINATE", the agent only prompts for human input only when a termination message is received or - the number of auto reply reaches the max_consecutive_auto_reply. - 3. When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops - when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - retrieve_config (dict or None): config for the retrieve agent. - To use default config, set to None. Otherwise, set to a dictionary with the following keys: - - task (Optional, str): the task of the retrieve chat. Possible values are "code", "qa" and "default". System - prompt will be different for different tasks. The default value is `default`, which supports both code and qa. - - client (Optional, qdrant_client.QdrantClient(":memory:")): A QdrantClient instance. If not provided, an in-memory instance will be assigned. Not recommended for production. - will be used. If you want to use other vector db, extend this class and override the `retrieve_docs` function. - - docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It can also be the path to a single file, - the url to a single file or a list of directories, files and urls. Default is None, which works only if the collection is already created. - - extra_docs (Optional, bool): when true, allows adding documents with unique IDs without overwriting existing ones; when false, it replaces existing documents using default IDs, risking collection overwrite., - when set to true it enables the system to assign unique IDs starting from "length+i" for new document chunks, preventing the replacement of existing documents and facilitating the addition of more content to the collection.. - By default, "extra_docs" is set to false, starting document IDs from zero. This poses a risk as new documents might overwrite existing ones, potentially causing unintended loss or alteration of data in the collection. - - collection_name (Optional, str): the name of the collection. - If key not provided, a default name `autogen-docs` will be used. - - model (Optional, str): the model to use for the retrieve chat. - If key not provided, a default model `gpt-4` will be used. - - chunk_token_size (Optional, int): the chunk token size for the retrieve chat. - If key not provided, a default size `max_tokens * 0.4` will be used. - - context_max_tokens (Optional, int): the context max token size for the retrieve chat. - If key not provided, a default size `max_tokens * 0.8` will be used. - - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are - "multi_lines" and "one_line". If key not provided, a default mode `multi_lines` will be used. - - must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True. - If chunk_mode is "one_line", this parameter will be ignored. - - embedding_model (Optional, str): the embedding model to use for the retrieve chat. - If key not provided, a default model `BAAI/bge-small-en-v1.5` will be used. All available models - can be found at `https://qdrant.github.io/fastembed/examples/Supported_Models/`. - - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None. - - customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "". - If not "" and the customized_answer_prefix is not in the answer, `Update Context` will be triggered. - - update_context (Optional, bool): if False, will not apply `Update Context` for interactive retrieval. Default is True. - - custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string. - The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name). - Default is None, tiktoken will be used and may not be accurate for non-OpenAI models. - - custom_text_split_function (Optional, Callable): a custom function to split a string into a list of strings. - Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`. - - custom_text_types (Optional, List[str]): a list of file types to be processed. Default is `autogen.retrieve_utils.TEXT_FORMATS`. - This only applies to files under the directories in `docs_path`. Explicitly included files and urls will be chunked regardless of their types. - - recursive (Optional, bool): whether to search documents recursively in the docs_path. Default is True. - - parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores. - - on_disk (Optional, bool): Whether to store the collection on disk. Default is False. - - quantization_config: Quantization configuration. If None, quantization will be disabled. - - hnsw_config: HNSW configuration. If None, default configuration will be used. - You can find more info about the hnsw configuration options at https://qdrant.tech/documentation/concepts/indexing/#vector-index. - API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection - - payload_indexing: Whether to create a payload index for the document field. Default is False. - You can find more info about the payload indexing options at https://qdrant.tech/documentation/concepts/indexing/#payload-index - API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_field_index - **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#init). + """Args: + name (str): name of the agent. + human_input_mode (str): whether to ask for human inputs every time a message is received. + Possible values are "ALWAYS", "TERMINATE", "NEVER". + 1. When "ALWAYS", the agent prompts for human input every time a message is received. + Under this mode, the conversation stops when the human input is "exit", + or when is_termination_msg is True and there is no human input. + 2. When "TERMINATE", the agent only prompts for human input only when a termination message is received or + the number of auto reply reaches the max_consecutive_auto_reply. + 3. When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops + when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + retrieve_config (dict or None): config for the retrieve agent. + To use default config, set to None. Otherwise, set to a dictionary with the following keys: + - task (Optional, str): the task of the retrieve chat. Possible values are "code", "qa" and "default". System + prompt will be different for different tasks. The default value is `default`, which supports both code and qa. + - client (Optional, qdrant_client.QdrantClient(":memory:")): A QdrantClient instance. If not provided, an in-memory instance will be assigned. Not recommended for production. + will be used. If you want to use other vector db, extend this class and override the `retrieve_docs` function. + - docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It can also be the path to a single file, + the url to a single file or a list of directories, files and urls. Default is None, which works only if the collection is already created. + - extra_docs (Optional, bool): when true, allows adding documents with unique IDs without overwriting existing ones; when false, it replaces existing documents using default IDs, risking collection overwrite., + when set to true it enables the system to assign unique IDs starting from "length+i" for new document chunks, preventing the replacement of existing documents and facilitating the addition of more content to the collection.. + By default, "extra_docs" is set to false, starting document IDs from zero. This poses a risk as new documents might overwrite existing ones, potentially causing unintended loss or alteration of data in the collection. + - collection_name (Optional, str): the name of the collection. + If key not provided, a default name `autogen-docs` will be used. + - model (Optional, str): the model to use for the retrieve chat. + If key not provided, a default model `gpt-4` will be used. + - chunk_token_size (Optional, int): the chunk token size for the retrieve chat. + If key not provided, a default size `max_tokens * 0.4` will be used. + - context_max_tokens (Optional, int): the context max token size for the retrieve chat. + If key not provided, a default size `max_tokens * 0.8` will be used. + - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are + "multi_lines" and "one_line". If key not provided, a default mode `multi_lines` will be used. + - must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True. + If chunk_mode is "one_line", this parameter will be ignored. + - embedding_model (Optional, str): the embedding model to use for the retrieve chat. + If key not provided, a default model `BAAI/bge-small-en-v1.5` will be used. All available models + can be found at `https://qdrant.github.io/fastembed/examples/Supported_Models/`. + - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None. + - customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "". + If not "" and the customized_answer_prefix is not in the answer, `Update Context` will be triggered. + - update_context (Optional, bool): if False, will not apply `Update Context` for interactive retrieval. Default is True. + - custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string. + The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name). + Default is None, tiktoken will be used and may not be accurate for non-OpenAI models. + - custom_text_split_function (Optional, Callable): a custom function to split a string into a list of strings. + Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`. + - custom_text_types (Optional, List[str]): a list of file types to be processed. Default is `autogen.retrieve_utils.TEXT_FORMATS`. + This only applies to files under the directories in `docs_path`. Explicitly included files and urls will be chunked regardless of their types. + - recursive (Optional, bool): whether to search documents recursively in the docs_path. Default is True. + - parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores. + - on_disk (Optional, bool): Whether to store the collection on disk. Default is False. + - quantization_config: Quantization configuration. If None, quantization will be disabled. + - hnsw_config: HNSW configuration. If None, default configuration will be used. + You can find more info about the hnsw configuration options at https://qdrant.tech/documentation/concepts/indexing/#vector-index. + API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection + - payload_indexing: Whether to create a payload index for the document field. Default is False. + You can find more info about the payload indexing options at https://qdrant.tech/documentation/concepts/indexing/#payload-index + API Reference: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_field_index + **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#init). """ warnings.warn( @@ -116,11 +115,10 @@ def __init__( self._payload_indexing = self._retrieve_config.get("payload_indexing", False) def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = ""): - """ - Args: - problem (str): the problem to be solved. - n_results (int): the number of results to be retrieved. Default is 20. - search_string (str): only docs that contain an exact match of this string will be retrieved. Default is "". + """Args: + problem (str): the problem to be solved. + n_results (int): the number of results to be retrieved. Default is 20. + search_string (str): only docs that contain an exact match of this string will be retrieved. Default is "". """ if not self._collection: print("Trying to create collection.") diff --git a/autogen/agentchat/contrib/reasoning_agent.py b/autogen/agentchat/contrib/reasoning_agent.py index 2224d9f315..58124d8ccf 100644 --- a/autogen/agentchat/contrib/reasoning_agent.py +++ b/autogen/agentchat/contrib/reasoning_agent.py @@ -5,7 +5,7 @@ import random import re import warnings -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Optional from ..agent import Agent from ..assistant_agent import AssistantAgent @@ -25,6 +25,7 @@ - Reply a single word 'TERMINATE' as an option if you believe the user's question is fully resolved. - Provide a brief description for each option. - Present your output in the specified format. +- If the question is a multi-choice question, you should carefully eliminate obviously wrong choices, look for contextual clues in the question, and use logical reasoning to select the most plausible answer. --- @@ -42,7 +43,6 @@ class ThinkNode: - def __init__(self, content: str, parent: Optional["ThinkNode"] = None) -> None: """A node in a tree structure representing a step in the reasoning process. @@ -82,7 +82,7 @@ def __init__(self, content: str, parent: Optional["ThinkNode"] = None) -> None: @property def _trajectory_arr(self) -> list[str]: - """Get the full path from root to this node as a list of strings. + """Gets the full path from root to this node as a list of strings. Returns: List[str]: List containing the content of each node from root to current node @@ -104,8 +104,12 @@ def trajectory(self) -> str: ans += f"\nStep {i + 1}: {option}" return ans - def backpropagate(self, reward: float): - """Update the score of this node and its parents using moving average.""" + def backpropagate(self, reward: float) -> None: + """Update the score of this node and its parents using moving average. + + Args: + reward (float): The reward to backpropagate up the tree. + """ node = self while node: node.visits += 1 @@ -160,8 +164,10 @@ def from_dict(cls, data: dict, parent: Optional["ThinkNode"] = None) -> "ThinkNo def visualize_tree(root: ThinkNode) -> None: - """ - Visualize the tree of thoughts using graphviz. + """Visualize the tree of thoughts using graphviz. + + Args: + root (ThinkNode): The root node of the tree. """ try: from graphviz import Digraph @@ -196,16 +202,14 @@ def add_nodes(node: ThinkNode, node_id: str = "0"): print("Make sure graphviz is installed on your system: https://graphviz.org/download/") -def extract_sft_dataset(root): - """ - Extract the best trajectory or multiple equally good trajectories - for SFT training. +def extract_sft_dataset(root: ThinkNode) -> list[dict]: + """Extract the best trajectory or multiple equally good trajectories for SFT training. Args: - root: The root node of the tree. + root (ThinkNonde): The root node of the tree. Returns: - List of best trajectories, where each trajectory is a pair of instruction and response. + List[Dict]: List of best trajectories, each one is a pair of instruction and response. """ instruction = root.content idx = len("# Question: ") + len(root.content) + 1 @@ -234,17 +238,16 @@ def _find_leaf_nodes(node): return best_trajectories -def extract_rlhf_preference_dataset(root, contrastive_threshold=0.2): - """ - Extract and generate preference pairs for RLHF training by comparing sibling nodes. +def extract_rlhf_preference_dataset(root: ThinkNode, contrastive_threshold: float = 0.2) -> list[dict]: + """Extract and generate preference pairs for RLHF training by comparing sibling nodes. Args: - root: The root node of the tree. - contrastive_threshold (float): between (0, 1), a distance measure that we are confidence to call + root (ThinkNode): The root node of the tree. + contrastive_threshold (float): between (0, 1), a distance measure that we are confident to call one is positive and another is negative. Returns: - A list of preference pairs, where each pair contains two responses and + List[Dict]: List of preference pairs, where each pair contains two responses and indicates which one is preferred. """ preference_pairs = [] @@ -252,7 +255,7 @@ def extract_rlhf_preference_dataset(root, contrastive_threshold=0.2): assert contrastive_threshold > 0 assert contrastive_threshold < 1 - def traverse_tree(node): + def traverse_tree(node) -> None: """Traverse the tree to compare sibling nodes and collect preferences.""" if not node.children: return # Leaf node, no comparisons needed @@ -296,22 +299,22 @@ def traverse_tree(node): class ReasoningAgent(AssistantAgent): def __init__( self, - name, - llm_config, - grader_llm_config=None, - max_depth=4, - beam_size=3, - answer_approach="pool", - verbose=True, + name: str, + llm_config: dict, + grader_llm_config: Optional[dict] = None, + max_depth: int = 4, + beam_size: int = 3, + answer_approach: str = "pool", + verbose: bool = True, reason_config: dict = {}, **kwargs, ) -> None: """Initialize a ReasoningAgent that uses tree-of-thought reasoning. Args: - name: Name of the agent - llm_config: Configuration for the language model - grader_llm_config: Optional separate configuration for the grader model. If not provided, uses llm_config + name (str): Name of the agent + llm_config(dict): Configuration for the language model + grader_llm_config(Optional[dict]): Optional separate configuration for the grader model. If not provided, uses llm_config max_depth (int): Maximum depth of the reasoning tree beam_size (int): DEPRECATED. Number of parallel reasoning paths to maintain answer_approach (str): DEPRECATED. Either "pool" or "best" - how to generate final answer @@ -383,14 +386,15 @@ def __init__( ) self._grader = AssistantAgent(name="tot_grader", llm_config=self._grader_llm_config) - def generate_forest_response(self, messages, sender, config=None): - """ - Generate a response using tree-of-thought reasoning. + def generate_forest_response( + self, messages: list[dict], sender: Agent, config: Optional[dict] = None + ) -> tuple[bool, str]: + """Generate a response using tree-of-thought reasoning. Args: - messages: Input messages to respond to - sender: Agent sending the messages - config: Optional configuration + messages (List[Dict[str, Any]]): Input messages to respond to + sender (Agent): Agent sending the messages + config (Optional[Dict[str, Any]]): Optional configuration Returns: Tuple[bool, str]: Success flag and generated response @@ -428,6 +432,7 @@ def rate_node(self, node: ThinkNode, ground_truth: str = None, is_outcome: bool Args: node (ThinkNode): Node containing the reasoning trajectory to evaluate + ground_truth (str): Optional ground truth to provide to the grader is_outcome (bool): indicates whether the rating is for an outcome (final answer) or a process (thinking trajectory). Returns: @@ -484,6 +489,7 @@ def rate_node(self, node: ThinkNode, ground_truth: str = None, is_outcome: bool else: prompt = f"Rate:\n{node.trajectory}" + self._grader.clear_history() self.send( message=prompt, recipient=self._grader, @@ -500,9 +506,8 @@ def rate_node(self, node: ThinkNode, ground_truth: str = None, is_outcome: bool reward = 0.0 # Default reward if parsing fails return reward - def _process_prompt(self, messages, sender): - """ - Process the incoming messages to extract the prompt and ground truth. + def _process_prompt(self, messages: list[dict], sender: Agent) -> tuple[Optional[str], Optional[str]]: + """Process the incoming messages to extract the prompt and ground truth. This method checks if the provided messages are None and retrieves the last message's content. It also looks for a specific keyword "GROUND_TRUTH" in the prompt to separate the main prompt @@ -510,6 +515,7 @@ def _process_prompt(self, messages, sender): Args: messages (List[Dict[str, Any]]): A list of message dictionaries containing the content to process. + sender (Agent): The agent sending the messages. Returns: Tuple[Optional[str], Optional[str]]: A tuple containing the processed prompt and the ground truth. @@ -529,19 +535,18 @@ def _process_prompt(self, messages, sender): ground_truth = None return prompt, ground_truth - def _beam_reply(self, prompt, ground_truth=""): + def _beam_reply(self, prompt: str, ground_truth: str = "") -> str: """Generate a response using tree-of-thought reasoning. Implements beam search through a tree of reasoning steps, using the thinker agent to generate possible next steps and the grader agent to evaluate paths. Args: - messages: Input messages to respond to - sender: Agent sending the messages - config: Optional configuration + prompt (str): The question or prompt to generate a response for. + ground_truth (str): The ground truth or correct answer for evaluation. Returns: - Tuple[bool, str]: Success flag and generated response + str: The generated response based on the reasoning process. """ root = ThinkNode(content=prompt, parent=None) self._root = root # save the root node for later visualization @@ -590,7 +595,7 @@ def _beam_reply(self, prompt, ground_truth=""): ) elif self._answer_approach == "pool": all_thoughts = "\n\n".join( - [f"--- Possibility {i+1} ---\n{node.trajectory}\n" for i, node in enumerate(final_answers)] + [f"--- Possibility {i + 1} ---\n{node.trajectory}\n" for i, node in enumerate(final_answers)] ) self.send( message=f"Answer the question {prompt}. You can utilize these students' thinking processes.\n\n{all_thoughts}", @@ -602,7 +607,16 @@ def _beam_reply(self, prompt, ground_truth=""): final_answer = self.chat_messages[self][-1]["content"].strip() return final_answer - def _mtcs_reply(self, prompt, ground_truth=""): + def _mtcs_reply(self, prompt: str, ground_truth: str = "") -> str: + """Generate a response using Monte Carlo Tree Search (MCTS) reasoning. + + Args: + prompt (str): The question or prompt to generate a response for. + ground_truth (str): The ground truth or correct answer for evaluation. + + Returns: + str: The generated response based on the reasoning process. + """ root = ThinkNode(content=prompt, parent=None) self._root = root answer_nodes = [] @@ -621,7 +635,8 @@ def _mtcs_reply(self, prompt, ground_truth=""): # More intensive analysis is needed in the future. choices_weights = [ # exploitation term + - (child.value / (child.visits + EPSILON)) + + (child.value / (child.visits + EPSILON)) + + # exploration term self._exploration_constant * math.sqrt(2 * math.log(node.visits + EPSILON) / (child.visits + EPSILON)) @@ -633,6 +648,9 @@ def _mtcs_reply(self, prompt, ground_truth=""): while not self._is_terminal(node): if len(node.children) == 0: self._expand(node) + if len(node.children) == 0: + node.content += "\nTERMINATE" + break node = random.choice(node.children) # Add answer (leaf) node and evaluate answer @@ -658,8 +676,7 @@ def _mtcs_reply(self, prompt, ground_truth=""): return best_ans_node.content def _expand(self, node: ThinkNode) -> list: - """ - Expand the node by generating possible next steps based on the current trajectory. + """Expand the node by generating possible next steps based on the current trajectory. This method sends a message to the thinker agent, asking for possible next steps that can be taken from the current node's trajectory. It processes the response to @@ -697,7 +714,15 @@ def _expand(self, node: ThinkNode) -> list: return [ThinkNode(content=option.strip().rstrip(), parent=node) for option in options] - def _is_terminal(self, node): + def _is_terminal(self, node: ThinkNode) -> bool: + """Check if the node is a terminal state in the reasoning process. + + Args: + node (ThinkNode): The node to check for terminal state. + + Returns: + bool: True if the node is terminal, False otherwise. + """ return node.depth >= self._max_depth or "TERMINATE" in node.content @property diff --git a/autogen/agentchat/contrib/retrieve_assistant_agent.py b/autogen/agentchat/contrib/retrieve_assistant_agent.py index e2e6c0a5cf..75a5c9dff9 100644 --- a/autogen/agentchat/contrib/retrieve_assistant_agent.py +++ b/autogen/agentchat/contrib/retrieve_assistant_agent.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import warnings -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Optional, Union from autogen.agentchat.agent import Agent from autogen.agentchat.assistant_agent import AssistantAgent diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py index 81d6accd98..26654f4625 100644 --- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py +++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py @@ -8,7 +8,7 @@ import os import re import uuid -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Any, Callable, Literal, Optional, Union from IPython import get_ipython @@ -104,8 +104,7 @@ def __init__( retrieve_config: Optional[dict] = None, # config for the retrieve agent **kwargs, ): - r""" - Args: + r"""Args: name (str): name of the agent. human_input_mode (str): whether to ask for human inputs every time a message is received. @@ -223,7 +222,6 @@ def __init__( `**kwargs` (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#init). Example: - Example of overriding retrieve_docs - If you have set up a customized vector db, and it's not compatible with chromadb, you can easily plug in it with below code. *[Deprecated]* use `vector_db` instead. You can extend VectorDB and pass it to the agent. @@ -325,16 +323,16 @@ def _init_db(self): if not self._vector_db: return - IS_TO_CHUNK = False # whether to chunk the raw files + is_to_chunk = False # whether to chunk the raw files if self._new_docs: - IS_TO_CHUNK = True + is_to_chunk = True if not self._docs_path: try: self._vector_db.get_collection(self._collection_name) logger.warning(f"`docs_path` is not provided. Use the existing collection `{self._collection_name}`.") self._overwrite = False self._get_or_create = True - IS_TO_CHUNK = False + is_to_chunk = False except ValueError: raise ValueError( "`docs_path` is not provided. " @@ -346,16 +344,16 @@ def _init_db(self): self._vector_db.get_collection(self._collection_name) logger.info(f"Use the existing collection `{self._collection_name}`.", color="green") except ValueError: - IS_TO_CHUNK = True + is_to_chunk = True else: - IS_TO_CHUNK = True + is_to_chunk = True self._vector_db.active_collection = self._vector_db.create_collection( self._collection_name, overwrite=self._overwrite, get_or_create=self._get_or_create ) docs = None - if IS_TO_CHUNK: + if is_to_chunk: if self.custom_text_split_function is not None: chunks, sources = split_files_to_chunks( get_files_from_dir(self._docs_path, self._custom_text_types, self._recursive), @@ -380,7 +378,7 @@ def _init_db(self): chunk_ids = ( [hashlib.blake2b(chunk.encode("utf-8")).hexdigest()[:HASH_LENGTH] for chunk in chunks] - if not self._vector_db.type == "qdrant" + if self._vector_db.type != "qdrant" else [str(uuid.UUID(hex=hashlib.md5(chunk.encode("utf-8")).hexdigest())) for chunk in chunks] ) chunk_ids_set = set(chunk_ids) @@ -420,7 +418,7 @@ def _check_update_context_before_send(self, sender, message, recipient, silent): else: msg_text = message - if "UPDATE CONTEXT" == msg_text.strip().upper(): + if msg_text.strip().upper() == "UPDATE CONTEXT": doc_contents = self._get_context(self._results) # Always use self.problem as the query text to retrieve docs, but each time we replace the context with the @@ -655,8 +653,8 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str = @staticmethod def message_generator(sender, recipient, context): - """ - Generate an initial message with the given context for the RetrieveUserProxyAgent. + """Generate an initial message with the given context for the RetrieveUserProxyAgent. + Args: sender (Agent): the sender agent. It should be the instance of RetrieveUserProxyAgent. recipient (Agent): the recipient agent. Usually it's the assistant agent. @@ -664,6 +662,7 @@ def message_generator(sender, recipient, context): - `problem` (str) - the problem to be solved. - `n_results` (int) - the number of results to be retrieved. Default is 20. - `search_string` (str) - only docs that contain an exact match of this string will be retrieved. Default is "". + Returns: str: the generated message ready to be sent to the recipient agent. """ @@ -681,7 +680,7 @@ def message_generator(sender, recipient, context): return message def run_code(self, code, **kwargs): - lang = kwargs.get("lang", None) + lang = kwargs.get("lang") if code.startswith("!") or code.startswith("pip") or lang in ["bash", "shell", "sh"]: return ( 0, diff --git a/autogen/agentchat/contrib/society_of_mind_agent.py b/autogen/agentchat/contrib/society_of_mind_agent.py index fbf2f15cc9..6533974ced 100644 --- a/autogen/agentchat/contrib/society_of_mind_agent.py +++ b/autogen/agentchat/contrib/society_of_mind_agent.py @@ -7,7 +7,7 @@ # ruff: noqa: E722 import copy import traceback -from typing import Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Callable, Literal, Optional, Union from autogen import Agent, ConversableAgent, GroupChat, GroupChatManager, OpenAIWrapper @@ -91,7 +91,6 @@ def _llm_response_preparer(self, prompt, messages): prompt (str): The prompt used to extract the final response from the transcript. messages (list): The messages generated as part of the inner monologue group chat. """ - _messages = [ { "role": "system", diff --git a/autogen/agentchat/contrib/swarm_agent.py b/autogen/agentchat/contrib/swarm_agent.py index f7aeeabce1..4c13a49559 100644 --- a/autogen/agentchat/contrib/swarm_agent.py +++ b/autogen/agentchat/contrib/swarm_agent.py @@ -14,7 +14,6 @@ from pydantic import BaseModel from autogen.oai import OpenAIWrapper -from autogen.tools import get_function_schema from ..agent import Agent from ..chat import ChatResult @@ -84,7 +83,7 @@ def __post_init__(self): self.agent = AfterWorkOption(self.agent.upper()) -class AFTER_WORK(AfterWork): +class AFTER_WORK(AfterWork): # noqa: N801 """Deprecated: Use AfterWork instead. This class will be removed in a future version (TBD).""" def __init__(self, *args, **kwargs): @@ -116,9 +115,9 @@ class OnCondition: def __post_init__(self): # Ensure valid types if self.target is not None: - assert isinstance(self.target, ConversableAgent) or isinstance( - self.target, dict - ), "'target' must be a ConversableAgent or a dict" + assert isinstance(self.target, ConversableAgent) or isinstance(self.target, dict), ( + "'target' must be a ConversableAgent or a dict" + ) # Ensure they have a condition if isinstance(self.condition, str): @@ -130,7 +129,7 @@ def __post_init__(self): assert isinstance(self.available, (Callable, str)), "'available' must be a callable or a string" -class ON_CONDITION(OnCondition): +class ON_CONDITION(OnCondition): # noqa: N801 """Deprecated: Use OnCondition instead. This class will be removed in a future version (TBD).""" def __init__(self, *args, **kwargs): @@ -238,7 +237,7 @@ def _create_nested_chats(agent: ConversableAgent, nested_chat_agents: list[Conve nested_chats["chat_queue"], reply_func_from_nested_chats=nested_chats.get("reply_func_from_nested_chats") or "summary_from_nested_chats", - config=nested_chats.get("config", None), + config=nested_chats.get("config"), trigger=lambda sender: True, position=0, use_async=nested_chats.get("use_async", False), @@ -616,8 +615,7 @@ def custom_afterwork_func(last_speaker: ConversableAgent, messages: List[Dict[st class SwarmResult(BaseModel): - """ - Encapsulates the possible return values for a swarm agent function. + """Encapsulates the possible return values for a swarm agent function. Args: values (str): The result values as a string. @@ -676,12 +674,11 @@ def transfer_to_agent_name() -> ConversableAgent: for transit in hand_to: if isinstance(transit, AfterWork): - assert isinstance( - transit.agent, (AfterWorkOption, ConversableAgent, str, Callable) - ), "Invalid After Work value" + assert isinstance(transit.agent, (AfterWorkOption, ConversableAgent, str, Callable)), ( + "Invalid After Work value" + ) agent._swarm_after_work = transit elif isinstance(transit, OnCondition): - if isinstance(transit.target, ConversableAgent): # Transition to agent @@ -769,7 +766,6 @@ def _generate_swarm_tool_reply( message = messages[-1] if "tool_calls" in message: - tool_call_count = len(message["tool_calls"]) # Loop through tool calls individually (so context can be updated after each function call) @@ -777,7 +773,6 @@ def _generate_swarm_tool_reply( tool_responses_inner = [] contents = [] for index in range(tool_call_count): - # Deep copy to ensure no changes to messages when we insert the context variables message_copy = copy.deepcopy(message) @@ -794,7 +789,6 @@ def _generate_swarm_tool_reply( # Inject the context variables into the tool call if it has the parameter sig = signature(func) if __CONTEXT_VARIABLES_PARAM_NAME__ in sig.parameters: - current_args = json.loads(tool_call["function"]["arguments"]) current_args[__CONTEXT_VARIABLES_PARAM_NAME__] = agent._context_variables tool_call["function"]["arguments"] = json.dumps(current_args) diff --git a/autogen/agentchat/contrib/text_analyzer_agent.py b/autogen/agentchat/contrib/text_analyzer_agent.py index 101ab7b70e..289d5eb2e7 100644 --- a/autogen/agentchat/contrib/text_analyzer_agent.py +++ b/autogen/agentchat/contrib/text_analyzer_agent.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Any, Dict, List, Literal, Optional, Tuple, Union +from typing import Any, Literal, Optional, Union from autogen.agentchat.agent import Agent from autogen.agentchat.assistant_agent import ConversableAgent @@ -26,16 +26,15 @@ def __init__( llm_config: Optional[Union[dict, bool]] = None, **kwargs, ): - """ - Args: - name (str): name of the agent. - system_message (str): system message for the ChatCompletion inference. - human_input_mode (str): This agent should NEVER prompt the human for input. - llm_config (dict or False): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) - for available options. - To disable llm-based auto reply, set to False. - **kwargs (dict): other kwargs in [ConversableAgent](../conversable_agent#init). + """Args: + name (str): name of the agent. + system_message (str): system message for the ChatCompletion inference. + human_input_mode (str): This agent should NEVER prompt the human for input. + llm_config (dict or False): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) + for available options. + To disable llm-based auto reply, set to False. + **kwargs (dict): other kwargs in [ConversableAgent](../conversable_agent#init). """ super().__init__( name=name, @@ -54,7 +53,8 @@ def _analyze_in_reply( ) -> tuple[bool, Union[str, dict, None]]: """Analyzes the given text as instructed, and returns the analysis as a message. Assumes exactly two messages containing the text to analyze and the analysis instructions. - See Teachability.analyze for an example of how to use this method.""" + See Teachability.analyze for an example of how to use this method. + """ if self.llm_config is False: raise ValueError("TextAnalyzerAgent requires self.llm_config to be set in its base class.") if messages is None: diff --git a/autogen/agentchat/contrib/vectordb/base.py b/autogen/agentchat/contrib/vectordb/base.py index d2f3e0685d..c52712b895 100644 --- a/autogen/agentchat/contrib/vectordb/base.py +++ b/autogen/agentchat/contrib/vectordb/base.py @@ -8,10 +8,8 @@ from typing import ( Any, Callable, - List, Optional, Protocol, - Tuple, TypedDict, Union, runtime_checkable, @@ -46,8 +44,7 @@ class Document(TypedDict): @runtime_checkable class VectorDB(Protocol): - """ - Abstract class for vector database. A vector database is responsible for storing and retrieving documents. + """Abstract class for vector database. A vector database is responsible for storing and retrieving documents. Attributes: active_collection: Any | The active collection in the vector database. Make get_collection faster. Default is None. @@ -71,8 +68,7 @@ class VectorDB(Protocol): ) def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> Any: - """ - Create a collection in the vector database. + """Create a collection in the vector database. Case 1. if the collection does not exist, create the collection. Case 2. the collection exists, if overwrite is True, it will overwrite the collection. Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection, @@ -89,8 +85,7 @@ def create_collection(self, collection_name: str, overwrite: bool = False, get_o ... def get_collection(self, collection_name: str = None) -> Any: - """ - Get the collection from the vector database. + """Get the collection from the vector database. Args: collection_name: str | The name of the collection. Default is None. If None, return the @@ -102,8 +97,7 @@ def get_collection(self, collection_name: str = None) -> Any: ... def delete_collection(self, collection_name: str) -> Any: - """ - Delete the collection from the vector database. + """Delete the collection from the vector database. Args: collection_name: str | The name of the collection. @@ -114,8 +108,7 @@ def delete_collection(self, collection_name: str) -> Any: ... def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False, **kwargs) -> None: - """ - Insert documents into the collection of the vector database. + """Insert documents into the collection of the vector database. Args: docs: List[Document] | A list of documents. Each document is a TypedDict `Document`. @@ -129,8 +122,7 @@ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: ... def update_docs(self, docs: list[Document], collection_name: str = None, **kwargs) -> None: - """ - Update documents in the collection of the vector database. + """Update documents in the collection of the vector database. Args: docs: List[Document] | A list of documents. @@ -143,8 +135,7 @@ def update_docs(self, docs: list[Document], collection_name: str = None, **kwarg ... def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None: - """ - Delete documents from the collection of the vector database. + """Delete documents from the collection of the vector database. Args: ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`. @@ -164,8 +155,7 @@ def retrieve_docs( distance_threshold: float = -1, **kwargs, ) -> QueryResults: - """ - Retrieve documents from the collection of the vector database based on the queries. + """Retrieve documents from the collection of the vector database based on the queries. Args: queries: List[str] | A list of queries. Each query is a string. @@ -184,8 +174,7 @@ def retrieve_docs( def get_docs_by_ids( self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs ) -> list[Document]: - """ - Retrieve documents from the collection of the vector database based on the ids. + """Retrieve documents from the collection of the vector database based on the ids. Args: ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None. @@ -202,16 +191,13 @@ def get_docs_by_ids( class VectorDBFactory: - """ - Factory class for creating vector databases. - """ + """Factory class for creating vector databases.""" PREDEFINED_VECTOR_DB = ["chroma", "pgvector", "mongodb", "qdrant"] @staticmethod def create_vector_db(db_type: str, **kwargs) -> VectorDB: - """ - Create a vector database. + """Create a vector database. Args: db_type: str | The type of the vector database. diff --git a/autogen/agentchat/contrib/vectordb/chromadb.py b/autogen/agentchat/contrib/vectordb/chromadb.py index accd05a4db..9a93f9ecc5 100644 --- a/autogen/agentchat/contrib/vectordb/chromadb.py +++ b/autogen/agentchat/contrib/vectordb/chromadb.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import os -from typing import Callable, List +from typing import Callable from .base import Document, ItemID, QueryResults, VectorDB from .utils import chroma_results_to_query_results, filter_results_by_distance, get_logger @@ -26,15 +26,12 @@ class ChromaVectorDB(VectorDB): - """ - A vector database that uses ChromaDB as the backend. - """ + """A vector database that uses ChromaDB as the backend.""" def __init__( self, *, client=None, path: str = "tmp/db", embedding_function: Callable = None, metadata: dict = None, **kwargs ) -> None: - """ - Initialize the vector database. + """Initialize the vector database. Args: client: chromadb.Client | The client object of the vector database. Default is None. @@ -71,8 +68,7 @@ def __init__( def create_collection( self, collection_name: str, overwrite: bool = False, get_or_create: bool = True ) -> Collection: - """ - Create a collection in the vector database. + """Create a collection in the vector database. Case 1. if the collection does not exist, create the collection. Case 2. the collection exists, if overwrite is True, it will overwrite the collection. Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection, @@ -114,8 +110,7 @@ def create_collection( raise ValueError(f"Collection {collection_name} already exists.") def get_collection(self, collection_name: str = None) -> Collection: - """ - Get the collection from the vector database. + """Get the collection from the vector database. Args: collection_name: str | The name of the collection. Default is None. If None, return the @@ -139,8 +134,7 @@ def get_collection(self, collection_name: str = None) -> Collection: return self.active_collection def delete_collection(self, collection_name: str) -> None: - """ - Delete the collection from the vector database. + """Delete the collection from the vector database. Args: collection_name: str | The name of the collection. @@ -170,8 +164,7 @@ def _batch_insert( collection.add(**collection_kwargs) def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None: - """ - Insert documents into the collection of the vector database. + """Insert documents into the collection of the vector database. Args: docs: List[Document] | A list of documents. Each document is a TypedDict `Document`. @@ -205,8 +198,7 @@ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: self._batch_insert(collection, embeddings, ids, metadatas, documents, upsert) def update_docs(self, docs: list[Document], collection_name: str = None) -> None: - """ - Update documents in the collection of the vector database. + """Update documents in the collection of the vector database. Args: docs: List[Document] | A list of documents. @@ -218,8 +210,7 @@ def update_docs(self, docs: list[Document], collection_name: str = None) -> None self.insert_docs(docs, collection_name, upsert=True) def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None: - """ - Delete documents from the collection of the vector database. + """Delete documents from the collection of the vector database. Args: ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`. @@ -240,8 +231,7 @@ def retrieve_docs( distance_threshold: float = -1, **kwargs, ) -> QueryResults: - """ - Retrieve documents from the collection of the vector database based on the queries. + """Retrieve documents from the collection of the vector database based on the queries. Args: queries: List[str] | A list of queries. Each query is a string. @@ -294,7 +284,6 @@ def _chroma_get_results_to_list_documents(data_dict) -> list[Document]: ] ``` """ - results = [] keys = [key for key in data_dict if data_dict[key] is not None] @@ -309,8 +298,7 @@ def _chroma_get_results_to_list_documents(data_dict) -> list[Document]: def get_docs_by_ids( self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs ) -> list[Document]: - """ - Retrieve documents from the collection of the vector database based on the ids. + """Retrieve documents from the collection of the vector database based on the ids. Args: ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None. diff --git a/autogen/agentchat/contrib/vectordb/mongodb.py b/autogen/agentchat/contrib/vectordb/mongodb.py index b1a199c495..0065273cb0 100644 --- a/autogen/agentchat/contrib/vectordb/mongodb.py +++ b/autogen/agentchat/contrib/vectordb/mongodb.py @@ -7,7 +7,7 @@ from collections.abc import Iterable, Mapping from copy import deepcopy from time import monotonic, sleep -from typing import Any, Callable, Dict, List, Literal, Set, Tuple, Union +from typing import Any, Callable, Literal, Union import numpy as np from pymongo import MongoClient, UpdateOne, errors @@ -32,9 +32,7 @@ def with_id_rename(docs: Iterable) -> list[dict[str, Any]]: class MongoDBAtlasVectorDB(VectorDB): - """ - A Collection object for MongoDB. - """ + """A Collection object for MongoDB.""" def __init__( self, @@ -47,8 +45,7 @@ def __init__( wait_until_index_ready: float = None, wait_until_document_ready: float = None, ): - """ - Initialize the vector database. + """Initialize the vector database. Args: connection_string: str | The MongoDB connection string to connect to. Default is ''. @@ -110,9 +107,9 @@ def _wait_for_index(self, collection: Collection, index_name: str, action: str = assert action in ["create", "delete"], f"{action=} must be create or delete." start = monotonic() while monotonic() - start < self._wait_until_index_ready: - if action == "create" and self._is_index_ready(collection, index_name): - return - elif action == "delete" and len(list(collection.list_search_indexes())) == 0: + if (action == "create" and self._is_index_ready(collection, index_name)) or ( + action == "delete" and len(list(collection.list_search_indexes())) == 0 + ): return sleep(_DELAY) @@ -137,8 +134,7 @@ def _get_embedding_size(self): return len(self.embedding_function(_SAMPLE_SENTENCE)[0]) def list_collections(self): - """ - List the collections in the vector database. + """List the collections in the vector database. Returns: List[str] | The list of collections. @@ -151,8 +147,7 @@ def create_collection( overwrite: bool = False, get_or_create: bool = True, ) -> Collection: - """ - Create a collection in the vector database and create a vector search index in the collection. + """Create a collection in the vector database and create a vector search index in the collection. Args: collection_name: str | The name of the collection. @@ -178,8 +173,7 @@ def create_collection( raise ValueError(f"Collection {collection_name} already exists.") def create_index_if_not_exists(self, index_name: str = "vector_index", collection: Collection = None) -> None: - """ - Creates a vector search index on the specified collection in MongoDB. + """Creates a vector search index on the specified collection in MongoDB. Args: MONGODB_INDEX (str, optional): The name of the vector search index to create. Defaults to "vector_search_index". @@ -189,8 +183,7 @@ def create_index_if_not_exists(self, index_name: str = "vector_index", collectio self.create_vector_search_index(collection, index_name) def get_collection(self, collection_name: str = None) -> Collection: - """ - Get the collection from the vector database. + """Get the collection from the vector database. Args: collection_name: str | The name of the collection. Default is None. If None, return the @@ -212,8 +205,7 @@ def get_collection(self, collection_name: str = None) -> Collection: return self.active_collection def delete_collection(self, collection_name: str) -> None: - """ - Delete the collection from the vector database. + """Delete the collection from the vector database. Args: collection_name: str | The name of the collection. @@ -363,9 +355,9 @@ def _insert_batch( return [] # Embed and create the documents embeddings = self.embedding_function(texts).tolist() - assert ( - len(embeddings) == n_texts - ), f"The number of embeddings produced by self.embedding_function ({len(embeddings)} does not match the number of texts provided to it ({n_texts})." + assert len(embeddings) == n_texts, ( + f"The number of embeddings produced by self.embedding_function ({len(embeddings)} does not match the number of texts provided to it ({n_texts})." + ) to_insert = [ {"_id": i, "content": t, "metadata": m, "embedding": e} for i, t, m, e in zip(ids, texts, metadatas, embeddings) @@ -386,7 +378,6 @@ def update_docs(self, docs: list[Document], collection_name: str = None, **kwarg collection_name: str | The name of the collection. Default is None. kwargs: Any | Use upsert=True` to insert documents whose ids are not present in collection. """ - n_docs = len(docs) logger.info(f"Preparing to embed and update {n_docs=}") # Compute the embeddings @@ -415,8 +406,7 @@ def update_docs(self, docs: list[Document], collection_name: str = None, **kwarg ) def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs): - """ - Delete documents from the collection of the vector database. + """Delete documents from the collection of the vector database. Args: ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`. @@ -428,8 +418,7 @@ def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs): def get_docs_by_ids( self, ids: list[ItemID] = None, collection_name: str = None, include: list[str] = None, **kwargs ) -> list[Document]: - """ - Retrieve documents from the collection of the vector database based on the ids. + """Retrieve documents from the collection of the vector database based on the ids. Args: ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None. @@ -464,8 +453,7 @@ def retrieve_docs( distance_threshold: float = -1, **kwargs, ) -> QueryResults: - """ - Retrieve documents from the collection of the vector database based on the queries. + """Retrieve documents from the collection of the vector database based on the queries. Args: queries: List[str] | A list of queries. Each query is a string. @@ -535,7 +523,6 @@ def _vector_search( List of tuples of length n_results from Collection. Each tuple contains a document dict and a score. """ - pipeline = [ { "$vectorSearch": { diff --git a/autogen/agentchat/contrib/vectordb/pgvectordb.py b/autogen/agentchat/contrib/vectordb/pgvectordb.py index df211bad00..65cd223c60 100644 --- a/autogen/agentchat/contrib/vectordb/pgvectordb.py +++ b/autogen/agentchat/contrib/vectordb/pgvectordb.py @@ -7,7 +7,7 @@ import os import re import urllib.parse -from typing import Callable, List, Optional, Union +from typing import Callable, Optional, Union import numpy as np from sentence_transformers import SentenceTransformer @@ -16,7 +16,7 @@ from .utils import get_logger try: - import pgvector + import pgvector # noqa: F401 from pgvector.psycopg import register_vector except ImportError: raise ImportError("Please install pgvector: `pip install pgvector`") @@ -31,8 +31,7 @@ class Collection: - """ - A Collection object for PGVector. + """A Collection object for PGVector. Attributes: client: The PGVector client. @@ -53,8 +52,7 @@ def __init__( metadata=None, get_or_create=None, ): - """ - Initialize the Collection object. + """Initialize the Collection object. Args: client: The PostgreSQL client. @@ -62,6 +60,7 @@ def __init__( embedding_function: The embedding function used to generate the vector representation. metadata: The metadata of the collection. get_or_create: The flag indicating whether to get or create the collection. + Returns: None """ @@ -89,8 +88,7 @@ def set_collection_name(self, collection_name) -> str: return self.name def add(self, ids: list[ItemID], documents: list, embeddings: list = None, metadatas: list = None) -> None: - """ - Add documents to the collection. + """Add documents to the collection. Args: ids (List[ItemID]): A list of document IDs. @@ -107,33 +105,28 @@ def add(self, ids: list[ItemID], documents: list, embeddings: list = None, metad for doc_id, embedding, metadata, document in zip(ids, embeddings, metadatas, documents): metadata = re.sub("'", '"', str(metadata)) sql_values.append((doc_id, embedding, metadata, document)) - sql_string = ( - f"INSERT INTO {self.name} (id, embedding, metadatas, documents)\n" f"VALUES (%s, %s, %s, %s);\n" - ) + sql_string = f"INSERT INTO {self.name} (id, embedding, metadatas, documents)\nVALUES (%s, %s, %s, %s);\n" elif embeddings is not None: for doc_id, embedding, document in zip(ids, embeddings, documents): sql_values.append((doc_id, embedding, document)) - sql_string = f"INSERT INTO {self.name} (id, embedding, documents) " f"VALUES (%s, %s, %s);\n" + sql_string = f"INSERT INTO {self.name} (id, embedding, documents) VALUES (%s, %s, %s);\n" elif metadatas is not None: for doc_id, metadata, document in zip(ids, metadatas, documents): metadata = re.sub("'", '"', str(metadata)) embedding = self.embedding_function(document) sql_values.append((doc_id, metadata, embedding, document)) - sql_string = ( - f"INSERT INTO {self.name} (id, metadatas, embedding, documents)\n" f"VALUES (%s, %s, %s, %s);\n" - ) + sql_string = f"INSERT INTO {self.name} (id, metadatas, embedding, documents)\nVALUES (%s, %s, %s, %s);\n" else: for doc_id, document in zip(ids, documents): embedding = self.embedding_function(document) sql_values.append((doc_id, document, embedding)) - sql_string = f"INSERT INTO {self.name} (id, documents, embedding)\n" f"VALUES (%s, %s, %s);\n" + sql_string = f"INSERT INTO {self.name} (id, documents, embedding)\nVALUES (%s, %s, %s);\n" logger.debug(f"Add SQL String:\n{sql_string}\n{sql_values}") cursor.executemany(sql_string, sql_values) cursor.close() def upsert(self, ids: list[ItemID], documents: list, embeddings: list = None, metadatas: list = None) -> None: - """ - Upsert documents into the collection. + """Upsert documents into the collection. Args: ids (List[ItemID]): A list of document IDs. @@ -191,8 +184,7 @@ def upsert(self, ids: list[ItemID], documents: list, embeddings: list = None, me cursor.close() def count(self) -> int: - """ - Get the total number of documents in the collection. + """Get the total number of documents in the collection. Returns: int: The total number of documents. @@ -209,8 +201,7 @@ def count(self) -> int: return total def table_exists(self, table_name: str) -> bool: - """ - Check if a table exists in the PostgreSQL database. + """Check if a table exists in the PostgreSQL database. Args: table_name (str): The name of the table to check. @@ -218,7 +209,6 @@ def table_exists(self, table_name: str) -> bool: Returns: bool: True if the table exists, False otherwise. """ - cursor = self.client.cursor() cursor.execute( """ @@ -241,8 +231,7 @@ def get( limit: Optional[Union[int, str]] = None, offset: Optional[Union[int, str]] = None, ) -> list[Document]: - """ - Retrieve documents from the collection. + """Retrieve documents from the collection. Args: ids (Optional[List]): A list of document IDs. @@ -313,8 +302,7 @@ def get( return retrieved_documents def update(self, ids: list, embeddings: list, metadatas: list, documents: list) -> None: - """ - Update documents in the collection. + """Update documents in the collection. Args: ids (List): A list of document IDs. @@ -342,8 +330,7 @@ def update(self, ids: list, embeddings: list, metadatas: list, documents: list) @staticmethod def euclidean_distance(arr1: list[float], arr2: list[float]) -> float: - """ - Calculate the Euclidean distance between two vectors. + """Calculate the Euclidean distance between two vectors. Parameters: - arr1 (List[float]): The first vector. @@ -357,8 +344,7 @@ def euclidean_distance(arr1: list[float], arr2: list[float]) -> float: @staticmethod def cosine_distance(arr1: list[float], arr2: list[float]) -> float: - """ - Calculate the cosine distance between two vectors. + """Calculate the cosine distance between two vectors. Parameters: - arr1 (List[float]): The first vector. @@ -372,8 +358,7 @@ def cosine_distance(arr1: list[float], arr2: list[float]) -> float: @staticmethod def inner_product_distance(arr1: list[float], arr2: list[float]) -> float: - """ - Calculate the Euclidean distance between two vectors. + """Calculate the Euclidean distance between two vectors. Parameters: - arr1 (List[float]): The first vector. @@ -394,8 +379,7 @@ def query( distance_threshold: Optional[float] = -1, include_embedding: Optional[bool] = False, ) -> QueryResults: - """ - Query documents in the collection. + """Query documents in the collection. Args: query_texts (List[str]): A list of query texts. @@ -433,7 +417,7 @@ def query( query = ( f"SELECT id, documents, embedding, metadatas " f"FROM {self.name} " - f"{clause} embedding {index_function} '{str(vector)}' {distance_threshold} " + f"{clause} embedding {index_function} '{vector!s}' {distance_threshold} " f"LIMIT {n_results}" ) cursor.execute(query) @@ -459,8 +443,7 @@ def query( @staticmethod def convert_string_to_array(array_string: str) -> list[float]: - """ - Convert a string representation of an array to a list of floats. + """Convert a string representation of an array to a list of floats. Parameters: - array_string (str): The string representation of the array. @@ -476,8 +459,7 @@ def convert_string_to_array(array_string: str) -> list[float]: return array def modify(self, metadata, collection_name: Optional[str] = None) -> None: - """ - Modify metadata for the collection. + """Modify metadata for the collection. Args: collection_name: The name of the collection. @@ -489,14 +471,11 @@ def modify(self, metadata, collection_name: Optional[str] = None) -> None: if collection_name: self.name = collection_name cursor = self.client.cursor() - cursor.execute( - "UPDATE collections" "SET metadata = '%s'" "WHERE collection_name = '%s';", (metadata, self.name) - ) + cursor.execute("UPDATE collectionsSET metadata = '%s'WHERE collection_name = '%s';", (metadata, self.name)) cursor.close() def delete(self, ids: list[ItemID], collection_name: Optional[str] = None) -> None: - """ - Delete documents from the collection. + """Delete documents from the collection. Args: ids (List[ItemID]): A list of document IDs to delete. @@ -513,8 +492,7 @@ def delete(self, ids: list[ItemID], collection_name: Optional[str] = None) -> No cursor.close() def delete_collection(self, collection_name: Optional[str] = None) -> None: - """ - Delete the entire collection. + """Delete the entire collection. Args: collection_name (Optional[str]): The name of the collection to delete. @@ -531,8 +509,7 @@ def delete_collection(self, collection_name: Optional[str] = None) -> None: def create_collection( self, collection_name: Optional[str] = None, dimension: Optional[Union[str, int]] = None ) -> None: - """ - Create a new collection. + """Create a new collection. Args: collection_name (Optional[str]): The name of the new collection. @@ -554,22 +531,20 @@ def create_collection( f"CREATE TABLE {self.name} (" f"documents text, id CHAR(8) PRIMARY KEY, metadatas JSONB, embedding vector({self.dimension}));" f"CREATE INDEX " - f'ON {self.name} USING hnsw (embedding vector_l2_ops) WITH (m = {self.metadata["hnsw:M"]}, ' - f'ef_construction = {self.metadata["hnsw:construction_ef"]});' + f"ON {self.name} USING hnsw (embedding vector_l2_ops) WITH (m = {self.metadata['hnsw:M']}, " + f"ef_construction = {self.metadata['hnsw:construction_ef']});" f"CREATE INDEX " - f'ON {self.name} USING hnsw (embedding vector_cosine_ops) WITH (m = {self.metadata["hnsw:M"]}, ' - f'ef_construction = {self.metadata["hnsw:construction_ef"]});' + f"ON {self.name} USING hnsw (embedding vector_cosine_ops) WITH (m = {self.metadata['hnsw:M']}, " + f"ef_construction = {self.metadata['hnsw:construction_ef']});" f"CREATE INDEX " - f'ON {self.name} USING hnsw (embedding vector_ip_ops) WITH (m = {self.metadata["hnsw:M"]}, ' - f'ef_construction = {self.metadata["hnsw:construction_ef"]});' + f"ON {self.name} USING hnsw (embedding vector_ip_ops) WITH (m = {self.metadata['hnsw:M']}, " + f"ef_construction = {self.metadata['hnsw:construction_ef']});" ) cursor.close() class PGVectorDB(VectorDB): - """ - A vector database that uses PGVector as the backend. - """ + """A vector database that uses PGVector as the backend.""" def __init__( self, @@ -585,8 +560,7 @@ def __init__( embedding_function: Callable = None, metadata: Optional[dict] = None, ) -> None: - """ - Initialize the vector database. + """Initialize the vector database. Note: connection_string or host + port + dbname must be specified @@ -641,8 +615,7 @@ def establish_connection( password: Optional[str] = None, connect_timeout: Optional[int] = 10, ) -> psycopg.Connection: - """ - Establishes a connection to a PostgreSQL database using psycopg. + """Establishes a connection to a PostgreSQL database using psycopg. Args: conn: An existing psycopg connection object. If provided, this connection will be used. @@ -711,8 +684,7 @@ def establish_connection( def create_collection( self, collection_name: str, overwrite: bool = False, get_or_create: bool = True ) -> Collection: - """ - Create a collection in the vector database. + """Create a collection in the vector database. Case 1. if the collection does not exist, create the collection. Case 2. the collection exists, if overwrite is True, it will overwrite the collection. Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection, @@ -773,8 +745,7 @@ def create_collection( raise ValueError(f"Collection {collection_name} already exists.") def get_collection(self, collection_name: str = None) -> Collection: - """ - Get the collection from the vector database. + """Get the collection from the vector database. Args: collection_name: str | The name of the collection. Default is None. If None, return the @@ -800,8 +771,7 @@ def get_collection(self, collection_name: str = None) -> Collection: return self.active_collection def delete_collection(self, collection_name: str) -> None: - """ - Delete the collection from the vector database. + """Delete the collection from the vector database. Args: collection_name: str | The name of the collection. @@ -837,8 +807,7 @@ def _batch_insert( collection.add(**collection_kwargs) def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None: - """ - Insert documents into the collection of the vector database. + """Insert documents into the collection of the vector database. Args: docs: List[Document] | A list of documents. Each document is a TypedDict `Document`. @@ -875,8 +844,7 @@ def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: self._batch_insert(collection, embeddings, ids, metadatas, documents, upsert) def update_docs(self, docs: list[Document], collection_name: str = None) -> None: - """ - Update documents in the collection of the vector database. + """Update documents in the collection of the vector database. Args: docs: List[Document] | A list of documents. @@ -888,8 +856,7 @@ def update_docs(self, docs: list[Document], collection_name: str = None) -> None self.insert_docs(docs, collection_name, upsert=True) def delete_docs(self, ids: list[ItemID], collection_name: str = None) -> None: - """ - Delete documents from the collection of the vector database. + """Delete documents from the collection of the vector database. Args: ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`. @@ -909,8 +876,7 @@ def retrieve_docs( n_results: int = 10, distance_threshold: float = -1, ) -> QueryResults: - """ - Retrieve documents from the collection of the vector database based on the queries. + """Retrieve documents from the collection of the vector database based on the queries. Args: queries: List[str] | A list of queries. Each query is a string. @@ -938,8 +904,7 @@ def retrieve_docs( def get_docs_by_ids( self, ids: list[ItemID] = None, collection_name: str = None, include=None, **kwargs ) -> list[Document]: - """ - Retrieve documents from the collection of the vector database based on the ids. + """Retrieve documents from the collection of the vector database based on the ids. Args: ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None. diff --git a/autogen/agentchat/contrib/vectordb/qdrant.py b/autogen/agentchat/contrib/vectordb/qdrant.py index 569f61ca04..20e7f44241 100644 --- a/autogen/agentchat/contrib/vectordb/qdrant.py +++ b/autogen/agentchat/contrib/vectordb/qdrant.py @@ -6,9 +6,8 @@ # SPDX-License-Identifier: MIT import abc import logging -import os from collections.abc import Sequence -from typing import Callable, List, Optional, Tuple, Union +from typing import Optional, Union from .base import Document, ItemID, QueryResults, VectorDB from .utils import get_logger @@ -75,9 +74,7 @@ def __call__(self, inputs: list[str]) -> list[Embeddings]: class QdrantVectorDB(VectorDB): - """ - A vector database implementation that uses Qdrant as the backend. - """ + """A vector database implementation that uses Qdrant as the backend.""" def __init__( self, @@ -89,8 +86,7 @@ def __init__( collection_options: dict = {}, **kwargs, ) -> None: - """ - Initialize the vector database. + """Initialize the vector database. Args: client: qdrant_client.QdrantClient | An instance of QdrantClient. @@ -107,8 +103,7 @@ def __init__( self.type = "qdrant" def create_collection(self, collection_name: str, overwrite: bool = False, get_or_create: bool = True) -> None: - """ - Create a collection in the vector database. + """Create a collection in the vector database. Case 1. if the collection does not exist, create the collection. Case 2. the collection exists, if overwrite is True, it will overwrite the collection. Case 3. the collection exists and overwrite is False, if get_or_create is True, it will get the collection, @@ -137,8 +132,7 @@ def create_collection(self, collection_name: str, overwrite: bool = False, get_o raise ValueError(f"Collection {collection_name} already exists.") def get_collection(self, collection_name: str = None): - """ - Get the collection from the vector database. + """Get the collection from the vector database. Args: collection_name: str | The name of the collection. @@ -163,8 +157,7 @@ def delete_collection(self, collection_name: str) -> None: return self.client.delete_collection(collection_name) def insert_docs(self, docs: list[Document], collection_name: str = None, upsert: bool = False) -> None: - """ - Insert documents into the collection of the vector database. + """Insert documents into the collection of the vector database. Args: docs: List[Document] | A list of documents. Each document is a TypedDict `Document`. @@ -200,8 +193,7 @@ def update_docs(self, docs: list[Document], collection_name: str = None) -> None raise ValueError("Some IDs do not exist. Skipping update") def delete_docs(self, ids: list[ItemID], collection_name: str = None, **kwargs) -> None: - """ - Delete documents from the collection of the vector database. + """Delete documents from the collection of the vector database. Args: ids: List[ItemID] | A list of document ids. Each id is a typed `ItemID`. @@ -221,8 +213,7 @@ def retrieve_docs( distance_threshold: float = 0, **kwargs, ) -> QueryResults: - """ - Retrieve documents from the collection of the vector database based on the queries. + """Retrieve documents from the collection of the vector database based on the queries. Args: queries: List[str] | A list of queries. Each query is a string. @@ -254,8 +245,7 @@ def retrieve_docs( def get_docs_by_ids( self, ids: list[ItemID] = None, collection_name: str = None, include=True, **kwargs ) -> list[Document]: - """ - Retrieve documents from the collection of the vector database based on the ids. + """Retrieve documents from the collection of the vector database based on the ids. Args: ids: List[ItemID] | A list of document ids. If None, will return all the documents. Default is None. @@ -307,9 +297,7 @@ def _scored_points_to_documents(self, scored_points: list[models.ScoredPoint]) - return [self._scored_point_to_document(scored_point) for scored_point in scored_points] def _validate_update_ids(self, collection_name: str, ids: list[str]) -> bool: - """ - Validates all the IDs exist in the collection - """ + """Validates all the IDs exist in the collection""" retrieved_ids = [ point.id for point in self.client.retrieve(collection_name, ids=ids, with_payload=False, with_vectors=False) ] @@ -321,9 +309,7 @@ def _validate_update_ids(self, collection_name: str, ids: list[str]) -> bool: return True def _validate_upsert_ids(self, collection_name: str, ids: list[str]) -> bool: - """ - Validate none of the IDs exist in the collection - """ + """Validate none of the IDs exist in the collection""" retrieved_ids = [ point.id for point in self.client.retrieve(collection_name, ids=ids, with_payload=False, with_vectors=False) ] diff --git a/autogen/agentchat/contrib/vectordb/utils.py b/autogen/agentchat/contrib/vectordb/utils.py index 1dd93391ef..b88dbd5ec8 100644 --- a/autogen/agentchat/contrib/vectordb/utils.py +++ b/autogen/agentchat/contrib/vectordb/utils.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import logging -from typing import Any, Dict, List +from typing import Any from termcolor import colored @@ -57,7 +57,6 @@ def filter_results_by_distance(results: QueryResults, distance_threshold: float Returns: QueryResults | A filtered results containing only distances smaller than the threshold. """ - if distance_threshold > 0: results = [[(key, value) for key, value in data if value < distance_threshold] for data in results] @@ -106,7 +105,6 @@ def chroma_results_to_query_results(data_dict: dict[str, list[list[Any]]], speci ] ``` """ - keys = [ key for key in data_dict diff --git a/autogen/agentchat/contrib/web_surfer.py b/autogen/agentchat/contrib/web_surfer.py index e8c5da2c21..871a881c13 100644 --- a/autogen/agentchat/contrib/web_surfer.py +++ b/autogen/agentchat/contrib/web_surfer.py @@ -5,16 +5,13 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import copy -import json import logging import re -from dataclasses import dataclass from datetime import datetime -from typing import Annotated, Any, Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Annotated, Any, Callable, Literal, Optional, Union -from ... import Agent, AssistantAgent, ConversableAgent, GroupChat, GroupChatManager, OpenAIWrapper, UserProxyAgent +from ... import Agent, AssistantAgent, ConversableAgent, OpenAIWrapper, UserProxyAgent from ...browser_utils import SimpleTextBrowser -from ...code_utils import content_str from ...oai.openai_utils import filter_config from ...token_count_utils import count_token, get_max_token_limit @@ -133,7 +130,7 @@ def _browser_state() -> tuple[str, str]: current_page = self.browser.viewport_current_page total_pages = len(self.browser.viewport_pages) - header += f"Viewport position: Showing page {current_page+1} of {total_pages}.\n" + header += f"Viewport position: Showing page {current_page + 1} of {total_pages}.\n" return (header, self.browser.viewport) @self._user_proxy.register_for_execution() diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py index a53607bdea..689c14522b 100644 --- a/autogen/agentchat/conversable_agent.py +++ b/autogen/agentchat/conversable_agent.py @@ -41,7 +41,6 @@ from ..coding.base import CodeExecutor from ..coding.factory import CodeExecutorFactory from ..exception_utils import InvalidCarryOverType, SenderRequired -from ..formatting_utils import colored from ..io.base import IOStream from ..messages.agent_messages import ( ClearConversableAgentHistoryMessage, @@ -49,8 +48,8 @@ ConversableAgentUsageSummaryMessage, ConversableAgentUsageSummaryNoCostIncurredMessage, ExecuteCodeBlockMessage, - ExecutedFunctionMessage, ExecuteFunctionMessage, + ExecutedFunctionMessage, GenerateCodeExecutionReplyMessage, TerminationAndHumanReplyMessage, UsingAutoReplyMessage, @@ -58,8 +57,7 @@ ) from ..oai.client import ModelClient, OpenAIWrapper from ..runtime_logging import log_event, log_function_use, log_new_agent, logging_enabled -from ..tools import Tool, get_function_schema, load_basemodels_if_needed, serialize_to_str -from ..tools.dependency_injection import inject_params +from ..tools import ChatContext, Tool, get_function_schema, load_basemodels_if_needed, serialize_to_str from .agent import Agent, LLMAgent from .chat import ChatResult, _post_process_carryover_item, a_initiate_chats, initiate_chats from .utils import consolidate_chat_info, gather_usage_summary @@ -108,7 +106,7 @@ def __post_init__(self): raise ValueError("Update function must be either a string or a callable") -class UPDATE_SYSTEM_MESSAGE(UpdateSystemMessage): +class UPDATE_SYSTEM_MESSAGE(UpdateSystemMessage): # noqa: N801 """Deprecated: Use UpdateSystemMessage instead. This class will be removed in a future version (TBD).""" def __init__(self, *args, **kwargs): @@ -161,60 +159,60 @@ def __init__( Union[list[Union[Callable, UpdateSystemMessage]], Callable, UpdateSystemMessage] ] = None, ): - """ - Args: - name (str): name of the agent. - system_message (str or list): system message for the ChatCompletion inference. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. - default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). - When set to 0, no auto reply will be generated. - human_input_mode (str): whether to ask for human inputs every time a message is received. - Possible values are "ALWAYS", "TERMINATE", "NEVER". - (1) When "ALWAYS", the agent prompts for human input every time a message is received. - Under this mode, the conversation stops when the human input is "exit", - or when is_termination_msg is True and there is no human input. - (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or - the number of auto reply reaches the max_consecutive_auto_reply. - (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops - when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. - function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions, also used for tool calls. - code_execution_config (dict or False): config for the code execution. - To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: - - work_dir (Optional, str): The working directory for the code execution. - If None, a default working directory will be used. - The default working directory is the "extensions" directory under - "path_to_autogen". - - use_docker (Optional, list, str or bool): The docker image to use for code execution. - Default is True, which means the code will be executed in a docker container. A default list of images will be used. - If a list or a str of image name(s) is provided, the code will be executed in a docker container - with the first image successfully pulled. - If False, the code will be executed in the current environment. - We strongly recommend using docker for code execution. - - timeout (Optional, int): The maximum execution time in seconds. - - last_n_messages (Experimental, int or str): The number of messages to look back for code execution. - If set to 'auto', it will scan backwards through all messages arriving since the agent last spoke, which is typically the last time execution was attempted. (Default: auto) - llm_config (dict or False or None): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) - for available options. - When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `llm_config` or in each config of 'config_list' in `llm_config`. - To disable llm-based auto reply, set to False. - When set to None, will use self.DEFAULT_CONFIG, which defaults to False. - default_auto_reply (str or dict): default auto reply when no code execution or llm-based reply is generated. - description (str): a short description of the agent. This description is used by other agents - (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) - chat_messages (dict or None): the previous chat messages that this agent had in the past with other agents. - Can be used to give the agent a memory by providing the chat history. This will allow the agent to - resume previous had conversations. Defaults to an empty chat history. - silent (bool or None): (Experimental) whether to print the message sent. If None, will use the value of - silent in each function. - context_variables (dict or None): Context variables that provide a persistent context for the agent. - Note: Will maintain a reference to the passed in context variables (enabling a shared context) - Only used in Swarms at this stage: - https://docs.ag2.ai/docs/reference/agentchat/contrib/swarm_agent - functions (List[Callable]): A list of functions to register with the agent. + """Args: + name (str): name of the agent. + system_message (str or list): system message for the ChatCompletion inference. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. + default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). + When set to 0, no auto reply will be generated. + human_input_mode (str): whether to ask for human inputs every time a message is received. + Possible values are "ALWAYS", "TERMINATE", "NEVER". + (1) When "ALWAYS", the agent prompts for human input every time a message is received. + Under this mode, the conversation stops when the human input is "exit", + or when is_termination_msg is True and there is no human input. + (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or + the number of auto reply reaches the max_consecutive_auto_reply. + (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops + when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. + function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions, also used for tool calls. + code_execution_config (dict or False): config for the code execution. + To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: + - work_dir (Optional, str): The working directory for the code execution. + If None, a default working directory will be used. + The default working directory is the "extensions" directory under + "path_to_autogen". + - use_docker (Optional, list, str or bool): The docker image to use for code execution. + Default is True, which means the code will be executed in a docker container. A default list of images will be used. + If a list or a str of image name(s) is provided, the code will be executed in a docker container + with the first image successfully pulled. + If False, the code will be executed in the current environment. + We strongly recommend using docker for code execution. + - timeout (Optional, int): The maximum execution time in seconds. + - last_n_messages (Experimental, int or str): The number of messages to look back for code execution. + If set to 'auto', it will scan backwards through all messages arriving since the agent last spoke, which is typically the last time execution was attempted. (Default: auto) + llm_config (dict or False or None): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) + for available options. + When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `llm_config` or in each config of 'config_list' in `llm_config`. + To disable llm-based auto reply, set to False. + When set to None, will use self.DEFAULT_CONFIG, which defaults to False. + default_auto_reply (str or dict): default auto reply when no code execution or llm-based reply is generated. + description (str): a short description of the agent. This description is used by other agents + (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) + chat_messages (dict or None): the previous chat messages that this agent had in the past with other agents. + Can be used to give the agent a memory by providing the chat history. This will allow the agent to + resume previous had conversations. Defaults to an empty chat history. + silent (bool or None): (Experimental) whether to print the message sent. If None, will use the value of + silent in each function. + context_variables (dict or None): Context variables that provide a persistent context for the agent. + Note: Will maintain a reference to the passed in context variables (enabling a shared context) + Only used in Swarms at this stage: + https://docs.ag2.ai/docs/reference/agentchat/contrib/swarm_agent + functions (List[Callable]): A list of functions to register with the agent. + update_agent_state_before_reply (List[Callable]): A list of functions, including UpdateSystemMessage's, called to update the agent before it replies. """ # we change code_execution_config below and we have to make sure we don't change the input # in case of UserProxyAgent, without this we could even change the default value {} @@ -222,6 +220,7 @@ def __init__( code_execution_config.copy() if hasattr(code_execution_config, "copy") else code_execution_config ) + self._validate_name(name) self._name = name # a dictionary of conversations, default value is list if chat_messages is None: @@ -356,6 +355,11 @@ def __init__( # Associate agent update state hooks self._register_update_agent_state_before_reply(update_agent_state_before_reply) + def _validate_name(self, name: str) -> None: + # Validation for name using regex to detect any whitespace + if re.search(r"\s", name): + raise ValueError(f"The name of the agent cannot contain any whitespace. The name provided is: '{name}'") + def _get_display_name(self): """Get the string representation of the agent. @@ -433,7 +437,6 @@ def _register_update_agent_state_before_reply(self, functions: Optional[Union[li for func in functions: if isinstance(func, UpdateSystemMessage): - # Wrapper function that allows this to be used in the update_agent_state hook # Its primary purpose, however, is just to update the agent's system message # Outer function to create a closure with the update function @@ -463,9 +466,9 @@ def update_system_message_wrapper( self.register_hook(hookable_method="update_agent_state", hook=func) def _validate_llm_config(self, llm_config): - assert llm_config in (None, False) or isinstance( - llm_config, dict - ), "llm_config must be a dict or False or None." + assert llm_config in (None, False) or isinstance(llm_config, dict), ( + "llm_config must be a dict or False or None." + ) if llm_config is None: llm_config = self.DEFAULT_CONFIG self.llm_config = self.DEFAULT_CONFIG if llm_config is None else llm_config @@ -611,7 +614,7 @@ def _get_chats_to_run( message = last_msg if callable(message): message = message(recipient, messages, sender, config) - # We only run chat that has a valid message. NOTE: This is prone to change dependin on applications. + # We only run chat that has a valid message. NOTE: This is prone to change depending on applications. if message: current_c["message"] = message chat_to_run.append(current_c) @@ -840,6 +843,7 @@ def register_nested_chats( **kwargs, ) -> None: """Register a nested chat reply function. + Args: chat_queue (list): a list of chat objects to be initiated. If use_async is used, then all messages in chat_queue must have a chat-id associated with them. trigger (Agent class, str, Agent instance, callable, or list): refer to `register_reply` for details. @@ -898,8 +902,8 @@ def wrapped_reply_func(recipient, messages=None, sender=None, config=None): ) def get_context(self, key: str, default: Any = None) -> Any: - """ - Get a context variable by key. + """Get a context variable by key. + Args: key: The key to look up default: Value to return if key doesn't exist @@ -909,8 +913,8 @@ def get_context(self, key: str, default: Any = None) -> Any: return self._context_variables.get(key, default) def set_context(self, key: str, value: Any) -> None: - """ - Set a context variable. + """Set a context variable. + Args: key: The key to set value: The value to associate with the key @@ -918,16 +922,16 @@ def set_context(self, key: str, value: Any) -> None: self._context_variables[key] = value def update_context(self, context_variables: dict[str, Any]) -> None: - """ - Update multiple context variables at once. + """Update multiple context variables at once. + Args: context_variables: Dictionary of variables to update/add """ self._context_variables.update(context_variables) def pop_context(self, key: str, default: Any = None) -> Any: - """ - Remove and return a context variable. + """Remove and return a context variable. + Args: key: The key to remove default: Value to return if key doesn't exist @@ -1023,8 +1027,7 @@ def _message_to_dict(message: Union[dict, str]) -> dict: @staticmethod def _normalize_name(name): - """ - LLMs sometimes ask functions while ignoring their own format requirements, this function should be used to replace invalid characters with "_". + """LLMs sometimes ask functions while ignoring their own format requirements, this function should be used to replace invalid characters with "_". Prefer _assert_valid_name for validating user configuration or input """ @@ -1032,8 +1035,7 @@ def _normalize_name(name): @staticmethod def _assert_valid_name(name): - """ - Ensure that configured names are valid, raises ValueError if not. + """Ensure that configured names are valid, raises ValueError if not. For munging LLM responses use _normalize_name to ensure LLM specified names don't break the API. """ @@ -1133,9 +1135,7 @@ def send( ```python { "content": lambda context: context["use_tool_msg"], - "context": { - "use_tool_msg": "Use tool X if they are relevant." - } + "context": {"use_tool_msg": "Use tool X if they are relevant."}, } ``` Next time, one agent can send a message B with a different "use_tool_msg". @@ -1183,9 +1183,7 @@ async def a_send( ```python { "content": lambda context: context["use_tool_msg"], - "context": { - "use_tool_msg": "Use tool X if they are relevant." - } + "context": {"use_tool_msg": "Use tool X if they are relevant."}, } ``` Next time, one agent can send a message B with a different "use_tool_msg". @@ -1262,7 +1260,7 @@ def receive( ValueError: if the message can't be converted into a valid ChatCompletion message. """ self._process_received_message(message, sender, silent) - if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False: + if request_reply is False or (request_reply is None and self.reply_at_receive[sender] is False): return reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender) if reply is not None: @@ -1299,7 +1297,7 @@ async def a_receive( ValueError: if the message can't be converted into a valid ChatCompletion message. """ self._process_received_message(message, sender, silent) - if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False: + if request_reply is False or (request_reply is None and self.reply_at_receive[sender] is False): return reply = await self.a_generate_reply(sender=sender) if reply is not None: @@ -1670,8 +1668,7 @@ def _reflection_with_llm( return response def _check_chat_queue_for_sender(self, chat_queue: list[dict[str, Any]]) -> list[dict[str, Any]]: - """ - Check the chat queue and add the "sender" key if it's missing. + """Check the chat queue and add the "sender" key if it's missing. Args: chat_queue (List[Dict[str, Any]]): A list of dictionaries containing chat information. @@ -1878,9 +1875,7 @@ def _generate_code_execution_reply_using_executor( # Find when the agent last spoke num_messages_to_scan = 0 for message in reversed(messages): - if "role" not in message: - break - elif message["role"] != "user": + if "role" not in message or message["role"] != "user": break else: num_messages_to_scan += 1 @@ -1929,9 +1924,7 @@ def generate_code_execution_reply( messages_to_scan = 0 for i in range(len(messages)): message = messages[-(i + 1)] - if "role" not in message: - break - elif message["role"] != "user": + if "role" not in message or message["role"] != "user": break else: messages_to_scan += 1 @@ -1964,8 +1957,7 @@ def generate_function_call_reply( sender: Optional[Agent] = None, config: Optional[Any] = None, ) -> tuple[bool, Union[dict, None]]: - """ - Generate a reply using function call. + """Generate a reply using function call. "function_call" replaced by "tool_calls" as of [OpenAI API v1.1.0](https://github.com/openai/openai-python/releases/tag/v1.1.0) See https://platform.openai.com/docs/api-reference/chat/create#chat-create-functions @@ -1975,7 +1967,7 @@ def generate_function_call_reply( if messages is None: messages = self._oai_messages[sender] message = messages[-1] - if "function_call" in message and message["function_call"]: + if message.get("function_call"): call_id = message.get("id", None) func_call = message["function_call"] func = self._function_map.get(func_call.get("name", None), None) @@ -2003,8 +1995,7 @@ async def a_generate_function_call_reply( sender: Optional[Agent] = None, config: Optional[Any] = None, ) -> tuple[bool, Union[dict, None]]: - """ - Generate a reply using async function call. + """Generate a reply using async function call. "function_call" replaced by "tool_calls" as of [OpenAI API v1.1.0](https://github.com/openai/openai-python/releases/tag/v1.1.0) See https://platform.openai.com/docs/api-reference/chat/create#chat-create-functions @@ -2564,6 +2555,7 @@ def run_code(self, code, **kwargs): """Run the code and return the result. Override this function to modify the way to run the code. + Args: code (str): the code to be executed. **kwargs: other keyword arguments. @@ -2782,6 +2774,7 @@ def generate_init_message(self, message: Union[dict, str, None], **kwargs) -> Un "carryover": a string or a list of string to specify the carryover information to be passed to this chat. It can be a string or a list of string. If provided, we will combine this carryover with the "message" content when generating the initial chat message. + Returns: str or dict: the processed message. """ @@ -2866,7 +2859,7 @@ def register_function(self, function_map: dict[str, Union[Callable, None]]): self._function_map = {k: v for k, v in self._function_map.items() if v is not None} def update_function_signature(self, func_sig: Union[str, dict], is_remove: None): - """update a function_signature in the LLM configuration for function_call. + """Update a function_signature in the LLM configuration for function_call. Args: func_sig (str or dict): description/name of the function to update/remove to the model. See: https://platform.openai.com/docs/api-reference/chat/create#chat/create-functions @@ -2875,7 +2868,6 @@ def update_function_signature(self, func_sig: Union[str, dict], is_remove: None) Deprecated as of [OpenAI API v1.1.0](https://github.com/openai/openai-python/releases/tag/v1.1.0) See https://platform.openai.com/docs/api-reference/chat/create#chat-create-function_call """ - if not isinstance(self.llm_config, dict): error_msg = "To update a function signature, agent must have an llm_config" logger.error(error_msg) @@ -2914,13 +2906,12 @@ def update_function_signature(self, func_sig: Union[str, dict], is_remove: None) self.client = OpenAIWrapper(**self.llm_config) def update_tool_signature(self, tool_sig: Union[str, dict], is_remove: bool): - """update a tool_signature in the LLM configuration for tool_call. + """Update a tool_signature in the LLM configuration for tool_call. Args: tool_sig (str or dict): description/name of the tool to update/remove to the model. See: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools is_remove: whether removing the tool from llm_config with name 'tool_sig' """ - if not self.llm_config: error_msg = "To update a tool signature, agent must have an llm_config" logger.error(error_msg) @@ -2967,13 +2958,14 @@ def function_map(self) -> dict[str, Callable]: """Return the function map.""" return self._function_map - def _wrap_function(self, func: F) -> F: - """Wrap the function to dump the return value to json. + def _wrap_function(self, func: F, inject_params: dict[str, Any] = {}) -> F: + """Wrap the function inject chat context parameters and to dump the return value to json. Handles both sync and async functions. Args: func: the function to be wrapped. + inject_params: the chat context parameters which will be passed to the function. Returns: The wrapped function. @@ -2982,7 +2974,7 @@ def _wrap_function(self, func: F) -> F: @load_basemodels_if_needed @functools.wraps(func) def _wrapped_func(*args, **kwargs): - retval = func(*args, **kwargs) + retval = func(*args, **kwargs, **inject_params) if logging_enabled(): log_function_use(self, func, kwargs, retval) return serialize_to_str(retval) @@ -2990,7 +2982,7 @@ def _wrapped_func(*args, **kwargs): @load_basemodels_if_needed @functools.wraps(func) async def _a_wrapped_func(*args, **kwargs): - retval = await func(*args, **kwargs) + retval = await func(*args, **kwargs, **inject_params) if logging_enabled(): log_function_use(self, func, kwargs, retval) return serialize_to_str(retval) @@ -3071,7 +3063,6 @@ def _decorator(func_or_tool: Union[F, Tool]) -> Tool: return _decorator def _register_for_llm(self, tool: Tool, api_style: Literal["tool", "function"]) -> None: - # register the function to the agent if there is LLM config, raise an exception otherwise if self.llm_config is None: raise RuntimeError("LLM config must be setup before registering a function for LLM.") @@ -3124,8 +3115,10 @@ def _decorator(func_or_tool: Union[Tool, F]) -> Tool: nonlocal name tool = Tool(func_or_tool=func_or_tool, name=name) + chat_context = ChatContext(self) + chat_context_params = {param: chat_context for param in tool._chat_context_param_names} - self.register_function({tool.name: self._wrap_function(tool.func)}) + self.register_function({tool.name: self._wrap_function(tool.func, chat_context_params)}) return tool @@ -3141,8 +3134,7 @@ def register_model_client(self, model_client_cls: ModelClient, **kwargs): self.client.register_model_client(model_client_cls, **kwargs) def register_hook(self, hookable_method: str, hook: Callable): - """ - Registers a hook to be called by a hookable method, in order to add a capability to the agent. + """Registers a hook to be called by a hookable method, in order to add a capability to the agent. Registered hooks are kept in lists (one per hookable method), and are called in their order of registration. Args: @@ -3155,8 +3147,7 @@ def register_hook(self, hookable_method: str, hook: Callable): hook_list.append(hook) def update_agent_state_before_reply(self, messages: list[dict]) -> None: - """ - Calls any registered capability hooks to update the agent's state. + """Calls any registered capability hooks to update the agent's state. Primarily used to update context variables. Will, potentially, modify the messages. """ @@ -3167,9 +3158,7 @@ def update_agent_state_before_reply(self, messages: list[dict]) -> None: hook(self, messages) def process_all_messages_before_reply(self, messages: list[dict]) -> list[dict]: - """ - Calls any registered capability hooks to process all messages, potentially modifying the messages. - """ + """Calls any registered capability hooks to process all messages, potentially modifying the messages.""" hook_list = self.hook_lists["process_all_messages_before_reply"] # If no hooks are registered, or if there are no messages to process, return the original message list. if len(hook_list) == 0 or messages is None: @@ -3182,11 +3171,9 @@ def process_all_messages_before_reply(self, messages: list[dict]) -> list[dict]: return processed_messages def process_last_received_message(self, messages: list[dict]) -> list[dict]: - """ - Calls any registered capability hooks to use and potentially modify the text of the last message, + """Calls any registered capability hooks to use and potentially modify the text of the last message, as long as the last message is not a function call or exit command. """ - # If any required condition is not met, return the original message list. hook_list = self.hook_lists["process_last_received_message"] if len(hook_list) == 0: diff --git a/autogen/agentchat/groupchat.py b/autogen/agentchat/groupchat.py index 425b817f3d..c1b2dbb7fe 100644 --- a/autogen/agentchat/groupchat.py +++ b/autogen/agentchat/groupchat.py @@ -11,11 +11,10 @@ import re import sys from dataclasses import dataclass, field -from typing import Callable, Dict, List, Literal, Optional, Tuple, Union +from typing import Callable, Literal, Optional, Union from ..code_utils import content_str from ..exception_utils import AgentNameConflict, NoEligibleSpeaker, UndefinedNextAgent -from ..formatting_utils import colored from ..graph_utils import check_graph_validity, invert_disallowed_to_allowed from ..io.base import IOStream from ..messages.agent_messages import ( @@ -369,8 +368,8 @@ def select_speaker_msg(self, agents: Optional[list[Agent]] = None) -> str: def select_speaker_prompt(self, agents: Optional[list[Agent]] = None) -> str: """Return the floating system prompt selecting the next speaker. This is always the *last* message in the context. - Will return None if the select_speaker_prompt_template is None.""" - + Will return None if the select_speaker_prompt_template is None. + """ if self.select_speaker_prompt_template is None: return None @@ -559,7 +558,6 @@ def _prepare_and_select_agents( def select_speaker(self, last_speaker: Agent, selector: ConversableAgent) -> Agent: """Select the next speaker (with requery).""" - # Prepare the list of available agents and select an agent if selection method allows (non-auto) selected_agent, agents, messages = self._prepare_and_select_agents(last_speaker) if selected_agent: @@ -573,7 +571,6 @@ def select_speaker(self, last_speaker: Agent, selector: ConversableAgent) -> Age async def a_select_speaker(self, last_speaker: Agent, selector: ConversableAgent) -> Agent: """Select the next speaker (with requery), asynchronously.""" - selected_agent, agents, messages = self._prepare_and_select_agents(last_speaker) if selected_agent: return selected_agent @@ -701,7 +698,6 @@ def _auto_select_speaker( Returns: Dict: a counter for mentioned agents. """ - # If no agents are passed in, assign all the group chat's agents if agents is None: agents = self.agents @@ -785,7 +781,6 @@ async def a_auto_select_speaker( Returns: Dict: a counter for mentioned agents. """ - # If no agents are passed in, assign all the group chat's agents if agents is None: agents = self.agents @@ -942,7 +937,8 @@ def _process_speaker_selection_result(self, result, last_speaker: ConversableAge """Checks the result of the auto_select_speaker function, returning the agent to speak. - Used by auto_select_speaker and a_auto_select_speaker.""" + Used by auto_select_speaker and a_auto_select_speaker. + """ if len(result.chat_history) > 0: # Use the final message, which will have the selected agent or reason for failure final_message = result.chat_history[-1]["content"] @@ -1107,9 +1103,7 @@ def last_speaker(self) -> Agent: def print_messages(recipient, messages, sender, config): # Print the message immediately - print( - f"Sender: {sender.name} | Recipient: {recipient.name} | Message: {messages[-1].get('content')}" - ) + print(f"Sender: {sender.name} | Recipient: {recipient.name} | Message: {messages[-1].get('content')}") print(f"Real Sender: {sender.last_speaker.name}") assert sender.last_speaker.name in messages[-1].get("content") return False, None # Required to ensure the agent communication flow continues @@ -1119,9 +1113,7 @@ def print_messages(recipient, messages, sender, config): agent_b = ConversableAgent("agent B", default_auto_reply="I'm agent B.") agent_c = ConversableAgent("agent C", default_auto_reply="I'm agent C.") for agent in [agent_a, agent_b, agent_c]: - agent.register_reply( - [ConversableAgent, None], reply_func=print_messages, config=None - ) + agent.register_reply([ConversableAgent, None], reply_func=print_messages, config=None) group_chat = GroupChat( [agent_a, agent_b, agent_c], messages=[], @@ -1130,9 +1122,7 @@ def print_messages(recipient, messages, sender, config): allow_repeat_speaker=True, ) chat_manager = GroupChatManager(group_chat) - groupchat_result = agent_a.initiate_chat( - chat_manager, message="Hi, there, I'm agent A." - ) + groupchat_result = agent_a.initiate_chat(chat_manager, message="Hi, there, I'm agent A.") ``` """ return self._last_speaker @@ -1306,7 +1296,6 @@ def resume( Returns: - Tuple[ConversableAgent, Dict]: A tuple containing the last agent who spoke and their message """ - # Convert messages from string to messages list, if needed if isinstance(messages, str): messages = self.messages_from_string(messages) @@ -1410,7 +1399,6 @@ async def a_resume( Returns: - Tuple[ConversableAgent, Dict]: A tuple containing the last agent who spoke and their message """ - # Convert messages from string to messages list, if needed if isinstance(messages, str): messages = self.messages_from_string(messages) @@ -1498,10 +1486,10 @@ async def a_resume( def _valid_resume_messages(self, messages: list[dict]): """Validates the messages used for resuming - args: + Args: messages (List[Dict]): list of messages to resume with - returns: + Returns: - bool: Whether they are valid for resuming """ # Must have messages to start with, otherwise they should run run_chat @@ -1525,15 +1513,14 @@ def _process_resume_termination( ): """Removes termination string, if required, and checks if termination may occur. - args: + Args: remove_termination_string (str or function): Remove the termination string from the last message to prevent immediate termination If a string is provided, this string will be removed from last message. If a function is provided, the last message will be passed to this function, and the function returns the string after processing. - returns: + Returns: None """ - last_message = messages[-1] # Replace any given termination string in the last message @@ -1557,10 +1544,10 @@ def _remove_termination_string(content: str) -> str: def messages_from_string(self, message_string: str) -> list[dict]: """Reads the saved state of messages in Json format for resume and returns as a messages list - args: + Args: - message_string: Json string, the saved state - returns: + Returns: - List[Dict]: List of messages """ try: @@ -1574,13 +1561,12 @@ def messages_to_string(self, messages: list[dict]) -> str: """Converts the provided messages into a Json string that can be used for resuming the chat. The state is made up of a list of messages - args: + Args: - messages (List[Dict]): set of messages to convert to a string - returns: + Returns: - str: Json representation of the messages which can be persisted for resuming later """ - return json.dumps(messages) def _raise_exception_on_async_reply_functions(self) -> None: @@ -1631,10 +1617,7 @@ def clear_agents_history(self, reply: dict, groupchat: GroupChat) -> str: nr_messages_to_preserve_provided = True else: for agent in groupchat.agents: - if agent.name == word: - agent_to_memory_clear = agent - break - elif agent.name == word[:-1]: # for the case when agent name is followed by dot or other sign + if agent.name == word or agent.name == word[:-1]: agent_to_memory_clear = agent break # preserve last tool call message if clear history called inside of tool response diff --git a/autogen/agentchat/realtime_agent/function_observer.py b/autogen/agentchat/realtime_agent/function_observer.py index 8417810051..a793c4c12f 100644 --- a/autogen/agentchat/realtime_agent/function_observer.py +++ b/autogen/agentchat/realtime_agent/function_observer.py @@ -44,7 +44,6 @@ async def call_function(self, call_id: str, name: str, kwargs: dict[str, Any]) - name (str): The name of the function to call. kwargs (Any[str, Any]): The arguments to pass to the function. """ - if name in self.agent.registred_realtime_tools: func = self.agent.registred_realtime_tools[name].func func = func if asyncio.iscoroutinefunction(func) else asyncify(func) diff --git a/autogen/agentchat/realtime_agent/oai_realtime_client.py b/autogen/agentchat/realtime_agent/oai_realtime_client.py index d074c20eb8..4756aacb3e 100644 --- a/autogen/agentchat/realtime_agent/oai_realtime_client.py +++ b/autogen/agentchat/realtime_agent/oai_realtime_client.py @@ -2,17 +2,22 @@ # # SPDX-License-Identifier: Apache-2.0 +import asyncio +import json +from collections.abc import AsyncGenerator from contextlib import asynccontextmanager from logging import Logger, getLogger -from typing import TYPE_CHECKING, Any, AsyncGenerator, Optional +from typing import TYPE_CHECKING, Any, Optional -from asyncer import TaskGroup, create_task_group +import httpx from openai import DEFAULT_MAX_RETRIES, NOT_GIVEN, AsyncOpenAI from openai.resources.beta.realtime.realtime import AsyncRealtimeConnection from .realtime_client import Role if TYPE_CHECKING: + from fastapi.websockets import WebSocket + from .realtime_client import RealtimeClientProtocol __all__ = ["OpenAIRealtimeClient", "Role"] @@ -168,8 +173,184 @@ async def read_events(self) -> AsyncGenerator[dict[str, Any], None]: self._connection = None -# needed for mypy to check if OpenAIRealtimeClient implements RealtimeClientProtocol +class OpenAIRealtimeWebRTCClient: + """(Experimental) Client for OpenAI Realtime API that uses WebRTC protocol.""" + + def __init__( + self, + *, + llm_config: dict[str, Any], + voice: str, + system_message: str, + websocket: "WebSocket", + logger: Optional[Logger] = None, + ) -> None: + """(Experimental) Client for OpenAI Realtime API. + + Args: + llm_config (dict[str, Any]): The config for the client. + """ + self._llm_config = llm_config + self._voice = voice + self._system_message = system_message + self._logger = logger + self._websocket = websocket + + config = llm_config["config_list"][0] + self._model: str = config["model"] + self._temperature: float = llm_config.get("temperature", 0.8) # type: ignore[union-attr] + self._config = config + + @property + def logger(self) -> Logger: + """Get the logger for the OpenAI Realtime API.""" + return self._logger or global_logger + + async def send_function_result(self, call_id: str, result: str) -> None: + """Send the result of a function call to the OpenAI Realtime API. + + Args: + call_id (str): The ID of the function call. + result (str): The result of the function call. + """ + await self._websocket.send_json( + { + "type": "conversation.item.create", + "item": { + "type": "function_call_output", + "call_id": call_id, + "output": result, + }, + } + ) + await self._websocket.send_json({"type": "response.create"}) + + async def send_text(self, *, role: Role, text: str) -> None: + """Send a text message to the OpenAI Realtime API. + + Args: + role (str): The role of the message. + text (str): The text of the message. + """ + # await self.connection.response.cancel() #why is this here? + await self._websocket.send_json( + { + "type": "connection.conversation.item.create", + "item": {"type": "message", "role": role, "content": [{"type": "input_text", "text": text}]}, + } + ) + # await self.connection.response.create() + + async def send_audio(self, audio: str) -> None: + """Send audio to the OpenAI Realtime API. + + Args: + audio (str): The audio to send. + """ + await self._websocket.send_json({"type": "input_audio_buffer.append", "audio": audio}) + + async def truncate_audio(self, audio_end_ms: int, content_index: int, item_id: str) -> None: + """Truncate audio in the OpenAI Realtime API. + + Args: + audio_end_ms (int): The end of the audio to truncate. + content_index (int): The index of the content to truncate. + item_id (str): The ID of the item to truncate. + """ + await self._websocket.send_json( + { + "type": "conversation.item.truncate", + "content_index": content_index, + "item_id": item_id, + "audio_end_ms": audio_end_ms, + } + ) + + async def session_update(self, session_options: dict[str, Any]) -> None: + """Send a session update to the OpenAI Realtime API. + + In the case of WebRTC we can not send it directly, but we can send it + to the javascript over the websocket, and rely on it to send session + update to OpenAI + + Args: + session_options (dict[str, Any]): The session options to update. + """ + logger = self.logger + logger.info(f"Sending session update: {session_options}") + # await self.connection.session.update(session=session_options) # type: ignore[arg-type] + await self._websocket.send_json({"type": "session.update", "session": session_options}) + logger.info("Sending session update finished") + + async def _initialize_session(self) -> None: + """Control initial session with OpenAI.""" + session_update = { + "turn_detection": {"type": "server_vad"}, + "voice": self._voice, + "instructions": self._system_message, + "modalities": ["audio", "text"], + "temperature": self._temperature, + } + await self.session_update(session_options=session_update) + + @asynccontextmanager + async def connect(self) -> AsyncGenerator[None, None]: + """Connect to the OpenAI Realtime API. + + In the case of WebRTC, we pass connection information over the + websocket, so that javascript on the other end of websocket open + actual connection to OpenAI + """ + try: + url = "https://api.openai.com/v1/realtime/sessions" + api_key = self._config.get("api_key", None) + headers = { + "Authorization": f"Bearer {api_key}", # Use os.getenv to get from environment + "Content-Type": "application/json", + } + data = { + # "model": "gpt-4o-realtime-preview-2024-12-17", + "model": self._model, + "voice": self._voice, + } + async with httpx.AsyncClient() as client: + response = await client.post(url, headers=headers, json=data) + response.raise_for_status() + json_data = response.json() + json_data["model"] = self._model + if self._websocket is not None: + await self._websocket.send_json({"type": "ag2.init", "config": json_data}) + await asyncio.sleep(10) + await self._initialize_session() + yield + finally: + pass + + async def read_events(self) -> AsyncGenerator[dict[str, Any], None]: + """Read messages from the OpenAI Realtime API. + Again, in case of WebRTC, we do not read OpenAI messages directly since we + do not hold connection to OpenAI. Instead we read messages from the websocket, and javascript + client on the other side of the websocket that is connected to OpenAI is relaying events to us. + """ + logger = self.logger + while True: + try: + message_json = await self._websocket.receive_text() + message = json.loads(message_json) + if "function" in message["type"]: + logger.info("Received function message", message) + yield message + except Exception: + break + + +# needed for mypy to check if OpenAIRealtimeWebRTCClient implements RealtimeClientProtocol if TYPE_CHECKING: _client: RealtimeClientProtocol = OpenAIRealtimeClient( llm_config={}, voice="alloy", system_message="You are a helpful AI voice assistant." ) + + def _rtc_client(websocket: "WebSocket") -> RealtimeClientProtocol: + return OpenAIRealtimeWebRTCClient( + llm_config={}, voice="alloy", system_message="You are a helpful AI voice assistant.", websocket=websocket + ) diff --git a/autogen/agentchat/realtime_agent/realtime_agent.py b/autogen/agentchat/realtime_agent/realtime_agent.py index b4e5d688b0..f372832d41 100644 --- a/autogen/agentchat/realtime_agent/realtime_agent.py +++ b/autogen/agentchat/realtime_agent/realtime_agent.py @@ -3,16 +3,19 @@ # SPDX-License-Identifier: Apache-2.0 from logging import Logger, getLogger -from typing import Any, Callable, Literal, Optional, TypeVar, Union +from typing import Any, Callable, Optional, TypeVar, Union import anyio -from asyncer import create_task_group, syncify +from asyncer import asyncify, create_task_group, syncify +from fastapi import WebSocket -from ...tools import Tool, get_function_schema +from ...tools import Tool from ..agent import Agent +from ..contrib.swarm_agent import AfterWorkOption, initiate_swarm_chat from ..conversable_agent import ConversableAgent from .function_observer import FunctionObserver -from .oai_realtime_client import OpenAIRealtimeClient, Role +from .oai_realtime_client import OpenAIRealtimeClient, OpenAIRealtimeWebRTCClient, Role +from .realtime_client import RealtimeClientProtocol from .realtime_observer import RealtimeObserver F = TypeVar("F", bound=Callable[..., Any]) @@ -21,14 +24,13 @@ SWARM_SYSTEM_MESSAGE = ( "You are a helpful voice assistant. Your task is to listen to user and to coordinate the tasks based on his/her inputs." - "Only call the 'answer_task_question' function when you have the answer from the user." - "You can communicate and will communicate using audio output only." + "You can and will communicate using audio output only." ) QUESTION_ROLE: Role = "user" QUESTION_MESSAGE = ( "I have a question/information for myself. DO NOT ANSWER YOURSELF, GET THE ANSWER FROM ME. " - "repeat the question to me **WITH AUDIO OUTPUT** and then call 'answer_task_question' AFTER YOU GET THE ANSWER FROM ME\n\n" + "repeat the question to me **WITH AUDIO OUTPUT** and AFTER YOU GET THE ANSWER FROM ME call 'answer_task_question'\n\n" "The question is: '{}'\n\n" ) QUESTION_TIMEOUT_SECONDS = 20 @@ -41,20 +43,22 @@ def __init__( self, *, name: str, - audio_adapter: RealtimeObserver, + audio_adapter: Optional[RealtimeObserver] = None, system_message: str = "You are a helpful AI Assistant.", llm_config: dict[str, Any], voice: str = "alloy", logger: Optional[Logger] = None, + websocket: Optional[WebSocket] = None, ): """(Experimental) Agent for interacting with the Realtime Clients. Args: name (str): The name of the agent. - audio_adapter (RealtimeObserver): The audio adapter for the agent. + audio_adapter (Optional[RealtimeObserver] = None): The audio adapter for the agent. system_message (str): The system message for the agent. llm_config (dict[str, Any], bool): The config for the agent. voice (str): The voice for the agent. + websocket (Optional[WebSocket] = None): WebSocket from WebRTC javascript client """ super().__init__( name=name, @@ -74,12 +78,20 @@ def __init__( self._logger = logger self._function_observer = FunctionObserver(logger=logger) self._audio_adapter = audio_adapter - self._realtime_client = OpenAIRealtimeClient( + self._realtime_client: RealtimeClientProtocol = OpenAIRealtimeClient( llm_config=llm_config, voice=voice, system_message=system_message, logger=logger ) + if websocket is not None: + self._realtime_client = OpenAIRealtimeWebRTCClient( + llm_config=llm_config, voice=voice, system_message=system_message, websocket=websocket, logger=logger + ) + self._voice = voice - self._observers: list[RealtimeObserver] = [self._function_observer, self._audio_adapter] + self._observers: list[RealtimeObserver] = [self._function_observer] + if self._audio_adapter: + # audio adapter is not needed for WebRTC + self._observers.append(self._audio_adapter) self._registred_realtime_tools: dict[str, Tool] = {} @@ -95,13 +107,17 @@ def __init__( self._initial_agent: Optional[ConversableAgent] = None self._agents: Optional[list[ConversableAgent]] = None + def _validate_name(self, name: str) -> None: + # RealtimeAgent does not need to validate the name + pass + @property def logger(self) -> Logger: """Get the logger for the agent.""" return self._logger or global_logger @property - def realtime_client(self) -> OpenAIRealtimeClient: + def realtime_client(self) -> RealtimeClientProtocol: """Get the OpenAI Realtime Client.""" return self._realtime_client @@ -154,10 +170,8 @@ async def run(self) -> None: """Run the agent.""" # everything is run in the same task group to enable easy cancellation using self._tg.cancel_scope.cancel() async with create_task_group() as self._tg: - # connect with the client first (establishes a connection and initializes a session) async with self._realtime_client.connect(): - # start the observers for observer in self._observers: self._tg.soonify(observer.run)(self) @@ -166,6 +180,15 @@ async def run(self) -> None: for observer in self._observers: await observer.wait_for_ready() + if self._start_swarm_chat and self._initial_agent and self._agents: + self._tg.soonify(asyncify(initiate_swarm_chat))( + initial_agent=self._initial_agent, + agents=self._agents, + user_agent=self, # type: ignore[arg-type] + messages="Find out what the user wants.", + after_work=AfterWorkOption.REVERT_TO_USER, + ) + # iterate over the events async for event in self.realtime_client.read_events(): for observer in self._observers: @@ -220,15 +243,13 @@ async def get_answer(self) -> str: return self._answer async def ask_question(self, question: str, question_timeout: int) -> None: - """ - Send a question for the user to the agent and wait for the answer. + """Send a question for the user to the agent and wait for the answer. If the answer is not received within the timeout, the question is repeated. Args: question: The question to ask the user. question_timeout: The time in seconds to wait for the answer. """ - self.reset_answer() await self._realtime_client.send_text(role=QUESTION_ROLE, text=question) @@ -260,7 +281,6 @@ def check_termination_and_human_reply( config: any the config for the agent """ - if not messages: return False, None diff --git a/autogen/agentchat/realtime_agent/realtime_client.py b/autogen/agentchat/realtime_agent/realtime_client.py index 2195a78f83..0e914bf419 100644 --- a/autogen/agentchat/realtime_agent/realtime_client.py +++ b/autogen/agentchat/realtime_agent/realtime_client.py @@ -2,7 +2,8 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, AsyncContextManager, AsyncGenerator, Literal, Protocol, runtime_checkable +from collections.abc import AsyncGenerator +from typing import Any, AsyncContextManager, Literal, Protocol, runtime_checkable __all__ = ["RealtimeClientProtocol", "Role"] diff --git a/autogen/agentchat/realtime_agent/twilio_audio_adapter.py b/autogen/agentchat/realtime_agent/twilio_audio_adapter.py index c8b275c5e7..aaca2fe3e4 100644 --- a/autogen/agentchat/realtime_agent/twilio_audio_adapter.py +++ b/autogen/agentchat/realtime_agent/twilio_audio_adapter.py @@ -4,17 +4,14 @@ import base64 import json -from logging import Logger, getLogger +from logging import Logger from typing import TYPE_CHECKING, Any, Optional -from openai.types.beta.realtime.realtime_server_event import RealtimeServerEvent - from .realtime_observer import RealtimeObserver if TYPE_CHECKING: from fastapi.websockets import WebSocket - from .realtime_agent import RealtimeAgent LOG_EVENT_TYPES = [ "error", diff --git a/autogen/agentchat/realtime_agent/websocket_audio_adapter.py b/autogen/agentchat/realtime_agent/websocket_audio_adapter.py index 7dc7030e67..ef76defa8d 100644 --- a/autogen/agentchat/realtime_agent/websocket_audio_adapter.py +++ b/autogen/agentchat/realtime_agent/websocket_audio_adapter.py @@ -4,15 +4,12 @@ import base64 import json -from logging import Logger, getLogger +from logging import Logger from typing import TYPE_CHECKING, Any, Optional -from openai.types.beta.realtime.realtime_server_event import RealtimeServerEvent - if TYPE_CHECKING: from fastapi.websockets import WebSocket - from .realtime_agent import RealtimeAgent from .realtime_observer import RealtimeObserver diff --git a/autogen/agentchat/user_proxy_agent.py b/autogen/agentchat/user_proxy_agent.py index 687e7e3150..54d50428b1 100644 --- a/autogen/agentchat/user_proxy_agent.py +++ b/autogen/agentchat/user_proxy_agent.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Callable, Dict, List, Literal, Optional, Union +from typing import Callable, Literal, Optional, Union from ..runtime_logging import log_new_agent, logging_enabled from .conversable_agent import ConversableAgent @@ -43,51 +43,50 @@ def __init__( description: Optional[str] = None, **kwargs, ): - """ - Args: - name (str): name of the agent. - is_termination_msg (function): a function that takes a message in the form of a dictionary - and returns a boolean value indicating if this received message is a termination message. - The dict can contain the following keys: "content", "role", "name", "function_call". - max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. - default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). - The limit only plays a role when human_input_mode is not "ALWAYS". - human_input_mode (str): whether to ask for human inputs every time a message is received. - Possible values are "ALWAYS", "TERMINATE", "NEVER". - (1) When "ALWAYS", the agent prompts for human input every time a message is received. - Under this mode, the conversation stops when the human input is "exit", - or when is_termination_msg is True and there is no human input. - (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or - the number of auto reply reaches the max_consecutive_auto_reply. - (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops - when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. - function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions. - code_execution_config (dict or False): config for the code execution. - To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: - - work_dir (Optional, str): The working directory for the code execution. - If None, a default working directory will be used. - The default working directory is the "extensions" directory under - "path_to_autogen". - - use_docker (Optional, list, str or bool): The docker image to use for code execution. - Default is True, which means the code will be executed in a docker container. A default list of images will be used. - If a list or a str of image name(s) is provided, the code will be executed in a docker container - with the first image successfully pulled. - If False, the code will be executed in the current environment. - We strongly recommend using docker for code execution. - - timeout (Optional, int): The maximum execution time in seconds. - - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. - default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. - llm_config (dict or False or None): llm inference configuration. - Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) - for available options. - Default to False, which disables llm-based auto reply. - When set to None, will use self.DEFAULT_CONFIG, which defaults to False. - system_message (str or List): system message for ChatCompletion inference. - Only used when llm_config is not False. Use it to reprogram the agent. - description (str): a short description of the agent. This description is used by other agents - (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) - **kwargs (dict): Please refer to other kwargs in - [ConversableAgent](conversable_agent#init). + """Args: + name (str): name of the agent. + is_termination_msg (function): a function that takes a message in the form of a dictionary + and returns a boolean value indicating if this received message is a termination message. + The dict can contain the following keys: "content", "role", "name", "function_call". + max_consecutive_auto_reply (int): the maximum number of consecutive auto replies. + default to None (no limit provided, class attribute MAX_CONSECUTIVE_AUTO_REPLY will be used as the limit in this case). + The limit only plays a role when human_input_mode is not "ALWAYS". + human_input_mode (str): whether to ask for human inputs every time a message is received. + Possible values are "ALWAYS", "TERMINATE", "NEVER". + (1) When "ALWAYS", the agent prompts for human input every time a message is received. + Under this mode, the conversation stops when the human input is "exit", + or when is_termination_msg is True and there is no human input. + (2) When "TERMINATE", the agent only prompts for human input only when a termination message is received or + the number of auto reply reaches the max_consecutive_auto_reply. + (3) When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops + when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True. + function_map (dict[str, callable]): Mapping function names (passed to openai) to callable functions. + code_execution_config (dict or False): config for the code execution. + To disable code execution, set to False. Otherwise, set to a dictionary with the following keys: + - work_dir (Optional, str): The working directory for the code execution. + If None, a default working directory will be used. + The default working directory is the "extensions" directory under + "path_to_autogen". + - use_docker (Optional, list, str or bool): The docker image to use for code execution. + Default is True, which means the code will be executed in a docker container. A default list of images will be used. + If a list or a str of image name(s) is provided, the code will be executed in a docker container + with the first image successfully pulled. + If False, the code will be executed in the current environment. + We strongly recommend using docker for code execution. + - timeout (Optional, int): The maximum execution time in seconds. + - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1. + default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated. + llm_config (dict or False or None): llm inference configuration. + Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create) + for available options. + Default to False, which disables llm-based auto reply. + When set to None, will use self.DEFAULT_CONFIG, which defaults to False. + system_message (str or List): system message for ChatCompletion inference. + Only used when llm_config is not False. Use it to reprogram the agent. + description (str): a short description of the agent. This description is used by other agents + (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message) + **kwargs (dict): Please refer to other kwargs in + [ConversableAgent](conversable_agent#init). """ super().__init__( name=name, diff --git a/autogen/agentchat/utils.py b/autogen/agentchat/utils.py index 74fa996165..7bb7c2df0d 100644 --- a/autogen/agentchat/utils.py +++ b/autogen/agentchat/utils.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import re -from typing import Any, Callable, Dict, List, Union +from typing import Any, Callable, Union from .agent import Agent @@ -27,9 +27,9 @@ def consolidate_chat_info(chat_info, uniform_sender=None) -> None: or summary_method in ("last_msg", "reflection_with_llm") ), "summary_method must be a string chosen from 'reflection_with_llm' or 'last_msg' or a callable, or None." if summary_method == "reflection_with_llm": - assert ( - sender.client is not None or c["recipient"].client is not None - ), "llm client must be set in either the recipient or sender when summary_method is reflection_with_llm." + assert sender.client is not None or c["recipient"].client is not None, ( + "llm client must be set in either the recipient or sender when summary_method is reflection_with_llm." + ) def gather_usage_summary(agents: list[Agent]) -> dict[dict[str, dict], dict[str, dict]]: @@ -44,33 +44,30 @@ def gather_usage_summary(agents: list[Agent]) -> dict[dict[str, dict], dict[str, - "usage_excluding_cached_inference": Cost information on the usage of tokens, excluding the tokens in cache. No larger than "usage_including_cached_inference". Example: - ```python { - "usage_including_cached_inference" : { + "usage_including_cached_inference": { "total_cost": 0.0006090000000000001, "gpt-35-turbo": { - "cost": 0.0006090000000000001, - "prompt_tokens": 242, - "completion_tokens": 123, - "total_tokens": 365 + "cost": 0.0006090000000000001, + "prompt_tokens": 242, + "completion_tokens": 123, + "total_tokens": 365, }, }, - - "usage_excluding_cached_inference" : { + "usage_excluding_cached_inference": { "total_cost": 0.0006090000000000001, "gpt-35-turbo": { - "cost": 0.0006090000000000001, - "prompt_tokens": 242, - "completion_tokens": 123, - "total_tokens": 365 + "cost": 0.0006090000000000001, + "prompt_tokens": 242, + "completion_tokens": 123, + "total_tokens": 365, }, - } + }, } ``` Note: - If none of the agents incurred any cost (not having a client), then the usage_including_cached_inference and usage_excluding_cached_inference will be `{'total_cost': 0}`. """ diff --git a/autogen/browser_utils.py b/autogen/browser_utils.py index c624d13749..25e25e75b9 100644 --- a/autogen/browser_utils.py +++ b/autogen/browser_utils.py @@ -5,12 +5,11 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import io -import json import mimetypes import os import re import uuid -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Optional, Union from urllib.parse import urljoin, urlparse import markdownify diff --git a/autogen/cache/__init__.py b/autogen/cache/__init__.py index 402f580e1d..68c5c8e889 100644 --- a/autogen/cache/__init__.py +++ b/autogen/cache/__init__.py @@ -7,4 +7,4 @@ from .abstract_cache_base import AbstractCache from .cache import Cache -__all__ = ["Cache", "AbstractCache"] +__all__ = ["AbstractCache", "Cache"] diff --git a/autogen/cache/abstract_cache_base.py b/autogen/cache/abstract_cache_base.py index d7d864508f..cabe75931b 100644 --- a/autogen/cache/abstract_cache_base.py +++ b/autogen/cache/abstract_cache_base.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import sys from types import TracebackType -from typing import Any, Optional, Protocol, Type +from typing import Any, Optional, Protocol if sys.version_info >= (3, 11): from typing import Self @@ -15,15 +15,13 @@ class AbstractCache(Protocol): - """ - This protocol defines the basic interface for cache operations. + """This protocol defines the basic interface for cache operations. Implementing classes should provide concrete implementations for these methods to handle caching mechanisms. """ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: - """ - Retrieve an item from the cache. + """Retrieve an item from the cache. Args: key (str): The key identifying the item in the cache. @@ -36,8 +34,7 @@ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: ... def set(self, key: str, value: Any) -> None: - """ - Set an item in the cache. + """Set an item in the cache. Args: key (str): The key under which the item is to be stored. @@ -46,15 +43,13 @@ def set(self, key: str, value: Any) -> None: ... def close(self) -> None: - """ - Close the cache. Perform any necessary cleanup, such as closing network connections or + """Close the cache. Perform any necessary cleanup, such as closing network connections or releasing resources. """ ... def __enter__(self) -> Self: - """ - Enter the runtime context related to this object. + """Enter the runtime context related to this object. The with statement will bind this method's return value to the target(s) specified in the as clause of the statement, if any. @@ -67,8 +62,7 @@ def __exit__( exc_value: Optional[BaseException], traceback: Optional[TracebackType], ) -> None: - """ - Exit the runtime context and close the cache. + """Exit the runtime context and close the cache. Args: exc_type: The exception type if an exception was raised in the context. diff --git a/autogen/cache/cache.py b/autogen/cache/cache.py index 1b2bc26f6b..a4206f246a 100644 --- a/autogen/cache/cache.py +++ b/autogen/cache/cache.py @@ -8,20 +8,19 @@ import sys from types import TracebackType -from typing import Any, Dict, Optional, Type, TypedDict, Union +from typing import Any from .abstract_cache_base import AbstractCache from .cache_factory import CacheFactory if sys.version_info >= (3, 11): - from typing import Self + pass else: - from typing_extensions import Self + pass class Cache(AbstractCache): - """ - A wrapper class for managing cache configuration and instances. + """A wrapper class for managing cache configuration and instances. This class provides a unified interface for creating and interacting with different types of cache (e.g., Redis, Disk). It abstracts the underlying @@ -41,8 +40,7 @@ class Cache(AbstractCache): @staticmethod def redis(cache_seed: str | int = 42, redis_url: str = "redis://localhost:6379/0") -> Cache: - """ - Create a Redis cache instance. + """Create a Redis cache instance. Args: cache_seed (Union[str, int], optional): A seed for the cache. Defaults to 42. @@ -55,8 +53,7 @@ def redis(cache_seed: str | int = 42, redis_url: str = "redis://localhost:6379/0 @staticmethod def disk(cache_seed: str | int = 42, cache_path_root: str = ".cache") -> Cache: - """ - Create a Disk cache instance. + """Create a Disk cache instance. Args: cache_seed (Union[str, int], optional): A seed for the cache. Defaults to 42. @@ -74,14 +71,14 @@ def cosmos_db( cache_seed: str | int = 42, client: any | None = None, ) -> Cache: - """ - Create a Cosmos DB cache instance with 'autogen_cache' as database ID. + """Create a Cosmos DB cache instance with 'autogen_cache' as database ID. Args: connection_string (str, optional): Connection string to the Cosmos DB account. container_id (str, optional): The container ID for the Cosmos DB account. cache_seed (Union[str, int], optional): A seed for the cache. client: Optional[CosmosClient]: Pass an existing Cosmos DB client. + Returns: Cache: A Cache instance configured for Cosmos DB. """ @@ -94,8 +91,7 @@ def cosmos_db( return Cache({"cache_seed": str(cache_seed), "cosmos_db_config": cosmos_db_config}) def __init__(self, config: dict[str, Any]): - """ - Initialize the Cache with the given configuration. + """Initialize the Cache with the given configuration. Validates the configuration keys and creates the cache instance. @@ -122,8 +118,7 @@ def __init__(self, config: dict[str, Any]): ) def __enter__(self) -> Cache: - """ - Enter the runtime context related to the cache object. + """Enter the runtime context related to the cache object. Returns: The cache instance for use within a context block. @@ -136,8 +131,7 @@ def __exit__( exc_value: BaseException | None, traceback: TracebackType | None, ) -> None: - """ - Exit the runtime context related to the cache object. + """Exit the runtime context related to the cache object. Cleans up the cache instance and handles any exceptions that occurred within the context. @@ -150,8 +144,7 @@ def __exit__( return self.cache.__exit__(exc_type, exc_value, traceback) def get(self, key: str, default: Any | None = None) -> Any | None: - """ - Retrieve an item from the cache. + """Retrieve an item from the cache. Args: key (str): The key identifying the item in the cache. @@ -164,8 +157,7 @@ def get(self, key: str, default: Any | None = None) -> Any | None: return self.cache.get(key, default) def set(self, key: str, value: Any) -> None: - """ - Set an item in the cache. + """Set an item in the cache. Args: key (str): The key under which the item is to be stored. @@ -174,8 +166,7 @@ def set(self, key: str, value: Any) -> None: self.cache.set(key, value) def close(self) -> None: - """ - Close the cache. + """Close the cache. Perform any necessary cleanup, such as closing connections or releasing resources. """ diff --git a/autogen/cache/cache_factory.py b/autogen/cache/cache_factory.py index b64328cfe7..6dabadf552 100644 --- a/autogen/cache/cache_factory.py +++ b/autogen/cache/cache_factory.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import logging import os -from typing import Any, Dict, Optional, Union +from typing import Any, Optional, Union from .abstract_cache_base import AbstractCache from .disk_cache import DiskCache @@ -20,8 +20,7 @@ def cache_factory( cache_path_root: str = ".cache", cosmosdb_config: Optional[dict[str, Any]] = None, ) -> AbstractCache: - """ - Factory function for creating cache instances. + """Factory function for creating cache instances. This function decides whether to create a RedisCache, DiskCache, or CosmosDBCache instance based on the provided parameters. If RedisCache is available and a redis_url is provided, @@ -39,7 +38,6 @@ def cache_factory( An instance of RedisCache, DiskCache, or CosmosDBCache. Examples: - Creating a Redis cache ```python @@ -53,11 +51,14 @@ def cache_factory( Creating a Cosmos DB cache: ```python - cosmos_cache = cache_factory("myseed", cosmosdb_config={ + cosmos_cache = cache_factory( + "myseed", + cosmosdb_config={ "connection_string": "your_connection_string", "database_id": "your_database_id", - "container_id": "your_container_id"} - ) + "container_id": "your_container_id", + }, + ) ``` """ diff --git a/autogen/cache/cosmos_db_cache.py b/autogen/cache/cosmos_db_cache.py index ff3301eda2..de558986ec 100644 --- a/autogen/cache/cosmos_db_cache.py +++ b/autogen/cache/cosmos_db_cache.py @@ -9,7 +9,7 @@ import pickle from typing import Any, Optional, TypedDict, Union -from azure.cosmos import CosmosClient, PartitionKey, exceptions +from azure.cosmos import CosmosClient, PartitionKey from azure.cosmos.exceptions import CosmosResourceNotFoundError from autogen.cache.abstract_cache_base import AbstractCache @@ -24,8 +24,7 @@ class CosmosDBConfig(TypedDict, total=False): class CosmosDBCache(AbstractCache): - """ - Synchronous implementation of AbstractCache using Azure Cosmos DB NoSQL API. + """Synchronous implementation of AbstractCache using Azure Cosmos DB NoSQL API. This class provides a concrete implementation of the AbstractCache interface using Azure Cosmos DB for caching data, with synchronous operations. @@ -37,8 +36,7 @@ class CosmosDBCache(AbstractCache): """ def __init__(self, seed: Union[str, int], cosmosdb_config: CosmosDBConfig): - """ - Initialize the CosmosDBCache instance. + """Initialize the CosmosDBCache instance. Args: seed (Union[str, int]): A seed or namespace for the cache, used as a partition key. @@ -59,8 +57,7 @@ def __init__(self, seed: Union[str, int], cosmosdb_config: CosmosDBConfig): @classmethod def create_cache(cls, seed: Union[str, int], cosmosdb_config: CosmosDBConfig): - """ - Factory method to create a CosmosDBCache instance based on the provided configuration. + """Factory method to create a CosmosDBCache instance based on the provided configuration. This method decides whether to use an existing CosmosClient or create a new one. """ if "client" in cosmosdb_config and isinstance(cosmosdb_config["client"], CosmosClient): @@ -83,8 +80,7 @@ def from_existing_client(cls, seed: Union[str, int], client: CosmosClient, datab return cls(str(seed), config) def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: - """ - Retrieve an item from the Cosmos DB cache. + """Retrieve an item from the Cosmos DB cache. Args: key (str): The key identifying the item in the cache. @@ -104,8 +100,7 @@ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: raise e def set(self, key: str, value: Any) -> None: - """ - Set an item in the Cosmos DB cache. + """Set an item in the Cosmos DB cache. Args: key (str): The key under which the item is to be stored. @@ -123,8 +118,7 @@ def set(self, key: str, value: Any) -> None: raise e def close(self) -> None: - """ - Close the Cosmos DB client. + """Close the Cosmos DB client. Perform any necessary cleanup, such as closing network connections. """ @@ -133,8 +127,7 @@ def close(self) -> None: pass def __enter__(self): - """ - Context management entry. + """Context management entry. Returns: self: The instance itself. @@ -142,8 +135,7 @@ def __enter__(self): return self def __exit__(self, exc_type: Optional[type], exc_value: Optional[Exception], traceback: Optional[Any]) -> None: - """ - Context management exit. + """Context management exit. Perform cleanup actions such as closing the Cosmos DB client. """ diff --git a/autogen/cache/disk_cache.py b/autogen/cache/disk_cache.py index 2838447cb6..7126c5e87e 100644 --- a/autogen/cache/disk_cache.py +++ b/autogen/cache/disk_cache.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import sys from types import TracebackType -from typing import Any, Optional, Type, Union +from typing import Any, Optional, Union import diskcache @@ -19,8 +19,7 @@ class DiskCache(AbstractCache): - """ - Implementation of AbstractCache using the DiskCache library. + """Implementation of AbstractCache using the DiskCache library. This class provides a concrete implementation of the AbstractCache interface using the diskcache library for caching data on disk. @@ -38,8 +37,7 @@ class DiskCache(AbstractCache): """ def __init__(self, seed: Union[str, int]): - """ - Initialize the DiskCache instance. + """Initialize the DiskCache instance. Args: seed (Union[str, int]): A seed or namespace for the cache. This is used to create @@ -49,8 +47,7 @@ def __init__(self, seed: Union[str, int]): self.cache = diskcache.Cache(seed) def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: - """ - Retrieve an item from the cache. + """Retrieve an item from the cache. Args: key (str): The key identifying the item in the cache. @@ -63,8 +60,7 @@ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: return self.cache.get(key, default) def set(self, key: str, value: Any) -> None: - """ - Set an item in the cache. + """Set an item in the cache. Args: key (str): The key under which the item is to be stored. @@ -73,8 +69,7 @@ def set(self, key: str, value: Any) -> None: self.cache.set(key, value) def close(self) -> None: - """ - Close the cache. + """Close the cache. Perform any necessary cleanup, such as closing file handles or releasing resources. @@ -82,8 +77,7 @@ def close(self) -> None: self.cache.close() def __enter__(self) -> Self: - """ - Enter the runtime context related to the object. + """Enter the runtime context related to the object. Returns: self: The instance itself. @@ -96,8 +90,7 @@ def __exit__( exc_value: Optional[BaseException], traceback: Optional[TracebackType], ) -> None: - """ - Exit the runtime context related to the object. + """Exit the runtime context related to the object. Perform cleanup actions such as closing the cache. diff --git a/autogen/cache/in_memory_cache.py b/autogen/cache/in_memory_cache.py index f080530e56..9cc564e5c5 100644 --- a/autogen/cache/in_memory_cache.py +++ b/autogen/cache/in_memory_cache.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import sys from types import TracebackType -from typing import Any, Dict, Optional, Type, Union +from typing import Any, Optional, Union from .abstract_cache_base import AbstractCache @@ -17,7 +17,6 @@ class InMemoryCache(AbstractCache): - def __init__(self, seed: Union[str, int] = ""): self._seed = str(seed) self._cache: dict[str, Any] = {} @@ -39,8 +38,7 @@ def close(self) -> None: pass def __enter__(self) -> Self: - """ - Enter the runtime context related to the object. + """Enter the runtime context related to the object. Returns: self: The instance itself. @@ -50,8 +48,7 @@ def __enter__(self) -> Self: def __exit__( self, exc_type: Optional[type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType] ) -> None: - """ - Exit the runtime context related to the object. + """Exit the runtime context related to the object. Args: exc_type: The exception type if an exception was raised in the context. diff --git a/autogen/cache/redis_cache.py b/autogen/cache/redis_cache.py index b87863f083..6453285e62 100644 --- a/autogen/cache/redis_cache.py +++ b/autogen/cache/redis_cache.py @@ -7,7 +7,7 @@ import pickle import sys from types import TracebackType -from typing import Any, Optional, Type, Union +from typing import Any, Optional, Union import redis @@ -20,8 +20,7 @@ class RedisCache(AbstractCache): - """ - Implementation of AbstractCache using the Redis database. + """Implementation of AbstractCache using the Redis database. This class provides a concrete implementation of the AbstractCache interface using the Redis database for caching data. @@ -41,8 +40,7 @@ class RedisCache(AbstractCache): """ def __init__(self, seed: Union[str, int], redis_url: str): - """ - Initialize the RedisCache instance. + """Initialize the RedisCache instance. Args: seed (Union[str, int]): A seed or namespace for the cache. This is used as a prefix for all cache keys. @@ -53,8 +51,7 @@ def __init__(self, seed: Union[str, int], redis_url: str): self.cache = redis.Redis.from_url(redis_url) def _prefixed_key(self, key: str) -> str: - """ - Get a namespaced key for the cache. + """Get a namespaced key for the cache. Args: key (str): The original key. @@ -65,8 +62,7 @@ def _prefixed_key(self, key: str) -> str: return f"autogen:{self.seed}:{key}" def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: - """ - Retrieve an item from the Redis cache. + """Retrieve an item from the Redis cache. Args: key (str): The key identifying the item in the cache. @@ -82,8 +78,7 @@ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]: return pickle.loads(result) def set(self, key: str, value: Any) -> None: - """ - Set an item in the Redis cache. + """Set an item in the Redis cache. Args: key (str): The key under which the item is to be stored. @@ -96,16 +91,14 @@ def set(self, key: str, value: Any) -> None: self.cache.set(self._prefixed_key(key), serialized_value) def close(self) -> None: - """ - Close the Redis client. + """Close the Redis client. Perform any necessary cleanup, such as closing network connections. """ self.cache.close() def __enter__(self) -> Self: - """ - Enter the runtime context related to the object. + """Enter the runtime context related to the object. Returns: self: The instance itself. @@ -115,8 +108,7 @@ def __enter__(self) -> Self: def __exit__( self, exc_type: Optional[type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[TracebackType] ) -> None: - """ - Exit the runtime context related to the object. + """Exit the runtime context related to the object. Perform cleanup actions such as closing the Redis client. diff --git a/autogen/code_utils.py b/autogen/code_utils.py index 07ec3d77b5..b282234bda 100644 --- a/autogen/code_utils.py +++ b/autogen/code_utils.py @@ -16,7 +16,7 @@ from concurrent.futures import ThreadPoolExecutor, TimeoutError from hashlib import md5 from types import SimpleNamespace -from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from typing import Callable, Optional, Union import docker @@ -42,7 +42,7 @@ TIMEOUT_MSG = "Timeout" DEFAULT_TIMEOUT = 600 WIN32 = sys.platform == "win32" -PATH_SEPARATOR = WIN32 and "\\" or "/" +PATH_SEPARATOR = (WIN32 and "\\") or "/" PYTHON_VARIANTS = ["python", "Python", "py"] logger = logging.getLogger(__name__) @@ -90,7 +90,7 @@ def content_str(content: Union[str, list[Union[UserMessageTextContentPart, UserM def infer_lang(code: str) -> str: - """infer the language for the code. + """Infer the language for the code. TODO: make it robust. """ if code.startswith("python ") or code.startswith("pip") or code.startswith("python3 "): @@ -473,7 +473,9 @@ def execute_code( image_list = ( ["python:3-slim", "python:3", "python:3-windowsservercore"] if use_docker is True - else [use_docker] if isinstance(use_docker, str) else use_docker + else [use_docker] + if isinstance(use_docker, str) + else use_docker ) for image in image_list: # check if the image exists @@ -737,7 +739,8 @@ def create_virtual_env(dir_path: str, **env_args) -> SimpleNamespace: **env_args: Any extra args to pass to the `EnvBuilder` Returns: - SimpleNamespace: the virtual env context object.""" + SimpleNamespace: the virtual env context object. + """ if not env_args: env_args = {"with_pip": True} env_builder = venv.EnvBuilder(**env_args) diff --git a/autogen/coding/__init__.py b/autogen/coding/__init__.py index 9d44868661..ed45459f94 100644 --- a/autogen/coding/__init__.py +++ b/autogen/coding/__init__.py @@ -12,11 +12,11 @@ __all__ = ( "CodeBlock", - "CodeResult", - "CodeExtractor", "CodeExecutor", "CodeExecutorFactory", - "MarkdownCodeExtractor", - "LocalCommandLineCodeExecutor", + "CodeExtractor", + "CodeResult", "DockerCommandLineCodeExecutor", + "LocalCommandLineCodeExecutor", + "MarkdownCodeExtractor", ) diff --git a/autogen/coding/base.py b/autogen/coding/base.py index edb3a6e320..4bb2197f41 100644 --- a/autogen/coding/base.py +++ b/autogen/coding/base.py @@ -7,13 +7,13 @@ from __future__ import annotations from collections.abc import Mapping -from typing import Any, List, Literal, Optional, Protocol, TypedDict, Union, runtime_checkable +from typing import Any, Literal, Optional, Protocol, TypedDict, Union, runtime_checkable from pydantic import BaseModel, Field from ..types import UserMessageImageContentPart, UserMessageTextContentPart -__all__ = ("CodeBlock", "CodeResult", "CodeExtractor", "CodeExecutor", "CodeExecutionConfig") +__all__ = ("CodeBlock", "CodeExecutionConfig", "CodeExecutor", "CodeExtractor", "CodeResult") class CodeBlock(BaseModel): diff --git a/autogen/coding/docker_commandline_code_executor.py b/autogen/coding/docker_commandline_code_executor.py index 395f61da27..ab33ccade5 100644 --- a/autogen/coding/docker_commandline_code_executor.py +++ b/autogen/coding/docker_commandline_code_executor.py @@ -14,7 +14,7 @@ from pathlib import Path from time import sleep from types import TracebackType -from typing import Any, ClassVar, Dict, List, Optional, Type, Union +from typing import Any, ClassVar import docker from docker.errors import ImageNotFound @@ -64,7 +64,7 @@ def __init__( image: str = "python:3-slim", container_name: str | None = None, timeout: int = 60, - work_dir: Path | str = Path("."), + work_dir: Path | str = Path(), bind_dir: Path | str | None = None, auto_remove: bool = True, stop_container: bool = True, @@ -190,8 +190,8 @@ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CommandLineCodeRe code_blocks (List[CodeBlock]): The code blocks to execute. Returns: - CommandlineCodeResult: The result of the code execution.""" - + CommandlineCodeResult: The result of the code execution. + """ if len(code_blocks) == 0: raise ValueError("No code blocks to execute.") @@ -225,7 +225,7 @@ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CommandLineCodeRe files.append(code_path) if not execute_code: - outputs.append(f"Code saved to {str(code_path)}\n") + outputs.append(f"Code saved to {code_path!s}\n") continue command = ["timeout", str(self._timeout), _cmd(lang), filename] diff --git a/autogen/coding/func_with_reqs.py b/autogen/coding/func_with_reqs.py index 1f842c9193..5fc373cb90 100644 --- a/autogen/coding/func_with_reqs.py +++ b/autogen/coding/func_with_reqs.py @@ -12,7 +12,7 @@ from dataclasses import dataclass, field from importlib.abc import SourceLoader from textwrap import dedent, indent -from typing import Any, Callable, Generic, List, Set, TypeVar, Union +from typing import Any, Callable, Generic, TypeVar, Union from typing_extensions import ParamSpec @@ -162,7 +162,7 @@ def wrapper(func: Callable[P, T]) -> FunctionWithRequirements[T, P]: def _build_python_functions_file( - funcs: list[FunctionWithRequirements[Any, P] | Callable[..., Any] | FunctionWithRequirementsStr] + funcs: list[FunctionWithRequirements[Any, P] | Callable[..., Any] | FunctionWithRequirementsStr], ) -> str: # First collect all global imports global_imports: set[str] = set() diff --git a/autogen/coding/jupyter/__init__.py b/autogen/coding/jupyter/__init__.py index a0be01d20d..a15fe09ff4 100644 --- a/autogen/coding/jupyter/__init__.py +++ b/autogen/coding/jupyter/__init__.py @@ -20,11 +20,11 @@ from .local_jupyter_server import LocalJupyterServer __all__ = [ - "JupyterConnectable", - "JupyterConnectionInfo", - "JupyterClient", - "LocalJupyterServer", "DockerJupyterServer", "EmbeddedIPythonCodeExecutor", + "JupyterClient", "JupyterCodeExecutor", + "JupyterConnectable", + "JupyterConnectionInfo", + "LocalJupyterServer", ] diff --git a/autogen/coding/jupyter/docker_jupyter_server.py b/autogen/coding/jupyter/docker_jupyter_server.py index f90090dee6..f52dd81d21 100644 --- a/autogen/coding/jupyter/docker_jupyter_server.py +++ b/autogen/coding/jupyter/docker_jupyter_server.py @@ -14,7 +14,6 @@ import uuid from pathlib import Path from types import TracebackType -from typing import Dict, Optional, Type, Union import docker diff --git a/autogen/coding/jupyter/helpers.py b/autogen/coding/jupyter/helpers.py index 3216e590ee..5f8cf1cc6d 100644 --- a/autogen/coding/jupyter/helpers.py +++ b/autogen/coding/jupyter/helpers.py @@ -11,6 +11,7 @@ def is_jupyter_kernel_gateway_installed() -> bool: + """Check if jupyter-kernel-gateway is installed.""" try: subprocess.run( ["jupyter", "kernelgateway", "--version"], diff --git a/autogen/coding/jupyter/jupyter_client.py b/autogen/coding/jupyter/jupyter_client.py index 5482c8537f..764e308fc3 100644 --- a/autogen/coding/jupyter/jupyter_client.py +++ b/autogen/coding/jupyter/jupyter_client.py @@ -9,7 +9,7 @@ import sys from dataclasses import dataclass from types import TracebackType -from typing import Any, Dict, List, Optional, Type, cast +from typing import Any, cast if sys.version_info >= (3, 11): from typing import Self @@ -71,7 +71,6 @@ def start_kernel(self, kernel_spec_name: str) -> str: Returns: str: ID of the started kernel """ - response = self._session.post( f"{self._get_api_base_url()}/api/kernels", headers=self._get_headers(), diff --git a/autogen/coding/jupyter/jupyter_code_executor.py b/autogen/coding/jupyter/jupyter_code_executor.py index afee16963d..a29778e811 100644 --- a/autogen/coding/jupyter/jupyter_code_executor.py +++ b/autogen/coding/jupyter/jupyter_code_executor.py @@ -7,12 +7,11 @@ import base64 import json import os -import re import sys import uuid from pathlib import Path from types import TracebackType -from typing import Any, ClassVar, List, Optional, Type, Union +from typing import Optional, Union from autogen.coding.utils import silence_pip @@ -22,7 +21,6 @@ from typing_extensions import Self -from ...agentchat.agent import LLMAgent from ..base import CodeBlock, CodeExecutor, CodeExtractor, IPythonCodeResult from ..markdown_code_extractor import MarkdownCodeExtractor from .base import JupyterConnectable, JupyterConnectionInfo @@ -35,7 +33,7 @@ def __init__( jupyter_server: Union[JupyterConnectable, JupyterConnectionInfo], kernel_name: str = "python3", timeout: int = 60, - output_dir: Union[Path, str] = Path("."), + output_dir: Union[Path, str] = Path(), ): """(Experimental) A code executor class that executes code statefully using a Jupyter server supplied to this class. diff --git a/autogen/coding/jupyter/local_jupyter_server.py b/autogen/coding/jupyter/local_jupyter_server.py index 6ea55c1f0a..518b2171f9 100644 --- a/autogen/coding/jupyter/local_jupyter_server.py +++ b/autogen/coding/jupyter/local_jupyter_server.py @@ -10,11 +10,9 @@ import json import secrets import signal -import socket import subprocess import sys from types import TracebackType -from typing import Optional, Type, Union, cast if sys.version_info >= (3, 11): from typing import Self diff --git a/autogen/coding/local_commandline_code_executor.py b/autogen/coding/local_commandline_code_executor.py index bcbab20ef2..98419d3083 100644 --- a/autogen/coding/local_commandline_code_executor.py +++ b/autogen/coding/local_commandline_code_executor.py @@ -14,7 +14,7 @@ from pathlib import Path from string import Template from types import SimpleNamespace -from typing import Any, Callable, ClassVar, Dict, List, Optional, Union +from typing import Any, Callable, ClassVar, Optional, Union from typing_extensions import ParamSpec @@ -73,7 +73,7 @@ def __init__( self, timeout: int = 60, virtual_env_context: Optional[SimpleNamespace] = None, - work_dir: Union[Path, str] = Path("."), + work_dir: Union[Path, str] = Path(), functions: list[Union[FunctionWithRequirements[Any, A], Callable[..., Any], FunctionWithRequirementsStr]] = [], functions_module: str = "functions", execution_policies: Optional[dict[str, bool]] = None, @@ -112,7 +112,6 @@ def __init__( functions_module (str): The module name under which functions are accessible. execution_policies (Optional[Dict[str, bool]]): A dictionary mapping languages to execution policies (True for execution, False for saving only). Defaults to class-wide DEFAULT_EXECUTION_POLICY. """ - if timeout < 1: raise ValueError("Timeout must be greater than or equal to 1.") @@ -189,8 +188,7 @@ def code_extractor(self) -> CodeExtractor: @staticmethod def sanitize_command(lang: str, code: str) -> None: - """ - Sanitize the code block to prevent dangerous commands. + """Sanitize the code block to prevent dangerous commands. This approach acknowledges that while Docker or similar containerization/sandboxing technologies provide a robust layer of security, not all users may have Docker installed or may choose not to use it. @@ -251,7 +249,8 @@ def execute_code_blocks(self, code_blocks: list[CodeBlock]) -> CommandLineCodeRe code_blocks (List[CodeBlock]): The code blocks to execute. Returns: - CommandLineCodeResult: The result of the code execution.""" + CommandLineCodeResult: The result of the code execution. + """ if not self._setup_functions_complete: self._setup_functions() return self._execute_code_dont_check_setup(code_blocks) @@ -296,7 +295,7 @@ def _execute_code_dont_check_setup(self, code_blocks: list[CodeBlock]) -> Comman if not execute_code: # Just return a message that the file is saved. - logs_all += f"Code saved to {str(written_file)}\n" + logs_all += f"Code saved to {written_file!s}\n" exitcode = 0 continue @@ -351,12 +350,13 @@ def __new__(cls, name, bases, classdict, *args, **kwargs): # type: ignore[no-un if alias is not None: def new(cls, *args, **kwargs): # type: ignore[no-untyped-def] - alias = getattr(cls, "_DeprecatedClassMeta__alias") + alias = cls._DeprecatedClassMeta__alias if alias is not None: warnings.warn( - "{} has been renamed to {}, the alias will be " - "removed in the future".format(cls.__name__, alias.__name__), + "{} has been renamed to {}, the alias will be removed in the future".format( + cls.__name__, alias.__name__ + ), DeprecationWarning, stacklevel=2, ) @@ -373,8 +373,9 @@ def new(cls, *args, **kwargs): # type: ignore[no-untyped-def] if alias is not None: warnings.warn( - "{} has been renamed to {}, the alias will be " - "removed in the future".format(b.__name__, alias.__name__), + "{} has been renamed to {}, the alias will be removed in the future".format( + b.__name__, alias.__name__ + ), DeprecationWarning, stacklevel=2, ) @@ -395,7 +396,7 @@ def __subclasscheck__(cls, subclass): # type: ignore[no-untyped-def] if subclass is cls: return True else: - return issubclass(subclass, getattr(cls, "_DeprecatedClassMeta__alias")) + return issubclass(subclass, cls._DeprecatedClassMeta__alias) # type: ignore[attr-defined] class LocalCommandlineCodeExecutor(metaclass=_DeprecatedClassMeta): diff --git a/autogen/coding/markdown_code_extractor.py b/autogen/coding/markdown_code_extractor.py index 8342ea2f92..01a9025940 100644 --- a/autogen/coding/markdown_code_extractor.py +++ b/autogen/coding/markdown_code_extractor.py @@ -5,7 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import re -from typing import Any, Dict, List, Optional, Union +from typing import Union from ..code_utils import CODE_BLOCK_PATTERN, UNKNOWN, content_str, infer_lang from ..types import UserMessageImageContentPart, UserMessageTextContentPart @@ -29,7 +29,6 @@ def extract_code_blocks( Returns: List[CodeBlock]: The extracted code blocks or an empty list. """ - text = content_str(message) match = re.findall(CODE_BLOCK_PATTERN, text, flags=re.DOTALL) if not match: diff --git a/autogen/exception_utils.py b/autogen/exception_utils.py index ac554d7a22..27f7a94816 100644 --- a/autogen/exception_utils.py +++ b/autogen/exception_utils.py @@ -6,13 +6,21 @@ # SPDX-License-Identifier: MIT from typing import Any +__all__ = [ + "AgentNameConflict", + "InvalidCarryOverType", + "NoEligibleSpeaker", + "SenderRequired", + "UndefinedNextAgent", +] -class AgentNameConflict(Exception): + +class AgentNameConflict(Exception): # noqa: N818 def __init__(self, msg: str = "Found multiple agents with the same name.", *args: Any, **kwargs: Any): super().__init__(msg, *args, **kwargs) -class NoEligibleSpeaker(Exception): +class NoEligibleSpeaker(Exception): # noqa: N818 """Exception raised for early termination of a GroupChat.""" def __init__(self, message: str = "No eligible speakers."): @@ -20,7 +28,7 @@ def __init__(self, message: str = "No eligible speakers."): super().__init__(self.message) -class SenderRequired(Exception): +class SenderRequired(Exception): # noqa: N818 """Exception raised when the sender is required but not provided.""" def __init__(self, message: str = "Sender is required but not provided."): @@ -28,7 +36,7 @@ def __init__(self, message: str = "Sender is required but not provided."): super().__init__(self.message) -class InvalidCarryOverType(Exception): +class InvalidCarryOverType(Exception): # noqa: N818 """Exception raised when the carryover type is invalid.""" def __init__( @@ -38,7 +46,7 @@ def __init__( super().__init__(self.message) -class UndefinedNextAgent(Exception): +class UndefinedNextAgent(Exception): # noqa: N818 """Exception raised when the provided next agents list does not overlap with agents in the group.""" def __init__(self, message: str = "The provided agents list does not overlap with agents in the group."): diff --git a/autogen/formatting_utils.py b/autogen/formatting_utils.py index 9c6ccb1676..8109f981df 100644 --- a/autogen/formatting_utils.py +++ b/autogen/formatting_utils.py @@ -75,3 +75,6 @@ def colored( force_color: bool | None = None, ) -> str: return str(text) + + +__all__ = ["colored"] diff --git a/autogen/graph_utils.py b/autogen/graph_utils.py index a495fb4131..fe77b0301c 100644 --- a/autogen/graph_utils.py +++ b/autogen/graph_utils.py @@ -5,15 +5,13 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import logging -from typing import Dict, List, Optional +from typing import Optional from autogen.agentchat import Agent def has_self_loops(allowed_speaker_transitions: dict) -> bool: - """ - Returns True if there are self loops in the allowed_speaker_transitions_Dict. - """ + """Returns True if there are self loops in the allowed_speaker_transitions_Dict.""" return any([key in value for key, value in allowed_speaker_transitions.items()]) @@ -21,8 +19,7 @@ def check_graph_validity( allowed_speaker_transitions_dict: dict, agents: list[Agent], ): - """ - allowed_speaker_transitions_dict: A dictionary of keys and list as values. The keys are the names of the agents, and the values are the names of the agents that the key agent can transition to. + """allowed_speaker_transitions_dict: A dictionary of keys and list as values. The keys are the names of the agents, and the values are the names of the agents that the key agent can transition to. agents: A list of Agents Checks for the following: @@ -31,12 +28,11 @@ def check_graph_validity( 2. Every key exists in agents. 3. Every value is a list of Agents (not string). - Warnings + Warnings: 1. Warning if there are isolated agent nodes 2. Warning if the set of agents in allowed_speaker_transitions do not match agents 3. Warning if there are duplicated agents in any values of `allowed_speaker_transitions_dict` """ - ### Errors # Check 1. The dictionary must have a structure of keys and list as values @@ -48,7 +44,7 @@ def check_graph_validity( raise ValueError("allowed_speaker_transitions_dict must be a dictionary with lists as values.") # Check 2. Every key exists in agents - if not all([key in agents for key in allowed_speaker_transitions_dict.keys()]): + if not all([key in agents for key in allowed_speaker_transitions_dict]): raise ValueError("allowed_speaker_transitions_dict has keys not in agents.") # Check 3. Every value is a list of Agents or empty list (not string). @@ -101,9 +97,7 @@ def check_graph_validity( def invert_disallowed_to_allowed(disallowed_speaker_transitions_dict: dict, agents: list[Agent]) -> dict: - """ - Start with a fully connected allowed_speaker_transitions_dict of all agents. Remove edges from the fully connected allowed_speaker_transitions_dict according to the disallowed_speaker_transitions_dict to form the allowed_speaker_transitions_dict. - """ + """Start with a fully connected allowed_speaker_transitions_dict of all agents. Remove edges from the fully connected allowed_speaker_transitions_dict according to the disallowed_speaker_transitions_dict to form the allowed_speaker_transitions_dict.""" # Create a fully connected allowed_speaker_transitions_dict of all agents allowed_speaker_transitions_dict = {agent: [other_agent for other_agent in agents] for agent in agents} @@ -119,9 +113,7 @@ def invert_disallowed_to_allowed(disallowed_speaker_transitions_dict: dict, agen def visualize_speaker_transitions_dict( speaker_transitions_dict: dict, agents: list[Agent], export_path: Optional[str] = None ): - """ - Visualize the speaker_transitions_dict using networkx. - """ + """Visualize the speaker_transitions_dict using networkx.""" try: import matplotlib.pyplot as plt import networkx as nx @@ -129,18 +121,18 @@ def visualize_speaker_transitions_dict( logging.fatal("Failed to import networkx or matplotlib. Try running 'pip install autogen[graphs]'") raise e - G = nx.DiGraph() + g = nx.DiGraph() # Add nodes - G.add_nodes_from([agent.name for agent in agents]) + g.add_nodes_from([agent.name for agent in agents]) # Add edges for key, value in speaker_transitions_dict.items(): for agent in value: - G.add_edge(key.name, agent.name) + g.add_edge(key.name, agent.name) # Visualize - nx.draw(G, with_labels=True, font_weight="bold") + nx.draw(g, with_labels=True, font_weight="bold") if export_path is not None: plt.savefig(export_path) diff --git a/autogen/interop/__init__.py b/autogen/interop/__init__.py index 8f070c8f24..7b6a47898c 100644 --- a/autogen/interop/__init__.py +++ b/autogen/interop/__init__.py @@ -9,4 +9,11 @@ from .pydantic_ai import PydanticAIInteroperability from .registry import register_interoperable_class -__all__ = ["Interoperability", "Interoperable", "register_interoperable_class"] +__all__ = [ + "CrewAIInteroperability", + "Interoperability", + "Interoperable", + "LangChainInteroperability", + "PydanticAIInteroperability", + "register_interoperable_class", +] diff --git a/autogen/interop/crewai/crewai.py b/autogen/interop/crewai/crewai.py index ce16b876b0..fa433bc5c5 100644 --- a/autogen/interop/crewai/crewai.py +++ b/autogen/interop/crewai/crewai.py @@ -18,8 +18,7 @@ def _sanitize_name(s: str) -> str: @register_interoperable_class("crewai") class CrewAIInteroperability: - """ - A class implementing the `Interoperable` protocol for converting CrewAI tools + """A class implementing the `Interoperable` protocol for converting CrewAI tools to a general `Tool` format. This class takes a `CrewAITool` and converts it into a standard `Tool` object. @@ -27,8 +26,7 @@ class CrewAIInteroperability: @classmethod def convert_tool(cls, tool: Any, **kwargs: Any) -> Tool: - """ - Converts a given CrewAI tool into a general `Tool` format. + """Converts a given CrewAI tool into a general `Tool` format. This method ensures that the provided tool is a valid `CrewAITool`, sanitizes the tool's name, processes its description, and prepares a function to interact @@ -76,7 +74,7 @@ def get_unsupported_reason(cls) -> Optional[str]: return "This submodule is only supported for Python versions 3.10, 3.11, and 3.12" try: - import crewai.tools + import crewai.tools # noqa: F401 except ImportError: return "Please install `interop-crewai` extra to use this module:\n\n\tpip install ag2[interop-crewai]" diff --git a/autogen/interop/interoperability.py b/autogen/interop/interoperability.py index 067571de5c..358dd8f9e1 100644 --- a/autogen/interop/interoperability.py +++ b/autogen/interop/interoperability.py @@ -1,7 +1,7 @@ # Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, Dict, List, Type +from typing import Any from ..tools import Tool from .interoperable import Interoperable @@ -11,8 +11,7 @@ class Interoperability: - """ - A class to handle interoperability between different tool types. + """A class to handle interoperability between different tool types. This class allows the conversion of tools to various interoperability classes and provides functionality for retrieving and registering interoperability classes. @@ -22,8 +21,7 @@ class Interoperability: @classmethod def convert_tool(cls, *, tool: Any, type: str, **kwargs: Any) -> Tool: - """ - Converts a given tool to an instance of a specified interoperability type. + """Converts a given tool to an instance of a specified interoperability type. Args: tool (Any): The tool object to be converted. @@ -41,8 +39,7 @@ def convert_tool(cls, *, tool: Any, type: str, **kwargs: Any) -> Tool: @classmethod def get_interoperability_class(cls, type: str) -> type[Interoperable]: - """ - Retrieves the interoperability class corresponding to the specified type. + """Retrieves the interoperability class corresponding to the specified type. Args: type (str): The type of the interoperability class to retrieve. @@ -64,8 +61,7 @@ def get_interoperability_class(cls, type: str) -> type[Interoperable]: @classmethod def get_supported_types(cls) -> list[str]: - """ - Returns a sorted list of all supported interoperability types. + """Returns a sorted list of all supported interoperability types. Returns: List[str]: A sorted list of strings representing the supported interoperability types. diff --git a/autogen/interop/interoperable.py b/autogen/interop/interoperable.py index 185e36089d..8e6023ca57 100644 --- a/autogen/interop/interoperable.py +++ b/autogen/interop/interoperable.py @@ -11,8 +11,7 @@ @runtime_checkable class Interoperable(Protocol): - """ - A Protocol defining the interoperability interface for tool conversion. + """A Protocol defining the interoperability interface for tool conversion. This protocol ensures that any class implementing it provides the method `convert_tool` to convert a given tool into a desired format or type. @@ -20,8 +19,7 @@ class Interoperable(Protocol): @classmethod def convert_tool(cls, tool: Any, **kwargs: Any) -> Tool: - """ - Converts a given tool to a desired format or type. + """Converts a given tool to a desired format or type. This method should be implemented by any class adhering to the `Interoperable` protocol. diff --git a/autogen/interop/langchain/langchain.py b/autogen/interop/langchain/langchain.py index c657b08e19..f1c19e402c 100644 --- a/autogen/interop/langchain/langchain.py +++ b/autogen/interop/langchain/langchain.py @@ -13,8 +13,7 @@ @register_interoperable_class("langchain") class LangChainInteroperability: - """ - A class implementing the `Interoperable` protocol for converting Langchain tools + """A class implementing the `Interoperable` protocol for converting Langchain tools into a general `Tool` format. This class takes a `LangchainTool` and converts it into a standard `Tool` object, @@ -24,8 +23,7 @@ class LangChainInteroperability: @classmethod def convert_tool(cls, tool: Any, **kwargs: Any) -> Tool: - """ - Converts a given Langchain tool into a general `Tool` format. + """Converts a given Langchain tool into a general `Tool` format. This method verifies that the provided tool is a valid `LangchainTool`, processes the tool's input and description, and returns a standardized @@ -67,7 +65,7 @@ def get_unsupported_reason(cls) -> Optional[str]: return "This submodule is only supported for Python versions 3.9 and above" try: - import langchain_core.tools + import langchain_core.tools # noqa: F401 except ImportError: return ( "Please install `interop-langchain` extra to use this module:\n\n\tpip install ag2[interop-langchain]" diff --git a/autogen/interop/pydantic_ai/pydantic_ai.py b/autogen/interop/pydantic_ai/pydantic_ai.py index 78c48dc0cd..e6f15db072 100644 --- a/autogen/interop/pydantic_ai/pydantic_ai.py +++ b/autogen/interop/pydantic_ai/pydantic_ai.py @@ -17,8 +17,7 @@ @register_interoperable_class("pydanticai") class PydanticAIInteroperability: - """ - A class implementing the `Interoperable` protocol for converting Pydantic AI tools + """A class implementing the `Interoperable` protocol for converting Pydantic AI tools into a general `Tool` format. This class takes a `PydanticAITool` and converts it into a standard `Tool` object, @@ -32,8 +31,7 @@ def inject_params( ctx: Any, tool: Any, ) -> Callable[..., Any]: - """ - Wraps the tool's function to inject context parameters and handle retries. + """Wraps the tool's function to inject context parameters and handle retries. This method ensures that context parameters are properly passed to the tool when invoked and that retries are managed according to the tool's settings. @@ -88,8 +86,7 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: @classmethod def convert_tool(cls, tool: Any, deps: Any = None, **kwargs: Any) -> AG2PydanticAITool: - """ - Converts a given Pydantic AI tool into a general `Tool` format. + """Converts a given Pydantic AI tool into a general `Tool` format. This method verifies that the provided tool is a valid `PydanticAITool`, handles context dependencies if necessary, and returns a standardized `Tool` object. @@ -155,7 +152,7 @@ def get_unsupported_reason(cls) -> Optional[str]: return "This submodule is only supported for Python versions 3.9 and above" try: - import pydantic_ai.tools + import pydantic_ai.tools # noqa: F401 except ImportError: return "Please install `interop-pydantic-ai` extra to use this module:\n\n\tpip install ag2[interop-pydantic-ai]" diff --git a/autogen/interop/pydantic_ai/pydantic_ai_tool.py b/autogen/interop/pydantic_ai/pydantic_ai_tool.py index e35d409870..651d713259 100644 --- a/autogen/interop/pydantic_ai/pydantic_ai_tool.py +++ b/autogen/interop/pydantic_ai/pydantic_ai_tool.py @@ -2,7 +2,7 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, Callable, Dict +from typing import Any, Callable from ...agentchat.conversable_agent import ConversableAgent from ...tools import Tool @@ -11,8 +11,7 @@ class PydanticAITool(Tool): - """ - A class representing a Pydantic AI Tool that extends the general Tool functionality + """A class representing a Pydantic AI Tool that extends the general Tool functionality with additional functionality specific to Pydantic AI tools. This class inherits from the Tool class and adds functionality for registering @@ -27,8 +26,7 @@ class PydanticAITool(Tool): def __init__( self, name: str, description: str, func: Callable[..., Any], parameters_json_schema: dict[str, Any] ) -> None: - """ - Initializes a PydanticAITool object with the provided name, description, + """Initializes a PydanticAITool object with the provided name, description, function, and parameter schema. Args: @@ -49,8 +47,7 @@ def __init__( } def register_for_llm(self, agent: ConversableAgent) -> None: - """ - Registers the tool with the ConversableAgent for use with a language model (LLM). + """Registers the tool with the ConversableAgent for use with a language model (LLM). This method updates the agent's tool signature to include the function schema, allowing the agent to invoke the tool correctly during interactions with the LLM. diff --git a/autogen/interop/registry.py b/autogen/interop/registry.py index cb10b701ac..e9685ca2be 100644 --- a/autogen/interop/registry.py +++ b/autogen/interop/registry.py @@ -2,11 +2,11 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Callable, Dict, Generic, List, Type, TypeVar +from typing import Callable, TypeVar from .interoperable import Interoperable -__all__ = ["register_interoperable_class", "InteroperableRegistry"] +__all__ = ["InteroperableRegistry", "register_interoperable_class"] InteroperableClass = TypeVar("InteroperableClass", bound=type[Interoperable]) diff --git a/autogen/io/__init__.py b/autogen/io/__init__.py index 143281f5cf..6b39286c88 100644 --- a/autogen/io/__init__.py +++ b/autogen/io/__init__.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from .base import InputStream, IOStream, OutputStream +from .base import IOStream, InputStream, OutputStream from .console import IOConsole from .websockets import IOWebsockets @@ -12,4 +12,4 @@ IOStream.set_global_default(IOConsole()) IOStream.set_default(IOConsole()) -__all__ = ("IOConsole", "IOStream", "InputStream", "OutputStream", "IOWebsockets") +__all__ = ("IOConsole", "IOStream", "IOWebsockets", "InputStream", "OutputStream") diff --git a/autogen/io/base.py b/autogen/io/base.py index 30f403b520..be49bc6e94 100644 --- a/autogen/io/base.py +++ b/autogen/io/base.py @@ -12,7 +12,7 @@ from autogen.messages.base_message import BaseMessage -__all__ = ("OutputStream", "InputStream", "IOStream") +__all__ = ("IOStream", "InputStream", "OutputStream") logger = logging.getLogger(__name__) diff --git a/autogen/io/console.py b/autogen/io/console.py index 62adab28cf..eb6e2989fd 100644 --- a/autogen/io/console.py +++ b/autogen/io/console.py @@ -50,7 +50,6 @@ def input(self, prompt: str = "", *, password: bool = False) -> str: str: The line read from the input stream. """ - if password: return getpass.getpass(prompt if prompt != "" else "Password: ") return input(prompt) diff --git a/autogen/logger/__init__.py b/autogen/logger/__init__.py index 01d367a3c3..2ed731f065 100644 --- a/autogen/logger/__init__.py +++ b/autogen/logger/__init__.py @@ -8,4 +8,4 @@ from .logger_factory import LoggerFactory from .sqlite_logger import SqliteLogger -__all__ = ("LoggerFactory", "SqliteLogger", "FileLogger") +__all__ = ("FileLogger", "LoggerFactory", "SqliteLogger") diff --git a/autogen/logger/base_logger.py b/autogen/logger/base_logger.py index b01c112da7..3943265bf1 100644 --- a/autogen/logger/base_logger.py +++ b/autogen/logger/base_logger.py @@ -9,7 +9,7 @@ import sqlite3 import uuid from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypeVar, Union +from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletion @@ -25,8 +25,7 @@ class BaseLogger(ABC): @abstractmethod def start(self) -> str: - """ - Open a connection to the logging database, and start recording. + """Open a connection to the logging database, and start recording. Returns: session_id (str): a unique id for the logging session @@ -46,8 +45,7 @@ def log_chat_completion( cost: float, start_time: str, ) -> None: - """ - Log a chat completion to database. + """Log a chat completion to database. In AutoGen, chat completions are somewhat complicated because they are handled by the `autogen.oai.OpenAIWrapper` class. One invocation to `create` can lead to multiple underlying OpenAI calls, depending on the llm_config list used, and @@ -68,8 +66,7 @@ def log_chat_completion( @abstractmethod def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any]) -> None: - """ - Log the birth of a new agent. + """Log the birth of a new agent. Args: agent (ConversableAgent): The agent to log. @@ -79,8 +76,7 @@ def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any]) -> N @abstractmethod def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None: - """ - Log an event for an agent. + """Log an event for an agent. Args: source (str or Agent): The source/creator of the event as a string name or an Agent instance @@ -91,8 +87,7 @@ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> @abstractmethod def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]]) -> None: - """ - Log the birth of a new OpenAIWrapper. + """Log the birth of a new OpenAIWrapper. Args: wrapper (OpenAIWrapper): The wrapper to log. @@ -102,8 +97,7 @@ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig @abstractmethod def log_new_client(self, client: AzureOpenAI | OpenAI, wrapper: OpenAIWrapper, init_args: dict[str, Any]) -> None: - """ - Log the birth of a new OpenAIWrapper. + """Log the birth of a new OpenAIWrapper. Args: wrapper (OpenAI): The OpenAI client to log. @@ -113,8 +107,7 @@ def log_new_client(self, client: AzureOpenAI | OpenAI, wrapper: OpenAIWrapper, i @abstractmethod def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None: - """ - Log the use of a registered function (could be a tool) + """Log the use of a registered function (could be a tool) Args: source (str or Agent): The source/creator of the event as a string name or an Agent instance @@ -125,14 +118,10 @@ def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any @abstractmethod def stop(self) -> None: - """ - Close the connection to the logging database, and stop logging. - """ + """Close the connection to the logging database, and stop logging.""" ... @abstractmethod def get_connection(self) -> None | sqlite3.Connection: - """ - Return a connection to the logging database. - """ + """Return a connection to the logging database.""" ... diff --git a/autogen/logger/file_logger.py b/autogen/logger/file_logger.py index 249dd11015..3b0433147e 100644 --- a/autogen/logger/file_logger.py +++ b/autogen/logger/file_logger.py @@ -11,7 +11,7 @@ import os import threading import uuid -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union +from typing import TYPE_CHECKING, Any, Callable, TypeVar from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletion @@ -92,9 +92,7 @@ def log_chat_completion( cost: float, start_time: str, ) -> None: - """ - Log a chat completion. - """ + """Log a chat completion.""" thread_id = threading.get_ident() source_name = None if isinstance(source, str): @@ -123,9 +121,7 @@ def log_chat_completion( self.logger.error(f"[file_logger] Failed to log chat completion: {e}") def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any] = {}) -> None: - """ - Log a new agent instance. - """ + """Log a new agent instance.""" thread_id = threading.get_ident() try: @@ -148,9 +144,7 @@ def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any] = {}) self.logger.error(f"[file_logger] Failed to log new agent: {e}") def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None: - """ - Log an event from an agent or a string source. - """ + """Log an event from an agent or a string source.""" from autogen import Agent # This takes an object o as input and returns a string. If the object o cannot be serialized, instead of raising an error, @@ -192,9 +186,7 @@ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> self.logger.error(f"[file_logger] Failed to log event {e}") def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]] = {}) -> None: - """ - Log a new wrapper instance. - """ + """Log a new wrapper instance.""" thread_id = threading.get_ident() try: @@ -229,9 +221,7 @@ def log_new_client( wrapper: OpenAIWrapper, init_args: dict[str, Any], ) -> None: - """ - Log a new client instance. - """ + """Log a new client instance.""" thread_id = threading.get_ident() try: @@ -251,9 +241,7 @@ def log_new_client( self.logger.error(f"[file_logger] Failed to log event {e}") def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None: - """ - Log a registered function(can be a tool) use from an agent or a string source. - """ + """Log a registered function(can be a tool) use from an agent or a string source.""" thread_id = threading.get_ident() try: diff --git a/autogen/logger/logger_factory.py b/autogen/logger/logger_factory.py index c3bab860a9..6670203104 100644 --- a/autogen/logger/logger_factory.py +++ b/autogen/logger/logger_factory.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Any, Dict, Literal, Optional +from typing import Any, Literal, Optional from autogen.logger.base_logger import BaseLogger from autogen.logger.file_logger import FileLogger @@ -14,10 +14,21 @@ class LoggerFactory: + """Factory class to create logger objects.""" + @staticmethod def get_logger( logger_type: Literal["sqlite", "file"] = "sqlite", config: Optional[dict[str, Any]] = None ) -> BaseLogger: + """Factory method to create logger objects. + + Args: + logger_type (Literal["sqlite", "file"], optional): Type of logger. Defaults to "sqlite". + config (Optional[dict[str, Any]], optional): Configuration for logger. Defaults to None. + + Returns: + BaseLogger: Logger object + """ if config is None: config = {} diff --git a/autogen/logger/logger_utils.py b/autogen/logger/logger_utils.py index f80f1eb426..ab73a75be8 100644 --- a/autogen/logger/logger_utils.py +++ b/autogen/logger/logger_utils.py @@ -7,12 +7,17 @@ import inspect from datetime import datetime, timezone from pathlib import Path, PurePath -from typing import Any, Dict, List, Tuple, Union +from typing import Any, Union __all__ = ("get_current_ts", "to_dict") def get_current_ts() -> str: + """Get current timestamp in UTC timezone. + + Returns: + str: Current timestamp in UTC timezone + """ return datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S.%f") @@ -21,6 +26,13 @@ def to_dict( exclude: tuple[str, ...] = (), no_recursive: tuple[Any, ...] = (), ) -> Any: + """Convert object to dictionary. + + Args: + obj (Union[int, float, str, bool, dict[Any, Any], list[Any], tuple[Any, ...], Any]): Object to convert + exclude (tuple[str, ...], optional): Keys to exclude. Defaults to (). + no_recursive (tuple[Any, ...], optional): Types to exclude from recursive conversion. Defaults to (). + """ if isinstance(obj, (int, float, str, bool)): return obj elif isinstance(obj, (Path, PurePath)): diff --git a/autogen/logger/sqlite_logger.py b/autogen/logger/sqlite_logger.py index 24bd7447e3..5c4d0d00a5 100644 --- a/autogen/logger/sqlite_logger.py +++ b/autogen/logger/sqlite_logger.py @@ -12,7 +12,7 @@ import sqlite3 import threading import uuid -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, TypeVar, Union +from typing import TYPE_CHECKING, Any, Callable, TypeVar from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletion @@ -43,6 +43,15 @@ def safe_serialize(obj: Any) -> str: + """Safely serialize an object to JSON. + + Args: + obj (Any): Object to serialize. + + Returns: + str: Serialized object. + """ + def default(o: Any) -> str: if hasattr(o, "to_json"): return str(o.to_json()) @@ -53,9 +62,16 @@ def default(o: Any) -> str: class SqliteLogger(BaseLogger): + """Sqlite logger class.""" + schema_version = 1 def __init__(self, config: dict[str, Any]): + """Initialize the SqliteLogger. + + Args: + config (dict[str, Any]): Configuration for the logger. + """ self.config = config try: @@ -198,8 +214,7 @@ def _apply_migration(self, migrations_dir: str = "./migrations") -> None: self._run_query(query=query, args=args) def _run_query(self, query: str, args: tuple[Any, ...] = ()) -> None: - """ - Executes a given SQL query. + """Executes a given SQL query. Args: query (str): The SQL query to execute. @@ -213,8 +228,7 @@ def _run_query(self, query: str, args: tuple[Any, ...] = ()) -> None: logger.error("[sqlite logger]Error running query with query %s and args %s: %s", query, args, e) def _run_query_script(self, script: str) -> None: - """ - Executes SQL script. + """Executes SQL script. Args: script (str): SQL script to execute. @@ -238,6 +252,19 @@ def log_chat_completion( cost: float, start_time: str, ) -> None: + """Log chat completion. + + Args: + invocation_id (uuid.UUID): Invocation ID. + client_id (int): Client ID. + wrapper_id (int): Wrapper ID. + source (str | Agent): Source of the chat completion. + request (dict[str, float | str | list[dict[str, str]]]): Request for the chat completion. + response (str | ChatCompletion): Response for the chat completion. + is_cached (int): Whether the response is cached. + cost (float): Cost of the chat completion. + start_time (str): Start time of the chat completion. + """ if self.con is None: return @@ -276,6 +303,12 @@ def log_chat_completion( self._run_query(query=query, args=args) def log_new_agent(self, agent: ConversableAgent, init_args: dict[str, Any]) -> None: + """Log new agent. + + Args: + agent (ConversableAgent): Agent to log. + init_args (dict[str, Any]): Initialization arguments of the agent + """ from autogen import Agent if self.con is None: @@ -318,6 +351,13 @@ class = excluded.class, self._run_query(query=query, args=args) def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> None: + """Log event. + + Args: + source (str | Agent): Source of the event. + name (str): Name of the event. + **kwargs (dict[str, Any]): Additional arguments for the event. + """ from autogen import Agent if self.con is None: @@ -353,6 +393,12 @@ def log_event(self, source: str | Agent, name: str, **kwargs: dict[str, Any]) -> self._run_query(query=query, args=args_str_based) def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig | list[LLMConfig]]) -> None: + """Log new wrapper. + + Args: + wrapper (OpenAIWrapper): Wrapper to log. + init_args (dict[str, LLMConfig | list[LLMConfig]]): Initialization arguments of the wrapper + """ if self.con is None: return @@ -383,7 +429,14 @@ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: dict[str, LLMConfig self._run_query(query=query, args=args) def log_function_use(self, source: str | Agent, function: F, args: dict[str, Any], returns: Any) -> None: + """Log function use. + Args: + source (str | Agent): Source of the function use. + function (F): Function to log. + args (dict[str, Any]): Arguments of the function. + returns (Any): Returns of the function. + """ if self.con is None: return @@ -418,6 +471,13 @@ def log_new_client( wrapper: OpenAIWrapper, init_args: dict[str, Any], ) -> None: + """Log new client. + + Args: + client (AzureOpenAI | OpenAI | CerebrasClient | GeminiClient | AnthropicClient | MistralAIClient | TogetherClient | GroqClient | CohereClient | OllamaClient | BedrockClient): Client to log. + wrapper (OpenAIWrapper): Wrapper of the client. + init_args (dict[str, Any]): Initialization arguments of the client. + """ if self.con is None: return @@ -450,10 +510,12 @@ def log_new_client( self._run_query(query=query, args=args) def stop(self) -> None: + """Stop the logger""" if self.con: self.con.close() def get_connection(self) -> None | sqlite3.Connection: + """Get connection.""" if self.con: return self.con return None diff --git a/autogen/math_utils.py b/autogen/math_utils.py index b2b4b5925f..6b427f9f12 100644 --- a/autogen/math_utils.py +++ b/autogen/math_utils.py @@ -34,6 +34,7 @@ def solve_problem(problem: str, **config) -> str: def remove_boxed(string: str) -> Optional[str]: """Source: https://github.com/hendrycks/math Extract the text within a \\boxed`{...}` environment. + Example: ```python > remove_boxed("\\boxed{\\frac{2}{3}}") @@ -85,6 +86,7 @@ def last_boxed_only_string(string: str) -> Optional[str]: def _fix_fracs(string: str) -> str: """Source: https://github.com/hendrycks/math Reformat fractions. + Examples: ``` >>> _fix_fracs("\\frac1b") @@ -130,6 +132,7 @@ def _fix_fracs(string: str) -> str: def _fix_a_slash_b(string: str) -> str: """Source: https://github.com/hendrycks/math Reformat fractions formatted as a/b to \\`frac{a}{b}`. + Example: ``` >>> _fix_a_slash_b("2/3") @@ -168,6 +171,7 @@ def _remove_right_units(string: str) -> str: def _fix_sqrt(string: str) -> str: """Source: https://github.com/hendrycks/math Reformat square roots. + Example: ``` >>> _fix_sqrt("\\sqrt3") diff --git a/autogen/messages/__init__.py b/autogen/messages/__init__.py index b21bb1c2a4..6a0d4338e8 100644 --- a/autogen/messages/__init__.py +++ b/autogen/messages/__init__.py @@ -4,4 +4,4 @@ from .base_message import BaseMessage, get_annotated_type_for_message_classes, wrap_message -__all__ = ["BaseMessage", "wrap_message", "get_annotated_type_for_message_classes"] +__all__ = ["BaseMessage", "get_annotated_type_for_message_classes", "wrap_message"] diff --git a/autogen/messages/agent_messages.py b/autogen/messages/agent_messages.py index 98d1aae147..914d61dc0c 100644 --- a/autogen/messages/agent_messages.py +++ b/autogen/messages/agent_messages.py @@ -20,26 +20,26 @@ __all__ = [ - "FunctionResponseMessage", - "ToolResponseMessage", + "ClearAgentsHistoryMessage", + "ClearConversableAgentHistoryMessage", + "ConversableAgentUsageSummaryMessage", + "ConversableAgentUsageSummaryNoCostIncurredMessage", + "ExecuteCodeBlockMessage", + "ExecuteFunctionMessage", "FunctionCallMessage", - "ToolCallMessage", - "TextMessage", + "FunctionResponseMessage", + "GenerateCodeExecutionReplyMessage", + "GroupChatResumeMessage", + "GroupChatRunChatMessage", "PostCarryoverProcessingMessage", - "ClearAgentsHistoryMessage", - "SpeakerAttemptSuccessfullMessage", + "SelectSpeakerMessage", "SpeakerAttemptFailedMultipleAgentsMessage", "SpeakerAttemptFailedNoAgentsMessage", - "GroupChatResumeMessage", - "GroupChatRunChatMessage", + "SpeakerAttemptSuccessfullMessage", "TerminationAndHumanReplyMessage", - "ExecuteCodeBlockMessage", - "ExecuteFunctionMessage", - "SelectSpeakerMessage", - "ClearConversableAgentHistoryMessage", - "GenerateCodeExecutionReplyMessage", - "ConversableAgentUsageSummaryMessage", - "ConversableAgentUsageSummaryNoCostIncurredMessage", + "TextMessage", + "ToolCallMessage", + "ToolResponseMessage", ] MessageRole = Literal["assistant", "function", "tool"] @@ -189,7 +189,7 @@ def print(self, f: Optional[Callable[..., Any]] = None) -> None: @wrap_message class TextMessage(BasePrintReceivedMessage): - content: Optional[Union[str, int, float, bool]] = None # type: ignore [assignment] + content: Optional[Union[str, int, float, bool, list[dict[str, str]]]] = None # type: ignore [assignment] def print(self, f: Optional[Callable[..., Any]] = None) -> None: f = f or print @@ -215,7 +215,7 @@ def create_received_message_model( # Role is neither function nor tool - if "function_call" in message and message["function_call"]: + if message.get("function_call"): return FunctionCallMessage( **message, sender_name=sender.name, @@ -223,7 +223,7 @@ def create_received_message_model( uuid=uuid, ) - if "tool_calls" in message and message["tool_calls"]: + if message.get("tool_calls"): return ToolCallMessage( **message, sender_name=sender.name, @@ -270,8 +270,8 @@ def __init__(self, *, uuid: Optional[UUID] = None, chat_info: dict[str, Any]): sender_name = chat_info["sender"].name recipient_name = chat_info["recipient"].name - summary_args = chat_info.get("summary_args", None) - max_turns = chat_info.get("max_turns", None) + summary_args = chat_info.get("summary_args") + max_turns = chat_info.get("max_turns") # Fix Callable in chat_info summary_method = chat_info.get("summary_method", "") @@ -680,7 +680,7 @@ def print(self, f: Optional[Callable[..., Any]] = None) -> None: f("Please select the next speaker from the following list:") agent_names = self.agent_names or [] for i, agent_name in enumerate(agent_names): - f(f"{i+1}: {agent_name}") + f(f"{i + 1}: {agent_name}") @wrap_message diff --git a/autogen/messages/base_message.py b/autogen/messages/base_message.py index 161c377939..f6470b2203 100644 --- a/autogen/messages/base_message.py +++ b/autogen/messages/base_message.py @@ -4,14 +4,14 @@ from abc import ABC -from typing import Annotated, Any, Callable, Literal, Optional, Type, TypeVar, Union +from typing import Annotated, Any, Callable, Literal, Optional, TypeVar, Union from uuid import UUID, uuid4 from pydantic import BaseModel, Field, create_model PetType = TypeVar("PetType", bound=Literal["cat", "dog"]) -__all__ = ["BaseMessage", "wrap_message", "get_annotated_type_for_message_classes"] +__all__ = ["BaseMessage", "get_annotated_type_for_message_classes", "wrap_message"] class BaseMessage(BaseModel, ABC): @@ -34,10 +34,10 @@ def camel2snake(name: str) -> str: return "".join(["_" + i.lower() if i.isupper() else i for i in name]).lstrip("_") -_message_classes: dict[str, Type[BaseModel]] = {} +_message_classes: dict[str, type[BaseModel]] = {} -def wrap_message(message_cls: Type[BaseMessage]) -> Type[BaseModel]: +def wrap_message(message_cls: type[BaseMessage]) -> type[BaseModel]: """Wrap a message class with a type field to be used in a union type This is needed for proper serialization and deserialization of messages in a union type. @@ -59,7 +59,7 @@ class WrapperBase(BaseModel): content: message_cls # type: ignore[valid-type] def __init__(self, *args: Any, **data: Any): - if set(data.keys()) <= {"type", "content"} and "content" in data: + if set(data.keys()) == {"type", "content"} and "content" in data: super().__init__(*args, **data) else: if "content" in data: @@ -71,18 +71,18 @@ def __init__(self, *args: Any, **data: Any): def print(self, f: Optional[Callable[..., Any]] = None) -> None: self.content.print(f) # type: ignore[attr-defined] - Wrapper = create_model(message_cls.__name__, __base__=WrapperBase) + wrapper_cls = create_model(message_cls.__name__, __base__=WrapperBase) - _message_classes[type_name] = Wrapper + _message_classes[type_name] = wrapper_cls - return Wrapper + return wrapper_cls -def get_annotated_type_for_message_classes() -> Type[Any]: +def get_annotated_type_for_message_classes() -> type[Any]: # this is a dynamic type so we need to disable the type checker union_type = Union[tuple(_message_classes.values())] # type: ignore[valid-type] return Annotated[union_type, Field(discriminator="type")] # type: ignore[return-value] -def get_message_classes() -> dict[str, Type[BaseModel]]: +def get_message_classes() -> dict[str, type[BaseModel]]: return _message_classes diff --git a/autogen/messages/client_messages.py b/autogen/messages/client_messages.py index 63c31b1090..05d7a83b8a 100644 --- a/autogen/messages/client_messages.py +++ b/autogen/messages/client_messages.py @@ -13,21 +13,36 @@ class ModelUsageSummary(BaseModel): + """Model usage summary.""" + model: str + """Model name.""" completion_tokens: int + """Number of tokens used for completion.""" cost: float + """Cost of the completion.""" prompt_tokens: int + """Number of tokens used for prompt.""" total_tokens: int + """Total number of tokens used.""" class ActualUsageSummary(BaseModel): + """Actual usage summary.""" + usages: Optional[list[ModelUsageSummary]] = None + """List of model usage summaries.""" total_cost: Optional[float] = None + """Total cost.""" class TotalUsageSummary(BaseModel): + """Total usage summary.""" + usages: Optional[list[ModelUsageSummary]] = None + """List of model usage summaries.""" total_cost: Optional[float] = None + """Total cost.""" Mode = Literal["both", "total", "actual"] @@ -58,9 +73,14 @@ def _change_usage_summary_format( @wrap_message class UsageSummaryMessage(BaseMessage): + """Usage summary message.""" + actual: ActualUsageSummary + """Actual usage summary.""" total: TotalUsageSummary + """Total usage summary.""" mode: Mode + """Mode to display the usage summary.""" def __init__( self, @@ -127,10 +147,13 @@ def print(self, f: Optional[Callable[..., Any]] = None) -> None: @wrap_message class StreamMessage(BaseMessage): - chunk_content: str + """Stream message.""" + + content: str + """Content of the message.""" - def __init__(self, *, uuid: Optional[UUID] = None, chunk_content: str) -> None: - super().__init__(uuid=uuid, chunk_content=chunk_content) + def __init__(self, *, uuid: Optional[UUID] = None, content: str) -> None: + super().__init__(uuid=uuid, content=content) def print(self, f: Optional[Callable[..., Any]] = None) -> None: f = f or print @@ -138,7 +161,7 @@ def print(self, f: Optional[Callable[..., Any]] = None) -> None: # Set the terminal text color to green f("\033[32m", end="") - f(self.chunk_content, end="", flush=True) + f(self.content, end="", flush=True) # Reset the terminal text color f("\033[0m\n") diff --git a/autogen/messages/print_message.py b/autogen/messages/print_message.py index f3a577f146..3f57b4993a 100644 --- a/autogen/messages/print_message.py +++ b/autogen/messages/print_message.py @@ -12,9 +12,14 @@ @wrap_message class PrintMessage(BaseMessage): + """Print message""" + objects: list[str] + """List of objects to print""" sep: str + """Separator between objects""" end: str + """End of the print""" def __init__( self, *objects: Any, sep: str = " ", end: str = "\n", flush: bool = False, uuid: Optional[UUID] = None diff --git a/autogen/oai/__init__.py b/autogen/oai/__init__.py index d3c474ccd1..68113589b0 100644 --- a/autogen/oai/__init__.py +++ b/autogen/oai/__init__.py @@ -18,16 +18,16 @@ ) __all__ = [ - "OpenAIWrapper", - "ModelClient", - "Completion", + "Cache", "ChatCompletion", - "get_config_list", + "Completion", + "ModelClient", + "OpenAIWrapper", + "config_list_from_dotenv", + "config_list_from_json", + "config_list_from_models", "config_list_gpt4_gpt35", "config_list_openai_aoai", - "config_list_from_models", - "config_list_from_json", - "config_list_from_dotenv", "filter_config", - "Cache", + "get_config_list", ] diff --git a/autogen/oai/anthropic.py b/autogen/oai/anthropic.py index ec5f68ec5d..9d4d3dd920 100644 --- a/autogen/oai/anthropic.py +++ b/autogen/oai/anthropic.py @@ -4,8 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -""" -Create an OpenAI-compatible client for the Anthropic API. +"""Create an OpenAI-compatible client for the Anthropic API. Example usage: Install the `anthropic` package by running `pip install --upgrade anthropic`. @@ -71,29 +70,25 @@ from __future__ import annotations -import copy import inspect import json import os import time import warnings -from typing import Annotated, Any, Dict, List, Optional, Tuple, Union +from typing import Any from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex from anthropic import __version__ as anthropic_version -from anthropic.types import Completion, Message, TextBlock, ToolUseBlock +from anthropic.types import TextBlock, ToolUseBlock from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import validate_parameter TOOL_ENABLED = anthropic_version >= "0.23.1" if TOOL_ENABLED: - from anthropic.types.tool_use_block_param import ( - ToolUseBlockParam, - ) + pass ANTHROPIC_PRICING_1k = { @@ -111,19 +106,19 @@ class AnthropicClient: def __init__(self, **kwargs: Any): - """ - Initialize the Anthropic API client. + """Initialize the Anthropic API client. + Args: api_key (str): The API key for the Anthropic API or set the `ANTHROPIC_API_KEY` environment variable. """ - self._api_key = kwargs.get("api_key", None) - self._aws_access_key = kwargs.get("aws_access_key", None) - self._aws_secret_key = kwargs.get("aws_secret_key", None) - self._aws_session_token = kwargs.get("aws_session_token", None) - self._aws_region = kwargs.get("aws_region", None) - self._gcp_project_id = kwargs.get("gcp_project_id", None) - self._gcp_region = kwargs.get("gcp_region", None) - self._gcp_auth_token = kwargs.get("gcp_auth_token", None) + self._api_key = kwargs.get("api_key") + self._aws_access_key = kwargs.get("aws_access_key") + self._aws_secret_key = kwargs.get("aws_secret_key") + self._aws_session_token = kwargs.get("aws_session_token") + self._aws_region = kwargs.get("aws_region") + self._gcp_project_id = kwargs.get("gcp_project_id") + self._gcp_region = kwargs.get("gcp_region") + self._gcp_auth_token = kwargs.get("gcp_auth_token") if not self._api_key: self._api_key = os.getenv("ANTHROPIC_API_KEY") @@ -175,7 +170,7 @@ def load_config(self, params: dict[str, Any]): """Load the configuration for the Anthropic API client.""" anthropic_params = {} - anthropic_params["model"] = params.get("model", None) + anthropic_params["model"] = params.get("model") assert anthropic_params["model"], "Please provide a `model` in the config_list to use the Anthropic API." anthropic_params["temperature"] = validate_parameter( @@ -320,8 +315,7 @@ def create(self, params: dict[str, Any]) -> ChatCompletion: return response_oai def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -359,7 +353,6 @@ def oai_messages_to_anthropic_messages(params: dict[str, Any]) -> list[dict[str, """Convert messages from OAI format to Anthropic format. We correct for any specific role orders and types, etc. """ - # Track whether we have tools passed in. If not, tool use / result messages should be converted to text messages. # Anthropic requires a tools parameter with the tools listed, if there are other messages with tool use or tool results. # This can occur when we don't need tool calling, such as for group chat speaker selection. diff --git a/autogen/oai/bedrock.py b/autogen/oai/bedrock.py index b624cc9125..3c5b994f56 100644 --- a/autogen/oai/bedrock.py +++ b/autogen/oai/bedrock.py @@ -4,8 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -""" -Create a compatible client for the Amazon Bedrock Converse API. +"""Create a compatible client for the Amazon Bedrock Converse API. Example usage: Install the `boto3` package by running `pip install --upgrade boto3`. @@ -21,7 +20,7 @@ "aws_region": "us-west-2", "aws_access_key": "", "aws_secret_key": "", - "price" : [0.003, 0.015] + "price": [0.003, 0.015], } ] @@ -37,7 +36,7 @@ import re import time import warnings -from typing import Any, Dict, List, Literal, Optional, Tuple +from typing import Any, Literal import boto3 import requests @@ -45,7 +44,6 @@ from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import validate_parameter @@ -56,14 +54,12 @@ class BedrockClient: _retries = 5 def __init__(self, **kwargs: Any): - """ - Initialises BedrockClient for Amazon's Bedrock Converse API - """ - self._aws_access_key = kwargs.get("aws_access_key", None) - self._aws_secret_key = kwargs.get("aws_secret_key", None) - self._aws_session_token = kwargs.get("aws_session_token", None) - self._aws_region = kwargs.get("aws_region", None) - self._aws_profile_name = kwargs.get("aws_profile_name", None) + """Initialises BedrockClient for Amazon's Bedrock Converse API""" + self._aws_access_key = kwargs.get("aws_access_key") + self._aws_secret_key = kwargs.get("aws_secret_key") + self._aws_session_token = kwargs.get("aws_session_token") + self._aws_region = kwargs.get("aws_region") + self._aws_profile_name = kwargs.get("aws_profile_name") if not self._aws_access_key: self._aws_access_key = os.getenv("AWS_ACCESS_KEY") @@ -104,7 +100,6 @@ def __init__(self, **kwargs: Any): or self._aws_secret_key is None or self._aws_secret_key == "" ): - # attempts to get client from attached role of managed service (lambda, ec2, ecs, etc.) self.bedrock_runtime = boto3.client(service_name="bedrock-runtime", config=bedrock_config) else: @@ -121,26 +116,21 @@ def message_retrieval(self, response): return [choice.message for choice in response.choices] def parse_custom_params(self, params: dict[str, Any]): - """ - Parses custom parameters for logic in this client class - """ - + """Parses custom parameters for logic in this client class""" # Should we separate system messages into its own request parameter, default is True # This is required because not all models support a system prompt (e.g. Mistral Instruct). self._supports_system_prompts = params.get("supports_system_prompts", True) def parse_params(self, params: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]: - """ - Loads the valid parameters required to invoke Bedrock Converse + """Loads the valid parameters required to invoke Bedrock Converse Returns a tuple of (base_params, additional_params) """ - base_params = {} additional_params = {} # Amazon Bedrock base model IDs are here: # https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html - self._model_id = params.get("model", None) + self._model_id = params.get("model") assert self._model_id, "Please provide the 'model` in the config_list to use Amazon Bedrock" # Parameters vary based on the model used. @@ -293,7 +283,6 @@ def extract_system_messages(messages: list[dict]) -> list: Returns: List[SystemMessage]: List of System messages. """ - """ system_messages = [message.get("content")[0]["text"] for message in messages if message.get("role") == "system"] return system_messages # ''.join(system_messages) @@ -311,14 +300,12 @@ def extract_system_messages(messages: list[dict]) -> list: def oai_messages_to_bedrock_messages( messages: list[dict[str, Any]], has_tools: bool, supports_system_prompts: bool ) -> list[dict]: - """ - Convert messages from OAI format to Bedrock format. + """Convert messages from OAI format to Bedrock format. We correct for any specific role orders and types, etc. AWS Bedrock requires messages to alternate between user and assistant roles. This function ensures that the messages are in the correct order and format for Bedrock by inserting "Please continue" messages as needed. This is the same method as the one in the Autogen Anthropic client """ - # Track whether we have tools passed in. If not, tool use / result messages should be converted to text messages. # Bedrock requires a tools parameter with the tools listed, if there are other messages with tool use or tool results. # This can occur when we don't need tool calling, such as for group chat speaker selection @@ -327,7 +314,7 @@ def oai_messages_to_bedrock_messages( # Take out system messages if the model supports it, otherwise leave them in. if supports_system_prompts: - messages = [x for x in messages if not x["role"] == "system"] + messages = [x for x in messages if x["role"] != "system"] else: # Replace role="system" with role="user" for msg in messages: @@ -505,7 +492,6 @@ def parse_image(image_url: str) -> tuple[bytes, str]: response = requests.get(image_url) # Check if the request was successful if response.status_code == 200: - content_type = response.headers.get("Content-Type") if not content_type.startswith("image"): content_type = "image/jpeg" @@ -575,8 +561,7 @@ def format_tool_calls(content): def convert_stop_reason_to_finish_reason( stop_reason: str, ) -> Literal["stop", "length", "tool_calls", "content_filter"]: - """ - Converts Bedrock finish reasons to our finish reasons, according to OpenAI: + """Converts Bedrock finish reasons to our finish reasons, according to OpenAI: - stop: if the model hit a natural stop point or a provided stop sequence, - length: if the maximum number of tokens specified in the request was reached, @@ -613,7 +598,6 @@ def convert_stop_reason_to_finish_reason( def calculate_cost(input_tokens: int, output_tokens: int, model_id: str) -> float: """Calculate the cost of the completion using the Bedrock pricing.""" - if model_id in PRICES_PER_K_TOKENS: input_cost_per_k, output_cost_per_k = PRICES_PER_K_TOKENS[model_id] input_cost = (input_tokens / 1000) * input_cost_per_k diff --git a/autogen/oai/cerebras.py b/autogen/oai/cerebras.py index 3cbcf3368f..ffdfd88c4b 100644 --- a/autogen/oai/cerebras.py +++ b/autogen/oai/cerebras.py @@ -8,12 +8,8 @@ Example: ```python - llm_config={ - "config_list": [{ - "api_type": "cerebras", - "model": "llama3.1-8b", - "api_key": os.environ.get("CEREBRAS_API_KEY") - }] + llm_config = { + "config_list": [{"api_type": "cerebras", "model": "llama3.1-8b", "api_key": os.environ.get("CEREBRAS_API_KEY")}] } agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) @@ -31,13 +27,12 @@ import os import time import warnings -from typing import Any, Dict, List, Optional +from typing import Any from cerebras.cloud.sdk import Cerebras, Stream from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -62,16 +57,15 @@ def __init__(self, api_key=None, **kwargs): if not self.api_key: self.api_key = os.getenv("CEREBRAS_API_KEY") - assert ( - self.api_key - ), "Please include the api_key in your config list entry for Cerebras or set the CEREBRAS_API_KEY env variable." + assert self.api_key, ( + "Please include the api_key in your config list entry for Cerebras or set the CEREBRAS_API_KEY env variable." + ) if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Crebras, it will be ignored.", UserWarning) def message_retrieval(self, response: ChatCompletion) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -100,10 +94,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: # Check that we have what we need to use Cerebras's API # We won't enforce the available models as they are likely to change - cerebras_params["model"] = params.get("model", None) - assert cerebras_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Cerebras model to use." + cerebras_params["model"] = params.get("model") + assert cerebras_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Cerebras model to use." + ) # Validate allowed Cerebras parameters # https://inference-docs.cerebras.ai/api-reference/chat-completions @@ -249,7 +243,6 @@ def oai_messages_to_cerebras_messages(messages: list[dict[str, Any]]) -> list[di """Convert messages from OAI format to Cerebras's format. We correct for any specific role orders and types. """ - cerebras_messages = copy.deepcopy(messages) # Remove the name field diff --git a/autogen/oai/client.py b/autogen/oai/client.py index 1cb8dc4415..4a359e93cf 100644 --- a/autogen/oai/client.py +++ b/autogen/oai/client.py @@ -10,7 +10,7 @@ import logging import sys import uuid -from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, Union, runtime_checkable +from typing import Any, Callable, Optional, Protocol, Union, runtime_checkable from pydantic import BaseModel, schema_json_of @@ -34,9 +34,8 @@ else: # raises exception if openai>=1 is installed and something is wrong with imports from openai import APIError, APITimeoutError, AzureOpenAI, OpenAI - from openai import __version__ as OPENAIVERSION + from openai import __version__ as openai_version from openai.lib._parsing._completions import type_to_response_format_param - from openai.resources import Completions from openai.types.chat import ChatCompletion from openai.types.chat.chat_completion import ChatCompletionMessage, Choice # type: ignore [attr-defined] from openai.types.chat.chat_completion_chunk import ( @@ -44,7 +43,6 @@ ChoiceDeltaToolCall, ChoiceDeltaToolCallFunction, ) - from openai.types.chat.parsed_chat_completion import ParsedChatCompletion, ParsedChatCompletionMessage from openai.types.completion import Completion from openai.types.completion_usage import CompletionUsage @@ -63,7 +61,7 @@ cerebras_import_exception: Optional[ImportError] = None except ImportError as e: - cerebras_AuthenticationError = cerebras_InternalServerError = cerebras_RateLimitError = Exception + cerebras_AuthenticationError = cerebras_InternalServerError = cerebras_RateLimitError = Exception # noqa: N816 cerebras_import_exception = e try: @@ -76,7 +74,7 @@ gemini_import_exception: Optional[ImportError] = None except ImportError as e: - gemini_InternalServerError = gemini_ResourceExhausted = Exception + gemini_InternalServerError = gemini_ResourceExhausted = Exception # noqa: N816 gemini_import_exception = e try: @@ -89,7 +87,7 @@ anthropic_import_exception: Optional[ImportError] = None except ImportError as e: - anthorpic_InternalServerError = anthorpic_RateLimitError = Exception + anthorpic_InternalServerError = anthorpic_RateLimitError = Exception # noqa: N816 anthropic_import_exception = e try: @@ -102,7 +100,7 @@ mistral_import_exception: Optional[ImportError] = None except ImportError as e: - mistral_SDKError = mistral_HTTPValidationError = Exception + mistral_SDKError = mistral_HTTPValidationError = Exception # noqa: N816 mistral_import_exception = e try: @@ -112,7 +110,7 @@ together_import_exception: Optional[ImportError] = None except ImportError as e: - together_TogetherException = Exception + together_TogetherException = Exception # noqa: N816 together_import_exception = e try: @@ -126,7 +124,7 @@ groq_import_exception: Optional[ImportError] = None except ImportError as e: - groq_InternalServerError = groq_RateLimitError = groq_APIConnectionError = Exception + groq_InternalServerError = groq_RateLimitError = groq_APIConnectionError = Exception # noqa: N816 groq_import_exception = e try: @@ -140,7 +138,7 @@ cohere_import_exception: Optional[ImportError] = None except ImportError as e: - cohere_InternalServerError = cohere_TooManyRequestsError = cohere_ServiceUnavailableError = Exception + cohere_InternalServerError = cohere_TooManyRequestsError = cohere_ServiceUnavailableError = Exception # noqa: N816 cohere_import_exception = e try: @@ -153,7 +151,7 @@ ollama_import_exception: Optional[ImportError] = None except ImportError as e: - ollama_RequestError = ollama_ResponseError = Exception + ollama_RequestError = ollama_ResponseError = Exception # noqa: N816 ollama_import_exception = e try: @@ -166,7 +164,7 @@ bedrock_import_exception: Optional[ImportError] = None except ImportError as e: - bedrock_BotoCoreError = bedrock_ClientError = Exception + bedrock_BotoCoreError = bedrock_ClientError = Exception # noqa: N816 bedrock_import_exception = e logger = logging.getLogger(__name__) @@ -182,8 +180,7 @@ class ModelClient(Protocol): - """ - A client class must implement the following methods: + """A client class must implement the following methods: - create must return a response object that implements the ModelClientResponseProtocol - cost must return the cost of the response - get_usage must return a dict with the following keys: @@ -215,8 +212,7 @@ def create(self, params: dict[str, Any]) -> ModelClientResponseProtocol: ... # def message_retrieval( self, response: ModelClientResponseProtocol ) -> Union[list[str], list[ModelClient.ModelClientResponseProtocol.Choice.Message]]: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -362,7 +358,7 @@ def _create_or_parse(*args, **kwargs): # If content is present, print it to the terminal and update response variables if content is not None: - iostream.send(StreamMessage(chunk_content=content)) + iostream.send(StreamMessage(content=content)) response_contents[choice.index] += content completion_tokens += 1 else: @@ -384,7 +380,7 @@ def _create_or_parse(*args, **kwargs): ), ) for i in range(len(response_contents)): - if OPENAIVERSION >= "1.5": # pragma: no cover + if openai_version >= "1.5": # pragma: no cover # OpenAI versions 1.5.0 and above choice = Choice( index=i, @@ -433,7 +429,7 @@ def cost(self, response: Union[ChatCompletion, Completion]) -> float: n_output_tokens = response.usage.completion_tokens if response.usage is not None else 0 # type: ignore [union-attr] if n_output_tokens is None: n_output_tokens = 0 - tmp_price1K = OAI_PRICE1K[model] + tmp_price1K = OAI_PRICE1K[model] # noqa: N806 # First value is input token rate, second value is output token rate if isinstance(tmp_price1K, tuple): return (tmp_price1K[0] * n_input_tokens + tmp_price1K[1] * n_output_tokens) / 1000 # type: ignore [no-any-return] @@ -483,13 +479,12 @@ def __init__( config_list: Optional[list[dict[str, Any]]] = None, **base_config: Any, ): - """ - Args: + """Args: config_list: a list of config dicts to override the base_config. They can contain additional kwargs as allowed in the [create](/docs/reference/oai/client#create) method. E.g., ```python - config_list=[ + config_list = [ { "model": "gpt-4", "api_key": os.environ.get("AZURE_OPENAI_API_KEY"), @@ -506,7 +501,7 @@ def __init__( { "model": "llama-7B", "base_url": "http://127.0.0.1:8080", - } + }, ] ``` @@ -514,7 +509,6 @@ def __init__( and additional kwargs. When using OpenAI or Azure OpenAI endpoints, please specify a non-empty 'model' either in `base_config` or in each config of `config_list`. """ - if logging_enabled(): log_new_wrapper(self, locals()) openai_config, extra_kwargs = self._separate_openai_config(base_config) @@ -770,6 +764,7 @@ def yes_or_no_filter(context, response): - allow_format_str_template (bool | None): Whether to allow format string template in the config. Default to false. - api_version (str | None): The api version. Default to None. E.g., "2024-02-01". + Raises: - RuntimeError: If all declared custom model clients are not registered - APIError: If any model client create call raises an APIError @@ -1059,8 +1054,7 @@ def _update_tool_calls_from_chunk( # future proofing for when tool calls other than function calls are supported if tool_calls_chunk.type and tool_calls_chunk.type != "function": raise NotImplementedError( - f"Tool call type {tool_calls_chunk.type} is currently not supported. " - "Only function calls are supported." + f"Tool call type {tool_calls_chunk.type} is currently not supported. Only function calls are supported." ) # Handle tool call diff --git a/autogen/oai/client_utils.py b/autogen/oai/client_utils.py index 6f417c90ba..e975c15654 100644 --- a/autogen/oai/client_utils.py +++ b/autogen/oai/client_utils.py @@ -8,20 +8,19 @@ import logging import warnings -from typing import Any, Dict, List, Optional, Tuple +from typing import Any def validate_parameter( params: dict[str, Any], param_name: str, allowed_types: tuple, - allow_None: bool, + allow_None: bool, # noqa: N803 default_value: Any, numerical_bound: tuple, allowed_values: list, ) -> Any: - """ - Validates a given config parameter, checking its type, values, and setting defaults + """Validates a given config parameter, checking its type, values, and setting defaults Parameters: params (Dict[str, Any]): Dictionary containing parameters to validate. param_name (str): The name of the parameter to validate. @@ -54,7 +53,6 @@ def validate_parameter( # If "safety_model" is missing or invalid in params, defaults to "default" ``` """ - if allowed_values is not None and not isinstance(allowed_values, list): raise TypeError(f"allowed_values should be a list or None, got {type(allowed_values).__name__}") @@ -81,11 +79,11 @@ def validate_parameter( ): warning = "has numerical bounds" if lower_bound is not None: - warning += f", >= {str(lower_bound)}" + warning += f", >= {lower_bound!s}" if upper_bound is not None: if lower_bound is not None: warning += " and" - warning += f" <= {str(upper_bound)}" + warning += f" <= {upper_bound!s}" if allow_None: warning += ", or can be None" @@ -107,8 +105,7 @@ def validate_parameter( def should_hide_tools(messages: list[dict[str, Any]], tools: list[dict[str, Any]], hide_tools_param: str) -> bool: - """ - Determines if tools should be hidden. This function is used to hide tools when they have been run, minimising the chance of the LLM choosing them when they shouldn't. + """Determines if tools should be hidden. This function is used to hide tools when they have been run, minimising the chance of the LLM choosing them when they shouldn't. Parameters: messages (List[Dict[str, Any]]): List of messages tools (List[Dict[str, Any]]): List of tools @@ -124,7 +121,6 @@ def should_hide_tools(messages: list[dict[str, Any]], tools: list[dict[str, Any] tools = params.get("tools", None) hide_tools = should_hide_tools(messages, tools, params["hide_tools"]) """ - if hide_tools_param == "never" or tools is None or len(tools) == 0: return False elif hide_tools_param == "if_any_run": diff --git a/autogen/oai/cohere.py b/autogen/oai/cohere.py index 3765902ce0..8cb4adf18c 100644 --- a/autogen/oai/cohere.py +++ b/autogen/oai/cohere.py @@ -35,14 +35,13 @@ import sys import time import warnings -from typing import Any, Dict, List, Optional +from typing import Any from cohere import Client as Cohere from cohere.types import ToolParameterDefinitionsValue, ToolResult from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import logging_formatter, validate_parameter @@ -74,20 +73,19 @@ def __init__(self, **kwargs): api_key (str): The API key for using Cohere (or environment variable COHERE_API_KEY needs to be set) """ # Ensure we have the api_key upon instantiation - self.api_key = kwargs.get("api_key", None) + self.api_key = kwargs.get("api_key") if not self.api_key: self.api_key = os.getenv("COHERE_API_KEY") - assert ( - self.api_key - ), "Please include the api_key in your config list entry for Cohere or set the COHERE_API_KEY env variable." + assert self.api_key, ( + "Please include the api_key in your config list entry for Cohere or set the COHERE_API_KEY env variable." + ) if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Cohere, it will be ignored.", UserWarning) def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -115,10 +113,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: # Check that we have what we need to use Cohere's API # We won't enforce the available models as they are likely to change - cohere_params["model"] = params.get("model", None) - assert cohere_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Cohere model to use." + cohere_params["model"] = params.get("model") + assert cohere_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Cohere model to use." + ) # Validate allowed Cohere parameters # https://docs.cohere.com/reference/chat @@ -175,7 +173,7 @@ def create(self, params: dict) -> ChatCompletion: total_tokens = 0 # Stream if in parameters - streaming = True if "stream" in params and params["stream"] else False + streaming = True if params.get("stream") else False cohere_finish = "stop" tool_calls = None ans = None @@ -225,7 +223,6 @@ def create(self, params: dict) -> ChatCompletion: cohere_finish = "tool_calls" tool_calls = [] for tool_call in response.tool_calls: - # if parameters are null, clear them out (Cohere can return a string "null" if no parameter values) tool_calls.append( @@ -270,11 +267,10 @@ def extract_to_cohere_tool_results(tool_call_id: str, content_output: str, all_t for tool_call in all_tool_calls: if tool_call["id"] == tool_call_id: - call = { "name": tool_call["function"]["name"], "parameters": json.loads( - tool_call["function"]["arguments"] if not tool_call["function"]["arguments"] == "" else "{}" + tool_call["function"]["arguments"] if tool_call["function"]["arguments"] != "" else "{}" ), } output = [{"value": content_output}] @@ -298,7 +294,6 @@ def oai_messages_to_cohere_messages( str: Preamble (system message) str: Message (the final user message) """ - cohere_messages = [] preamble = "" @@ -306,7 +301,6 @@ def oai_messages_to_cohere_messages( if "tools" in params: cohere_tools = [] for tool in params["tools"]: - # build list of properties parameters = {} @@ -351,7 +345,6 @@ def oai_messages_to_cohere_messages( # tool_results go into tool_results parameter messages_length = len(messages) for index, message in enumerate(messages): - if "role" in message and message["role"] == "system": # System message if preamble == "": @@ -422,7 +415,6 @@ def oai_messages_to_cohere_messages( return cohere_messages, preamble, "" else: - # We need to get the last message to assign to the message field for Cohere, # if the last message is a user message, use that, otherwise put in 'continue'. if cohere_messages[-1]["role"] == "USER": diff --git a/autogen/oai/completion.py b/autogen/oai/completion.py index 0768de778e..8060e0fc58 100644 --- a/autogen/oai/completion.py +++ b/autogen/oai/completion.py @@ -10,7 +10,7 @@ import time from collections import defaultdict from time import sleep -from typing import Callable, Dict, List, Optional, Union +from typing import Callable, Optional, Union import numpy as np @@ -40,12 +40,12 @@ RateLimitError, Timeout, ) - from openai import Completion as openai_Completion + from openai import Completion as OpenAICompletion ERROR = None assert openai.__version__ < "1" except (AssertionError, ImportError): - openai_Completion = object + OpenAICompletion = object # The autogen.Completion class requires openai<1 ERROR = AssertionError("(Deprecated) The autogen.Completion class requires openai<1 and diskcache. ") @@ -57,7 +57,7 @@ logger.addHandler(_ch) -class Completion(openai_Completion): +class Completion(OpenAICompletion): """`(openai<1)` A class for OpenAI completion API. It also supports: ChatCompletion, Azure OpenAI API. @@ -81,7 +81,7 @@ class Completion(openai_Completion): } # price per 1k tokens - price1K = { + price1K = { # noqa: N815 "text-ada-001": 0.0004, "text-babbage-001": 0.0005, "text-curie-001": 0.002, @@ -256,12 +256,8 @@ def _get_response(cls, config: dict, raise_on_ratelimit_or_timeout=False, use_ca sleep(retry_wait_time) except (RateLimitError, Timeout) as err: time_left = max_retry_period - (time.time() - start_time + retry_wait_time) - if ( - time_left > 0 - and isinstance(err, RateLimitError) - or time_left > request_timeout - and isinstance(err, Timeout) - and "request_timeout" not in config + if (time_left > 0 and isinstance(err, RateLimitError)) or ( + time_left > request_timeout and isinstance(err, Timeout) and "request_timeout" not in config ): if isinstance(err, Timeout): request_timeout <<= 1 @@ -471,7 +467,7 @@ def _eval(cls, config: dict, prune=True, eval_only=False): prune and target_output_tokens and avg_n_tokens <= target_output_tokens * (1 - ratio) - and (num_completions < config_n or num_completions == config_n and data_limit == data_length) + and (num_completions < config_n or (num_completions == config_n and data_limit == data_length)) ): # update valid n cls._max_valid_n_per_max_tokens[region_key] = valid_n = cls._max_valid_n_per_max_tokens.get( @@ -769,7 +765,7 @@ def create( "model": "llama-7B", "base_url": "http://127.0.0.1:8080", "api_type": "openai", - } + }, ], prompt="Hi", ) @@ -953,7 +949,7 @@ def eval_func(responses, **data): An example agg_method in str: ```python - agg_method = 'median' + agg_method = "median" ``` An example agg_method in a Callable: @@ -964,7 +960,7 @@ def eval_func(responses, **data): An example agg_method in a dict of Callable: ```python - agg_method={'median_success': np.median, 'avg_success': np.mean} + agg_method = {"median_success": np.median, "avg_success": np.mean} ``` return_responses_and_per_instance_result (bool): Whether to also return responses @@ -1063,7 +1059,7 @@ def cost(cls, response: dict): usage = response["usage"] n_input_tokens = usage["prompt_tokens"] n_output_tokens = usage.get("completion_tokens", 0) - price1K = cls.price1K[model] + price1K = cls.price1K[model] # noqa: N806 if isinstance(price1K, tuple): return (price1K[0] * n_input_tokens + price1K[1] * n_output_tokens) / 1000 return price1K * (n_input_tokens + n_output_tokens) / 1000 diff --git a/autogen/oai/gemini.py b/autogen/oai/gemini.py index af2d017fcc..b4f626b348 100644 --- a/autogen/oai/gemini.py +++ b/autogen/oai/gemini.py @@ -6,26 +6,27 @@ # SPDX-License-Identifier: MIT """Create a OpenAI-compatible client for Gemini features. - Example: ```python - llm_config={ - "config_list": [{ - "api_type": "google", - "model": "gemini-pro", - "api_key": os.environ.get("GOOGLE_GEMINI_API_KEY"), - "safety_settings": [ + llm_config = { + "config_list": [ + { + "api_type": "google", + "model": "gemini-pro", + "api_key": os.environ.get("GOOGLE_GEMINI_API_KEY"), + "safety_settings": [ {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_ONLY_HIGH"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_ONLY_HIGH"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_ONLY_HIGH"}, - {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_ONLY_HIGH"} - ], - "top_p":0.5, - "max_tokens": 2048, - "temperature": 1.0, - "top_k": 5 + {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_ONLY_HIGH"}, + ], + "top_p": 0.5, + "max_tokens": 2048, + "temperature": 1.0, + "top_k": 5, } - ]} + ] + } agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) ``` @@ -48,31 +49,31 @@ import re import time import warnings -from collections.abc import Mapping from io import BytesIO -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any import google.generativeai as genai -import PIL import requests import vertexai +from PIL import Image from google.ai.generativelanguage import Content, FunctionCall, FunctionDeclaration, FunctionResponse, Part, Tool from google.ai.generativelanguage_v1beta.types import Schema from google.auth.credentials import Credentials +from google.generativeai.types import GenerateContentResponse from jsonschema import ValidationError from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from PIL import Image -from pydantic import BaseModel from vertexai.generative_models import ( Content as VertexAIContent, ) from vertexai.generative_models import FunctionDeclaration as vaiFunctionDeclaration +from vertexai.generative_models import ( + GenerationResponse as VertexAIGenerationResponse, +) from vertexai.generative_models import GenerativeModel from vertexai.generative_models import HarmBlockThreshold as VertexAIHarmBlockThreshold from vertexai.generative_models import HarmCategory as VertexAIHarmCategory -from vertexai.generative_models import Image as VertexAIImage from vertexai.generative_models import Part as VertexAIPart from vertexai.generative_models import SafetySetting as VertexAISafetySetting from vertexai.generative_models import ( @@ -110,9 +111,9 @@ def _initialize_vertexai(self, **params): if "location" in params: vertexai_init_args["location"] = params["location"] if "credentials" in params: - assert isinstance( - params["credentials"], Credentials - ), "Object type google.auth.credentials.Credentials is expected!" + assert isinstance(params["credentials"], Credentials), ( + "Object type google.auth.credentials.Credentials is expected!" + ) vertexai_init_args["credentials"] = params["credentials"] if vertexai_init_args: vertexai.init(**vertexai_init_args) @@ -136,7 +137,7 @@ def __init__(self, **kwargs): location (str): Compute region to be used, like 'us-west1'. This parameter is only valid in case no API key is specified. """ - self.api_key = kwargs.get("api_key", None) + self.api_key = kwargs.get("api_key") if not self.api_key: self.api_key = os.getenv("GOOGLE_GEMINI_API_KEY") if self.api_key is None: @@ -147,16 +148,15 @@ def __init__(self, **kwargs): else: self.use_vertexai = False if not self.use_vertexai: - assert ("project_id" not in kwargs) and ( - "location" not in kwargs - ), "Google Cloud project and compute location cannot be set when using an API Key!" + assert ("project_id" not in kwargs) and ("location" not in kwargs), ( + "Google Cloud project and compute location cannot be set when using an API Key!" + ) if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Gemini. It will be ignored.", UserWarning) def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -179,13 +179,12 @@ def get_usage(response) -> dict: } def create(self, params: dict) -> ChatCompletion: - if self.use_vertexai: self._initialize_vertexai(**params) else: - assert ("project_id" not in params) and ( - "location" not in params - ), "Google Cloud project and compute location cannot be set when using an API Key!" + assert ("project_id" not in params) and ("location" not in params), ( + "Google Cloud project and compute location cannot be set when using an API Key!" + ) model_name = params.get("model", "gemini-pro") if model_name == "gemini-pro-vision": @@ -203,7 +202,7 @@ def create(self, params: dict) -> ChatCompletion: messages = params.get("messages", []) stream = params.get("stream", False) n_response = params.get("n", 1) - system_instruction = params.get("system_instruction", None) + system_instruction = params.get("system_instruction") response_validation = params.get("response_validation", True) if "tools" in params: tools = self._tools_to_gemini_tools(params["tools"]) @@ -265,11 +264,22 @@ def create(self, params: dict) -> ChatCompletion: ans = "" random_id = random.randint(0, 10000) prev_function_calls = [] - for part in response.parts: + if isinstance(response, GenerateContentResponse): + parts = response.parts + elif isinstance(response, VertexAIGenerationResponse): # or hasattr(response, "candidates"): + # google.generativeai also raises an error len(candidates) != 1: + if len(response.candidates) != 1: + raise ValueError( + f"Unexpected number of candidates in the response. Expected 1, got {len(response.candidates)}" + ) + parts = response.candidates[0].content.parts + else: + raise ValueError(f"Unexpected response type: {type(response)}") + + for part in parts: # Function calls if fn_call := part.function_call: - # If we have a repeated function call, ignore it if fn_call not in prev_function_calls: autogen_tool_calls.append( @@ -332,7 +342,7 @@ def _oai_content_to_gemini_content(self, message: dict[str, Any]) -> tuple[list, """Convert AutoGen content to Gemini parts, catering for text and tool calls""" rst = [] - if message["role"] == "tool": + if "role" in message and message["role"] == "tool": # Tool call recommendation function_name = self.tool_call_function_map[message["tool_call_id"]] @@ -355,7 +365,6 @@ def _oai_content_to_gemini_content(self, message: dict[str, Any]) -> tuple[list, return rst, "tool" elif "tool_calls" in message and len(message["tool_calls"]) != 0: for tool_call in message["tool_calls"]: - function_id = tool_call["id"] function_name = tool_call["function"]["name"] self.tool_call_function_map[function_id] = function_name @@ -468,13 +477,7 @@ def _oai_messages_to_gemini_messages(self, messages: list[dict[str, Any]]) -> li if self.use_vertexai else rst.append(Content(parts=parts, role=role)) ) - elif part_type == "tool": - rst.append( - VertexAIContent(parts=parts, role="function") - if self.use_vertexai - else rst.append(Content(parts=parts, role="function")) - ) - elif part_type == "tool_call": + elif part_type == "tool" or part_type == "tool_call": rst.append( VertexAIContent(parts=parts, role="function") if self.use_vertexai @@ -527,7 +530,6 @@ def _oai_messages_to_gemini_messages(self, messages: list[dict[str, Any]]) -> li def _tools_to_gemini_tools(self, tools: list[dict[str, Any]]) -> list[Tool]: """Create Gemini tools (as typically requires Callables)""" - functions = [] for tool in tools: if self.use_vertexai: @@ -608,9 +610,9 @@ def _create_gemini_function_declaration_schema(json_data) -> Schema: return param_schema + @staticmethod def _create_gemini_function_parameters(function_parameter: dict[str, any]) -> dict[str, any]: """Convert function parameters to Gemini format, recursive""" - function_parameter["type_"] = function_parameter["type"].upper() # Parameter properties and items @@ -687,7 +689,6 @@ def get_image_data(image_file: str, use_b64=True) -> bytes: def calculate_gemini_cost(use_vertexai: bool, input_tokens: int, output_tokens: int, model_name: str) -> float: - def total_cost_mil(cost_per_mil_input: float, cost_per_mil_output: float): # Cost per million return cost_per_mil_input * input_tokens / 1e6 + cost_per_mil_output * output_tokens / 1e6 diff --git a/autogen/oai/groq.py b/autogen/oai/groq.py index 3330e99e52..59d48b7e4a 100644 --- a/autogen/oai/groq.py +++ b/autogen/oai/groq.py @@ -8,13 +8,9 @@ Example: ```python - llm_config={ - "config_list": [{ - "api_type": "groq", - "model": "mixtral-8x7b-32768", - "api_key": os.environ.get("GROQ_API_KEY") - } - ]} + llm_config = { + "config_list": [{"api_type": "groq", "model": "mixtral-8x7b-32768", "api_key": os.environ.get("GROQ_API_KEY")}] + } agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) ``` @@ -31,13 +27,12 @@ import os import time import warnings -from typing import Any, Dict, List, Optional +from typing import Any from groq import Groq, Stream from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -60,21 +55,20 @@ def __init__(self, **kwargs): api_key (str): The API key for using Groq (or environment variable GROQ_API_KEY needs to be set) """ # Ensure we have the api_key upon instantiation - self.api_key = kwargs.get("api_key", None) + self.api_key = kwargs.get("api_key") if not self.api_key: self.api_key = os.getenv("GROQ_API_KEY") - assert ( - self.api_key - ), "Please include the api_key in your config list entry for Groq or set the GROQ_API_KEY env variable." + assert self.api_key, ( + "Please include the api_key in your config list entry for Groq or set the GROQ_API_KEY env variable." + ) if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Groq API, it will be ignored.", UserWarning) - self.base_url = kwargs.get("base_url", None) + self.base_url = kwargs.get("base_url") def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -102,10 +96,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: # Check that we have what we need to use Groq's API # We won't enforce the available models as they are likely to change - groq_params["model"] = params.get("model", None) - assert groq_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Groq model to use." + groq_params["model"] = params.get("model") + assert groq_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Groq model to use." + ) # Validate allowed Groq parameters # https://console.groq.com/docs/api-reference#chat @@ -261,7 +255,6 @@ def oai_messages_to_groq_messages(messages: list[dict[str, Any]]) -> list[dict[s """Convert messages from OAI format to Groq's format. We correct for any specific role orders and types. """ - groq_messages = copy.deepcopy(messages) # Remove the name field diff --git a/autogen/oai/mistral.py b/autogen/oai/mistral.py index 54a998a390..24adbc11d5 100644 --- a/autogen/oai/mistral.py +++ b/autogen/oai/mistral.py @@ -8,13 +8,11 @@ Example: ```python - llm_config={ - "config_list": [{ - "api_type": "mistral", - "model": "open-mixtral-8x22b", - "api_key": os.environ.get("MISTRAL_API_KEY") - } - ]} + llm_config = { + "config_list": [ + {"api_type": "mistral", "model": "open-mixtral-8x22b", "api_key": os.environ.get("MISTRAL_API_KEY")} + ] + } agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) ``` @@ -27,12 +25,11 @@ NOTE: Requires mistralai package version >= 1.0.1 """ -import inspect import json import os import time import warnings -from typing import Any, Dict, List, Optional, Union +from typing import Any, Union # Mistral libraries # pip install mistralai @@ -49,7 +46,6 @@ from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -63,15 +59,14 @@ def __init__(self, **kwargs): Args: api_key (str): The API key for using Mistral.AI (or environment variable MISTRAL_API_KEY needs to be set) """ - # Ensure we have the api_key upon instantiation - self.api_key = kwargs.get("api_key", None) + self.api_key = kwargs.get("api_key") if not self.api_key: self.api_key = os.getenv("MISTRAL_API_KEY", None) - assert ( - self.api_key - ), "Please specify the 'api_key' in your config list entry for Mistral or set the MISTRAL_API_KEY env variable." + assert self.api_key, ( + "Please specify the 'api_key' in your config list entry for Mistral or set the MISTRAL_API_KEY env variable." + ) if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Mistral.AI, it will be ignored.", UserWarning) @@ -80,7 +75,6 @@ def __init__(self, **kwargs): def message_retrieval(self, response: ChatCompletion) -> Union[list[str], list[ChatCompletionMessage]]: """Retrieve the messages from the response.""" - return [choice.message for choice in response.choices] def cost(self, response) -> float: @@ -91,10 +85,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: mistral_params = {} # 1. Validate models - mistral_params["model"] = params.get("model", None) - assert mistral_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Mistral.ai model to use." + mistral_params["model"] = params.get("model") + assert mistral_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Mistral.ai model to use." + ) # 2. Validate allowed Mistral.AI parameters mistral_params["temperature"] = validate_parameter(params, "temperature", (int, float), True, 0.7, None, None) @@ -240,7 +234,6 @@ def get_usage(response: ChatCompletion) -> dict: def tool_def_to_mistral(tool_definitions: list[dict[str, Any]]) -> list[dict[str, Any]]: """Converts AutoGen tool definition to a mistral tool format""" - mistral_tools = [] for autogen_tool in tool_definitions: @@ -260,7 +253,6 @@ def tool_def_to_mistral(tool_definitions: list[dict[str, Any]]) -> list[dict[str def calculate_mistral_cost(input_tokens: int, output_tokens: int, model_name: str) -> float: """Calculate the cost of the mistral response.""" - # Prices per 1 thousand tokens # https://mistral.ai/technology/ model_cost_map = { diff --git a/autogen/oai/ollama.py b/autogen/oai/ollama.py index 57c7183b11..cb259a7dd2 100644 --- a/autogen/oai/ollama.py +++ b/autogen/oai/ollama.py @@ -8,12 +8,7 @@ Example: ```python - llm_config={ - "config_list": [{ - "api_type": "ollama", - "model": "mistral:7b-instruct-v0.3-q6_K" - } - ]} + llm_config = {"config_list": [{"api_type": "ollama", "model": "mistral:7b-instruct-v0.3-q6_K"}]} agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) ``` @@ -33,7 +28,7 @@ import re import time import warnings -from typing import Any, Dict, List, Optional, Tuple +from typing import Any import ollama from fix_busted_json import repair_json @@ -41,7 +36,6 @@ from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from pydantic import BaseModel from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -91,8 +85,7 @@ def __init__(self, **kwargs): warnings.warn("response_format is not supported for Ollama, it will be ignored.", UserWarning) def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -126,10 +119,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: # There are other, advanced, parameters such as format, system (to override system message), template, raw, etc. - not used # We won't enforce the available models - ollama_params["model"] = params.get("model", None) - assert ollama_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Ollama model to use." + ollama_params["model"] = params.get("model") + assert ollama_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Ollama model to use." + ) ollama_params["stream"] = validate_parameter(params, "stream", bool, True, False, None, None) @@ -262,7 +255,6 @@ def create(self, params: dict) -> ChatCompletion: total_tokens = prompt_tokens + completion_tokens if response is not None: - # Defaults ollama_finish = "stop" tool_calls = None @@ -277,9 +269,7 @@ def create(self, params: dict) -> ChatCompletion: # Process tools in the response if self._tools_in_conversation: - if self._native_tool_calls: - if not ollama_params["stream"]: response_content = response["message"]["content"] @@ -303,7 +293,6 @@ def create(self, params: dict) -> ChatCompletion: random_id += 1 elif not self._native_tool_calls: - # Try to convert the response to a tool call object response_toolcalls = response_to_tool_call(ans) @@ -366,7 +355,6 @@ def oai_messages_to_ollama_messages(self, messages: list[dict[str, Any]], tools: """Convert messages from OAI format to Ollama's format. We correct for any specific role orders and types, and convert tools to messages (as Ollama can't use tool messages) """ - ollama_messages = copy.deepcopy(messages) # Remove the name field @@ -470,7 +458,6 @@ def oai_messages_to_ollama_messages(self, messages: list[dict[str, Any]], tools: def response_to_tool_call(response_string: str) -> Any: """Attempts to convert the response to an object, aimed to align with function format `[{},{}]`""" - # We try and detect the list[dict] format: # Pattern 1 is [{},{}] # Pattern 2 is {} (without the [], so could be a single function call) @@ -481,7 +468,6 @@ def response_to_tool_call(response_string: str) -> Any: matches = re.findall(pattern, response_string.strip()) for match in matches: - # It has matched, extract it and load it json_str = match.strip() data_object = None @@ -530,7 +516,6 @@ def response_to_tool_call(response_string: str) -> Any: def _object_to_tool_call(data_object: Any) -> list[dict]: """Attempts to convert an object to a valid tool call object List[Dict] and returns it, if it can, otherwise None""" - # If it's a dictionary and not a list then wrap in a list if isinstance(data_object, dict): data_object = [data_object] diff --git a/autogen/oai/openai_utils.py b/autogen/oai/openai_utils.py index 77da5a6279..ed34d6fad9 100644 --- a/autogen/oai/openai_utils.py +++ b/autogen/oai/openai_utils.py @@ -12,7 +12,7 @@ import tempfile import time from pathlib import Path -from typing import Any, Dict, List, Optional, Set, Union +from typing import Any, Optional, Union from dotenv import find_dotenv, load_dotenv from openai import OpenAI @@ -141,14 +141,14 @@ def get_config_list( Example: ```python # Define a list of API keys - api_keys = ['key1', 'key2', 'key3'] + api_keys = ["key1", "key2", "key3"] # Optionally, define a list of base URLs corresponding to each API key - base_urls = ['https://api.service1.com', 'https://api.service2.com', 'https://api.service3.com'] + base_urls = ["https://api.service1.com", "https://api.service2.com", "https://api.service3.com"] # Optionally, define the API type and version if they are common for all keys - api_type = 'azure' - api_version = '2024-02-01' + api_type = "azure" + api_version = "2024-02-01" # Call the get_config_list function to get a list of configuration dictionaries config_list = get_config_list(api_keys, base_urls, api_type, api_version) @@ -309,8 +309,7 @@ def config_list_from_models( exclude: Optional[str] = None, model_list: Optional[list[str]] = None, ) -> list[dict[str, Any]]: - """ - Get a list of configs for API calls with models specified in the model list. + """Get a list of configs for API calls with models specified in the model list. This function extends `config_list_openai_aoai` by allowing to clone its' out for each of the models provided. Each configuration will have a 'model' key with the model name as its value. This is particularly useful when @@ -330,15 +329,15 @@ def config_list_from_models( Example: ```python # Define the path where the API key files are located - key_file_path = '/path/to/key/files' + key_file_path = "/path/to/key/files" # Define the file names for the OpenAI and Azure OpenAI API keys and bases - openai_api_key_file = 'key_openai.txt' - aoai_api_key_file = 'key_aoai.txt' - aoai_api_base_file = 'base_aoai.txt' + openai_api_key_file = "key_openai.txt" + aoai_api_key_file = "key_aoai.txt" + aoai_api_base_file = "base_aoai.txt" # Define the list of models for which to create configurations - model_list = ['gpt-4', 'gpt-3.5-turbo'] + model_list = ["gpt-4", "gpt-3.5-turbo"] # Call the function to get a list of configuration dictionaries config_list = config_list_from_models( @@ -346,7 +345,7 @@ def config_list_from_models( openai_api_key_file=openai_api_key_file, aoai_api_key_file=aoai_api_key_file, aoai_api_base_file=aoai_api_base_file, - model_list=model_list + model_list=model_list, ) # The `config_list` will contain configurations for the specified models, for example: @@ -416,6 +415,7 @@ def filter_config( intersection with the acceptable values. exclude (bool): If False (the default value), configs that match the filter will be included in the returned list. If True, configs that match the filter will be excluded in the returned list. + Returns: list of dict: A list of configuration dictionaries that meet all the criteria specified in `filter_dict`. @@ -424,16 +424,16 @@ def filter_config( ```python # Example configuration list with various models and API types configs = [ - {'model': 'gpt-3.5-turbo'}, - {'model': 'gpt-4'}, - {'model': 'gpt-3.5-turbo', 'api_type': 'azure'}, - {'model': 'gpt-3.5-turbo', 'tags': ['gpt35_turbo', 'gpt-35-turbo']}, + {"model": "gpt-3.5-turbo"}, + {"model": "gpt-4"}, + {"model": "gpt-3.5-turbo", "api_type": "azure"}, + {"model": "gpt-3.5-turbo", "tags": ["gpt35_turbo", "gpt-35-turbo"]}, ] # Define filter criteria to select configurations for the 'gpt-3.5-turbo' model # that are also using the 'azure' API type filter_criteria = { - 'model': ['gpt-3.5-turbo'], # Only accept configurations for 'gpt-3.5-turbo' - 'api_type': ['azure'] # Only accept configurations for 'azure' API type + "model": ["gpt-3.5-turbo"], # Only accept configurations for 'gpt-3.5-turbo' + "api_type": ["azure"], # Only accept configurations for 'azure' API type } # Apply the filter to the configuration list filtered_configs = filter_config(configs, filter_criteria) @@ -441,7 +441,7 @@ def filter_config( # [{'model': 'gpt-3.5-turbo', 'api_type': 'azure', ...}] # Define a filter to select a given tag filter_criteria = { - 'tags': ['gpt35_turbo'], + "tags": ["gpt35_turbo"], } # Apply the filter to the configuration list filtered_configs = filter_config(configs, filter_criteria) @@ -456,7 +456,6 @@ def filter_config( dictionaries that do not have that key will also be considered a match. """ - if filter_dict: return [ item @@ -481,8 +480,7 @@ def config_list_from_json( file_location: Optional[str] = "", filter_dict: Optional[dict[str, Union[list[Union[str, None]], set[Union[str, None]]]]] = None, ) -> list[dict[str, Any]]: - """ - Retrieves a list of API configurations from a JSON stored in an environment variable or a file. + """Retrieves a list of API configurations from a JSON stored in an environment variable or a file. This function attempts to parse JSON data from the given `env_or_file` parameter. If `env_or_file` is an environment variable containing JSON data, it will be used directly. Otherwise, it is assumed to be a filename, @@ -507,7 +505,7 @@ def config_list_from_json( # We can retrieve a filtered list of configurations like this: filter_criteria = {"model": ["gpt-3.5-turbo"]} - configs = config_list_from_json('CONFIG_JSON', filter_dict=filter_criteria) + configs = config_list_from_json("CONFIG_JSON", filter_dict=filter_criteria) # The 'configs' variable will now contain only the configurations that match the filter criteria. ``` @@ -548,16 +546,11 @@ def get_config( api_type: Optional[str] = None, api_version: Optional[str] = None, ) -> dict[str, Any]: - """ - Constructs a configuration dictionary for a single model with the provided API configurations. + """Constructs a configuration dictionary for a single model with the provided API configurations. Example: ```python - config = get_config( - api_key="sk-abcdef1234567890", - base_url="https://api.openai.com", - api_version="v1" - ) + config = get_config(api_key="sk-abcdef1234567890", base_url="https://api.openai.com", api_version="v1") # The 'config' variable will now contain: # { # "api_key": "sk-abcdef1234567890", @@ -590,8 +583,7 @@ def config_list_from_dotenv( model_api_key_map: Optional[dict[str, Any]] = None, filter_dict: Optional[dict[str, Union[list[Union[str, None]], set[Union[str, None]]]]] = None, ) -> list[dict[str, Union[str, set[str]]]]: - """ - Load API configurations from a specified .env file or environment variables and construct a list of configurations. + """Load API configurations from a specified .env file or environment variables and construct a list of configurations. This function will: - Load API keys from a provided .env file or from existing environment variables. @@ -689,9 +681,7 @@ def config_list_from_dotenv( def retrieve_assistants_by_name(client: OpenAI, name: str) -> list[Assistant]: - """ - Return the assistants with the given name from OAI assistant API - """ + """Return the assistants with the given name from OAI assistant API""" assistants = client.beta.assistants.list() candidate_assistants = [] for assistant in assistants.data: @@ -711,7 +701,6 @@ def detect_gpt_assistant_api_version() -> str: def create_gpt_vector_store(client: OpenAI, name: str, fild_ids: list[str]) -> Any: """Create a openai vector store for gpt assistant""" - try: vector_store = client.beta.vector_stores.create(name=name) except Exception as e: @@ -735,7 +724,6 @@ def create_gpt_assistant( client: OpenAI, name: str, instructions: str, model: str, assistant_config: dict[str, Any] ) -> Assistant: """Create a openai gpt assistant""" - assistant_create_kwargs = {} gpt_assistant_api_version = detect_gpt_assistant_api_version() tools = assistant_config.get("tools", []) @@ -784,7 +772,6 @@ def create_gpt_assistant( def update_gpt_assistant(client: OpenAI, assistant_id: str, assistant_config: dict[str, Any]) -> Assistant: """Update openai gpt assistant""" - gpt_assistant_api_version = detect_gpt_assistant_api_version() assistant_update_kwargs = {} diff --git a/autogen/oai/together.py b/autogen/oai/together.py index ac55cb81b3..f66fa948c3 100644 --- a/autogen/oai/together.py +++ b/autogen/oai/together.py @@ -8,13 +8,15 @@ Example: ```python - llm_config={ - "config_list": [{ - "api_type": "together", - "model": "mistralai/Mixtral-8x7B-Instruct-v0.1", - "api_key": os.environ.get("TOGETHER_API_KEY") + llm_config = { + "config_list": [ + { + "api_type": "together", + "model": "mistralai/Mixtral-8x7B-Instruct-v0.1", + "api_key": os.environ.get("TOGETHER_API_KEY"), } - ]} + ] + } agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) ``` @@ -27,24 +29,16 @@ from __future__ import annotations -import base64 import copy import os -import random -import re import time import warnings -from collections.abc import Mapping -from io import BytesIO -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any -import requests from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall from openai.types.chat.chat_completion import ChatCompletionMessage, Choice from openai.types.completion_usage import CompletionUsage -from PIL import Image -from pydantic import BaseModel -from together import Together, error +from together import Together from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -59,20 +53,19 @@ def __init__(self, **kwargs): api_key (str): The API key for using Together.AI (or environment variable TOGETHER_API_KEY needs to be set) """ # Ensure we have the api_key upon instantiation - self.api_key = kwargs.get("api_key", None) + self.api_key = kwargs.get("api_key") if not self.api_key: self.api_key = os.getenv("TOGETHER_API_KEY") if "response_format" in kwargs and kwargs["response_format"] is not None: warnings.warn("response_format is not supported for Together.AI, it will be ignored.", UserWarning) - assert ( - self.api_key - ), "Please include the api_key in your config list entry for Together.AI or set the TOGETHER_API_KEY env variable." + assert self.api_key, ( + "Please include the api_key in your config list entry for Together.AI or set the TOGETHER_API_KEY env variable." + ) def message_retrieval(self, response) -> list: - """ - Retrieve and return a list of strings or a list of Choice.Message from the response. + """Retrieve and return a list of strings or a list of Choice.Message from the response. NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. @@ -99,10 +92,10 @@ def parse_params(self, params: dict[str, Any]) -> dict[str, Any]: together_params = {} # Check that we have what we need to use Together.AI's API - together_params["model"] = params.get("model", None) - assert together_params[ - "model" - ], "Please specify the 'model' in your config list entry to nominate the Together.AI model to use." + together_params["model"] = params.get("model") + assert together_params["model"], ( + "Please specify the 'model' in your config list entry to nominate the Together.AI model to use." + ) # Validate allowed Together.AI parameters # https://github.com/togethercomputer/together-python/blob/94ffb30daf0ac3e078be986af7228f85f79bde99/src/together/resources/completions.py#L44 @@ -225,7 +218,6 @@ def oai_messages_to_together_messages(messages: list[dict[str, Any]]) -> list[di """Convert messages from OAI format to Together.AI format. We correct for any specific role orders and types. """ - together_messages = copy.deepcopy(messages) # If we have a message with role='tool', which occurs when a function is executed, change it to 'user' @@ -312,7 +304,6 @@ def oai_messages_to_together_messages(messages: list[dict[str, Any]]) -> list[di def calculate_together_cost(input_tokens: int, output_tokens: int, model_name: str) -> float: """Cost calculation for inference""" - if model_name in chat_lang_code_model_sizes or model_name in mixture_model_sizes: cost_per_mil = 0 @@ -320,7 +311,7 @@ def calculate_together_cost(input_tokens: int, output_tokens: int, model_name: s if model_name in chat_lang_code_model_sizes: size_in_b = chat_lang_code_model_sizes[model_name] - for top_size in chat_lang_code_model_costs.keys(): + for top_size in chat_lang_code_model_costs: if size_in_b <= top_size: cost_per_mil = chat_lang_code_model_costs[top_size] break @@ -329,7 +320,7 @@ def calculate_together_cost(input_tokens: int, output_tokens: int, model_name: s # Mixture-of-experts size_in_b = mixture_model_sizes[model_name] - for top_size in mixture_costs.keys(): + for top_size in mixture_costs: if size_in_b <= top_size: cost_per_mil = mixture_costs[top_size] break diff --git a/autogen/retrieve_utils.py b/autogen/retrieve_utils.py index 6b9df68ff3..091b862ee8 100644 --- a/autogen/retrieve_utils.py +++ b/autogen/retrieve_utils.py @@ -8,7 +8,7 @@ import hashlib import os import re -from typing import Callable, List, Tuple, Union +from typing import Callable, Union from urllib.parse import urlparse import chromadb @@ -19,7 +19,7 @@ if chromadb.__version__ < "0.4.15": from chromadb.api import API else: - from chromadb.api import ClientAPI as API + from chromadb.api import ClientAPI as API # noqa: N814 import logging import chromadb.utils.embedding_functions as ef @@ -166,7 +166,6 @@ def split_files_to_chunks( custom_text_split_function: Callable = None, ) -> tuple[list[str], list[dict]]: """Split a list of files into chunks of max_tokens.""" - chunks = [] sources = [] @@ -288,7 +287,9 @@ def _generate_file_name_from_url(url: str, max_length=255) -> str: hash = hashlib.blake2b(url_bytes).hexdigest() parsed_url = urlparse(url) file_name = os.path.basename(url) - file_name = f"{parsed_url.netloc}_{file_name}_{hash[:min(8, max_length-len(parsed_url.netloc)-len(file_name)-1)]}" + file_name = ( + f"{parsed_url.netloc}_{file_name}_{hash[: min(8, max_length - len(parsed_url.netloc) - len(file_name) - 1)]}" + ) return file_name @@ -380,7 +381,6 @@ def create_vector_db_from_dir( extra_docs (Optional, bool): whether to add more documents in the collection. Default is False Returns: - The chromadb client. """ if client is None: @@ -423,7 +423,7 @@ def create_vector_db_from_dir( end_idx = i + min(40000, len(chunks) - i) collection.upsert( documents=chunks[i:end_idx], - ids=[f"doc_{j+length}" for j in range(i, end_idx)], # unique for each doc + ids=[f"doc_{j + length}" for j in range(i, end_idx)], # unique for each doc metadatas=sources[i:end_idx], ) except ValueError as e: @@ -458,7 +458,6 @@ def query_vector_db( functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`. Returns: - The query result. The format is: ```python diff --git a/autogen/runtime_logging.py b/autogen/runtime_logging.py index 02abf2e80c..1f597cde3b 100644 --- a/autogen/runtime_logging.py +++ b/autogen/runtime_logging.py @@ -9,7 +9,7 @@ import logging import sqlite3 import uuid -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, TypeVar, Union +from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar from openai import AzureOpenAI, OpenAI from openai.types.chat import ChatCompletion @@ -42,8 +42,8 @@ def start( logger_type: Literal["sqlite", "file"] = "sqlite", config: dict[str, Any] | None = None, ) -> str: - """ - Start logging for the runtime. + """Start logging for the runtime. + Args: logger (BaseLogger): A logger instance logger_type (str): The type of logger to use (default: sqlite) diff --git a/autogen/token_count_utils.py b/autogen/token_count_utils.py index defb163674..c1c8be3112 100644 --- a/autogen/token_count_utils.py +++ b/autogen/token_count_utils.py @@ -7,7 +7,7 @@ import json import logging import re -from typing import Dict, List, Union +from typing import Union import tiktoken @@ -82,6 +82,7 @@ def token_left(input: Union[str, list, dict], model="gpt-3.5-turbo-0613") -> int def count_token(input: Union[str, list, dict], model: str = "gpt-3.5-turbo-0613") -> int: """Count number of tokens used by an OpenAI model. + Args: input: (str, list, dict): Input to the model. model: (str): Model name. @@ -232,9 +233,9 @@ def num_tokens_from_functions(functions, model="gpt-3.5-turbo-0613") -> int: if "parameters" in function: parameters = function["parameters"] if "properties" in parameters: - for propertiesKey in parameters["properties"]: - function_tokens += len(encoding.encode(propertiesKey)) - v = parameters["properties"][propertiesKey] + for properties_key in parameters["properties"]: + function_tokens += len(encoding.encode(properties_key)) + v = parameters["properties"][properties_key] for field in v: if field == "type": function_tokens += 2 diff --git a/autogen/tools/__init__.py b/autogen/tools/__init__.py index 1ff3f27766..04542b4d3b 100644 --- a/autogen/tools/__init__.py +++ b/autogen/tools/__init__.py @@ -10,8 +10,8 @@ "BaseContext", "ChatContext", "Depends", + "Tool", "get_function_schema", "load_basemodels_if_needed", "serialize_to_str", - "Tool", ] diff --git a/autogen/tools/dependency_injection.py b/autogen/tools/dependency_injection.py index f1a7129b67..9af9f42c07 100644 --- a/autogen/tools/dependency_injection.py +++ b/autogen/tools/dependency_injection.py @@ -6,40 +6,107 @@ import sys from abc import ABC from functools import wraps -from typing import Any, Callable, Iterable, get_type_hints +from typing import TYPE_CHECKING, Any, Callable, Iterable, Optional, Union, get_type_hints from fast_depends import Depends as FastDepends from fast_depends import inject from fast_depends.dependencies import model +from autogen.agentchat import Agent + +if TYPE_CHECKING: + from ..agentchat.conversable_agent import ConversableAgent + __all__ = [ "BaseContext", "ChatContext", "Depends", "Field", + "get_context_params", "inject_params", ] class BaseContext(ABC): + """Base class for context classes. + + This is the base class for defining various context types that may be used + throughout the application. It serves as a parent for specific context classes. + """ + pass class ChatContext(BaseContext): - messages: list[str] = [] + """ChatContext class that extends BaseContext. + + This class is used to represent a chat context that holds a list of messages. + It inherits from `BaseContext` and adds the `messages` attribute. + """ + + def __init__(self, agent: "ConversableAgent") -> None: + """Initializes the ChatContext with an agent. + + Args: + agent: The agent to use for retrieving chat messages. + """ + self._agent = agent + + @property + def chat_messages(self) -> dict[Agent, list[dict[Any, Any]]]: + """The messages in the chat. + + Returns: + A dictionary of agents and their messages. + """ + return self._agent.chat_messages + + @property + def last_message(self) -> Optional[dict[str, Any]]: + """The last message in the chat. + + Returns: + The last message in the chat. + """ + return self._agent.last_message() + +def Depends(x: Any) -> Any: # noqa: N802 + """Creates a dependency for injection based on the provided context or type. -def Depends(x: Any) -> Any: + Args: + x: The context or dependency to be injected. + + Returns: + A FastDepends object that will resolve the dependency for injection. + """ if isinstance(x, BaseContext): return FastDepends(lambda: x) return FastDepends(x) -def _is_base_context_param(param: inspect.Parameter) -> bool: +def get_context_params(func: Callable[..., Any], subclass: Union[type[BaseContext], type[ChatContext]]) -> list[str]: + """Gets the names of the context parameters in a function signature. + + Args: + func: The function to inspect for context parameters. + subclass: The subclass to search for. + + Returns: + A list of parameter names that are instances of the specified subclass. + """ + + sig = inspect.signature(func) + return [p.name for p in sig.parameters.values() if _is_context_param(p, subclass=subclass)] + + +def _is_context_param( + param: inspect.Parameter, subclass: Union[type[BaseContext], type[ChatContext]] = BaseContext +) -> bool: # param.annotation.__args__[0] is used to handle Annotated[MyContext, Depends(MyContext(b=2))] param_annotation = param.annotation.__args__[0] if hasattr(param.annotation, "__args__") else param.annotation - return isinstance(param_annotation, type) and issubclass(param_annotation, BaseContext) + return isinstance(param_annotation, type) and issubclass(param_annotation, subclass) def _is_depends_param(param: inspect.Parameter) -> bool: @@ -61,13 +128,24 @@ def _remove_injected_params_from_signature(func: Callable[..., Any]) -> Callable func = _fix_staticmethod(func) sig = inspect.signature(func) - params_to_remove = [p.name for p in sig.parameters.values() if _is_base_context_param(p) or _is_depends_param(p)] + params_to_remove = [p.name for p in sig.parameters.values() if _is_context_param(p) or _is_depends_param(p)] _remove_params(func, sig, params_to_remove) return func class Field: + """Represents a description field for use in type annotations. + + This class is used to store a description for an annotated field, often used for + documenting or validating fields in a context or data model. + """ + def __init__(self, description: str) -> None: + """Initializes the Field with a description. + + Args: + description: The description text for the field. + """ self._description = description @property @@ -102,6 +180,17 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: def inject_params(f: Callable[..., Any]) -> Callable[..., Any]: + """Injects parameters into a function, removing injected dependencies from its signature. + + This function is used to modify a function by injecting dependencies and removing + injected parameters from the function's signature. + + Args: + f: The function to modify with dependency injection. + + Returns: + The modified function with injected dependencies and updated signature. + """ # This is a workaround for Python 3.9+ where staticmethod.__func__ is accessible if sys.version_info >= (3, 9) and isinstance(f, staticmethod) and hasattr(f, "__func__"): f = _fix_staticmethod(f) diff --git a/autogen/tools/function_utils.py b/autogen/tools/function_utils.py index 2b7e458d2c..7d87f28eb6 100644 --- a/autogen/tools/function_utils.py +++ b/autogen/tools/function_utils.py @@ -8,7 +8,7 @@ import inspect import json from logging import getLogger -from typing import Annotated, Any, Callable, Dict, ForwardRef, List, Optional, Set, Tuple, Type, TypeVar, Union +from typing import Annotated, Any, Callable, ForwardRef, Optional, TypeVar, Union from pydantic import BaseModel, Field from typing_extensions import Literal, get_args, get_origin @@ -208,6 +208,7 @@ def get_missing_annotations(typed_signature: inspect.Signature, required: list[s """Get the missing annotations of a function Ignores the parameters with default values as they are not required to be annotated, but logs a warning. + Args: typed_signature: The signature of the function with type annotations required: The required parameters of the function @@ -236,11 +237,11 @@ def get_function_schema(f: Callable[..., Any], *, name: Optional[str] = None, de TypeError: If the function is not annotated Examples: - ```python def f(a: Annotated[str, "Parameter a"], b: int = 2, c: Annotated[float, "Parameter c"] = 0.1) -> None: pass + get_function_schema(f, description="function f") # {'type': 'function', diff --git a/autogen/tools/tool.py b/autogen/tools/tool.py index 98a3d97841..d5391f1fd4 100644 --- a/autogen/tools/tool.py +++ b/autogen/tools/tool.py @@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, Any, Callable, Optional, Union from ..tools.function_utils import get_function_schema -from .dependency_injection import inject_params +from .dependency_injection import ChatContext, get_context_params, inject_params if TYPE_CHECKING: from ..agentchat.conversable_agent import ConversableAgent @@ -15,8 +15,7 @@ class Tool: - """ - A class representing a Tool that can be used by an agent for various tasks. + """A class representing a Tool that can be used by an agent for various tasks. This class encapsulates a tool with a name, description, and an executable function. The tool can be registered with a ConversableAgent for use either with an LLM or for direct execution. @@ -45,13 +44,15 @@ def __init__( self._name: str = name or func_or_tool.name self._description: str = description or func_or_tool.description self._func: Callable[..., Any] = func_or_tool.func - elif inspect.isfunction(func_or_tool): + self._chat_context_param_names: list[str] = func_or_tool._chat_context_param_names + elif inspect.isfunction(func_or_tool) or inspect.ismethod(func_or_tool): + self._chat_context_param_names = get_context_params(func_or_tool, subclass=ChatContext) self._func = inject_params(func_or_tool) self._name = name or func_or_tool.__name__ self._description = description or func_or_tool.__doc__ or "" else: raise ValueError( - f"Parameter 'func_or_tool' must be a function or a Tool instance, it is '{type(func_or_tool)}' instead." + f"Parameter 'func_or_tool' must be a function, method or a Tool instance, it is '{type(func_or_tool)}' instead." ) @property @@ -67,8 +68,7 @@ def func(self) -> Callable[..., Any]: return self._func def register_for_llm(self, agent: "ConversableAgent") -> None: - """ - Registers the tool for use with a ConversableAgent's language model (LLM). + """Registers the tool for use with a ConversableAgent's language model (LLM). This method registers the tool so that it can be invoked by the agent during interactions with the language model. @@ -79,8 +79,7 @@ def register_for_llm(self, agent: "ConversableAgent") -> None: agent.register_for_llm()(self) def register_for_execution(self, agent: "ConversableAgent") -> None: - """ - Registers the tool for direct execution by a ConversableAgent. + """Registers the tool for direct execution by a ConversableAgent. This method registers the tool so that it can be executed by the agent, typically outside of the context of an LLM interaction. @@ -91,8 +90,7 @@ def register_for_execution(self, agent: "ConversableAgent") -> None: agent.register_for_execution()(self) def __call__(self, *args: Any, **kwargs: Any) -> Any: - """ - Execute the tool by calling its underlying function with the provided arguments. + """Execute the tool by calling its underlying function with the provided arguments. Args: *args: Positional arguments to pass to the tool diff --git a/autogen/types.py b/autogen/types.py index be865907ab..897fc58dbc 100644 --- a/autogen/types.py +++ b/autogen/types.py @@ -4,17 +4,25 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Dict, List, Literal, TypedDict, Union +from typing import Literal, TypedDict, Union MessageContentType = Union[str, list[Union[dict, str]], None] class UserMessageTextContentPart(TypedDict): + """Represents a text content part of a user message""" + type: Literal["text"] + """The type of the content part. Always "text" for text content parts.""" text: str + """The text content of the part.""" class UserMessageImageContentPart(TypedDict): + """Represents an image content part of a user message""" + type: Literal["image_url"] + """The type of the content part. Always "image_url" for image content parts.""" # Ignoring the other "detail param for now" image_url: dict[Literal["url"], str] + """The URL of the image.""" diff --git a/autogen/version.py b/autogen/version.py index 8c91541ec9..4d5df8dc5f 100644 --- a/autogen/version.py +++ b/autogen/version.py @@ -2,4 +2,4 @@ # # SPDX-License-Identifier: Apache-2.0 -__version__ = "0.6.1" +__version__ = "0.7.0" diff --git a/notebook/agentchat_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat.ipynb index e674f54397..5f12143913 100644 --- a/notebook/agentchat_RetrieveChat.ipynb +++ b/notebook/agentchat_RetrieveChat.ipynb @@ -61,7 +61,6 @@ ], "source": [ "import json\n", - "import os\n", "\n", "import chromadb\n", "\n", @@ -2336,7 +2335,7 @@ ], "source": [ "for i in range(len(questions)):\n", - " print(f\"\\n\\n>>>>>>>>>>>> Below are outputs of Case {i+1} <<<<<<<<<<<<\\n\\n\")\n", + " print(f\"\\n\\n>>>>>>>>>>>> Below are outputs of Case {i + 1} <<<<<<<<<<<<\\n\\n\")\n", "\n", " # reset the assistant. Always reset the assistant before starting a new conversation.\n", " assistant.reset()\n", @@ -2765,7 +2764,7 @@ ], "source": [ "for i in range(len(questions)):\n", - " print(f\"\\n\\n>>>>>>>>>>>> Below are outputs of Case {i+1} <<<<<<<<<<<<\\n\\n\")\n", + " print(f\"\\n\\n>>>>>>>>>>>> Below are outputs of Case {i + 1} <<<<<<<<<<<<\\n\\n\")\n", "\n", " # reset the assistant. Always reset the assistant before starting a new conversation.\n", " assistant.reset()\n", diff --git a/notebook/agentchat_RetrieveChat_mongodb.ipynb b/notebook/agentchat_RetrieveChat_mongodb.ipynb index afd290c973..eb8489028b 100644 --- a/notebook/agentchat_RetrieveChat_mongodb.ipynb +++ b/notebook/agentchat_RetrieveChat_mongodb.ipynb @@ -54,10 +54,8 @@ } ], "source": [ - "import json\n", "import os\n", "\n", - "import autogen\n", "from autogen import AssistantAgent\n", "from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n", "\n", diff --git a/notebook/agentchat_RetrieveChat_pgvector.ipynb b/notebook/agentchat_RetrieveChat_pgvector.ipynb index 31073362f1..2f79bb9d79 100644 --- a/notebook/agentchat_RetrieveChat_pgvector.ipynb +++ b/notebook/agentchat_RetrieveChat_pgvector.ipynb @@ -84,10 +84,8 @@ } ], "source": [ - "import json\n", "import os\n", "\n", - "import chromadb\n", "import psycopg\n", "from sentence_transformers import SentenceTransformer\n", "\n", diff --git a/notebook/agentchat_agentoptimizer.ipynb b/notebook/agentchat_agentoptimizer.ipynb index cf7c27c920..3bddcaf7b3 100644 --- a/notebook/agentchat_agentoptimizer.ipynb +++ b/notebook/agentchat_agentoptimizer.ipynb @@ -1,466 +1,464 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# AgentOptimizer: An Agentic Way to Train Your LLM Agent\n", - "\n", - "AutoGen offers conversable agents powered by LLM, tool, or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n", - "Please find documentation about this feature [here](https://docs.ag2.ai/docs/Use-Cases/agent_chat).\n", - "\n", - "In traditional ML pipeline, we train a model by updating its parameter according to the loss on the training set, while in the era of LLM agents, how should we train an agent? Here, we take an initial step towards the agent training. Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI, we draw an analogy between model parameters and agent functions/skills, and update agent’s functions/skills based on its historical performance on the training set. As an agentic way of training an agent, our approach help enhance the agents’ abilities without requiring access to the LLMs parameters.\n", - "\n", - "In this notebook, we introduce a new class, ‘AgentOptimizer’, which is able to improve the function list of one Assistant-UserProxy pair according to the historical conversation histories.\n", - "This feature would support agents in improving their ability to solve problems of the same type as previous tasks.\n", - "Specifically, given a set of training data, AgentOptimizer would iteratively prompt the LLM to optimize the existing function list of the AssistantAgent and UserProxyAgent with code implementation if necessary. It also includes two strategies, roll-back, and early-stop, to streamline the training process.\n", - "In the example scenario, we test the proposed AgentOptimizer in solving problems from the [MATH dataset](https://github.com/hendrycks/math). \n", - "\n", - "![AgentOptimizer](https://media.githubusercontent.com/media/ag2ai/ag2/main/website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.png)\n", - "\n", - "More information could be found in the [paper](https://arxiv.org/abs/2402.11359).\n", - "\n", - "Authors:\n", - "- [Shaokun Zhang](https://github.com/skzhang1), Ph.D. student at the The Pennsylvania State University\n", - "- [Jieyu Zhang](https://jieyuz2.github.io), Ph.D. student at the University of Washington" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "import copy\n", - "import json\n", - "import os\n", - "from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n", - "\n", - "from openai import BadRequestError\n", - "\n", - "import autogen\n", - "from autogen import config_list_from_json\n", - "from autogen.agentchat import Agent\n", - "from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer\n", - "from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent\n", - "from autogen.code_utils import extract_code\n", - "from autogen.math_utils import get_answer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MathUserProxy with function_call\n", - "\n", - "This agent is a customized MathUserProxy inherits from its [parent class](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/contrib/math_user_proxy_agent.py).\n", - "\n", - "It supports using both function_call and python to solve math problems.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "def is_termination_msg_mathchat(message):\n", - " \"\"\"Check if a message is a termination message.\"\"\"\n", - " if isinstance(message, dict):\n", - " message = message.get(\"content\")\n", - " if message is None:\n", - " return False\n", - " cb = extract_code(message)\n", - " contain_code = False\n", - " for c in cb:\n", - " if c[0] == \"python\":\n", - " contain_code = True\n", - " break\n", - " if message.rstrip().find(\"TERMINATE\") >= 0:\n", - " return True\n", - " return not contain_code and get_answer(message) is not None and get_answer(message) != \"\"\n", - "\n", - "\n", - "class MathUserProxyAgent(MathUserProxyAgent):\n", - " MAX_CONSECUTIVE_AUTO_REPLY = 15\n", - " DEFAULT_REPLY = \"Continue. Please keep solving the problem until you need to query. (If you get to the answer, put it in \\\\boxed{}.)\"\n", - " PROMPTS = \"\"\"Let's solve a math problem.\n", - "Query requirements:\n", - "You should always use the 'print' function for the output and use fractions/radical forms instead of decimals.\n", - "You can use packages like sympy to help you.\n", - "You must follow the formats below to write your code:\n", - "```python\n", - "# your code\n", - "```\n", - "If some packages are missing, you could also suggest a code to install the corresponding package.\n", - "\n", - "Please follow this process:\n", - "1. Solve the problem step by step (do not over-divide the steps).\n", - "2. Take out any queries that can be asked through Python code (for example, any calculations or equations that can be calculated) and functions you know in the context of this conversation.\n", - "\n", - "Please\n", - "(1) do not mix suggested Python codes and function calls in one step.\n", - "(2) You MUST remember that you don’t have a function named \"python\" available.\n", - "\n", - "You must follow the formats below to write your Python code:\n", - "```python\n", - "# your code\n", - "```\n", - "\n", - "3. Wait for me to give the results or wait for the executed results of the function call.\n", - "4. Continue if you think the result is correct. If the result is invalid or unexpected, please correct your query or reasoning.\n", - "\n", - "After all the queries are run and you get the answer, put the answer in \\\\boxed{}.\n", - "\n", - "Problem:\n", - "\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " name: Optional[str] = \"MathChatAgent\",\n", - " is_termination_msg: Optional[Callable[[Dict], bool]] = is_termination_msg_mathchat,\n", - " human_input_mode: Literal[\"ALWAYS\", \"NEVER\", \"TERMINATE\"] = \"NEVER\",\n", - " default_auto_reply: Optional[Union[str, Dict, None]] = DEFAULT_REPLY,\n", - " max_invalid_q_per_step=3,\n", - " **kwargs,\n", - " ):\n", - " super().__init__(\n", - " name=name,\n", - " is_termination_msg=is_termination_msg,\n", - " human_input_mode=human_input_mode,\n", - " default_auto_reply=default_auto_reply,\n", - " max_invalid_q_per_step=max_invalid_q_per_step,\n", - " **kwargs,\n", - " )\n", - " del self._reply_func_list[2]\n", - " self.register_reply([Agent, None], MathUserProxyAgent._generate_math_reply, position=4)\n", - " del self._reply_func_list[3]\n", - " self.register_reply(\n", - " trigger=autogen.ConversableAgent, reply_func=MathUserProxyAgent.generate_function_call_reply, position=3\n", - " )\n", - " self.register_reply(\n", - " trigger=autogen.ConversableAgent, reply_func=MathUserProxyAgent._check_final_result, position=0\n", - " )\n", - "\n", - " self.max_function_call_trial = 3\n", - " self.query = None\n", - " self.answer = None\n", - " self.is_correct = None\n", - "\n", - " def generate_function_call_reply(\n", - " self,\n", - " messages: Optional[List[Dict]] = None,\n", - " sender: Optional[autogen.ConversableAgent] = None,\n", - " config: Optional[Any] = None,\n", - " ) -> Tuple[bool, Union[Dict, None]]:\n", - " \"\"\"Generate a reply using function call.\"\"\"\n", - " if messages is None:\n", - " messages = self._oai_messages[sender]\n", - " message = messages[-1]\n", - " if \"function_call\" in message:\n", - " is_exec_success, func_return = self.execute_function(message[\"function_call\"])\n", - " if is_exec_success:\n", - " self.max_function_call_trial = 3\n", - " return True, func_return\n", - " else:\n", - " if self.max_function_call_trial == 0:\n", - " error_message = func_return[\"content\"]\n", - " self.max_function_call_trial = 3\n", - " return (\n", - " True,\n", - " \"The func is executed failed many times. \"\n", - " + error_message\n", - " + \". Please directly reply me with TERMINATE. We need to terminate the conversation.\",\n", - " )\n", - " else:\n", - " revise_prompt = \"You may make a wrong function call (It may due the arguments you provided doesn't fit the function arguments like missing required positional argument). \\\n", - " If you think this error occurs due to you make a wrong function arguments input and you could make it success, please try to call this function again using the correct arguments. \\\n", - " Otherwise, the error may be caused by the function itself. Please directly reply me with TERMINATE. We need to terminate the conversation. \"\n", - " error_message = func_return[\"content\"]\n", - " return True, \"The func is executed failed.\" + error_message + revise_prompt\n", - " return False, None\n", - "\n", - " def initiate_chat(\n", - " self,\n", - " recipient,\n", - " answer: None,\n", - " silent: Optional[bool] = False,\n", - " **context,\n", - " ):\n", - " self.query = context[\"problem\"]\n", - " if not isinstance(answer, str):\n", - " answer = str(answer)\n", - " if answer.endswith(\".0\"):\n", - " answer = answer[:-2]\n", - " self._answer = answer\n", - " else:\n", - " self._answer = answer\n", - "\n", - " self.is_correct = None\n", - "\n", - " self._prepare_chat(recipient, True)\n", - " error_message = None\n", - " try:\n", - " prompt = self.PROMPTS + context[\"problem\"]\n", - " self.send(prompt, recipient, silent=silent)\n", - " except BadRequestError as e:\n", - " error_message = str(e)\n", - " self.is_correct = 0\n", - " print(\"error information: {}\".format(error_message))\n", - "\n", - " recipient.reset()\n", - " is_correct = copy.deepcopy(self.is_correct)\n", - " self._reset()\n", - " return is_correct\n", - "\n", - " def _check_final_result(\n", - " self,\n", - " messages: Optional[List[Dict]] = None,\n", - " sender: Optional[autogen.Agent] = None,\n", - " config: Optional[Any] = None,\n", - " ):\n", - "\n", - " messages = messages[-1]\n", - " if isinstance(messages, dict):\n", - " messages = messages.get(\"content\")\n", - " if messages is None:\n", - " return False, None\n", - "\n", - " cb = extract_code(messages)\n", - " contain_code = False\n", - " for c in cb:\n", - " if c[0] == \"python\":\n", - " contain_code = True\n", - " break\n", - " if not contain_code and get_answer(messages) is not None and get_answer(messages) != \"\":\n", - " if get_answer(messages) == self._answer:\n", - " self.is_correct = 1\n", - " return True, \"The result is Correct. Please reply me with TERMINATE.\"\n", - " else:\n", - " self.is_correct = 0\n", - " return False, None\n", - " else:\n", - " return False, None\n", - "\n", - " def _reset(self):\n", - " super()._reset()\n", - " self.max_function_call_trial = 3\n", - " self.is_correct = None\n", - " self.query = None\n", - " self.answer = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load dataset\n", - "\n", - "MATAH dataset contains 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. \n", - "\n", - "We strictly follow the [train](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/train/algebra.jsonl)/[test](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/algebra.jsonl) splits of [Craft](https://github.com/lifan-yuan/CRAFT). Please specific your own path to the dataset. Here we sample the first 10 algebra problems as examples. " - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "test_data, train_data = [], []\n", - "with open(\"MATH/dataset/algebra.jsonl\", \"r\", encoding=\"utf-8\") as f:\n", - " for line in f:\n", - " test_data.append(json.loads(line))\n", - "with open(\"MATH/dataset/train/algebra.jsonl\", \"r\", encoding=\"utf-8\") as f:\n", - " for line in f:\n", - " train_data.append(json.loads(line))\n", - "test_data, train_data = test_data[0:10], train_data[0:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Agents construction\n", - "\n", - "Constructing MathUserProxyAgent and AssistantAgent used in solving these problems. Here, we use gpt-4-1106-preview to construct the AssistantAgent. " - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "llm_config = {\n", - " \"config_list\": [\n", - " {\n", - " \"model\": \"gpt-4-1106-preview\",\n", - " \"api_type\": \"azure\",\n", - " \"api_key\": os.environ[\"AZURE_OPENAI_API_KEY\"],\n", - " \"base_url\": \"https://ENDPOINT.openai.azure.com/\",\n", - " \"api_version\": \"2023-07-01-preview\",\n", - " }\n", - " ]\n", - "}\n", - "\n", - "assistant = autogen.AssistantAgent(\n", - " name=\"assistant\",\n", - " system_message=\"You are a helpful assistant.\",\n", - " llm_config=llm_config,\n", - ")\n", - "user_proxy = MathUserProxyAgent(\n", - " name=\"mathproxyagent\",\n", - " human_input_mode=\"NEVER\",\n", - " code_execution_config={\"work_dir\": \"_output\", \"use_docker\": False},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test without agent optimizations \n", - "\n", - "Below is the code to get the performance without the agents optimization process. \n", - "\n", - "In this case, the AssistantAgent and MathUserProxyAgent don't have any function calls but solely solve problems with Python." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum = 0\n", - "for index, query in enumerate(test_data):\n", - " is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", - " print(is_correct)\n", - " sum += is_correct\n", - "success_rate_without_agent_training = sum / 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Agent Training \n", - "\n", - "Then, we use the AgentOptimizer to iteratively optimize the agents by optimizing the function calls according to the historical conversations and performance.\n", - "The AgentOptimizer yields register_for_llm and register_for_executor at each iteration, which are subsequently utilized to update the assistant and user_proxy agents, respectively. \n", - "Here we optimize these two agents for ten epochs. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "EPOCH = 10\n", - "optimizer_model = \"gpt-4-1106-preview\"\n", - "optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config, optimizer_model=optimizer_model)\n", - "for i in range(EPOCH):\n", - " for index, query in enumerate(train_data):\n", - " is_correct = user_proxy.initiate_chat(assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", - " history = assistant.chat_messages_for_summary(user_proxy)\n", - " optimizer.record_one_conversation(history, is_satisfied=is_correct)\n", - " register_for_llm, register_for_exector = optimizer.step()\n", - " for item in register_for_llm:\n", - " assistant.update_function_signature(**item)\n", - " if len(register_for_exector.keys()) > 0:\n", - " user_proxy.register_function(function_map=register_for_exector)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test with agent optimizations \n", - "\n", - "After agent optimization, the agents obtained a list of functions from the AgentOptimizers after 10 optimization iterations as shown below.\n", - "\n", - "We then show the final performances with/without the agent optimization process. We observe the agents after optimization are obviously better.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum = 0\n", - "for index, query in enumerate(test_data):\n", - " is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", - " sum += is_correct\n", - "success_rate_with_agent_training = sum / 10" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AgentOptimizer: An Agentic Way to Train Your LLM Agent\n", + "\n", + "AutoGen offers conversable agents powered by LLM, tool, or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n", + "Please find documentation about this feature [here](https://docs.ag2.ai/docs/Use-Cases/agent_chat).\n", + "\n", + "In traditional ML pipeline, we train a model by updating its parameter according to the loss on the training set, while in the era of LLM agents, how should we train an agent? Here, we take an initial step towards the agent training. Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI, we draw an analogy between model parameters and agent functions/skills, and update agent’s functions/skills based on its historical performance on the training set. As an agentic way of training an agent, our approach help enhance the agents’ abilities without requiring access to the LLMs parameters.\n", + "\n", + "In this notebook, we introduce a new class, ‘AgentOptimizer’, which is able to improve the function list of one Assistant-UserProxy pair according to the historical conversation histories.\n", + "This feature would support agents in improving their ability to solve problems of the same type as previous tasks.\n", + "Specifically, given a set of training data, AgentOptimizer would iteratively prompt the LLM to optimize the existing function list of the AssistantAgent and UserProxyAgent with code implementation if necessary. It also includes two strategies, roll-back, and early-stop, to streamline the training process.\n", + "In the example scenario, we test the proposed AgentOptimizer in solving problems from the [MATH dataset](https://github.com/hendrycks/math). \n", + "\n", + "![AgentOptimizer](https://media.githubusercontent.com/media/ag2ai/ag2/main/website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.png)\n", + "\n", + "More information could be found in the [paper](https://arxiv.org/abs/2402.11359).\n", + "\n", + "Authors:\n", + "- [Shaokun Zhang](https://github.com/skzhang1), Ph.D. student at the The Pennsylvania State University\n", + "- [Jieyu Zhang](https://jieyuz2.github.io), Ph.D. student at the University of Washington" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "import json\n", + "import os\n", + "from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union\n", + "\n", + "from openai import BadRequestError\n", + "\n", + "import autogen\n", + "from autogen.agentchat import Agent\n", + "from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer\n", + "from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent\n", + "from autogen.code_utils import extract_code\n", + "from autogen.math_utils import get_answer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MathUserProxy with function_call\n", + "\n", + "This agent is a customized MathUserProxy inherits from its [parent class](https://github.com/ag2ai/ag2/blob/main/autogen/agentchat/contrib/math_user_proxy_agent.py).\n", + "\n", + "It supports using both function_call and python to solve math problems.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def is_termination_msg_mathchat(message):\n", + " \"\"\"Check if a message is a termination message.\"\"\"\n", + " if isinstance(message, dict):\n", + " message = message.get(\"content\")\n", + " if message is None:\n", + " return False\n", + " cb = extract_code(message)\n", + " contain_code = False\n", + " for c in cb:\n", + " if c[0] == \"python\":\n", + " contain_code = True\n", + " break\n", + " if message.rstrip().find(\"TERMINATE\") >= 0:\n", + " return True\n", + " return not contain_code and get_answer(message) is not None and get_answer(message) != \"\"\n", + "\n", + "\n", + "class MathUserProxyAgent(MathUserProxyAgent):\n", + " MAX_CONSECUTIVE_AUTO_REPLY = 15\n", + " DEFAULT_REPLY = \"Continue. Please keep solving the problem until you need to query. (If you get to the answer, put it in \\\\boxed{}.)\"\n", + " PROMPTS = \"\"\"Let's solve a math problem.\n", + "Query requirements:\n", + "You should always use the 'print' function for the output and use fractions/radical forms instead of decimals.\n", + "You can use packages like sympy to help you.\n", + "You must follow the formats below to write your code:\n", + "```python\n", + "# your code\n", + "```\n", + "If some packages are missing, you could also suggest a code to install the corresponding package.\n", + "\n", + "Please follow this process:\n", + "1. Solve the problem step by step (do not over-divide the steps).\n", + "2. Take out any queries that can be asked through Python code (for example, any calculations or equations that can be calculated) and functions you know in the context of this conversation.\n", + "\n", + "Please\n", + "(1) do not mix suggested Python codes and function calls in one step.\n", + "(2) You MUST remember that you don’t have a function named \"python\" available.\n", + "\n", + "You must follow the formats below to write your Python code:\n", + "```python\n", + "# your code\n", + "```\n", + "\n", + "3. Wait for me to give the results or wait for the executed results of the function call.\n", + "4. Continue if you think the result is correct. If the result is invalid or unexpected, please correct your query or reasoning.\n", + "\n", + "After all the queries are run and you get the answer, put the answer in \\\\boxed{}.\n", + "\n", + "Problem:\n", + "\"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " name: Optional[str] = \"MathChatAgent\",\n", + " is_termination_msg: Optional[Callable[[Dict], bool]] = is_termination_msg_mathchat,\n", + " human_input_mode: Literal[\"ALWAYS\", \"NEVER\", \"TERMINATE\"] = \"NEVER\",\n", + " default_auto_reply: Optional[Union[str, Dict, None]] = DEFAULT_REPLY,\n", + " max_invalid_q_per_step=3,\n", + " **kwargs,\n", + " ):\n", + " super().__init__(\n", + " name=name,\n", + " is_termination_msg=is_termination_msg,\n", + " human_input_mode=human_input_mode,\n", + " default_auto_reply=default_auto_reply,\n", + " max_invalid_q_per_step=max_invalid_q_per_step,\n", + " **kwargs,\n", + " )\n", + " del self._reply_func_list[2]\n", + " self.register_reply([Agent, None], MathUserProxyAgent._generate_math_reply, position=4)\n", + " del self._reply_func_list[3]\n", + " self.register_reply(\n", + " trigger=autogen.ConversableAgent, reply_func=MathUserProxyAgent.generate_function_call_reply, position=3\n", + " )\n", + " self.register_reply(\n", + " trigger=autogen.ConversableAgent, reply_func=MathUserProxyAgent._check_final_result, position=0\n", + " )\n", + "\n", + " self.max_function_call_trial = 3\n", + " self.query = None\n", + " self.answer = None\n", + " self.is_correct = None\n", + "\n", + " def generate_function_call_reply(\n", + " self,\n", + " messages: Optional[List[Dict]] = None,\n", + " sender: Optional[autogen.ConversableAgent] = None,\n", + " config: Optional[Any] = None,\n", + " ) -> Tuple[bool, Union[Dict, None]]:\n", + " \"\"\"Generate a reply using function call.\"\"\"\n", + " if messages is None:\n", + " messages = self._oai_messages[sender]\n", + " message = messages[-1]\n", + " if \"function_call\" in message:\n", + " is_exec_success, func_return = self.execute_function(message[\"function_call\"])\n", + " if is_exec_success:\n", + " self.max_function_call_trial = 3\n", + " return True, func_return\n", + " else:\n", + " if self.max_function_call_trial == 0:\n", + " error_message = func_return[\"content\"]\n", + " self.max_function_call_trial = 3\n", + " return (\n", + " True,\n", + " \"The func is executed failed many times. \"\n", + " + error_message\n", + " + \". Please directly reply me with TERMINATE. We need to terminate the conversation.\",\n", + " )\n", + " else:\n", + " revise_prompt = \"You may make a wrong function call (It may due the arguments you provided doesn't fit the function arguments like missing required positional argument). \\\n", + " If you think this error occurs due to you make a wrong function arguments input and you could make it success, please try to call this function again using the correct arguments. \\\n", + " Otherwise, the error may be caused by the function itself. Please directly reply me with TERMINATE. We need to terminate the conversation. \"\n", + " error_message = func_return[\"content\"]\n", + " return True, \"The func is executed failed.\" + error_message + revise_prompt\n", + " return False, None\n", + "\n", + " def initiate_chat(\n", + " self,\n", + " recipient,\n", + " answer: None,\n", + " silent: Optional[bool] = False,\n", + " **context,\n", + " ):\n", + " self.query = context[\"problem\"]\n", + " if not isinstance(answer, str):\n", + " answer = str(answer)\n", + " if answer.endswith(\".0\"):\n", + " answer = answer[:-2]\n", + " self._answer = answer\n", + " else:\n", + " self._answer = answer\n", + "\n", + " self.is_correct = None\n", + "\n", + " self._prepare_chat(recipient, True)\n", + " error_message = None\n", + " try:\n", + " prompt = self.PROMPTS + context[\"problem\"]\n", + " self.send(prompt, recipient, silent=silent)\n", + " except BadRequestError as e:\n", + " error_message = str(e)\n", + " self.is_correct = 0\n", + " print(\"error information: {}\".format(error_message))\n", + "\n", + " recipient.reset()\n", + " is_correct = copy.deepcopy(self.is_correct)\n", + " self._reset()\n", + " return is_correct\n", + "\n", + " def _check_final_result(\n", + " self,\n", + " messages: Optional[List[Dict]] = None,\n", + " sender: Optional[autogen.Agent] = None,\n", + " config: Optional[Any] = None,\n", + " ):\n", + " messages = messages[-1]\n", + " if isinstance(messages, dict):\n", + " messages = messages.get(\"content\")\n", + " if messages is None:\n", + " return False, None\n", + "\n", + " cb = extract_code(messages)\n", + " contain_code = False\n", + " for c in cb:\n", + " if c[0] == \"python\":\n", + " contain_code = True\n", + " break\n", + " if not contain_code and get_answer(messages) is not None and get_answer(messages) != \"\":\n", + " if get_answer(messages) == self._answer:\n", + " self.is_correct = 1\n", + " return True, \"The result is Correct. Please reply me with TERMINATE.\"\n", + " else:\n", + " self.is_correct = 0\n", + " return False, None\n", + " else:\n", + " return False, None\n", + "\n", + " def _reset(self):\n", + " super()._reset()\n", + " self.max_function_call_trial = 3\n", + " self.is_correct = None\n", + " self.query = None\n", + " self.answer = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load dataset\n", + "\n", + "MATAH dataset contains 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. \n", + "\n", + "We strictly follow the [train](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/train/algebra.jsonl)/[test](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/algebra.jsonl) splits of [Craft](https://github.com/lifan-yuan/CRAFT). Please specific your own path to the dataset. Here we sample the first 10 algebra problems as examples. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "test_data, train_data = [], []\n", + "with open(\"MATH/dataset/algebra.jsonl\", \"r\", encoding=\"utf-8\") as f:\n", + " for line in f:\n", + " test_data.append(json.loads(line))\n", + "with open(\"MATH/dataset/train/algebra.jsonl\", \"r\", encoding=\"utf-8\") as f:\n", + " for line in f:\n", + " train_data.append(json.loads(line))\n", + "test_data, train_data = test_data[0:10], train_data[0:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Agents construction\n", + "\n", + "Constructing MathUserProxyAgent and AssistantAgent used in solving these problems. Here, we use gpt-4-1106-preview to construct the AssistantAgent. " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "llm_config = {\n", + " \"config_list\": [\n", + " {\n", + " \"model\": \"gpt-4-1106-preview\",\n", + " \"api_type\": \"azure\",\n", + " \"api_key\": os.environ[\"AZURE_OPENAI_API_KEY\"],\n", + " \"base_url\": \"https://ENDPOINT.openai.azure.com/\",\n", + " \"api_version\": \"2023-07-01-preview\",\n", + " }\n", + " ]\n", + "}\n", + "\n", + "assistant = autogen.AssistantAgent(\n", + " name=\"assistant\",\n", + " system_message=\"You are a helpful assistant.\",\n", + " llm_config=llm_config,\n", + ")\n", + "user_proxy = MathUserProxyAgent(\n", + " name=\"mathproxyagent\",\n", + " human_input_mode=\"NEVER\",\n", + " code_execution_config={\"work_dir\": \"_output\", \"use_docker\": False},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test without agent optimizations \n", + "\n", + "Below is the code to get the performance without the agents optimization process. \n", + "\n", + "In this case, the AssistantAgent and MathUserProxyAgent don't have any function calls but solely solve problems with Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum = 0\n", + "for index, query in enumerate(test_data):\n", + " is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", + " print(is_correct)\n", + " sum += is_correct\n", + "success_rate_without_agent_training = sum / 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Agent Training \n", + "\n", + "Then, we use the AgentOptimizer to iteratively optimize the agents by optimizing the function calls according to the historical conversations and performance.\n", + "The AgentOptimizer yields register_for_llm and register_for_executor at each iteration, which are subsequently utilized to update the assistant and user_proxy agents, respectively. \n", + "Here we optimize these two agents for ten epochs. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EPOCH = 10\n", + "optimizer_model = \"gpt-4-1106-preview\"\n", + "optimizer = AgentOptimizer(max_actions_per_step=3, llm_config=llm_config, optimizer_model=optimizer_model)\n", + "for i in range(EPOCH):\n", + " for index, query in enumerate(train_data):\n", + " is_correct = user_proxy.initiate_chat(assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", + " history = assistant.chat_messages_for_summary(user_proxy)\n", + " optimizer.record_one_conversation(history, is_satisfied=is_correct)\n", + " register_for_llm, register_for_exector = optimizer.step()\n", + " for item in register_for_llm:\n", + " assistant.update_function_signature(**item)\n", + " if len(register_for_exector.keys()) > 0:\n", + " user_proxy.register_function(function_map=register_for_exector)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Test with agent optimizations \n", + "\n", + "After agent optimization, the agents obtained a list of functions from the AgentOptimizers after 10 optimization iterations as shown below.\n", + "\n", + "We then show the final performances with/without the agent optimization process. We observe the agents after optimization are obviously better.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sum = 0\n", + "for index, query in enumerate(test_data):\n", + " is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n", + " sum += is_correct\n", + "success_rate_with_agent_training = sum / 10" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------------------------------Functions learned------------------------------------------------\n", - "evaluate_expression: Evaluate arithmetic or mathematical expressions provided as strings.\n", - "\n", - "calculate_compound_interest_principal: Calculate the principal amount needed to achieve a certain future value with quarterly compound interest.\n", - "\n", - "solve_linear_system: Solve a system of linear equations represented as coefficients and variables.\n", - "\n", - "------------------------------------------------Summary------------------------------------------------\n", - "\n", - "success_rate_without_agent_training: 60.0%\n", - "\n", - "success_rate_with_agent_training: 90.0%\n", - "\n" - ] - } - ], - "source": [ - "print(\n", - " \"------------------------------------------------Functions learned------------------------------------------------\"\n", - ")\n", - "for func in assistant.llm_config[\"functions\"]:\n", - " print(func[\"name\"] + \": \" + func[\"description\"] + \"\\n\")\n", - "print(\"------------------------------------------------Summary------------------------------------------------\\n\")\n", - "print(\"success_rate_without_agent_training: {average}%\\n\".format(average=success_rate_without_agent_training * 100))\n", - "print(\"success_rate_with_agent_training: {average}%\\n\".format(average=success_rate_with_agent_training * 100))" - ] - } - ], - "metadata": { - "front_matter": { - "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.", - "tags": [ - "optimization", - "tool/function" - ] - }, - "kernelspec": { - "display_name": "py3.9", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------------------------------Functions learned------------------------------------------------\n", + "evaluate_expression: Evaluate arithmetic or mathematical expressions provided as strings.\n", + "\n", + "calculate_compound_interest_principal: Calculate the principal amount needed to achieve a certain future value with quarterly compound interest.\n", + "\n", + "solve_linear_system: Solve a system of linear equations represented as coefficients and variables.\n", + "\n", + "------------------------------------------------Summary------------------------------------------------\n", + "\n", + "success_rate_without_agent_training: 60.0%\n", + "\n", + "success_rate_with_agent_training: 90.0%\n", + "\n" + ] } + ], + "source": [ + "print(\n", + " \"------------------------------------------------Functions learned------------------------------------------------\"\n", + ")\n", + "for func in assistant.llm_config[\"functions\"]:\n", + " print(func[\"name\"] + \": \" + func[\"description\"] + \"\\n\")\n", + "print(\"------------------------------------------------Summary------------------------------------------------\\n\")\n", + "print(\"success_rate_without_agent_training: {average}%\\n\".format(average=success_rate_without_agent_training * 100))\n", + "print(\"success_rate_with_agent_training: {average}%\\n\".format(average=success_rate_with_agent_training * 100))" + ] + } + ], + "metadata": { + "front_matter": { + "description": "AgentOptimizer is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.", + "tags": [ + "optimization", + "tool/function" + ] + }, + "kernelspec": { + "display_name": "py3.9", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 2 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/notebook/agentchat_azr_ai_search.ipynb b/notebook/agentchat_azr_ai_search.ipynb index bffc8680e5..d0a85fc740 100644 --- a/notebook/agentchat_azr_ai_search.ipynb +++ b/notebook/agentchat_azr_ai_search.ipynb @@ -108,7 +108,6 @@ "import json\n", "import os\n", "\n", - "import requests\n", "from azure.identity import DefaultAzureCredential\n", "from azure.search.documents import SearchClient\n", "from dotenv import load_dotenv\n", @@ -359,7 +358,6 @@ ], "source": [ "if __name__ == \"__main__\":\n", - " import asyncio\n", "\n", " async def main():\n", " with Cache.disk() as cache:\n", diff --git a/notebook/agentchat_dalle_and_gpt4v.ipynb b/notebook/agentchat_dalle_and_gpt4v.ipynb index d4278c96d1..708f661860 100644 --- a/notebook/agentchat_dalle_and_gpt4v.ipynb +++ b/notebook/agentchat_dalle_and_gpt4v.ipynb @@ -28,25 +28,19 @@ "metadata": {}, "outputs": [], "source": [ - "import json\n", "import os\n", - "import pdb\n", - "import random\n", "import re\n", - "import time\n", - "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n", + "from typing import Dict, List, Optional, Union\n", "\n", - "import matplotlib.pyplot as plt\n", "import PIL\n", - "import requests\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", "from diskcache import Cache\n", "from openai import OpenAI\n", - "from PIL import Image\n", - "from termcolor import colored\n", "\n", "import autogen\n", "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n", - "from autogen.agentchat.contrib.img_utils import _to_pil, get_image_data, get_pil_image, gpt4v_formatter\n", + "from autogen.agentchat.contrib.img_utils import _to_pil, get_image_data, get_pil_image\n", "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent" ] }, @@ -117,8 +111,7 @@ "outputs": [], "source": [ "def dalle_call(client: OpenAI, model: str, prompt: str, size: str, quality: str, n: int) -> str:\n", - " \"\"\"\n", - " Generate an image using OpenAI's DALL-E model and cache the result.\n", + " \"\"\"Generate an image using OpenAI's DALL-E model and cache the result.\n", "\n", " This function takes a prompt and other parameters to generate an image using OpenAI's DALL-E model.\n", " It checks if the result is already cached; if so, it returns the cached image data. Otherwise,\n", @@ -177,8 +170,7 @@ "outputs": [], "source": [ "def extract_img(agent: Agent) -> PIL.Image:\n", - " \"\"\"\n", - " Extracts an image from the last message of an agent and converts it to a PIL image.\n", + " \"\"\"Extracts an image from the last message of an agent and converts it to a PIL image.\n", "\n", " This function searches the last message sent by the given agent for an image tag,\n", " extracts the image data, and then converts this data into a PIL (Python Imaging Library) image object.\n", @@ -379,8 +371,7 @@ "source": [ "class DalleCreator(AssistantAgent):\n", " def __init__(self, n_iters=2, **kwargs):\n", - " \"\"\"\n", - " Initializes a DalleCreator instance.\n", + " \"\"\"Initializes a DalleCreator instance.\n", "\n", " This agent facilitates the creation of visualizations through a collaborative effort among\n", " its child agents: dalle and critics.\n", @@ -621,11 +612,11 @@ ], "metadata": { "front_matter": { - "description": "Multimodal agent chat with DALL-E and GPT-4v.", - "tags": [ - "multimodal", - "gpt-4v" - ] + "description": "Multimodal agent chat with DALL-E and GPT-4v.", + "tags": [ + "multimodal", + "gpt-4v" + ] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", diff --git a/notebook/agentchat_databricks_dbrx.ipynb b/notebook/agentchat_databricks_dbrx.ipynb index 74d391e5e4..f6bd140109 100644 --- a/notebook/agentchat_databricks_dbrx.ipynb +++ b/notebook/agentchat_databricks_dbrx.ipynb @@ -507,10 +507,9 @@ "metadata": {}, "outputs": [], "source": [ - "class Databricks_AutoGenLogger:\n", + "class DatabricksAutoGenLogger:\n", " def __init__(self):\n", " from pyspark.sql import SparkSession\n", - " import autogen\n", "\n", " self.spark = SparkSession.builder.getOrCreate()\n", " self.logger_config = {\"dbname\": \"logs.db\"}\n", @@ -635,7 +634,7 @@ "user_proxy = autogen.UserProxyAgent(name=\"user\", code_execution_config=False)\n", "\n", "# Before initiating chat, start logging:\n", - "logs = Databricks_AutoGenLogger()\n", + "logs = DatabricksAutoGenLogger()\n", "logs.start()\n", "try:\n", " user_proxy.initiate_chat(assistant, message=\"What is MLflow?\", max_turns=1)\n", diff --git a/notebook/agentchat_function_call_async.ipynb b/notebook/agentchat_function_call_async.ipynb index 3abe706acb..685fb08413 100644 --- a/notebook/agentchat_function_call_async.ipynb +++ b/notebook/agentchat_function_call_async.ipynb @@ -37,6 +37,7 @@ "metadata": {}, "outputs": [], "source": [ + "import asyncio\n", "import time\n", "\n", "from typing_extensions import Annotated\n", @@ -100,7 +101,7 @@ "@coder.register_for_llm(description=\"create a timer for N seconds\")\n", "async def timer(num_seconds: Annotated[str, \"Number of seconds in the timer.\"]) -> str:\n", " for i in range(int(num_seconds)):\n", - " time.sleep(1)\n", + " asyncio.sleep(1)\n", " # should print to stdout\n", " return \"Timer is done!\"\n", "\n", @@ -197,7 +198,7 @@ ], "source": [ "with Cache.disk() as cache:\n", - " await user_proxy.a_initiate_chat( # noqa: F704\n", + " await user_proxy.a_initiate_chat(\n", " coder,\n", " message=\"Create a timer for 5 seconds and then a stopwatch for 5 seconds.\",\n", " cache=cache,\n", @@ -344,7 +345,7 @@ "\"\"\"\n", "\n", "with Cache.disk() as cache:\n", - " await user_proxy.a_initiate_chat( # noqa: F704\n", + " await user_proxy.a_initiate_chat(\n", " manager,\n", " message=message,\n", " cache=cache,\n", @@ -369,7 +370,7 @@ ] }, "kernelspec": { - "display_name": "flaml_dev", + "display_name": ".venv-3.9", "language": "python", "name": "python3" }, @@ -383,7 +384,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/notebook/agentchat_function_call_code_writing.ipynb b/notebook/agentchat_function_call_code_writing.ipynb index 2f80cc3421..c80b0e5b64 100644 --- a/notebook/agentchat_function_call_code_writing.ipynb +++ b/notebook/agentchat_function_call_code_writing.ipynb @@ -167,7 +167,7 @@ "def see_file(filename: Annotated[str, \"Name and path of file to check.\"]):\n", " with open(default_path + filename, \"r\") as file:\n", " lines = file.readlines()\n", - " formatted_lines = [f\"{i+1}:{line}\" for i, line in enumerate(lines)]\n", + " formatted_lines = [f\"{i + 1}:{line}\" for i, line in enumerate(lines)]\n", " file_contents = \"\".join(formatted_lines)\n", "\n", " return 0, file_contents\n", diff --git a/notebook/agentchat_graph_rag_neo4j.ipynb b/notebook/agentchat_graph_rag_neo4j.ipynb index 5306b70396..0da6a7d906 100644 --- a/notebook/agentchat_graph_rag_neo4j.ipynb +++ b/notebook/agentchat_graph_rag_neo4j.ipynb @@ -15,7 +15,7 @@ "\n", "```bash\n", "pip install ag2[neo4j]\n", - "```", + "```\n", ":::\n", "````" ] @@ -249,22 +249,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", "\n", "Which company is the employer?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", "\n", "The employer is BUZZ Co.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", "\n", "What policies does it have?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", "\n", "BUZZ Co. has several policies, including:\n", "\n", @@ -288,32 +288,32 @@ "These policies cover a wide range of employment aspects, from leave and separation to confidentiality and security.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", "\n", "What's Buzz's equal employment opprtunity policy?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", "\n", "The specific content of BUZZ Co.'s Equal Employment Opportunity policy is not provided in the document. However, it is mentioned that BUZZ Co. has an Equal Employment Opportunity policy, which typically would state the company's commitment to providing equal employment opportunities and non-discrimination in the workplace.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", "\n", "What does Civic Responsibility state?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", "\n", "The document does not provide any information about a Civic Responsibility policy. Therefore, I don't have details on what BUZZ Co.'s Civic Responsibility might state.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to buzz_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", "\n", "Does Donald Trump work there?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mbuzz_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", "\n", "The document does not provide any information about specific individuals, including whether Donald Trump works at BUZZ Co. Therefore, I don't have any details on the employment of Donald Trump at BUZZ Co.\n", "\n", @@ -488,22 +488,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n", "\n", "Which company is the employer?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n", "\n", "The employer is BUZZ Co.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n", "\n", "What polices does it have?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n", "\n", "BUZZ Co. has several policies outlined in its Employee Handbook, including:\n", "\n", @@ -531,22 +531,22 @@ "These policies are designed to guide employees in their conduct and responsibilities at BUZZ Co.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n", "\n", "What does Civic Responsibility state?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n", "\n", "The Civic Responsibility policy at BUZZ Co. states that the company will pay employees the difference between their salary and any amount paid by the government, unless prohibited by law, for up to a maximum of ten days of jury duty. Additionally, BUZZ Co. will pay employees the difference between their salary and any amount paid by the government or any other source for serving as an Election Day worker at the polls on official election days, not to exceed two elections in one given calendar year.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to paul_graham_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to paul_graham_agent):\n", "\n", "Which policy listed above does civic responsibility belong to?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mpaul_graham_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mpaul_graham_agent\u001b[0m (to user_proxy):\n", "\n", "The Civic Responsibility policy belongs to the \"Leave Benefits and Other Work Policies\" section of the BUZZ Co. Employee Handbook.\n", "\n", @@ -588,8 +588,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Incrementally add new documents to the existing knoweldge graph." - ] + "### Incrementally add new documents to the existing knoweledge graph." + ] }, { "cell_type": "code", @@ -644,12 +644,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n", "\n", "What is Equal Employment Opportunity Policy at BUZZ?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n", "\n", "The Equal Employment Opportunity (EEO) and Anti-Discrimination Policy at BUZZ Co. is designed to ensure full compliance with all applicable anti-discrimination laws and regulations. BUZZ Co. is an equal opportunity employer and strictly prohibits any form of discrimination or harassment. The policy ensures equal employment opportunities for all employees and applicants, regardless of race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other legally protected characteristic.\n", "\n", @@ -658,12 +658,12 @@ "BUZZ Co. enforces this policy by posting required notices, including EEO statements in job advertisements, posting job openings with state agencies, and prohibiting retaliation against individuals who report discrimination or harassment. Employees are required to report incidents of discrimination or harassment, which are promptly investigated, and appropriate corrective action is taken.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to rag_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to rag_agent):\n", "\n", "What is prohibited sexual harassment stated in the policy?\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mrag_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mrag_agent\u001b[0m (to user_proxy):\n", "\n", "Prohibited sexual harassment, as stated in BUZZ Co.'s policy, includes unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature. Such conduct is considered prohibited sexual harassment when:\n", "\n", @@ -674,12 +674,12 @@ "The policy also encompasses any unwelcome conduct based on other protected characteristics, such as race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, or protected veteran status. Such conduct becomes unlawful when continued employment is made contingent upon the employee's toleration of the offensive conduct, or when the conduct is so severe or pervasive that it creates a work environment that would be considered intimidating, hostile, or abusive by a reasonable person.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33muser_proxy\u001B[0m (to paul_graham_agent):\n", + "\u001b[33muser_proxy\u001b[0m (to paul_graham_agent):\n", "\n", "List the name of 5 other policies at Buzz.\n", "\n", "--------------------------------------------------------------------------------\n", - "\u001B[33mpaul_graham_agent\u001B[0m (to user_proxy):\n", + "\u001b[33mpaul_graham_agent\u001b[0m (to user_proxy):\n", "\n", "Certainly! Here are five other policies at BUZZ Co.:\n", "\n", diff --git a/notebook/agentchat_graph_rag_neo4j_native.ipynb b/notebook/agentchat_graph_rag_neo4j_native.ipynb new file mode 100644 index 0000000000..c38cb9554e --- /dev/null +++ b/notebook/agentchat_graph_rag_neo4j_native.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Neo4j's native GraphRAG SDK with AG2 agents for Question & Answering\n", + "\n", + "AG2 provides GraphRAG integration through agent capabilities. This is an example utilizing the integration of Neo4j's native GraphRAG SDK.\n", + "The Neo4j native query engine enables the construction of a knowledge graph from a single text or PDF file. Additionally, you can define custom entities, relationships, or schemas to guide the graph-building process. Once created, you can integrate the RAG capabilities into AG2 agents to query the knowledge graph effectively. \n", + "\n", + "````{=mdx}\n", + ":::info Requirements\n", + "To install the neo4j GraphRAG SDK with OpenAI LLM\n", + "\n", + "```bash\n", + "sudo apt-get install graphviz graphviz-dev\n", + "pip install pygraphviz\n", + "pip install \"neo4j-graphrag[openai, experimental]\"\n", + "```\n", + ":::\n", + "````\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Configuration and OpenAI API Key\n", + "\n", + "By default, in order to use OpenAI LLM with Neo4j you need to have an OpenAI key in your environment variable `OPENAI_API_KEY`.\n", + "\n", + "You can utilize an OAI_CONFIG_LIST file and extract the OpenAI API key and put it in the environment, as will be shown in the following cell.\n", + "\n", + "Alternatively, you can load the environment variable yourself.\n", + "\n", + "````{=mdx}\n", + ":::tip\n", + "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n", + ":::\n", + "````\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import autogen\n", + "\n", + "config_list = autogen.config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n", + "\n", + "# Put the OpenAI API key into the environment\n", + "os.environ[\"OPENAI_API_KEY\"] = config_list[0][\"api_key\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# This is needed to allow nested asyncio calls for Neo4j in Jupyter\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Set up LLM models\n", + "\n", + "**Important** \n", + "- **Default Models**:\n", + " - **Knowledge Graph Construction** OpenAI's `GPT-4o` with `json_object` output `temperature=0.0`.\n", + " - **Question Answering**: OpenAI's `GPT-4o` with `temperature=0.0`.\n", + " - **Embedding**: OpenAI's `text-embedding-3-large`. You need to provide its dimension for the query engine later.\n", + "\n", + "- **Customization**:\n", + " You can change these defaults by setting the following parameters on the `Neo4jNativeGraphQueryEngine`:\n", + " - `llm`: Specify a LLM instance with a llm you like for graph construction, it **must support json format response**\n", + " - `query_llm`: Specify a LLM instance with a llm you like for querying. **Don't use json format response.**\n", + " - `embedding`: Specify a Embedder instance with a embedding model.\n", + "\n", + "Learn more about configuring other LLM providers for agents [here](https://github.com/neo4j/neo4j-graphrag-python?tab=readme-ov-file#optional-dependencies).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from neo4j_graphrag.embeddings import OpenAIEmbeddings\n", + "from neo4j_graphrag.llm.openai_llm import OpenAILLM\n", + "\n", + "llm = OpenAILLM(\n", + " model_name=\"gpt-4o\",\n", + " model_params={\n", + " \"response_format\": {\"type\": \"json_object\"}, # Json format response is required for the LLM\n", + " \"temperature\": 0,\n", + " },\n", + ")\n", + "\n", + "query_llm = OpenAILLM(\n", + " model_name=\"gpt-4o\",\n", + " model_params={\"temperature\": 0}, # Don't use json format response for the query LLM\n", + ")\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Imports\n", + "from autogen import ConversableAgent, UserProxyAgent\n", + "from autogen.agentchat.contrib.graph_rag.document import Document, DocumentType\n", + "from autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine import Neo4jNativeGraphQueryEngine\n", + "from autogen.agentchat.contrib.graph_rag.neo4j_native_graph_rag_capability import Neo4jNativeGraphCapability" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Knowledge Graph with Your Own Data\n", + "\n", + "**Note:** You need to have a Neo4j database running. If you are running one in a Docker container, please ensure your Docker network is setup to allow access to it. \n", + "\n", + "In this example, the Neo4j endpoint is set to host=\"bolt://172.17.0.3\" and port=7687, please adjust accordingly. For how to spin up a Neo4j with Docker, you can refer to [this](https://docs.llamaindex.ai/en/stable/examples/property_graph/property_graph_neo4j/#:~:text=stores%2Dneo4j-,Docker%20Setup,%C2%B6,-To%20launch%20Neo4j)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A Simple Example\n", + "\n", + "In this example, the graph schema is auto-generated. Entities and relationships are created as they fit into the data\n", + "\n", + "Neo4j GraphRAG SDK supports single document of 2 input types -- txt and pdf (images will be skipped). \n", + "\n", + "We start by creating a Neo4j knowledge graph with a sample text." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# load documents\n", + "# To use text data, you need to:\n", + "# 1. Specify the type as TEXT\n", + "# 2. Pass the path to the text file\n", + "\n", + "input_path = \"../test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt\"\n", + "input_document = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First we need to use the query engine to initialize the database. It performs the follows steps:\n", + "\n", + "1. Clears the existing database.\n", + "2. Extracts graph nodes and relationships from the input data to build a knowledge graph.\n", + "3. Creates a vector index for efficient retrieval.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing all nodes and relationships in the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Database cleared successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Initializing the knowledge graph builders...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Building the knowledge graph...\n", + "INFO:neo4j_graphrag.experimental.pipeline.config.runner:PIPELINE_RUNNER: starting pipeline with run_params=defaultdict(, {'schema': {'entities': [], 'relations': [], 'potential_schema': None}, 'splitter': {'text': '**BUZZ Co. EMPLOYEE HANDBOOK**\\n\\n**EMPLOYEE RECEIPT AND ACCEPTANCE**\\n\\nI acknowledge receipt of the BUZZ Co. Employee Handbook and understand it is my responsibility to read and know its contents. This Handbook is not an employment contract. Unless I have a written employment agreement with BUZZ Co. that states otherwise, my employment is at-will. I can resign at any time, with or without notice or cause, and BUZZ Co. can terminate my employment at any time, with or without notice or cause.\\n\\nSignature: _________________________\\n\\nPrint Name: ________________________\\n\\nDate: ____________________________\\n\\n**CONFIDENTIALITY POLICY AND PLEDGE**\\n\\nAll non-public information about BUZZ Co., its members, or donors is confidential. Employees may not disclose this information to anyone outside BUZZ Co., or to other employees who do not need it to perform their duties. Disclosure of confidential information will result in disciplinary action, including possible termination.\\n\\nSignature: _________________________\\n\\nPrint Name: ________________________\\n\\nDate: ____________________________\\n\\n**TABLE OF CONTENTS**\\n\\nI. MISSION\\nII. OVERVIEW\\nIII. VOLUNTARY AT-WILL EMPLOYMENT\\nIV. EQUAL EMPLOYMENT OPPORTUNITY\\nV. POLICY AGAINST WORKPLACE HARASSMENT\\nVI. SOLICITATION\\nVII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY\\nVIII. EMPLOYMENT POLICIES AND PRACTICES\\nIX. POSITION DESCRIPTION AND SALARY ADMINISTRATION\\nX. WORK REVIEW\\nXI. ECONOMIC BENEFITS AND INSURANCE\\nXII. LEAVE BENEFITS AND OTHER WORK POLICIES\\nXIII. REIMBURSEMENT OF EXPENSES\\nXIV. SEPARATION\\nXV. RETURN OF PROPERTY\\nXVI. REVIEW OF PERSONNEL AND WORK PRACTICES\\nXVII. PERSONNEL RECORDS\\nXVIII. OUTSIDE EMPLOYMENT\\nXIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION\\nXX. COMPUTER AND INFORMATION SECURITY\\nXXI. INTERNET ACCEPTABLE USE POLICY\\n\\n**I. MISSION**\\n\\n[Insert BUZZ Co.\\'s Mission Statement Here]\\n\\n**II. OVERVIEW**\\n\\nThis Handbook provides guidelines about BUZZ Co.\\'s policies and procedures. It is not a contract and does not guarantee employment for any specific period. With the exception of the at-will employment policy, these guidelines may be changed by BUZZ Co. at any time without notice. The Board of Directors establishes personnel policies, and the Executive Director administers them.\\n\\n**III. VOLUNTARY AT-WILL EMPLOYMENT**\\n\\nUnless an employee has a written employment agreement stating otherwise, all employment at BUZZ Co. is \"at-will.\" Employees may be terminated with or without cause, and employees may leave their employment with or without cause.\\n\\n**IV. EQUAL EMPLOYMENT OPPORTUNITY**\\n\\n[Content not provided in the original document, but should be included here. State BUZZ Co.\\'s commitment to equal employment opportunity and non-discrimination.]\\n\\n**V. POLICY AGAINST WORKPLACE HARASSMENT**\\n\\n[Content not provided in the original document. State BUZZ Co.\\'s policy against all forms of workplace harassment and the procedures for reporting such incidents.]\\n\\n**VI. SOLICITATION**\\n\\nEmployees may not solicit for any unauthorized purpose during work time. Non-working employees may not solicit working employees. Non-employees may not solicit on BUZZ Co. premises. Distribution of materials requires prior approval from the Executive Director.\\n\\n**VII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY**\\n\\n**A. Hours of Work:**\\nNormal work week is five 7-hour days, typically 9:00 a.m. - 5:00 p.m., Monday-Friday, with a one-hour unpaid lunch. Work schedules may vary with Executive Director approval.\\n\\n**B. Attendance and Punctuality:**\\nRegular attendance is expected. Notify your supervisor and the office manager as soon as possible if you are absent, late, or need to leave early. For absences longer than one day, call your supervisor before each workday. Excessive absences or tardiness may result in disciplinary action, up to termination.\\n\\n**C. Overtime:**\\nNon-Exempt Employees will be paid overtime (1.5 times the regular rate) for hours worked over 40 in a work week, or double time for work on Sundays or holidays. Overtime must be pre-approved by the Executive Director.\\n\\n**VIII. EMPLOYMENT POLICIES AND PRACTICES**\\n\\n**A. Definition of Terms:**\\n\\n1. **Employer:** BUZZ Co.\\n2. **Full-Time Employee:** Works 35+ hours/week.\\n3. **Part-Time Employee:** Works 17.5 - 35 hours/week.\\n4. **Exempt Employee:** Salaried, exempt from FLSA overtime rules.\\n5. **Non-Exempt Employee:** Hourly, non-exempt from FLSA overtime rules.\\n6. **Temporary Employee:** Employed for a specific period less than six months.\\n\\n**IX. POSITION DESCRIPTION AND SALARY ADMINISTRATION**\\n\\nEach position has a written job description. Paychecks are distributed on the 15th and last day of each month. Timesheets are due within two days of each pay period.\\n\\n**X. WORK REVIEW**\\n\\nOngoing work review with supervisors. Annual performance reviews provide an opportunity to discuss the past year, set goals, and strengthen the working relationship.\\n\\n**XI. ECONOMIC BENEFITS AND INSURANCE**\\n\\n**A. Health/Life Insurance:**\\nBUZZ Co. offers health and dental insurance to eligible full-time and part-time employees after the first full month of employment.\\n\\n**B. Social Security/Medicare/Medicaid:**\\nBUZZ Co. participates in these programs.\\n\\n**C. Workers\\' Compensation and Unemployment Insurance:**\\nEmployees are covered by Workers\\' Compensation. BUZZ Co. participates in the District of Columbia unemployment program.\\n\\n**D. Retirement Plan:**\\nAvailable to eligible full-time and part-time employees (21+ years old). BUZZ Co. contributes after one year of vested employment.\\n\\n**E. Tax Deferred Annuity Plan:**\\nOffered through payroll deduction at the employee\\'s expense.\\n\\n**XII. LEAVE BENEFITS AND OTHER WORK POLICIES**\\n\\n**A. Holidays:**\\n11.5 paid holidays per year for Full-Time Employees, pro-rated for Part-Time.\\n\\n**B. Vacation:**\\nFull-time employees earn 10 days after the first year, 15 days after the third year and 20 days after the fourth year. Prorated for Part-Time employees.\\n\\n**C. Sick Leave:**\\nOne day per month for Full-Time, pro-rated for Part-Time, up to a 30-day maximum.\\n\\n**D. Personal Leave:**\\n3 days per year after six months of employment for Full-Time, pro-rated for Part-Time.\\n\\n**E. Military Leave:**\\nUnpaid leave in accordance with applicable law.\\n\\n**F. Civic Responsibility:**\\nBUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government, unless prohibited by law, up to a maximum of ten days of jury duty.\\nBUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government or any other source, unless prohibited by law for serving as an Election Day worker at the polls on official election days (not to exceed two elections in one given calendar year).\\n\\n**G. Parental Leave:**\\n24 hours of unpaid leave per year for school-related events under the DC Parental Leave Act.\\n\\n**H. Bereavement Leave:**\\n5 days for immediate family, 3 days for other close relatives.\\n\\n**I. Extended Personal Leave:**\\nUp to eight weeks unpaid leave may be granted after one year of employment.\\n\\n**J. Severe Weather Conditions:**\\nBUZZ Co. follows Federal Government office closures.\\n\\n**K. Meetings and Conferences:**\\nTime off with pay may be granted for work-related educational opportunities.\\n\\n**XIII. REIMBURSEMENT OF EXPENSES**\\n\\nReimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\\n\\n**XIV. SEPARATION**\\n\\nEmployees are encouraged to give at least 10 business days\\' written notice. Exit interviews will be scheduled. Employees who resign or are terminated will receive accrued, unused vacation benefits.\\n\\n**XV. RETURN OF PROPERTY**\\n\\nEmployees must return all BUZZ Co. property upon separation or request.\\n\\n**XVI. REVIEW OF PERSONNEL ACTION**\\n\\nEmployees may request a review of personnel actions, first with their supervisor, then with the Executive Director.\\n\\n**XVII. PERSONNEL RECORDS**\\n\\nPersonnel records are confidential. Employees must promptly report changes in personnel data. Accurate time records are required.\\n\\n**XVIII. OUTSIDE EMPLOYMENT**\\n\\nOutside employment is permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest.\\n\\n**XIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION**\\n\\nEmployees must sign a non-disclosure agreement. Unauthorized disclosure of confidential information is prohibited.\\n\\n**XX. COMPUTER AND INFORMATION SECURITY**\\n\\nBUZZ Co. computer systems are for business use, with limited personal use allowed. All data is BUZZ Co. property and may be monitored. Do not use systems for offensive or illegal activities. Follow security procedures.\\n\\n**XXI. INTERNET ACCEPTABLE USE POLICY**\\n\\nInternet access is for business use. Do not use the Internet for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\nRevised {Date}\\n\\nApproved by the Executive Committee of the BUZZ Co. Board of Directors\\n'}})\n", + "INFO:neo4j_graphrag.experimental.components.lexical_graph:Document node not created in the lexical graph because no document metadata is provided\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE RANGE INDEX __entity__id IF NOT EXISTS FOR (e:__KGBuilder__) ON (e.id)` has no effect.} {description: `RANGE INDEX __entity__id FOR (e:__KGBuilder__) ON (e.id)` already exists.} {position: None} for query: 'CREATE INDEX __entity__id IF NOT EXISTS FOR (n:__KGBuilder__) ON (n.id)'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Knowledge graph built successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:neo4j_graphrag.indexes:Creating vector index named 'vector-index-name'\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE VECTOR INDEX `vector-index-name` IF NOT EXISTS FOR (e:Chunk) ON (e.embedding) OPTIONS {indexConfig: {`vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn}}` has no effect.} {description: `VECTOR INDEX `vector-index-name` FOR (e:Chunk) ON (e.embedding)` already exists.} {position: None} for query: 'CREATE VECTOR INDEX $name IF NOT EXISTS FOR (n:Chunk) ON n.embedding OPTIONS { indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Vector index 'vector-index-name' created successfully.\n" + ] + } + ], + "source": [ + "query_engine = Neo4jNativeGraphQueryEngine(\n", + " host=\"bolt://172.17.0.3\", # Change\n", + " port=7687, # if needed\n", + " username=\"neo4j\", # Change if you reset username\n", + " password=\"password\", # Change if you reset password\n", + " llm=llm, # change to the LLM model you want to use\n", + " embeddings=embeddings, # change to the embeddings model you want to use\n", + " query_llm=query_llm, # change to the query LLM model you want to use\n", + " embedding_dimension=3072, # must match the dimension of the embeddings model\n", + ")\n", + "\n", + "# initialize the database (it will delete any pre-existing data)\n", + "query_engine.init_db(input_document)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add capability to a ConversableAgent and query them\n", + "The rag capability enables the agent to perform local search on the knowledge graph using the vector index created in the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "Who is the employer?\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "The employer is BUZZ Co.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What benefits are entitled to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. are entitled to several benefits, including:\n", + "\n", + "1. **Health/Life Insurance:** Health and dental insurance are offered to eligible full-time and part-time employees after the first full month of employment.\n", + "2. **Social Security/Medicare/Medicaid:** BUZZ Co. participates in these programs.\n", + "3. **Workers' Compensation and Unemployment Insurance:** Employees are covered by Workers' Compensation, and BUZZ Co. participates in the District of Columbia unemployment program.\n", + "4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\n", + "5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\n", + "6. **Leave Benefits:** Includes paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\n", + "7. **Reimbursement of Expenses:** Reimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What responsibilities apply to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. have several responsibilities, including:\n", + "\n", + "1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can lead to disciplinary action, including termination.\n", + "\n", + "2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action.\n", + "\n", + "3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\n", + "\n", + "4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\n", + "\n", + "5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\n", + "\n", + "6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\n", + "\n", + "7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\n", + "\n", + "8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\n", + "\n", + "9. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.\n", + "\n", + "These responsibilities ensure the smooth operation and integrity of BUZZ Co.'s work environment.\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "ChatResult(chat_id=None, chat_history=[{'content': 'Who is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What benefits are entitled to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Employees at BUZZ Co. are entitled to several benefits, including:\\n\\n1. **Health/Life Insurance:** Health and dental insurance are offered to eligible full-time and part-time employees after the first full month of employment.\\n2. **Social Security/Medicare/Medicaid:** BUZZ Co. participates in these programs.\\n3. **Workers' Compensation and Unemployment Insurance:** Employees are covered by Workers' Compensation, and BUZZ Co. participates in the District of Columbia unemployment program.\\n4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\\n5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\\n6. **Leave Benefits:** Includes paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\\n7. **Reimbursement of Expenses:** Reimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\", 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What responsibilities apply to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can lead to disciplinary action, including termination.\\n\\n2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\\n\\n6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\n9. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.\\n\\nThese responsibilities ensure the smooth operation and integrity of BUZZ Co.'s work environment.\", 'role': 'user', 'name': 'buzz_agent'}], summary=\"Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can lead to disciplinary action, including termination.\\n\\n2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\\n\\n6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\n9. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.\\n\\nThese responsibilities ensure the smooth operation and integrity of BUZZ Co.'s work environment.\", cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What benefits are entitled to employees?', 'What responsibilities apply to employees?', 'exit'])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ConversableAgent (no LLM configuration)\n", + "graph_rag_agent = ConversableAgent(\n", + " name=\"buzz_agent\",\n", + " human_input_mode=\"NEVER\",\n", + ")\n", + "\n", + "# Associate the capability with the agent\n", + "graph_rag_capability = Neo4jNativeGraphCapability(query_engine)\n", + "graph_rag_capability.add_to_agent(graph_rag_agent)\n", + "\n", + "# Create a user proxy agent to converse with our RAG agent\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"ALWAYS\",\n", + ")\n", + "\n", + "user_proxy.initiate_chat(graph_rag_agent, message=\"Who is the employer?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Revisit the example by defining custom entities, relations and schema\n", + "\n", + "By providing custom entities, relations and schema, you could guide the engine to create a graph that better extracts the structure within the data. Custom schema must use provided entities and relations.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Custom entities, relations and schema that fits the document\n", + "\n", + "entities = [\"EMPLOYEE\", \"EMPLOYER\", \"POLICY\", \"BENEFIT\", \"POSITION\", \"DEPARTMENT\", \"CONTRACT\", \"RESPONSIBILITY\"]\n", + "relations = [\n", + " \"FOLLOWS\",\n", + " \"PROVIDES\",\n", + " \"APPLIES_TO\",\n", + " \"ASSIGNED_TO\",\n", + " \"PART_OF\",\n", + " \"REQUIRES\",\n", + " \"ENTITLED_TO\",\n", + " \"REPORTS_TO\",\n", + "]\n", + "\n", + "potential_schema = [\n", + " (\"EMPLOYEE\", \"FOLLOWS\", \"POLICY\"),\n", + " (\"EMPLOYEE\", \"ASSIGNED_TO\", \"POSITION\"),\n", + " (\"EMPLOYEE\", \"REPORTS_TO\", \"DEPARTMENT\"),\n", + " (\"EMPLOYER\", \"PROVIDES\", \"BENEFIT\"),\n", + " (\"EMPLOYER\", \"REQUIRES\", \"RESPONSIBILITY\"),\n", + " (\"POLICY\", \"APPLIES_TO\", \"EMPLOYEE\"),\n", + " (\"POLICY\", \"APPLIES_TO\", \"CONTRACT\"),\n", + " (\"POLICY\", \"REQUIRES\", \"RESPONSIBILITY\"),\n", + " (\"BENEFIT\", \"ENTITLED_TO\", \"EMPLOYEE\"),\n", + " (\"POSITION\", \"PART_OF\", \"DEPARTMENT\"),\n", + " (\"POSITION\", \"ASSIGNED_TO\", \"EMPLOYEE\"),\n", + " (\"CONTRACT\", \"REQUIRES\", \"RESPONSIBILITY\"),\n", + " (\"CONTRACT\", \"APPLIES_TO\", \"EMPLOYEE\"),\n", + " (\"RESPONSIBILITY\", \"ASSIGNED_TO\", \"POSITION\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing all nodes and relationships in the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Database cleared successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Initializing the knowledge graph builders...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Building the knowledge graph...\n", + "INFO:neo4j_graphrag.experimental.pipeline.config.runner:PIPELINE_RUNNER: starting pipeline with run_params=defaultdict(, {'schema': {'entities': [SchemaEntity(label='EMPLOYEE', description='', properties=[]), SchemaEntity(label='EMPLOYER', description='', properties=[]), SchemaEntity(label='POLICY', description='', properties=[]), SchemaEntity(label='BENEFIT', description='', properties=[]), SchemaEntity(label='POSITION', description='', properties=[]), SchemaEntity(label='DEPARTMENT', description='', properties=[]), SchemaEntity(label='CONTRACT', description='', properties=[]), SchemaEntity(label='RESPONSIBILITY', description='', properties=[])], 'relations': [SchemaRelation(label='FOLLOWS', description='', properties=[]), SchemaRelation(label='PROVIDES', description='', properties=[]), SchemaRelation(label='APPLIES_TO', description='', properties=[]), SchemaRelation(label='ASSIGNED_TO', description='', properties=[]), SchemaRelation(label='PART_OF', description='', properties=[]), SchemaRelation(label='REQUIRES', description='', properties=[]), SchemaRelation(label='ENTITLED_TO', description='', properties=[]), SchemaRelation(label='REPORTS_TO', description='', properties=[])], 'potential_schema': [('EMPLOYEE', 'FOLLOWS', 'POLICY'), ('EMPLOYEE', 'ASSIGNED_TO', 'POSITION'), ('EMPLOYEE', 'REPORTS_TO', 'DEPARTMENT'), ('EMPLOYER', 'PROVIDES', 'BENEFIT'), ('EMPLOYER', 'REQUIRES', 'RESPONSIBILITY'), ('POLICY', 'APPLIES_TO', 'EMPLOYEE'), ('POLICY', 'APPLIES_TO', 'CONTRACT'), ('POLICY', 'REQUIRES', 'RESPONSIBILITY'), ('BENEFIT', 'ENTITLED_TO', 'EMPLOYEE'), ('POSITION', 'PART_OF', 'DEPARTMENT'), ('POSITION', 'ASSIGNED_TO', 'EMPLOYEE'), ('CONTRACT', 'REQUIRES', 'RESPONSIBILITY'), ('CONTRACT', 'APPLIES_TO', 'EMPLOYEE'), ('RESPONSIBILITY', 'ASSIGNED_TO', 'POSITION')]}, 'splitter': {'text': '**BUZZ Co. EMPLOYEE HANDBOOK**\\n\\n**EMPLOYEE RECEIPT AND ACCEPTANCE**\\n\\nI acknowledge receipt of the BUZZ Co. Employee Handbook and understand it is my responsibility to read and know its contents. This Handbook is not an employment contract. Unless I have a written employment agreement with BUZZ Co. that states otherwise, my employment is at-will. I can resign at any time, with or without notice or cause, and BUZZ Co. can terminate my employment at any time, with or without notice or cause.\\n\\nSignature: _________________________\\n\\nPrint Name: ________________________\\n\\nDate: ____________________________\\n\\n**CONFIDENTIALITY POLICY AND PLEDGE**\\n\\nAll non-public information about BUZZ Co., its members, or donors is confidential. Employees may not disclose this information to anyone outside BUZZ Co., or to other employees who do not need it to perform their duties. Disclosure of confidential information will result in disciplinary action, including possible termination.\\n\\nSignature: _________________________\\n\\nPrint Name: ________________________\\n\\nDate: ____________________________\\n\\n**TABLE OF CONTENTS**\\n\\nI. MISSION\\nII. OVERVIEW\\nIII. VOLUNTARY AT-WILL EMPLOYMENT\\nIV. EQUAL EMPLOYMENT OPPORTUNITY\\nV. POLICY AGAINST WORKPLACE HARASSMENT\\nVI. SOLICITATION\\nVII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY\\nVIII. EMPLOYMENT POLICIES AND PRACTICES\\nIX. POSITION DESCRIPTION AND SALARY ADMINISTRATION\\nX. WORK REVIEW\\nXI. ECONOMIC BENEFITS AND INSURANCE\\nXII. LEAVE BENEFITS AND OTHER WORK POLICIES\\nXIII. REIMBURSEMENT OF EXPENSES\\nXIV. SEPARATION\\nXV. RETURN OF PROPERTY\\nXVI. REVIEW OF PERSONNEL AND WORK PRACTICES\\nXVII. PERSONNEL RECORDS\\nXVIII. OUTSIDE EMPLOYMENT\\nXIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION\\nXX. COMPUTER AND INFORMATION SECURITY\\nXXI. INTERNET ACCEPTABLE USE POLICY\\n\\n**I. MISSION**\\n\\n[Insert BUZZ Co.\\'s Mission Statement Here]\\n\\n**II. OVERVIEW**\\n\\nThis Handbook provides guidelines about BUZZ Co.\\'s policies and procedures. It is not a contract and does not guarantee employment for any specific period. With the exception of the at-will employment policy, these guidelines may be changed by BUZZ Co. at any time without notice. The Board of Directors establishes personnel policies, and the Executive Director administers them.\\n\\n**III. VOLUNTARY AT-WILL EMPLOYMENT**\\n\\nUnless an employee has a written employment agreement stating otherwise, all employment at BUZZ Co. is \"at-will.\" Employees may be terminated with or without cause, and employees may leave their employment with or without cause.\\n\\n**IV. EQUAL EMPLOYMENT OPPORTUNITY**\\n\\n[Content not provided in the original document, but should be included here. State BUZZ Co.\\'s commitment to equal employment opportunity and non-discrimination.]\\n\\n**V. POLICY AGAINST WORKPLACE HARASSMENT**\\n\\n[Content not provided in the original document. State BUZZ Co.\\'s policy against all forms of workplace harassment and the procedures for reporting such incidents.]\\n\\n**VI. SOLICITATION**\\n\\nEmployees may not solicit for any unauthorized purpose during work time. Non-working employees may not solicit working employees. Non-employees may not solicit on BUZZ Co. premises. Distribution of materials requires prior approval from the Executive Director.\\n\\n**VII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY**\\n\\n**A. Hours of Work:**\\nNormal work week is five 7-hour days, typically 9:00 a.m. - 5:00 p.m., Monday-Friday, with a one-hour unpaid lunch. Work schedules may vary with Executive Director approval.\\n\\n**B. Attendance and Punctuality:**\\nRegular attendance is expected. Notify your supervisor and the office manager as soon as possible if you are absent, late, or need to leave early. For absences longer than one day, call your supervisor before each workday. Excessive absences or tardiness may result in disciplinary action, up to termination.\\n\\n**C. Overtime:**\\nNon-Exempt Employees will be paid overtime (1.5 times the regular rate) for hours worked over 40 in a work week, or double time for work on Sundays or holidays. Overtime must be pre-approved by the Executive Director.\\n\\n**VIII. EMPLOYMENT POLICIES AND PRACTICES**\\n\\n**A. Definition of Terms:**\\n\\n1. **Employer:** BUZZ Co.\\n2. **Full-Time Employee:** Works 35+ hours/week.\\n3. **Part-Time Employee:** Works 17.5 - 35 hours/week.\\n4. **Exempt Employee:** Salaried, exempt from FLSA overtime rules.\\n5. **Non-Exempt Employee:** Hourly, non-exempt from FLSA overtime rules.\\n6. **Temporary Employee:** Employed for a specific period less than six months.\\n\\n**IX. POSITION DESCRIPTION AND SALARY ADMINISTRATION**\\n\\nEach position has a written job description. Paychecks are distributed on the 15th and last day of each month. Timesheets are due within two days of each pay period.\\n\\n**X. WORK REVIEW**\\n\\nOngoing work review with supervisors. Annual performance reviews provide an opportunity to discuss the past year, set goals, and strengthen the working relationship.\\n\\n**XI. ECONOMIC BENEFITS AND INSURANCE**\\n\\n**A. Health/Life Insurance:**\\nBUZZ Co. offers health and dental insurance to eligible full-time and part-time employees after the first full month of employment.\\n\\n**B. Social Security/Medicare/Medicaid:**\\nBUZZ Co. participates in these programs.\\n\\n**C. Workers\\' Compensation and Unemployment Insurance:**\\nEmployees are covered by Workers\\' Compensation. BUZZ Co. participates in the District of Columbia unemployment program.\\n\\n**D. Retirement Plan:**\\nAvailable to eligible full-time and part-time employees (21+ years old). BUZZ Co. contributes after one year of vested employment.\\n\\n**E. Tax Deferred Annuity Plan:**\\nOffered through payroll deduction at the employee\\'s expense.\\n\\n**XII. LEAVE BENEFITS AND OTHER WORK POLICIES**\\n\\n**A. Holidays:**\\n11.5 paid holidays per year for Full-Time Employees, pro-rated for Part-Time.\\n\\n**B. Vacation:**\\nFull-time employees earn 10 days after the first year, 15 days after the third year and 20 days after the fourth year. Prorated for Part-Time employees.\\n\\n**C. Sick Leave:**\\nOne day per month for Full-Time, pro-rated for Part-Time, up to a 30-day maximum.\\n\\n**D. Personal Leave:**\\n3 days per year after six months of employment for Full-Time, pro-rated for Part-Time.\\n\\n**E. Military Leave:**\\nUnpaid leave in accordance with applicable law.\\n\\n**F. Civic Responsibility:**\\nBUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government, unless prohibited by law, up to a maximum of ten days of jury duty.\\nBUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government or any other source, unless prohibited by law for serving as an Election Day worker at the polls on official election days (not to exceed two elections in one given calendar year).\\n\\n**G. Parental Leave:**\\n24 hours of unpaid leave per year for school-related events under the DC Parental Leave Act.\\n\\n**H. Bereavement Leave:**\\n5 days for immediate family, 3 days for other close relatives.\\n\\n**I. Extended Personal Leave:**\\nUp to eight weeks unpaid leave may be granted after one year of employment.\\n\\n**J. Severe Weather Conditions:**\\nBUZZ Co. follows Federal Government office closures.\\n\\n**K. Meetings and Conferences:**\\nTime off with pay may be granted for work-related educational opportunities.\\n\\n**XIII. REIMBURSEMENT OF EXPENSES**\\n\\nReimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\\n\\n**XIV. SEPARATION**\\n\\nEmployees are encouraged to give at least 10 business days\\' written notice. Exit interviews will be scheduled. Employees who resign or are terminated will receive accrued, unused vacation benefits.\\n\\n**XV. RETURN OF PROPERTY**\\n\\nEmployees must return all BUZZ Co. property upon separation or request.\\n\\n**XVI. REVIEW OF PERSONNEL ACTION**\\n\\nEmployees may request a review of personnel actions, first with their supervisor, then with the Executive Director.\\n\\n**XVII. PERSONNEL RECORDS**\\n\\nPersonnel records are confidential. Employees must promptly report changes in personnel data. Accurate time records are required.\\n\\n**XVIII. OUTSIDE EMPLOYMENT**\\n\\nOutside employment is permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest.\\n\\n**XIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION**\\n\\nEmployees must sign a non-disclosure agreement. Unauthorized disclosure of confidential information is prohibited.\\n\\n**XX. COMPUTER AND INFORMATION SECURITY**\\n\\nBUZZ Co. computer systems are for business use, with limited personal use allowed. All data is BUZZ Co. property and may be monitored. Do not use systems for offensive or illegal activities. Follow security procedures.\\n\\n**XXI. INTERNET ACCEPTABLE USE POLICY**\\n\\nInternet access is for business use. Do not use the Internet for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\nRevised {Date}\\n\\nApproved by the Executive Committee of the BUZZ Co. Board of Directors\\n'}})\n", + "INFO:neo4j_graphrag.experimental.components.lexical_graph:Document node not created in the lexical graph because no document metadata is provided\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE RANGE INDEX __entity__id IF NOT EXISTS FOR (e:__KGBuilder__) ON (e.id)` has no effect.} {description: `RANGE INDEX __entity__id FOR (e:__KGBuilder__) ON (e.id)` already exists.} {position: None} for query: 'CREATE INDEX __entity__id IF NOT EXISTS FOR (n:__KGBuilder__) ON (n.id)'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Knowledge graph built successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:neo4j_graphrag.indexes:Creating vector index named 'vector-index-name'\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE VECTOR INDEX `vector-index-name` IF NOT EXISTS FOR (e:Chunk) ON (e.embedding) OPTIONS {indexConfig: {`vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn}}` has no effect.} {description: `VECTOR INDEX `vector-index-name` FOR (e:Chunk) ON (e.embedding)` already exists.} {position: None} for query: 'CREATE VECTOR INDEX $name IF NOT EXISTS FOR (n:Chunk) ON n.embedding OPTIONS { indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Vector index 'vector-index-name' created successfully.\n" + ] + } + ], + "source": [ + "query_engine = Neo4jNativeGraphQueryEngine(\n", + " host=\"bolt://172.17.0.3\", # Change\n", + " port=7687, # if needed\n", + " username=\"neo4j\", # Change if you reset username\n", + " password=\"password\", # Change if you reset password\n", + " llm=llm, # change to the LLM model you want to use\n", + " embeddings=embeddings, # change to the embeddings model you want to use\n", + " query_llm=query_llm, # change to the query LLM model you want to use\n", + " embedding_dimension=3072, # must match the dimension of the embeddings model\n", + " entities=entities,\n", + " relations=relations,\n", + " potential_schema=potential_schema,\n", + ")\n", + "\n", + "# initialize the database (it will delete any pre-existing data)\n", + "query_engine.init_db(input_document)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query the graph rag agent again\n", + "If you inspect the database, you should find more nodes are created in the graph for each chunk of data this time. \n", + "However, given the simple structure of input, the difference is not apparent in querying.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "Who is the employer?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "The employer is BUZZ Co.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What benefits are entitled to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. are entitled to several benefits, including:\n", + "\n", + "1. **Health/Life Insurance:** Health and dental insurance are offered to eligible full-time and part-time employees after the first full month of employment.\n", + "\n", + "2. **Social Security/Medicare/Medicaid:** BUZZ Co. participates in these programs.\n", + "\n", + "3. **Workers' Compensation and Unemployment Insurance:** Employees are covered by Workers' Compensation, and BUZZ Co. participates in the District of Columbia unemployment program.\n", + "\n", + "4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\n", + "\n", + "5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\n", + "\n", + "6. **Leave Benefits:** Includes paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\n", + "\n", + "7. **Reimbursement of Expenses:** Reimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What responsibilities apply to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. have several responsibilities, including:\n", + "\n", + "1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can result in disciplinary action, including termination.\n", + "\n", + "2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may lead to disciplinary action.\n", + "\n", + "3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\n", + "\n", + "4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\n", + "\n", + "5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\n", + "\n", + "6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\n", + "\n", + "7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\n", + "\n", + "8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "ChatResult(chat_id=None, chat_history=[{'content': 'Who is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What benefits are entitled to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Employees at BUZZ Co. are entitled to several benefits, including:\\n\\n1. **Health/Life Insurance:** Health and dental insurance are offered to eligible full-time and part-time employees after the first full month of employment.\\n\\n2. **Social Security/Medicare/Medicaid:** BUZZ Co. participates in these programs.\\n\\n3. **Workers' Compensation and Unemployment Insurance:** Employees are covered by Workers' Compensation, and BUZZ Co. participates in the District of Columbia unemployment program.\\n\\n4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\\n\\n5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\\n\\n6. **Leave Benefits:** Includes paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\\n\\n7. **Reimbursement of Expenses:** Reimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts.\", 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What responsibilities apply to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can result in disciplinary action, including termination.\\n\\n2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may lead to disciplinary action.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\\n\\n6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.', 'role': 'user', 'name': 'buzz_agent'}], summary='Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure can result in disciplinary action, including termination.\\n\\n2. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may lead to disciplinary action.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n5. **Outside Employment:** Employees can engage in outside employment as long as it does not interfere with their job performance at BUZZ Co. or create a conflict of interest.\\n\\n6. **Non-Disclosure Agreement:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** Employees must use BUZZ Co. computer systems primarily for business purposes, with limited personal use allowed. They must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Use:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What benefits are entitled to employees?', 'What responsibilities apply to employees?', 'exit'])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ConversableAgent (no LLM configuration)\n", + "graph_rag_agent = ConversableAgent(\n", + " name=\"buzz_agent\",\n", + " human_input_mode=\"NEVER\",\n", + ")\n", + "\n", + "# Associate the capability with the agent\n", + "graph_rag_capability = Neo4jNativeGraphCapability(query_engine)\n", + "graph_rag_capability.add_to_agent(graph_rag_agent)\n", + "\n", + "# Create a user proxy agent to converse with our RAG agent\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"ALWAYS\",\n", + ")\n", + "\n", + "user_proxy.initiate_chat(graph_rag_agent, message=\"Who is the employer?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Another example with pdf format input\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# load documents\n", + "\n", + "# To use pdf data, you need to\n", + "# 1. Specify the type as PDF\n", + "# 2. Pass the path to the PDF file\n", + "input_path = \"../test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.pdf\"\n", + "input_document = [Document(doctype=DocumentType.PDF, path_or_url=input_path)]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Clearing all nodes and relationships in the database...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Database cleared successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Initializing the knowledge graph builders...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Building the knowledge graph...\n", + "INFO:neo4j_graphrag.experimental.pipeline.config.runner:PIPELINE_RUNNER: starting pipeline with run_params=defaultdict(, {'schema': {'entities': [], 'relations': [], 'potential_schema': None}, 'pdf_loader': {'filepath': '../test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.pdf'}})\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE RANGE INDEX __entity__id IF NOT EXISTS FOR (e:__KGBuilder__) ON (e.id)` has no effect.} {description: `RANGE INDEX __entity__id FOR (e:__KGBuilder__) ON (e.id)` already exists.} {position: None} for query: 'CREATE INDEX __entity__id IF NOT EXISTS FOR (n:__KGBuilder__) ON (n.id)'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Knowledge graph built successfully.\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Creating vector index 'vector-index-name'...\n", + "INFO:neo4j_graphrag.indexes:Creating vector index named 'vector-index-name'\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE VECTOR INDEX `vector-index-name` IF NOT EXISTS FOR (e:Chunk) ON (e.embedding) OPTIONS {indexConfig: {`vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn}}` has no effect.} {description: `VECTOR INDEX `vector-index-name` FOR (e:Chunk) ON (e.embedding)` already exists.} {position: None} for query: 'CREATE VECTOR INDEX $name IF NOT EXISTS FOR (n:Chunk) ON n.embedding OPTIONS { indexConfig: { `vector.dimensions`: toInteger($dimensions), `vector.similarity_function`: $similarity_fn } }'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Vector index 'vector-index-name' created successfully.\n" + ] + } + ], + "source": [ + "query_engine = Neo4jNativeGraphQueryEngine(\n", + " host=\"bolt://172.17.0.3\", # Change\n", + " port=7687, # if needed\n", + " username=\"neo4j\", # Change if you reset username\n", + " password=\"password\", # Change if you reset password\n", + " llm=llm, # change to the LLM model you want to use\n", + " embeddings=embeddings, # change to the embeddings model you want to use\n", + " query_llm=query_llm, # change to the query LLM model you want to use\n", + " embedding_dimension=3072, # must match the dimension of the embeddings model\n", + ")\n", + "\n", + "# initialize the database (it will delete any pre-existing data)\n", + "query_engine.init_db(input_document)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "Who is the employer?\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "The employer is BUZZ Co.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What benefits are entitled to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. are entitled to several benefits, including:\n", + "\n", + "1. **Health/Life Insurance:** Offered to eligible full-time and part-time employees after the first full month of employment.\n", + "2. **Social Security/Medicare/Medicaid:** Participation in these programs.\n", + "3. **Workers' Compensation and Unemployment Insurance:** Coverage under Workers' Compensation and participation in the District of Columbia unemployment program.\n", + "4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\n", + "5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\n", + "6. **Leave Benefits:** Including paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\n", + "7. **Reimbursement of Expenses:** For reasonable and necessary business expenses, including travel, with prior approval and receipts.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to buzz_agent):\n", + "\n", + "What responsibilities apply to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mbuzz_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. have several responsibilities, including:\n", + "\n", + "1. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action, up to termination.\n", + "\n", + "2. **Overtime:** Non-exempt employees must have overtime pre-approved by the Executive Director.\n", + "\n", + "3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\n", + "\n", + "4. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure will result in disciplinary action, including possible termination.\n", + "\n", + "5. **Outside Employment:** Permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest.\n", + "\n", + "6. **Non-Disclosure of Confidential Information:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\n", + "\n", + "7. **Computer and Information Security:** BUZZ Co. computer systems are for business use, with limited personal use allowed. Employees must follow security procedures and not use systems for offensive or illegal activities.\n", + "\n", + "8. **Internet Acceptable Use Policy:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\n", + "\n", + "9. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\n", + "\n", + "10. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "ChatResult(chat_id=None, chat_history=[{'content': 'Who is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What benefits are entitled to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Employees at BUZZ Co. are entitled to several benefits, including:\\n\\n1. **Health/Life Insurance:** Offered to eligible full-time and part-time employees after the first full month of employment.\\n2. **Social Security/Medicare/Medicaid:** Participation in these programs.\\n3. **Workers' Compensation and Unemployment Insurance:** Coverage under Workers' Compensation and participation in the District of Columbia unemployment program.\\n4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\\n5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\\n6. **Leave Benefits:** Including paid holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\\n7. **Reimbursement of Expenses:** For reasonable and necessary business expenses, including travel, with prior approval and receipts.\", 'role': 'user', 'name': 'buzz_agent'}, {'content': 'What responsibilities apply to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action, up to termination.\\n\\n2. **Overtime:** Non-exempt employees must have overtime pre-approved by the Executive Director.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure will result in disciplinary action, including possible termination.\\n\\n5. **Outside Employment:** Permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest.\\n\\n6. **Non-Disclosure of Confidential Information:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** BUZZ Co. computer systems are for business use, with limited personal use allowed. Employees must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Acceptable Use Policy:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\n9. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n10. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.', 'role': 'user', 'name': 'buzz_agent'}], summary='Employees at BUZZ Co. have several responsibilities, including:\\n\\n1. **Attendance and Punctuality:** Regular attendance is expected. Employees must notify their supervisor and the office manager if they are absent, late, or need to leave early. Excessive absences or tardiness may result in disciplinary action, up to termination.\\n\\n2. **Overtime:** Non-exempt employees must have overtime pre-approved by the Executive Director.\\n\\n3. **Return of Property:** Employees must return all BUZZ Co. property upon separation or request.\\n\\n4. **Confidentiality:** Employees must not disclose non-public information about BUZZ Co., its members, or donors. Unauthorized disclosure will result in disciplinary action, including possible termination.\\n\\n5. **Outside Employment:** Permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest.\\n\\n6. **Non-Disclosure of Confidential Information:** Employees must sign a non-disclosure agreement and are prohibited from unauthorized disclosure of confidential information.\\n\\n7. **Computer and Information Security:** BUZZ Co. computer systems are for business use, with limited personal use allowed. Employees must follow security procedures and not use systems for offensive or illegal activities.\\n\\n8. **Internet Acceptable Use Policy:** Internet access is for business use, and employees must not use it for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage.\\n\\n9. **Personnel Records:** Employees must promptly report changes in personnel data and maintain accurate time records.\\n\\n10. **Reimbursement of Expenses:** Employees must obtain prior approval and provide receipts for reimbursement of reasonable and necessary business expenses, including travel.', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What benefits are entitled to employees?', 'What responsibilities apply to employees?', 'exit'])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ConversableAgent (no LLM configuration)\n", + "graph_rag_agent = ConversableAgent(\n", + " name=\"buzz_agent\",\n", + " human_input_mode=\"NEVER\",\n", + ")\n", + "\n", + "# Associate the capability with the agent\n", + "graph_rag_capability = Neo4jNativeGraphCapability(query_engine)\n", + "graph_rag_capability.add_to_agent(graph_rag_agent)\n", + "\n", + "# Create a user proxy agent to converse with our RAG agent\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"ALWAYS\",\n", + ")\n", + "\n", + "user_proxy.initiate_chat(graph_rag_agent, message=\"Who is the employer?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Incrementally add new documents to the existing knowledge graph.\n", + "We add another document and build it into the existing graph" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Building the knowledge graph...\n", + "INFO:neo4j_graphrag.experimental.pipeline.config.runner:PIPELINE_RUNNER: starting pipeline with run_params=defaultdict(, {'schema': {'entities': [], 'relations': [], 'potential_schema': None}, 'splitter': {'text': 'Signed in\\nSkip to Main Content\\n Rotten Tomatoes\\n\\nWhat\\'s the Tomatometer®?\\nCritics\\nMOVIES TV SHOWS SHOP NEW NEWS SHOWTIMES\\nTRENDING ON RT\\nFuriosa First Reviews\\nMost Anticipated 2025 Movies\\nBest Movies of All Time\\nTV Premiere Dates\\nMain image for The Matrix\\nThe Matrix\\nR, Now Playing, 2h 16m, Sci-Fi/ Action\\n\\n LIST\\nVideos\\nRotten Tomatoes is Wrong About... The Matrix Sequels\\n51:31\\nRotten Tomatoes is Wrong About... The Matrix Sequels\\nContent collapsed.\\n‘The Matrix’ Government Lobby Scene | Rotten Tomatoes’ 21 Most Memorable Moments\\n2:31\\n‘The Matrix’ Government Lobby Scene | Rotten Tomatoes’ 21 Most Memorable Moments\\nContent collapsed.\\nView more videos\\nmovie poster\\nTomatometer\\n207 Reviews\\nAudience Score\\n250,000+ Ratings\\nNeo (Keanu Reeves) believes that Morpheus (Laurence Fishburne), an elusive figure considered to be the most dangerous man alive, can answer his question -- What is the Matrix? Neo is contacted by Trinity (Carrie-Anne Moss), a beautiful stranger who leads him into an underworld where he meets Morpheus. They fight a brutal battle for their lives against a cadre of viciously intelligent secret agents. It is a truth that could cost Neo something more precious than his life.\\nContent collapsed.\\nRead More\\nfandango\\nNow in Theaters\\nNow Playing\\nBuy tickets for The Matrix\\nWhere to Watch\\nWhat to Know\\nReviews\\nCast & Crew\\nMore Like This\\nRelated News\\nVideos\\nPhotos\\nMedia Info\\n\\nNext\\nWhere To Watch\\nThe Matrix\\nfandango\\nIn Theaters\\nvudu\\nFandango at Home\\namazon-prime-video-us\\nPrime Video\\nnetflix\\nNetflix\\nWatch The Matrix with a subscription on Netflix, rent on Fandango at Home, Prime Video, or buy on Fandango at Home, Prime Video.\\nThe Matrix\\nWhat To Know\\n Critics Consensus\\nThanks to the Wachowskis\\' imaginative vision, The Matrix is a smartly crafted combination of spectacular action and groundbreaking special effects.\\n\\nRead Critics Reviews\\nCritics Reviews\\nView All (207) Critics Reviews\\n Critic\\'s profile\\nCritic\\'s Name\\nMike Clark\\nPublication\\nUSA Today\\nTOP CRITIC\\nThe review\\nThough The Matrix ultimately overdoses on gloom-and-doom grunge, over-elaborate cool and especially running time, it\\'s just clever enough to enable [the Wachowskis] to nix the sophomore jinx following 1996\\'s Bound.\\nContent collapsed.\\nFresh score.\\nRated: 2.5/4 •\\nDate\\nJul 13, 2023\\nFull Review\\n Critic\\'s profile\\nCritic\\'s Name\\nJoe Morgenstern\\nPublication\\nWall Street Journal\\nTOP CRITIC\\nThe review\\nI know almost nothing of the intricate techniques that make The Matrix as eye-filling as it is brain-numbing, but I can tell you that... [its visuals] are wondrous to behold.\\nContent collapsed.\\nFresh score.\\nDate\\nJul 13, 2023\\nFull Review\\n Critic\\'s profile\\nCritic\\'s Name\\nManohla Dargis\\nPublication\\nL.A. Weekly\\nTOP CRITIC\\nThe review\\nThere\\'s more form than content in The Matrix, but Bill Pope\\'s swooping, noir-inflected cinematography is wonderfully complemented by Owen Paterson\\'s inventive production design, a great soundtrack and the best fight choreography this side of Hong Kong.\\nContent collapsed.\\nFresh score.\\nDate\\nJul 13, 2023\\nFull Review\\n Critic\\'s profile\\nCritic\\'s Name\\nSean Axmaker\\nPublication\\nStream on Demand\\nThe review\\nThe Wachowskis proceed to turn the world as we know it into a virtual reality landscape with a 20th century tech-noir look (lit with a sickly green hue, like the glow of an old IBM computer screen) and the physics of a video game.\\nContent collapsed.\\nFresh score.\\nDate\\nSep 9, 2023\\nFull Review\\n Critic\\'s profile\\nCritic\\'s Name\\nDan DiNicola\\nPublication\\nThe Daily Gazette (Schenectady, NY)\\nThe review\\nA $60 million, high-tech assault on the senses and a pretentious insult to the mind.\\nContent collapsed.\\nRotten score.\\nDate\\nJul 15, 2023\\nFull Review\\n Critic\\'s profile\\nCritic\\'s Name\\nChristopher Brandon\\nPublication\\nTNT\\'s Rough Cut\\nThe review\\nA cyberpunk thriller that\\'s so much fun to watch, it could single-handedly reboot the genre.\\nContent collapsed.\\nFresh score.\\nDate\\nJul 13, 2023\\nFull Review\\nRead all reviews\\n\\nNext\\nAudience Reviews\\nView All (1000+) audience reviews\\nCritic\\'s Name\\ncasearmitage\\nThe review\\nTwenty five years later, it still holds up. You have to see it to believe it.\\nContent collapsed.\\nRated 5/5 Stars • Rated 5 out of 5 stars\\nDate\\n04/04/24\\nFull Review\\nCritic\\'s Name\\nNathanael\\nThe review\\nOf course The Matrix is absolutely amazing, but the 4DX thing was pretty cool too!\\nContent collapsed.\\nRated 5/5 Stars • Rated 5 out of 5 stars\\nDate\\n12/14/21\\nFull Review\\nCritic\\'s Name\\nAverage P\\nThe review\\nSo good that you entirely forget the nutty premise that we\\'re all being kept in suspended animation wired into some kind of giant mainframe which creates more energy than it requires. Oh well, whatever. Great adventure story.\\nContent collapsed.\\nRated 5/5 Stars • Rated 5 out of 5 stars\\nDate\\n05/23/24\\nFull Review\\nCritic\\'s Name\\nFirst name L\\nThe review\\nEvent cinema in at it\\'s best, it\\'s often mocked, equally celebrated, for a reason.\\nContent collapsed.\\nRated 4/5 Stars • Rated 4 out of 5 stars\\nDate\\n05/14/24\\nFull Review\\nCritic\\'s Name\\nJace E\\nThe review\\nThe Matrix is one of the most ambitious and captivating Sci-Fi films of all time. The action is electric, the characters are strong and there are legendary moments peppered throughout. Where the film is lacking is in the actual human aspect; the romance is underdeveloped and Neo\\'s life in the Matrix is superficial (why do we need a scene of his boss disciplining him?). That being said, the fight sequences are incredibly entertaining and the entire idea of the Matrix plays with your mind. I honestly fell asleep multiple times watching the film and had to go back and watch it again. From the slow motion to the muted colors, it was easy to doze off. However, once the climactic moments hit, I was enthralled. The helicopter scene is great and the final fight sequences hit hard. I\\'m curious where the franchise will go from here, but this film is a brilliant start. Best Character: Tank Best Quote: \"There\\'s a difference between knowing the path and walking the path.\" - Morpheus Best Scene: The Bullet Dodge Best Piece of Score: \"He\\'s the One Alright\"\\nContent collapsed.\\nRated 4/5 Stars • Rated 4 out of 5 stars\\nDate\\n05/13/24\\nFull Review\\nCritic\\'s Name\\nAntonio M\\nThe review\\nHa fatto la storia del cinema cyberpunk. Un cult intramontabile.\\nContent collapsed.\\nRated 5/5 Stars • Rated 5 out of 5 stars\\nDate\\n05/09/24\\nFull Review\\nRead all reviews\\n\\nNext\\nCast & Crew\\nLilly Wachowski thumbnail image\\nLilly Wachowski\\nDirector\\n Lana Wachowski thumbnail image\\nLana Wachowski\\nDirector\\n Keanu Reeves thumbnail image\\nKeanu Reeves\\nThomas A. Anderson\\n Laurence Fishburne thumbnail image\\nLaurence Fishburne\\nMorpheus\\n Carrie-Anne Moss thumbnail image\\nCarrie-Anne Moss\\nTrinity\\n Hugo Weaving thumbnail image\\nHugo Weaving\\nAgent Smith\\nContent collapsed.\\nShow More Cast & Crew\\nMore Like This\\nView All Movies in Theaters\\n The Matrix Reloaded poster\\nCertified fresh score.\\n74%\\nFresh audience score.\\n72%\\nThe Matrix Reloaded\\n\\n WATCHLIST\\nThe Matrix Revolutions poster\\nRotten score.\\n34%\\nFresh audience score.\\n60%\\nThe Matrix Revolutions\\n\\n WATCHLIST\\nDemolition Man poster\\nFresh score.\\n63%\\nFresh audience score.\\n67%\\nDemolition Man\\n\\n WATCHLIST\\nUniversal Soldier: The Return poster\\nRotten score.\\n5%\\nRotten audience score.\\n24%\\nUniversal Soldier: The Return\\n\\n WATCHLIST\\nTerminator 3: Rise of the Machines poster\\nFresh score.\\n70%\\nRotten audience score.\\n46%\\nTerminator 3: Rise of the Machines\\n\\n WATCHLIST\\n\\nDiscover more movies and TV shows.\\nView More\\n\\nNext\\nRelated Movie News\\nView All Related Movie News\\n\\n25 Memorable Movie Lines of the Last 25 Years\\nContent collapsed.\\n\\nSterling K. Brown’s Five Favorite Films\\nContent collapsed.\\n\\nThe Matrix vs. John Wick\\nContent collapsed.\\n\\nNext\\nVideos\\nView All videos\\nThe Matrix\\nRotten Tomatoes is Wrong About... The Matrix Sequels\\n51:31\\nRotten Tomatoes is Wrong About... The Matrix Sequels\\nContent collapsed.\\n‘The Matrix’ Government Lobby Scene | Rotten Tomatoes’ 21 Most Memorable Moments\\n2:31\\n‘The Matrix’ Government Lobby Scene | Rotten Tomatoes’ 21 Most Memorable Moments\\nContent collapsed.\\nView more videos\\n\\nNext\\nPhotos\\nView All The Matrix photos\\nThe Matrix\\n\\nThe Matrix photo 1\\n\\nThe Matrix photo 2\\n\\nThe Matrix photo 3\\n\\nThe Matrix photo 4\\n\\nAnthony Ray Parker as Dozer, Keanu Reeves as Thomas A. Anderson/Neo, Carrie-Anne Moss as Trinity, and Laurence Fishburne as Morpheus in Nebuchadnezzer\\'s cockpit.\\n\\nKeanu Reeves and Carrie-Anne Moss in Warner Brothers\\' The Matrix.\\n\\nLaurence Fishburne in Warner Brothers\\' The Matrix\\n\\nCarrie-Anne Moss and Keanu Reeves in Warner Brothers\\' The Matrix\\n\\nCarrie-Anne Moss in Warner Brothers\\' The Matrix\\n\\nKeanu Reeves in Warner Brothers\\' The Matrix\\nView more photos\\n\\nNext\\nMovie Info\\nSynopsis\\nNeo (Keanu Reeves) believes that Morpheus (Laurence Fishburne), an elusive figure considered to be the most dangerous man alive, can answer his question -- What is the Matrix? Neo is contacted by Trinity (Carrie-Anne Moss), a beautiful stranger who leads him into an underworld where he meets Morpheus. They fight a brutal battle for their lives against a cadre of viciously intelligent secret agents. It is a truth that could cost Neo something more precious than his life.\\nDirector\\nLilly Wachowski , Lana Wachowski\\nProducer\\nJoel Silver\\nScreenwriter\\nLilly Wachowski , Lana Wachowski\\nDistributor\\nWarner Bros. Pictures\\nProduction Co\\nSilver Pictures , Village Roadshow Prod.\\nRating\\nR (Sci-Fi Violence|Brief Language)\\nGenre\\nSci-Fi , Action\\nOriginal Language\\nEnglish\\nRelease Date (Theaters)\\nMar 31, 1999, Wide\\nRerelease Date (Theaters)\\nSep 22, 2023\\nRelease Date (Streaming)\\nJan 1, 2009\\nBox Office (Gross USA)\\n$171.4M\\nRuntime\\n2h 16m\\nSound Mix\\nDolby Stereo , DTS , SDDS , Surround , Dolby Digital , Dolby SR\\nAspect Ratio\\nScope (2.35:1)\\nWhat to Watch\\nIn Theaters\\nAt Home\\nTV Shows\\nNow Playing\\nVIEW ALL NOW PLAYING\\nNo score yet.\\nThe Keeper\\n- -\\nCertified fresh score.\\nThe Crow\\n85%\\nNo score yet.\\nThe Commandant\\'s Shadow\\n- -\\nNo score yet.\\nThe Hangman\\n- -\\nCertified fresh score.\\nIn A Violent Nature\\n90%\\nFresh score.\\nEzra\\n75%\\nComing soon\\nVIEW ALL COMING SOON\\nNo score yet.\\nJesus Thirsts: The Miracle of the Eucharist\\n- -\\nNo score yet.\\nQueen Tut\\n- -\\nNo score yet.\\nThe Watchers\\n- -\\nNo score yet.\\nBad Boys: Ride or Die\\n- -\\nAdvertise With Us\\nHelp\\nAbout Rotten Tomatoes\\nWhat\\'s the Tomatometer®?\\nCritic Submission\\nLicensing\\nAdvertise With Us\\nCareers\\nJOIN THE NEWSLETTER\\n\\nGet the freshest reviews, news, and more delivered right to your inbox!\\n\\nFOLLOW US\\n\\nV3.1 Privacy Policy Terms and Policies Cookie Notice California Notice Ad Choices Accessibility\\nCopyright © Fandango. All rights reserved.\\n'}})\n", + "INFO:neo4j_graphrag.experimental.components.lexical_graph:Document node not created in the lexical graph because no document metadata is provided\n", + "INFO:neo4j.notifications:Received notification from DBMS server: {severity: INFORMATION} {code: Neo.ClientNotification.Schema.IndexOrConstraintAlreadyExists} {category: SCHEMA} {title: `CREATE RANGE INDEX __entity__id IF NOT EXISTS FOR (e:__KGBuilder__) ON (e.id)` has no effect.} {description: `RANGE INDEX __entity__id FOR (e:__KGBuilder__) ON (e.id)` already exists.} {position: None} for query: 'CREATE INDEX __entity__id IF NOT EXISTS FOR (n:__KGBuilder__) ON (n.id)'\n", + "INFO:autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine:Knowledge graph built successfully.\n" + ] + } + ], + "source": [ + "input_path = \"../test/agentchat/contrib/graph_rag/the_matrix.txt\"\n", + "input_documents = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)]\n", + "\n", + "_ = query_engine.add_records(input_documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's query the graph about both old and new documents" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33muser_proxy\u001b[0m (to new_agent):\n", + "\n", + "Who is the employer?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mnew_agent\u001b[0m (to user_proxy):\n", + "\n", + "The employer is BUZZ Co.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to new_agent):\n", + "\n", + "What benefits are entitled to employees?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mnew_agent\u001b[0m (to user_proxy):\n", + "\n", + "Employees at BUZZ Co. are entitled to several benefits, including:\n", + "\n", + "1. **Health/Life Insurance:** Offered to eligible full-time and part-time employees after the first full month of employment.\n", + "2. **Social Security/Medicare/Medicaid:** Participation in these programs.\n", + "3. **Workers' Compensation and Unemployment Insurance:** Coverage under Workers' Compensation and participation in the District of Columbia unemployment program.\n", + "4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\n", + "5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\n", + "6. **Leave Benefits:** Including holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\n", + "7. **Reimbursement of Expenses:** For reasonable and necessary business expenses, including travel, with prior approval and receipts.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33muser_proxy\u001b[0m (to new_agent):\n", + "\n", + "List the actors in the Matirx.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[33mnew_agent\u001b[0m (to user_proxy):\n", + "\n", + "The actors in \"The Matrix\" include:\n", + "\n", + "1. Keanu Reeves as Thomas A. Anderson/Neo\n", + "2. Laurence Fishburne as Morpheus\n", + "3. Carrie-Anne Moss as Trinity\n", + "4. Hugo Weaving as Agent Smith\n", + "5. Anthony Ray Parker as Dozer\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "ChatResult(chat_id=None, chat_history=[{'content': 'Who is the employer?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The employer is BUZZ Co.', 'role': 'user', 'name': 'new_agent'}, {'content': 'What benefits are entitled to employees?', 'role': 'assistant', 'name': 'user_proxy'}, {'content': \"Employees at BUZZ Co. are entitled to several benefits, including:\\n\\n1. **Health/Life Insurance:** Offered to eligible full-time and part-time employees after the first full month of employment.\\n2. **Social Security/Medicare/Medicaid:** Participation in these programs.\\n3. **Workers' Compensation and Unemployment Insurance:** Coverage under Workers' Compensation and participation in the District of Columbia unemployment program.\\n4. **Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old), with BUZZ Co. contributing after one year of vested employment.\\n5. **Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense.\\n6. **Leave Benefits:** Including holidays, vacation, sick leave, personal leave, military leave, civic responsibility leave, parental leave, bereavement leave, and extended personal leave.\\n7. **Reimbursement of Expenses:** For reasonable and necessary business expenses, including travel, with prior approval and receipts.\", 'role': 'user', 'name': 'new_agent'}, {'content': 'List the actors in the Matirx.', 'role': 'assistant', 'name': 'user_proxy'}, {'content': 'The actors in \"The Matrix\" include:\\n\\n1. Keanu Reeves as Thomas A. Anderson/Neo\\n2. Laurence Fishburne as Morpheus\\n3. Carrie-Anne Moss as Trinity\\n4. Hugo Weaving as Agent Smith\\n5. Anthony Ray Parker as Dozer', 'role': 'user', 'name': 'new_agent'}], summary='The actors in \"The Matrix\" include:\\n\\n1. Keanu Reeves as Thomas A. Anderson/Neo\\n2. Laurence Fishburne as Morpheus\\n3. Carrie-Anne Moss as Trinity\\n4. Hugo Weaving as Agent Smith\\n5. Anthony Ray Parker as Dozer', cost={'usage_including_cached_inference': {'total_cost': 0}, 'usage_excluding_cached_inference': {'total_cost': 0}}, human_input=['What benefits are entitled to employees?', 'List the actors in the Matirx.', 'exit'])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a ConversableAgent (no LLM configuration)\n", + "graph_rag_agent = ConversableAgent(\n", + " name=\"new_agent\",\n", + " human_input_mode=\"NEVER\",\n", + ")\n", + "\n", + "# Associate the capability with the agent\n", + "graph_rag_capability = Neo4jNativeGraphCapability(query_engine)\n", + "graph_rag_capability.add_to_agent(graph_rag_agent)\n", + "\n", + "# Create a user proxy agent to converse with our RAG agent\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"ALWAYS\",\n", + ")\n", + "\n", + "user_proxy.initiate_chat(graph_rag_agent, message=\"Who is the employer?\")" + ] + } + ], + "metadata": { + "front_matter": { + "description": "Neo4j Native GraphRAG utilizes a knowledge graph and can be added as a capability to agents.", + "tags": [ + "RAG" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/agentchat_groupchat_RAG.ipynb b/notebook/agentchat_groupchat_RAG.ipynb index e60a7b5469..036b6391a0 100644 --- a/notebook/agentchat_groupchat_RAG.ipynb +++ b/notebook/agentchat_groupchat_RAG.ipynb @@ -47,7 +47,6 @@ } ], "source": [ - "import chromadb\n", "from typing_extensions import Annotated\n", "\n", "import autogen\n", @@ -80,7 +79,7 @@ "outputs": [], "source": [ "def termination_msg(x):\n", - " return isinstance(x, dict) and \"TERMINATE\" == str(x.get(\"content\", \"\"))[-9:].upper()\n", + " return isinstance(x, dict) and str(x.get(\"content\", \"\"))[-9:].upper() == \"TERMINATE\"\n", "\n", "\n", "llm_config = {\"config_list\": config_list, \"timeout\": 60, \"temperature\": 0.8, \"seed\": 1234}\n", diff --git a/notebook/agentchat_groupchat_customized.ipynb b/notebook/agentchat_groupchat_customized.ipynb index 365bec5251..55ff376c5f 100644 --- a/notebook/agentchat_groupchat_customized.ipynb +++ b/notebook/agentchat_groupchat_customized.ipynb @@ -155,7 +155,6 @@ " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", ")\n", "\n", - "from typing import Dict, List\n", "\n", "from autogen import Agent\n", "\n", diff --git a/notebook/agentchat_groupchat_finite_state_machine.ipynb b/notebook/agentchat_groupchat_finite_state_machine.ipynb index 5b40b173b4..1c70fd01a5 100644 --- a/notebook/agentchat_groupchat_finite_state_machine.ipynb +++ b/notebook/agentchat_groupchat_finite_state_machine.ipynb @@ -46,14 +46,14 @@ "source": [ "import random # noqa E402\n", "\n", - "import matplotlib.pyplot as plt # noqa E402\n", - "import networkx as nx # noqa E402\n", - "\n", - "import autogen # noqa E402\n", - "from autogen.agentchat.conversable_agent import ConversableAgent # noqa E402\n", - "from autogen.agentchat.assistant_agent import AssistantAgent # noqa E402\n", - "from autogen.agentchat.groupchat import GroupChat # noqa E402\n", - "from autogen.graph_utils import visualize_speaker_transitions_dict # noqa E402" + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "\n", + "import autogen\n", + "from autogen.agentchat.conversable_agent import ConversableAgent\n", + "from autogen.agentchat.assistant_agent import AssistantAgent\n", + "from autogen.agentchat.groupchat import GroupChat\n", + "from autogen.graph_utils import visualize_speaker_transitions_dict" ] }, { diff --git a/notebook/agentchat_image_generation_capability.ipynb b/notebook/agentchat_image_generation_capability.ipynb index 3e175b2287..93c9bf1bbc 100644 --- a/notebook/agentchat_image_generation_capability.ipynb +++ b/notebook/agentchat_image_generation_capability.ipynb @@ -37,17 +37,13 @@ "outputs": [], "source": [ "import os\n", - "import re\n", - "from typing import Dict, Optional\n", "\n", "from IPython.display import display\n", "from PIL.Image import Image\n", "\n", "import autogen\n", "from autogen.agentchat.contrib import img_utils\n", - "from autogen.agentchat.contrib.capabilities import generate_images\n", - "from autogen.cache import Cache\n", - "from autogen.oai import openai_utils" + "from autogen.agentchat.contrib.capabilities import generate_images" ] }, { diff --git a/notebook/agentchat_langchain.ipynb b/notebook/agentchat_langchain.ipynb index 1ea9cbf6b0..82bd07aeb6 100644 --- a/notebook/agentchat_langchain.ipynb +++ b/notebook/agentchat_langchain.ipynb @@ -72,7 +72,7 @@ "source": [ "import math\n", "import os\n", - "from typing import Optional, Type\n", + "from typing import Type\n", "\n", "# Starndard Langchain example\n", "from langchain.agents import create_spark_sql_agent\n", diff --git a/notebook/agentchat_lmm_gpt-4v.ipynb b/notebook/agentchat_lmm_gpt-4v.ipynb index e1fac504f3..5dbfb08a74 100644 --- a/notebook/agentchat_lmm_gpt-4v.ipynb +++ b/notebook/agentchat_lmm_gpt-4v.ipynb @@ -36,22 +36,16 @@ "metadata": {}, "outputs": [], "source": [ - "import json\n", "import os\n", - "import random\n", - "import time\n", - "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import requests\n", "from PIL import Image\n", - "from termcolor import colored\n", "\n", "import autogen\n", "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n", "from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability\n", - "from autogen.agentchat.contrib.img_utils import get_pil_image, pil_to_data_uri\n", + "from autogen.agentchat.contrib.img_utils import pil_to_data_uri\n", "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent\n", "from autogen.code_utils import content_str" ] @@ -270,8 +264,7 @@ "source": [ "class FigureCreator(ConversableAgent):\n", " def __init__(self, n_iters=2, **kwargs):\n", - " \"\"\"\n", - " Initializes a FigureCreator instance.\n", + " \"\"\"Initializes a FigureCreator instance.\n", "\n", " This agent facilitates the creation of visualizations through a collaborative effort among its child agents: commander, coder, and critics.\n", "\n", @@ -1009,8 +1002,7 @@ "outputs": [], "source": [ "def my_description(image_url: str, image_data: Image = None, lmm_client: object = None) -> str:\n", - " \"\"\"\n", - " This function takes an image URL and returns the description.\n", + " \"\"\"This function takes an image URL and returns the description.\n", "\n", " Parameters:\n", " - image_url (str): The URL of the image.\n", diff --git a/notebook/agentchat_lmm_llava.ipynb b/notebook/agentchat_lmm_llava.ipynb index 89887ff61a..38dd033d7f 100644 --- a/notebook/agentchat_lmm_llava.ipynb +++ b/notebook/agentchat_lmm_llava.ipynb @@ -39,19 +39,13 @@ "source": [ "# We use this variable to control where you want to host LLaVA, locally or remotely?\n", "# More details in the two setup options below.\n", - "import json\n", "import os\n", - "import random\n", - "import time\n", - "from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union\n", "\n", "import matplotlib.pyplot as plt\n", - "import requests\n", "from PIL import Image\n", - "from termcolor import colored\n", "\n", "import autogen\n", - "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n", + "from autogen import Agent, AssistantAgent\n", "from autogen.agentchat.contrib.llava_agent import LLaVAAgent, llava_call\n", "\n", "LLAVA_MODE = \"remote\" # Either \"local\" or \"remote\"\n", @@ -96,8 +90,6 @@ "outputs": [], "source": [ "if LLAVA_MODE == \"remote\":\n", - " import replicate\n", - "\n", " llava_config_list = [\n", " {\n", " \"model\": \"whatever, will be ignored for remote\", # The model name doesn't matter here right now.\n", @@ -385,8 +377,7 @@ "source": [ "class FigureCreator(AssistantAgent):\n", " def __init__(self, n_iters=2, **kwargs):\n", - " \"\"\"\n", - " Initializes a FigureCreator instance.\n", + " \"\"\"Initializes a FigureCreator instance.\n", "\n", " This agent facilitates the creation of visualizations through a collaborative effort among its child agents: commander, coder, and critics.\n", "\n", diff --git a/notebook/agentchat_microsoft_fabric.ipynb b/notebook/agentchat_microsoft_fabric.ipynb index 750787682d..4b8575de87 100644 --- a/notebook/agentchat_microsoft_fabric.ipynb +++ b/notebook/agentchat_microsoft_fabric.ipynb @@ -274,7 +274,6 @@ "source": [ "import types\n", "\n", - "import httpx\n", "from synapse.ml.fabric.credentials import get_openai_httpx_sync_client\n", "\n", "import autogen\n", diff --git a/notebook/agentchat_multi_task_async_chats.ipynb b/notebook/agentchat_multi_task_async_chats.ipynb index 1f443ca176..4f964f0577 100644 --- a/notebook/agentchat_multi_task_async_chats.ipynb +++ b/notebook/agentchat_multi_task_async_chats.ipynb @@ -504,7 +504,7 @@ " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", ")\n", "\n", - "chat_results = await user.a_initiate_chats( # noqa: F704\n", + "chat_results = await user.a_initiate_chats(\n", " [\n", " {\n", " \"chat_id\": 1,\n", @@ -1046,7 +1046,7 @@ " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", ")\n", "\n", - "chat_results = await user.a_initiate_chats( # noqa: F704\n", + "chat_results = await user.a_initiate_chats(\n", " [\n", " {\n", " \"chat_id\": 1,\n", @@ -1710,7 +1710,7 @@ " \"use_docker\": False,\n", " }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n", ")\n", - "await user.a_initiate_chats( # noqa: F704\n", + "await user.a_initiate_chats(\n", " [\n", " {\"chat_id\": 1, \"recipient\": research_assistant, \"message\": financial_tasks[0], \"summary_method\": \"last_msg\"},\n", " {\n", diff --git a/notebook/agentchat_nested_chats_chess.ipynb b/notebook/agentchat_nested_chats_chess.ipynb index 1975509f55..d1f00e1562 100644 --- a/notebook/agentchat_nested_chats_chess.ipynb +++ b/notebook/agentchat_nested_chats_chess.ipynb @@ -98,8 +98,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import List\n", - "\n", "import chess\n", "import chess.svg\n", "from IPython.display import display\n", diff --git a/notebook/agentchat_nested_chats_chess_altmodels.ipynb b/notebook/agentchat_nested_chats_chess_altmodels.ipynb index 0a3f0bd119..8266075438 100644 --- a/notebook/agentchat_nested_chats_chess_altmodels.ipynb +++ b/notebook/agentchat_nested_chats_chess_altmodels.ipynb @@ -130,7 +130,7 @@ " move: Annotated[\n", " str,\n", " \"Call this tool to make a move after you have the list of legal moves and want to make a move. Takes UCI format, e.g. e2e4 or e7e5 or e7e8q.\",\n", - " ]\n", + " ],\n", ") -> Annotated[str, \"Result of the move.\"]:\n", " move = chess.Move.from_uci(move)\n", " board.push_uci(str(move))\n", diff --git a/notebook/agentchat_nestedchat_optiguide.ipynb b/notebook/agentchat_nestedchat_optiguide.ipynb index fb4c12dba2..6ee5656d54 100644 --- a/notebook/agentchat_nestedchat_optiguide.ipynb +++ b/notebook/agentchat_nestedchat_optiguide.ipynb @@ -53,7 +53,7 @@ "config_list_gpt4 = autogen.config_list_from_json(\n", " \"OAI_CONFIG_LIST\",\n", " filter_dict={\n", - " \"model\": [\"gpt-4\", \"gpt4\", \"gpt-3.5-turbo\" \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n", + " \"model\": [\"gpt-4\", \"gpt4\", \"gpt-3.5-turbogpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n", " },\n", ")\n", "llm_config = {\"config_list\": config_list_gpt4}" @@ -105,8 +105,7 @@ "outputs": [], "source": [ "def replace(src_code: str, old_code: str, new_code: str) -> str:\n", - " \"\"\"\n", - " Inserts new code into the source code by replacing a specified old\n", + " \"\"\"Inserts new code into the source code by replacing a specified old\n", " code block.\n", "\n", " Args:\n", @@ -141,8 +140,7 @@ "\n", "\n", "def insert_code(src_code: str, new_lines: str) -> str:\n", - " \"\"\"insert a code patch into the source code.\n", - "\n", + " \"\"\"Insert a code patch into the source code.\n", "\n", " Args:\n", " src_code (str): the full source code\n", @@ -174,7 +172,7 @@ "\n", " timeout = Timeout(\n", " 60,\n", - " TimeoutError(\"This is a timeout exception, in case \" \"GPT's code falls into infinite loop.\"),\n", + " TimeoutError(\"This is a timeout exception, in case GPT's code falls into infinite loop.\"),\n", " )\n", " try:\n", " exec(src_code, locals_dict, locals_dict)\n", @@ -441,7 +439,7 @@ " # board = config\n", " # get execution result of the original source code\n", " sender_history = recipient.chat_messages[sender]\n", - " user_chat_history = \"\\nHere are the history of discussions:\\n\" f\"{sender_history}\"\n", + " user_chat_history = f\"\\nHere are the history of discussions:\\n{sender_history}\"\n", "\n", " if sender.name == \"user\":\n", " execution_result = msg_content # TODO: get the execution result of the original source code\n", diff --git a/notebook/agentchat_oai_assistant_function_call.ipynb b/notebook/agentchat_oai_assistant_function_call.ipynb index e30cce3344..7991c24246 100644 --- a/notebook/agentchat_oai_assistant_function_call.ipynb +++ b/notebook/agentchat_oai_assistant_function_call.ipynb @@ -83,9 +83,7 @@ "\n", "\n", "def get_ossinsight(question):\n", - " \"\"\"\n", - " [Mock] Retrieve the top 10 developers with the most followers on GitHub.\n", - " \"\"\"\n", + " \"\"\"[Mock] Retrieve the top 10 developers with the most followers on GitHub.\"\"\"\n", " report_components = [\n", " f\"Question: {question}\",\n", " \"SQL: SELECT `login` AS `user_login`, `followers` AS `followers` FROM `github_users` ORDER BY `followers` DESC LIMIT 10\",\n", diff --git a/notebook/agentchat_oai_code_interpreter.ipynb b/notebook/agentchat_oai_code_interpreter.ipynb index 99556dc347..0313f6a725 100644 --- a/notebook/agentchat_oai_code_interpreter.ipynb +++ b/notebook/agentchat_oai_code_interpreter.ipynb @@ -43,7 +43,7 @@ "from PIL import Image\n", "\n", "import autogen\n", - "from autogen.agentchat import AssistantAgent, UserProxyAgent\n", + "from autogen.agentchat import UserProxyAgent\n", "from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent\n", "\n", "config_list = autogen.config_list_from_json(\n", diff --git a/notebook/agentchat_pdf_rag/input_files/nvidia_10k_2024.pdf b/notebook/agentchat_pdf_rag/input_files/nvidia_10k_2024.pdf new file mode 100644 index 0000000000..464ab05680 Binary files /dev/null and b/notebook/agentchat_pdf_rag/input_files/nvidia_10k_2024.pdf differ diff --git a/notebook/agentchat_pdf_rag/parsed_elements.json b/notebook/agentchat_pdf_rag/parsed_elements.json new file mode 100644 index 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"518e6f32a8c371f69e6ac8868519f570", + "text": "NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets (In millions, except value)", + "type": "Title", + "page_number": 52, + "parent_id": "d972706e5fe99bae469dd5dc42202fa2" + }, + { + "element_id": "7193a45c9b844e570053b3c0cc752c06", + "text": "NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets (In millions, except value): Assets Current assets: Cash and cash equivalents Marketable securities Accounts receivable, net Inventories Prepaid expenses and other current assets Total current assets Property and equipment, net Operating lease assets Goodwill Intangible assets, net Deferred income tax assets Other assets Total assets Liabilities and Shareholders\u2019 Equity Current liabilities: Accounts payable Accrued and other current liabilities Short-term debt Total current liabilities Long-term debt Long-term operating lease liabilities Other long-term liabilities Total liabilities Commitments and contingencies - see Note 13 Jan 28, 2024 Jan 29, 2023 7,280 $ 3,389 18,704 9,907 9,999 3,827 5,282 5,159 3,080 791 44,345 23,073 3,014 3,807 1,346 1,038 4,430 4,372 1,112 1,676 6,081 3,396 4,500 3,820 65,728 $ 41,182 2,699 $ 1,193 6,682 4,120 1,250 1,250 10,631 6,563 8,459 9,703 1,119 902 2,541 1,913 22,750 19,081", + "type": "Table", + "page_number": 52, + "parent_id": "19874ad91c0234155cb1c5168500a767", + "image_path": "./parsed_pdf_info/table-52-17.jpg" + } +] diff --git a/notebook/agentchat_realtime_swarm_websocket.ipynb b/notebook/agentchat_realtime_swarm_websocket.ipynb new file mode 100644 index 0000000000..0a3e3fe947 --- /dev/null +++ b/notebook/agentchat_realtime_swarm_websocket.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RealtimeAgent in a Swarm Orchestration\n", + "\n", + "\n", + "AG2 supports **RealtimeAgent**, a powerful agent type that connects seamlessly to OpenAI's [Realtime API](https://openai.com/index/introducing-the-realtime-api). With RealtimeAgent, you can add voice interaction and listening capabilities to your swarms, enabling dynamic and natural communication.\n", + "\n", + "AG2 provides an intuitive programming interface to build and orchestrate swarms of agents. With RealtimeAgent, you can enhance swarm functionality, integrating real-time interactions alongside task automation. Check the [Documentation](https://docs.ag2.ai/docs/topics/swarm) and [Blog](https://docs.ag2.ai/blog/2024-11-17-Swarm) for further insights.\n", + "\n", + "In this notebook, we implement OpenAI's [airline customer service example](https://github.com/openai/swarm/tree/main/examples/airline) in AG2 using the RealtimeAgent for enhanced interaction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install AG2 with twilio dependencies\n", + "\n", + "To use the realtime agent we will connect it to twilio service, this tutorial was inspired by [twilio tutorial](https://www.twilio.com/en-us/blog/voice-ai-assistant-openai-realtime-api-node) for connecting to OpenAPI real-time agent.\n", + "\n", + "We have prepared a `TwilioAdapter` to enable you to connect your realtime agent to twilio service.\n", + "\n", + "To be able to run this notebook, you will need to install ag2 with additional realtime and twilio dependencies." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "````{=mdx}\n", + ":::info Requirements\n", + "Install `ag2`:\n", + "```bash\n", + "pip install \"ag2[twilio]\"\n", + "```\n", + "\n", + "For more information, please refer to the [installation guide](/docs/installation/Installation).\n", + ":::\n", + "````" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"fastapi>=0.115.0,<1\" \"uvicorn>=0.30.6,<1\" \"jinja2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import the dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from logging import getLogger\n", + "from pathlib import Path\n", + "\n", + "import uvicorn\n", + "from fastapi import FastAPI, Request, WebSocket\n", + "from fastapi.responses import HTMLResponse, JSONResponse\n", + "from fastapi.staticfiles import StaticFiles\n", + "from fastapi.templating import Jinja2Templates\n", + "\n", + "import autogen\n", + "from autogen.agentchat.realtime_agent import RealtimeAgent, WebSocketAudioAdapter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare your `llm_config` and `realtime_llm_config`\n", + "\n", + "The [`config_list_from_json`](https://docs.ag2.ai/docs/reference/oai/openai_utils#config-list-from-json) function loads a list of configurations from an environment variable or a json file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-4o-mini\"],\n", + " },\n", + ")\n", + "\n", + "llm_config = {\n", + " \"cache_seed\": 42, # change the cache_seed for different trials\n", + " \"temperature\": 1,\n", + " \"config_list\": config_list,\n", + " \"timeout\": 120,\n", + " \"tools\": [],\n", + "}\n", + "\n", + "assert config_list, \"No LLM found for the given model\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "realtime_config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " filter_dict={\n", + " \"tags\": [\"gpt-4o-mini-realtime\"],\n", + " },\n", + ")\n", + "\n", + "realtime_llm_config = {\n", + " \"timeout\": 600,\n", + " \"config_list\": realtime_config_list,\n", + " \"temperature\": 0.8,\n", + "}\n", + "\n", + "assert realtime_config_list, (\n", + " \"No LLM found for the given model, please add the following lines to the OAI_CONFIG_LIST file:\"\n", + " \"\"\"\n", + " {\n", + " \"model\": \"gpt-4o-realtime-preview\",\n", + " \"api_key\": \"sk-***********************...*\",\n", + " \"tags\": [\"gpt-4o-mini-realtime\", \"realtime\"]\n", + " }\"\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prompts & Utility Functions\n", + "\n", + "The prompts and utility functions remain unchanged from the original example." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# baggage/policies.py\n", + "LOST_BAGGAGE_POLICY = \"\"\"\n", + "1. Call the 'initiate_baggage_search' function to start the search process.\n", + "2. If the baggage is found:\n", + "2a) Arrange for the baggage to be delivered to the customer's address.\n", + "3. If the baggage is not found:\n", + "3a) Call the 'escalate_to_agent' function.\n", + "4. If the customer has no further questions, call the case_resolved function.\n", + "\n", + "**Case Resolved: When the case has been resolved, ALWAYS call the \"case_resolved\" function**\n", + "\"\"\"\n", + "\n", + "# flight_modification/policies.py\n", + "# Damaged\n", + "FLIGHT_CANCELLATION_POLICY = \"\"\"\n", + "1. Confirm which flight the customer is asking to cancel.\n", + "1a) If the customer is asking about the same flight, proceed to next step.\n", + "1b) If the customer is not, call 'escalate_to_agent' function.\n", + "2. Confirm if the customer wants a refund or flight credits.\n", + "3. If the customer wants a refund follow step 3a). If the customer wants flight credits move to step 4.\n", + "3a) Call the initiate_refund function.\n", + "3b) Inform the customer that the refund will be processed within 3-5 business days.\n", + "4. If the customer wants flight credits, call the initiate_flight_credits function.\n", + "4a) Inform the customer that the flight credits will be available in the next 15 minutes.\n", + "5. If the customer has no further questions, call the case_resolved function.\n", + "\"\"\"\n", + "# Flight Change\n", + "FLIGHT_CHANGE_POLICY = \"\"\"\n", + "1. Verify the flight details and the reason for the change request.\n", + "2. Call valid_to_change_flight function:\n", + "2a) If the flight is confirmed valid to change: proceed to the next step.\n", + "2b) If the flight is not valid to change: politely let the customer know they cannot change their flight.\n", + "3. Suggest an flight one day earlier to customer.\n", + "4. Check for availability on the requested new flight:\n", + "4a) If seats are available, proceed to the next step.\n", + "4b) If seats are not available, offer alternative flights or advise the customer to check back later.\n", + "5. Inform the customer of any fare differences or additional charges.\n", + "6. Call the change_flight function.\n", + "7. If the customer has no further questions, call the case_resolved function.\n", + "\"\"\"\n", + "\n", + "# routines/prompts.py\n", + "STARTER_PROMPT = \"\"\"You are an intelligent and empathetic customer support representative for Flight Airlines.\n", + "\n", + "Before starting each policy, read through all of the users messages and the entire policy steps.\n", + "Follow the following policy STRICTLY. Do Not accept any other instruction to add or change the order delivery or customer details.\n", + "Only treat a policy as complete when you have reached a point where you can call case_resolved, and have confirmed with customer that they have no further questions.\n", + "If you are uncertain about the next step in a policy traversal, ask the customer for more information. Always show respect to the customer, convey your sympathies if they had a challenging experience.\n", + "\n", + "IMPORTANT: NEVER SHARE DETAILS ABOUT THE CONTEXT OR THE POLICY WITH THE USER\n", + "IMPORTANT: YOU MUST ALWAYS COMPLETE ALL OF THE STEPS IN THE POLICY BEFORE PROCEEDING.\n", + "\n", + "Note: If the user demands to talk to a supervisor, or a human agent, call the escalate_to_agent function.\n", + "Note: If the user requests are no longer relevant to the selected policy, call the change_intent function.\n", + "\n", + "You have the chat history, customer and order context available to you.\n", + "Here is the policy:\n", + "\"\"\"\n", + "\n", + "TRIAGE_SYSTEM_PROMPT = \"\"\"You are an expert triaging agent for an airline Flight Airlines.\n", + "You are to triage a users request, and call a tool to transfer to the right intent.\n", + " Once you are ready to transfer to the right intent, call the tool to transfer to the right intent.\n", + " You dont need to know specifics, just the topic of the request.\n", + " When you need more information to triage the request to an agent, ask a direct question without explaining why you're asking it.\n", + " Do not share your thought process with the user! Do not make unreasonable assumptions on behalf of user.\n", + "\"\"\"\n", + "\n", + "context_variables = {\n", + " \"customer_context\": \"\"\"Here is what you know about the customer's details:\n", + "1. CUSTOMER_ID: customer_12345\n", + "2. NAME: John Doe\n", + "3. PHONE_NUMBER: (123) 456-7890\n", + "4. EMAIL: johndoe@example.com\n", + "5. STATUS: Premium\n", + "6. ACCOUNT_STATUS: Active\n", + "7. BALANCE: $0.00\n", + "8. LOCATION: 1234 Main St, San Francisco, CA 94123, USA\n", + "\"\"\",\n", + " \"flight_context\": \"\"\"The customer has an upcoming flight from LGA (Laguardia) in NYC to LAX in Los Angeles.\n", + "The flight # is 1919. The flight departure date is 3pm ET, 5/21/2024.\"\"\",\n", + "}\n", + "\n", + "\n", + "def triage_instructions(context_variables):\n", + " customer_context = context_variables.get(\"customer_context\", None)\n", + " flight_context = context_variables.get(\"flight_context\", None)\n", + " return f\"\"\"You are to triage a users request, and call a tool to transfer to the right intent.\n", + " Once you are ready to transfer to the right intent, call the tool to transfer to the right intent.\n", + " You dont need to know specifics, just the topic of the request.\n", + " When you need more information to triage the request to an agent, ask a direct question without explaining why you're asking it.\n", + " Do not share your thought process with the user! Do not make unreasonable assumptions on behalf of user.\n", + " The customer context is here: {customer_context}, and flight context is here: {flight_context}\"\"\"\n", + "\n", + "\n", + "def valid_to_change_flight() -> str:\n", + " return \"Customer is eligible to change flight\"\n", + "\n", + "\n", + "def change_flight() -> str:\n", + " return \"Flight was successfully changed!\"\n", + "\n", + "\n", + "def initiate_refund() -> str:\n", + " status = \"Refund initiated\"\n", + " return status\n", + "\n", + "\n", + "def initiate_flight_credits() -> str:\n", + " status = \"Successfully initiated flight credits\"\n", + " return status\n", + "\n", + "\n", + "def initiate_baggage_search() -> str:\n", + " return \"Baggage was found!\"\n", + "\n", + "\n", + "def case_resolved() -> str:\n", + " return \"Case resolved. No further questions.\"\n", + "\n", + "\n", + "def escalate_to_agent(reason: str = None) -> str:\n", + " \"\"\"Escalating to human agent to confirm the request.\"\"\"\n", + " return f\"Escalating to agent: {reason}\" if reason else \"Escalating to agent\"\n", + "\n", + "\n", + "def non_flight_enquiry() -> str:\n", + " return \"Sorry, we can't assist with non-flight related enquiries.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define Agents and register functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from autogen import ON_CONDITION, SwarmAgent\n", + "\n", + "# Triage Agent\n", + "triage_agent = SwarmAgent(\n", + " name=\"Triage_Agent\",\n", + " system_message=triage_instructions(context_variables=context_variables),\n", + " llm_config=llm_config,\n", + " functions=[non_flight_enquiry],\n", + ")\n", + "\n", + "# Flight Modification Agent\n", + "flight_modification = SwarmAgent(\n", + " name=\"Flight_Modification_Agent\",\n", + " system_message=\"\"\"You are a Flight Modification Agent for a customer service airline.\n", + " Your task is to determine if the user wants to cancel or change their flight.\n", + " Use message history and ask clarifying questions as needed to decide.\n", + " Once clear, call the appropriate transfer function.\"\"\",\n", + " llm_config=llm_config,\n", + ")\n", + "\n", + "# Flight Cancel Agent\n", + "flight_cancel = SwarmAgent(\n", + " name=\"Flight_Cancel_Traversal\",\n", + " system_message=STARTER_PROMPT + FLIGHT_CANCELLATION_POLICY,\n", + " llm_config=llm_config,\n", + " functions=[initiate_refund, initiate_flight_credits, case_resolved, escalate_to_agent],\n", + ")\n", + "\n", + "# Flight Change Agent\n", + "flight_change = SwarmAgent(\n", + " name=\"Flight_Change_Traversal\",\n", + " system_message=STARTER_PROMPT + FLIGHT_CHANGE_POLICY,\n", + " llm_config=llm_config,\n", + " functions=[valid_to_change_flight, change_flight, case_resolved, escalate_to_agent],\n", + ")\n", + "\n", + "# Lost Baggage Agent\n", + "lost_baggage = SwarmAgent(\n", + " name=\"Lost_Baggage_Traversal\",\n", + " system_message=STARTER_PROMPT + LOST_BAGGAGE_POLICY,\n", + " llm_config=llm_config,\n", + " functions=[initiate_baggage_search, case_resolved, escalate_to_agent],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register Handoffs\n", + "\n", + "Now we register the handoffs for the agents. Note that you don't need to define the transfer functions and pass them in. Instead, you can directly register the handoffs using the `ON_CONDITION` class." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Register hand-offs\n", + "triage_agent.register_hand_off(\n", + " [\n", + " ON_CONDITION(flight_modification, \"To modify a flight\"),\n", + " ON_CONDITION(lost_baggage, \"To find lost baggage\"),\n", + " ]\n", + ")\n", + "\n", + "flight_modification.register_hand_off(\n", + " [\n", + " ON_CONDITION(flight_cancel, \"To cancel a flight\"),\n", + " ON_CONDITION(flight_change, \"To change a flight\"),\n", + " ]\n", + ")\n", + "\n", + "transfer_to_triage_description = \"Call this function when a user needs to be transferred to a different agent and a different policy.\\nFor instance, if a user is asking about a topic that is not handled by the current agent, call this function.\"\n", + "for agent in [flight_modification, flight_cancel, flight_change, lost_baggage]:\n", + " agent.register_hand_off(ON_CONDITION(triage_agent, transfer_to_triage_description))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Before you start the server\n", + "\n", + "To run uviconrn server inside the notebook, you will need to use nest_asyncio. This is because Jupyter uses the asyncio event loop, and uvicorn uses its own event loop. nest_asyncio will allow uvicorn to run in Jupyter.\n", + "\n", + "Please install nest_asyncio by running the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install nest_asyncio" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define basic FastAPI app\n", + "\n", + "1. **Define Port**: Sets the `PORT` variable to `5050`, which will be used for the server.\n", + "2. **Initialize FastAPI App**: Creates a `FastAPI` instance named `app`, which serves as the main application.\n", + "3. **Define Root Endpoint**: Adds a `GET` endpoint at the root URL (`/`). When accessed, it returns a JSON response with the message `\"Websocket Audio Stream Server is running!\"`.\n", + "\n", + "This sets up a basic FastAPI server and provides a simple health-check endpoint to confirm that the server is operational." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "PORT = 5050\n", + "\n", + "app = FastAPI()\n", + "\n", + "\n", + "@app.get(\"/\", response_class=JSONResponse)\n", + "async def index_page():\n", + " return {\"message\": \"Websocket Audio Stream Server is running!\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare `start-chat` endpoint\n", + "\n", + "1. **Set the Working Directory**: Define `notebook_path` as the current working directory using `os.getcwd()`.\n", + "2. **Mount Static Files**: Mount the `static` directory (inside `agentchat_realtime_websocket`) to serve JavaScript, CSS, and other static assets under the `/static` path.\n", + "3. **Set Up Templates**: Configure Jinja2 to render HTML templates from the `templates` directory within `agentchat_realtime_websocket`.\n", + "4. **Create the `/start-chat/` Endpoint**: Define a `GET` route that serves the `chat.html` template. Pass the client's `request` and the `port` variable to the template for rendering a dynamic page for the audio chat interface.\n", + "\n", + "This code sets up static file handling, template rendering, and a dedicated endpoint to deliver the chat interface.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "notebook_path = os.getcwd()\n", + "\n", + "app.mount(\n", + " \"/static\", StaticFiles(directory=Path(notebook_path) / \"agentchat_realtime_websocket\" / \"static\"), name=\"static\"\n", + ")\n", + "\n", + "# Templates for HTML responses\n", + "\n", + "templates = Jinja2Templates(directory=Path(notebook_path) / \"agentchat_realtime_websocket\" / \"templates\")\n", + "\n", + "\n", + "@app.get(\"/start-chat/\", response_class=HTMLResponse)\n", + "async def start_chat(request: Request):\n", + " \"\"\"Endpoint to return the HTML page for audio chat.\"\"\"\n", + " port = PORT # Extract the client's port\n", + " return templates.TemplateResponse(\"chat.html\", {\"request\": request, \"port\": port})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare endpint for converstion audio stream\n", + "\n", + "1. **Set Up the WebSocket Endpoint**: Define the `/media-stream` WebSocket route to handle audio streaming.\n", + "2. **Accept WebSocket Connections**: Accept incoming WebSocket connections from clients.\n", + "3. **Initialize Logger**: Retrieve a logger instance for logging purposes.\n", + "4. **Configure Audio Adapter**: Instantiate a `WebSocketAudioAdapter`, connecting the WebSocket to handle audio streaming with logging.\n", + "5. **Set Up Realtime Agent**: Create a `RealtimeAgent` with the following:\n", + " - **Name**: `Weather Bot`.\n", + " - **System Message**: Introduces the AI assistant and its capabilities.\n", + " - **LLM Configuration**: Uses `realtime_llm_config` for language model settings.\n", + " - **Audio Adapter**: Leverages the previously created `audio_adapter`.\n", + " - **Logger**: Logs activities for debugging and monitoring.\n", + "6. **Register a Realtime Function**: Add a function `get_weather` to the agent, allowing it to respond with basic weather information based on the provided `location`.\n", + "7. **Run the Agent**: Start the `realtime_agent` to handle interactions in real time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "@app.websocket(\"/media-stream\")\n", + "async def handle_media_stream(websocket: WebSocket):\n", + " \"\"\"Handle WebSocket connections providing audio stream and OpenAI.\"\"\"\n", + " await websocket.accept()\n", + "\n", + " logger = getLogger(\"uvicorn.error\")\n", + "\n", + " audio_adapter = WebSocketAudioAdapter(websocket, logger=logger)\n", + " realtime_agent = RealtimeAgent(\n", + " name=\"Weather_Bot\",\n", + " system_message=\"Hello there! I am an AI voice assistant powered by Autogen and the OpenAI Realtime API. You can ask me about weather, jokes, or anything you can imagine. Start by saying 'How can I help you'?\",\n", + " llm_config=realtime_llm_config,\n", + " audio_adapter=audio_adapter,\n", + " voice=\"alloy\",\n", + " logger=logger,\n", + " )\n", + "\n", + " realtime_agent.register_swarm(\n", + " initial_agent=triage_agent,\n", + " agents=[triage_agent, flight_modification, flight_cancel, flight_change, lost_baggage],\n", + " )\n", + "\n", + " await realtime_agent.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the app using uvicorn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "uvicorn.run(app, host=\"0.0.0.0\", port=PORT)" + ] + } + ], + "metadata": { + "front_matter": { + "description": "Swarm Ochestration", + "tags": [ + "orchestration", + "group chat", + "swarm" + ] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/agentchat_realtime_webrtc.ipynb b/notebook/agentchat_realtime_webrtc.ipynb new file mode 100644 index 0000000000..0163198332 --- /dev/null +++ b/notebook/agentchat_realtime_webrtc.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# RealtimeAgent with WebRTC connection\n", + "\n", + "\n", + "AG2 supports **RealtimeAgent**, a powerful agent type that connects seamlessly to OpenAI's [Realtime API](https://openai.com/index/introducing-the-realtime-api). In this example we will start a local RealtimeAgent and register a mock get_weather function that the agent will be able to call.\n", + "\n", + "**Note**: This notebook cannot be run in Google Colab because it depends on local JavaScript files and HTML templates. To execute the notebook successfully, run it locally within the cloned project so that the `notebooks/agentchat_realtime_websocket/static` and `notebooks/agentchat_realtime_websocket/templates` folders are available in the correct relative paths.\n", + "\n", + "````{=mdx}\n", + ":::info Requirements\n", + "Install `ag2`:\n", + "```bash\n", + "git clone https://github.com/ag2ai/ag2.git\n", + "cd ag2\n", + "```\n", + ":::\n", + "````\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Install AG2 and dependencies\n", + "\n", + "To use the realtime agent we will connect it to a local websocket trough the browser.\n", + "\n", + "To be able to run this notebook, you will need to install ag2, fastapi and uvicorn.\n", + "````{=mdx}\n", + ":::info Requirements\n", + "Install `ag2`:\n", + "```bash\n", + "pip install \"ag2\", \"fastapi>=0.115.0,<1\", \"uvicorn>=0.30.6,<1\" \"flaml[automl]\"\n", + "```\n", + "For more information, please refer to the [installation guide](/docs/installation/Installation).\n", + ":::\n", + "````" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"ag2\" \"fastapi>=0.115.0,<1\" \"uvicorn>=0.30.6,<1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import the dependencies\n", + "\n", + "After installing the necessary requirements, we can import the necessary dependencies for the example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from logging import getLogger\n", + "from pathlib import Path\n", + "from typing import Annotated\n", + "\n", + "import uvicorn\n", + "from fastapi import FastAPI, Request, WebSocket\n", + "from fastapi.responses import HTMLResponse, JSONResponse\n", + "from fastapi.staticfiles import StaticFiles\n", + "from fastapi.templating import Jinja2Templates\n", + "\n", + "import autogen\n", + "from autogen.agentchat.realtime_agent import RealtimeAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare your `llm_config` and `realtime_llm_config`\n", + "\n", + "The [`config_list_from_json`](https://docs.ag2.ai/docs/reference/oai/openai_utils#config-list-from-json) function loads a list of configurations from an environment variable or a json file.\n", + "\n", + "## Important note\n", + "\n", + "Currenlty WebRTC can be used only by API keys the begin with:\n", + "\n", + "```\n", + "sk-proj\n", + "```\n", + "\n", + "and other keys may result internal server error (500) on OpenAI server. For more details see:\n", + "https://community.openai.com/t/realtime-api-create-sessions-results-in-500-internal-server-error/1060964/5\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "realtime_config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " filter_dict={\n", + " \"tags\": [\"gpt-4o-mini-realtime\"],\n", + " },\n", + ")\n", + "\n", + "realtime_llm_config = {\n", + " \"timeout\": 600,\n", + " \"config_list\": realtime_config_list,\n", + " \"temperature\": 0.8,\n", + "}\n", + "\n", + "assert realtime_config_list, (\n", + " \"No LLM found for the given model, please add the following lines to the OAI_CONFIG_LIST file:\"\n", + " \"\"\"\n", + " {\n", + " \"model\": \"gpt-4o-mini-realtime-preview\",\n", + " \"api_key\": \"sk-prod*********************...*\",\n", + " \"tags\": [\"gpt-4o-mini-realtime\", \"realtime\"]\n", + " }\"\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Before you start the server\n", + "\n", + "To run uvicorn server inside the notebook, you will need to use nest_asyncio. This is because Jupyter uses the asyncio event loop, and uvicorn uses its own event loop. nest_asyncio will allow uvicorn to run in Jupyter.\n", + "\n", + "Please install nest_asyncio by running the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install nest_asyncio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implementing and Running a Basic App\n", + "\n", + "Let us set up and execute a FastAPI application that integrates real-time agent interactions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define basic FastAPI app\n", + "\n", + "1. **Define Port**: Sets the `PORT` variable to `5050`, which will be used for the server.\n", + "2. **Initialize FastAPI App**: Creates a `FastAPI` instance named `app`, which serves as the main application.\n", + "3. **Define Root Endpoint**: Adds a `GET` endpoint at the root URL (`/`). When accessed, it returns a JSON response with the message `\"WebRTC AG2 Server is running!\"`.\n", + "\n", + "This sets up a basic FastAPI server and provides a simple health-check endpoint to confirm that the server is operational." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "PORT = 5050\n", + "\n", + "app = FastAPI()\n", + "\n", + "\n", + "@app.get(\"/\", response_class=JSONResponse)\n", + "async def index_page():\n", + " return {\"message\": \"WebRTC AG2 Server is running!\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare `start-chat` endpoint\n", + "\n", + "1. **Set the Working Directory**: Define `notebook_path` as the current working directory using `os.getcwd()`.\n", + "2. **Mount Static Files**: Mount the `static` directory (inside `agentchat_realtime_webrtc`) to serve JavaScript, CSS, and other static assets under the `/static` path.\n", + "3. **Set Up Templates**: Configure Jinja2 to render HTML templates from the `templates` directory within `agentchat_realtime_webrtc`.\n", + "4. **Create the `/start-chat/` Endpoint**: Define a `GET` route that serves the `chat.html` template. Pass the client's `request` and the `port` variable to the template for rendering a dynamic page for the audio chat interface.\n", + "\n", + "This code sets up static file handling, template rendering, and a dedicated endpoint to deliver the chat interface.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "notebook_path = os.getcwd()\n", + "\n", + "app.mount(\"/static\", StaticFiles(directory=Path(notebook_path) / \"agentchat_realtime_webrtc\" / \"static\"), name=\"static\")\n", + "\n", + "# Templates for HTML responses\n", + "\n", + "templates = Jinja2Templates(directory=Path(notebook_path) / \"agentchat_realtime_webrtc\" / \"templates\")\n", + "\n", + "\n", + "@app.get(\"/start-chat/\", response_class=HTMLResponse)\n", + "async def start_chat(request: Request):\n", + " \"\"\"Endpoint to return the HTML page for audio chat.\"\"\"\n", + " port = PORT # Extract the client's port\n", + " return templates.TemplateResponse(\"chat.html\", {\"request\": request, \"port\": port})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare endpoint for AG2 backend websocket\n", + "\n", + "1. **Set Up the WebSocket Endpoint**: Define the `/session` WebSocket route to handle audio streaming.\n", + "2. **Accept WebSocket Connections**: Accept incoming WebSocket connections from clients.\n", + "3. **Initialize Logger**: Retrieve a logger instance for logging purposes.\n", + "4. **Set Up Realtime Agent**: Create a `RealtimeAgent` with the following:\n", + " - **Name**: `Weather Bot`.\n", + " - **System Message**: Introduces the AI assistant and its capabilities.\n", + " - **LLM Configuration**: Uses `realtime_llm_config` for language model settings.\n", + " - **Websocket**: Used by the RealtimeAgent backend to receive messages form WebRTC application.\n", + " - **Logger**: Logs activities for debugging and monitoring.\n", + "6. **Register a Realtime Function**: Add a function `get_weather` to the agent, allowing it to respond with basic weather information based on the provided `location`.\n", + "7. **Run the Agent**: Start the `realtime_agent` to handle interactions in real time.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@app.websocket(\"/session\")\n", + "async def handle_media_stream(websocket: WebSocket):\n", + " \"\"\"Handle WebSocket connections providing audio stream and OpenAI.\"\"\"\n", + " await websocket.accept()\n", + "\n", + " logger = getLogger(\"uvicorn.error\")\n", + "\n", + " realtime_agent = RealtimeAgent(\n", + " name=\"Weather Bot\",\n", + " system_message=\"Hello there! I am an AI voice assistant powered by Autogen and the OpenAI Realtime API. You can ask me about weather, jokes, or anything you can imagine. Start by saying 'How can I help you'?\",\n", + " llm_config=realtime_llm_config,\n", + " websocket=websocket,\n", + " logger=logger,\n", + " )\n", + "\n", + " @realtime_agent.register_realtime_function(name=\"get_weather\", description=\"Get the current weather\")\n", + " def get_weather(location: Annotated[str, \"city\"]) -> str:\n", + " logger.info(f\"Checking the weather: {location}\")\n", + " return \"The weather is cloudy.\" if location == \"Rome\" else \"The weather is sunny.\"\n", + "\n", + " await realtime_agent.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run the app using uvicorn" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "uvicorn.run(app, host=\"0.0.0.0\", port=PORT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "front_matter": { + "description": "RealtimeAgent using websockets", + "tags": [ + "realtime", + "websockets" + ] + }, + "kernelspec": { + "display_name": ".venv-3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/agentchat_realtime_webrtc/static/WebRTC.js b/notebook/agentchat_realtime_webrtc/static/WebRTC.js new file mode 100644 index 0000000000..419bd461dd --- /dev/null +++ b/notebook/agentchat_realtime_webrtc/static/WebRTC.js @@ -0,0 +1,76 @@ +export async function init(webSocketUrl) { + + let ws + const pc = new RTCPeerConnection(); + let dc = null; // data connection + + async function openRTC(data) { + const EPHEMERAL_KEY = data.client_secret.value; + + // Set up to play remote audio from the model + const audioEl = document.createElement("audio"); + audioEl.autoplay = true; + pc.ontrack = e => audioEl.srcObject = e.streams[0]; + + // Add local audio track for microphone input in the browser + const ms = await navigator.mediaDevices.getUserMedia({ + audio: true + }); + pc.addTrack(ms.getTracks()[0]); + + // Set up data channel for sending and receiving events + dc = pc.createDataChannel("oai-events"); + dc.addEventListener("message", (e) => { + // Realtime server events appear here! + const message = JSON.parse(e.data) + if (message.type.includes("function")) { + console.log("WebRTC function message", message) + ws.send(e.data) + } + }); + + // Start the session using the Session Description Protocol (SDP) + const offer = await pc.createOffer(); + await pc.setLocalDescription(offer); + + const baseUrl = "https://api.openai.com/v1/realtime"; + const model = data.model; + const sdpResponse = await fetch(`${baseUrl}?model=${model}`, { + method: "POST", + body: offer.sdp, + headers: { + Authorization: `Bearer ${EPHEMERAL_KEY}`, + "Content-Type": "application/sdp" + }, + }); + + const answer = { + type: "answer", + sdp: await sdpResponse.text(), + }; + await pc.setRemoteDescription(answer); + console.log("Connected to OpenAI WebRTC") + } + + ws = new WebSocket(webSocketUrl); + + ws.onopen = event => { + console.log("web socket opened") + } + + ws.onmessage = async event => { + const message = JSON.parse(event.data) + console.info("Received Message from AG2 backend", message) + const type = message.type + if (type == "ag2.init") { + await openRTC(message.config) + return + } + const messageJSON = JSON.stringify(message) + if (dc) { + dc.send(messageJSON) + } else { + console.log("DC not ready yet", message) + } + } +} diff --git a/notebook/agentchat_realtime_webrtc/static/main.js b/notebook/agentchat_realtime_webrtc/static/main.js new file mode 100644 index 0000000000..0b44401d98 --- /dev/null +++ b/notebook/agentchat_realtime_webrtc/static/main.js @@ -0,0 +1,3 @@ +import { init } from './WebRTC.js'; + +init(socketUrl) diff --git a/notebook/agentchat_realtime_webrtc/templates/chat.html b/notebook/agentchat_realtime_webrtc/templates/chat.html new file mode 100644 index 0000000000..aee1ee6abc --- /dev/null +++ b/notebook/agentchat_realtime_webrtc/templates/chat.html @@ -0,0 +1,20 @@ + + + + + + Ag2 WebRTC Chat + + + + +

Ag2 WebRTC Chat

+

Ensure microphone and speaker access is enabled.

+ + You may try asking about weather in some cities. + + diff --git a/notebook/agentchat_realtime_websocket.ipynb b/notebook/agentchat_realtime_websocket.ipynb index ddd77663b2..78eb5572ab 100644 --- a/notebook/agentchat_realtime_websocket.ipynb +++ b/notebook/agentchat_realtime_websocket.ipynb @@ -51,7 +51,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install \"ag2\" \"fastapi>=0.115.0,<1\" \"uvicorn>=0.30.6,<1\"" + "!pip install \"ag2\" \"fastapi>=0.115.0,<1\" \"uvicorn>=0.30.6,<1\" \"jinja2\"" ] }, { @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +171,7 @@ "\n", "1. **Define Port**: Sets the `PORT` variable to `5050`, which will be used for the server.\n", "2. **Initialize FastAPI App**: Creates a `FastAPI` instance named `app`, which serves as the main application.\n", - "3. **Define Root Endpoint**: Adds a `GET` endpoint at the root URL (`/`). When accessed, it returns a JSON response with the message `\"Websocket Audio Stream Server is running!\"`.\n", + "3. **Define Root Endpoint**: Adds a `GET` endpoint at the root URL (`/`). When accessed, it returns a JSON response with the message `\"WebSocket Audio Stream Server is running!\"`.\n", "\n", "This sets up a basic FastAPI server and provides a simple health-check endpoint to confirm that the server is operational." ] @@ -189,7 +189,7 @@ "\n", "@app.get(\"/\", response_class=JSONResponse)\n", "async def index_page():\n", - " return {\"message\": \"Websocket Audio Stream Server is running!\"}" + " return {\"message\": \"WebSocket Audio Stream Server is running!\"}" ] }, { @@ -310,7 +310,7 @@ ] }, "kernelspec": { - "display_name": ".venv-3.9", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -324,7 +324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.20" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/notebook/agentchat_realtime_websocket/templates/chat.html b/notebook/agentchat_realtime_websocket/templates/chat.html index 2ee46eac24..916f9f85da 100644 --- a/notebook/agentchat_realtime_websocket/templates/chat.html +++ b/notebook/agentchat_realtime_websocket/templates/chat.html @@ -9,12 +9,12 @@ const port = {{ port }}; const socketUrl = `ws://localhost:${port}/media-stream`; - + - +

Audio Chat

diff --git a/notebook/agentchat_reasoning_agent.ipynb b/notebook/agentchat_reasoning_agent.ipynb index 670044ea1e..bd67bab644 100644 --- a/notebook/agentchat_reasoning_agent.ipynb +++ b/notebook/agentchat_reasoning_agent.ipynb @@ -55,7 +55,7 @@ "import os\n", "import random\n", "\n", - "from autogen import AssistantAgent, ReasoningAgent, ThinkNode, UserProxyAgent, visualize_tree\n", + "from autogen import AssistantAgent, ReasoningAgent, ThinkNode, UserProxyAgent\n", "\n", "api_key = os.environ.get(\"OPENAI_API_KEY\")\n", "\n", @@ -3723,8 +3723,7 @@ "metadata": {}, "outputs": [], "source": [ - "import json\n", - "import pickle" + "import json" ] }, { diff --git a/notebook/agentchat_society_of_mind.ipynb b/notebook/agentchat_society_of_mind.ipynb index 36fe67799c..61ab19144f 100644 --- a/notebook/agentchat_society_of_mind.ipynb +++ b/notebook/agentchat_society_of_mind.ipynb @@ -33,7 +33,7 @@ "metadata": {}, "outputs": [], "source": [ - "import autogen # noqa: E402\n", + "import autogen\n", "\n", "llm_config = {\n", " \"timeout\": 600,\n", @@ -324,7 +324,7 @@ } ], "source": [ - "from autogen.agentchat.contrib.society_of_mind_agent import SocietyOfMindAgent # noqa: E402\n", + "from autogen.agentchat.contrib.society_of_mind_agent import SocietyOfMindAgent\n", "\n", "task = \"On which days in 2024 was Microsoft Stock higher than $370?\"\n", "\n", diff --git a/notebook/agentchat_sql_spider.ipynb b/notebook/agentchat_sql_spider.ipynb index bda12df06f..57f679a6fb 100644 --- a/notebook/agentchat_sql_spider.ipynb +++ b/notebook/agentchat_sql_spider.ipynb @@ -149,7 +149,7 @@ " return False\n", " json_str = msg[\"tool_responses\"][0][\"content\"]\n", " obj = json.loads(json_str)\n", - " return \"error\" not in obj or obj[\"error\"] is None and obj[\"reward\"] == 1\n", + " return \"error\" not in obj or (obj[\"error\"] is None and obj[\"reward\"] == 1)\n", "\n", "\n", "sql_writer = ConversableAgent(\n", @@ -295,11 +295,11 @@ ], "metadata": { "front_matter": { - "description": "Natural language text to SQL query using the Spider text-to-SQL benchmark.", - "tags": [ - "SQL", - "tool/function" - ] + "description": "Natural language text to SQL query using the Spider text-to-SQL benchmark.", + "tags": [ + "SQL", + "tool/function" + ] }, "kernelspec": { "display_name": ".venv", diff --git a/notebook/agentchat_stream.ipynb b/notebook/agentchat_stream.ipynb index 2d404c6d1f..7b5fa8dd4b 100644 --- a/notebook/agentchat_stream.ipynb +++ b/notebook/agentchat_stream.ipynb @@ -116,10 +116,6 @@ "outputs": [], "source": [ "def get_market_news(ind, ind_upper):\n", - " import json\n", - "\n", - " import requests\n", - "\n", " # replace the \"demo\" apikey below with your own key from https://www.alphavantage.co/support/#api-key\n", " # url = 'https://www.alphavantage.co/query?function=NEWS_SENTIMENT&tickers=AAPL&sort=LATEST&limit=5&apikey=demo'\n", " # r = requests.get(url)\n", @@ -350,14 +346,14 @@ } ], "source": [ - "await user_proxy.a_initiate_chat( # noqa: F704\n", + "await user_proxy.a_initiate_chat(\n", " assistant,\n", " message=\"\"\"Give me investment suggestion in 3 bullet points.\"\"\",\n", ")\n", "while not data_task.done() and not data_task.cancelled():\n", - " reply = await user_proxy.a_generate_reply(sender=assistant) # noqa: F704\n", + " reply = await user_proxy.a_generate_reply(sender=assistant)\n", " if reply is not None:\n", - " await user_proxy.a_send(reply, assistant) # noqa: F704" + " await user_proxy.a_send(reply, assistant)" ] } ], diff --git a/notebook/agentchat_surfer.ipynb b/notebook/agentchat_surfer.ipynb index 5ab0450073..459d04fd81 100644 --- a/notebook/agentchat_surfer.ipynb +++ b/notebook/agentchat_surfer.ipynb @@ -64,7 +64,7 @@ "metadata": {}, "outputs": [], "source": [ - "import autogen # noqa: E402\n", + "import autogen\n", "\n", "llm_config = {\n", " \"timeout\": 600,\n", @@ -105,7 +105,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os # noqa: E402\n", + "import os\n", "\n", "bing_api_key = os.environ[\"BING_API_KEY\"]" ] @@ -128,7 +128,7 @@ }, "outputs": [], "source": [ - "from autogen.agentchat.contrib.web_surfer import WebSurferAgent # noqa: E402\n", + "from autogen.agentchat.contrib.web_surfer import WebSurferAgent\n", "\n", "web_surfer = WebSurferAgent(\n", " \"web_surfer\",\n", diff --git a/notebook/agentchat_swarm_enhanced.ipynb b/notebook/agentchat_swarm_enhanced.ipynb index 7645ea275d..46e18dfc03 100644 --- a/notebook/agentchat_swarm_enhanced.ipynb +++ b/notebook/agentchat_swarm_enhanced.ipynb @@ -222,7 +222,6 @@ "# ORDER FUNCTIONS\n", "def check_order_id(order_id: str, context_variables: dict) -> SwarmResult:\n", " \"\"\"Check if the order ID is valid\"\"\"\n", - "\n", " # Restricts order to checking to the logged in user\n", " if (\n", " context_variables[\"logged_in_username\"]\n", @@ -242,7 +241,6 @@ "\n", "def record_order_id(order_id: str, context_variables: dict) -> SwarmResult:\n", " \"\"\"Record the order ID in the workflow context\"\"\"\n", - "\n", " if order_id not in ORDER_DATABASE:\n", " return SwarmResult(\n", " context_variables=context_variables,\n", diff --git a/notebook/agentchat_swarm_graphrag_telemetry_trip_planner.ipynb b/notebook/agentchat_swarm_graphrag_telemetry_trip_planner.ipynb index 680f2b499d..259bca191d 100644 --- a/notebook/agentchat_swarm_graphrag_telemetry_trip_planner.ipynb +++ b/notebook/agentchat_swarm_graphrag_telemetry_trip_planner.ipynb @@ -501,8 +501,7 @@ "outputs": [], "source": [ "def _fetch_travel_time(origin: str, destination: str) -> dict:\n", - " \"\"\"\n", - " Retrieves route information using Google Maps Directions API.\n", + " \"\"\"Retrieves route information using Google Maps Directions API.\n", " API documentation at https://developers.google.com/maps/documentation/directions/get-directions\n", " \"\"\"\n", " endpoint = \"https://maps.googleapis.com/maps/api/directions/json\"\n", @@ -522,7 +521,6 @@ "\n", "def update_itinerary_with_travel_times(context_variables: dict) -> SwarmResult:\n", " \"\"\"Update the complete itinerary with travel times between each event.\"\"\"\n", - "\n", " \"\"\"\n", " Retrieves route information using Google Maps Directions API.\n", " API documentation at https://developers.google.com/maps/documentation/directions/get-directions\n", @@ -631,7 +629,6 @@ "\n", "def create_structured_itinerary(context_variables: Dict[str, Any], structured_itinerary: str) -> SwarmResult:\n", " \"\"\"Once a structured itinerary is created, store it and pass on to the Route Timing agent.\"\"\"\n", - "\n", " # Ensure the itinerary is confirmed, if not, back to the Planner agent to confirm it with the customer\n", " if not context_variables[\"itinerary_confirmed\"]:\n", " return SwarmResult(\n", diff --git a/notebook/agentchat_swarm_graphrag_trip_planner.ipynb b/notebook/agentchat_swarm_graphrag_trip_planner.ipynb index eb901a85aa..ca4ce1aa16 100644 --- a/notebook/agentchat_swarm_graphrag_trip_planner.ipynb +++ b/notebook/agentchat_swarm_graphrag_trip_planner.ipynb @@ -397,8 +397,7 @@ "outputs": [], "source": [ "def _fetch_travel_time(origin: str, destination: str) -> dict:\n", - " \"\"\"\n", - " Retrieves route information using Google Maps Directions API.\n", + " \"\"\"Retrieves route information using Google Maps Directions API.\n", " API documentation at https://developers.google.com/maps/documentation/directions/get-directions\n", " \"\"\"\n", " endpoint = \"https://maps.googleapis.com/maps/api/directions/json\"\n", @@ -418,7 +417,6 @@ "\n", "def update_itinerary_with_travel_times(context_variables: dict) -> SwarmResult:\n", " \"\"\"Update the complete itinerary with travel times between each event.\"\"\"\n", - "\n", " \"\"\"\n", " Retrieves route information using Google Maps Directions API.\n", " API documentation at https://developers.google.com/maps/documentation/directions/get-directions\n", @@ -527,7 +525,6 @@ "\n", "def create_structured_itinerary(context_variables: Dict[str, Any], structured_itinerary: str) -> SwarmResult:\n", " \"\"\"Once a structured itinerary is created, store it and pass on to the Route Timing agent.\"\"\"\n", - "\n", " # Ensure the itinerary is confirmed, if not, back to the Planner agent to confirm it with the customer\n", " if not context_variables[\"itinerary_confirmed\"]:\n", " return SwarmResult(\n", diff --git a/notebook/agentchat_tabular_data_rag_workflow.ipynb b/notebook/agentchat_tabular_data_rag_workflow.ipynb new file mode 100644 index 0000000000..e34f03950c --- /dev/null +++ b/notebook/agentchat_tabular_data_rag_workflow.ipynb @@ -0,0 +1,589 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Agentic RAG workflow on tabular data from a PDF file\n", + "\n", + "In this notebook, we're building a workflow to extract accurate tabular data information from a PDF file.\n", + "\n", + "The following bullets summarize the notebook, with highlights being:\n", + "\n", + "- Parse the PDF file and extract tables into images (optional).\n", + "- A single RAG agent fails to get the accurate information from tabular data.\n", + "- An agentic workflow using a groupchat is able to extract information accurately:\n", + " - the agentic workflow uses a RAG agent to extract document metadata (e.g. the image of a data table using just the table name)\n", + " - the table image is converted to Markdown through a multi-modal agent\n", + " - finally, an assistant agent answers the original question with an LLM" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "````{=mdx}\n", + ":::info Requirements\n", + "Unstructured-IO is a dependency for this notebook to parse the PDF. Please install AG2 (with the neo4j extra) and the dependencies:\n", + "\n", + "- Install Poppler https://pdf2image.readthedocs.io/en/latest/installation.html\n", + "- Install Tesseract https://tesseract-ocr.github.io/tessdoc/Installation.html\n", + "- pip install ag2[neo4j], unstructured==0.16.11, pi-heif==0.21.0, unstructured_inference==0.8.1, unstructured.pytesseract==0.3.13, pytesseract==0.3.13\n", + ":::\n", + "````\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set Configuration and OpenAI API Key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import autogen\n", + "\n", + "config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-4o\"],\n", + " },\n", + ")\n", + "os.environ[\"OPENAI_API_KEY\"] = config_list[0][\"api_key\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parse PDF file\n", + "\n", + "**Skip and use parsed files to run the rest.**\n", + "This step is expensive and time consuming, please skip if you don't need to generate the full data set. The **estimated cost is from $10 to $15 to parse the pdf file and build the knowledge graph with entire parsed output**.\n", + "\n", + "For the notebook, we use a common finanical document, [Nvidia 2024 10-K](https://investor.nvidia.com/financial-info/sec-filings/sec-filings-details/default.aspx?FilingId=17293267) as an example ([file download link](https://d18rn0p25nwr6d.cloudfront.net/CIK-0001045810/1cbe8fe7-e08a-46e3-8dcc-b429fc06c1a4.pdf)).\n", + "\n", + "We use Unstructured-IO to parse the PDF, the table and image from the PDF are extracted out as .jpg files.\n", + "\n", + "All parsed output are saved in a JSON file." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from unstructured.partition.pdf import partition_pdf\n", + "from unstructured.staging.base import elements_to_json\n", + "\n", + "file_elements = partition_pdf(\n", + " filename=\"./input_files/nvidia_10k_2024.pdf\",\n", + " strategy=\"hi_res\",\n", + " languages=[\"eng\"],\n", + " infer_table_structure=True,\n", + " extract_images_in_pdf=True,\n", + " extract_image_block_output_dir=\"./parsed_pdf_info\",\n", + " extract_image_block_types=[\"Image\", \"Table\"],\n", + " extract_forms=False,\n", + " form_extraction_skip_tables=False,\n", + ")\n", + "\n", + "elements_to_json(elements=file_elements, filename=\"parsed_elements.json\", encoding=\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create sample dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "output_elements = []\n", + "keys_to_extract = [\"element_id\", \"text\", \"type\"]\n", + "metadata_keys = [\"page_number\", \"parent_id\", \"image_path\"]\n", + "text_types = set([\"Text\", \"UncategorizedText\", \"NarrativeText\"])\n", + "element_length = len(file_elements)\n", + "for idx in range(element_length):\n", + " data = file_elements[idx].to_dict()\n", + " new_data = {key: data[key] for key in keys_to_extract}\n", + " metadata = data[\"metadata\"]\n", + " for key in metadata_keys:\n", + " if key in metadata:\n", + " new_data[key] = metadata[key]\n", + " if data[\"type\"] == \"Table\":\n", + " if idx > 0:\n", + " pre_data = file_elements[idx - 1].to_dict()\n", + " if pre_data[\"type\"] in text_types:\n", + " new_data[\"text\"] = pre_data[\"text\"] + new_data[\"text\"]\n", + " if idx < element_length - 1:\n", + " post_data = file_elements[idx + 1].to_dict()\n", + " if post_data[\"type\"] in text_types:\n", + " new_data[\"text\"] = new_data[\"text\"] + post_data[\"text\"]\n", + " output_elements.append(new_data)\n", + "\n", + "with open(\"proessed_elements.json\", \"w\", encoding=\"utf-8\") as file:\n", + " json.dump(output_elements, file, indent=4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports\n", + "\n", + "**If you want to skip the parsing of the PDF file, you can start here.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# This is needed to allow nested asyncio calls for Neo4j in Jupyter\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()\n", + "\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "from llama_index.llms.openai import OpenAI\n", + "\n", + "from autogen import AssistantAgent, ConversableAgent, UserProxyAgent\n", + "\n", + "# load documents\n", + "from autogen.agentchat.contrib.graph_rag.document import Document, DocumentType\n", + "from autogen.agentchat.contrib.graph_rag.neo4j_graph_query_engine import Neo4jGraphQueryEngine\n", + "from autogen.agentchat.contrib.graph_rag.neo4j_graph_rag_capability import Neo4jGraphCapability\n", + "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a knowledge graph with sample data\n", + "\n", + "To save time and cost, we use a small subset of the data for the notebook.\n", + "\n", + "**This does not change the fact that the native RAG agent solution failed to provide the correct answer.**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "22c02a975b784c5db13ea02163bd140a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Parsing nodes: 0%| | 0/1 [00:00.\".\n", + " For example, when you got message \"The image path for the table titled XYZ is \"./parsed_pdf_info/abcde\".\",\n", + " you will reply \"Please extract table from the following image and convert it to Markdown.\n", + " .\"\n", + " \"\"\",\n", + " llm_config=llm_config,\n", + " human_input_mode=\"NEVER\",\n", + ")\n", + "\n", + "image2table_convertor = MultimodalConversableAgent(\n", + " name=\"image2table_convertor\",\n", + " system_message=\"\"\"\n", + " You are an image to table convertor. You will process an image of one or multiple consecutive tables.\n", + " You need to follow the following steps in sequence,\n", + " 1. extract the complete table contents and structure.\n", + " 2. Make sure the structure is complete and no information is left out. Otherwise, start from step 1 again.\n", + " 3. Correct typos in the text fields.\n", + " 4. In the end, output the table(s) in Markdown.\n", + " \"\"\",\n", + " llm_config={\"config_list\": config_list, \"max_tokens\": 300},\n", + " human_input_mode=\"NEVER\",\n", + " max_consecutive_auto_reply=1,\n", + ")\n", + "\n", + "conclusion = AssistantAgent(\n", + " name=\"conclusion\",\n", + " system_message=\"\"\"You are a helpful assistant.\n", + " Base on the history of the groupchat, answer the original question from User_proxy.\n", + " \"\"\",\n", + " llm_config=llm_config,\n", + " human_input_mode=\"NEVER\", # Never ask for human input.\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "What is goodwill asset (in millions) for 2024 in table NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: table_assistant\n", + "\u001b[0m\n", + "\u001b[33mtable_assistant\u001b[0m (to chat_manager):\n", + "\n", + "Find image_path for Table: NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: nvidia_rag\n", + "\u001b[0m\n", + "\u001b[33mnvidia_rag\u001b[0m (to chat_manager):\n", + "\n", + "The image path for the table titled \"NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets\" is \"./parsed_pdf_info/table-52-17.jpg\".\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: img_request_format\n", + "\u001b[0m\n", + "\u001b[33mimg_request_format\u001b[0m (to chat_manager):\n", + "\n", + "Please extract table from the following image and convert it to Markdown.\n", + ".\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: image2table_convertor\n", + "\u001b[0m\n", + "\u001b[33mimage2table_convertor\u001b[0m (to chat_manager):\n", + "\n", + "Here is the extracted table from the image in Markdown format:\n", + "\n", + "```markdown\n", + "| | Jan 28, 2024 | Jan 29, 2023 |\n", + "|------------------------------------------|--------------|--------------|\n", + "| **Assets** | | |\n", + "| Current assets: | | |\n", + "|     Cash and cash equivalents | $7,280 | $3,389 |\n", + "|     Marketable securities | $18,704 | $9,907 |\n", + "|     Accounts receivable, net | $9,999 | $3,827 |\n", + "|     Inventories | $5,282 | $5,159 |\n", + "|     Prepaid expenses and other current assets | $3,080 | $791 |\n", + "| Total current assets | $44,345 | $23,073 |\n", + "| Property and equipment, net | $3,914 | $3,807 |\n", + "| Operating lease assets | $1,346 | $1,038 |\n", + "| Goodwill | $4,430 | $4,372 |\n", + "| Intangible assets, net | $1,112 | $1,676 |\n", + "| Deferred income tax assets | $6,081 | $3,396 |\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: conclusion\n", + "\u001b[0m\n", + "\u001b[33mconclusion\u001b[0m (to chat_manager):\n", + "\n", + "The goodwill asset for NVIDIA Corporation as of January 28, 2024, is $4,430 million.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: User_proxy\n", + "\u001b[0m\n", + "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n", + "\n", + "What is the total current assets from the table NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets?\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: table_assistant\n", + "\u001b[0m\n", + "\u001b[33mtable_assistant\u001b[0m (to chat_manager):\n", + "\n", + "The total current assets from the table \"NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets\" are $44,345 million as of January 28, 2024.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: nvidia_rag\n", + "\u001b[0m\n", + "\u001b[33mnvidia_rag\u001b[0m (to chat_manager):\n", + "\n", + "Yes, that's correct! The total current assets for NVIDIA Corporation and Subsidiaries as of January 28, 2024, are $44,345 million, according to the table \"NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets.\" If you have any more questions or need further details, feel free to ask!\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\u001b[32m\n", + "Next speaker: img_request_format\n", + "\u001b[0m\n", + "\u001b[33mimg_request_format\u001b[0m (to chat_manager):\n", + "\n", + "Great, if you have any more questions or need further clarification, feel free to ask!\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "groupchat = autogen.GroupChat(\n", + " agents=[\n", + " user_proxy,\n", + " table_assistant,\n", + " rag_agent,\n", + " img_request_format,\n", + " image2table_convertor,\n", + " conclusion,\n", + " ],\n", + " messages=[],\n", + " speaker_selection_method=\"round_robin\",\n", + ")\n", + "manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n", + "chat_result = user_proxy.initiate_chat(\n", + " manager,\n", + " message=\"What is goodwill asset (in millions) for 2024 in table NVIDIA Corporation and Subsidiaries Consolidated Balance Sheets?\",\n", + ")" + ] + } + ], + "metadata": { + "front_matter": { + "description": "Agentic RAG workflow on tabular data from a PDF file", + "tags": [ + "RAG", + "groupchat" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/agentchat_teachable_oai_assistants.ipynb b/notebook/agentchat_teachable_oai_assistants.ipynb index ce4a599374..d7c0e0e7a6 100644 --- a/notebook/agentchat_teachable_oai_assistants.ipynb +++ b/notebook/agentchat_teachable_oai_assistants.ipynb @@ -173,9 +173,7 @@ "\n", "\n", "def get_ossinsight(question):\n", - " \"\"\"\n", - " Retrieve the top 10 developers with the most followers on GitHub.\n", - " \"\"\"\n", + " \"\"\"Retrieve the top 10 developers with the most followers on GitHub.\"\"\"\n", " url = \"https://api.ossinsight.io/explorer/answer\"\n", " headers = {\"Content-Type\": \"application/json\"}\n", " data = {\"question\": question, \"ignoreCache\": True}\n", diff --git a/notebook/agentchat_video_transcript_translate_with_whisper.ipynb b/notebook/agentchat_video_transcript_translate_with_whisper.ipynb index 4da24e20cb..ea3189092a 100644 --- a/notebook/agentchat_video_transcript_translate_with_whisper.ipynb +++ b/notebook/agentchat_video_transcript_translate_with_whisper.ipynb @@ -209,7 +209,7 @@ " except FileNotFoundError:\n", " return \"The specified audio file could not be found.\"\n", " except Exception as e:\n", - " return f\"An unexpected error occurred: {str(e)}\"" + " return f\"An unexpected error occurred: {e!s}\"" ] }, { diff --git a/notebook/agentchat_websockets.ipynb b/notebook/agentchat_websockets.ipynb index b3f7e87879..00d421d2ce 100644 --- a/notebook/agentchat_websockets.ipynb +++ b/notebook/agentchat_websockets.ipynb @@ -26,7 +26,7 @@ "Some extra dependencies are needed for this notebook, which can be installed via pip:\n", "\n", "```bash\n", - "pip install autogen[websockets] fastapi uvicorn\n", + "pip install ag2[websockets] fastapi uvicorn\n", "```\n", "\n", "For more information, please refer to the [installation guide](/docs/installation/Installation).\n", @@ -63,7 +63,7 @@ "config_list = autogen.config_list_from_json(\n", " env_or_file=\"OAI_CONFIG_LIST\",\n", " filter_dict={\n", - " \"model\": [\"gpt-4o\", \"gpt-4o-mini\"],\n", + " \"model\": [\"gpt-4o-mini\"],\n", " },\n", ")" ] @@ -89,7 +89,7 @@ "source": [ "## Defining `on_connect` function\n", "\n", - "An `on_connect` function is a crucial part of applications that utilize websockets, acting as an event handler that is called whenever a new client connection is established. This function is designed to initiate any necessary setup, communication protocols, or data exchange procedures specific to the newly connected client. Essentially, it lays the groundwork for the interactive session that follows, configuring how the server and the client will communicate and what initial actions are to be taken once a connection is made. Now, let's delve into the details of how to define this function, especially in the context of using the AutoGen framework with websockets.\n", + "An `on_connect` function is a crucial part of applications that utilize websockets, acting as an event handler that is called whenever a new client connection is established. This function is designed to initiate any necessary setup, communication protocols, or data exchange procedures specific to the newly connected client. Essentially, it lays the groundwork for the interactive session that follows, configuring how the server and the client will communicate and what initial actions are to be taken once a connection is made. Now, let's delve into the details of how to define this function, especially in the context of using the AG2 framework with websockets.\n", "\n", "\n", "Upon a client's connection to the websocket server, the server automatically initiates a new instance of the [`IOWebsockets`](https://docs.ag2.ai/docs/reference/io/websockets#iowebsockets) class. This instance is crucial for managing the data flow between the server and the client. The `on_connect` function leverages this instance to set up the communication protocol, define interaction rules, and initiate any preliminary data exchanges or configurations required for the client-server interaction to proceed smoothly.\n" @@ -145,7 +145,7 @@ " f\" - on_connect(): Initiating chat with agent {agent} using message '{initial_msg}'\",\n", " flush=True,\n", " )\n", - " user_proxy.initiate_chat( # noqa: F704\n", + " user_proxy.initiate_chat(\n", " agent,\n", " message=initial_msg,\n", " )\n", @@ -234,7 +234,7 @@ "source": [ "## Testing websockets server running inside FastAPI server with HTML/JS client\n", "\n", - "The code snippets below outlines an approach for testing an `on_connect` function in a web environment using [FastAPI](https://fastapi.tiangolo.com/) to serve a simple interactive HTML page. This method allows users to send messages through a web interface, which are then processed by the server running the AutoGen framework via websockets. Here's a step-by-step explanation:\n", + "The code snippets below outlines an approach for testing an `on_connect` function in a web environment using [FastAPI](https://fastapi.tiangolo.com/) to serve a simple interactive HTML page. This method allows users to send messages through a web interface, which are then processed by the server running the AG2 framework via websockets. Here's a step-by-step explanation:\n", "\n", "1. **FastAPI Application Setup**: The code initiates by importing necessary libraries and setting up a FastAPI application. FastAPI is a modern, fast web framework for building APIs with Python 3.7+ based on standard Python type hints.\n", "\n", @@ -246,21 +246,21 @@ "\n", "5. **Starting the FastAPI Application**: Lastly, the FastAPI application is started using Uvicorn, an ASGI server, configured with the app and additional parameters as needed. The server is then launched to serve the FastAPI application, making the interactive HTML page accessible to users.\n", "\n", - "This method of testing allows for interactive communication between the user and the server, providing a practical way to demonstrate and evaluate the behavior of the on_connect function in real-time. Users can send messages through the webpage, and the server processes these messages as per the logic defined in the on_connect function, showcasing the capabilities and responsiveness of the AutoGen framework's websocket handling in a user-friendly manner." + "This method of testing allows for interactive communication between the user and the server, providing a practical way to demonstrate and evaluate the behavior of the on_connect function in real-time. Users can send messages through the webpage, and the server processes these messages as per the logic defined in the on_connect function, showcasing the capabilities and responsiveness of the AG2 framework's websocket handling in a user-friendly manner." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "5e55dc06", "metadata": {}, "outputs": [], "source": [ - "from contextlib import asynccontextmanager # noqa: E402\n", - "from pathlib import Path # noqa: E402\n", + "from contextlib import asynccontextmanager\n", + "from pathlib import Path\n", "\n", - "from fastapi import FastAPI # noqa: E402\n", - "from fastapi.responses import HTMLResponse # noqa: E402\n", + "from fastapi import FastAPI\n", + "from fastapi.responses import HTMLResponse\n", "\n", "PORT = 8000\n", "\n", @@ -268,7 +268,7 @@ "\n", "\n", " \n", - " Autogen websocket test\n", + " AG2 websocket test\n", " \n", " \n", "

WebSocket Chat

\n", @@ -322,11 +322,11 @@ "metadata": {}, "outputs": [], "source": [ - "import uvicorn # noqa: E402\n", + "import uvicorn\n", "\n", "config = uvicorn.Config(app)\n", "server = uvicorn.Server(config)\n", - "await server.serve() # noqa: F704" + "await server.serve()" ] }, { @@ -380,18 +380,101 @@ "metadata": {}, "outputs": [], "source": [ - "from http.server import HTTPServer, SimpleHTTPRequestHandler # noqa: E402\n", + "from http.server import HTTPServer, SimpleHTTPRequestHandler\n", "\n", "PORT = 8000\n", "\n", - "html = \"\"\"\n", + "# Format JSON-based messages for display\n", + "js_formatters = \"\"\"\n", + "function formatMessageContent(content) {\n", + " if (content && typeof content === 'object') {\n", + " // Create a copy without uuid\n", + " const formatted = {};\n", + " for (const [key, value] of Object.entries(content)) {\n", + " if (key !== 'uuid') {\n", + " formatted[key] = value;\n", + " }\n", + " }\n", + " return JSON.stringify(formatted, null, 2);\n", + " }\n", + " return String(content);\n", + "}\n", + "\n", + "function format_message(data) {\n", + " try {\n", + " let msg = typeof data === 'string' ? JSON.parse(data) : data;\n", + " let formatted = {\n", + " type: msg.type || '',\n", + " content: formatMessageContent(msg.content)\n", + " };\n", + "\n", + " // Add any additional fields\n", + " for (const [key, value] of Object.entries(msg)) {\n", + " if (key !== 'type' && key !== 'content') {\n", + " formatted[key] = value;\n", + " }\n", + " }\n", + "\n", + " return JSON.stringify(formatted, null, 2);\n", + " } catch (e) {\n", + " return String(data);\n", + " }\n", + "}\n", + "\"\"\"\n", + "\n", + "html = f\"\"\"\n", "\n", "\n", " \n", - " Autogen websocket test\n", + " AG2 websocket\n", + " \n", + " \n", " \n", " \n", - "

WebSocket Chat

\n", + "

AG2 Structured Messages w/ websockets

\n", "
\n", " \n", " \n", @@ -399,44 +482,47 @@ "
    \n", "
\n", " \n", " \n", "\n", "\"\"\"\n", "\n", + "# The rest of the server setup code remains the same\n", "with TemporaryDirectory() as temp_dir:\n", - " # create a simple HTTP webpage\n", " path = Path(temp_dir) / \"chat.html\"\n", " with open(path, \"w\") as f:\n", " f.write(html)\n", "\n", - " #\n", " class MyRequestHandler(SimpleHTTPRequestHandler):\n", " def __init__(self, *args, **kwargs):\n", " super().__init__(*args, directory=temp_dir, **kwargs)\n", "\n", - " def do_GET(self):\n", + " def do_GET(self): # noqa: N802\n", " if self.path == \"/\":\n", " self.path = \"/chat.html\"\n", " return SimpleHTTPRequestHandler.do_GET(self)\n", "\n", " handler = MyRequestHandler\n", "\n", - " with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8080) as uri:\n", + " with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8082) as uri:\n", " print(f\"Websocket server started at {uri}.\", flush=True)\n", "\n", " with HTTPServer((\"\", PORT), handler) as httpd:\n", @@ -446,14 +532,6 @@ " except KeyboardInterrupt:\n", " print(\" - HTTP server stopped.\", flush=True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "19656d0e", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -465,7 +543,7 @@ ] }, "kernelspec": { - "display_name": "venv", + "display_name": ".venv-3.9", "language": "python", "name": "python3" }, @@ -479,7 +557,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.20" } }, "nbformat": 4, diff --git a/notebook/agentchats_sequential_chats.ipynb b/notebook/agentchats_sequential_chats.ipynb index f822955a5b..9f4209f53f 100644 --- a/notebook/agentchats_sequential_chats.ipynb +++ b/notebook/agentchats_sequential_chats.ipynb @@ -811,9 +811,9 @@ " print(\"Human input in the middle:\", chat_res.human_input)\n", " print(\"Conversation cost: \", chat_res.cost)\n", " if i == 1:\n", - " assert (\n", - " len(chat_res.chat_history) == 4\n", - " ), f\"The chat history should contain at most 4 messages because max_turns is set to 2 in the {i}-th chat.\"\n", + " assert len(chat_res.chat_history) == 4, (\n", + " f\"The chat history should contain at most 4 messages because max_turns is set to 2 in the {i}-th chat.\"\n", + " )\n", " print(\"\\n\\n\")" ] }, diff --git a/notebook/async_human_input.ipynb b/notebook/async_human_input.ipynb index 1db58f271c..892726bcf4 100644 --- a/notebook/async_human_input.ipynb +++ b/notebook/async_human_input.ipynb @@ -31,12 +31,11 @@ "outputs": [], "source": [ "import asyncio\n", - "from typing import Any, Callable, Dict, List, Optional, Tuple, Union\n", + "from typing import Dict, Optional, Union\n", "\n", "import nest_asyncio\n", "\n", "from autogen import AssistantAgent\n", - "from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent\n", "from autogen.agentchat.user_proxy_agent import UserProxyAgent" ] }, @@ -187,7 +186,7 @@ " )\n", "\n", "\n", - "await main() # noqa: F704" + "await main()" ] } ], diff --git a/notebook/autogen_uniformed_api_calling.ipynb b/notebook/autogen_uniformed_api_calling.ipynb index 12914c3914..a2eb057a4d 100644 --- a/notebook/autogen_uniformed_api_calling.ipynb +++ b/notebook/autogen_uniformed_api_calling.ipynb @@ -122,8 +122,7 @@ "\n", "\n", "def model_call_example_function(model: str, message: str, cache_seed: int = 41, print_cost: bool = False):\n", - " \"\"\"\n", - " A helper function that demonstrates how to call different models using the OpenAIWrapper class.\n", + " \"\"\"A helper function that demonstrates how to call different models using the OpenAIWrapper class.\n", " Note the name `OpenAIWrapper` is not accurate, as now it is a wrapper for multiple models, not just OpenAI.\n", " This might be changed in the future.\n", " \"\"\"\n", diff --git a/notebook/dependency_injection.ipynb b/notebook/dependency_injection.ipynb deleted file mode 100644 index 4a7c6f01d4..0000000000 --- a/notebook/dependency_injection.ipynb +++ /dev/null @@ -1,446 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from abc import ABC\n", - "from inspect import signature\n", - "from typing import Annotated, Any, Callable, Optional, Union\n", - "\n", - "from fast_depends import Depends as FastDepends\n", - "from fast_depends import inject\n", - "from pydantic import BaseModel\n", - "\n", - "from autogen.tools import Tool" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class BaseContext(ABC):\n", - " pass\n", - "\n", - "\n", - "class ChatContext(BaseContext):\n", - " messages: list[str] = []\n", - "\n", - "\n", - "def Depends(x: Any) -> Any:\n", - " if isinstance(x, BaseContext):\n", - " return FastDepends(lambda: x)\n", - "\n", - " return FastDepends(x)\n", - "\n", - "\n", - "class Agent:\n", - " def __init__(self):\n", - " self.tools: dict[str, Callable[..., Any]] = {}\n", - "\n", - " def _remove_injected_params_from_signature(self, func: Callable[..., Any]) -> None:\n", - " remove_from_signature = []\n", - " sig = signature(func)\n", - " for param in sig.parameters.values():\n", - " param_annotation = (\n", - " param.annotation.__args__[0] if hasattr(param.annotation, \"__args__\") else param.annotation\n", - " )\n", - " if isinstance(param_annotation, type) and issubclass(param_annotation, BaseContext):\n", - " remove_from_signature.append(param.name)\n", - "\n", - " new_signature = sig.replace(\n", - " parameters=[p for p in sig.parameters.values() if p.name not in remove_from_signature]\n", - " )\n", - " func.__signature__ = new_signature\n", - "\n", - " # Coopied from ConversableAgent\n", - "\n", - " def _create_tool_if_needed(\n", - " self, func_or_tool: Union[Tool, Callable[..., Any]], name: Optional[str], description: Optional[str]\n", - " ) -> Tool:\n", - "\n", - " if isinstance(func_or_tool, Tool):\n", - " tool: Tool = func_or_tool\n", - " tool._name = name or tool.name\n", - " tool._description = description or tool.description\n", - "\n", - " return tool\n", - "\n", - " if isinstance(func_or_tool, Callable):\n", - " # Only next 2 lines are different from the original\n", - " func: Callable[..., Any] = inject(func_or_tool)\n", - " self._remove_injected_params_from_signature(func)\n", - "\n", - " name = name or func.__name__\n", - "\n", - " tool = Tool(name=name, description=description, func=func)\n", - "\n", - " return tool\n", - "\n", - " raise ValueError(\n", - " \"Parameter 'func_or_tool' must be a function or a Tool instance, it is '{type(func_or_tool)}' instead.\"\n", - " )\n", - "\n", - " def register_for_llm(\n", - " self, *, name: Optional[str] = None, description: Optional[str] = None\n", - " ) -> Callable[[Callable[..., Any]], Tool]:\n", - " def decorator(func_or_tool: Union[Callable[..., Any], Tool]) -> Tool:\n", - " nonlocal name, description\n", - "\n", - " tool = self._create_tool_if_needed(func_or_tool, name, description)\n", - "\n", - " return tool\n", - "\n", - " return decorator\n", - "\n", - " def register_for_execution(self, name: Optional[str] = None) -> Callable[[Tool], Tool]:\n", - " def decorator(func_or_tool: Tool) -> Tool:\n", - " nonlocal name\n", - "\n", - " tool = self._create_tool_if_needed(func_or_tool, name, None)\n", - "\n", - " self.tools[tool.name] = tool.func\n", - "\n", - " return tool\n", - "\n", - " return decorator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Context injection\n", - "\n", - "#### Title:\n", - "As a developer, I want to assign a default context value for function parameters to streamline dependency injection in tool definitions.\n", - "\n", - "#### Description:\n", - "This feature allows developers to define functions with parameters that include default values derived from a context object. The context object can be instantiated and injected without manual handling, simplifying API usage.\n", - "\n", - "#### Acceptance Criteria:\n", - "- Functions can specify a default context value as part of their signature.\n", - "- Context values are automatically injected during function execution.\n", - "- The default context parameter must be compatible with the defined BaseContext.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "class MyContext(BaseContext, BaseModel):\n", - " b: int\n", - "\n", - "\n", - "ctx = MyContext(b=2)\n", - "\n", - "agent = Agent()\n", - "\n", - "\n", - "@agent.register_for_llm(description=\"Example function\")\n", - "@agent.register_for_execution()\n", - "def f(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(ctx)],\n", - " # We support the following syntaxes as well:\n", - " # ctx: Annotated[MyContext, Depends(MyContext(b=2))],\n", - " # ctx: MyContext = Depends(MyContext(b=2)), # non-annotated version\n", - " # ctx: MyContext = MyContext(b=2), # non-annotated version for subclasses of BaseContext\n", - ") -> int:\n", - " return a + ctx.b" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "assert \"f\" in agent.tools\n", - "assert isinstance(agent.tools[\"f\"], Callable)\n", - "assert str(signature(agent.tools[\"f\"])) == \"(a: int) -> int\"" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "assert f(1) == 3\n", - "ctx.b = 4\n", - "assert f(1) == 5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Different agent context in each function invocation\n", - "\n", - "#### Title:\n", - "As a developer, I want to inject context objects dynamically into functions without explicitly modifying their signatures.\n", - "\n", - "#### Description:\n", - "This feature allows context objects to be dynamically assigned to functions during registration. The function signature remains unchanged, and the context is removed from the interface seen by LLMs or external users.\n", - "\n", - "#### Acceptance Criteria:\n", - "- Context injection occurs transparently during function execution.\n", - "- Registered functions do not expose the ctx parameter in their final API interface.\n", - "- Functions retain full type safety and compatibility with the BaseContext.\n", - "- Unit tests validate dynamic injection scenarios.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "agent1 = Agent()\n", - "agent2 = Agent()\n", - "user_proxy = Agent()\n", - "\n", - "\n", - "def _f(\n", - " a: int,\n", - " ctx: MyContext,\n", - ") -> int:\n", - " ctx.b = ctx.b + 1\n", - " return a + ctx.b\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent1.register_for_llm(description=\"Example function\")\n", - "def f1(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(MyContext(b=2))],\n", - ") -> int:\n", - " return _f(a, ctx)\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent1.register_for_llm(description=\"Example function\")\n", - "def f2(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(MyContext(b=2))],\n", - ") -> int:\n", - " return _f(a, ctx)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "assert \"f1\" in user_proxy.tools\n", - "assert \"f2\" in user_proxy.tools\n", - "assert isinstance(user_proxy.tools[\"f1\"], Callable)\n", - "assert isinstance(user_proxy.tools[\"f2\"], Callable)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "assert f1(1) == 4\n", - "assert f1(1) == 5\n", - "assert f2(1) == 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## User Story 3\n", - "\n", - "#### Title:\n", - "As a developer, I want multiple agents to share a single context object for efficient and synchronized execution.\n", - "#### Description:\n", - "Enable multiple agents or users to share the same context object. This allows for coordinated interactions and consistency in function behavior across users while minimizing resource overhead.\n", - "#### Acceptance Criteria:\n", - "- A shared context object can be assigned to multiple users.\n", - "- Shared context updates reflect immediately across all agents.\n", - "- Unit tests validate behavior with shared context across agents.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "agent1 = Agent()\n", - "agent2 = Agent()\n", - "user_proxy = Agent()\n", - "\n", - "ctx = MyContext(b=2)\n", - "\n", - "\n", - "def _f(\n", - " a: int,\n", - " ctx: MyContext,\n", - ") -> int:\n", - " ctx.b = ctx.b + 1\n", - " return a + ctx.b\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent1.register_for_llm(description=\"Example function\")\n", - "def f1(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(ctx)],\n", - ") -> int:\n", - " return _f(a, ctx)\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent2.register_for_llm(description=\"Example function\")\n", - "def f2(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(ctx)],\n", - ") -> int:\n", - " return _f(a, ctx)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "assert \"f1\" in user_proxy.tools\n", - "assert \"f2\" in user_proxy.tools\n", - "assert isinstance(user_proxy.tools[\"f1\"], Callable)\n", - "assert isinstance(user_proxy.tools[\"f2\"], Callable)\n", - "assert str(signature(user_proxy.tools[\"f1\"])) == \"(a: int) -> int\"\n", - "assert str(signature(user_proxy.tools[\"f2\"])) == \"(a: int) -> int\"\n", - "assert user_proxy.tools[\"f1\"](1) == 4\n", - "assert user_proxy.tools[\"f1\"](1) == 5\n", - "assert user_proxy.tools[\"f2\"](1) == 6\n", - "assert user_proxy.tools[\"f2\"](1) == 7\n", - "assert user_proxy.tools[\"f1\"](1) == 8" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Test Signature and context injection" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "agent = Agent()\n", - "user_proxy = Agent()\n", - "ctx = MyContext(b=2)\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent.register_for_llm(description=\"Example function\")\n", - "def test_1(\n", - " a: int,\n", - " ctx: Annotated[MyContext, Depends(ctx)],\n", - ") -> int:\n", - " return a + ctx.b\n", - "\n", - "\n", - "assert \"test_1\" in user_proxy.tools\n", - "assert isinstance(user_proxy.tools[\"test_1\"], Callable)\n", - "assert str(signature(user_proxy.tools[\"test_1\"])) == \"(a: int) -> int\"\n", - "assert user_proxy.tools[\"test_1\"](1) == 3" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "agent = Agent()\n", - "user_proxy = Agent()\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent.register_for_llm(description=\"Example function\")\n", - "def test_2(\n", - " a: int,\n", - " ctx: MyContext = Depends(MyContext(b=3)),\n", - ") -> int:\n", - " return a + ctx.b\n", - "\n", - "\n", - "assert \"test_2\" in user_proxy.tools\n", - "assert isinstance(user_proxy.tools[\"test_2\"], Callable)\n", - "assert str(signature(user_proxy.tools[\"test_2\"])) == \"(a: int) -> int\"\n", - "assert user_proxy.tools[\"test_2\"](1) == 4" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "agent = Agent()\n", - "user_proxy = Agent()\n", - "\n", - "\n", - "@user_proxy.register_for_execution()\n", - "@agent.register_for_llm(description=\"Example function\")\n", - "def test_3(\n", - " a: int,\n", - " ctx: MyContext = MyContext(b=4),\n", - ") -> int:\n", - " return a + ctx.b\n", - "\n", - "\n", - "assert \"test_3\" in user_proxy.tools\n", - "assert isinstance(user_proxy.tools[\"test_3\"], Callable)\n", - "assert str(signature(user_proxy.tools[\"test_3\"])) == \"(a: int) -> int\"\n", - "assert user_proxy.tools[\"test_3\"](1) == 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebook/gpt_assistant_agent_function_call.ipynb b/notebook/gpt_assistant_agent_function_call.ipynb index be91088262..f864f5a764 100644 --- a/notebook/gpt_assistant_agent_function_call.ipynb +++ b/notebook/gpt_assistant_agent_function_call.ipynb @@ -53,8 +53,6 @@ }, "outputs": [], "source": [ - "from typing import Annotated, Literal\n", - "\n", "import requests\n", "\n", "import autogen\n", @@ -88,8 +86,7 @@ "outputs": [], "source": [ "def get_dad_jokes(search_term: str, page: int = 1, limit: int = 10) -> str:\n", - " \"\"\"\n", - " Fetches a list of dad jokes based on a search term.\n", + " \"\"\"Fetches a list of dad jokes based on a search term.\n", "\n", " Parameters:\n", " - search_term: The search term to find jokes about.\n", @@ -152,8 +149,7 @@ "outputs": [], "source": [ "def write_to_txt(content: str, filename: str = \"dad_jokes.txt\"):\n", - " \"\"\"\n", - " Writes a formatted string to a text file.\n", + " \"\"\"Writes a formatted string to a text file.\n", " Parameters:\n", "\n", " - content: The formatted string to write.\n", diff --git a/notebook/lats_search.ipynb b/notebook/lats_search.ipynb index 4edd6b1f84..e75d4905e9 100644 --- a/notebook/lats_search.ipynb +++ b/notebook/lats_search.ipynb @@ -29,10 +29,9 @@ "import json\n", "import logging\n", "import os\n", - "import uuid\n", "from typing import Any, Dict, List\n", "\n", - "from autogen import AssistantAgent, ConversableAgent, GroupChat, UserProxyAgent, config_list_from_json" + "from autogen import AssistantAgent, ConversableAgent, UserProxyAgent" ] }, { @@ -137,8 +136,7 @@ "\n", "class Reflection(BaseModel):\n", " reflections: str = Field(\n", - " description=\"The critique and reflections on the sufficiency, superfluency,\"\n", - " \" and general quality of the response\"\n", + " description=\"The critique and reflections on the sufficiency, superfluency, and general quality of the response\"\n", " )\n", " score: int = Field(\n", " description=\"Score from 0-10 on the quality of the candidate response.\",\n", @@ -206,9 +204,7 @@ " self.backpropagate(reflection.normalized_score)\n", "\n", " def __repr__(self) -> str:\n", - " return (\n", - " f\"\"\n", - " )\n", + " return f\"\"\n", "\n", " @property\n", " def is_solved(self) -> bool:\n", @@ -507,8 +503,8 @@ "\n", " return Reflection(reflections=reflections, score=score, found_solution=found_solution)\n", " except Exception as e:\n", - " logging.error(f\"Error in reflection_chain: {str(e)}\", exc_info=True)\n", - " return Reflection(reflections=f\"Error in reflection: {str(e)}\", score=0, found_solution=False)" + " logging.error(f\"Error in reflection_chain: {e!s}\", exc_info=True)\n", + " return Reflection(reflections=f\"Error in reflection: {e!s}\", score=0, found_solution=False)" ] }, { @@ -625,7 +621,7 @@ " return TreeState(root=root, input=state[\"input\"])\n", "\n", " except Exception as e:\n", - " logging.error(f\"Error in generate_initial_response: {str(e)}\", exc_info=True)\n", + " logging.error(f\"Error in generate_initial_response: {e!s}\", exc_info=True)\n", " return TreeState(root=None, input=state[\"input\"])" ] }, @@ -734,7 +730,7 @@ " return TreeState(root=root, input=state[\"input\"])\n", "\n", " except Exception as e:\n", - " logging.error(f\"Error in generate_initial_response: {str(e)}\", exc_info=True)\n", + " logging.error(f\"Error in generate_initial_response: {e!s}\", exc_info=True)\n", " return TreeState(root=None, input=state[\"input\"])" ] }, @@ -777,7 +773,7 @@ " else:\n", " candidates.append(str(response))\n", " except Exception as e:\n", - " logging.error(f\"Error generating candidate: {str(e)}\")\n", + " logging.error(f\"Error generating candidate: {e!s}\")\n", " candidates.append(\"Failed to generate candidate.\")\n", "\n", " if not candidates:\n", @@ -869,7 +865,6 @@ " logger = logging.getLogger(__name__)\n", "\n", " try:\n", - "\n", " state = {\"input\": input_query, \"root\": None}\n", " try:\n", " state = generate_initial_response(state)\n", @@ -878,7 +873,7 @@ " return \"Failed to generate initial response.\"\n", " logger.info(\"Initial response generated successfully\")\n", " except Exception as e:\n", - " logger.error(f\"Error generating initial response: {str(e)}\", exc_info=True)\n", + " logger.error(f\"Error generating initial response: {e!s}\", exc_info=True)\n", " return \"Failed to generate initial response due to an unexpected error.\"\n", "\n", " for iteration in range(max_iterations):\n", @@ -896,7 +891,7 @@ " )\n", " logger.info(f\"Completed iteration {iteration + 1}\")\n", " except Exception as e:\n", - " logger.error(f\"Error during iteration {iteration + 1}: {str(e)}\", exc_info=True)\n", + " logger.error(f\"Error during iteration {iteration + 1}: {e!s}\", exc_info=True)\n", " continue\n", "\n", " if not isinstance(state, dict) or \"root\" not in state or state[\"root\"] is None:\n", @@ -913,8 +908,8 @@ " logger.info(\"LATS search completed successfully\")\n", " return result\n", " except Exception as e:\n", - " logger.error(f\"An unexpected error occurred during LATS execution: {str(e)}\", exc_info=True)\n", - " return f\"An unexpected error occurred: {str(e)}\"" + " logger.error(f\"An unexpected error occurred during LATS execution: {e!s}\", exc_info=True)\n", + " return f\"An unexpected error occurred: {e!s}\"" ] }, { @@ -959,8 +954,8 @@ " print(f\"Question: {question}\")\n", " print(f\"Answer: {result}\")\n", " except Exception as e:\n", - " logger.error(f\"An error occurred while processing the question: {str(e)}\", exc_info=True)\n", - " print(f\"An error occurred: {str(e)}\")\n", + " logger.error(f\"An error occurred while processing the question: {e!s}\", exc_info=True)\n", + " print(f\"An error occurred: {e!s}\")\n", " finally:\n", " print(\"---\")" ] diff --git a/notebook/oai_chatgpt_gpt4.ipynb b/notebook/oai_chatgpt_gpt4.ipynb index af1edb3922..9e7699ab51 100644 --- a/notebook/oai_chatgpt_gpt4.ipynb +++ b/notebook/oai_chatgpt_gpt4.ipynb @@ -83,8 +83,6 @@ }, "outputs": [], "source": [ - "import logging\n", - "\n", "import datasets\n", "\n", "import autogen\n", diff --git a/notebook/tools_chat_context_dependency_injection.ipynb b/notebook/tools_chat_context_dependency_injection.ipynb new file mode 100644 index 0000000000..a3fbe8dfd4 --- /dev/null +++ b/notebook/tools_chat_context_dependency_injection.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chat Context Dependency Injection\n", + "\n", + "In this tutorial, we’ll build upon the concepts introduced in the [Tools with Dependency Injection](https://github.com/ag2ai/ag2/blob/main/notebook/tools_dependency_injection.ipynb) notebook to demonstrate how to use `ChatContext` for more advanced workflows.\n", + "\n", + "By leveraging `ChatContext`, we can track the flow of conversations, ensuring proper function execution order. For example, before retrieving a user’s account balance, we’ll ensure the user has logged in first. This approach prevents unauthorized actions and enhances security.\n", + "\n", + "**Benefits of Using `ChatContext`**\n", + "- Flow Control: It helps enforce the correct sequence of function calls.\n", + "- Enhanced Security: Ensures actions depend on preconditions like authentication.\n", + "- Simplified Debugging: Logs conversation history, making it easier to trace issues.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "To install `AG2`, simply run the following command:\n", + "\n", + "```bash\n", + "pip install ag2\n", + "```\n", + "\n", + "\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from typing import Annotated, Literal\n", + "\n", + "from pydantic import BaseModel\n", + "\n", + "from autogen.agentchat import AssistantAgent, UserProxyAgent\n", + "from autogen.tools.dependency_injection import BaseContext, ChatContext, Depends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define BaseContext Class\n", + "\n", + "The following `BaseContext` class and helper functions are adapted from the previous tutorial. They define the structure for securely handling account data and operations like login and balance retrieval." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class Account(BaseContext, BaseModel):\n", + " username: str\n", + " password: str\n", + " currency: Literal[\"USD\", \"EUR\"] = \"USD\"\n", + "\n", + "\n", + "alice_account = Account(username=\"alice\", password=\"password123\")\n", + "bob_account = Account(username=\"bob\", password=\"password456\")\n", + "\n", + "account_ballace_dict = {\n", + " (alice_account.username, alice_account.password): 300,\n", + " (bob_account.username, bob_account.password): 200,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper Functions\n", + "\n", + "These functions validate account credentials and retrieve account balances." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def _verify_account(account: Account):\n", + " if (account.username, account.password) not in account_ballace_dict:\n", + " raise ValueError(\"Invalid username or password\")\n", + "\n", + "\n", + "def _get_balance(account: Account):\n", + " _verify_account(account)\n", + " return f\"Your balance is {account_ballace_dict[(account.username, account.password)]}{account.currency}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Agent Configuration\n", + "\n", + "Configure the agents for the interaction.\n", + "\n", + "- `config_list` defines the LLM configurations, including the model and API key.\n", + "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n", + "- `AssistantAgent` represents the AI agent, configured with the LLM settings." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "config_list = [{\"model\": \"gpt-4o-mini\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", + "agent = AssistantAgent(\n", + " name=\"agent\",\n", + " llm_config={\"config_list\": config_list},\n", + ")\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy_1\",\n", + " human_input_mode=\"NEVER\",\n", + " llm_config=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Injecting a ChatContext Parameter\n", + "\n", + "Now let’s upgrade the example from the previous tutorial by introducing the `ChatContext` parameter. This enhancement allows us to enforce proper execution order in the workflow, ensuring that users log in before accessing sensitive data like account balances.\n", + "\n", + "The following functions will be registered:\n", + "\n", + "- `login`: Verifies the user’s credentials and ensures they are logged in.\n", + "- `get_balance`: Retrieves the account balance but only if the user has successfully logged in first.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "@user_proxy.register_for_execution()\n", + "@agent.register_for_llm(description=\"Login\")\n", + "def login(\n", + " account: Annotated[Account, Depends(bob_account)],\n", + ") -> str:\n", + " _verify_account(account)\n", + " return \"You are logged in\"\n", + "\n", + "\n", + "@user_proxy.register_for_execution()\n", + "@agent.register_for_llm(description=\"Get balance\")\n", + "def get_balance(\n", + " account: Annotated[Account, Depends(bob_account)],\n", + " chat_context: ChatContext,\n", + ") -> str:\n", + " _verify_account(account)\n", + "\n", + " # Extract the list of messages exchanged with the first agent in the conversation.\n", + " # The chat_context.chat_messages is a dictionary where keys are agents (objects)\n", + " # and values are lists of message objects. We take the first value (messages of the first agent).\n", + " messages_with_first_agent = list(chat_context.chat_messages.values())[0]\n", + "\n", + " login_function_called = False\n", + " for message in messages_with_first_agent:\n", + " if \"tool_calls\" in message and message[\"tool_calls\"][0][\"function\"][\"name\"] == \"login\":\n", + " login_function_called = True\n", + " break\n", + "\n", + " if not login_function_called:\n", + " raise ValueError(\"Please login first\")\n", + "\n", + " balance = _get_balance(account)\n", + " return balance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we initiate a chat to retrieve the balance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_proxy.initiate_chat(agent, message=\"Get users balance\", max_turns=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "front_matter": { + "description": "Chat Context Dependency Injection", + "tags": [ + "tools", + "dependency injection", + "function calling" + ] + }, + "kernelspec": { + "display_name": ".venv-develop", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebook/tools_dependency_injection.ipynb b/notebook/tools_dependency_injection.ipynb index 0dd9ba489c..c34dd9fdcd 100644 --- a/notebook/tools_dependency_injection.ipynb +++ b/notebook/tools_dependency_injection.ipynb @@ -1,8 +1,53 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tools with Dependency Injection\n", + "\n", + "[Dependency Injection](https://en.wikipedia.org/wiki/Dependency_injection) is a secure way to connect external functions to agents without exposing sensitive data such as passwords, tokens, or personal information. This approach ensures that sensitive information remains protected while still allowing agents to perform their tasks effectively, even when working with large language models (LLMs).\n", + "\n", + "In this guide, we'll explore how to build secure workflows that handle sensitive data safely.\n", + "\n", + "As an example, we'll create an agent that retrieves user's account balance. The best part is that sensitive data like username and password are never shared with the LLM. Instead, it's securely injected directly into the function at runtime, keeping it safe while maintaining seamless functionality.\n", + "\n", + "\n", + "## Why Dependency Injection Is Essential\n", + "\n", + "Here's why dependency injection is a game-changer for secure LLM workflows:\n", + "\n", + "- **Enhanced Security**: Your sensitive data is never directly exposed to the LLM.\n", + "- **Simplified Development**: Secure data can be seamlessly accessed by functions without requiring complex configurations.\n", + "- **Unmatched Flexibility**: It supports safe integration of diverse workflows, allowing you to scale and adapt with ease.\n", + "\n", + "In this guide, we'll explore how to set up dependency injection and build secure workflows. Let's dive in!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "To install `AG2`, simply run the following command:\n", + "\n", + "```bash\n", + "pip install ag2\n", + "```\n", + "\n", + "\n", + "### Imports\n", + "\n", + "The functionality demonstrated in this guide is located in the `autogen.tools.dependency_injection` module. This module provides key components for dependency injection:\n", + "\n", + "- `BaseContext`: abstract base class used to define and encapsulate data contexts, such as user account information, which can then be injected into functions or agents securely.\n", + "- `Depends`: a function used to declare and inject dependencies, either from a context (like `BaseContext`) or a function, ensuring sensitive data is provided securely without direct exposure." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -17,77 +62,123 @@ ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "config_list = [{\"model\": \"gpt-4o-mini\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", - "assistant = ConversableAgent(\n", - " name=\"assistant\",\n", - " llm_config={\"config_list\": config_list},\n", - ")\n", - "user_proxy = UserProxyAgent(\n", - " name=\"user_proxy_1\",\n", - " human_input_mode=\"NEVER\",\n", - " llm_config=False,\n", - ")" + "### Define a BaseContext Class\n", + "We start by defining a `BaseContext` class for accounts. This will act as the base structure for dependency injection. By using this approach, sensitive information like usernames and passwords is never exposed to the LLM." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "account_ballace_dict = {\n", - " (\"alice\", \"password123\"): 300,\n", - " (\"bob\", \"password456\"): 200,\n", - " (\"charlie\", \"password789\"): 100,\n", - "}\n", - "\n", - "\n", "class Account(BaseContext, BaseModel):\n", " username: str\n", " password: str\n", " currency: Literal[\"USD\", \"EUR\"] = \"USD\"\n", "\n", "\n", + "alice_account = Account(username=\"alice\", password=\"password123\")\n", "bob_account = Account(username=\"bob\", password=\"password456\")\n", "\n", - "\n", + "account_ballace_dict = {\n", + " (alice_account.username, alice_account.password): 300,\n", + " (bob_account.username, bob_account.password): 200,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper Functions\n", + "To ensure that the provided account is valid and retrieve its balance, we create two helper functions." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ "def _verify_account(account: Account):\n", " if (account.username, account.password) not in account_ballace_dict:\n", " raise ValueError(\"Invalid username or password\")\n", "\n", "\n", - "@user_proxy.register_for_execution()\n", - "@assistant.register_for_llm(description=\"Get the balance of the account\")\n", - "def get_balance(\n", - " # Account which will be injected to the function\n", - " account: Annotated[Account, Depends(bob_account)],\n", - " # It is also possible to use the following syntax to define the dependency\n", - " # account: Account = Depends(bob_account),\n", - ") -> str:\n", + "def _get_balance(account: Account):\n", " _verify_account(account)\n", - " return f\"Your balance is {account_ballace_dict[(account.username, account.password)]}{account.currency}\"\n", + " return f\"Your balance is {account_ballace_dict[(account.username, account.password)]}{account.currency}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Agent Configuration\n", "\n", + "Configure the agents for the interaction.\n", "\n", + "- `config_list` defines the LLM configurations, including the model and API key.\n", + "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n", + "- `AssistantAgent` represents the AI agent, configured with the LLM settings." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "config_list = [{\"model\": \"gpt-4o-mini\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", + "\n", + "assistant = ConversableAgent(\n", + " name=\"assistant\",\n", + " llm_config={\"config_list\": config_list},\n", + ")\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy_1\",\n", + " human_input_mode=\"NEVER\",\n", + " llm_config=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Injecting a BaseContext Parameter\n", + "\n", + "In the example below we register the function and use dependency injection to automatically inject the bob_account Account object into the function. This `account` parameter will not be visible to the LLM.\n", + "\n", + "**Note:** You can also use `account: Account = Depends(bob_account)` as an alternative syntax." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ "@user_proxy.register_for_execution()\n", - "@assistant.register_for_llm(description=\"Set the balance of the account\")\n", - "def update_balance_with_interest(\n", - " amount: Annotated[float, \"Ammount of money to set\"],\n", + "@assistant.register_for_llm(description=\"Get the balance of the account\")\n", + "def get_balance_1(\n", " # Account which will be injected to the function\n", " account: Annotated[Account, Depends(bob_account)],\n", - " # Default interest which will be injected to the function\n", - " interest: Annotated[float, Depends(lambda: 0.02)],\n", " # It is also possible to use the following syntax to define the dependency\n", - " # interest: float = Depends(lambda: 0.02),\n", + " # account: Account = Depends(bob_account),\n", ") -> str:\n", - " _verify_account(account)\n", - " account_ballace_dict[(account.username, account.password)] += amount * (1 + interest)\n", - "\n", - " return \"Balanace has been updated successfully\"" + " return _get_balance(account)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we initiate a chat to retrieve the balance." ] }, { @@ -96,100 +187,230 @@ "metadata": {}, "outputs": [], "source": [ - "message = \"Update balance with 500 and verify if the balance was set correctly\"\n", - "user_proxy.initiate_chat(assistant, message=message, max_turns=2)" + "user_proxy.initiate_chat(assistant, message=\"Get the user's account balance\", max_turns=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Multiple Agents With Different Parameters" + "## Injecting Parameters Without BaseContext\n", + "\n", + "Sometimes, you might not want to use `BaseContext`. Here's how to inject simple parameters directly.\n", + "\n", + "### Agent Configuration\n", + "\n", + "Configure the agents for the interaction.\n", + "\n", + "- `config_list` defines the LLM configurations, including the model and API key.\n", + "- `UserProxyAgent` simulates user inputs without requiring actual human interaction (set to `NEVER`).\n", + "- `AssistantAgent` represents the AI agent, configured with the LLM settings." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "config_list = [{\"model\": \"gpt-4o-mini\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]\n", - "llm_config = {\"config_list\": config_list}\n", - "assistant_1 = ConversableAgent(\n", - " name=\"assistant_1\",\n", - " llm_config={\"config_list\": config_list},\n", - ")\n", - "assistant_2 = ConversableAgent(\n", - " name=\"assistant_2\",\n", + "assistant = ConversableAgent(\n", + " name=\"assistant\",\n", " llm_config={\"config_list\": config_list},\n", ")\n", "user_proxy = UserProxyAgent(\n", " name=\"user_proxy_1\",\n", " human_input_mode=\"NEVER\",\n", " llm_config=False,\n", - ")\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register the Function with Direct Parameter Injection\n", + "Instead of injecting a full context like `Account`, you can directly inject individual parameters, such as the username and password, into a function. This allows for more granular control over the data injected into the function, and still ensures that sensitive information is managed securely.\n", "\n", - "groupchat = GroupChat(agents=[user_proxy, assistant_1, assistant_2], messages=[], max_round=5)\n", - "manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)" + "Here’s how you can set it up:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ - "account_ballace_dict = {\n", - " (\"alice\", \"password123\"): 300,\n", - " (\"bob\", \"password456\"): 200,\n", - " (\"charlie\", \"password789\"): 100,\n", - "}\n", + "def get_username() -> str:\n", + " return \"bob\"\n", "\n", "\n", - "class Account(BaseContext, BaseModel):\n", - " username: str\n", - " password: str\n", - " currency: Literal[\"USD\", \"EUR\"] = \"USD\"\n", + "def get_password() -> str:\n", + " return \"password456\"\n", "\n", "\n", - "alice_account = Account(username=\"alice\", password=\"password123\")\n", - "bob_account = Account(username=\"bob\", password=\"password456\")\n", + "@user_proxy.register_for_execution()\n", + "@assistant.register_for_llm(description=\"Get the balance of the account\")\n", + "def get_balance_2(\n", + " username: Annotated[str, Depends(get_username)],\n", + " password: Annotated[str, Depends(get_password)],\n", + " # or use lambdas\n", + " # username: Annotated[str, Depends(lambda: \"bob\")],\n", + " # password: Annotated[str, Depends(lambda: \"password456\")],\n", + ") -> str:\n", + " account = Account(username=username, password=password)\n", + " return _get_balance(account)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initiate the Chat\n", + "As before, initiate a chat to test the function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "user_proxy.initiate_chat(assistant, message=\"Get users balance\", max_turns=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aligning Contexts to Agents\n", "\n", + "You can match specific dependencies, such as 3rd party system credentials, with specific agents by using tools with dependency injection.\n", "\n", - "def _verify_account(account: Account):\n", - " if (account.username, account.password) not in account_ballace_dict:\n", - " raise ValueError(\"Invalid username or password\")\n", + "In this example we have 2 external systems and have 2 related login credentials. We don't want or need the LLM to be aware of these credentials.\n", + "\n", + "### Mock third party systems" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Mock third party system functions\n", + "# Imagine that these use the username and password to authenticate\n", "\n", "\n", - "def _get_balance(account: Account):\n", - " _verify_account(account)\n", - " return f\"Your balance is {account_ballace_dict[(account.username, account.password)]}{account.currency}\"\n", + "def weather_api_call(username: str, password: str, location: str) -> str:\n", + " print(f\"Accessing third party Weather System using username {username}\")\n", + " return \"It's sunny and 40 degrees Celsius in Sydney, Australia.\"\n", + "\n", + "\n", + "def my_ticketing_system_availability(username: str, password: str, concert: str) -> bool:\n", + " print(f\"Accessing third party Ticketing System using username {username}\")\n", + " return False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Agents" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "weather_agent = ConversableAgent(\n", + " name=\"weather_agent\",\n", + " system_message=\"You are a Weather Agent, you can only get the weather.\",\n", + " description=\"Weather Agent solely used for getting weather.\",\n", + " llm_config={\"config_list\": config_list},\n", + ")\n", + "\n", + "ticket_agent = ConversableAgent(\n", + " name=\"ticket_agent\",\n", + " system_message=\"You are a Ticketing Agent, you can only get ticket availability.\",\n", + " description=\"Ticketing Agent solely used for getting ticket availability.\",\n", + " llm_config={\"config_list\": config_list},\n", + ")\n", + "user_proxy = UserProxyAgent(\n", + " name=\"user_proxy_1\",\n", + " human_input_mode=\"NEVER\",\n", + " llm_config=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create BaseContext class for credentials" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "# We create a class based on BaseContext\n", + "class ThirdPartyCredentials(BaseContext, BaseModel):\n", + " username: str\n", + " password: str" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create Credentials and Functions with Dependency Injection" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Weather API\n", + "weather_account = ThirdPartyCredentials(username=\"ag2weather\", password=\"wbkvEehV1A\")\n", "\n", "\n", "@user_proxy.register_for_execution()\n", - "@assistant_1.register_for_llm(description=\"Get the balance of the account\")\n", - "def get_balance_for_assistant_1(\n", - " account: Annotated[Account, Depends(alice_account)],\n", + "@weather_agent.register_for_llm(description=\"Get the weather for a location\")\n", + "def get_weather(\n", + " location: str,\n", + " credentials: Annotated[ThirdPartyCredentials, Depends(weather_account)],\n", ") -> str:\n", - " return _get_balance(account)\n", + " # Access the Weather API using the credentials\n", + " return weather_api_call(username=credentials.username, password=credentials.password, location=location)\n", + "\n", + "\n", + "# Ticketing System\n", + "ticket_system_account = ThirdPartyCredentials(username=\"ag2tickets\", password=\"EZRIVeVWvA\")\n", "\n", "\n", "@user_proxy.register_for_execution()\n", - "@assistant_2.register_for_llm(description=\"Get the balance of the account\")\n", - "def get_balance_for_assistant_2(\n", - " account: Annotated[Account, Depends(bob_account)],\n", - ") -> str:\n", - " return _get_balance(account)" + "@ticket_agent.register_for_llm(description=\"Get the availability of tickets for a concert\")\n", + "def tickets_available(\n", + " concert_name: str,\n", + " credentials: Annotated[ThirdPartyCredentials, Depends(ticket_system_account)],\n", + ") -> bool:\n", + " return my_ticketing_system_availability(\n", + " username=credentials.username, password=credentials.password, concert=concert_name\n", + " )" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "message = \"Both assistants, please get the balance of the account\"\n", - "user_proxy.initiate_chat(manager, message=message, max_turns=1)" + "### Create Group Chat and run" ] }, { @@ -197,12 +418,29 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "groupchat = GroupChat(agents=[user_proxy, weather_agent, ticket_agent], messages=[], max_round=5)\n", + "manager = GroupChatManager(groupchat=groupchat, llm_config={\"config_list\": config_list})\n", + "\n", + "message = (\n", + " \"Start by getting the weather for Sydney, Australia, and follow that up by checking \"\n", + " \"if there are tickets for the 'AG2 Live' concert.\"\n", + ")\n", + "user_proxy.initiate_chat(manager, message=message, max_turns=1)" + ] } ], "metadata": { + "front_matter": { + "description": "Tools Dependency Injection", + "tags": [ + "tools", + "dependency injection", + "function calling" + ] + }, "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -216,7 +454,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.16" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index c9a6b572c6..c578574108 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,257 @@ +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + [metadata] license_file = "LICENSE" description-file = "README.md" +[project] +name = "pyautogen" +description = "A programming framework for agentic AI" +readme = "README.md" +authors = [ + {name = "Chi Wang", email = "support@ag2.ai"}, + {name = "Qingyun Wu", email = "support@ag2.ai"}, +] + +keywords = [ + "ai", + "agent", + "autogen", + "ag2", + "pyautogen", + "ag2.ai", + "ag2ai", + "agentic" +] + +requires-python = ">=3.9,<3.14" + +dynamic = ["version"] + +classifiers = [ + "Development Status :: 5 - Production/Stable", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python", + "Programming Language :: Python :: Implementation :: CPython", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Operating System :: OS Independent", + "Topic :: Software Development :: Libraries :: Application Frameworks", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Software Development :: Libraries", + "Topic :: Software Development", + "Intended Audience :: Developers", + "Intended Audience :: Information Technology", +] + +dependencies = [ + "openai>=1.58", + "diskcache", + "termcolor", + "flaml", + # numpy is installed by flaml, but we want to pin the version to below 2.x (see https://github.com/microsoft/autogen/issues/1960) + "numpy>=2.1; python_version>='3.13'", # numpy 2.1+ required for Python 3.13 + "numpy>=1.24.0,<2.0.0; python_version<'3.13'", # numpy 1.24+ for older Python versions + "python-dotenv", + "tiktoken", + # Disallowing 2.6.0 can be removed when this is fixed https://github.com/pydantic/pydantic/issues/8705 + "pydantic>=1.10,<3,!=2.6.0", # could be both V1 and V2 + "docker", + "packaging", + "websockets>=14,<15", + "asyncer==0.0.8", + "fast_depends>=2.4.12,<3", +] + +[project.optional-dependencies] + +# public distributions +jupyter-executor = [ + "jupyter-kernel-gateway", + "websocket-client", + "requests", + "jupyter-client>=8.6.0", + "ipykernel>=6.29.0", +] + +retrievechat = [ + "protobuf==4.25.3", + "chromadb==0.5.3", + "sentence_transformers", + "pypdf", + "ipython", + "beautifulsoup4", + "markdownify", +] + +retrievechat-pgvector = [ + "pyautogen[retrievechat]", + "pgvector>=0.2.5", + "psycopg[binary]>=3.1.18; platform_system=='Windows' or platform_system=='Darwin'", + "psycopg>=3.1.18; platform_system=='Linux'", +] + +retrievechat-mongodb = [ + "pyautogen[retrievechat]", + "pymongo>=4.0.0", +] + +retrievechat-qdrant = [ + "pyautogen[retrievechat]", + "qdrant_client", + "fastembed>=0.3.1", +] + +graph-rag-falkor-db = [ + "graphrag_sdk==0.3.3", + "falkordb>=1.0.10" +] + +neo4j = [ + "docx2txt==0.8", + "llama-index==0.12.5", + "llama-index-graph-stores-neo4j==0.4.2", + "llama-index-core==0.12.5", + "llama-index-readers-web==0.3.3", +] + +# used for agentchat_realtime_swarm notebook and realtime agent twilio demo +twilio = [ + "fastapi>=0.115.0,<1", + "uvicorn>=0.30.6,<1", + "twilio>=9.3.2" +] + +interop-crewai = [ + "crewai[tools]>=0.86,<1; python_version>='3.10' and python_version<'3.13'", + "weaviate-client==4.10.2; python_version>='3.10' and python_version<'3.13'", + # crewai uses litellm, litellm introduced uvloop as deps with version 1.57.5 which does not support win32 + "litellm<1.57.5; sys_platform=='win32'", +] +interop-langchain = ["langchain-community>=0.3.12,<1"] +interop-pydantic-ai = ["pydantic-ai==0.0.13"] +interop =[ + "pyautogen[interop-crewai, interop-langchain, interop-pydantic-ai]", +] + +# pysqlite3-binary used so it doesn't need to compile pysqlite3 +autobuild = ["chromadb", "sentence-transformers", "huggingface-hub", "pysqlite3-binary"] + +blendsearch = ["flaml[blendsearch]"] +mathchat = ["sympy", "pydantic==1.10.9", "wolframalpha"] +captainagent = ["pyautogen[autobuild]", "pandas"] +teachable = ["chromadb"] +lmm = ["replicate", "pillow"] +graph = ["networkx", "matplotlib"] +gemini = [ + "google-generativeai>=0.5,<1", + "google-cloud-aiplatform", + "google-auth", + "pillow", + "pydantic", + "jsonschema", +] +together = ["together>=1.2"] +websurfer = ["beautifulsoup4", "markdownify", "pdfminer.six", "pathvalidate"] +redis = ["redis"] +cosmosdb = ["azure-cosmos>=4.2.0"] +websockets = ["websockets>=14.0,<15"] +long-context = ["llmlingua<0.3"] +anthropic = ["anthropic[vertex]>=0.23.1"] +cerebras = ["cerebras_cloud_sdk>=1.0.0"] +mistral = ["mistralai>=1.0.1"] +groq = ["groq>=0.9.0"] +cohere = ["cohere>=5.5.8"] +ollama = ["ollama>=0.3.3", "fix_busted_json>=0.0.18"] +bedrock = ["boto3>=1.34.149"] + +## dev dependencies + +# test dependencies +test = [ + "ipykernel", + "nbconvert", + "nbformat", + "pre-commit", + "pytest-cov>=5", + "pytest-asyncio", + "pytest>=8,<9", + "pandas", + "fastapi>=0.115.0,<1", +] + +docs = [ + "pydoc-markdown", + "pyyaml==6.0.2", + "termcolor", + "nbclient", +] + +types = [ + "mypy==1.9.0", + "pyautogen[test, jupyter-executor, interop]", +] + +lint = [ + "ruff==0.9.1", + "codespell==2.3.0", + "pyupgrade-directories==0.3.0", +] + +dev = [ + "pyautogen[lint,test,types,docs]", + "pre-commit==4.0.1", + "detect-secrets==1.5.0", + "uv==0.5.16", +] + + +[project.urls] +Homepage = "https://ag2.ai/" +Documentation = "https://docs.ag2.ai/docs/Home" +Tracker = "https://github.com/ag2ai/ag2/issues" +Source = "https://github.com/ag2ai/ag2" +Discord = "https://discord.gg/pAbnFJrkgZ" + +[tool.hatch.version] +path = "autogen/version.py" + +[tool.hatch.build] +skip-excluded-dirs = true +exclude = ["/test", "/notebook"] + +[tool.hatch.build.targets.wheel] +packages = ["autogen"] +only-include = ["autogen", "autogen/agentchat/contrib/captainagent/tools"] + +[tool.hatch.build.targets.sdist] +exclude = ["test", "notebook"] + +[tool.hatch.build.targets.wheel.sources] +"autogen" = "autogen" +"autogen/agentchat/contrib/captainagent/tools" = "autogen/agentchat/contrib/captainagent/tools" + [tool.pytest.ini_options] addopts = '--cov=. --cov-append --cov-branch --cov-report=xml -m "not conda"' -markers = ["conda: test related to conda forge distribution"] +testpaths = [ + "test", +] +markers = [ + "conda: test related to conda forge distribution", + "all", + "openai", + "gemini", + "redis", + "docker", +] [tool.black] # https://github.com/psf/black @@ -14,21 +260,44 @@ exclude = "(.eggs|.git|.hg|.mypy_cache|.venv|_build|buck-out|build|dist)" [tool.ruff] +fix = true line-length = 120 +target-version = 'py39' +#include = ["autogen", "test", "docs"] +#exclude = [] [tool.ruff.lint] # Enable Pyflakes `E` and `F` codes by default. select = [ - "E", - "W", # see: https://pypi.org/project/pycodestyle - "F", # see: https://pypi.org/project/pyflakes - # "D", # see: https://pypi.org/project/pydocstyle - # "N", # see: https://pypi.org/project/pep8-naming - # "S", # see: https://pypi.org/project/flake8-bandit - "I", # see: https://pypi.org/project/isort/ + "E", # pycodestyle errors https://docs.astral.sh/ruff/rules/#error-e + "W", # pycodestyle warnings https://docs.astral.sh/ruff/rules/#warning-w + "C90", # mccabe https://docs.astral.sh/ruff/rules/#mccabe-c90 + "N", # pep8-naming https://docs.astral.sh/ruff/rules/#pep8-naming-n +# "D", # pydocstyle https://docs.astral.sh/ruff/rules/#pydocstyle-d + "I", # isort https://docs.astral.sh/ruff/rules/#isort-i + "F", # pyflakes https://docs.astral.sh/ruff/rules/#pyflakes-f + "ASYNC", # flake8-async https://docs.astral.sh/ruff/rules/#flake8-async-async +# "C4", # flake8-comprehensions https://docs.astral.sh/ruff/rules/#flake8-comprehensions-c4 +# "B", # flake8-bugbear https://docs.astral.sh/ruff/rules/#flake8-bugbear-b + "Q", # flake8-quotes https://docs.astral.sh/ruff/rules/#flake8-quotes-q +# "T20", # flake8-print https://docs.astral.sh/ruff/rules/#flake8-print-t20 +# "SIM", # flake8-simplify https://docs.astral.sh/ruff/rules/#flake8-simplify-sim +# "PT", # flake8-pytest-style https://docs.astral.sh/ruff/rules/#flake8-pytest-style-pt +# "PTH", # flake8-use-pathlib https://docs.astral.sh/ruff/rules/#flake8-use-pathlib-pth +# "TCH", # flake8-type-checking https://docs.astral.sh/ruff/rules/#flake8-type-checking-tch +# "RUF", # Ruff-specific rules https://docs.astral.sh/ruff/rules/#ruff-specific-rules-ruf +# "PERF", # Perflint https://docs.astral.sh/ruff/rules/#perflint-perf + "RUF022", # Sort __all__ https://docs.astral.sh/ruff/rules/unsorted-dunder-all/ ] -ignore = ["E501", "F401", "F403", "C901"] +ignore = ["E501", "F403", "C901", + "E402", + "E721", + "ASYNC109", + "E501", # line too long, handled by formatter later + "D100", "D101", "D102", "D103", "D104", + "C901", # too complex +] # Exclude a variety of commonly ignored directories. exclude = [ ".eggs", @@ -45,12 +314,21 @@ exclude = [ "math_utils\\.py$", "**/cap/py/autogencap/proto/*", ] -unfixable = ["F401"] [tool.ruff.lint.mccabe] # Unlike Flake8, default to a complexity level of 10. max-complexity = 10 +[tool.ruff.lint.isort] +case-sensitive = true + +[tool.ruff.format] +docstring-code-format = true + +[tool.ruff.lint.pydocstyle] +convention = "google" + + [tool.mypy] files = [ "autogen/logger", diff --git a/scripts/devcontainer/generate-devcontainers.py b/scripts/devcontainer/generate-devcontainers.py new file mode 100644 index 0000000000..b6a6e04dbb --- /dev/null +++ b/scripts/devcontainer/generate-devcontainers.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +# A script to generate devcontainer files for different python versions + +from pathlib import Path + +from jinja2 import Template + +# List of python versions to generate devcontainer files for +PYTHON_VERSIONS = ["3.9", "3.10", "3.11", "3.12", "3.13"] +DEFAULT = "3.9" + +DEVCONTAINER_JSON_TEMPLATE = Path("scripts/devcontainer/templates/devcontainer.json.jinja") + + +def generate_devcontainer_json_file(python_version: str) -> None: + print(f"Generating devcontainer.json for python {python_version}") + + with open(DEVCONTAINER_JSON_TEMPLATE, "r") as f: + content = f.read() + + # Replace python_version with the current version using jinja template + template = Template(content) + data = { + "python_version": python_version, + } + devcontainer_content = template.render(data) + + file_dir = ( + Path("./.devcontainer/") if python_version == DEFAULT else Path(f"./.devcontainer/python-{python_version}/") + ) + file_dir.mkdir(parents=True, exist_ok=True) + + with open(file_dir / "devcontainer.json", "w") as f: + f.write(devcontainer_content + "\n") + + +def generate_devcontainer_files() -> None: + for python_version in PYTHON_VERSIONS: + # Delete existing devcontainer files + files_to_delete = [] + if python_version == DEFAULT: + files_to_delete = [Path("./.devcontainer/devcontainer.json")] + + files_to_delete = files_to_delete + [ + Path(f"./.devcontainer/python-{python_version}/devcontainer.json"), + Path(f"./.devcontainer/python-{python_version}/"), + ] + for file in files_to_delete: + if file.exists(): + if file.is_file(): + file.unlink() + elif file.is_dir(): + file.rmdir() + + generate_devcontainer_json_file(python_version) + + +if __name__ == "__main__": + generate_devcontainer_files() diff --git a/scripts/devcontainer/generate-devcontainers.sh b/scripts/devcontainer/generate-devcontainers.sh new file mode 100755 index 0000000000..312116052b --- /dev/null +++ b/scripts/devcontainer/generate-devcontainers.sh @@ -0,0 +1,3 @@ +#!/usr/bin/env bash + +python3 scripts/devcontainer/generate-devcontainers.py diff --git a/scripts/devcontainer/templates/devcontainer.json.jinja b/scripts/devcontainer/templates/devcontainer.json.jinja new file mode 100644 index 0000000000..c08e324021 --- /dev/null +++ b/scripts/devcontainer/templates/devcontainer.json.jinja @@ -0,0 +1,84 @@ +// Do not edit this file directly. +// This file is auto generated from the template file `scripts/devcontainer/templates/devcontainer.json.jinja`. +// If you need to make changes, please update the template file and regenerate this file +// by running pre-commit command `pre-commit run --all-files` +// or by manually running the script `./scripts/devcontainer/generate-devcontainers.sh`. +{ + "name": "python-{{ python_version }}", + "image": "mcr.microsoft.com/devcontainers/python:{{ python_version }}", + "secrets": { + "OAI_CONFIG_LIST": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are working on OpenAI-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "TOGETHER_API_KEY": { + "description": "This key is optional and only needed if you are working with Together API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "ANTHROPIC_API_KEY": { + "description": "This key is optional and only needed if you are working with Anthropic API-related code. Leave it blank if not required. You can always set it later as an environment variable in the codespace terminal." + }, + "AZURE_OPENAI_API_KEY": { + "description": "This key is optional and only needed if you are using Azure's OpenAI services. For it to work, you must also set the related environment variables: AZURE_API_ENDPOINT, AZURE_API_VERSION. Leave it blank if not required. You can always set these variables later in the codespace terminal." + }, + "AZURE_API_ENDPOINT": { + "description": "This key is required if you are using Azure's OpenAI services. It must be used in conjunction with AZURE_OPENAI_API_KEY, AZURE_API_VERSION to ensure proper configuration. You can always set these variables later as environment variables in the codespace terminal." + }, + "AZURE_API_VERSION": { + "description": "This key is required to specify the version of the Azure API you are using. Set this along with AZURE_OPENAI_API_KEY, AZURE_API_ENDPOINT for Azure OpenAI services. These variables can be configured later as environment variables in the codespace terminal." + }, + }, + "shutdownAction": "stopContainer", + "workspaceFolder": "/workspaces/ag2", + "runArgs": [ + "--name", + "python-{{ python_version }}-ag2", + "--env-file", + "${localWorkspaceFolder}/.devcontainer/devcontainer.env" + ], + "remoteEnv": {}, + "features": { + "ghcr.io/devcontainers/features/common-utils:2": { + "installZsh": true, + "installOhMyZsh": true, + "configureZshAsDefaultShell": true, + "username": "vscode", + "userUid": "1000", + "userGid": "1000" + // "upgradePackages": "true" + }, + "ghcr.io/devcontainers/features/node:1": {}, + "ghcr.io/devcontainers/features/git:1": {}, + "ghcr.io/devcontainers/features/git-lfs:1": {}, + "ghcr.io/rocker-org/devcontainer-features/quarto-cli:1": {} + }, + "updateContentCommand": "bash .devcontainer/setup.sh", + "customizations": { + "vscode": { + "settings": { + "python.linting.enabled": true, + "python.testing.pytestEnabled": true, + "editor.formatOnSave": true, + "editor.codeActionsOnSave": { + "source.organizeImports": "always" + }, + "[python]": { + "editor.defaultFormatter": "ms-python.vscode-pylance" + }, + "editor.rulers": [ + 80 + ] + }, + "extensions": [ + "ms-python.python", + "ms-toolsai.jupyter", + "ms-toolsai.vscode-jupyter-cell-tags", + "ms-toolsai.jupyter-keymap", + "ms-toolsai.jupyter-renderers", + "ms-toolsai.vscode-jupyter-slideshow", + "ms-python.vscode-pylance" + ] + } + } +} diff --git a/scripts/lint.sh b/scripts/lint.sh new file mode 100755 index 0000000000..1d43b9901d --- /dev/null +++ b/scripts/lint.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash + +echo "Running ruff linter (isort, flake, pyupgrade, etc. replacement)..." +ruff check + +echo "Running ruff formatter (black replacement)..." +ruff format diff --git a/scripts/pre-commit-license-check.py b/scripts/pre-commit-license-check.py index 2745b60796..068f65831f 100755 --- a/scripts/pre-commit-license-check.py +++ b/scripts/pre-commit-license-check.py @@ -77,7 +77,7 @@ def get_files_to_check() -> List[Path]: """Determine which files to check based on environment.""" try: if "--all-files" in sys.argv: - return list(Path(".").rglob("*.py")) + return list(Path().rglob("*.py")) if os.getenv("GITHUB_ACTIONS") == "true": return get_github_pr_files() diff --git a/scripts/pre-commit-lint.sh b/scripts/pre-commit-lint.sh new file mode 100755 index 0000000000..a0f99e5652 --- /dev/null +++ b/scripts/pre-commit-lint.sh @@ -0,0 +1,34 @@ +#!/usr/bin/env bash + +# from: https://jaredkhan.com/blog/mypy-pre-commit + +# A script for running mypy, +# with all its dependencies installed. + +set -o errexit + +# Change directory to the project root directory. +cd "$(dirname "$0")"/.. + +# Install the dependencies into the mypy env. +# Note that this can take seconds to run. +# In my case, I need to use a custom index URL. +# Avoid pip spending time quietly retrying since +# likely cause of failure is lack of VPN connection. +pip uninstall pyautogen --yes --quiet + +pip install --editable ".[lint]" \ + --retries 1 \ + --no-input \ + --quiet + +# Run on all files, +# ignoring the paths passed to this script, +# so as not to miss type errors. +# My repo makes use of namespace packages. +# Use the namespace-packages flag +# and specify the package to run on explicitly. +# Note that we do not use --ignore-missing-imports, +# as this can give us false confidence in our results. +# mypy fastagency +./scripts/lint.sh diff --git a/scripts/pre-commit-mypy-run.sh b/scripts/pre-commit-mypy-run.sh index 1e2bd7beba..b48d4587ed 100755 --- a/scripts/pre-commit-mypy-run.sh +++ b/scripts/pre-commit-mypy-run.sh @@ -10,6 +10,8 @@ set -o errexit # Change directory to the project root directory. cd "$(dirname "$0")"/.. +pip uninstall pyautogen --yes --quiet + pip install -q -e .[types] mypy diff --git a/scripts/test.sh b/scripts/test.sh new file mode 100755 index 0000000000..cdeb69da56 --- /dev/null +++ b/scripts/test.sh @@ -0,0 +1,7 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +#!/usr/bin/env bash + +pytest --ff -vv --durations=10 --durations-min=1.0 "$@" diff --git a/scripts/test_skip_openai.sh b/scripts/test_skip_openai.sh index a9b4155c71..64fc91de76 100755 --- a/scripts/test_skip_openai.sh +++ b/scripts/test_skip_openai.sh @@ -4,4 +4,4 @@ #!/usr/bin/env bash -pytest test --ignore=test/agentchat/contrib --skip-openai --durations=10 --durations-min=1.0 +bash scripts/test.sh -m "not openai" --ignore=test/agentchat/contrib "$@" diff --git a/setup.py b/setup.py deleted file mode 100644 index ae4aadb5af..0000000000 --- a/setup.py +++ /dev/null @@ -1,191 +0,0 @@ -# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai -# -# SPDX-License-Identifier: Apache-2.0 -# -# Portions derived from https://github.com/microsoft/autogen are under the MIT License. -# SPDX-License-Identifier: MIT -import os -import platform -import sys - -import setuptools - -here = os.path.abspath(os.path.dirname(__file__)) - -with open("README.md", "r", encoding="UTF-8") as fh: - long_description = fh.read() - -# Get the code version -version = {} -with open(os.path.join(here, "autogen/version.py")) as fp: - exec(fp.read(), version) -__version__ = version["__version__"] - - -current_os = platform.system() - -install_requires = [ - "openai>=1.58", - "diskcache", - "termcolor", - "flaml", - # numpy is installed by flaml, but we want to pin the version to below 2.x (see https://github.com/microsoft/autogen/issues/1960) - "numpy>=2.1; python_version>='3.13'", # numpy 2.1+ required for Python 3.13 - "numpy>=1.24.0,<2.0.0; python_version<'3.13'", # numpy 1.24+ for older Python versions - "python-dotenv", - "tiktoken", - # Disallowing 2.6.0 can be removed when this is fixed https://github.com/pydantic/pydantic/issues/8705 - "pydantic>=1.10,<3,!=2.6.0", # could be both V1 and V2 - "docker", - "packaging", - "websockets>=14,<15", - "asyncer==0.0.8", - "fast_depends>=2.4.12,<3", -] - -test = [ - "ipykernel", - "nbconvert", - "nbformat", - "pre-commit", - "pytest-cov>=5", - "pytest-asyncio", - "pytest>=8,<9", - "pandas", - "fastapi>=0.115.0,<1", -] - -jupyter_executor = [ - "jupyter-kernel-gateway", - "websocket-client", - "requests", - "jupyter-client>=8.6.0", - "ipykernel>=6.29.0", -] - -retrieve_chat = [ - "protobuf==4.25.3", - "chromadb==0.5.3", - "sentence_transformers", - "pypdf", - "ipython", - "beautifulsoup4", - "markdownify", -] - -retrieve_chat_pgvector = [*retrieve_chat, "pgvector>=0.2.5"] - -graph_rag_falkor_db = ["graphrag_sdk==0.3.3", "falkordb>=1.0.10"] - -neo4j = [ - "docx2txt==0.8", - "llama-index==0.12.5", - "llama-index-graph-stores-neo4j==0.4.2", - "llama-index-core==0.12.5", -] - -# used for agentchat_realtime_swarm notebook and realtime agent twilio demo -twilio = ["fastapi>=0.115.0,<1", "uvicorn>=0.30.6,<1", "twilio>=9.3.2"] - -interop_crewai = [ - "crewai[tools]>=0.86,<1; python_version>='3.10' and python_version<'3.13'", - "weaviate-client==4.10.2; python_version>='3.10' and python_version<'3.13'", -] -interop_langchain = ["langchain-community>=0.3.12,<1"] -interop_pydantic_ai = ["pydantic-ai==0.0.13"] -interop = interop_crewai + interop_langchain + interop_pydantic_ai - -types = ["mypy==1.9.0"] + test + jupyter_executor + interop - -if current_os in ["Windows", "Darwin"]: - retrieve_chat_pgvector.extend(["psycopg[binary]>=3.1.18"]) -elif current_os == "Linux": - retrieve_chat_pgvector.extend(["psycopg>=3.1.18"]) - -# pysqlite3-binary used so it doesn't need to compile pysqlite3 -autobuild = ["chromadb", "sentence-transformers", "huggingface-hub", "pysqlite3-binary"] - -# NOTE: underscores in pip install, e.g. pip install ag2[graph_rag_falkor_db], will automatically -# convert to hyphens. So, do not include underscores in the name of extras. - -# ** IMPORTANT: IF ADDING EXTRAS ** -# PLEASE add them in the setup_ag2.py and setup_autogen.py files - -extra_require = { - "test": test, - "blendsearch": ["flaml[blendsearch]"], - "mathchat": ["sympy", "pydantic==1.10.9", "wolframalpha"], - "retrievechat": retrieve_chat, - "retrievechat-pgvector": retrieve_chat_pgvector, - "retrievechat-mongodb": [*retrieve_chat, "pymongo>=4.0.0"], - "retrievechat-qdrant": [*retrieve_chat, "qdrant_client", "fastembed>=0.3.1"], - "graph-rag-falkor-db": graph_rag_falkor_db, - "autobuild": autobuild, - "captainagent": autobuild + ["pandas"], - "teachable": ["chromadb"], - "lmm": ["replicate", "pillow"], - "graph": ["networkx", "matplotlib"], - "gemini": [ - "google-generativeai>=0.5,<1", - "google-cloud-aiplatform", - "google-auth", - "pillow", - "pydantic", - "jsonschema", - ], - "together": ["together>=1.2"], - "websurfer": ["beautifulsoup4", "markdownify", "pdfminer.six", "pathvalidate"], - "redis": ["redis"], - "cosmosdb": ["azure-cosmos>=4.2.0"], - "websockets": ["websockets>=14.0,<15"], - "jupyter-executor": jupyter_executor, - "types": types, - "long-context": ["llmlingua<0.3"], - "anthropic": ["anthropic[vertex]>=0.23.1"], - "cerebras": ["cerebras_cloud_sdk>=1.0.0"], - "mistral": ["mistralai>=1.0.1"], - "groq": ["groq>=0.9.0"], - "cohere": ["cohere>=5.5.8"], - "ollama": ["ollama>=0.3.3", "fix_busted_json>=0.0.18"], - "bedrock": ["boto3>=1.34.149"], - "twilio": twilio, - "interop-crewai": interop_crewai, - "interop-langchain": interop_langchain, - "interop-pydantic-ai": interop_pydantic_ai, - "interop": interop, - "neo4j": neo4j, -} - -setuptools.setup( - name="pyautogen", - version=__version__, - author="Chi Wang & Qingyun Wu", - author_email="support@ag2.ai", - description="A programming framework for agentic AI", - long_description=long_description, - long_description_content_type="text/markdown", - url="https://github.com/ag2ai/ag2", - packages=setuptools.find_namespace_packages( - include=[ - "autogen*", - "autogen.agentchat.contrib.captainagent.tools*", - ], - exclude=["test"], - ), - package_data={ - "autogen.agentchat.contrib.captainagent": [ - "tools/tool_description.tsv", - "tools/requirements.txt", - ] - }, - include_package_data=True, - install_requires=install_requires, - extras_require=extra_require, - classifiers=[ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: Apache Software License", - "Operating System :: OS Independent", - ], - license="Apache Software License 2.0", - python_requires=">=3.9,<3.14", -) diff --git a/test/agentchat/contrib/agent_eval/test_agent_eval.py b/test/agentchat/contrib/agent_eval/test_agent_eval.py index 7345a13576..37ee052eee 100644 --- a/test/agentchat/contrib/agent_eval/test_agent_eval.py +++ b/test/agentchat/contrib/agent_eval/test_agent_eval.py @@ -10,15 +10,11 @@ import pytest -import autogen from autogen.agentchat.contrib.agent_eval.agent_eval import generate_criteria, quantify_criteria from autogen.agentchat.contrib.agent_eval.criterion import Criterion from autogen.agentchat.contrib.agent_eval.task import Task -from ....conftest import reason, skip_openai # noqa: E402 - -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" +from ....conftest import Credentials def remove_ground_truth(test_case: str): @@ -30,31 +26,8 @@ def remove_ground_truth(test_case: str): return str(test_details), correctness -if not skip_openai: - openai_config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - # The Retrieval tool requires at least gpt-3.5-turbo-1106 (newer versions are supported) or gpt-4-turbo-preview models. - # https://platform.openai.com/docs/models/overview - filter_dict={ - "api_type": ["openai"], - "model": [ - "gpt-4o-mini", - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - ], - }, - ) - - aoai_config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"api_type": ["azure"]}, - ) - +@pytest.fixture +def task() -> Task: success_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_successful.txt").read() response_successful = remove_ground_truth(success_str)[0] failed_str = open("test/test_files/agenteval-in-out/samples/sample_math_response_failed.txt").read() @@ -67,14 +40,12 @@ def remove_ground_truth(test_case: str): "failed_response": response_failed, } ) + return task -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_generate_criteria(): - criteria = generate_criteria(task=task, llm_config={"config_list": aoai_config_list}) +@pytest.mark.openai +def test_generate_criteria(credentials_azure: Credentials, task: Task): + criteria = generate_criteria(task=task, llm_config={"config_list": credentials_azure.config_list}) assert criteria assert len(criteria) > 0 assert criteria[0].description @@ -82,11 +53,8 @@ def test_generate_criteria(): assert criteria[0].accepted_values -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_quantify_criteria(): +@pytest.mark.openai +def test_quantify_criteria(credentials_azure: Credentials, task: Task): criteria_file = "test/test_files/agenteval-in-out/samples/sample_math_criteria.json" criteria = open(criteria_file).read() criteria = Criterion.parse_json_str(criteria) @@ -95,7 +63,7 @@ def test_quantify_criteria(): test_case, ground_truth = remove_ground_truth(test_case) quantified = quantify_criteria( - llm_config={"config_list": aoai_config_list}, + llm_config={"config_list": credentials_azure.config_list}, criteria=criteria, task=task, test_case=test_case, diff --git a/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py b/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py index 581ac8b815..d9fa2b6f99 100755 --- a/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py +++ b/test/agentchat/contrib/capabilities/chat_with_teachable_agent.py @@ -10,7 +10,7 @@ from autogen.agentchat.contrib.capabilities.teachability import Teachability from autogen.formatting_utils import colored -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import KEY_LOC, OAI_CONFIG_LIST # Specify the model to use. GPT-3.5 is less reliable than GPT-4 at learning from user input. filter_dict = {"model": ["gpt-4o-mini"]} @@ -46,7 +46,6 @@ def create_teachable_agent(reset_db=False): def interact_freely_with_user(): """Starts a free-form chat between the user and a teachable agent.""" - # Create the agents. print(colored("\nLoading previous memory (if any) from disk.", "light_cyan")) teachable_agent = create_teachable_agent(reset_db=False) diff --git a/test/agentchat/contrib/capabilities/test_image_generation_capability.py b/test/agentchat/contrib/capabilities/test_image_generation_capability.py index 4175d664fd..be09064d03 100644 --- a/test/agentchat/contrib/capabilities/test_image_generation_capability.py +++ b/test/agentchat/contrib/capabilities/test_image_generation_capability.py @@ -6,7 +6,6 @@ # SPDX-License-Identifier: MIT import itertools import os -import sys import tempfile from typing import Any @@ -28,7 +27,7 @@ else: skip_requirement = False -from ....conftest import MOCK_OPEN_AI_API_KEY, skip_openai # noqa: E402 +from ....conftest import MOCK_OPEN_AI_API_KEY filter_dict = {"model": ["gpt-4o-mini"]} @@ -60,7 +59,7 @@ def dalle_image_generator(dalle_config: dict[str, Any], resolution: str, quality def api_key(): - return MOCK_OPEN_AI_API_KEY if skip_openai else os.environ.get("OPENAI_API_KEY") + return os.environ.get("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) @pytest.fixture @@ -92,7 +91,7 @@ def image_gen_capability(): return generate_images.ImageGeneration(image_generator) -@pytest.mark.skipif(skip_openai, reason="Requested to skip.") +@pytest.mark.openai @pytest.mark.skipif(skip_requirement, reason="Dependencies are not installed.") def test_dalle_image_generator(dalle_config: dict[str, Any]): """Tests DalleImageGenerator capability to generate images by calling the OpenAI API.""" @@ -116,7 +115,6 @@ def test_dalle_image_generator_cache_key( gen_config_1: A tuple containing the resolution, quality, and prompt for the first image generator. gen_config_2: A tuple containing the resolution, quality, and prompt for the second image generator. """ - dalle_generator_1 = dalle_image_generator(dalle_config, resolution=gen_config_1[0], quality=gen_config_1[1]) dalle_generator_2 = dalle_image_generator(dalle_config, resolution=gen_config_2[0], quality=gen_config_2[1]) diff --git a/test/agentchat/contrib/capabilities/test_teachable_agent.py b/test/agentchat/contrib/capabilities/test_teachable_agent.py index c5ab3a9853..e3285e27b8 100755 --- a/test/agentchat/contrib/capabilities/test_teachable_agent.py +++ b/test/agentchat/contrib/capabilities/test_teachable_agent.py @@ -8,18 +8,17 @@ import pytest -from autogen import ConversableAgent, config_list_from_json +from autogen import ConversableAgent from autogen.formatting_utils import colored -from ....conftest import skip_openai # noqa: E402 -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import Credentials try: from autogen.agentchat.contrib.capabilities.teachability import Teachability except ImportError: skip = True else: - skip = skip_openai + skip = False # Specify the model to use by uncommenting one of the following lines. @@ -29,17 +28,12 @@ filter_dict = {"tags": ["gpt-4o-mini"]} -def create_teachable_agent(reset_db=False, verbosity=0): +def create_teachable_agent(credentials: Credentials, reset_db=False, verbosity=0): """Instantiates a teachable agent using the settings from the top of this file.""" - # Load LLM inference endpoints from an env variable or a file - # See https://docs.ag2.ai/docs/FAQ#set-your-api-endpoints - # and OAI_CONFIG_LIST_sample - config_list = config_list_from_json(env_or_file=OAI_CONFIG_LIST, filter_dict=filter_dict, file_location=KEY_LOC) - # Start by instantiating any agent that inherits from ConversableAgent. teachable_agent = ConversableAgent( name="teachable_agent", - llm_config={"config_list": config_list, "timeout": 120, "cache_seed": None}, # Disable caching. + llm_config={"config_list": credentials.config_list, "timeout": 120, "cache_seed": None}, # Disable caching. ) # Instantiate the Teachability capability. Its parameters are all optional. @@ -67,11 +61,12 @@ def check_agent_response(teachable_agent, user, correct_answer): return 0 -def use_question_answer_phrasing(): +def use_question_answer_phrasing(credentials: Credentials): """Tests whether the teachable agent can answer a question after being taught the answer in a previous chat.""" print(colored("\nTEST QUESTION-ANSWER PHRASING", "light_cyan")) num_errors, num_tests = 0, 0 teachable_agent, teachability = create_teachable_agent( + credentials, reset_db=True, verbosity=0, # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists. ) # For a clean test, clear the agent's memory. @@ -101,11 +96,12 @@ def use_question_answer_phrasing(): return num_errors, num_tests -def use_task_advice_pair_phrasing(): +def use_task_advice_pair_phrasing(credentials: Credentials): """Tests whether the teachable agent can demonstrate a new skill after being taught a task-advice pair in a previous chat.""" print(colored("\nTEST TASK-ADVICE PHRASING", "light_cyan")) num_errors, num_tests = 0, 0 teachable_agent, teachability = create_teachable_agent( + credentials, reset_db=True, # For a clean test, clear the teachable agent's memory. verbosity=3, # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists. ) @@ -132,21 +128,22 @@ def use_task_advice_pair_phrasing(): return num_errors, num_tests +@pytest.mark.openai @pytest.mark.skipif( skip, reason="do not run if dependency is not installed or requested to skip", ) -def test_teachability_code_paths(): +def test_teachability_code_paths(credentials_gpt_4o_mini: Credentials): """Runs this file's unit tests.""" total_num_errors, total_num_tests = 0, 0 num_trials = 1 # Set to a higher number to get a more accurate error rate. for trial in range(num_trials): - num_errors, num_tests = use_question_answer_phrasing() + num_errors, num_tests = use_question_answer_phrasing(credentials_gpt_4o_mini) total_num_errors += num_errors total_num_tests += num_tests - num_errors, num_tests = use_task_advice_pair_phrasing() + num_errors, num_tests = use_task_advice_pair_phrasing(credentials_gpt_4o_mini) total_num_errors += num_errors total_num_tests += num_tests @@ -163,18 +160,19 @@ def test_teachability_code_paths(): ) +@pytest.mark.openai @pytest.mark.skipif( skip, reason="do not run if dependency is not installed or requested to skip", ) -def test_teachability_accuracy(): +def test_teachability_accuracy(credentials_gpt_4o_mini: Credentials): """A very cheap and fast test of teachability accuracy.""" print(colored("\nTEST TEACHABILITY ACCURACY", "light_cyan")) num_trials = 10 # The expected probability of failure is about 0.3 on each trial. for trial in range(num_trials): teachable_agent, teachability = create_teachable_agent( - reset_db=True, verbosity=0 + credentials_gpt_4o_mini, reset_db=True, verbosity=0 ) # For a clean test, clear the agent's memory. user = ConversableAgent("user", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER") diff --git a/test/agentchat/contrib/capabilities/test_transform_messages.py b/test/agentchat/contrib/capabilities/test_transform_messages.py index bb86cf55f7..c274a31a58 100644 --- a/test/agentchat/contrib/capabilities/test_transform_messages.py +++ b/test/agentchat/contrib/capabilities/test_transform_messages.py @@ -12,28 +12,18 @@ from autogen.agentchat.contrib.capabilities.transform_messages import TransformMessages from autogen.agentchat.contrib.capabilities.transforms import MessageHistoryLimiter, MessageTokenLimiter -from ....conftest import skip_openai # noqa: E402 -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import Credentials -@pytest.mark.skipif(skip_openai, reason="Requested to skip openai test.") -def test_transform_messages_capability(): +@pytest.mark.openai +def test_transform_messages_capability(credentials_gpt_4o_mini: Credentials) -> None: """Test the TransformMessages capability to handle long contexts. This test is a replica of test_transform_chat_history_with_agents in test_context_handling.py """ with tempfile.TemporaryDirectory() as temp_dir: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - KEY_LOC, - filter_dict={ - "model": "gpt-4o-mini", - }, - ) - - assistant = autogen.AssistantAgent( - "assistant", llm_config={"config_list": config_list}, max_consecutive_auto_reply=1 - ) + llm_config = credentials_gpt_4o_mini.llm_config + assistant = autogen.AssistantAgent("assistant", llm_config=llm_config, max_consecutive_auto_reply=1) context_handling = TransformMessages( transforms=[ @@ -66,7 +56,7 @@ def test_transform_messages_capability(): clear_history=False, ) except Exception as e: - assert False, f"Chat initiation failed with error {str(e)}" + assert False, f"Chat initiation failed with error {e!s}" if __name__ == "__main__": diff --git a/test/agentchat/contrib/capabilities/test_transforms.py b/test/agentchat/contrib/capabilities/test_transforms.py index 2038991bfc..64e415549a 100644 --- a/test/agentchat/contrib/capabilities/test_transforms.py +++ b/test/agentchat/contrib/capabilities/test_transforms.py @@ -5,8 +5,7 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import copy -from typing import Any, Dict, List -from unittest.mock import MagicMock, patch +from typing import Any import pytest @@ -297,7 +296,6 @@ def test_message_token_limiter_get_logs(message_token_limiter, messages, expecte @pytest.mark.parametrize("text_compressor", get_text_compressors()) def test_text_compression(text_compressor): """Test the TextMessageCompressor transform.""" - compressor = TextMessageCompressor(text_compressor=text_compressor) text = "Run this test with a long string. " diff --git a/test/agentchat/contrib/capabilities/test_transforms_util.py b/test/agentchat/contrib/capabilities/test_transforms_util.py index 6647226f0b..c5091f9b86 100644 --- a/test/agentchat/contrib/capabilities/test_transforms_util.py +++ b/test/agentchat/contrib/capabilities/test_transforms_util.py @@ -6,7 +6,6 @@ # SPDX-License-Identifier: MIT import itertools import tempfile -from typing import Dict, Tuple import pytest diff --git a/test/agentchat/contrib/capabilities/test_vision_capability.py b/test/agentchat/contrib/capabilities/test_vision_capability.py index 9ba6dd9ec9..765ec2ef99 100644 --- a/test/agentchat/contrib/capabilities/test_vision_capability.py +++ b/test/agentchat/contrib/capabilities/test_vision_capability.py @@ -12,7 +12,7 @@ from autogen.agentchat.conversable_agent import ConversableAgent try: - from PIL import Image + from PIL import Image # noqa: F401 from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability except ImportError: @@ -40,7 +40,7 @@ def vision_capability(lmm_config): @pytest.fixture def conversable_agent(): - return ConversableAgent(name="conversable agent", llm_config=False) + return ConversableAgent(name="conversable_agent", llm_config=False) @pytest.mark.skipif( diff --git a/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.pdf b/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.pdf new file mode 100644 index 0000000000..43cb3114b8 Binary files /dev/null and b/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.pdf differ diff --git a/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt b/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt new file mode 100644 index 0000000000..3f7b973785 --- /dev/null +++ b/test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt @@ -0,0 +1 @@ +**BUZZ Co. EMPLOYEE HANDBOOK** **EMPLOYEE RECEIPT AND ACCEPTANCE** I acknowledge receipt of the BUZZ Co. Employee Handbook and understand it is my responsibility to read and know its contents. This Handbook is not an employment contract. Unless I have a written employment agreement with BUZZ Co. that states otherwise, my employment is at-will. I can resign at any time, with or without notice or cause, and BUZZ Co. can terminate my employment at any time, with or without notice or cause. Signature: _________________________ Print Name: ________________________ Date: ____________________________ **CONFIDENTIALITY POLICY AND PLEDGE** All non-public information about BUZZ Co., its members, or donors is confidential. Employees may not disclose this information to anyone outside BUZZ Co., or to other employees who do not need it to perform their duties. Disclosure of confidential information will result in disciplinary action, including possible termination. Signature: _________________________ Print Name: ________________________ Date: ____________________________ **TABLE OF CONTENTS** I. MISSION II. OVERVIEW III. VOLUNTARY AT-WILL EMPLOYMENT IV. EQUAL EMPLOYMENT OPPORTUNITY V. POLICY AGAINST WORKPLACE HARASSMENT VI. SOLICITATION VII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY VIII. EMPLOYMENT POLICIES AND PRACTICES IX. POSITION DESCRIPTION AND SALARY ADMINISTRATION X. WORK REVIEW XI. ECONOMIC BENEFITS AND INSURANCE XII. LEAVE BENEFITS AND OTHER WORK POLICIES XIII. REIMBURSEMENT OF EXPENSES XIV. SEPARATION XV. RETURN OF PROPERTY XVI. REVIEW OF PERSONNEL AND WORK PRACTICES XVII. PERSONNEL RECORDS XVIII. OUTSIDE EMPLOYMENT XIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION XX. COMPUTER AND INFORMATION SECURITY XXI. INTERNET ACCEPTABLE USE POLICY **I. MISSION** [Insert BUZZ Co.'s Mission Statement Here] **II. OVERVIEW** This Handbook provides guidelines about BUZZ Co.'s policies and procedures. It is not a contract and does not guarantee employment for any specific period. With the exception of the at-will employment policy, these guidelines may be changed by BUZZ Co. at any time without notice. The Board of Directors establishes personnel policies, and the Executive Director administers them. **III. VOLUNTARY AT-WILL EMPLOYMENT** Unless an employee has a written employment agreement stating otherwise, all employment at BUZZ Co. is "at-will." Employees may be terminated with or without cause, and employees may leave their employment with or without cause. **IV. EQUAL EMPLOYMENT OPPORTUNITY** [Content not provided in the original document, but should be included here. State BUZZ Co.'s commitment to equal employment opportunity and non-discrimination.] **V. POLICY AGAINST WORKPLACE HARASSMENT** [Content not provided in the original document. State BUZZ Co.'s policy against all forms of workplace harassment and the procedures for reporting such incidents.] **VI. SOLICITATION** Employees may not solicit for any unauthorized purpose during work time. Non-working employees may not solicit working employees. Non-employees may not solicit on BUZZ Co. premises. Distribution of materials requires prior approval from the Executive Director. **VII. HOURS OF WORK, ATTENDANCE, AND PUNCTUALITY** **A. Hours of Work:** Normal work week is five 7-hour days, typically 9:00 a.m. - 5:00 p.m., Monday-Friday, with a one-hour unpaid lunch. Work schedules may vary with Executive Director approval. **B. Attendance and Punctuality:** Regular attendance is expected. Notify your supervisor and the office manager as soon as possible if you are absent, late, or need to leave early. For absences longer than one day, call your supervisor before each workday. Excessive absences or tardiness may result in disciplinary action, up to termination. **C. Overtime:** Non-Exempt Employees will be paid overtime (1.5 times the regular rate) for hours worked over 40 in a work week, or double time for work on Sundays or holidays. Overtime must be pre-approved by the Executive Director. **VIII. EMPLOYMENT POLICIES AND PRACTICES** **A. Definition of Terms:** 1. **Employer:** BUZZ Co. 2. **Full-Time Employee:** Works 35+ hours/week. 3. **Part-Time Employee:** Works 17.5 - 35 hours/week. 4. **Exempt Employee:** Salaried, exempt from FLSA overtime rules. 5. **Non-Exempt Employee:** Hourly, non-exempt from FLSA overtime rules. 6. **Temporary Employee:** Employed for a specific period less than six months. **IX. POSITION DESCRIPTION AND SALARY ADMINISTRATION** Each position has a written job description. Paychecks are distributed on the 15th and last day of each month. Timesheets are due within two days of each pay period. **X. WORK REVIEW** Ongoing work review with supervisors. Annual performance reviews provide an opportunity to discuss the past year, set goals, and strengthen the working relationship. **XI. ECONOMIC BENEFITS AND INSURANCE** **A. Health/Life Insurance:** BUZZ Co. offers health and dental insurance to eligible full-time and part-time employees after the first full month of employment. **B. Social Security/Medicare/Medicaid:** BUZZ Co. participates in these programs. **C. Workers' Compensation and Unemployment Insurance:** Employees are covered by Workers' Compensation. BUZZ Co. participates in the District of Columbia unemployment program. **D. Retirement Plan:** Available to eligible full-time and part-time employees (21+ years old). BUZZ Co. contributes after one year of vested employment. **E. Tax Deferred Annuity Plan:** Offered through payroll deduction at the employee's expense. **XII. LEAVE BENEFITS AND OTHER WORK POLICIES** **A. Holidays:** 11.5 paid holidays per year for Full-Time Employees, pro-rated for Part-Time. **B. Vacation:** Full-time employees earn 10 days after the first year, 15 days after the third year and 20 days after the fourth year. Prorated for Part-Time employees. **C. Sick Leave:** One day per month for Full-Time, pro-rated for Part-Time, up to a 30-day maximum. **D. Personal Leave:** 3 days per year after six months of employment for Full-Time, pro-rated for Part-Time. **E. Military Leave:** Unpaid leave in accordance with applicable law. **F. Civic Responsibility:** BUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government, unless prohibited by law, up to a maximum of ten days of jury duty. BUZZ Co. will pay employees the difference between his or her salary and any amount paid by the government or any other source, unless prohibited by law for serving as an Election Day worker at the polls on official election days (not to exceed two elections in one given calendar year). **G. Parental Leave:** 24 hours of unpaid leave per year for school-related events under the DC Parental Leave Act. **H. Bereavement Leave:** 5 days for immediate family, 3 days for other close relatives. **I. Extended Personal Leave:** Up to eight weeks unpaid leave may be granted after one year of employment. **J. Severe Weather Conditions:** BUZZ Co. follows Federal Government office closures. **K. Meetings and Conferences:** Time off with pay may be granted for work-related educational opportunities. **XIII. REIMBURSEMENT OF EXPENSES** Reimbursement for reasonable and necessary business expenses, including travel, with prior approval and receipts. **XIV. SEPARATION** Employees are encouraged to give at least 10 business days' written notice. Exit interviews will be scheduled. Employees who resign or are terminated will receive accrued, unused vacation benefits. **XV. RETURN OF PROPERTY** Employees must return all BUZZ Co. property upon separation or request. **XVI. REVIEW OF PERSONNEL ACTION** Employees may request a review of personnel actions, first with their supervisor, then with the Executive Director. **XVII. PERSONNEL RECORDS** Personnel records are confidential. Employees must promptly report changes in personnel data. Accurate time records are required. **XVIII. OUTSIDE EMPLOYMENT** Outside employment is permitted as long as it does not interfere with BUZZ Co. job performance or create a conflict of interest. **XIX. NON-DISCLOSURE OF CONFIDENTIAL INFORMATION** Employees must sign a non-disclosure agreement. Unauthorized disclosure of confidential information is prohibited. **XX. COMPUTER AND INFORMATION SECURITY** BUZZ Co. computer systems are for business use, with limited personal use allowed. All data is BUZZ Co. property and may be monitored. Do not use systems for offensive or illegal activities. Follow security procedures. **XXI. INTERNET ACCEPTABLE USE POLICY** Internet access is for business use. Do not use the Internet for illegal, offensive, or unauthorized personal activities. BUZZ Co. may monitor Internet usage. Revised {Date} Approved by the Executive Committee of the BUZZ Co. Board of Directors diff --git a/test/agentchat/contrib/graph_rag/Toast_financial_report.pdf b/test/agentchat/contrib/graph_rag/Toast_financial_report.pdf new file mode 100644 index 0000000000..300a276f73 Binary files /dev/null and b/test/agentchat/contrib/graph_rag/Toast_financial_report.pdf differ diff --git a/test/agentchat/contrib/graph_rag/paul_graham_essay.txt b/test/agentchat/contrib/graph_rag/paul_graham_essay.txt deleted file mode 100644 index 6ebd858793..0000000000 --- a/test/agentchat/contrib/graph_rag/paul_graham_essay.txt +++ /dev/null @@ -1,353 +0,0 @@ - - -What I Worked On - -February 2021 - -Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. - -The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights. - -The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer. - -I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear. - -With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1] - -The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer. - -Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter. - -Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored. - -I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI. - -AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words. - -There weren't any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn't happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do. - -For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence. - -I had gotten into a program at Cornell that didn't make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose "Artificial Intelligence." When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover. - -I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went. - -I don't remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that's told "the dog is sitting on the chair" translates this into some formal representation and adds it to the list of things it knows. - -What these programs really showed was that there's a subset of natural language that's a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike. - -So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It's scary to think how little I knew about Lisp hacking when I started writing that book. But there's nothing like writing a book about something to help you learn it. The book, On Lisp, wasn't published till 1993, but I wrote much of it in grad school. - -Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting. - -The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good. - -There were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work. - -I wanted not just to build things, but to build things that would last. - -In this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I'd spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn't become obsolete. Some of the best ones were hundreds of years old. - -And moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn't have a boss, or even need to get research funding. - -I had always liked looking at paintings. Could I make them? I had no idea. I'd never imagined it was even possible. I knew intellectually that people made art — that it didn't just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous. - -That fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything. - -So now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis. - -I didn't see a way out of this situation. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school. - -Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay "Yes, I think so. I'll give you something to read in a few days." - -I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely. - -Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went. - -I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design. - -Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian. - -Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2] - -I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. - -Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3] - -While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting. Whereas a still life you can, if you want, copy pixel by pixel from what you're seeing. You don't want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it's been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it's the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4] - -I liked painting still lives because I was curious about what I was seeing. In everyday life, we aren't consciously aware of much we're seeing. Most visual perception is handled by low-level processes that merely tell your brain "that's a water droplet" without telling you details like where the lightest and darkest points are, or "that's a bush" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there's a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted. - -This is not the only way to paint. I'm not 100% sure it's even a good way to paint. But it seemed a good enough bet to be worth trying. - -Our teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn't teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US. - -I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5] - -Interleaf had done something pretty bold. Inspired by Emacs, they'd added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I've had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn't know C and didn't want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish. - -The good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans. - -I learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it's better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it. - -But the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it's good to be the "entry level" option, even though that will be less prestigious, because if you're not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign. - -When I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life. - -In the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style. - -A signature style is the visual equivalent of what in show business is known as a "schtick": something that immediately identifies the work as yours and no one else's. For example, when you see a painting that looks like a certain kind of cartoon, you know it's by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That's not always why artists have a signature style, but it's usually why buyers pay a lot for such work. [6] - -There were plenty of earnest students too: kids who "could draw" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers. - -I learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn't much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7] - -Asterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there's a tiny corner that's not rich, or at least wasn't in 1993. It's called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York. - -I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.) - -The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant. - -She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want. - -Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet. - -If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us. - -Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an "internet storefront" was something we already knew how to build. - -So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course. - -We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser. - -This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server. - -Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server. - -We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves. - -At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on. - -We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server. - -It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company. - -(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.) - -In September, Robert rebelled. "We've been working on this for a month," he said, "and it's still not done." This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker. - -It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo. - -We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8] - -There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful. - -There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us. - -We did a lot of things right by accident like that. For example, we did what's now called "doing things that don't scale," although at the time we would have described it as "being so lame that we're driven to the most desperate measures to get users." The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d'etre of our software was that people could use it to make their own stores. But anything to get users. - -We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too. - -Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by "business" and thought we needed a "business person" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do. - -Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny. - -Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards. - -It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned. - -The next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn't realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo's prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf. - -Yahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they'd never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I'd painted anything that I'd half forgotten why I was doing this. My brain had been entirely full of software and men's shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint. - -When I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan. - -But I really was quitting to paint, and I started immediately. There was no time to lose. I'd already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That's what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me. - -So I tried to paint, but I just didn't seem to have any energy or ambition. Part of the problem was that I didn't know many people in California. I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them. - -When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet). - -Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh. - -Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc. - -I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it. - -Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did. - -By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra. - -I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open source project. - -Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it. - -The subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge. - -The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10] - -Wow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything. - -This had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11] - -In the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12] - -I've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too. - -I knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging. - -One of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip. - -It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one. - -Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office. - -One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out. - -Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders. - -When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on. - -One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made. - -So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out "But not me!" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment. - -Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on. - -As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13] - -Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel. - -There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn't go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we'd been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us. - -YC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking "Wow, that means they got all the returns." But once again, this was not due to any particular insight on our part. We didn't know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn't have known where to start. [14] - -The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they'd start startups instead? We wouldn't feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn't make much money out of it, we'd at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft. - -We'd use the building I owned in Cambridge as our headquarters. We'd all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we'd bring in experts on startups to give talks. - -We knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get "deal flow," as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who'd already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we'd intended. - -We invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs. - -The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16] - -Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them. - -As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the "YC GDP," but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates. - -I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things. - -In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity. - -HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17] - -As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC. - -YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. - -There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: "No one works harder than the boss." He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard. - -One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. "You know," he said, "you should make sure Y Combinator isn't the last cool thing you do." - -At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would. - -In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else. - -I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners. - -When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned. - -She died on January 15, 2014. We knew this was coming, but it was still hard when it did. - -I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.) - -What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18] - -I spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway. - -I realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around. - -I started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren't about startups. Then in March 2015 I started working on Lisp again. - -The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn't originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what's the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19] - -McCarthy didn't realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy's interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn't match. It was this that attracted me in college, though I didn't understand why at the time. - -McCarthy's 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you'd want in a programming language. So these had to be added, and when they were, they weren't defined using McCarthy's original axiomatic approach. That wouldn't have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you'd have had to run it, and computers then weren't powerful enough. - -Now they are, though. Now you could continue using McCarthy's axiomatic approach till you'd defined a complete programming language. And as long as every change you made to McCarthy's Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long. - -I wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it's an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test. - -I had to ban myself from writing essays during most of this time, or I'd never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you're working on an interpreter written in itself, it's hard to keep track of what's happening at what level, and errors can be practically encrypted by the time you get them. - -So I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I'd ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I'd check Twitter or HN and see someone asking "Does Paul Graham still code?" - -Working on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years. - -In the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England. - -In the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code. - -Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it. - - - - - - - - - -Notes - -[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting. - -[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way. - -[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists. - -[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters. - -[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer. - -[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive. - -[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price. - -[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores. - -[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them. - -[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things. - -[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version. - -[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era. - -Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete). - -Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be? - -[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator. - -I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square. - -[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded. - -[15] I've never liked the term "deal flow," because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed. - -[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now. - -[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance. - -[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree. - -[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper. - -But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved. - - - -Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this. diff --git a/test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py b/test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py index a52a43112c..d72ad34c03 100644 --- a/test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py +++ b/test/agentchat/contrib/graph_rag/test_falkor_graph_rag.py @@ -9,8 +9,6 @@ import pytest from graphrag_sdk import Attribute, AttributeType, Entity, Ontology, Relation -from ....conftest import reason, skip_openai # noqa: E402 - try: from autogen.agentchat.contrib.graph_rag.document import Document, DocumentType from autogen.agentchat.contrib.graph_rag.falkor_graph_query_engine import ( @@ -22,16 +20,16 @@ else: skip = False -reason = "do not run on MacOS or windows OR dependency is not installed OR " + reason +reason = "do not run on MacOS or windows OR dependency is not installed" +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) def test_falkor_db_query_engine(): - """ - Test FalkorDB Query Engine. + """Test FalkorDB Query Engine. 1. create a test FalkorDB Query Engine with a schema. 2. Initialize it with an input txt file. 3. Query it with a question and verify the result contains the critical information. diff --git a/test/agentchat/contrib/graph_rag/test_native_neo4j_graph_rag.py b/test/agentchat/contrib/graph_rag/test_native_neo4j_graph_rag.py new file mode 100644 index 0000000000..58962ccdc5 --- /dev/null +++ b/test/agentchat/contrib/graph_rag/test_native_neo4j_graph_rag.py @@ -0,0 +1,155 @@ +# Copyright (c) 2023 - 2025, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +import logging +import sys + +import pytest + +from ....conftest import reason + +try: + from autogen.agentchat.contrib.graph_rag.document import Document, DocumentType + from autogen.agentchat.contrib.graph_rag.neo4j_native_graph_query_engine import ( + GraphStoreQueryResult, + Neo4jNativeGraphQueryEngine, + ) + +except ImportError: + skip = True +else: + skip = False + +# Configure the logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +reason = "do not run on MacOS or windows OR dependency is not installed OR " + reason + + +# Test fixture for creating and initializing a query engine +@pytest.fixture(scope="module") +def neo4j_native_query_engine(): + input_path = "./test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt" + input_document = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)] + + # best practice to use upper-case + entities = ["EMPLOYEE", "EMPLOYER", "POLICY", "BENEFIT", "POSITION", "DEPARTMENT", "CONTRACT", "RESPONSIBILITY"] + relations = [ + "FOLLOWS", + "PROVIDES", + "APPLIES_TO", + "DEFINED_AS", + "ASSIGNED_TO", + "PART_OF", + "MANAGES", + "REQUIRES", + "ENTITLED_TO", + "REPORTS_TO", + ] + + potential_schema = [ + ("EMPLOYEE", "FOLLOWS", "POLICY"), + ("EMPLOYEE", "APPLIES_TO", "CONTRACT"), + ("EMPLOYEE", "ASSIGNED_TO", "POSITION"), + ("EMPLOYEE", "ENTITLED_TO", "BENEFIT"), + ("EMPLOYEE", "REPORTS_TO", "EMPLOYER"), + ("EMPLOYEE", "REPORTS_TO", "DEPARTMENT"), + ("EMPLOYER", "PROVIDES", "BENEFIT"), + ("EMPLOYER", "MANAGES", "DEPARTMENT"), + ("EMPLOYER", "REQUIRES", "RESPONSIBILITY"), + ("POLICY", "APPLIES_TO", "EMPLOYEE"), + ("POLICY", "APPLIES_TO", "CONTRACT"), + ("POLICY", "DEFINED_AS", "RESPONSIBILITY"), + ("POLICY", "REQUIRES", "RESPONSIBILITY"), + ("BENEFIT", "PROVIDES", "EMPLOYEE"), + ("BENEFIT", "ENTITLED_TO", "EMPLOYEE"), + ("POSITION", "PART_OF", "DEPARTMENT"), + ("POSITION", "ASSIGNED_TO", "EMPLOYEE"), + ("DEPARTMENT", "PART_OF", "EMPLOYER"), + ("DEPARTMENT", "MANAGES", "EMPLOYEE"), + ("CONTRACT", "PROVIDES", "EMPLOYEE"), + ("CONTRACT", "REQUIRES", "RESPONSIBILITY"), + ("CONTRACT", "APPLIES_TO", "EMPLOYEE"), + ("RESPONSIBILITY", "ASSIGNED_TO", "POSITION"), + ("RESPONSIBILITY", "DEFINED_AS", "POLICY"), + ] + + query_engine = Neo4jNativeGraphQueryEngine( + host="bolt://172.17.0.3", # Change + port=7687, # if needed + username="neo4j", # Change if you reset username + password="password", # Change if you reset password + entities=entities, + relations=relations, + potential_schema=potential_schema, + ) + + # Ingest data and initialize the database + query_engine.init_db(input_doc=input_document) + return query_engine + + +# Test fixture for auto generated knowledge graph +@pytest.fixture(scope="module") +def neo4j_native_query_engine_auto(): + input_path = "./test/agentchat/contrib/graph_rag/BUZZ_Employee_Handbook.txt" + + input_document = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)] + + query_engine = Neo4jNativeGraphQueryEngine( + host="bolt://172.17.0.3", # Change + port=7687, # if needed + username="neo4j", # Change if you reset username + password="password", # Change if you reset password + ) + + # Ingest data and initialize the database + query_engine.init_db(input_doc=input_document) + return query_engine + + +@pytest.mark.openai +@pytest.mark.skipif( + sys.platform in ["darwin", "win32"] or skip, + reason=reason, +) +def test_neo4j_native_query_engine(neo4j_native_query_engine): + """Test querying with initialized knowledge graph""" + question = "Which company is the employer?" + query_result: GraphStoreQueryResult = neo4j_native_query_engine.query(question=question) + + logger.info(query_result.answer) + assert query_result.answer.find("BUZZ") >= 0 + + +@pytest.mark.openai +@pytest.mark.skipif( + sys.platform in ["darwin", "win32"] or skip, + reason=reason, +) +def test_neo4j_native_query_auto(neo4j_native_query_engine_auto): + """Test querying with auto-generated property graph""" + question = "Which company is the employer?" + query_result: GraphStoreQueryResult = neo4j_native_query_engine_auto.query(question=question) + + logger.info(query_result.answer) + assert query_result.answer.find("BUZZ") >= 0 + + +def test_neo4j_add_records(neo4j_native_query_engine): + """Test the add_records functionality of the Neo4j Query Engine.""" + input_path = "./test/agentchat/contrib/graph_rag/the_matrix.txt" + input_documents = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)] + + # Add records to the existing graph + _ = neo4j_native_query_engine.add_records(input_documents) + + # Verify the new data is in the graph + question = "Who acted in 'The Matrix'?" + query_result: GraphStoreQueryResult = neo4j_native_query_engine.query(question=question) + + logger.info(query_result.answer) + + assert query_result.answer.find("Keanu Reeves") >= 0 diff --git a/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py b/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py index 54d69613a3..27c3c966b6 100644 --- a/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py +++ b/test/agentchat/contrib/graph_rag/test_neo4j_graph_rag.py @@ -10,7 +10,7 @@ import pytest -from ....conftest import reason, skip_openai # noqa: E402 +from ....conftest import reason try: from autogen.agentchat.contrib.graph_rag.document import Document, DocumentType @@ -106,9 +106,7 @@ def neo4j_query_engine(): # Test fixture to test auto-generation without given schema @pytest.fixture(scope="module") def neo4j_query_engine_auto(): - """ - Test the engine with auto-generated property graph - """ + """Test the engine with auto-generated property graph""" query_engine = Neo4jGraphQueryEngine( username="neo4j", password="password", @@ -116,18 +114,17 @@ def neo4j_query_engine_auto(): port=7687, database="neo4j", ) - query_engine.connect_db() # Connect to the existing graph + query_engine.init_db() return query_engine +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) def test_neo4j_query_engine(neo4j_query_engine): - """ - Test querying functionality of the Neo4j Query Engine. - """ + """Test querying functionality of the Neo4j Query Engine.""" question = "Which company is the employer?" # Query the database @@ -138,14 +135,13 @@ def test_neo4j_query_engine(neo4j_query_engine): assert query_result.answer.find("BUZZ") >= 0 +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) def test_neo4j_add_records(neo4j_query_engine): - """ - Test the add_records functionality of the Neo4j Query Engine. - """ + """Test the add_records functionality of the Neo4j Query Engine.""" input_path = "./test/agentchat/contrib/graph_rag/the_matrix.txt" input_documents = [Document(doctype=DocumentType.TEXT, path_or_url=input_path)] @@ -161,14 +157,13 @@ def test_neo4j_add_records(neo4j_query_engine): assert query_result.answer.find("Keanu Reeves") >= 0 +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) def test_neo4j_auto(neo4j_query_engine_auto): - """ - Test querying with auto-generated property graph - """ + """Test querying with auto-generated property graph""" question = "Which company is the employer?" query_result: GraphStoreQueryResult = neo4j_query_engine_auto.query(question=question) @@ -176,14 +171,13 @@ def test_neo4j_auto(neo4j_query_engine_auto): assert query_result.answer.find("BUZZ") >= 0 +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) def test_neo4j_json_auto(neo4j_query_engine_with_json): - """ - Test querying with auto-generated property graph from a JSON file. - """ + """Test querying with auto-generated property graph from a JSON file.""" question = "What are current layout detection models in the LayoutParser model zoo?" query_result: GraphStoreQueryResult = neo4j_query_engine_with_json.query(question=question) diff --git a/test/agentchat/contrib/retrievechat/test_pgvector_retrievechat.py b/test/agentchat/contrib/retrievechat/test_pgvector_retrievechat.py index 231baa404c..713a0c1f0f 100644 --- a/test/agentchat/contrib/retrievechat/test_pgvector_retrievechat.py +++ b/test/agentchat/contrib/retrievechat/test_pgvector_retrievechat.py @@ -11,13 +11,12 @@ import pytest from sentence_transformers import SentenceTransformer -from autogen import AssistantAgent, config_list_from_json +from autogen import AssistantAgent -from ....conftest import skip_openai # noqa: E402 -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import Credentials try: - import pgvector + import pgvector # noqa: F401 from autogen.agentchat.contrib.retrieve_user_proxy_agent import ( RetrieveUserProxyAgent, @@ -31,18 +30,13 @@ test_dir = os.path.join(os.path.dirname(__file__), "../../..", "test_files") +@pytest.mark.openai @pytest.mark.skipif( - skip or skip_openai, + skip, reason="dependency is not installed OR requested to skip", ) -def test_retrievechat(): +def test_retrievechat(credentials_gpt_4o_mini: Credentials): conversations = {} - # ChatCompletion.start_logging(conversations) # deprecated in v0.2 - - config_list = config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) assistant = AssistantAgent( name="assistant", @@ -50,7 +44,7 @@ def test_retrievechat(): llm_config={ "timeout": 600, "seed": 42, - "config_list": config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) @@ -72,7 +66,7 @@ def test_retrievechat(): ], "custom_text_types": ["non-existent-type"], "chunk_token_size": 2000, - "model": config_list[0]["model"], + "model": credentials_gpt_4o_mini.config_list[0]["model"], "vector_db": "pgvector", # PGVector database "collection_name": "test_collection", "db_config": { diff --git a/test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py b/test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py index 8fb93e6018..6f5e822927 100755 --- a/test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py +++ b/test/agentchat/contrib/retrievechat/test_qdrant_retrievechat.py @@ -11,13 +11,12 @@ import pytest -from autogen import AssistantAgent, config_list_from_json +from autogen import AssistantAgent -from ....conftest import skip_openai # noqa: E402 -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import Credentials try: - import fastembed + import fastembed # noqa: F401 from qdrant_client import QdrantClient from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import ( @@ -31,35 +30,29 @@ QDRANT_INSTALLED = False try: - import openai + import openai # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai - -test_dir = os.path.join(os.path.dirname(__file__), "../../..", "test_files") + skip = False +@pytest.mark.openai @pytest.mark.skipif( sys.platform in ["darwin", "win32"] or not QDRANT_INSTALLED or skip, reason="do not run on MacOS or windows OR dependency is not installed OR requested to skip", ) -def test_retrievechat(): +def test_retrievechat(credentials_gpt_4o_mini: Credentials): conversations = {} # ChatCompletion.start_logging(conversations) # deprecated in v0.2 - config_list = config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) - assistant = AssistantAgent( name="assistant", system_message="You are a helpful assistant.", llm_config={ "timeout": 600, "seed": 42, - "config_list": config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) @@ -82,6 +75,7 @@ def test_retrievechat(): print(conversations) +@pytest.mark.openai @pytest.mark.skipif(not QDRANT_INSTALLED, reason="qdrant_client is not installed") def test_qdrant_filter(): client = QdrantClient(":memory:") @@ -97,8 +91,10 @@ def test_qdrant_filter(): assert len(results["ids"][0]) == 4 +@pytest.mark.openai @pytest.mark.skipif(not QDRANT_INSTALLED, reason="qdrant_client is not installed") def test_qdrant_search(): + test_dir = os.path.join(os.path.dirname(__file__), "../../..", "test_files") client = QdrantClient(":memory:") create_qdrant_from_dir(test_dir, client=client) diff --git a/test/agentchat/contrib/retrievechat/test_retrievechat.py b/test/agentchat/contrib/retrievechat/test_retrievechat.py index f5561e8ebb..143e633d15 100755 --- a/test/agentchat/contrib/retrievechat/test_retrievechat.py +++ b/test/agentchat/contrib/retrievechat/test_retrievechat.py @@ -10,14 +10,11 @@ import pytest -import autogen - -from ....conftest import reason, skip_openai # noqa: E402 -from ...test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ....conftest import Credentials, reason try: import chromadb - import openai + import openai # noqa: F401 from chromadb.utils import embedding_functions as ef from autogen import AssistantAgent @@ -32,26 +29,22 @@ reason = "do not run on MacOS or windows OR dependency is not installed OR " + reason +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip or skip_openai, + sys.platform in ["darwin", "win32"] or skip, reason=reason, ) -def test_retrievechat(): +def test_retrievechat(credentials_gpt_4o_mini: Credentials): conversations = {} # autogen.ChatCompletion.start_logging(conversations) # deprecated in v0.2 - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) - assistant = AssistantAgent( name="assistant", system_message="You are a helpful assistant.", llm_config={ "timeout": 600, "seed": 42, - "config_list": config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) @@ -63,7 +56,7 @@ def test_retrievechat(): retrieve_config={ "docs_path": "./website/docs", "chunk_token_size": 2000, - "model": config_list[0]["model"], + "model": credentials_gpt_4o_mini.config_list[0]["model"], "client": chromadb.PersistentClient(path="/tmp/chromadb"), "embedding_function": sentence_transformer_ef, "get_or_create": True, diff --git a/test/agentchat/contrib/test_agent_builder.py b/test/agentchat/contrib/test_agent_builder.py index 8966333db9..a4ae12231c 100755 --- a/test/agentchat/contrib/test_agent_builder.py +++ b/test/agentchat/contrib/test_agent_builder.py @@ -11,14 +11,13 @@ import pytest -from autogen.agentchat.contrib.agent_builder import AgentBuilder +from autogen.agentchat.contrib.captainagent.agent_builder import AgentBuilder -from ...conftest import reason, skip_openai # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ...conftest import KEY_LOC, OAI_CONFIG_LIST try: - import chromadb - import huggingface_hub + import chromadb # noqa: F401 + import huggingface_hub # noqa: F401 except ImportError: skip = True else: @@ -40,23 +39,24 @@ def _config_check(config): assert agent_config.get("system_message", None) is not None -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_build(): - builder = AgentBuilder( +@pytest.fixture +def builder() -> AgentBuilder: + return AgentBuilder( config_file_or_env=OAI_CONFIG_LIST, config_file_location=KEY_LOC, builder_model_tags=["gpt-4o"], agent_model_tags=["gpt-4o"], ) + + +@pytest.mark.openai +def test_build(builder: AgentBuilder): building_task = ( "Find a paper on arxiv by programming, and analyze its application in some domain. " "For example, find a recent paper about gpt-4 on arxiv " "and find its potential applications in software." ) - agent_list, agent_config = builder.build( + _, agent_config = builder.build( building_task=building_task, default_llm_config={"temperature": 0}, code_execution_config={ @@ -72,23 +72,18 @@ def test_build(): assert len(agent_config["agent_configs"]) <= builder.max_agents +@pytest.mark.openai @pytest.mark.skipif( - skip_openai or skip, - reason=reason + "OR dependency not installed", + skip, + reason="dependency not installed", ) -def test_build_from_library(): - builder = AgentBuilder( - config_file_or_env=OAI_CONFIG_LIST, - config_file_location=KEY_LOC, - builder_model_tags=["gpt-4o"], - agent_model_tags=["gpt-4o"], - ) +def test_build_from_library(builder: AgentBuilder): building_task = ( "Find a paper on arxiv by programming, and analyze its application in some domain. " "For example, find a recent paper about gpt-4 on arxiv " "and find its potential applications in software." ) - agent_list, agent_config = builder.build_from_library( + _, agent_config = builder.build_from_library( building_task=building_task, library_path_or_json=f"{here}/example_agent_builder_library.json", default_llm_config={"temperature": 0}, @@ -107,7 +102,7 @@ def test_build_from_library(): builder.clear_all_agents() # test embedding similarity selection - agent_list, agent_config = builder.build_from_library( + _, agent_config = builder.build_from_library( building_task=building_task, library_path_or_json=f"{here}/example_agent_builder_library.json", default_llm_config={"temperature": 0}, @@ -125,17 +120,8 @@ def test_build_from_library(): assert len(agent_config["agent_configs"]) <= builder.max_agents -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_save(): - builder = AgentBuilder( - config_file_or_env=OAI_CONFIG_LIST, - config_file_location=KEY_LOC, - builder_model_tags=["gpt-4o"], - agent_model_tags=["gpt-4o"], - ) +@pytest.mark.openai +def test_save(builder: AgentBuilder): building_task = ( "Find a paper on arxiv by programming, and analyze its application in some domain. " "For example, find a recent paper about gpt-4 on arxiv " @@ -162,24 +148,12 @@ def test_save(): _config_check(saved_configs) -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_load(): - builder = AgentBuilder( - config_file_or_env=OAI_CONFIG_LIST, - config_file_location=KEY_LOC, - # builder_model=["gpt-4", "gpt-4-1106-preview"], - # agent_model=["gpt-4", "gpt-4-1106-preview"], - builder_model_tags=["gpt-4o"], - agent_model_tags=["gpt-4o"], - ) - +@pytest.mark.openai +def test_load(builder: AgentBuilder): config_save_path = f"{here}/example_test_agent_builder_config.json" json.load(open(config_save_path)) - agent_list, loaded_agent_configs = builder.load( + _, loaded_agent_configs = builder.load( config_save_path, code_execution_config={ "last_n_messages": 2, @@ -193,18 +167,8 @@ def test_load(): _config_check(loaded_agent_configs) -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_clear_agent(): - builder = AgentBuilder( - config_file_or_env=OAI_CONFIG_LIST, - config_file_location=KEY_LOC, - builder_model_tags=["gpt-4o"], - agent_model_tags=["gpt-4o"], - ) - +@pytest.mark.openai +def test_clear_agent(builder: AgentBuilder): config_save_path = f"{here}/example_test_agent_builder_config.json" builder.load( config_save_path, diff --git a/test/agentchat/contrib/test_agent_optimizer.py b/test/agentchat/contrib/test_agent_optimizer.py index 472dfe15b1..9c5572a84c 100644 --- a/test/agentchat/contrib/test_agent_optimizer.py +++ b/test/agentchat/contrib/test_agent_optimizer.py @@ -8,27 +8,19 @@ import pytest -import autogen from autogen import AssistantAgent, UserProxyAgent from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer -from ...conftest import reason, skip_openai -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ...conftest import Credentials here = os.path.abspath(os.path.dirname(__file__)) -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_record_conversation(): +@pytest.mark.openai +def test_record_conversation(credentials_all: Credentials): problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}" - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) + config_list = credentials_all.config_list llm_config = { "config_list": config_list, "timeout": 60, @@ -60,17 +52,11 @@ def test_record_conversation(): assert len(optimizer._trial_conversations_performance) == 0 -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_step(): +@pytest.mark.openai +def test_step(credentials_all: Credentials): problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}" - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) + config_list = credentials_all.config_list llm_config = { "config_list": config_list, "timeout": 60, diff --git a/test/agentchat/contrib/test_captainagent.py b/test/agentchat/contrib/test_captainagent.py index 50c1233c62..18f589c19c 100644 --- a/test/agentchat/contrib/test_captainagent.py +++ b/test/agentchat/contrib/test_captainagent.py @@ -5,27 +5,23 @@ import pytest -from autogen import UserProxyAgent, config_list_from_json -from autogen.agentchat.contrib.captainagent import CaptainAgent +from autogen import UserProxyAgent +from autogen.agentchat.contrib.captainagent.captainagent import CaptainAgent -from ...conftest import reason, skip_openai # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ...conftest import KEY_LOC, OAI_CONFIG_LIST, Credentials, reason try: - import chromadb - import huggingface_hub + import chromadb # noqa: F401 + import huggingface_hub # noqa: F401 except ImportError: skip = True else: skip = False -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_captain_agent_from_scratch(): - config_list = config_list_from_json(OAI_CONFIG_LIST, file_location=KEY_LOC) +@pytest.mark.openai +def test_captain_agent_from_scratch(credentials_all: Credentials): + config_list = credentials_all.config_list llm_config = { "temperature": 0, "config_list": config_list, @@ -62,13 +58,13 @@ def test_captain_agent_from_scratch(): print(result) +@pytest.mark.openai @pytest.mark.skipif( - skip_openai or skip, + skip, reason=reason, ) -def test_captain_agent_with_library(): - - config_list = config_list_from_json(OAI_CONFIG_LIST, file_location=KEY_LOC) +def test_captain_agent_with_library(credentials_all: Credentials): + config_list = credentials_all.config_list llm_config = { "temperature": 0, "config_list": config_list, diff --git a/test/agentchat/contrib/test_gpt_assistant.py b/test/agentchat/contrib/test_gpt_assistant.py index 5d0ec9ec4e..2a698d4ab7 100755 --- a/test/agentchat/contrib/test_gpt_assistant.py +++ b/test/agentchat/contrib/test_gpt_assistant.py @@ -7,66 +7,33 @@ #!/usr/bin/env python3 -m pytest import os -import sys import uuid from unittest.mock import MagicMock import openai import pytest -import autogen from autogen import OpenAIWrapper, UserProxyAgent from autogen.agentchat.contrib.gpt_assistant_agent import GPTAssistantAgent from autogen.oai.openai_utils import detect_gpt_assistant_api_version, retrieve_assistants_by_name -from ...conftest import reason, skip_openai # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 - -if not skip_openai: - openai_config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - # The Retrieval tool requires at least gpt-3.5-turbo-1106 (newer versions are supported) or gpt-4-turbo-preview models. - # https://platform.openai.com/docs/models/overview - filter_dict={ - "api_type": ["openai"], - "model": [ - "gpt-4o-mini", - "gpt-4o", - "gpt-4-turbo", - "gpt-4-turbo-preview", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - ], - }, - ) - aoai_config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"api_type": ["azure"], "tags": ["assistant"]}, - ) - +from ...conftest import Credentials -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_config_list() -> None: - assert len(openai_config_list) > 0 - assert len(aoai_config_list) > 0 +@pytest.mark.openai +@pytest.mark.parametrize("provider", ["openai", "azure"]) +def test_gpt_assistant_chat_openai( + provider: str, credentials_gpt_4o_mini: Credentials, credentials_azure: Credentials +) -> None: + if provider == "openai": + _test_gpt_assistant_chat(credentials_gpt_4o_mini) + elif provider == "azure": + _test_gpt_assistant_chat(credentials_azure) + else: + raise ValueError(f"Invalid provider: {provider}") -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_gpt_assistant_chat() -> None: - for gpt_config in [openai_config_list, aoai_config_list]: - _test_gpt_assistant_chat({"config_list": gpt_config}) - _test_gpt_assistant_chat(gpt_config[0]) - -def _test_gpt_assistant_chat(gpt_config) -> None: +def _test_gpt_assistant_chat(credentials: Credentials) -> None: ossinsight_api_schema = { "name": "ossinsight_data_api", "parameters": { @@ -90,7 +57,7 @@ def ask_ossinsight(question: str) -> str: name = f"For test_gpt_assistant_chat {uuid.uuid4()}" analyst = GPTAssistantAgent( name=name, - llm_config=gpt_config, + llm_config=credentials.llm_config, assistant_config={"tools": [{"type": "function", "function": ossinsight_api_schema}]}, instructions="Hello, Open Source Project Analyst. You'll conduct comprehensive evaluations of open source projects or organizations on the GitHub platform", ) @@ -130,18 +97,21 @@ def ask_ossinsight(question: str) -> str: assert threads_count == 0 -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_get_assistant_instructions() -> None: - for gpt_config in [openai_config_list, aoai_config_list]: - _test_get_assistant_instructions(gpt_config) +@pytest.mark.openai +@pytest.mark.parametrize("provider", ["openai", "azure"]) +def test_get_assistant_instructions( + provider: str, credentials_gpt_4o_mini: Credentials, credentials_azure: Credentials +) -> None: + if provider == "openai": + _test_get_assistant_instructions(credentials_gpt_4o_mini) + elif provider == "azure": + _test_get_assistant_instructions(credentials_azure) + else: + raise ValueError(f"Invalid provider: {provider}") -def _test_get_assistant_instructions(gpt_config) -> None: - """ - Test function to create a new GPTAssistantAgent, set its instructions, retrieve the instructions, +def _test_get_assistant_instructions(credentials: Credentials) -> None: + """Test function to create a new GPTAssistantAgent, set its instructions, retrieve the instructions, and assert that the retrieved instructions match the set instructions. """ name = f"For test_get_assistant_instructions {uuid.uuid4()}" @@ -149,7 +119,7 @@ def _test_get_assistant_instructions(gpt_config) -> None: name, instructions="This is a test", llm_config={ - "config_list": gpt_config, + "config_list": credentials.config_list, }, ) @@ -159,18 +129,21 @@ def _test_get_assistant_instructions(gpt_config) -> None: assert instruction_match is True -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_gpt_assistant_instructions_overwrite() -> None: - for gpt_config in [openai_config_list, aoai_config_list]: - _test_gpt_assistant_instructions_overwrite(gpt_config) +@pytest.mark.openai +@pytest.mark.parametrize("provider", ["openai", "azure"]) +def test_gpt_assistant_instructions_overwrite( + provider: str, credentials_gpt_4o_mini: Credentials, credentials_azure: Credentials +) -> None: + if provider == "openai": + _test_gpt_assistant_instructions_overwrite(credentials_gpt_4o_mini) + elif provider == "azure": + _test_gpt_assistant_instructions_overwrite(credentials_azure) + else: + raise ValueError(f"Invalid provider: {provider}") -def _test_gpt_assistant_instructions_overwrite(gpt_config) -> None: - """ - Test that the instructions of a GPTAssistantAgent can be overwritten or not depending on the value of the +def _test_gpt_assistant_instructions_overwrite(credentials: Credentials) -> None: + """Test that the instructions of a GPTAssistantAgent can be overwritten or not depending on the value of the `overwrite_instructions` parameter when creating a new assistant with the same ID. Steps: @@ -179,7 +152,6 @@ def _test_gpt_assistant_instructions_overwrite(gpt_config) -> None: 3. Create a new GPTAssistantAgent with the same ID but different instructions and `overwrite_instructions=True`. 4. Check that the instructions of the assistant have been overwritten with the new ones. """ - name = f"For test_gpt_assistant_instructions_overwrite {uuid.uuid4()}" instructions1 = "This is a test #1" instructions2 = "This is a test #2" @@ -188,7 +160,7 @@ def _test_gpt_assistant_instructions_overwrite(gpt_config) -> None: name, instructions=instructions1, llm_config={ - "config_list": gpt_config, + "config_list": credentials.config_list, }, ) @@ -198,7 +170,7 @@ def _test_gpt_assistant_instructions_overwrite(gpt_config) -> None: name, instructions=instructions2, llm_config={ - "config_list": gpt_config, + "config_list": credentials.config_list, # keep it to test older version of assistant config "assistant_id": assistant_id, }, @@ -213,13 +185,9 @@ def _test_gpt_assistant_instructions_overwrite(gpt_config) -> None: assert instruction_match is True -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_gpt_assistant_existing_no_instructions() -> None: - """ - Test function to check if the GPTAssistantAgent can retrieve instructions for an existing assistant +@pytest.mark.openai +def test_gpt_assistant_existing_no_instructions(credentials_gpt_4o_mini: Credentials) -> None: + """Test function to check if the GPTAssistantAgent can retrieve instructions for an existing assistant even if the assistant was created with no instructions initially. """ name = f"For test_gpt_assistant_existing_no_instructions {uuid.uuid4()}" @@ -229,7 +197,7 @@ def test_gpt_assistant_existing_no_instructions() -> None: name, instructions=instructions, llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) @@ -240,7 +208,7 @@ def test_gpt_assistant_existing_no_instructions() -> None: assistant = GPTAssistantAgent( name, llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, assistant_config={"assistant_id": assistant_id}, ) @@ -253,17 +221,13 @@ def test_gpt_assistant_existing_no_instructions() -> None: assert instruction_match is True -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_get_assistant_files() -> None: - """ - Test function to create a new GPTAssistantAgent, set its instructions, retrieve the instructions, +@pytest.mark.openai +def test_get_assistant_files(credentials_gpt_4o_mini: Credentials) -> None: + """Test function to create a new GPTAssistantAgent, set its instructions, retrieve the instructions, and assert that the retrieved instructions match the set instructions. """ current_file_path = os.path.abspath(__file__) - openai_client = OpenAIWrapper(config_list=openai_config_list)._clients[0]._oai_client + openai_client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list)._clients[0]._oai_client file = openai_client.files.create(file=open(current_file_path, "rb"), purpose="assistants") name = f"For test_get_assistant_files {uuid.uuid4()}" gpt_assistant_api_version = detect_gpt_assistant_api_version() @@ -273,7 +237,7 @@ def test_get_assistant_files() -> None: name, instructions="This is a test", llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, "tools": [{"type": "retrieval"}], "file_ids": [file.id], }, @@ -298,15 +262,9 @@ def test_get_assistant_files() -> None: assert expected_file_id in retrieved_file_ids -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_assistant_retrieval() -> None: - """ - Test function to check if the GPTAssistantAgent can retrieve the same assistant - """ - +@pytest.mark.openai +def test_assistant_retrieval(credentials_gpt_4o_mini: Credentials) -> None: + """Test function to check if the GPTAssistantAgent can retrieve the same assistant""" name = f"For test_assistant_retrieval {uuid.uuid4()}" function_1_schema = { @@ -320,7 +278,7 @@ def test_assistant_retrieval() -> None: "description": "This is a test function 2", } - openai_client = OpenAIWrapper(config_list=openai_config_list)._clients[0]._oai_client + openai_client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list)._clients[0]._oai_client current_file_path = os.path.abspath(__file__) file_1 = openai_client.files.create(file=open(current_file_path, "rb"), purpose="assistants") @@ -328,7 +286,7 @@ def test_assistant_retrieval() -> None: try: all_llm_config = { - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, } assistant_config = { "tools": [ @@ -375,13 +333,9 @@ def test_assistant_retrieval() -> None: assert len(candidates) == 0 -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_assistant_mismatch_retrieval() -> None: +@pytest.mark.openai +def test_assistant_mismatch_retrieval(credentials_gpt_4o_mini: Credentials) -> None: """Test function to check if the GPTAssistantAgent can filter out the mismatch assistant""" - name = f"For test_assistant_retrieval {uuid.uuid4()}" function_1_schema = { @@ -400,7 +354,7 @@ def test_assistant_mismatch_retrieval() -> None: "description": "This is a test function 3", } - openai_client = OpenAIWrapper(config_list=openai_config_list)._clients[0]._oai_client + openai_client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list)._clients[0]._oai_client current_file_path = os.path.abspath(__file__) file_1 = openai_client.files.create(file=open(current_file_path, "rb"), purpose="assistants") file_2 = openai_client.files.create(file=open(current_file_path, "rb"), purpose="assistants") @@ -415,7 +369,7 @@ def test_assistant_mismatch_retrieval() -> None: {"type": "code_interpreter"}, ], "file_ids": [file_1.id, file_2.id], - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, } name = f"For test_assistant_retrieval {uuid.uuid4()}" @@ -449,7 +403,7 @@ def test_assistant_mismatch_retrieval() -> None: {"type": "function", "function": function_3_schema}, ], "file_ids": [file_2.id, file_1.id], - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, } assistant_tools_mistaching = GPTAssistantAgent( name, @@ -475,13 +429,9 @@ def test_assistant_mismatch_retrieval() -> None: assert len(candidates) == 0 -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_gpt_assistant_tools_overwrite() -> None: - """ - Test that the tools of a GPTAssistantAgent can be overwritten or not depending on the value of the +@pytest.mark.openai +def test_gpt_assistant_tools_overwrite(credentials_gpt_4o_mini: Credentials) -> None: + """Test that the tools of a GPTAssistantAgent can be overwritten or not depending on the value of the `overwrite_tools` parameter when creating a new assistant with the same ID. Steps: @@ -490,7 +440,6 @@ def test_gpt_assistant_tools_overwrite() -> None: 3. Create a new GPTAssistantAgent with the same ID but different tools and `overwrite_tools=True`. 4. Check that the tools of the assistant have been overwritten with the new ones. """ - original_tools = [ { "type": "function", @@ -565,7 +514,7 @@ def test_gpt_assistant_tools_overwrite() -> None: assistant_org = GPTAssistantAgent( name, llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, assistant_config={ "tools": original_tools, @@ -579,7 +528,7 @@ def test_gpt_assistant_tools_overwrite() -> None: assistant = GPTAssistantAgent( name, llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, assistant_config={ "assistant_id": assistant_id, @@ -597,13 +546,10 @@ def test_gpt_assistant_tools_overwrite() -> None: assert retrieved_tools_name == [tool["function"]["name"] for tool in new_tools] -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_gpt_reflection_with_llm() -> None: +@pytest.mark.openai +def test_gpt_reflection_with_llm(credentials_gpt_4o_mini: Credentials) -> None: gpt_assistant = GPTAssistantAgent( - name="assistant", llm_config={"config_list": openai_config_list, "assistant_id": None} + name="assistant", llm_config={"config_list": credentials_gpt_4o_mini.config_list, "assistant_id": None} ) user_proxy = UserProxyAgent( @@ -619,7 +565,7 @@ def test_gpt_reflection_with_llm() -> None: # use the assistant configuration agent_using_assistant_config = GPTAssistantAgent( name="assistant", - llm_config={"config_list": openai_config_list}, + llm_config={"config_list": credentials_gpt_4o_mini.config_list}, assistant_config={"assistant_id": gpt_assistant.assistant_id}, ) result = user_proxy.initiate_chat( @@ -628,13 +574,9 @@ def test_gpt_reflection_with_llm() -> None: assert result is not None -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_assistant_tool_and_function_role_messages() -> None: - """ - Tests that internally generated roles ('tool', 'function') are correctly mapped to +@pytest.mark.openai +def test_assistant_tool_and_function_role_messages(credentials_gpt_4o_mini: Credentials) -> None: + """Tests that internally generated roles ('tool', 'function') are correctly mapped to OpenAI Assistant API-compatible role ('assistant') before sending to the OpenAI API to prevent BadRequestError when using GPTAssistantAgent with other tool-calling agents. @@ -644,7 +586,7 @@ def test_assistant_tool_and_function_role_messages() -> None: assistant = GPTAssistantAgent( name, llm_config={ - "config_list": openai_config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) diff --git a/test/agentchat/contrib/test_img_utils.py b/test/agentchat/contrib/test_img_utils.py index 49efad1013..bb4518bd45 100755 --- a/test/agentchat/contrib/test_img_utils.py +++ b/test/agentchat/contrib/test_img_utils.py @@ -67,9 +67,7 @@ def test_read_pil(self): def are_b64_images_equal(x: str, y: str): - """ - Asserts that two base64 encoded images are equal. - """ + """Asserts that two base64 encoded images are equal.""" img1 = get_pil_image(x) img2 = get_pil_image(y) return (np.array(img1) == np.array(img2)).all() @@ -110,9 +108,7 @@ def test_local_image(self): @pytest.mark.skipif(skip, reason="dependency is not installed") class TestLlavaFormater(unittest.TestCase): def test_no_images(self): - """ - Test the llava_formatter function with a prompt containing no images. - """ + """Test the llava_formatter function with a prompt containing no images.""" prompt = "This is a test." expected_output = (prompt, []) result = llava_formatter(prompt) @@ -120,9 +116,7 @@ def test_no_images(self): @patch("autogen.agentchat.contrib.img_utils.get_image_data") def test_with_images(self, mock_get_image_data): - """ - Test the llava_formatter function with a prompt containing images. - """ + """Test the llava_formatter function with a prompt containing images.""" # Mock the get_image_data function to return a fixed string. mock_get_image_data.return_value = raw_encoded_image @@ -133,9 +127,7 @@ def test_with_images(self, mock_get_image_data): @patch("autogen.agentchat.contrib.img_utils.get_image_data") def test_with_ordered_images(self, mock_get_image_data): - """ - Test the llava_formatter function with ordered image tokens. - """ + """Test the llava_formatter function with ordered image tokens.""" # Mock the get_image_data function to return a fixed string. mock_get_image_data.return_value = raw_encoded_image @@ -148,9 +140,7 @@ def test_with_ordered_images(self, mock_get_image_data): @pytest.mark.skipif(skip, reason="dependency is not installed") class TestGpt4vFormatter(unittest.TestCase): def test_no_images(self): - """ - Test the gpt4v_formatter function with a prompt containing no images. - """ + """Test the gpt4v_formatter function with a prompt containing no images.""" prompt = "This is a test." expected_output = [{"type": "text", "text": prompt}] result = gpt4v_formatter(prompt) @@ -158,9 +148,7 @@ def test_no_images(self): @patch("autogen.agentchat.contrib.img_utils.get_image_data") def test_with_images(self, mock_get_image_data): - """ - Test the gpt4v_formatter function with a prompt containing images. - """ + """Test the gpt4v_formatter function with a prompt containing images.""" # Mock the get_image_data function to return a fixed string. mock_get_image_data.return_value = raw_encoded_image @@ -175,9 +163,7 @@ def test_with_images(self, mock_get_image_data): @patch("autogen.agentchat.contrib.img_utils.get_pil_image") def test_with_images_for_pil(self, mock_get_pil_image): - """ - Test the gpt4v_formatter function with a prompt containing images. - """ + """Test the gpt4v_formatter function with a prompt containing images.""" # Mock the get_image_data function to return a fixed string. mock_get_pil_image.return_value = raw_pil_image @@ -191,9 +177,7 @@ def test_with_images_for_pil(self, mock_get_pil_image): self.assertEqual(result, expected_output) def test_with_images_for_url(self): - """ - Test the gpt4v_formatter function with a prompt containing images. - """ + """Test the gpt4v_formatter function with a prompt containing images.""" prompt = "This is a test with an image ." expected_output = [ {"type": "text", "text": "This is a test with an image "}, @@ -205,9 +189,7 @@ def test_with_images_for_url(self): @patch("autogen.agentchat.contrib.img_utils.get_image_data") def test_multiple_images(self, mock_get_image_data): - """ - Test the gpt4v_formatter function with a prompt containing multiple images. - """ + """Test the gpt4v_formatter function with a prompt containing multiple images.""" # Mock the get_image_data function to return a fixed string. mock_get_image_data.return_value = raw_encoded_image @@ -228,18 +210,14 @@ def test_multiple_images(self, mock_get_image_data): @pytest.mark.skipif(skip, reason="dependency is not installed") class TestExtractImgPaths(unittest.TestCase): def test_no_images(self): - """ - Test the extract_img_paths function with a paragraph containing no images. - """ + """Test the extract_img_paths function with a paragraph containing no images.""" paragraph = "This is a test paragraph with no images." expected_output = [] result = extract_img_paths(paragraph) self.assertEqual(result, expected_output) def test_with_images(self): - """ - Test the extract_img_paths function with a paragraph containing images. - """ + """Test the extract_img_paths function with a paragraph containing images.""" paragraph = ( "This is a test paragraph with images http://example.com/image1.jpg and http://example.com/image2.png." ) @@ -248,18 +226,14 @@ def test_with_images(self): self.assertEqual(result, expected_output) def test_mixed_case(self): - """ - Test the extract_img_paths function with mixed case image extensions. - """ + """Test the extract_img_paths function with mixed case image extensions.""" paragraph = "Mixed case extensions http://example.com/image.JPG and http://example.com/image.Png." expected_output = ["http://example.com/image.JPG", "http://example.com/image.Png"] result = extract_img_paths(paragraph) self.assertEqual(result, expected_output) def test_local_paths(self): - """ - Test the extract_img_paths function with local file paths. - """ + """Test the extract_img_paths function with local file paths.""" paragraph = "Local paths image1.jpeg and image2.GIF." expected_output = ["image1.jpeg", "image2.GIF"] result = extract_img_paths(paragraph) diff --git a/test/agentchat/contrib/test_llamaindex_conversable_agent.py b/test/agentchat/contrib/test_llamaindex_conversable_agent.py index 2f8a3fa900..a7bb83aaee 100644 --- a/test/agentchat/contrib/test_llamaindex_conversable_agent.py +++ b/test/agentchat/contrib/test_llamaindex_conversable_agent.py @@ -14,7 +14,7 @@ from autogen.agentchat.contrib.llamaindex_conversable_agent import LLamaIndexConversableAgent from autogen.agentchat.conversable_agent import ConversableAgent -from ...conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai +from ...conftest import MOCK_OPEN_AI_API_KEY, reason skip_reasons = [reason] try: @@ -30,21 +30,20 @@ pass -openaiKey = MOCK_OPEN_AI_API_KEY +openai_key = MOCK_OPEN_AI_API_KEY @pytest.mark.skipif(skip_for_dependencies, reason=skip_reason) @patch("llama_index.core.agent.ReActAgent.chat") def test_group_chat_with_llama_index_conversable_agent(chat_mock: MagicMock) -> None: - """ - Tests the group chat functionality with two MultimodalConversable Agents. + """Tests the group chat functionality with two MultimodalConversable Agents. Verifies that the chat is correctly limited by the max_round parameter. Each agent is set to describe an image in a unique style, but the chat should not exceed the specified max_rounds. """ llm = OpenAI( model="gpt-4o", temperature=0.0, - api_key=openaiKey, + api_key=openai_key, ) chat_mock.return_value = AgentChatResponse( diff --git a/test/agentchat/contrib/test_llava.py b/test/agentchat/contrib/test_llava.py index 118c83ccb1..7e967d5a2b 100755 --- a/test/agentchat/contrib/test_llava.py +++ b/test/agentchat/contrib/test_llava.py @@ -11,8 +11,6 @@ import pytest -import autogen - from ...conftest import MOCK_OPEN_AI_API_KEY try: diff --git a/test/agentchat/contrib/test_lmm.py b/test/agentchat/contrib/test_lmm.py index d1e42cfeef..de39870046 100755 --- a/test/agentchat/contrib/test_lmm.py +++ b/test/agentchat/contrib/test_lmm.py @@ -98,12 +98,10 @@ def test_print_received_message(self): @pytest.mark.skipif(skip, reason="Dependency not installed") def test_group_chat_with_lmm(): - """ - Tests the group chat functionality with two MultimodalConversable Agents. + """Tests the group chat functionality with two MultimodalConversable Agents. Verifies that the chat is correctly limited by the max_round parameter. Each agent is set to describe an image in a unique style, but the chat should not exceed the specified max_rounds. """ - # Configuration parameters max_round = 5 max_consecutive_auto_reply = 10 diff --git a/test/agentchat/contrib/test_reasoning_agent.py b/test/agentchat/contrib/test_reasoning_agent.py index 28d0afa281..db9a80ddf7 100644 --- a/test/agentchat/contrib/test_reasoning_agent.py +++ b/test/agentchat/contrib/test_reasoning_agent.py @@ -6,24 +6,21 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -import json import os import random import sys -from typing import Dict, List from unittest.mock import MagicMock, call, patch import pytest -import autogen from autogen.agentchat.contrib.reasoning_agent import ReasoningAgent, ThinkNode, visualize_tree sys.path.append(os.path.join(os.path.dirname(__file__), "../..")) -from ...conftest import reason, skip_openai # noqa: E402 +from ...conftest import reason skip_reasons = [reason] try: - from graphviz import Digraph + from graphviz import Digraph # noqa: F401 skip_for_dependencies = False skip_reason = "" @@ -106,7 +103,7 @@ def test_think_node_from_dict(): assert node.children == [] -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai def test_reasoning_agent_init(reasoning_agent): """Test ReasoningAgent initialization""" assert reasoning_agent.name == "reasoning_agent" diff --git a/test/agentchat/contrib/test_society_of_mind_agent.py b/test/agentchat/contrib/test_society_of_mind_agent.py index 10d09f4e1d..ed62fa374f 100755 --- a/test/agentchat/contrib/test_society_of_mind_agent.py +++ b/test/agentchat/contrib/test_society_of_mind_agent.py @@ -6,8 +6,6 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -import os -import sys from typing import Annotated import pytest @@ -15,11 +13,7 @@ import autogen from autogen.agentchat.contrib.society_of_mind_agent import SocietyOfMindAgent -from ...conftest import skip_openai # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 - -if not skip_openai: - config_list = autogen.config_list_from_json(env_or_file=OAI_CONFIG_LIST, file_location=KEY_LOC) +from ...conftest import Credentials def test_society_of_mind_agent(): @@ -212,14 +206,11 @@ def custom_preparer(self, messages): assert external_agent.chat_messages[soc][-1]["content"] == "All tests passed." -@pytest.mark.skipif( - skip_openai, - reason="do not run openai tests", -) -def test_function_calling(): - llm_config = {"config_list": config_list} +@pytest.mark.openai +def test_function_calling(credentials_all: Credentials): + llm_config = {"config_list": credentials_all.config_list} inner_llm_config = { - "config_list": config_list, + "config_list": credentials_all.config_list, "functions": [ { "name": "reverse_print", @@ -290,13 +281,10 @@ def test_function_calling(): ) -@pytest.mark.skipif( - skip_openai, - reason="do not run openai tests", -) -def test_tool_use(): - llm_config = {"config_list": config_list} - inner_llm_config = {"config_list": config_list} +@pytest.mark.openai +def test_tool_use(credentials_all: Credentials): + llm_config = credentials_all.llm_config + inner_llm_config = credentials_all.llm_config external_agent = autogen.ConversableAgent( "external_agent", diff --git a/test/agentchat/contrib/test_swarm.py b/test/agentchat/contrib/test_swarm.py index 524c7533d5..d5f48ddaee 100644 --- a/test/agentchat/contrib/test_swarm.py +++ b/test/agentchat/contrib/test_swarm.py @@ -1,14 +1,13 @@ # Copyright (c) 2023 - 2025, Owners of https://github.com/ag2ai # # SPDX-License-Identifier: Apache-2.0 -from typing import Any, Dict, List, Union +from typing import Any, Union from unittest.mock import MagicMock, patch import pytest from autogen.agentchat.agent import Agent from autogen.agentchat.contrib.swarm_agent import ( - __CONTEXT_VARIABLES_PARAM_NAME__, __TOOL_EXECUTOR_NAME__, AfterWork, AfterWorkOption, @@ -97,7 +96,6 @@ def test_on_condition(): def test_receiving_agent(): """Test the receiving agent based on various starting messages""" - # 1. Test with a single message - should always be the initial agent messages_one_no_name = [{"role": "user", "content": "Initial message"}] @@ -157,10 +155,10 @@ def test_resume_speaker(): ] # Patch initiate_chat on agents so we can monitor which started the conversation - with patch.object(test_initial_agent, "initiate_chat") as mock_initial_chat, patch.object( - test_second_agent, "initiate_chat" - ) as mock_second_chat: - + with ( + patch.object(test_initial_agent, "initiate_chat") as mock_initial_chat, + patch.object(test_second_agent, "initiate_chat") as mock_second_chat, + ): mock_chat_result = MagicMock() mock_chat_result.chat_history = multiple_messages @@ -302,7 +300,6 @@ def test_temporary_user_proxy(): def test_context_variables_updating_multi_tools(): """Test context variables handling in tool calls""" - testing_llm_config = { "config_list": [ { @@ -363,7 +360,6 @@ def mock_generate_oai_reply_tool(*args, **kwargs): def test_function_transfer(): """Tests a function call that has a transfer to agent in the SwarmResult""" - testing_llm_config = { "config_list": [ { @@ -778,7 +774,6 @@ def mock_generate_oai_reply_tool(*args, **kwargs): def test_prepare_swarm_agents(): """Test preparation of swarm agents including tool executor setup""" - testing_llm_config = { "config_list": [ { @@ -830,7 +825,6 @@ def test_func2(): def test_create_nested_chats(): """Test creation of nested chat agents and registration of handoffs""" - testing_llm_config = { "config_list": [ { @@ -941,7 +935,6 @@ def test_setup_context_variables(): def test_cleanup_temp_user_messages(): """Test cleanup of temporary user messages""" - chat_result = MagicMock() chat_result.chat_history = [ {"role": "user", "name": "_User", "content": "Test 1"}, diff --git a/test/agentchat/contrib/test_web_surfer.py b/test/agentchat/contrib/test_web_surfer.py index 4b41019975..0eac02bcac 100755 --- a/test/agentchat/contrib/test_web_surfer.py +++ b/test/agentchat/contrib/test_web_surfer.py @@ -11,11 +11,9 @@ import pytest -from autogen import UserProxyAgent, config_list_from_json -from autogen.oai.openai_utils import filter_config +from autogen import UserProxyAgent -from ...conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ...conftest import MOCK_OPEN_AI_API_KEY, Credentials BLOG_POST_URL = "https://docs.ag2.ai/blog/2023-04-21-LLM-tuning-math" BLOG_POST_TITLE = "Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH - AG2" @@ -35,9 +33,6 @@ else: skip_bing = False -if not skip_openai: - config_list = config_list_from_json(env_or_file=OAI_CONFIG_LIST, file_location=KEY_LOC) - @pytest.mark.skipif( skip_all, @@ -98,25 +93,19 @@ def test_web_surfer() -> None: response = function_map["summarize_page"]() +@pytest.mark.openai @pytest.mark.skipif( - skip_all or skip_openai, - reason="dependency is not installed OR" + reason, + skip_all, + reason="dependency is not installed", ) -def test_web_surfer_oai() -> None: - llm_config = {"config_list": config_list, "timeout": 180, "cache_seed": 42} - - # adding Azure name variations to the model list - model = ["gpt-4o", "gpt-4o-mini"] - model += [m.replace(".", "") for m in model] +def test_web_surfer_oai(credentials_gpt_4o_mini: Credentials, credentials_gpt_4o: Credentials) -> None: + llm_config = {"config_list": credentials_gpt_4o.config_list, "timeout": 180, "cache_seed": 42} summarizer_llm_config = { - "config_list": filter_config(config_list, dict(model=model)), # type: ignore[no-untyped-call] + "config_list": credentials_gpt_4o_mini.config_list, "timeout": 180, } - assert len(llm_config["config_list"]) > 0 # type: ignore[arg-type] - assert len(summarizer_llm_config["config_list"]) > 0 - page_size = 4096 web_surfer = WebSurferAgent( "web_surfer", diff --git a/test/agentchat/contrib/vectordb/test_chromadb.py b/test/agentchat/contrib/vectordb/test_chromadb.py index 6966269136..c9193576d9 100644 --- a/test/agentchat/contrib/vectordb/test_chromadb.py +++ b/test/agentchat/contrib/vectordb/test_chromadb.py @@ -14,7 +14,7 @@ try: import chromadb import chromadb.errors - import sentence_transformers + import sentence_transformers # noqa: F401 from autogen.agentchat.contrib.vectordb.chromadb import ChromaVectorDB except ImportError: diff --git a/test/agentchat/contrib/vectordb/test_mongodb.py b/test/agentchat/contrib/vectordb/test_mongodb.py index 055ec22b5f..6de1418c71 100644 --- a/test/agentchat/contrib/vectordb/test_mongodb.py +++ b/test/agentchat/contrib/vectordb/test_mongodb.py @@ -8,15 +8,14 @@ import os import random from time import monotonic, sleep -from typing import List import pytest from autogen.agentchat.contrib.vectordb.base import Document try: - import pymongo - import sentence_transformers + import pymongo # noqa: F401 + import sentence_transformers # noqa: F401 from autogen.agentchat.contrib.vectordb.mongodb import MongoDBAtlasVectorDB except ImportError: @@ -143,8 +142,7 @@ def collection_name(): def test_create_collection(db, collection_name): - """ - def create_collection(collection_name: str, + """Def create_collection(collection_name: str, overwrite: bool = False) -> Collection Create a collection in the vector database. - Case 1. if the collection does not exist, create the collection. diff --git a/test/agentchat/contrib/vectordb/test_pgvectordb.py b/test/agentchat/contrib/vectordb/test_pgvectordb.py index 8e3a52bc0c..0fe572bc52 100644 --- a/test/agentchat/contrib/vectordb/test_pgvectordb.py +++ b/test/agentchat/contrib/vectordb/test_pgvectordb.py @@ -13,9 +13,9 @@ from ....conftest import reason try: - import pgvector + import pgvector # noqa: F401 import psycopg - import sentence_transformers + import sentence_transformers # noqa: F401 from autogen.agentchat.contrib.vectordb.pgvectordb import PGVectorDB except ImportError: diff --git a/test/agentchat/contrib/vectordb/test_vectordb_utils.py b/test/agentchat/contrib/vectordb/test_vectordb_utils.py index 7f9758d4a5..a53467cb19 100644 --- a/test/agentchat/contrib/vectordb/test_vectordb_utils.py +++ b/test/agentchat/contrib/vectordb/test_vectordb_utils.py @@ -6,10 +6,6 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -import os -import sys - -import pytest from autogen.agentchat.contrib.vectordb.utils import chroma_results_to_query_results, filter_results_by_distance diff --git a/test/agentchat/extensions/tsp.py b/test/agentchat/extensions/tsp.py index 7b1e7600f3..f3b57dd539 100644 --- a/test/agentchat/extensions/tsp.py +++ b/test/agentchat/extensions/tsp.py @@ -9,11 +9,8 @@ Triangular inequality is not required in this problem. """ -import math -import pdb import random -import sys -from itertools import combinations, permutations +from itertools import permutations def solve_tsp(dists: dict) -> float: @@ -28,7 +25,7 @@ def solve_tsp(dists: dict) -> float: """ # Get the unique nodes from the distance matrix nodes = set() - for pair in dists.keys(): + for pair in dists: nodes.add(pair[0]) nodes.add(pair[1]) diff --git a/test/agentchat/realtime_agent/conftest.py b/test/agentchat/realtime_agent/conftest.py deleted file mode 100644 index 6186837502..0000000000 --- a/test/agentchat/realtime_agent/conftest.py +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai -# -# SPDX-License-Identifier: Apache-2.0 - -import pytest - -import autogen - -from ...conftest import MOCK_OPEN_AI_API_KEY # noqa: E402 -from ..test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST -from .realtime_test_utils import Credentials - - -@pytest.fixture -def credentials() -> Credentials: - """Fixture to load the LLM config.""" - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-realtime"], - }, - file_location=KEY_LOC, - ) - assert config_list, "No config list found" - - return Credentials( - llm_config={ - "config_list": config_list, - "temperature": 0.6, - } - ) - - -@pytest.fixture -def mock_credentials() -> Credentials: - llm_config = { - "config_list": [ - { - "model": "gpt-4o", - "api_key": MOCK_OPEN_AI_API_KEY, - }, - ], - "temperature": 0.8, - } - - return Credentials(llm_config=llm_config) diff --git a/test/agentchat/realtime_agent/realtime_test_utils.py b/test/agentchat/realtime_agent/realtime_test_utils.py index 6707d50cd6..dde870ed48 100644 --- a/test/agentchat/realtime_agent/realtime_test_utils.py +++ b/test/agentchat/realtime_agent/realtime_test_utils.py @@ -10,7 +10,7 @@ from anyio import Event from openai import NotGiven, OpenAI -__all__ = ["Credentials", "text_to_speech", "trace"] +__all__ = ["text_to_speech", "trace"] def text_to_speech( @@ -73,27 +73,3 @@ def _inner(*args: Any, **kwargs: Any) -> Any: return _inner # type: ignore[return-value] return decorator - - -class Credentials: - """Credentials for the OpenAI API.""" - - def __init__(self, llm_config: dict[str, Any]) -> None: - self.llm_config = llm_config - - def sanitize(self) -> dict[str, Any]: - llm_config = self.llm_config.copy() - for config in llm_config["config_list"]: - if "api_key" in config: - config["api_key"] = "********" - return llm_config - - def __repr__(self) -> str: - return repr(self.sanitize()) - - def __str___(self) -> str: - return str(self.sanitize()) - - @property - def openai_api_key(self) -> str: - return self.llm_config["config_list"][0]["api_key"] # type: ignore[no-any-return] diff --git a/test/agentchat/realtime_agent/test_e2e.py b/test/agentchat/realtime_agent/test_e2e.py index 439745d451..61a2415bf0 100644 --- a/test/agentchat/realtime_agent/test_e2e.py +++ b/test/agentchat/realtime_agent/test_e2e.py @@ -3,7 +3,7 @@ # SPDX-License-Identifier: Apache-2.0 from logging import getLogger -from typing import Annotated, Any +from typing import Annotated from unittest.mock import MagicMock import pytest @@ -14,22 +14,22 @@ from autogen.agentchat.realtime_agent import RealtimeAgent, RealtimeObserver, WebSocketAudioAdapter -from ...conftest import reason, skip_openai # noqa: E402 -from .realtime_test_utils import Credentials, text_to_speech, trace +from ...conftest import Credentials +from .realtime_test_utils import text_to_speech, trace logger = getLogger(__name__) -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai class TestE2E: - async def _test_e2e(self, credentials: Credentials) -> None: + async def _test_e2e(self, credentials_gpt_4o_realtime: Credentials) -> None: """End-to-end test for the RealtimeAgent. Create a FastAPI app with a WebSocket endpoint that handles audio stream and OpenAI. """ - llm_config = credentials.llm_config - openai_api_key = credentials.openai_api_key + llm_config = credentials_gpt_4o_realtime.llm_config + openai_api_key = credentials_gpt_4o_realtime.openai_api_key # Event for synchronization and tracking state weather_func_called_event = Event() @@ -94,18 +94,18 @@ def get_weather(location: Annotated[str, "city"]) -> str: assert "Seattle" in last_response_transcript, "Weather response did not include the location" assert "cloudy" in last_response_transcript, "Weather response did not include the weather condition" - @pytest.mark.asyncio() - async def test_e2e(self, credentials: Credentials) -> None: + @pytest.mark.asyncio + async def test_e2e(self, credentials_gpt_4o_realtime: Credentials) -> None: """End-to-end test for the RealtimeAgent. Retry the test up to 3 times if it fails. Sometimes the test fails due to voice not being recognized by the OpenAI API. """ i = 0 - count = 3 + count = 5 while True: try: - await self._test_e2e(credentials=credentials) + await self._test_e2e(credentials_gpt_4o_realtime=credentials_gpt_4o_realtime) return # Exit the function if the test passes except Exception as e: logger.warning( diff --git a/test/agentchat/realtime_agent/test_oai_realtime_client.py b/test/agentchat/realtime_agent/test_oai_realtime_client.py index 54a23b865b..0519e39d01 100644 --- a/test/agentchat/realtime_agent/test_oai_realtime_client.py +++ b/test/agentchat/realtime_agent/test_oai_realtime_client.py @@ -2,7 +2,6 @@ # # SPDX-License-Identifier: Apache-2.0 -from typing import Any from unittest.mock import MagicMock import pytest @@ -11,14 +10,13 @@ from autogen.agentchat.realtime_agent.oai_realtime_client import OpenAIRealtimeClient from autogen.agentchat.realtime_agent.realtime_client import RealtimeClientProtocol -from ...conftest import reason, skip_openai # noqa: E402 -from .realtime_test_utils import Credentials +from ...conftest import Credentials class TestOAIRealtimeClient: @pytest.fixture - def client(self, credentials: Credentials) -> RealtimeClientProtocol: - llm_config = credentials.llm_config + def client(self, credentials_gpt_4o_realtime: Credentials) -> RealtimeClientProtocol: + llm_config = credentials_gpt_4o_realtime.llm_config return OpenAIRealtimeClient( llm_config=llm_config, voice="alloy", @@ -35,8 +33,8 @@ def test_init(self, mock_credentials: Credentials) -> None: ) assert isinstance(client, RealtimeClientProtocol) - @pytest.mark.skipif(skip_openai, reason=reason) - @pytest.mark.asyncio() + @pytest.mark.openai + @pytest.mark.asyncio async def test_not_connected(self, client: OpenAIRealtimeClient) -> None: with pytest.raises(RuntimeError, match=r"Client is not connected, call connect\(\) first."): with move_on_after(1) as scope: @@ -45,8 +43,8 @@ async def test_not_connected(self, client: OpenAIRealtimeClient) -> None: assert not scope.cancelled_caught - @pytest.mark.skipif(skip_openai, reason=reason) - @pytest.mark.asyncio() + @pytest.mark.openai + @pytest.mark.asyncio async def test_start_read_events(self, client: OpenAIRealtimeClient) -> None: mock = MagicMock() @@ -67,8 +65,8 @@ async def test_start_read_events(self, client: OpenAIRealtimeClient) -> None: assert calls_kwargs[0]["type"] == "session.created" assert calls_kwargs[1]["type"] == "session.updated" - @pytest.mark.skipif(skip_openai, reason=reason) - @pytest.mark.asyncio() + @pytest.mark.openai + @pytest.mark.asyncio async def test_send_text(self, client: OpenAIRealtimeClient) -> None: mock = MagicMock() diff --git a/test/agentchat/realtime_agent/test_realtime_agent.py b/test/agentchat/realtime_agent/test_realtime_agent.py new file mode 100644 index 0000000000..17ee6fd560 --- /dev/null +++ b/test/agentchat/realtime_agent/test_realtime_agent.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any, Callable +from unittest.mock import MagicMock + +import pytest + +from autogen.agentchat.realtime_agent import RealtimeAgent +from autogen.tools.tool import Tool + +from ...conftest import Credentials + + +def f(a: int, b: int = 3) -> int: + return a + b + + +async def f_async(a: int, b: int = 3) -> int: + return a + b + + +class A: + def f(self, a: int, b: int = 3) -> int: + return a + b + + async def f_async(self, a: int, b: int = 3) -> int: + return a + b + + @staticmethod + def f_static(a: int, b: int = 3) -> int: + return a + b + + @staticmethod + async def f_static_async(a: int, b: int = 3) -> int: + return a + b + + +class TestRealtimeAgent: + @pytest.fixture + def agent(self, mock_credentials: Credentials) -> RealtimeAgent: + return RealtimeAgent( + name="realtime_agent", + llm_config=mock_credentials.llm_config, + audio_adapter=MagicMock(), + ) + + @pytest.fixture + def expected_tools(self) -> dict[str, Any]: + return { + "type": "function", + "description": "Example function", + "name": "f", + "parameters": { + "type": "object", + "properties": { + "a": {"type": "integer", "description": "a"}, + "b": {"type": "integer", "description": "b", "default": 3}, + }, + "required": ["a"], + }, + } + + @pytest.mark.parametrize( + ("func", "func_name", "is_async", "expected"), + [ + (f, "f", False, 4), + (f_async, "f_async", True, 4), + (A().f, "f", False, 4), + (A().f_async, "f_async", True, 4), + (A.f_static, "f_static", False, 4), + (A.f_static_async, "f_static_async", True, 4), + ], + ) + @pytest.mark.asyncio + async def test_register_tools( + self, + agent: RealtimeAgent, + expected_tools: dict[str, Any], + func: Callable[..., Any], + func_name: str, + is_async: bool, + expected: str, + ) -> None: + agent.register_realtime_function(description="Example function")(func) + + assert isinstance(agent._registred_realtime_tools[func_name], Tool) + + expected_tools["name"] = func_name + assert agent._registred_realtime_tools[func_name].realtime_tool_schema == expected_tools + + retval = agent._registred_realtime_tools[func_name].func(1) + actual = await retval if is_async else retval + + assert actual == expected diff --git a/test/agentchat/realtime_agent/test_realtime_observer.py b/test/agentchat/realtime_agent/test_realtime_observer.py index 3bc1835aab..e402afdd2b 100644 --- a/test/agentchat/realtime_agent/test_realtime_observer.py +++ b/test/agentchat/realtime_agent/test_realtime_observer.py @@ -8,9 +8,8 @@ import pytest from asyncer import create_task_group -from openai.types.beta.realtime.realtime_server_event import RealtimeServerEvent -from autogen.agentchat.realtime_agent import RealtimeAgent, RealtimeObserver +from autogen.agentchat.realtime_agent import RealtimeObserver class MyObserver(RealtimeObserver): @@ -38,7 +37,7 @@ async def on_event(self, event: dict[str, Any]) -> None: class TestRealtimeObserver: - @pytest.mark.asyncio() + @pytest.mark.asyncio async def test_shutdown(self) -> None: mock = MagicMock() observer = MyObserver(mock) diff --git a/test/agentchat/realtime_agent/test_submodule.py b/test/agentchat/realtime_agent/test_submodule.py index bafd8a649e..4ff29253f9 100644 --- a/test/agentchat/realtime_agent/test_submodule.py +++ b/test/agentchat/realtime_agent/test_submodule.py @@ -7,9 +7,4 @@ def test_import() -> None: - from autogen.agentchat.realtime_agent import ( - FunctionObserver, - RealtimeAgent, - TwilioAudioAdapter, - WebSocketAudioAdapter, - ) + pass diff --git a/test/agentchat/realtime_agent/test_swarm_start.py b/test/agentchat/realtime_agent/test_swarm_start.py new file mode 100644 index 0000000000..026c7ac2e9 --- /dev/null +++ b/test/agentchat/realtime_agent/test_swarm_start.py @@ -0,0 +1,131 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +from logging import getLogger +from typing import Annotated +from unittest.mock import MagicMock + +import pytest +from anyio import Event, move_on_after, sleep +from asyncer import create_task_group +from fastapi import FastAPI, WebSocket +from fastapi.testclient import TestClient + +from autogen.agentchat.conversable_agent import ConversableAgent +from autogen.agentchat.realtime_agent import RealtimeAgent, RealtimeObserver, WebSocketAudioAdapter +from autogen.tools.dependency_injection import Field as AG2Field + +from ...conftest import Credentials +from .realtime_test_utils import text_to_speech, trace + +logger = getLogger(__name__) + + +@pytest.mark.openai +class TestSwarmE2E: + async def _test_e2e(self, credentials_gpt_4o_realtime: Credentials, credentials_gpt_4o_mini: Credentials) -> None: + """End-to-end test for the RealtimeAgent. + + Create a FastAPI app with a WebSocket endpoint that handles audio stream and OpenAI. + + """ + openai_api_key = credentials_gpt_4o_realtime.openai_api_key + + # Event for synchronization and tracking state + weather_func_called_event = Event() + weather_func_mock = MagicMock() + + app = FastAPI() + mock_observer = MagicMock(spec=RealtimeObserver) + + @app.websocket("/media-stream") + async def handle_media_stream(websocket: WebSocket) -> None: + """Handle WebSocket connections providing audio stream and OpenAI.""" + await websocket.accept() + + audio_adapter = WebSocketAudioAdapter(websocket) + agent = RealtimeAgent( + name="Weather_Bot", + llm_config=credentials_gpt_4o_realtime.llm_config, + audio_adapter=audio_adapter, + ) + + agent.register_observer(mock_observer) + + @trace(weather_func_mock, postcall_event=weather_func_called_event) + def get_weather(location: Annotated[str, AG2Field(description="city")]) -> str: + return "The weather is cloudy." if location == "Seattle" else "The weather is sunny." + + weatherman = ConversableAgent( + name="Weatherman", + system_message="You are a weatherman. You can answer questions about the weather.", + llm_config=credentials_gpt_4o_mini.llm_config, + functions=[get_weather], + ) + + agent.register_swarm( + initial_agent=weatherman, + agents=[weatherman], + ) + + async with create_task_group() as tg: + tg.soonify(agent.run)() + await sleep(25) # Run for 10 seconds + tg.cancel_scope.cancel() + + assert tg.cancel_scope.cancel_called, "Task group was not cancelled" + + await websocket.close() + + client = TestClient(app) + with client.websocket_connect("/media-stream") as websocket: + await sleep(5) + websocket.send_json( + { + "event": "media", + "media": { + "timestamp": 0, + "payload": text_to_speech(text="How is the weather in Seattle?", openai_api_key=openai_api_key), + }, + } + ) + + # Wait for the weather function to be called or timeout + with move_on_after(10) as scope: + await weather_func_called_event.wait() + assert weather_func_called_event.is_set(), "Weather function was not called within the expected time" + assert not scope.cancel_called, "Cancel scope was called before the weather function was called" + + # Verify the function call details + weather_func_mock.assert_called_with(location="Seattle") + + last_response_transcript = mock_observer.on_event.call_args_list[-1][0][0]["response"]["output"][0][ + "content" + ][0]["transcript"] + assert "Seattle" in last_response_transcript, "Weather response did not include the location" + assert "cloudy" in last_response_transcript, "Weather response did not include the weather condition" + + @pytest.mark.asyncio + async def test_e2e(self, credentials_gpt_4o_realtime: Credentials, credentials_gpt_4o_mini: Credentials) -> None: + """End-to-end test for the RealtimeAgent. + + Retry the test up to 5 times if it fails. Sometimes the test fails due to voice not being recognized by the OpenAI API. + + """ + i = 0 + count = 5 + while True: + try: + await self._test_e2e( + credentials_gpt_4o_realtime=credentials_gpt_4o_realtime, + credentials_gpt_4o_mini=credentials_gpt_4o_mini, + ) + return # Exit the function if the test passes + except Exception as e: + logger.warning( + f"Test 'TestSwarmE2E.test_e2e' failed on attempt {i + 1} with exception: {e}", stack_info=True + ) + if i + 1 >= count: + raise + i += 1 diff --git a/test/agentchat/test_agent_logging.py b/test/agentchat/test_agent_logging.py index ba9317945c..2884509dbc 100644 --- a/test/agentchat/test_agent_logging.py +++ b/test/agentchat/test_agent_logging.py @@ -5,7 +5,6 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import json -import os import sqlite3 import sys import uuid @@ -15,8 +14,7 @@ import autogen import autogen.runtime_logging -from ..conftest import skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ..conftest import Credentials TEACHER_MESSAGE = """ You are roleplaying a math teacher, and your job is to help your students with linear algebra. @@ -42,20 +40,6 @@ "SELECT source_id, source_name, event_name, agent_module, agent_class_name, json_state, timestamp FROM events" ) -if not skip_openai: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - file_location=KEY_LOC, - ) - - llm_config = {"config_list": config_list} - - num_of_configs = len(config_list) -############################################################### - @pytest.fixture(scope="function") def db_connection(): @@ -67,18 +51,19 @@ def db_connection(): autogen.runtime_logging.stop() +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip_openai, + sys.platform in ["darwin", "win32"], reason="do not run on MacOS or windows OR dependency is not installed OR requested to skip", ) -def test_two_agents_logging(db_connection): +def test_two_agents_logging(credentials: Credentials, db_connection): cur = db_connection.cursor() teacher = autogen.AssistantAgent( "teacher", system_message=TEACHER_MESSAGE, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, - llm_config=llm_config, + llm_config=credentials.llm_config, max_consecutive_auto_reply=2, ) @@ -86,7 +71,7 @@ def test_two_agents_logging(db_connection): "student", system_message=STUDENT_MESSAGE, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, - llm_config=llm_config, + llm_config=credentials.llm_config, max_consecutive_auto_reply=1, ) @@ -103,9 +88,9 @@ def test_two_agents_logging(db_connection): session_id = rows[0]["session_id"] for idx, row in enumerate(rows): - assert ( - row["invocation_id"] and str(uuid.UUID(row["invocation_id"], version=4)) == row["invocation_id"] - ), "invocation id is not valid uuid" + assert row["invocation_id"] and str(uuid.UUID(row["invocation_id"], version=4)) == row["invocation_id"], ( + "invocation id is not valid uuid" + ) assert row["client_id"], "client id is empty" assert row["wrapper_id"], "wrapper id is empty" assert row["session_id"] and row["session_id"] == session_id @@ -162,7 +147,7 @@ def test_two_agents_logging(db_connection): cur.execute(OAI_CLIENTS_QUERY) rows = cur.fetchall() - assert len(rows) == num_of_configs * 2 # two agents + assert len(rows) == len(credentials.config_list) * 2 # two agents session_id = rows[0]["session_id"] for row in rows: @@ -190,18 +175,19 @@ def test_two_agents_logging(db_connection): assert row["timestamp"], "timestamp is empty" +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip_openai, + sys.platform in ["darwin", "win32"], reason="do not run on MacOS or windows OR dependency is not installed OR requested to skip", ) -def test_groupchat_logging(db_connection): +def test_groupchat_logging(credentials_gpt_4o: Credentials, credentials_gpt_4o_mini: Credentials, db_connection): cur = db_connection.cursor() teacher = autogen.AssistantAgent( "teacher", system_message=TEACHER_MESSAGE, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, - llm_config=llm_config, + llm_config=credentials_gpt_4o.llm_config, max_consecutive_auto_reply=2, ) @@ -209,7 +195,7 @@ def test_groupchat_logging(db_connection): "student", system_message=STUDENT_MESSAGE, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, - llm_config=llm_config, + llm_config=credentials_gpt_4o_mini.llm_config, max_consecutive_auto_reply=1, ) @@ -217,7 +203,7 @@ def test_groupchat_logging(db_connection): agents=[teacher, student], messages=[], max_round=3, speaker_selection_method="round_robin" ) - group_chat_manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config) + group_chat_manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=credentials_gpt_4o_mini.llm_config) student.initiate_chat( group_chat_manager, @@ -242,7 +228,9 @@ def test_groupchat_logging(db_connection): # Verify oai clients cur.execute(OAI_CLIENTS_QUERY) rows = cur.fetchall() - assert len(rows) == num_of_configs * 3 # three agents + assert len(rows) == len(credentials_gpt_4o_mini.config_list) * 2 + len( + credentials_gpt_4o.config_list + ) # two agents and chat manager # Verify oai wrappers cur.execute(OAI_WRAPPERS_QUERY) diff --git a/test/agentchat/test_agent_setup_with_use_docker_settings.py b/test/agentchat/test_agent_setup_with_use_docker_settings.py index dc3440db78..1d1bfc1bce 100644 --- a/test/agentchat/test_agent_setup_with_use_docker_settings.py +++ b/test/agentchat/test_agent_setup_with_use_docker_settings.py @@ -5,7 +5,6 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import os -import sys import pytest @@ -15,14 +14,12 @@ is_docker_running, ) -from ..conftest import reason, skip_openai - def docker_running(): return is_docker_running() or in_docker_container() -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai def test_agent_setup_with_code_execution_off(): user_proxy = UserProxyAgent( name="test_agent", @@ -33,7 +30,7 @@ def test_agent_setup_with_code_execution_off(): assert user_proxy._code_execution_config is False -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai def test_agent_setup_with_use_docker_false(): user_proxy = UserProxyAgent( name="test_agent", @@ -44,7 +41,7 @@ def test_agent_setup_with_use_docker_false(): assert user_proxy._code_execution_config["use_docker"] is False -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai def test_agent_setup_with_env_variable_false_and_docker_running(monkeypatch): monkeypatch.setenv("AUTOGEN_USE_DOCKER", "False") @@ -56,7 +53,8 @@ def test_agent_setup_with_env_variable_false_and_docker_running(monkeypatch): assert user_proxy._code_execution_config["use_docker"] is False -@pytest.mark.skipif(skip_openai or (not docker_running()), reason=reason + " OR docker not running") +@pytest.mark.openai +@pytest.mark.skipif((not docker_running()), reason="docker not running") def test_agent_setup_with_default_and_docker_running(monkeypatch): monkeypatch.delenv("AUTOGEN_USE_DOCKER", raising=False) @@ -72,7 +70,8 @@ def test_agent_setup_with_default_and_docker_running(monkeypatch): assert user_proxy._code_execution_config["use_docker"] is True -@pytest.mark.skipif(skip_openai or (docker_running()), reason=reason + " OR docker running") +@pytest.mark.openai +@pytest.mark.skipif((docker_running()), reason="docker running") def test_raises_error_agent_setup_with_default_and_docker_not_running(monkeypatch): monkeypatch.delenv("AUTOGEN_USE_DOCKER", raising=False) with pytest.raises(RuntimeError): @@ -82,7 +81,8 @@ def test_raises_error_agent_setup_with_default_and_docker_not_running(monkeypatc ) -@pytest.mark.skipif(skip_openai or (docker_running()), reason=" OR docker running") +@pytest.mark.openai +@pytest.mark.skipif((docker_running()), reason="docker running") def test_raises_error_agent_setup_with_env_variable_true_and_docker_not_running(monkeypatch): monkeypatch.setenv("AUTOGEN_USE_DOCKER", "True") @@ -93,7 +93,8 @@ def test_raises_error_agent_setup_with_env_variable_true_and_docker_not_running( ) -@pytest.mark.skipif(skip_openai or (not docker_running()), reason=" OR docker not running") +@pytest.mark.openai +@pytest.mark.skipif((not docker_running()), reason="docker not running") def test_agent_setup_with_env_variable_true_and_docker_running(monkeypatch): monkeypatch.setenv("AUTOGEN_USE_DOCKER", "True") diff --git a/test/agentchat/test_agent_usage.py b/test/agentchat/test_agent_usage.py index 903b11c351..f1a5ff23e3 100755 --- a/test/agentchat/test_agent_usage.py +++ b/test/agentchat/test_agent_usage.py @@ -11,42 +11,27 @@ import pytest -import autogen from autogen import AssistantAgent, UserProxyAgent, gather_usage_summary -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -@pytest.mark.skipif(skip_openai, reason=reason) -def test_gathering(): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) +@pytest.mark.openai +def test_gathering(credentials_gpt_4o: Credentials, credentials_gpt_4o_mini: Credentials): assistant1 = AssistantAgent( "assistant", system_message="You are a helpful assistant.", - llm_config={ - "config_list": config_list, - "model": "gpt-4o-mini", - }, + llm_config=credentials_gpt_4o_mini.llm_config, ) assistant2 = AssistantAgent( "assistant", system_message="You are a helpful assistant.", - llm_config={ - "config_list": config_list, - "model": "gpt-4o-mini", - }, + llm_config=credentials_gpt_4o_mini.llm_config, ) assistant3 = AssistantAgent( "assistant", system_message="You are a helpful assistant.", - llm_config={ - "config_list": config_list, - "model": "gpt-4o", - }, + llm_config=credentials_gpt_4o.llm_config, ) assistant1.client.total_usage_summary = { @@ -83,13 +68,9 @@ def test_gathering(): print("Total usage summary:", total_usage_summary) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_agent_usage(): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) +@pytest.mark.openai +def test_agent_usage(credentials: Credentials): + config_list = credentials.config_list assistant = AssistantAgent( "assistant", system_message="You are a helpful assistant.", diff --git a/test/agentchat/test_agentchat_utils.py b/test/agentchat/test_agentchat_utils.py index 8c38a6855e..fb4a68a7fe 100644 --- a/test/agentchat/test_agentchat_utils.py +++ b/test/agentchat/test_agentchat_utils.py @@ -4,7 +4,7 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Dict, List, Union +from typing import Union import pytest diff --git a/test/agentchat/test_assistant_agent.py b/test/agentchat/test_assistant_agent.py index 2d4c077ac9..7b87346b35 100755 --- a/test/agentchat/test_assistant_agent.py +++ b/test/agentchat/test_assistant_agent.py @@ -11,29 +11,24 @@ import pytest -import autogen from autogen.agentchat import AssistantAgent, UserProxyAgent -from ..conftest import reason, skip_openai # noqa: E402 +from ..conftest import Credentials -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" here = os.path.abspath(os.path.dirname(__file__)) +@pytest.mark.openai @pytest.mark.skipif( - sys.platform in ["darwin", "win32"] or skip_openai, - reason="do not run on MacOS or windows OR " + reason, + sys.platform in ["darwin", "win32"], + reason="do not run on MacOS or windows", ) -def test_ai_user_proxy_agent(): +def test_ai_user_proxy_agent(credentials_gpt_4o_mini: Credentials): conversations = {} # autogen.ChatCompletion.start_logging(conversations) - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) + config_list = credentials_gpt_4o_mini.config_list + assistant = AssistantAgent( "assistant", system_message="You are a helpful assistant.", @@ -66,13 +61,9 @@ def test_ai_user_proxy_agent(): print("Result summary:", res.summary) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_gpt35(human_input_mode="NEVER", max_consecutive_auto_reply=5): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) +@pytest.mark.openai +def test_gpt4omini(credentials_gpt_4o_mini: Credentials, human_input_mode="NEVER", max_consecutive_auto_reply=5): + config_list = credentials_gpt_4o_mini.config_list llm_config = { "cache_seed": 42, "config_list": config_list, @@ -110,13 +101,11 @@ def test_gpt35(human_input_mode="NEVER", max_consecutive_auto_reply=5): assert not isinstance(user.use_docker, bool) # None or str -@pytest.mark.skipif(skip_openai, reason=reason) -def test_create_execute_script(human_input_mode="NEVER", max_consecutive_auto_reply=3): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) +@pytest.mark.openai +def test_create_execute_script( + credentials_gpt_4o_mini: Credentials, human_input_mode="NEVER", max_consecutive_auto_reply=3 +): + config_list = credentials_gpt_4o_mini.config_list conversations = {} # autogen.ChatCompletion.start_logging(conversations) llm_config = { @@ -163,15 +152,9 @@ def test_create_execute_script(human_input_mode="NEVER", max_consecutive_auto_re # autogen.ChatCompletion.stop_logging() -@pytest.mark.skipif(skip_openai, reason=reason) -def test_tsp(human_input_mode="NEVER", max_consecutive_auto_reply=2): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "tags": ["gpt-4o"], - }, - ) +@pytest.mark.openai +def test_tsp(credentials_gpt_4o_mini: Credentials, human_input_mode="NEVER", max_consecutive_auto_reply=2): + config_list = credentials_gpt_4o_mini.config_list hard_questions = [ "What if we must go from node 1 to node 2?", "Can we double all distances?", diff --git a/test/agentchat/test_async.py b/test/agentchat/test_async.py index ed3d123068..23e884180a 100755 --- a/test/agentchat/test_async.py +++ b/test/agentchat/test_async.py @@ -12,8 +12,7 @@ import autogen -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials def get_market_news(ind, ind_upper): @@ -57,10 +56,10 @@ def get_market_news(ind, ind_upper): return feeds_summary -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai @pytest.mark.asyncio -async def test_async_groupchat(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) +async def test_async_groupchat(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list # create an AssistantAgent instance named "assistant" assistant = autogen.AssistantAgent( @@ -91,10 +90,10 @@ async def test_async_groupchat(): assert len(user_proxy.chat_messages) > 0 -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai @pytest.mark.asyncio -async def test_stream(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) +async def test_stream(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list data = asyncio.Future() async def add_stock_price_data(): diff --git a/test/agentchat/test_async_chats.py b/test/agentchat/test_async_chats.py index 1b1e3a1a4a..415b230e9a 100755 --- a/test/agentchat/test_async_chats.py +++ b/test/agentchat/test_async_chats.py @@ -10,21 +10,15 @@ import pytest -import autogen from autogen import AssistantAgent, UserProxyAgent -from ..conftest import skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -@pytest.mark.skipif(skip_openai, reason="requested to skip openai tests") +@pytest.mark.openai @pytest.mark.asyncio -async def test_async_chats(): - config_list_4omini = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) +async def test_async_chats(credentials_gpt_4o_mini: Credentials): + config_list_4omini = credentials_gpt_4o_mini.config_list financial_tasks = [ """What are the full names of NVDA and TESLA.""", diff --git a/test/agentchat/test_async_get_human_input.py b/test/agentchat/test_async_get_human_input.py index d9fa2115d4..be95762dcd 100755 --- a/test/agentchat/test_async_get_human_input.py +++ b/test/agentchat/test_async_get_human_input.py @@ -13,14 +13,13 @@ import autogen -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai @pytest.mark.asyncio -async def test_async_get_human_input(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) +async def test_async_get_human_input(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list # create an AssistantAgent instance named "assistant" assistant = autogen.AssistantAgent( @@ -44,10 +43,10 @@ async def test_async_get_human_input(): print("Human input:", res.human_input) -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai @pytest.mark.asyncio -async def test_async_max_turn(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) +async def test_async_max_turn(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list # create an AssistantAgent instance named "assistant" assistant = autogen.AssistantAgent( @@ -69,9 +68,9 @@ async def test_async_max_turn(): print("Result summary:", res.summary) print("Human input:", res.human_input) print("chat history:", res.chat_history) - assert ( - len(res.chat_history) == 6 - ), f"Chat history should have 6 messages because max_turns is set to 3 (and user keep request try again) but has {len(res.chat_history)}." + assert len(res.chat_history) == 6, ( + f"Chat history should have 6 messages because max_turns is set to 3 (and user keep request try again) but has {len(res.chat_history)}." + ) if __name__ == "__main__": diff --git a/test/agentchat/test_cache_agent.py b/test/agentchat/test_cache_agent.py index e54d6c9b20..3c49f17a25 100644 --- a/test/agentchat/test_cache_agent.py +++ b/test/agentchat/test_cache_agent.py @@ -14,29 +14,30 @@ from autogen.agentchat import AssistantAgent, UserProxyAgent from autogen.cache import Cache -from ..conftest import skip_openai, skip_redis # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials, skip_redis try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: - skip_openai_tests = True + skip_tests = True else: - skip_openai_tests = False or skip_openai + skip_tests = False try: - import redis + import redis # noqa: F401 except ImportError: skip_redis_tests = True else: skip_redis_tests = False or skip_redis -@pytest.mark.skipif(skip_openai_tests, reason="openai not installed OR requested to skip") -def test_legacy_disk_cache(): +@pytest.mark.openai +@pytest.mark.skipif(skip_tests, reason="openai not installed") +def test_legacy_disk_cache(credentials_gpt_4o_mini: Credentials): random_cache_seed = int.from_bytes(os.urandom(2), "big") start_time = time.time() cold_cache_messages = run_conversation( + credentials_gpt_4o_mini, cache_seed=random_cache_seed, ) end_time = time.time() @@ -44,6 +45,7 @@ def test_legacy_disk_cache(): start_time = time.time() warm_cache_messages = run_conversation( + credentials_gpt_4o_mini, cache_seed=random_cache_seed, ) end_time = time.time() @@ -52,18 +54,19 @@ def test_legacy_disk_cache(): assert duration_with_warm_cache < duration_with_cold_cache -@pytest.mark.skipif(skip_openai_tests or skip_redis_tests, reason="redis not installed OR requested to skip") -def test_redis_cache(): +@pytest.mark.openai +@pytest.mark.skipif(skip_tests or skip_redis_tests, reason="redis not installed OR openai not installed") +def test_redis_cache(credentials_gpt_4o_mini: Credentials): random_cache_seed = int.from_bytes(os.urandom(2), "big") redis_url = os.getenv("REDIS_URL", "redis://localhost:6379/0") start_time = time.time() with Cache.redis(random_cache_seed, redis_url) as cache_client: - cold_cache_messages = run_conversation(cache_seed=None, cache=cache_client) + cold_cache_messages = run_conversation(credentials_gpt_4o_mini, cache_seed=None, cache=cache_client) end_time = time.time() duration_with_cold_cache = end_time - start_time start_time = time.time() - warm_cache_messages = run_conversation(cache_seed=None, cache=cache_client) + warm_cache_messages = run_conversation(credentials_gpt_4o_mini, cache_seed=None, cache=cache_client) end_time = time.time() duration_with_warm_cache = end_time - start_time assert cold_cache_messages == warm_cache_messages @@ -71,29 +74,30 @@ def test_redis_cache(): random_cache_seed = int.from_bytes(os.urandom(2), "big") with Cache.redis(random_cache_seed, redis_url) as cache_client: - cold_cache_messages = run_groupchat_conversation(cache=cache_client) + cold_cache_messages = run_groupchat_conversation(credentials_gpt_4o_mini, cache=cache_client) end_time = time.time() duration_with_cold_cache = end_time - start_time start_time = time.time() - warm_cache_messages = run_groupchat_conversation(cache=cache_client) + warm_cache_messages = run_groupchat_conversation(credentials_gpt_4o_mini, cache=cache_client) end_time = time.time() duration_with_warm_cache = end_time - start_time assert cold_cache_messages == warm_cache_messages assert duration_with_warm_cache < duration_with_cold_cache -@pytest.mark.skipif(skip_openai_tests, reason="openai not installed OR requested to skip") -def test_disk_cache(): +@pytest.mark.openai +@pytest.mark.skipif(skip_tests, reason="openai not installed") +def test_disk_cache(credentials_gpt_4o_mini: Credentials): random_cache_seed = int.from_bytes(os.urandom(2), "big") start_time = time.time() with Cache.disk(random_cache_seed) as cache_client: - cold_cache_messages = run_conversation(cache_seed=None, cache=cache_client) + cold_cache_messages = run_conversation(credentials_gpt_4o_mini, cache_seed=None, cache=cache_client) end_time = time.time() duration_with_cold_cache = end_time - start_time start_time = time.time() - warm_cache_messages = run_conversation(cache_seed=None, cache=cache_client) + warm_cache_messages = run_conversation(credentials_gpt_4o_mini, cache_seed=None, cache=cache_client) end_time = time.time() duration_with_warm_cache = end_time - start_time assert cold_cache_messages == warm_cache_messages @@ -101,26 +105,22 @@ def test_disk_cache(): random_cache_seed = int.from_bytes(os.urandom(2), "big") with Cache.disk(random_cache_seed) as cache_client: - cold_cache_messages = run_groupchat_conversation(cache=cache_client) + cold_cache_messages = run_groupchat_conversation(credentials_gpt_4o_mini, cache=cache_client) end_time = time.time() duration_with_cold_cache = end_time - start_time start_time = time.time() - warm_cache_messages = run_groupchat_conversation(cache=cache_client) + warm_cache_messages = run_groupchat_conversation(credentials_gpt_4o_mini, cache=cache_client) end_time = time.time() duration_with_warm_cache = end_time - start_time assert cold_cache_messages == warm_cache_messages assert duration_with_warm_cache < duration_with_cold_cache -def run_conversation(cache_seed, human_input_mode="NEVER", max_consecutive_auto_reply=5, cache=None): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - ) +def run_conversation( + credentials: Credentials, cache_seed, human_input_mode="NEVER", max_consecutive_auto_reply=5, cache=None +): + config_list = credentials.config_list llm_config = { "cache_seed": cache_seed, "config_list": config_list, @@ -158,16 +158,8 @@ def run_conversation(cache_seed, human_input_mode="NEVER", max_consecutive_auto_ return user.chat_messages[assistant] -def run_groupchat_conversation(cache, human_input_mode="NEVER", max_consecutive_auto_reply=5): - KEY_LOC = "notebook" - OAI_CONFIG_LIST = "OAI_CONFIG_LIST" - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - ) +def run_groupchat_conversation(credentials: Credentials, cache, human_input_mode="NEVER", max_consecutive_auto_reply=5): + config_list = credentials.config_list llm_config = { "cache_seed": None, "config_list": config_list, diff --git a/test/agentchat/test_chats.py b/test/agentchat/test_chats.py index 7a2cb57600..ae66fd1d88 100755 --- a/test/agentchat/test_chats.py +++ b/test/agentchat/test_chats.py @@ -6,37 +6,35 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -import os -import sys -from typing import Annotated, Literal +from collections.abc import Generator +from tempfile import TemporaryDirectory +from typing import Annotated, Literal, TypeVar import pytest import autogen -from autogen import AssistantAgent, GroupChat, GroupChatManager, UserProxyAgent, filter_config, initiate_chats +from autogen import AssistantAgent, GroupChat, GroupChatManager, UserProxyAgent, initiate_chats from autogen.agentchat.chat import _post_process_carryover_item -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -config_list = ( - [] - if skip_openai - else autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) -) -config_list_4omini = ( - [] - if skip_openai - else autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) -) +@pytest.fixture +def work_dir() -> Generator[str, None, None]: + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def groupchat_work_dir() -> Generator[str, None, None]: + with TemporaryDirectory() as temp_dir: + yield temp_dir + + +@pytest.fixture +def tasks_work_dir() -> Generator[str, None, None]: + with TemporaryDirectory() as temp_dir: + yield temp_dir def test_chat_messages_for_summary(): @@ -60,8 +58,10 @@ def test_chat_messages_for_summary(): assert len(messages) == 2 -@pytest.mark.skipif(skip_openai, reason=reason) -def test_chats_group(): +@pytest.mark.openai +def test_chats_group( + credentials_gpt_4o_mini: Credentials, work_dir: str, groupchat_work_dir: str, tasks_work_dir: str +) -> None: financial_tasks = [ """What are the full names of NVDA and TESLA.""", """Give lucky numbers for them.""", @@ -75,7 +75,7 @@ def test_chats_group(): human_input_mode="NEVER", code_execution_config={ "last_n_messages": 1, - "work_dir": "groupchat", + "work_dir": work_dir, "use_docker": False, }, is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"), @@ -83,12 +83,12 @@ def test_chats_group(): financial_assistant = AssistantAgent( name="Financial_assistant", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, ) writer = AssistantAgent( name="Writer", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, system_message=""" You are a professional writer, known for your insightful and engaging articles. @@ -102,7 +102,7 @@ def test_chats_group(): system_message="""Critic. Double check plan, claims, code from other agents and provide feedback. Check whether the plan includes adding verifiable info such as source URL. Reply "TERMINATE" in the end when everything is done. """, - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, ) groupchat_1 = GroupChat(agents=[user_proxy, financial_assistant, critic], messages=[], max_round=3) @@ -112,10 +112,10 @@ def test_chats_group(): manager_1 = GroupChatManager( groupchat=groupchat_1, name="Research_manager", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, code_execution_config={ "last_n_messages": 1, - "work_dir": "groupchat", + "work_dir": groupchat_work_dir, "use_docker": False, }, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, @@ -123,10 +123,10 @@ def test_chats_group(): manager_2 = GroupChatManager( groupchat=groupchat_2, name="Writing_manager", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, code_execution_config={ "last_n_messages": 1, - "work_dir": "groupchat", + "work_dir": groupchat_work_dir, "use_docker": False, }, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, @@ -138,7 +138,7 @@ def test_chats_group(): is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"), code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -169,8 +169,8 @@ def test_chats_group(): print(all_res[1].summary) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_chats(): +@pytest.mark.openai +def test_chats(credentials_gpt_4o_mini: Credentials): import random class Function: @@ -197,17 +197,17 @@ def luck_number_message(sender, recipient, context): func = Function() financial_assistant_1 = AssistantAgent( name="Financial_assistant_1", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, function_map={"get_random_number": func.get_random_number}, ) financial_assistant_2 = AssistantAgent( name="Financial_assistant_2", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, function_map={"get_random_number": func.get_random_number}, ) writer = AssistantAgent( name="Writer", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, system_message=""" You are a professional writer, known for @@ -223,7 +223,7 @@ def luck_number_message(sender, recipient, context): is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -299,8 +299,8 @@ def my_summary_method(recipient, sender, summary_args): # print(blogpost.summary, insights_and_blogpost) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_chats_general(): +@pytest.mark.openai +def test_chats_general(credentials_gpt_4o_mini: Credentials, tasks_work_dir: str): financial_tasks = [ """What are the full names of NVDA and TESLA.""", """Give lucky numbers for them.""", @@ -311,15 +311,15 @@ def test_chats_general(): financial_assistant_1 = AssistantAgent( name="Financial_assistant_1", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, ) financial_assistant_2 = AssistantAgent( name="Financial_assistant_2", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, ) writer = AssistantAgent( name="Writer", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, system_message=""" You are a professional writer, known for @@ -335,7 +335,7 @@ def test_chats_general(): is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -347,7 +347,7 @@ def test_chats_general(): max_consecutive_auto_reply=3, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -403,8 +403,8 @@ def my_summary_method(recipient, sender, summary_args): # print(blogpost.summary, insights_and_blogpost) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_chats_exceptions(): +@pytest.mark.openai +def test_chats_exceptions(credentials_gpt_4o: Credentials, tasks_work_dir: str): financial_tasks = [ """What are the full names of NVDA and TESLA.""", """Give lucky numbers for them.""", @@ -413,11 +413,11 @@ def test_chats_exceptions(): financial_assistant_1 = AssistantAgent( name="Financial_assistant_1", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o.llm_config, ) financial_assistant_2 = AssistantAgent( name="Financial_assistant_2", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o.llm_config, ) user = UserProxyAgent( name="User", @@ -425,7 +425,7 @@ def test_chats_exceptions(): is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -436,7 +436,7 @@ def test_chats_exceptions(): is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -487,10 +487,10 @@ def test_chats_exceptions(): ) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_chats_w_func(): +@pytest.mark.openai +def test_chats_w_func(credentials_gpt_4o_mini: Credentials, tasks_work_dir: str): llm_config = { - "config_list": config_list_4omini, + "config_list": credentials_gpt_4o_mini.config_list, "timeout": 120, } @@ -508,12 +508,12 @@ def test_chats_w_func(): max_consecutive_auto_reply=10, code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, ) - CurrencySymbol = Literal["USD", "EUR"] + CurrencySymbol = TypeVar("CurrencySymbol", bound=Literal["USD", "EUR"]) def exchange_rate(base_currency: CurrencySymbol, quote_currency: CurrencySymbol) -> float: if base_currency == quote_currency: @@ -543,9 +543,9 @@ def currency_calculator( print(res.summary, res.cost, res.chat_history) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_udf_message_in_chats(): - llm_config_35 = {"config_list": config_list_4omini} +@pytest.mark.openai +def test_udf_message_in_chats(credentials_gpt_4o_mini: Credentials, tasks_work_dir: str) -> None: + llm_config_40mini = credentials_gpt_4o_mini.llm_config research_task = """ ## NVDA (NVIDIA Corporation) @@ -575,11 +575,11 @@ def my_writing_task(sender, recipient, context): researcher = autogen.AssistantAgent( name="Financial_researcher", - llm_config=llm_config_35, + llm_config=llm_config_40mini, ) writer = autogen.AssistantAgent( name="Writer", - llm_config=llm_config_35, + llm_config=llm_config_40mini, system_message=""" You are a professional writer, known for your insightful and engaging articles. @@ -594,7 +594,7 @@ def my_writing_task(sender, recipient, context): is_termination_msg=lambda x: x.get("content", "") and x.get("content", "").rstrip().endswith("TERMINATE"), code_execution_config={ "last_n_messages": 1, - "work_dir": "tasks", + "work_dir": tasks_work_dir, "use_docker": False, }, # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly. ) @@ -615,7 +615,7 @@ def my_writing_task(sender, recipient, context): "message": my_writing_task, "max_turns": 2, # max number of turns for the conversation (added for demo purposes, generally not necessarily needed) "summary_method": "reflection_with_llm", - "work_dir": "tasks", + "work_dir": tasks_work_dir, }, ] ) @@ -625,9 +625,9 @@ def my_writing_task(sender, recipient, context): def test_post_process_carryover_item(): gemini_carryover_item = {"content": "How can I help you?", "role": "model"} - assert ( - _post_process_carryover_item(gemini_carryover_item) == gemini_carryover_item["content"] - ), "Incorrect carryover postprocessing" + assert _post_process_carryover_item(gemini_carryover_item) == gemini_carryover_item["content"], ( + "Incorrect carryover postprocessing" + ) carryover_item = "How can I help you?" assert _post_process_carryover_item(carryover_item) == carryover_item, "Incorrect carryover postprocessing" diff --git a/test/agentchat/test_conversable_agent.py b/test/agentchat/test_conversable_agent.py index 235cf74c63..e8d181bda9 100755 --- a/test/agentchat/test_conversable_agent.py +++ b/test/agentchat/test_conversable_agent.py @@ -23,16 +23,10 @@ from autogen.agentchat.conversable_agent import register_function from autogen.exception_utils import InvalidCarryOverType, SenderRequired -from ..conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials here = os.path.abspath(os.path.dirname(__file__)) -gpt4_config_list = [ - {"model": "gpt-4o"}, - {"model": "gpt-4o-mini"}, -] - @pytest.fixture def conversable_agent(): @@ -45,6 +39,15 @@ def conversable_agent(): ) +@pytest.mark.parametrize("name", ["agent name", "agent_name ", " agent\nname", " agent\tname"]) +def test_conversable_agent_name_with_white_space_raises_error(name: str) -> None: + with pytest.raises( + ValueError, + match=f"The name of the agent cannot contain any whitespace. The name provided is: '{name}'", + ): + ConversableAgent(name=name) + + def test_sync_trigger(): agent = ConversableAgent("a0", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER") agent1 = ConversableAgent("a1", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER") @@ -393,9 +396,9 @@ def test_conversable_agent(): pre_len = len(dummy_agent_1.chat_messages[dummy_agent_2]) with pytest.raises(ValueError): dummy_agent_1.receive({"message": "hello"}, dummy_agent_2) - assert pre_len == len( - dummy_agent_1.chat_messages[dummy_agent_2] - ), "When the message is not an valid openai message, it should not be appended to the oai conversation." + assert pre_len == len(dummy_agent_1.chat_messages[dummy_agent_2]), ( + "When the message is not an valid openai message, it should not be appended to the oai conversation." + ) # monkeypatch.setattr(sys, "stdin", StringIO("exit")) dummy_agent_1.send("TERMINATE", dummy_agent_2) # send a str @@ -412,9 +415,9 @@ def test_conversable_agent(): with pytest.raises(ValueError): dummy_agent_1.send({"message": "hello"}, dummy_agent_2) - assert pre_len == len( - dummy_agent_1.chat_messages[dummy_agent_2] - ), "When the message is not a valid openai message, it should not be appended to the oai conversation." + assert pre_len == len(dummy_agent_1.chat_messages[dummy_agent_2]), ( + "When the message is not a valid openai message, it should not be appended to the oai conversation." + ) # update system message dummy_agent_1.update_system_message("new system message") @@ -457,16 +460,16 @@ def add_num(num_to_be_added): messages = [{"function_call": {"name": "add_num", "arguments": '{ "num_to_be_added": 5 }'}, "role": "assistant"}] # when sender is None, messages is provided - assert ( - dummy_agent_2.generate_reply(messages=messages, sender=None)["content"] == 15 - ), "generate_reply not working when sender is None" + assert dummy_agent_2.generate_reply(messages=messages, sender=None)["content"] == 15, ( + "generate_reply not working when sender is None" + ) # when sender is provided, messages is None dummy_agent_1 = ConversableAgent(name="dummy_agent_1", llm_config=False, human_input_mode="ALWAYS") dummy_agent_2._oai_messages[dummy_agent_1] = messages - assert ( - dummy_agent_2.generate_reply(messages=None, sender=dummy_agent_1)["content"] == 15 - ), "generate_reply not working when messages is None" + assert dummy_agent_2.generate_reply(messages=None, sender=dummy_agent_1)["content"] == 15, ( + "generate_reply not working when messages is None" + ) dummy_agent_2.register_reply(["str", None], ConversableAgent.generate_oai_reply) with pytest.raises(SenderRequired): @@ -507,83 +510,81 @@ async def test_a_generate_reply_with_messages_and_sender_none(conversable_agent) pytest.fail(f"Unexpected exception: {e}") -def test_update_function_signature_and_register_functions() -> None: - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent = ConversableAgent(name="agent", llm_config={"config_list": gpt4_config_list}) +def test_update_function_signature_and_register_functions(mock_credentials: Credentials) -> None: + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) - def exec_python(cell: str) -> None: - pass + def exec_python(cell: str) -> None: + pass - def exec_sh(script: str) -> None: - pass + def exec_sh(script: str) -> None: + pass - agent.update_function_signature( - { - "name": "python", - "description": "run cell in ipython and return the execution result.", - "parameters": { - "type": "object", - "properties": { - "cell": { - "type": "string", - "description": "Valid Python cell to execute.", - } - }, - "required": ["cell"], + agent.update_function_signature( + { + "name": "python", + "description": "run cell in ipython and return the execution result.", + "parameters": { + "type": "object", + "properties": { + "cell": { + "type": "string", + "description": "Valid Python cell to execute.", + } }, + "required": ["cell"], }, - is_remove=False, - ) + }, + is_remove=False, + ) - functions = agent.llm_config["functions"] - assert {f["name"] for f in functions} == {"python"} + functions = agent.llm_config["functions"] + assert {f["name"] for f in functions} == {"python"} - agent.update_function_signature( - { - "name": "sh", - "description": "run a shell script and return the execution result.", - "parameters": { - "type": "object", - "properties": { - "script": { - "type": "string", - "description": "Valid shell script to execute.", - } - }, - "required": ["script"], + agent.update_function_signature( + { + "name": "sh", + "description": "run a shell script and return the execution result.", + "parameters": { + "type": "object", + "properties": { + "script": { + "type": "string", + "description": "Valid shell script to execute.", + } }, + "required": ["script"], }, - is_remove=False, - ) + }, + is_remove=False, + ) - functions = agent.llm_config["functions"] - assert {f["name"] for f in functions} == {"python", "sh"} + functions = agent.llm_config["functions"] + assert {f["name"] for f in functions} == {"python", "sh"} - # register the functions - agent.register_function( - function_map={ - "python": exec_python, - "sh": exec_sh, - } - ) - assert set(agent.function_map.keys()) == {"python", "sh"} - assert agent.function_map["python"] == exec_python - assert agent.function_map["sh"] == exec_sh + # register the functions + agent.register_function( + function_map={ + "python": exec_python, + "sh": exec_sh, + } + ) + assert set(agent.function_map.keys()) == {"python", "sh"} + assert agent.function_map["python"] == exec_python + assert agent.function_map["sh"] == exec_sh - # remove the functions - agent.register_function( - function_map={ - "python": None, - } - ) + # remove the functions + agent.register_function( + function_map={ + "python": None, + } + ) - assert set(agent.function_map.keys()) == {"sh"} - assert agent.function_map["sh"] == exec_sh + assert set(agent.function_map.keys()) == {"sh"} + assert agent.function_map["sh"] == exec_sh def test__wrap_function_sync(): - CurrencySymbol = Literal["USD", "EUR"] + CurrencySymbol = Literal["USD", "EUR"] # noqa: N806 class Currency(BaseModel): currency: CurrencySymbol = Field(description="Currency code") @@ -621,7 +622,7 @@ def currency_calculator( @pytest.mark.asyncio async def test__wrap_function_async(): - CurrencySymbol = Literal["USD", "EUR"] + CurrencySymbol = Literal["USD", "EUR"] # noqa: N806 class Currency(BaseModel): currency: CurrencySymbol = Field(description="Currency code") @@ -661,96 +662,21 @@ def get_origin(d: dict[str, Callable[..., Any]]) -> dict[str, Callable[..., Any] return {k: v._origin for k, v in d.items()} -def test_register_for_llm(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent3 = ConversableAgent(name="agent3", llm_config={"config_list": gpt4_config_list}) - agent2 = ConversableAgent(name="agent2", llm_config={"config_list": gpt4_config_list}) - agent1 = ConversableAgent(name="agent1", llm_config={"config_list": gpt4_config_list}) +def test_register_for_llm(mock_credentials: Credentials) -> None: + agent3 = ConversableAgent(name="agent3", llm_config=mock_credentials.llm_config) + agent2 = ConversableAgent(name="agent2", llm_config=mock_credentials.llm_config) + agent1 = ConversableAgent(name="agent1", llm_config=mock_credentials.llm_config) - @agent3.register_for_llm() - @agent2.register_for_llm(name="python") - @agent1.register_for_llm(description="run cell in ipython and return the execution result.") - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: - pass - - expected1 = [ - { - "type": "function", - "function": { - "description": "run cell in ipython and return the execution result.", - "name": "exec_python", - "parameters": { - "type": "object", - "properties": { - "cell": { - "type": "string", - "description": "Valid Python cell to execute.", - } - }, - "required": ["cell"], - }, - }, - } - ] - expected2 = copy.deepcopy(expected1) - expected2[0]["function"]["name"] = "python" - expected3 = expected2 - - assert agent1.llm_config["tools"] == expected1 - assert agent2.llm_config["tools"] == expected2 - assert agent3.llm_config["tools"] == expected3 - - @agent3.register_for_llm() - @agent2.register_for_llm() - @agent1.register_for_llm(name="sh", description="run a shell script and return the execution result.") - async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> str: - pass - - expected1 = expected1 + [ - { - "type": "function", - "function": { - "name": "sh", - "description": "run a shell script and return the execution result.", - "parameters": { - "type": "object", - "properties": { - "script": { - "type": "string", - "description": "Valid shell script to execute.", - } - }, - "required": ["script"], - }, - }, - } - ] - expected2 = expected2 + [expected1[1]] - expected3 = expected3 + [expected1[1]] - - assert agent1.llm_config["tools"] == expected1 - assert agent2.llm_config["tools"] == expected2 - assert agent3.llm_config["tools"] == expected3 - - -def test_register_for_llm_api_style_function(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent3 = ConversableAgent(name="agent3", llm_config={"config_list": gpt4_config_list}) - agent2 = ConversableAgent(name="agent2", llm_config={"config_list": gpt4_config_list}) - agent1 = ConversableAgent(name="agent1", llm_config={"config_list": gpt4_config_list}) - - @agent3.register_for_llm(api_style="function") - @agent2.register_for_llm(name="python", api_style="function") - @agent1.register_for_llm( - description="run cell in ipython and return the execution result.", api_style="function" - ) - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: - pass + @agent3.register_for_llm() + @agent2.register_for_llm(name="python") + @agent1.register_for_llm(description="run cell in ipython and return the execution result.") + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: + pass - expected1 = [ - { + expected1 = [ + { + "type": "function", + "function": { "description": "run cell in ipython and return the execution result.", "name": "exec_python", "parameters": { @@ -763,26 +689,27 @@ def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: }, "required": ["cell"], }, - } - ] - expected2 = copy.deepcopy(expected1) - expected2[0]["name"] = "python" - expected3 = expected2 - - assert agent1.llm_config["functions"] == expected1 - assert agent2.llm_config["functions"] == expected2 - assert agent3.llm_config["functions"] == expected3 - - @agent3.register_for_llm(api_style="function") - @agent2.register_for_llm(api_style="function") - @agent1.register_for_llm( - name="sh", description="run a shell script and return the execution result.", api_style="function" - ) - async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> str: - pass + }, + } + ] + expected2 = copy.deepcopy(expected1) + expected2[0]["function"]["name"] = "python" + expected3 = expected2 + + assert agent1.llm_config["tools"] == expected1 + assert agent2.llm_config["tools"] == expected2 + assert agent3.llm_config["tools"] == expected3 + + @agent3.register_for_llm() + @agent2.register_for_llm() + @agent1.register_for_llm(name="sh", description="run a shell script and return the execution result.") + async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> str: + pass - expected1 = expected1 + [ - { + expected1 = expected1 + [ + { + "type": "function", + "function": { "name": "sh", "description": "run a shell script and return the execution result.", "parameters": { @@ -795,42 +722,106 @@ async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> s }, "required": ["script"], }, - } - ] - expected2 = expected2 + [expected1[1]] - expected3 = expected3 + [expected1[1]] + }, + } + ] + expected2 = expected2 + [expected1[1]] + expected3 = expected3 + [expected1[1]] + + assert agent1.llm_config["tools"] == expected1 + assert agent2.llm_config["tools"] == expected2 + assert agent3.llm_config["tools"] == expected3 + - assert agent1.llm_config["functions"] == expected1 - assert agent2.llm_config["functions"] == expected2 - assert agent3.llm_config["functions"] == expected3 +def test_register_for_llm_api_style_function(mock_credentials: Credentials): + agent3 = ConversableAgent(name="agent3", llm_config=mock_credentials.llm_config) + agent2 = ConversableAgent(name="agent2", llm_config=mock_credentials.llm_config) + agent1 = ConversableAgent(name="agent1", llm_config=mock_credentials.llm_config) + @agent3.register_for_llm(api_style="function") + @agent2.register_for_llm(name="python", api_style="function") + @agent1.register_for_llm(description="run cell in ipython and return the execution result.", api_style="function") + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: + pass -def test_register_for_llm_without_description(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent = ConversableAgent(name="agent", llm_config={"config_list": gpt4_config_list}) + expected1 = [ + { + "description": "run cell in ipython and return the execution result.", + "name": "exec_python", + "parameters": { + "type": "object", + "properties": { + "cell": { + "type": "string", + "description": "Valid Python cell to execute.", + } + }, + "required": ["cell"], + }, + } + ] + expected2 = copy.deepcopy(expected1) + expected2[0]["name"] = "python" + expected3 = expected2 + + assert agent1.llm_config["functions"] == expected1 + assert agent2.llm_config["functions"] == expected2 + assert agent3.llm_config["functions"] == expected3 + + @agent3.register_for_llm(api_style="function") + @agent2.register_for_llm(api_style="function") + @agent1.register_for_llm( + name="sh", description="run a shell script and return the execution result.", api_style="function" + ) + async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> str: + pass + + expected1 = expected1 + [ + { + "name": "sh", + "description": "run a shell script and return the execution result.", + "parameters": { + "type": "object", + "properties": { + "script": { + "type": "string", + "description": "Valid shell script to execute.", + } + }, + "required": ["script"], + }, + } + ] + expected2 = expected2 + [expected1[1]] + expected3 = expected3 + [expected1[1]] + + assert agent1.llm_config["functions"] == expected1 + assert agent2.llm_config["functions"] == expected2 + assert agent3.llm_config["functions"] == expected3 - @agent.register_for_llm() - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: - pass - assert exec_python.description == "" +def test_register_for_llm_without_description(mock_credentials: Credentials): + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) + @agent.register_for_llm() + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: + pass + + assert exec_python.description == "" -def test_register_for_llm_with_docstring(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent = ConversableAgent(name="agent", llm_config={"config_list": gpt4_config_list}) - @agent.register_for_llm() - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: - """Execute a Python cell.""" - pass +def test_register_for_llm_with_docstring(mock_credentials: Credentials): + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) - assert exec_python.description == "Execute a Python cell." + @agent.register_for_llm() + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: + """Execute a Python cell.""" + pass + assert exec_python.description == "Execute a Python cell." -def test_register_for_llm_without_LLM(): + +def test_register_for_llm_without_LLM(): # noqa: N802 agent = ConversableAgent(name="agent", llm_config=None) with pytest.raises( AssertionError, @@ -858,103 +849,84 @@ def test_register_for_llm_without_model_name(): ConversableAgent(name="agent", llm_config={"config_list": [{"model": ""}]}) -def test_register_for_execution(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent = ConversableAgent(name="agent", llm_config={"config_list": [{"model": "gpt-4o"}]}) - user_proxy_1 = UserProxyAgent(name="user_proxy_1") - user_proxy_2 = UserProxyAgent(name="user_proxy_2") - - @user_proxy_2.register_for_execution(name="python") - @agent.register_for_execution() - @agent.register_for_llm(description="run cell in ipython and return the execution result.") - @user_proxy_1.register_for_execution() - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]): - pass - - expected_function_map_1 = {"exec_python": exec_python.func} - assert get_origin(agent.function_map) == expected_function_map_1 - assert get_origin(user_proxy_1.function_map) == expected_function_map_1 - - expected_function_map_2 = {"python": exec_python.func} - assert get_origin(user_proxy_2.function_map) == expected_function_map_2 - - @agent.register_for_execution() - @agent.register_for_llm(description="run a shell script and return the execution result.") - @user_proxy_1.register_for_execution(name="sh") - async def exec_sh(script: Annotated[str, "Valid shell script to execute."]): - pass - - expected_function_map = { - "exec_python": exec_python.func, - "sh": exec_sh.func, - } - assert get_origin(agent.function_map) == expected_function_map - assert get_origin(user_proxy_1.function_map) == expected_function_map +def test_register_for_execution(mock_credentials: Credentials): + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) + user_proxy_1 = UserProxyAgent(name="user_proxy_1") + user_proxy_2 = UserProxyAgent(name="user_proxy_2") + @user_proxy_2.register_for_execution(name="python") + @agent.register_for_execution() + @agent.register_for_llm(description="run cell in ipython and return the execution result.") + @user_proxy_1.register_for_execution() + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]): + pass -def test_register_functions(): - with pytest.MonkeyPatch.context() as mp: - mp.setenv("OPENAI_API_KEY", MOCK_OPEN_AI_API_KEY) - agent = ConversableAgent(name="agent", llm_config={"config_list": gpt4_config_list}) - user_proxy = UserProxyAgent(name="user_proxy") + expected_function_map_1 = {"exec_python": exec_python.func} + assert get_origin(agent.function_map) == expected_function_map_1 + assert get_origin(user_proxy_1.function_map) == expected_function_map_1 - def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: - pass + expected_function_map_2 = {"python": exec_python.func} + assert get_origin(user_proxy_2.function_map) == expected_function_map_2 - register_function( - exec_python, - caller=agent, - executor=user_proxy, - description="run cell in ipython and return the execution result.", - ) + @agent.register_for_execution() + @agent.register_for_llm(description="run a shell script and return the execution result.") + @user_proxy_1.register_for_execution(name="sh") + async def exec_sh(script: Annotated[str, "Valid shell script to execute."]): + pass - expected_function_map = {"exec_python": exec_python} - assert get_origin(user_proxy.function_map).keys() == expected_function_map.keys() + expected_function_map = { + "exec_python": exec_python.func, + "sh": exec_sh.func, + } + assert get_origin(agent.function_map) == expected_function_map + assert get_origin(user_proxy_1.function_map) == expected_function_map - expected = [ - { - "type": "function", - "function": { - "description": "run cell in ipython and return the execution result.", - "name": "exec_python", - "parameters": { - "type": "object", - "properties": { - "cell": { - "type": "string", - "description": "Valid Python cell to execute.", - } - }, - "required": ["cell"], + +def test_register_functions(mock_credentials: Credentials): + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) + user_proxy = UserProxyAgent(name="user_proxy") + + def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str: + pass + + register_function( + exec_python, + caller=agent, + executor=user_proxy, + description="run cell in ipython and return the execution result.", + ) + + expected_function_map = {"exec_python": exec_python} + assert get_origin(user_proxy.function_map).keys() == expected_function_map.keys() + + expected = [ + { + "type": "function", + "function": { + "description": "run cell in ipython and return the execution result.", + "name": "exec_python", + "parameters": { + "type": "object", + "properties": { + "cell": { + "type": "string", + "description": "Valid Python cell to execute.", + } }, + "required": ["cell"], }, - } - ] - assert agent.llm_config["tools"] == expected - - -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -def test_function_registration_e2e_sync() -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - file_location=KEY_LOC, - ) + }, + } + ] + assert agent.llm_config["tools"] == expected - llm_config = { - "config_list": config_list, - } +@pytest.mark.openai +def test_function_registration_e2e_sync(credentials_gpt_4o_mini: Credentials) -> None: coder = autogen.AssistantAgent( name="chatbot", system_message="For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.", - llm_config=llm_config, + llm_config=credentials_gpt_4o_mini.llm_config, ) # create a UserProxyAgent instance named "user_proxy" @@ -1002,7 +974,7 @@ def stopwatch(num_seconds: Annotated[str, "Number of seconds in the stopwatch."] # 'await' is used to pause and resume code execution for async IO operations. # Without 'await', an async function returns a coroutine object but doesn't execute the function. # With 'await', the async function is executed and the current function is paused until the awaited function returns a result. - user_proxy.initiate_chat( # noqa: F704 + user_proxy.initiate_chat( coder, message="Create a timer for 1 second and then a stopwatch for 2 seconds.", ) @@ -1011,28 +983,13 @@ def stopwatch(num_seconds: Annotated[str, "Number of seconds in the stopwatch."] stopwatch_mock.assert_called_once_with(num_seconds="2") -@pytest.mark.skipif( - skip_openai, - reason=reason, -) -@pytest.mark.asyncio() -async def test_function_registration_e2e_async() -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) - - llm_config = { - "config_list": config_list, - } - +@pytest.mark.openai +@pytest.mark.asyncio +async def test_function_registration_e2e_async(credentials_gpt_4o: Credentials) -> None: coder = autogen.AssistantAgent( name="chatbot", system_message="For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.", - llm_config=llm_config, + llm_config=credentials_gpt_4o.llm_config, ) # create a UserProxyAgent instance named "user_proxy" @@ -1080,7 +1037,7 @@ def stopwatch(num_seconds: Annotated[str, "Number of seconds in the stopwatch."] # 'await' is used to pause and resume code execution for async IO operations. # Without 'await', an async function returns a coroutine object but doesn't execute the function. # With 'await', the async function is executed and the current function is paused until the awaited function returns a result. - await user_proxy.a_initiate_chat( # noqa: F704 + await user_proxy.a_initiate_chat( coder, message="Create a timer for 1 second and then a stopwatch for 2 seconds.", ) @@ -1089,15 +1046,13 @@ def stopwatch(num_seconds: Annotated[str, "Number of seconds in the stopwatch."] stopwatch_mock.assert_called_once_with(num_seconds="2") -@pytest.mark.skipif(skip_openai, reason=reason) -def test_max_turn(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) - +@pytest.mark.openai +def test_max_turn(credentials_gpt_4o_mini: Credentials) -> None: # create an AssistantAgent instance named "assistant" assistant = autogen.AssistantAgent( name="assistant", max_consecutive_auto_reply=10, - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o_mini.llm_config, ) user_proxy = autogen.UserProxyAgent(name="user", human_input_mode="ALWAYS", code_execution_config=False) @@ -1111,8 +1066,8 @@ def test_max_turn(): assert len(res.chat_history) <= 6 -@pytest.mark.skipif(skip_openai, reason=reason) -def test_message_func(): +@pytest.mark.openai +def test_message_func(credentials_gpt_4o_mini: Credentials): import random class Function: @@ -1122,11 +1077,6 @@ def get_random_number(self): self.call_count += 1 return str(random.randint(0, 100)) - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) - def my_message_play(sender, recipient, context): final_msg = {} final_msg["content"] = "Let's play a game." @@ -1148,7 +1098,7 @@ def my_message_play(sender, recipient, context): name="Player", system_message="You will use function `get_random_number` to get a random number. Stop only when you get at least 1 even number and 1 odd number. Reply TERMINATE to stop.", description="A player that makes function_calls.", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o_mini.llm_config, function_map={"get_random_number": func.get_random_number}, ) @@ -1167,8 +1117,8 @@ def my_message_play(sender, recipient, context): print(chat_res_play.summary) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_summary(): +@pytest.mark.openai +def test_summary(credentials_gpt_4o_mini: Credentials): import random class Function: @@ -1178,10 +1128,6 @@ def get_random_number(self): self.call_count += 1 return str(random.randint(0, 100)) - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, file_location=KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]} - ) - def my_message_play(sender, recipient, context): final_msg = {} final_msg["content"] = "Let's play a game." @@ -1207,7 +1153,7 @@ def my_summary(sender, recipient, summary_args): name="Player", system_message="You will use function `get_random_number` to get a random number. Stop only when you get at least 1 even number and 1 odd number. Reply TERMINATE to stop.", description="A player that makes function_calls.", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o_mini.llm_config, function_map={"get_random_number": func.get_random_number}, ) @@ -1402,7 +1348,6 @@ def bob_initiate_chat(agent: ConversableAgent, text: Literal["past", "future"]): def test_http_client(): - import httpx with pytest.raises(TypeError): @@ -1423,7 +1368,6 @@ def test_http_client(): def test_adding_duplicate_function_warning(): - config_base = [{"base_url": "http://0.0.0.0:8000", "api_key": "NULL"}] agent = autogen.ConversableAgent( @@ -1532,7 +1476,7 @@ def test_handle_carryover(): assert proc_content_empty_carryover == content, "Incorrect carryover processing" -@pytest.mark.skipif(skip_openai, reason=reason) +@pytest.mark.openai def test_context_variables(): # Test initialization with context_variables initial_context = {"test_key": "test_value", "number": 42, "nested": {"inner": "value"}} @@ -1593,9 +1537,8 @@ def test_context_variables(): assert agent._context_variables == expected_final_context -@pytest.mark.skipif(skip_openai, reason=reason) def test_invalid_functions_parameter(): - """Test initialization with valid and invalid parameters""" + """Test initialization with valid and invali d parameters""" # Invalid functions parameter with pytest.raises(TypeError): diff --git a/test/agentchat/test_dependancy_injection.py b/test/agentchat/test_dependancy_injection.py index bbcad42d88..6d6e135c67 100644 --- a/test/agentchat/test_dependancy_injection.py +++ b/test/agentchat/test_dependancy_injection.py @@ -8,40 +8,121 @@ import pytest from pydantic import BaseModel -import autogen from autogen.agentchat import ConversableAgent, UserProxyAgent -from autogen.tools import BaseContext, Depends +from autogen.tools import BaseContext, ChatContext, Depends -from ..conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -class TestDependencyInjection: - class MyContext(BaseContext, BaseModel): - b: int - - @pytest.fixture() - def mock_llm_config(self) -> None: - return { - "config_list": [ - {"model": "gpt-4o", "api_key": MOCK_OPEN_AI_API_KEY}, - ], - } - - @pytest.fixture() - def llm_config(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - file_location=KEY_LOC, - ) - return { - "config_list": config_list, - } +class MyContext(BaseContext, BaseModel): + b: int + + +def f_with_annotated( + a: int, + ctx: Annotated[MyContext, Depends(MyContext(b=2))], + chat_ctx: Annotated[ChatContext, "Chat context"], + c: Annotated[int, "c description"] = 3, +) -> int: + assert isinstance(chat_ctx, ChatContext) + return a + ctx.b + c + + +async def f_with_annotated_async( + a: int, + ctx: Annotated[MyContext, Depends(MyContext(b=2))], + chat_ctx: ChatContext, + c: Annotated[int, "c description"] = 3, +) -> int: + assert isinstance(chat_ctx, ChatContext) + return a + ctx.b + c + + +def f_without_annotated( + a: int, + chat_ctx: ChatContext, + ctx: MyContext = Depends(MyContext(b=3)), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + c + + +async def f_without_annotated_async( + a: int, + ctx: MyContext = Depends(MyContext(b=3)), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + c + + +def f_with_annotated_and_depends( + a: int, + ctx: MyContext = MyContext(b=4), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + c + + +async def f_with_annotated_and_depends_async( + a: int, + ctx: MyContext = MyContext(b=4), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + c + - @pytest.fixture() +def f_with_multiple_depends( + a: int, + ctx: Annotated[MyContext, Depends(MyContext(b=2))], + ctx2: Annotated[MyContext, Depends(MyContext(b=3))], + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + ctx2.b + c + + +async def f_with_multiple_depends_async( + a: int, + ctx: Annotated[MyContext, Depends(MyContext(b=2))], + ctx2: Annotated[MyContext, Depends(MyContext(b=3))], + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx.b + ctx2.b + c + + +def f_wihout_base_context( + a: int, + ctx: Annotated[int, Depends(lambda a: a + 2)], + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx + c + + +async def f_wihout_base_context_async( + a: int, + ctx: Annotated[int, Depends(lambda a: a + 2)], + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx + c + + +def f_with_default_depends( + a: int, + ctx: int = Depends(lambda a: a + 2), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx + c + + +async def f_with_default_depends_async( + a: int, + ctx: int = Depends(lambda a: a + 2), + c: Annotated[int, "c description"] = 3, +) -> int: + return a + ctx + c + + +class TestDependencyInjection: + @pytest.fixture def expected_tools(self) -> list[dict[str, Any]]: return [ { @@ -61,92 +142,6 @@ def expected_tools(self) -> list[dict[str, Any]]: } ] - def f_with_annotated( - a: int, - ctx: Annotated[MyContext, Depends(MyContext(b=2))], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - async def f_with_annotated_async( - a: int, - ctx: Annotated[MyContext, Depends(MyContext(b=2))], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - def f_without_annotated( - a: int, - ctx: MyContext = Depends(MyContext(b=3)), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - async def f_without_annotated_async( - a: int, - ctx: MyContext = Depends(MyContext(b=3)), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - def f_with_annotated_and_depends( - a: int, - ctx: MyContext = MyContext(b=4), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - async def f_with_annotated_and_depends_async( - a: int, - ctx: MyContext = MyContext(b=4), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + c - - def f_with_multiple_depends( - a: int, - ctx: Annotated[MyContext, Depends(MyContext(b=2))], - ctx2: Annotated[MyContext, Depends(MyContext(b=3))], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + ctx2.b + c - - async def f_with_multiple_depends_async( - a: int, - ctx: Annotated[MyContext, Depends(MyContext(b=2))], - ctx2: Annotated[MyContext, Depends(MyContext(b=3))], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx.b + ctx2.b + c - - def f_wihout_base_context( - a: int, - ctx: Annotated[int, Depends(lambda a: a + 2)], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx + c - - async def f_wihout_base_context_async( - a: int, - ctx: Annotated[int, Depends(lambda a: a + 2)], - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx + c - - def f_with_default_depends( - a: int, - ctx: int = Depends(lambda a: a + 2), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx + c - - async def f_with_default_depends_async( - a: int, - ctx: int = Depends(lambda a: a + 2), - c: Annotated[int, "c description"] = 3, - ) -> int: - return a + ctx + c - @pytest.mark.parametrize( ("func", "func_name", "is_async", "expected"), [ @@ -164,17 +159,17 @@ async def f_with_default_depends_async( (f_with_default_depends_async, "f_with_default_depends_async", True, "7"), ], ) - @pytest.mark.asyncio() + @pytest.mark.asyncio async def test_register_tools( self, - mock_llm_config: dict[str, Any], + mock_credentials: Credentials, expected_tools: list[dict[str, Any]], func: Callable[..., Any], func_name: str, is_async: bool, expected: str, ) -> None: - agent = ConversableAgent(name="agent", llm_config=mock_llm_config) + agent = ConversableAgent(name="agent", llm_config=mock_credentials.llm_config) agent.register_for_llm(description="Example function")(func) agent.register_for_execution()(func) @@ -188,15 +183,15 @@ async def test_register_tools( assert actual == expected - @pytest.mark.skipif(skip_openai, reason=reason) + @pytest.mark.openai @pytest.mark.parametrize("is_async", [False, True]) - @pytest.mark.asyncio() - async def test_end2end(self, llm_config: dict[str, Any], is_async: bool) -> None: + @pytest.mark.asyncio + async def test_end2end(self, credentials_gpt_4o_mini, is_async: bool) -> None: class UserContext(BaseContext, BaseModel): username: str password: str - agent = ConversableAgent(name="agent", llm_config=llm_config) + agent = ConversableAgent(name="agent", llm_config=credentials_gpt_4o_mini.llm_config) user = UserContext(username="user23", password="password23") users = [user] @@ -218,7 +213,13 @@ def _login(user: UserContext) -> str: @user_proxy.register_for_execution() @agent.register_for_llm(description="Login function") - async def login(user: Annotated[UserContext, Depends(user)]) -> str: + async def login( + user: Annotated[UserContext, Depends(user)], + chat_ctx: ChatContext, + ) -> str: + expected = {"arguments": "{}", "name": "login"} + assert chat_ctx.last_message["tool_calls"][0]["function"] == expected + return _login(user) await user_proxy.a_initiate_chat(agent, message="Please login", max_turns=2) diff --git a/test/agentchat/test_function_and_tool_calling.py b/test/agentchat/test_function_and_tool_calling.py index d3776ce206..b92b48e298 100644 --- a/test/agentchat/test_function_and_tool_calling.py +++ b/test/agentchat/test_function_and_tool_calling.py @@ -6,7 +6,7 @@ # SPDX-License-Identifier: MIT import json import sys -from typing import Any, Callable, Dict, List +from typing import Any, Callable import pytest @@ -291,7 +291,7 @@ def test_generate_function_call_reply_on_function_call_message(is_function_async assert (finished, retval) == (True, _function_use_message_1_error_expected_reply) -@pytest.mark.asyncio() +@pytest.mark.asyncio @pytest.mark.parametrize("is_function_async", [True, False]) async def test_a_generate_function_call_reply_on_function_call_message(is_function_async: bool) -> None: agent = ConversableAgent(name="agent", llm_config=False) @@ -388,7 +388,7 @@ def test_generate_tool_calls_reply_on_function_call_message(is_function_async: b assert (finished, retval) == (True, _tool_use_message_1_error_expected_reply) -@pytest.mark.asyncio() +@pytest.mark.asyncio @pytest.mark.parametrize("is_function_async", [True, False]) async def test_a_generate_tool_calls_reply_on_function_call_message(is_function_async: bool) -> None: agent = ConversableAgent(name="agent", llm_config=False) diff --git a/test/agentchat/test_function_call.py b/test/agentchat/test_function_call.py index ed52f55cf1..95cc7c4225 100755 --- a/test/agentchat/test_function_call.py +++ b/test/agentchat/test_function_call.py @@ -6,6 +6,7 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest +import asyncio import json import sys @@ -14,26 +15,19 @@ import autogen from autogen.math_utils import eval_math_responses -from ..conftest import skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials, reason try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai + skip = False -@pytest.mark.skipif(skip, reason="openai not installed OR requested to skip") -def test_eval_math_responses(): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-mini", "gpt-4o"], - }, - file_location=KEY_LOC, - ) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_eval_math_responses(credentials_gpt_4o_mini: Credentials): functions = [ { "name": "eval_math_responses", @@ -55,7 +49,7 @@ def test_eval_math_responses(): }, }, ] - client = autogen.OpenAIWrapper(config_list=config_list) + client = autogen.OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create( messages=[ { @@ -168,14 +162,12 @@ def raise_exception(): @pytest.mark.asyncio async def test_a_execute_function(): - import time - from autogen.agentchat import UserProxyAgent # Create an async function async def add_num(num_to_be_added): given_num = 10 - time.sleep(1) + asyncio.sleep(1) return str(num_to_be_added + given_num) user = UserProxyAgent(name="test", function_map={"add_num": add_num}) @@ -227,20 +219,14 @@ def get_number(): assert (await user.a_execute_function(func_call))[1]["content"] == "42" +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.10"), - reason="do not run if openai is not installed OR reeusted to skip OR py!=3.10", + reason=reason, ) -def test_update_function(): - config_list_gpt4 = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o", "gpt-4o-mini"], - }, - file_location=KEY_LOC, - ) +def test_update_function(credentials_gpt_4o_mini: Credentials): llm_config = { - "config_list": config_list_gpt4, + "config_list": credentials_gpt_4o_mini.config_list, "seed": 42, "functions": [], } diff --git a/test/agentchat/test_function_call_groupchat.py b/test/agentchat/test_function_call_groupchat.py index 6812e14dff..d16a7a8d93 100755 --- a/test/agentchat/test_function_call_groupchat.py +++ b/test/agentchat/test_function_call_groupchat.py @@ -12,8 +12,7 @@ import autogen -from ..conftest import reason, skip_openai -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials func_def = { "name": "get_random_number", @@ -25,10 +24,7 @@ } -@pytest.mark.skipif( - skip_openai, - reason=reason, -) +@pytest.mark.openai @pytest.mark.parametrize( "key, value, sync", [ @@ -38,7 +34,7 @@ ], ) @pytest.mark.asyncio -async def test_function_call_groupchat(key, value, sync): +async def test_function_call_groupchat(credentials_gpt_4o_mini: Credentials, key, value, sync): import random class Function: @@ -49,21 +45,9 @@ def get_random_number(self): return random.randint(0, 100) # llm_config without functions - config_list_4omini_no_tools = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) - llm_config_no_function = {"config_list": config_list_4omini_no_tools} - - # llm_config with functions - config_list_4omini = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) + llm_config_no_function = credentials_gpt_4o_mini.llm_config llm_config = { - "config_list": config_list_4omini, + "config_list": credentials_gpt_4o_mini.config_list, key: value, } diff --git a/test/agentchat/test_groupchat.py b/test/agentchat/test_groupchat.py index ef9420eedc..18869acbed 100755 --- a/test/agentchat/test_groupchat.py +++ b/test/agentchat/test_groupchat.py @@ -143,11 +143,7 @@ def _test_selection_method(method: str): "This is bob speaking.", "This is charlie speaking.", ] * 2 - elif method == "auto": - agent1.initiate_chat(group_chat_manager, message="This is alice speaking.") - assert len(agent1.chat_messages[group_chat_manager]) == 6 - assert len(groupchat.messages) == 6 - elif method == "random": + elif method == "auto" or method == "random": agent1.initiate_chat(group_chat_manager, message="This is alice speaking.") assert len(agent1.chat_messages[group_chat_manager]) == 6 assert len(groupchat.messages) == 6 @@ -527,7 +523,7 @@ def test_send_intros(): assert "The first agent." in messages[0]["content"] assert "The second agent." in messages[0]["content"] assert "The third agent." in messages[0]["content"] - assert "The initiating message." == messages[1]["content"] + assert messages[1]["content"] == "The initiating message." assert messages[2]["content"] == agent1._default_auto_reply # Reset and start again @@ -551,7 +547,7 @@ def test_send_intros(): for a in [agent1, agent2, agent3]: messages = agent1.chat_messages[group_chat_manager2] assert len(messages) == 2 - assert "The initiating message." == messages[0]["content"] + assert messages[0]["content"] == "The initiating message." assert messages[1]["content"] == agent1._default_auto_reply @@ -1051,14 +1047,12 @@ def custom_speaker_selection_func(last_speaker: Agent, groupchat: GroupChat) -> def test_custom_speaker_selection_with_transition_graph(): - """ - In this test, although speaker_selection_method is defined, the speaker transitions are also defined. + """In this test, although speaker_selection_method is defined, the speaker transitions are also defined. There are 26 agents here, a to z. The speaker transitions are defined such that the agents can transition to the next alphabet. In addition, because we want the transition order to be a,u,t,o,g,e,n, we also define the speaker transitions for these agents. The speaker_selection_method is defined to return the next agent in the expected sequence. """ - # For loop that creates UserProxyAgent with names from a to z agents = [ autogen.UserProxyAgent( @@ -1092,9 +1086,7 @@ def test_custom_speaker_selection_with_transition_graph(): previous_agent = current_agent def custom_speaker_selection_func(last_speaker: Agent, groupchat: GroupChat) -> Optional[Agent]: - """ - Define a customized speaker selection function. - """ + """Define a customized speaker selection function.""" expected_sequence = ["a", "u", "t", "o", "g", "e", "n"] last_speaker_char = last_speaker.name @@ -1128,11 +1120,9 @@ def custom_speaker_selection_func(last_speaker: Agent, groupchat: GroupChat) -> def test_custom_speaker_selection_overrides_transition_graph(): - """ - In this test, team A engineer can transition to team A executor and team B engineer, but team B engineer cannot transition to team A executor. + """In this test, team A engineer can transition to team A executor and team B engineer, but team B engineer cannot transition to team A executor. The expected behaviour is that the custom speaker selection function will override the constraints of the graph. """ - # For loop that creates UserProxyAgent with names from a to z agents = [ autogen.UserProxyAgent( @@ -1269,11 +1259,9 @@ def test_role_for_select_speaker_messages(): def test_select_speaker_message_and_prompt_templates(): - """ - In this test, two agents are part of a group chat which has customized select speaker message and select speaker prompt templates. Both valid and empty string values will be used. + """In this test, two agents are part of a group chat which has customized select speaker message and select speaker prompt templates. Both valid and empty string values will be used. The expected behaviour is that the customized speaker selection message and prompts will override the default values or throw exceptions if empty. """ - agent1 = autogen.ConversableAgent( "Alice", description="A wonderful employee named Alice.", @@ -1364,11 +1352,9 @@ def test_select_speaker_message_and_prompt_templates(): def test_speaker_selection_agent_name_match(): - """ - In this test a group chat, with auto speaker selection, the speaker name match + """In this test a group chat, with auto speaker selection, the speaker name match function is tested against the extended name match regex. """ - user_proxy = autogen.UserProxyAgent( name="User_proxy", system_message="A human admin.", @@ -1498,8 +1484,7 @@ def test_role_for_reflection_summary(): def test_speaker_selection_auto_process_result(): - """ - Tests the return result of the 2-agent chat used for speaker selection for the auto method. + """Tests the return result of the 2-agent chat used for speaker selection for the auto method. The last message of the messages passed in will contain a pass or fail. If passed, the message will contain the name of the correct agent and that agent will be returned. If failed, the message will contain the reason for failure for the last attempt and the next @@ -1543,9 +1528,9 @@ def test_speaker_selection_auto_process_result(): assert groupchat._process_speaker_selection_result(chat_result, cmo, agent_list) == pm ### Agent not selected successfully - chat_result.chat_history[3][ - "content" - ] = "[AGENT SELECTION FAILED]Select speaker attempt #3 of 3 failed as it did not include any agent names." + chat_result.chat_history[3]["content"] = ( + "[AGENT SELECTION FAILED]Select speaker attempt #3 of 3 failed as it did not include any agent names." + ) # The next speaker in the list will be selected, which will be the Product_Manager (as the last speaker is the Chief_Marketing_Officer) assert groupchat._process_speaker_selection_result(chat_result, cmo, agent_list) == pm @@ -1558,8 +1543,7 @@ def test_speaker_selection_auto_process_result(): def test_speaker_selection_validate_speaker_name(): - """ - Tests the speaker name validation function used to evaluate the return result of the LLM + """Tests the speaker name validation function used to evaluate the return result of the LLM during speaker selection in 'auto' mode. Function: _validate_speaker_name @@ -1572,7 +1556,6 @@ def test_speaker_selection_validate_speaker_name(): When returning a message, it will include the 'override_role' key and value to support the GroupChat role_for_select_speaker_messages attribute """ - # Group Chat setup cmo = autogen.ConversableAgent( name="Chief_Marketing_Officer", @@ -1727,11 +1710,9 @@ def test_speaker_selection_validate_speaker_name(): def test_select_speaker_auto_messages(): - """ - In this test, two agents are part of a group chat which has customized select speaker "auto" multiple and no-name prompt messages. Both valid and empty string values will be used. + """In this test, two agents are part of a group chat which has customized select speaker "auto" multiple and no-name prompt messages. Both valid and empty string values will be used. The expected behaviour is that the customized speaker selection "auto" messages will override the default values or throw exceptions if empty. """ - agent1 = autogen.ConversableAgent( "Alice", description="A wonderful employee named Alice.", @@ -1863,7 +1844,6 @@ def test_manager_messages_from_string(): def test_manager_resume_functions(): """Tests functions within the resume chat functionality""" - # Setup coder = AssistantAgent(name="Coder", llm_config=None) groupchat = GroupChat(messages=[], agents=[coder]) @@ -2012,7 +1992,6 @@ def termination_func(x: str) -> str: def test_manager_resume_returns(): """Tests the return resume chat functionality""" - # Test the return agent and message is correct coder = AssistantAgent(name="Coder", llm_config=None) groupchat = GroupChat(messages=[], agents=[coder]) @@ -2045,7 +2024,6 @@ def test_manager_resume_returns(): def test_manager_resume_messages(): """Tests that the messages passed into resume are the correct format""" - coder = AssistantAgent(name="Coder", llm_config=None) groupchat = GroupChat(messages=[], agents=[coder]) manager = GroupChatManager(groupchat) @@ -2122,7 +2100,6 @@ def get_usage(response): def test_select_speaker_transform_messages(): """Tests adding transform messages to a GroupChat for speaker selection when in 'auto' mode""" - # Test adding a TransformMessages to a group chat test_add_transforms = transform_messages.TransformMessages( transforms=[ @@ -2162,7 +2139,6 @@ def test_select_speaker_transform_messages(): def test_manager_resume_message_assignment(): """Tests that the messages passed in are assigned to agents correctly""" - # Setup agent_a = AssistantAgent(name="Agent_A", llm_config=None) agent_b = AssistantAgent(name="Agent_B", llm_config=None) diff --git a/test/agentchat/test_human_input.py b/test/agentchat/test_human_input.py index 54fd68597f..0af1d84145 100755 --- a/test/agentchat/test_human_input.py +++ b/test/agentchat/test_human_input.py @@ -12,19 +12,21 @@ import autogen -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials -@pytest.mark.skipif(skip_openai, reason=reason) -def test_get_human_input(): - config_list = autogen.config_list_from_json(OAI_CONFIG_LIST, KEY_LOC, filter_dict={"tags": ["gpt-4o-mini"]}) - +@pytest.mark.openai +def test_get_human_input(credentials_gpt_4o_mini: Credentials): # create an AssistantAgent instance named "assistant" assistant = autogen.AssistantAgent( name="assistant", max_consecutive_auto_reply=2, - llm_config={"timeout": 600, "cache_seed": 41, "config_list": config_list, "temperature": 0}, + llm_config={ + "timeout": 600, + "cache_seed": 41, + "config_list": credentials_gpt_4o_mini.config_list, + "temperature": 0, + }, ) user_proxy = autogen.UserProxyAgent(name="user", human_input_mode="ALWAYS", code_execution_config=False) diff --git a/test/agentchat/test_math_user_proxy_agent.py b/test/agentchat/test_math_user_proxy_agent.py index 343fbd3ba7..e137f34b21 100755 --- a/test/agentchat/test_math_user_proxy_agent.py +++ b/test/agentchat/test_math_user_proxy_agent.py @@ -6,52 +6,45 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -import os import sys import pytest -import autogen from autogen.agentchat.contrib.math_user_proxy_agent import ( MathUserProxyAgent, _add_print_to_last_line, _remove_print, ) -from ..conftest import skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai + skip = False +@pytest.mark.openai @pytest.mark.skipif( skip or sys.platform in ["darwin", "win32"], reason="do not run on MacOS or windows", ) -def test_math_user_proxy_agent(): +def test_math_user_proxy_agent( + credentials_gpt_4o_mini: Credentials, +): from autogen.agentchat.assistant_agent import AssistantAgent conversations = {} # autogen.ChatCompletion.start_logging(conversations) - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - ) assistant = AssistantAgent( "assistant", system_message="You are a helpful assistant.", llm_config={ "cache_seed": 42, - "config_list": config_list, + "config_list": credentials_gpt_4o_mini.config_list, }, ) diff --git a/test/agentchat/test_nested.py b/test/agentchat/test_nested.py index b63a0c70ac..5e08b747d0 100755 --- a/test/agentchat/test_nested.py +++ b/test/agentchat/test_nested.py @@ -11,8 +11,7 @@ import autogen from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability -from ..conftest import reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST # noqa: E402 +from ..conftest import Credentials class MockAgentReplies(AgentCapability): @@ -32,16 +31,11 @@ def mock_reply(recipient, messages, sender, config): agent.register_reply([autogen.Agent, None], mock_reply, position=2) -@pytest.mark.skipif(skip_openai, reason=reason) -def test_nested(): - config_list = autogen.config_list_from_json(env_or_file=OAI_CONFIG_LIST, file_location=KEY_LOC) - config_list_4omini = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-4o-mini"]}, - ) - llm_config = {"config_list": config_list} - +@pytest.mark.openai +def test_nested( + credentials_gpt_4o_mini: Credentials, + credentials_gpt_4o: Credentials, +): tasks = [ """What's the date today?""", """Make a pleasant joke about it.""", @@ -49,7 +43,7 @@ def test_nested(): inner_assistant = autogen.AssistantAgent( "Inner-assistant", - llm_config=llm_config, + llm_config=credentials_gpt_4o_mini.llm_config, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, ) @@ -75,7 +69,7 @@ def test_nested(): manager = autogen.GroupChatManager( groupchat=groupchat, is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0, - llm_config=llm_config, + llm_config=credentials_gpt_4o.llm_config, code_execution_config={ "work_dir": "coding", "use_docker": False, @@ -90,7 +84,7 @@ def test_nested(): assistant_2 = autogen.AssistantAgent( name="Assistant", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, # is_termination_msg=lambda x: x.get("content", "") == "", ) @@ -118,7 +112,7 @@ def test_nested(): writer = autogen.AssistantAgent( name="Writer", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, system_message=""" You are a professional writer, known for your insightful and engaging articles. @@ -129,7 +123,7 @@ def test_nested(): autogen.AssistantAgent( name="Reviewer", - llm_config={"config_list": config_list_4omini}, + llm_config=credentials_gpt_4o_mini.llm_config, system_message=""" You are a compliance reviewer, known for your thoroughness and commitment to standards. Your task is to scrutinize content for any harmful elements or regulatory violations, ensuring @@ -163,9 +157,9 @@ def writing_message(recipient, messages, sender, config): def test_sync_nested_chat(): def is_termination(msg): - if isinstance(msg, str) and msg == "FINAL_RESULT": - return True - elif isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT": + if (isinstance(msg, str) and msg == "FINAL_RESULT") or ( + isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT" + ): return True return False @@ -202,9 +196,9 @@ def is_termination(msg): @pytest.mark.asyncio async def test_async_nested_chat(): def is_termination(msg): - if isinstance(msg, str) and msg == "FINAL_RESULT": - return True - elif isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT": + if (isinstance(msg, str) and msg == "FINAL_RESULT") or ( + isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT" + ): return True return False @@ -243,9 +237,9 @@ def is_termination(msg): @pytest.mark.asyncio async def test_async_nested_chat_chat_id_validation(): def is_termination(msg): - if isinstance(msg, str) and msg == "FINAL_RESULT": - return True - elif isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT": + if (isinstance(msg, str) and msg == "FINAL_RESULT") or ( + isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT" + ): return True return False @@ -280,9 +274,9 @@ def is_termination(msg): def test_sync_nested_chat_in_group(): def is_termination(msg): - if isinstance(msg, str) and msg == "FINAL_RESULT": - return True - elif isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT": + if (isinstance(msg, str) and msg == "FINAL_RESULT") or ( + isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT" + ): return True return False @@ -327,9 +321,9 @@ def is_termination(msg): @pytest.mark.asyncio async def test_async_nested_chat_in_group(): def is_termination(msg): - if isinstance(msg, str) and msg == "FINAL_RESULT": - return True - elif isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT": + if (isinstance(msg, str) and msg == "FINAL_RESULT") or ( + isinstance(msg, dict) and msg.get("content") == "FINAL_RESULT" + ): return True return False diff --git a/test/agentchat/test_structured_output.py b/test/agentchat/test_structured_output.py index 4c5ad3e771..7290054f79 100644 --- a/test/agentchat/test_structured_output.py +++ b/test/agentchat/test_structured_output.py @@ -13,26 +13,19 @@ import autogen -from ..conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai # noqa: E402 -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST - - -@pytest.mark.skipif(skip_openai, reason=reason) -def test_structured_output(): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "model": ["gpt-4o", "gpt-4o-mini"], - }, - ) +from ..conftest import Credentials + +@pytest.mark.openai +def test_structured_output(credentials_gpt_4o: Credentials): class ResponseModel(BaseModel): question: str short_answer: str reasoning: str difficulty: float + config_list = credentials_gpt_4o.config_list + for config in config_list: config["response_format"] = ResponseModel @@ -80,9 +73,13 @@ def format(self) -> str: @pytest.fixture -def mock_assistant(): +def mock_assistant(mock_credentials: Credentials) -> autogen.AssistantAgent: """Set up a mocked AssistantAgent with a predefined response format.""" - config_list = [{"model": "gpt-4o", "api_key": MOCK_OPEN_AI_API_KEY, "response_format": MathReasoning}] + config_list = mock_credentials.config_list + + for config in config_list: + config["response_format"] = MathReasoning + llm_config = {"config_list": config_list, "cache_seed": 43} assistant = autogen.AssistantAgent( @@ -112,7 +109,7 @@ def mock_assistant(): return assistant -def test_structured_output_formatting(mock_assistant): +def test_structured_output_formatting(mock_assistant: autogen.AssistantAgent) -> None: """Test that the AssistantAgent correctly formats structured output.""" user_proxy = autogen.UserProxyAgent( name="User_proxy", diff --git a/test/agentchat/test_tool_calls.py b/test/agentchat/test_tool_calls.py index 8804e86a20..83db779987 100755 --- a/test/agentchat/test_tool_calls.py +++ b/test/agentchat/test_tool_calls.py @@ -8,7 +8,6 @@ import inspect import json -import os import sys import pytest @@ -17,23 +16,20 @@ from autogen.math_utils import eval_math_responses from autogen.oai.client import TOOL_ENABLED -from .test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST +from ..conftest import Credentials try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: - skip_openai = True + skip = True else: - from ..conftest import skip_openai + skip = False -@pytest.mark.skipif(skip_openai or not TOOL_ENABLED, reason="openai>=1.1.0 not installed or requested to skip") -def test_eval_math_responses(): - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - KEY_LOC, - filter_dict={"tags": ["tool"]}, - ) +@pytest.mark.openai +@pytest.mark.skipif(skip or not TOOL_ENABLED, reason="openai>=1.1.0 not installed or requested to skip") +def test_eval_math_responses(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list tools = [ { "type": "function", @@ -84,18 +80,10 @@ def test_eval_math_responses(): print(eval_math_responses(**arguments)) -@pytest.mark.skipif(skip_openai or not TOOL_ENABLED, reason="openai>=1.1.0 not installed or requested to skip") -def test_eval_math_responses_api_style_function(): - # config_list = autogen.config_list_from_models( - # KEY_LOC, - # model_list=["gpt-4-0613", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k"], - # ) - - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - KEY_LOC, - filter_dict={"tags": ["tool"]}, - ) +@pytest.mark.openai +@pytest.mark.skipif(skip or not TOOL_ENABLED, reason="openai>=1.1.0 not installed or requested to skip") +def test_eval_math_responses_api_style_function(credentials_gpt_4o_mini: Credentials): + config_list = credentials_gpt_4o_mini.config_list functions = [ { "name": "eval_math_responses", @@ -142,20 +130,14 @@ def test_eval_math_responses_api_style_function(): print(eval_math_responses(**arguments)) +@pytest.mark.openai @pytest.mark.skipif( - skip_openai or not TOOL_ENABLED or not sys.version.startswith("3.10"), + skip or not TOOL_ENABLED or not sys.version.startswith("3.10"), reason="do not run if openai is <1.1.0 or py!=3.10 or requested to skip", ) -def test_update_tool(): - config_list_gpt4 = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) +def test_update_tool(credentials_gpt_4o: Credentials): llm_config = { - "config_list": config_list_gpt4, + "config_list": credentials_gpt_4o.config_list, "seed": 42, "tools": [], } @@ -190,9 +172,9 @@ def test_update_tool(): messages1 = assistant.chat_messages[user_proxy][-1]["content"] print("Message:", messages1) print("Summary:", res.summary) - assert ( - messages1.replace("TERMINATE", "") == res.summary - ), "Message (removing TERMINATE) and summary should be the same" + assert messages1.replace("TERMINATE", "") == res.summary, ( + "Message (removing TERMINATE) and summary should be the same" + ) assistant.update_tool_signature("greet_user", is_remove=True) res = user_proxy.initiate_chat( diff --git a/test/coding/test_commandline_code_executor.py b/test/coding/test_commandline_code_executor.py index db29500e95..5c744bdfb9 100644 --- a/test/coding/test_commandline_code_executor.py +++ b/test/coding/test_commandline_code_executor.py @@ -15,14 +15,14 @@ import pytest from autogen.agentchat.conversable_agent import ConversableAgent -from autogen.code_utils import WIN32, decide_use_docker, is_docker_running +from autogen.code_utils import decide_use_docker, is_docker_running from autogen.coding.base import CodeBlock, CodeExecutor from autogen.coding.docker_commandline_code_executor import DockerCommandLineCodeExecutor from autogen.coding.factory import CodeExecutorFactory from autogen.coding.local_commandline_code_executor import LocalCommandLineCodeExecutor sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -from conftest import MOCK_OPEN_AI_API_KEY, skip_docker # noqa: E402 +from conftest import MOCK_OPEN_AI_API_KEY, skip_docker if skip_docker or not is_docker_running() or not decide_use_docker(use_docker=None): skip_docker_test = True @@ -285,9 +285,9 @@ def test_policy_override(): if lang not in custom_policy: assert executor.execution_policies[lang] == should_execute, f"Policy for {lang} should not be changed" - assert set(executor.execution_policies.keys()) == set( - default_policy.keys() - ), "Execution policies should only contain known languages" + assert set(executor.execution_policies.keys()) == set(default_policy.keys()), ( + "Execution policies should only contain known languages" + ) def _test_restart(executor: CodeExecutor) -> None: @@ -342,9 +342,9 @@ def _test_conversable_agent_code_execution(executor: CodeExecutor) -> None: def test_dangerous_commands(lang, code, expected_message): with pytest.raises(ValueError) as exc_info: LocalCommandLineCodeExecutor.sanitize_command(lang, code) - assert expected_message in str( - exc_info.value - ), f"Expected message '{expected_message}' not found in '{str(exc_info.value)}'" + assert expected_message in str(exc_info.value), ( + f"Expected message '{expected_message}' not found in '{exc_info.value!s}'" + ) @pytest.mark.parametrize("cls", classes_to_test) diff --git a/test/coding/test_embedded_ipython_code_executor.py b/test/coding/test_embedded_ipython_code_executor.py index d0698ce99d..372a84d906 100644 --- a/test/coding/test_embedded_ipython_code_executor.py +++ b/test/coding/test_embedded_ipython_code_executor.py @@ -17,7 +17,7 @@ from autogen.coding.base import CodeBlock, CodeExecutor from autogen.coding.factory import CodeExecutorFactory -from ..conftest import MOCK_OPEN_AI_API_KEY, skip_docker # noqa: E402 +from ..conftest import MOCK_OPEN_AI_API_KEY, skip_docker try: from autogen.coding.jupyter import ( @@ -78,7 +78,7 @@ def test_create(cls) -> None: @pytest.mark.parametrize("cls", classes_to_test) def test_init(cls) -> None: executor = cls(timeout=10, kernel_name="python3", output_dir=".") - assert executor._timeout == 10 and executor._kernel_name == "python3" and executor._output_dir == Path(".") + assert executor._timeout == 10 and executor._kernel_name == "python3" and executor._output_dir == Path() # Try invalid output directory. with pytest.raises(ValueError, match="Output directory .* does not exist."): diff --git a/test/coding/test_markdown_code_extractor.py b/test/coding/test_markdown_code_extractor.py index b63bc67531..66c6e2940d 100644 --- a/test/coding/test_markdown_code_extractor.py +++ b/test/coding/test_markdown_code_extractor.py @@ -59,9 +59,7 @@ def scrape(url): text = soup.find("div", {"id": "bodyContent"}).text return title, text ``` -""".replace( - "\n", "\r\n" -) +""".replace("\n", "\r\n") _message_5 = """ Test bash script: diff --git a/test/coding/test_user_defined_functions.py b/test/coding/test_user_defined_functions.py index 96d8419748..a81e30049e 100644 --- a/test/coding/test_user_defined_functions.py +++ b/test/coding/test_user_defined_functions.py @@ -205,7 +205,6 @@ def add_two_numbers(a: int, b: int) -> int: @pytest.mark.parametrize("cls", classes_to_test) def test_cant_load_broken_str_function_with_reqs(cls) -> None: - with pytest.raises(ValueError): _ = FunctionWithRequirements.from_str( ''' diff --git a/test/conftest.py b/test/conftest.py index 5f98689fda..6a6938e3bd 100644 --- a/test/conftest.py +++ b/test/conftest.py @@ -4,19 +4,26 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT +from pathlib import Path +from typing import Any, Optional + import pytest -skip_openai = False +import autogen + skip_redis = False skip_docker = False -reason = "requested to skip" + +KEY_LOC = str((Path(__file__).parents[1] / "notebook").resolve()) +OAI_CONFIG_LIST = "OAI_CONFIG_LIST" MOCK_OPEN_AI_API_KEY = "sk-mockopenaiAPIkeysinexpectedformatsfortestingonly" +reason = "requested to skip" + -# Registers command-line options like '--skip-openai' and '--skip-redis' via pytest hook. +# Registers command-line options like '--skip-docker' and '--skip-redis' via pytest hook. # When these flags are set, it indicates that tests requiring OpenAI or Redis (respectively) should be skipped. def pytest_addoption(parser): - parser.addoption("--skip-openai", action="store_true", help="Skip all tests that require openai") parser.addoption("--skip-redis", action="store_true", help="Skip all tests that require redis") parser.addoption("--skip-docker", action="store_true", help="Skip all tests that require docker") @@ -24,9 +31,137 @@ def pytest_addoption(parser): # pytest hook implementation extracting command line args and exposing it globally @pytest.hookimpl(tryfirst=True) def pytest_configure(config): - global skip_openai - skip_openai = config.getoption("--skip-openai", False) global skip_redis skip_redis = config.getoption("--skip-redis", False) global skip_docker skip_docker = config.getoption("--skip-docker", False) + + +class Credentials: + """Credentials for the OpenAI API.""" + + def __init__(self, llm_config: dict[str, Any]) -> None: + self.llm_config = llm_config + + def sanitize(self) -> dict[str, Any]: + llm_config = self.llm_config.copy() + for config in llm_config["config_list"]: + if "api_key" in config: + config["api_key"] = "********" + return llm_config + + def __repr__(self) -> str: + return repr(self.sanitize()) + + def __str___(self) -> str: + return str(self.sanitize()) + + @property + def config_list(self) -> list[dict[str, Any]]: + return self.llm_config["config_list"] # type: ignore[no-any-return] + + @property + def openai_api_key(self) -> str: + return self.llm_config["config_list"][0]["api_key"] # type: ignore[no-any-return] + + +def get_credentials(filter_dict: Optional[dict[str, Any]] = None, temperature: float = 0.0) -> Credentials: + """Fixture to load the LLM config.""" + config_list = autogen.config_list_from_json( + OAI_CONFIG_LIST, + filter_dict=filter_dict, + file_location=KEY_LOC, + ) + assert config_list, "No config list found" + + return Credentials( + llm_config={ + "config_list": config_list, + "temperature": temperature, + } + ) + + +def get_openai_credentials(filter_dict: Optional[dict[str, Any]] = None, temperature: float = 0.0) -> Credentials: + config_list = [ + conf + for conf in get_credentials(filter_dict, temperature).config_list + if "api_type" not in conf or conf["api_type"] == "openai" + ] + assert config_list, "No OpenAI config list found" + + return Credentials( + llm_config={ + "config_list": config_list, + "temperature": temperature, + } + ) + + +@pytest.fixture +def credentials_azure() -> Credentials: + return get_credentials(filter_dict={"api_type": ["azure"]}) + + +@pytest.fixture +def credentials_azure_gpt_35_turbo() -> Credentials: + return get_credentials(filter_dict={"api_type": ["azure"], "tags": ["gpt-3.5-turbo"]}) + + +@pytest.fixture +def credentials_azure_gpt_35_turbo_instruct() -> Credentials: + return get_credentials( + filter_dict={"tags": ["gpt-35-turbo-instruct", "gpt-3.5-turbo-instruct"], "api_type": ["azure"]} + ) + + +@pytest.fixture +def credentials_all() -> Credentials: + return get_credentials() + + +@pytest.fixture +def credentials_gpt_4o_mini() -> Credentials: + return get_openai_credentials(filter_dict={"tags": ["gpt-4o-mini"]}) + + +@pytest.fixture +def credentials_gpt_4o() -> Credentials: + return get_openai_credentials(filter_dict={"tags": ["gpt-4o"]}) + + +@pytest.fixture +def credentials_gpt_4o_realtime() -> Credentials: + return get_openai_credentials(filter_dict={"tags": ["gpt-4o-realtime"]}, temperature=0.6) + + +@pytest.fixture +def credentials() -> Credentials: + return get_credentials(filter_dict={"tags": ["gpt-4o"]}) + + +def get_mock_credentials(model: str, temperature: float = 0.6) -> Credentials: + llm_config = { + "config_list": [ + { + "model": model, + "api_key": MOCK_OPEN_AI_API_KEY, + }, + ], + "temperature": temperature, + } + + return Credentials(llm_config=llm_config) + + +@pytest.fixture +def mock_credentials() -> Credentials: + return get_mock_credentials(model="gpt-4o") + + +def pytest_sessionfinish(session, exitstatus): + # Exit status 5 means there were no tests collected + # so we should set the exit status to 1 + # https://docs.pytest.org/en/stable/reference/exit-codes.html + if exitstatus == 5: + session.exitstatus = 0 diff --git a/test/interop/crewai/test_crewai.py b/test/interop/crewai/test_crewai.py index 43b11f2212..7d9fb47ae7 100644 --- a/test/interop/crewai/test_crewai.py +++ b/test/interop/crewai/test_crewai.py @@ -8,12 +8,10 @@ import pytest -import autogen from autogen import AssistantAgent, UserProxyAgent from autogen.interop import Interoperable -from ...agentchat.test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST -from ...conftest import MOCK_OPEN_AI_API_KEY, reason, skip_openai +from ...conftest import MOCK_OPEN_AI_API_KEY, Credentials if sys.version_info >= (3, 10) and sys.version_info < (3, 13): from autogen.interop.crewai import CrewAIInteroperability @@ -58,15 +56,8 @@ def test_convert_tool(self) -> None: assert self.tool.func(args=args) == "Hello, World!" - @pytest.mark.skipif(skip_openai, reason=reason) - def test_with_llm(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o-mini"], - }, - file_location=KEY_LOC, - ) + @pytest.mark.openai + def test_with_llm(self, credentials_gpt_4o_mini: Credentials) -> None: user_proxy = UserProxyAgent( name="User", human_input_mode="NEVER", @@ -74,7 +65,7 @@ def test_with_llm(self) -> None: chatbot = AssistantAgent( name="chatbot", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o_mini.llm_config, ) self.tool.register_for_execution(user_proxy) diff --git a/test/interop/langchain/test_langchain.py b/test/interop/langchain/test_langchain.py index 50bd0d3f50..5c009016be 100644 --- a/test/interop/langchain/test_langchain.py +++ b/test/interop/langchain/test_langchain.py @@ -2,20 +2,18 @@ # # SPDX-License-Identifier: Apache-2.0 -import os import sys +from unittest.mock import MagicMock import pytest from langchain.tools import tool as langchain_tool from pydantic import BaseModel, Field -import autogen from autogen import AssistantAgent, UserProxyAgent from autogen.interop import Interoperable from autogen.interop.langchain import LangChainInteroperability -from ...agentchat.test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST -from ...conftest import reason, skip_openai +from ...conftest import Credentials # skip if python version is not >= 3.9 @@ -28,9 +26,12 @@ def setup(self) -> None: class SearchInput(BaseModel): query: str = Field(description="should be a search query") + self.mock = MagicMock() + @langchain_tool("search-tool", args_schema=SearchInput, return_direct=True) # type: ignore[misc] def search(query: SearchInput) -> str: """Look up things online.""" + self.mock(query) return "LangChain Integration" self.model_type = search.args_schema @@ -50,15 +51,10 @@ def test_convert_tool(self) -> None: tool_input = self.model_type(query="LangChain") # type: ignore[misc] assert self.tool.func(tool_input=tool_input) == "LangChain Integration" - @pytest.mark.skipif(skip_openai, reason=reason) - def test_with_llm(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) + @pytest.mark.openai + def test_with_llm(self, credentials_gpt_4o: Credentials) -> None: + llm_config = credentials_gpt_4o.llm_config + user_proxy = UserProxyAgent( name="User", human_input_mode="NEVER", @@ -66,20 +62,15 @@ def test_with_llm(self) -> None: chatbot = AssistantAgent( name="chatbot", - llm_config={"config_list": config_list}, + llm_config=llm_config, ) self.tool.register_for_execution(user_proxy) self.tool.register_for_llm(chatbot) - user_proxy.initiate_chat(recipient=chatbot, message="search for LangChain", max_turns=2) - - for message in user_proxy.chat_messages[chatbot]: - if "tool_responses" in message: - assert message["tool_responses"][0]["content"] == "LangChain Integration" - return + user_proxy.initiate_chat(recipient=chatbot, message="search for LangChain", max_turns=5) - assert False, "No tool response found in chat messages" + self.mock.assert_called() def test_get_unsupported_reason(self) -> None: assert LangChainInteroperability.get_unsupported_reason() is None @@ -92,9 +83,12 @@ def test_get_unsupported_reason(self) -> None: class TestLangChainInteroperabilityWithoutPydanticInput: @pytest.fixture(autouse=True) def setup(self) -> None: + self.mock = MagicMock() + @langchain_tool def search(query: str, max_length: int) -> str: """Look up things online.""" + self.mock(query, max_length) return f"LangChain Integration, max_length: {max_length}" self.tool = LangChainInteroperability.convert_tool(search) @@ -107,15 +101,9 @@ def test_convert_tool(self) -> None: tool_input = self.model_type(query="LangChain", max_length=100) # type: ignore[misc] assert self.tool.func(tool_input=tool_input) == "LangChain Integration, max_length: 100" - @pytest.mark.skipif(skip_openai, reason=reason) - def test_with_llm(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) + @pytest.mark.openai + def test_with_llm(self, credentials_gpt_4o: Credentials) -> None: + llm_config = credentials_gpt_4o.llm_config user_proxy = UserProxyAgent( name="User", human_input_mode="NEVER", @@ -123,7 +111,7 @@ def test_with_llm(self) -> None: chatbot = AssistantAgent( name="chatbot", - llm_config={"config_list": config_list}, + llm_config=llm_config, system_message=""" When using the search tool, input should be: { @@ -138,14 +126,9 @@ def test_with_llm(self) -> None: self.tool.register_for_execution(user_proxy) self.tool.register_for_llm(chatbot) - user_proxy.initiate_chat(recipient=chatbot, message="search for LangChain, Use max 100 characters", max_turns=2) - - for message in user_proxy.chat_messages[chatbot]: - if "tool_responses" in message: - assert message["tool_responses"][0]["content"] == "LangChain Integration, max_length: 100" - return + user_proxy.initiate_chat(recipient=chatbot, message="search for LangChain, Use max 100 characters", max_turns=5) - assert False, "No tool response found in chat messages" + self.mock.assert_called() @pytest.mark.skipif(sys.version_info >= (3, 9), reason="LangChain Interoperability is supported") diff --git a/test/interop/pydantic_ai/test_pydantic_ai.py b/test/interop/pydantic_ai/test_pydantic_ai.py index dd1dd40fde..0d3b7c54d6 100644 --- a/test/interop/pydantic_ai/test_pydantic_ai.py +++ b/test/interop/pydantic_ai/test_pydantic_ai.py @@ -2,7 +2,6 @@ # # SPDX-License-Identifier: Apache-2.0 -import os import random import sys from inspect import signature @@ -13,13 +12,11 @@ from pydantic_ai import RunContext from pydantic_ai.tools import Tool as PydanticAITool -import autogen from autogen import AssistantAgent, UserProxyAgent from autogen.interop import Interoperable from autogen.interop.pydantic_ai import PydanticAIInteroperability -from ...agentchat.test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST -from ...conftest import reason, skip_openai +from ...conftest import Credentials # skip if python version is not >= 3.9 @@ -47,24 +44,14 @@ def test_convert_tool(self) -> None: assert self.tool.description == "Roll a six-sided dice and return the result." assert self.tool.func() in ["1", "2", "3", "4", "5", "6"] - @pytest.mark.skipif(skip_openai, reason=reason) - def test_with_llm(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) + @pytest.mark.openai + def test_with_llm(self, credentials_gpt_4o: Credentials) -> None: user_proxy = UserProxyAgent( name="User", human_input_mode="NEVER", ) - chatbot = AssistantAgent( - name="chatbot", - llm_config={"config_list": config_list}, - ) + chatbot = AssistantAgent(name="chatbot", llm_config=credentials_gpt_4o.llm_config) self.tool.register_for_execution(user_proxy) self.tool.register_for_llm(chatbot) @@ -83,7 +70,6 @@ def test_with_llm(self) -> None: sys.version_info < (3, 9), reason="Only Python 3.9 and above are supported for LangchainInteroperability" ) class TestPydanticAIInteroperabilityDependencyInjection: - def test_dependency_injection(self) -> None: def f( ctx: RunContext[int], # type: ignore[valid-type] @@ -199,15 +185,8 @@ def test_expected_tools(self) -> None: assert chatbot.llm_config["tools"] == expected_tools # type: ignore[index] - @pytest.mark.skipif(skip_openai, reason=reason) - def test_with_llm(self) -> None: - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "tags": ["gpt-4o"], - }, - file_location=KEY_LOC, - ) + @pytest.mark.openai + def test_with_llm(self, credentials_gpt_4o: Credentials) -> None: user_proxy = UserProxyAgent( name="User", human_input_mode="NEVER", @@ -215,7 +194,7 @@ def test_with_llm(self) -> None: chatbot = AssistantAgent( name="chatbot", - llm_config={"config_list": config_list}, + llm_config=credentials_gpt_4o.llm_config, ) self.tool.register_for_execution(user_proxy) diff --git a/test/interop/pydantic_ai/test_pydantic_ai_tool.py b/test/interop/pydantic_ai/test_pydantic_ai_tool.py index 0f4eb92577..967c24d9e2 100644 --- a/test/interop/pydantic_ai/test_pydantic_ai_tool.py +++ b/test/interop/pydantic_ai/test_pydantic_ai_tool.py @@ -3,7 +3,6 @@ # SPDX-License-Identifier: Apache-2.0 import sys -import unittest import pytest from pydantic_ai.tools import Tool as PydanticAITool diff --git a/test/io/test_base.py b/test/io/test_base.py index 5c6bdcf20e..667bcf8ce7 100644 --- a/test/io/test_base.py +++ b/test/io/test_base.py @@ -5,9 +5,9 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT from threading import Thread -from typing import Any, List +from typing import Any -from autogen.io import IOConsole, IOStream, IOWebsockets +from autogen.io import IOConsole, IOStream from autogen.messages.base_message import BaseMessage diff --git a/test/io/test_websockets.py b/test/io/test_websockets.py index 1f5b1c1eb1..f315b0188e 100644 --- a/test/io/test_websockets.py +++ b/test/io/test_websockets.py @@ -7,7 +7,7 @@ import json from pprint import pprint from tempfile import TemporaryDirectory -from typing import Any, Callable, Dict, Optional +from typing import Any, Callable, Optional from unittest.mock import MagicMock from uuid import UUID @@ -17,13 +17,9 @@ import autogen from autogen.cache.cache import Cache from autogen.io import IOWebsockets -from autogen.io.base import IOStream from autogen.messages.base_message import BaseMessage, wrap_message -from ..conftest import skip_openai - -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" +from ..conftest import Credentials # Check if the websockets module is available try: @@ -112,8 +108,8 @@ def on_connect(iostream: IOWebsockets) -> None: print("Test passed.", flush=True) - @pytest.mark.skipif(skip_openai, reason="requested to skip") - def test_chat(self) -> None: + @pytest.mark.openai + def test_chat(self, credentials_gpt_4o_mini: Credentials) -> None: print("Testing setup", flush=True) mock = MagicMock() @@ -125,19 +121,8 @@ def on_connect(iostream: IOWebsockets) -> None: initial_msg = iostream.input() - config_list = autogen.config_list_from_json( - OAI_CONFIG_LIST, - filter_dict={ - "model": [ - "gpt-4o-mini", - "gpt-4o", - ], - }, - file_location=KEY_LOC, - ) - llm_config = { - "config_list": config_list, + "config_list": credentials_gpt_4o_mini.config_list, "stream": True, } @@ -165,7 +150,7 @@ def on_connect(iostream: IOWebsockets) -> None: flush=True, ) try: - user_proxy.initiate_chat( # noqa: F704 + user_proxy.initiate_chat( agent, message=initial_msg, cache=cache, diff --git a/test/messages/test_agent_messages.py b/test/messages/test_agent_messages.py index 8b72bc587d..396b7d77dc 100644 --- a/test/messages/test_agent_messages.py +++ b/test/messages/test_agent_messages.py @@ -18,9 +18,8 @@ ConversableAgentUsageSummaryMessage, ConversableAgentUsageSummaryNoCostIncurredMessage, ExecuteCodeBlockMessage, - ExecutedFunctionMessage, ExecuteFunctionMessage, - FunctionCall, + ExecutedFunctionMessage, FunctionCallMessage, FunctionResponseMessage, GenerateCodeExecutionReplyMessage, @@ -36,14 +35,11 @@ SpeakerAttemptSuccessfullMessage, TerminationAndHumanReplyMessage, TextMessage, - ToolCall, ToolCallMessage, - ToolResponse, ToolResponseMessage, UsingAutoReplyMessage, create_received_message_model, ) -from autogen.oai.client import OpenAIWrapper @pytest.fixture(autouse=True) @@ -305,9 +301,34 @@ def test_print( class TestTextMessage: - def test_print_context_message(self, uuid: UUID, sender: ConversableAgent, recipient: ConversableAgent) -> None: - message = {"content": "hello {name}", "context": {"name": "there"}} - + @pytest.mark.parametrize( + "message, expected_content", + [ + ( + {"content": "hello {name}", "context": {"name": "there"}}, + "hello {name}", + ), + ( + { + "content": [ + { + "type": "text", + "text": "Please extract table from the following image and convert it to Markdown.", + } + ] + }, + "Please extract table from the following image and convert it to Markdown.", + ), + ], + ) + def test_print_messages( + self, + uuid: UUID, + sender: ConversableAgent, + recipient: ConversableAgent, + message: dict[str, Any], + expected_content: str, + ) -> None: actual = create_received_message_model(uuid=uuid, message=message, sender=sender, recipient=recipient) assert isinstance(actual, TextMessage) @@ -315,7 +336,7 @@ def test_print_context_message(self, uuid: UUID, sender: ConversableAgent, recip "type": "text", "content": { "uuid": uuid, - "content": "hello {name}", + "content": message["content"], "sender_name": "sender", "recipient_name": "recipient", }, @@ -325,11 +346,9 @@ def test_print_context_message(self, uuid: UUID, sender: ConversableAgent, recip mock = MagicMock() actual.print(f=mock) - # print(mock.call_args_list) - expected_call_args_list = [ call("\x1b[33msender\x1b[0m (to recipient):\n", flush=True), - call("hello {name}", flush=True), + call(expected_content, flush=True), call( "\n", "--------------------------------------------------------------------------------", @@ -713,7 +732,7 @@ def test_print(self, uuid: UUID) -> None: class TestGroupChatRunChatMessage: def test_print(self, uuid: UUID) -> None: speaker = ConversableAgent( - "assistant uno", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER" + "assistant_uno", max_consecutive_auto_reply=0, llm_config=False, human_input_mode="NEVER" ) silent = False @@ -724,7 +743,7 @@ def test_print(self, uuid: UUID) -> None: "type": "group_chat_run_chat", "content": { "uuid": uuid, - "speaker_name": "assistant uno", + "speaker_name": "assistant_uno", "verbose": True, }, } @@ -735,7 +754,7 @@ def test_print(self, uuid: UUID) -> None: # print(mock.call_args_list) - expected_call_args_list = [call("\x1b[32m\nNext speaker: assistant uno\n\x1b[0m", flush=True)] + expected_call_args_list = [call("\x1b[32m\nNext speaker: assistant_uno\n\x1b[0m", flush=True)] assert mock.call_args_list == expected_call_args_list diff --git a/test/messages/test_base_message.py b/test/messages/test_base_message.py index 7e74a25065..cb3a99e6fe 100644 --- a/test/messages/test_base_message.py +++ b/test/messages/test_base_message.py @@ -2,9 +2,8 @@ # # SPDX-License-Identifier: Apache-2.0 -from contextlib import contextmanager -from typing import Generator, Type -from uuid import uuid4 +from collections.abc import Generator +from uuid import UUID import pytest from pydantic import BaseModel @@ -12,13 +11,12 @@ from autogen.messages.base_message import ( BaseMessage, _message_classes, - get_annotated_type_for_message_classes, wrap_message, ) -@pytest.fixture() -def TestMessage() -> Generator[Type[BaseMessage], None, None]: +@pytest.fixture +def TestMessage() -> Generator[type[BaseMessage], None, None]: # noqa: N802 org_message_classes = _message_classes.copy() try: @@ -35,10 +33,8 @@ class TestMessage(BaseMessage): class TestBaseMessage: - def test_model_dump_validate(self, TestMessage: Type[BaseModel]) -> None: - uuid = uuid4() - - print(f"{TestMessage=}") + def test_model_dump_validate(self, TestMessage: type[BaseModel], uuid: UUID) -> None: # noqa: N803 + # print(f"{TestMessage=}") message = TestMessage(uuid=uuid, sender="sender", receiver="receiver", content="Hello, World!") @@ -59,3 +55,19 @@ def test_model_dump_validate(self, TestMessage: Type[BaseModel]) -> None: model = TestMessage(**expected) assert model.model_dump() == expected + + def test_single_content_parameter_message(self, uuid: UUID) -> None: + @wrap_message + class TestSingleContentParameterMessage(BaseMessage): + content: str + + message = TestSingleContentParameterMessage(uuid=uuid, content="Hello, World!") + + expected = {"type": "test_single_content_parameter", "content": {"content": "Hello, World!", "uuid": uuid}} + assert message.model_dump() == expected + + model = TestSingleContentParameterMessage.model_validate(expected) + assert model.model_dump() == expected + + model = TestSingleContentParameterMessage(**expected) + assert model.model_dump() == expected diff --git a/test/messages/test_client_messages.py b/test/messages/test_client_messages.py index 78124ffab6..061246bf22 100644 --- a/test/messages/test_client_messages.py +++ b/test/messages/test_client_messages.py @@ -9,10 +9,7 @@ import pytest from autogen.messages.client_messages import ( - ActualUsageSummary, - ModelUsageSummary, StreamMessage, - TotalUsageSummary, UsageSummaryMessage, _change_usage_summary_format, ) @@ -321,15 +318,15 @@ def test_usage_summary_print_none_actual_and_total( class TestStreamMessage: def test_print(self, uuid: UUID) -> None: - chunk_content = "random stream chunk content" - stream_message = StreamMessage(uuid=uuid, chunk_content=chunk_content) + content = "random stream chunk content" + stream_message = StreamMessage(uuid=uuid, content=content) assert isinstance(stream_message, StreamMessage) expected_model_dump = { "type": "stream", "content": { "uuid": uuid, - "chunk_content": chunk_content, + "content": content, }, } assert stream_message.model_dump() == expected_model_dump diff --git a/test/oai/_test_completion.py b/test/oai/_test_completion.py deleted file mode 100755 index fece9ed42c..0000000000 --- a/test/oai/_test_completion.py +++ /dev/null @@ -1,454 +0,0 @@ -# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai -# -# SPDX-License-Identifier: Apache-2.0 -# -# Portions derived from https://github.com/microsoft/autogen are under the MIT License. -# SPDX-License-Identifier: MIT -#!/usr/bin/env python3 -m pytest - -import json -import os -import sys -from functools import partial - -import datasets -import numpy as np -import pytest - -import autogen -from autogen.code_utils import ( - eval_function_completions, - generate_assertions, - generate_code, - implement, -) -from autogen.math_utils import eval_math_responses, solve_problem -from test.oai.test_utils import KEY_LOC, OAI_CONFIG_LIST - -here = os.path.abspath(os.path.dirname(__file__)) - - -def yes_or_no_filter(context, response, **_): - return context.get("yes_or_no_choice", False) is False or any( - text in ["Yes.", "No."] for text in autogen.Completion.extract_text(response) - ) - - -def valid_json_filter(response, **_): - for text in autogen.Completion.extract_text(response): - try: - json.loads(text) - return True - except ValueError: - pass - return False - - -def test_filter(): - try: - import openai - except ImportError as exc: - print(exc) - return - config_list = autogen.config_list_from_models( - KEY_LOC, exclude="aoai", model_list=["text-ada-001", "gpt-4o-mini", "text-davinci-003"] - ) - response = autogen.Completion.create( - context={"yes_or_no_choice": True}, - config_list=config_list, - prompt="Is 37 a prime number? Please answer 'Yes.' or 'No.'", - filter_func=yes_or_no_filter, - ) - assert ( - autogen.Completion.extract_text(response)[0] in ["Yes.", "No."] - or not response["pass_filter"] - and response["config_id"] == 2 - ) - response = autogen.Completion.create( - context={"yes_or_no_choice": False}, - config_list=config_list, - prompt="Is 37 a prime number?", - filter_func=yes_or_no_filter, - ) - assert response["model"] == "text-ada-001" - response = autogen.Completion.create( - config_list=config_list, - prompt="How to construct a json request to Bing API to search for 'latest AI news'? Return the JSON request.", - filter_func=valid_json_filter, - ) - assert response["config_id"] == 2 or response["pass_filter"], "the response must pass filter unless all fail" - assert not response["pass_filter"] or json.loads(autogen.Completion.extract_text(response)[0]) - - -def test_chatcompletion(): - params = autogen.ChatCompletion._construct_params( - context=None, - config={"model": "unknown"}, - prompt="hi", - ) - assert "messages" in params - params = autogen.Completion._construct_params( - context=None, - config={"model": "unknown"}, - prompt="hi", - ) - assert "messages" not in params - params = autogen.Completion._construct_params( - context=None, - config={"model": "gpt-4o"}, - prompt="hi", - ) - assert "messages" in params - params = autogen.Completion._construct_params( - context={"name": "there"}, - config={"model": "unknown"}, - prompt="hi {name}", - allow_format_str_template=True, - ) - assert params["prompt"] == "hi there" - params = autogen.Completion._construct_params( - context={"name": "there"}, - config={"model": "unknown"}, - prompt="hi {name}", - ) - assert params["prompt"] != "hi there" - - -def test_multi_model(): - try: - import openai - except ImportError as exc: - print(exc) - return - response = autogen.Completion.create( - config_list=autogen.config_list_gpt4_gpt35(KEY_LOC), - prompt="Hi", - ) - print(response) - - -def test_nocontext(): - try: - import diskcache - import openai - except ImportError as exc: - print(exc) - return - response = autogen.Completion.create( - model="text-ada-001", - prompt="1+1=", - max_tokens=1, - use_cache=False, - request_timeout=10, - config_list=autogen.config_list_openai_aoai(KEY_LOC, exclude="aoai"), - ) - print(response) - code, _ = generate_code( - config_list=autogen.config_list_from_json( - OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={ - "model": { - "gpt-4o-mini", - "gpt-4o", - }, - }, - ), - messages=[ - { - "role": "system", - "content": "You want to become a better assistant by learning new skills and improving your existing ones.", - }, - { - "role": "user", - "content": "Write reusable code to use web scraping to get information from websites.", - }, - ], - ) - print(code) - - solution, cost = solve_problem("1+1=", config_list=autogen.config_list_gpt4_gpt35(KEY_LOC)) - print(solution, cost) - - -@pytest.mark.skipif( - sys.platform == "win32", - reason="do not run on windows", -) -def test_humaneval(num_samples=1): - gpt35_config_list = autogen.config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - filter_dict={ - "model": { - "gpt-4o-mini", - "gpt-4o", - }, - }, - file_location=KEY_LOC, - ) - assertions = partial(generate_assertions, config_list=gpt35_config_list) - eval_with_generated_assertions = partial( - eval_function_completions, - assertions=assertions, - ) - - seed = 41 - data = datasets.load_dataset("openai_humaneval")["test"].shuffle(seed=seed) - n_tune_data = 20 - tune_data = [ - { - "definition": data[x]["prompt"], - "test": data[x]["test"], - "entry_point": data[x]["entry_point"], - } - for x in range(n_tune_data) - ] - test_data = [ - { - "definition": data[x]["prompt"], - "test": data[x]["test"], - "entry_point": data[x]["entry_point"], - } - for x in range(n_tune_data, len(data)) - ] - autogen.Completion.clear_cache(cache_path_root="{here}/cache") - autogen.Completion.set_cache(seed) - try: - import diskcache - import openai - except ImportError as exc: - print(exc) - return - autogen.Completion.clear_cache(400) - # no error should be raised - response = autogen.Completion.create( - context=test_data[0], - config_list=autogen.config_list_from_models(KEY_LOC, model_list=["gpt-4o-mini"]), - prompt="", - max_tokens=1, - max_retry_period=0, - raise_on_ratelimit_or_timeout=False, - ) - # assert response == -1 - config_list = autogen.config_list_openai_aoai(KEY_LOC) - # a minimal tuning example - config, _ = autogen.Completion.tune( - data=tune_data, - metric="success", - mode="max", - eval_func=eval_function_completions, - n=1, - prompt="{definition}", - allow_format_str_template=True, - config_list=config_list, - ) - response = autogen.Completion.create(context=test_data[0], config_list=config_list, **config) - # a minimal tuning example for tuning chat completion models using the Completion class - config, _ = autogen.Completion.tune( - data=tune_data, - metric="succeed_assertions", - mode="max", - eval_func=eval_with_generated_assertions, - n=1, - model="text-davinci-003", - prompt="{definition}", - allow_format_str_template=True, - config_list=config_list, - ) - response = autogen.Completion.create(context=test_data[0], config_list=config_list, **config) - # a minimal tuning example for tuning chat completion models using the ChatCompletion class - config_list = autogen.config_list_openai_aoai(KEY_LOC) - config, _ = autogen.ChatCompletion.tune( - data=tune_data, - metric="expected_success", - mode="max", - eval_func=eval_function_completions, - n=1, - messages=[{"role": "user", "content": "{definition}"}], - config_list=config_list, - allow_format_str_template=True, - request_timeout=120, - ) - response = autogen.ChatCompletion.create(context=test_data[0], config_list=config_list, **config) - print(response) - from openai import RateLimitError - - try: - code, cost, selected = implement(tune_data[1], [{**config_list[-1], **config}]) - except RateLimitError: - code, cost, selected = implement( - tune_data[1], - [{**config_list[0], "model": "text-ada-001", "prompt": config["messages"]["content"]}], - assertions=assertions, - ) - print(code) - print(cost) - assert selected == 0 - print(eval_function_completions([code], **tune_data[1])) - # a more comprehensive tuning example - config2, analysis = autogen.Completion.tune( - data=tune_data, - metric="success", - mode="max", - eval_func=eval_with_generated_assertions, - log_file_name="logs/humaneval.log", - inference_budget=0.002, - optimization_budget=2, - num_samples=num_samples, - # logging_level=logging.INFO, - prompt=[ - "{definition}", - "# Python 3{definition}", - "Complete the following Python function:{definition}", - ], - stop=[["\nclass", "\ndef", "\nif", "\nprint"], None], # the stop sequences - config_list=config_list, - allow_format_str_template=True, - ) - print(config2) - print(analysis.best_result) - print(test_data[0]) - response = autogen.Completion.create(context=test_data[0], config_list=config_list, **config2) - print(response) - autogen.Completion.data = test_data[:num_samples] - result = autogen.Completion._eval(analysis.best_config, prune=False, eval_only=True) - print("result without pruning", result) - result = autogen.Completion.test(test_data[:num_samples], config_list=config_list, **config2) - print(result) - try: - code, cost, selected = implement( - tune_data[1], [{**config_list[-2], **config2}, {**config_list[-1], **config}], assertions=assertions - ) - except RateLimitError: - code, cost, selected = implement( - tune_data[1], - [ - {**config_list[-3], **config2}, - {**config_list[0], "model": "text-ada-001", "prompt": config["messages"]["content"]}, - ], - assertions=assertions, - ) - print(code) - print(cost) - print(selected) - print(eval_function_completions([code], **tune_data[1])) - - -def test_math(num_samples=-1): - try: - import diskcache - import openai - except ImportError as exc: - print(exc) - return - - seed = 41 - data = datasets.load_dataset("competition_math") - train_data = data["train"].shuffle(seed=seed) - test_data = data["test"].shuffle(seed=seed) - n_tune_data = 20 - tune_data = [ - { - "problem": train_data[x]["problem"], - "solution": train_data[x]["solution"], - } - for x in range(len(train_data)) - if train_data[x]["level"] == "Level 1" - ][:n_tune_data] - test_data = [ - { - "problem": test_data[x]["problem"], - "solution": test_data[x]["solution"], - } - for x in range(len(test_data)) - if test_data[x]["level"] == "Level 1" - ] - print( - "max tokens in tuning data's canonical solutions", - max([len(x["solution"].split()) for x in tune_data]), - ) - print(len(tune_data), len(test_data)) - # prompt template - prompts = [ - lambda data: "%s Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{}." - % data["problem"] - ] - - autogen.Completion.set_cache(seed) - config_list = autogen.config_list_openai_aoai(KEY_LOC) - vanilla_config = { - "model": "text-ada-001", - "temperature": 1, - "max_tokens": 1024, - "n": 1, - "prompt": prompts[0], - "stop": "###", - } - test_data_sample = test_data[0:3] - result = autogen.Completion.test(test_data_sample, eval_math_responses, config_list=config_list, **vanilla_config) - result = autogen.Completion.test( - test_data_sample, - eval_math_responses, - agg_method="median", - config_list=config_list, - **vanilla_config, - ) - - def my_median(results): - return np.median(results) - - def my_average(results): - return np.mean(results) - - result = autogen.Completion.test( - test_data_sample, - eval_math_responses, - agg_method=my_median, - **vanilla_config, - ) - result = autogen.Completion.test( - test_data_sample, - eval_math_responses, - agg_method={ - "expected_success": my_median, - "success": my_average, - "success_vote": my_average, - "votes": np.mean, - }, - **vanilla_config, - ) - - print(result) - - config, _ = autogen.Completion.tune( - data=tune_data, # the data for tuning - metric="expected_success", # the metric to optimize - mode="max", # the optimization mode - eval_func=eval_math_responses, # the evaluation function to return the success metrics - # log_file_name="logs/math.log", # the log file name - inference_budget=0.002, # the inference budget (dollar) - optimization_budget=0.01, # the optimization budget (dollar) - num_samples=num_samples, - prompt=prompts, # the prompt templates to choose from - stop="###", # the stop sequence - config_list=config_list, - ) - print("tuned config", config) - result = autogen.Completion.test(test_data_sample, config_list=config_list, **config) - print("result from tuned config:", result) - print("empty responses", eval_math_responses([], None)) - - -if __name__ == "__main__": - import openai - - config_list = autogen.config_list_openai_aoai(KEY_LOC) - assert len(config_list) >= 3, config_list - openai.api_key = os.environ["OPENAI_API_KEY"] - - # test_filter() - # test_chatcompletion() - # test_multi_model() - # test_nocontext() - # test_humaneval(1) - test_math(1) diff --git a/test/oai/test_anthropic.py b/test/oai/test_anthropic.py index 1a9fb29969..ba8ecdc5ea 100644 --- a/test/oai/test_anthropic.py +++ b/test/oai/test_anthropic.py @@ -7,7 +7,6 @@ #!/usr/bin/env python3 -m pytest import os -from unittest.mock import MagicMock, patch import pytest @@ -24,7 +23,7 @@ reason = "Anthropic dependency not installed!" -@pytest.fixture() +@pytest.fixture def mock_completion(): class MockCompletion: def __init__( @@ -48,7 +47,7 @@ def __init__( return MockCompletion -@pytest.fixture() +@pytest.fixture def anthropic_client(): return AnthropicClient(api_key="dummy_api_key") @@ -66,7 +65,7 @@ def test_initialization_missing_api_key(): AnthropicClient(api_key="dummy_api_key") -@pytest.fixture() +@pytest.fixture def anthropic_client_with_aws_credentials(): return AnthropicClient( aws_access_key="dummy_access_key", @@ -76,7 +75,7 @@ def anthropic_client_with_aws_credentials(): ) -@pytest.fixture() +@pytest.fixture def anthropic_client_with_vertexai_credentials(): return AnthropicClient( gcp_project_id="dummy_project_id", @@ -92,31 +91,31 @@ def test_intialization(anthropic_client): @pytest.mark.skipif(skip, reason=reason) def test_intialization_with_aws_credentials(anthropic_client_with_aws_credentials): - assert ( - anthropic_client_with_aws_credentials.aws_access_key == "dummy_access_key" - ), "`aws_access_key` should be correctly set in the config" - assert ( - anthropic_client_with_aws_credentials.aws_secret_key == "dummy_secret_key" - ), "`aws_secret_key` should be correctly set in the config" - assert ( - anthropic_client_with_aws_credentials.aws_session_token == "dummy_session_token" - ), "`aws_session_token` should be correctly set in the config" - assert ( - anthropic_client_with_aws_credentials.aws_region == "us-west-2" - ), "`aws_region` should be correctly set in the config" + assert anthropic_client_with_aws_credentials.aws_access_key == "dummy_access_key", ( + "`aws_access_key` should be correctly set in the config" + ) + assert anthropic_client_with_aws_credentials.aws_secret_key == "dummy_secret_key", ( + "`aws_secret_key` should be correctly set in the config" + ) + assert anthropic_client_with_aws_credentials.aws_session_token == "dummy_session_token", ( + "`aws_session_token` should be correctly set in the config" + ) + assert anthropic_client_with_aws_credentials.aws_region == "us-west-2", ( + "`aws_region` should be correctly set in the config" + ) @pytest.mark.skipif(skip, reason=reason) def test_initialization_with_vertexai_credentials(anthropic_client_with_vertexai_credentials): - assert ( - anthropic_client_with_vertexai_credentials.gcp_project_id == "dummy_project_id" - ), "`gcp_project_id` should be correctly set in the config" - assert ( - anthropic_client_with_vertexai_credentials.gcp_region == "us-west-2" - ), "`gcp_region` should be correctly set in the config" - assert ( - anthropic_client_with_vertexai_credentials.gcp_auth_token == "dummy_auth_token" - ), "`gcp_auth_token` should be correctly set in the config" + assert anthropic_client_with_vertexai_credentials.gcp_project_id == "dummy_project_id", ( + "`gcp_project_id` should be correctly set in the config" + ) + assert anthropic_client_with_vertexai_credentials.gcp_region == "us-west-2", ( + "`gcp_region` should be correctly set in the config" + ) + assert anthropic_client_with_vertexai_credentials.gcp_auth_token == "dummy_auth_token", ( + "`gcp_auth_token` should be correctly set in the config" + ) # Test cost calculation diff --git a/test/oai/test_bedrock.py b/test/oai/test_bedrock.py index b059ee138d..54a553f62c 100644 --- a/test/oai/test_bedrock.py +++ b/test/oai/test_bedrock.py @@ -34,7 +34,6 @@ def __init__(self, text, choices, usage, cost, model): @pytest.fixture def bedrock_client(): - # Set Bedrock client with some default values client = BedrockClient() @@ -49,7 +48,6 @@ def bedrock_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason=skip_reason) def test_initialization(): - # Creation works without an api_key as it's handled in the parameter parsing BedrockClient() @@ -150,13 +148,13 @@ def test_create_response(mock_chat, bedrock_client): response = bedrock_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Bedrock response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Bedrock response", ( + "Response content should match expected output" + ) assert response.id == "mock_bedrock_response_id", "Response ID should match the mocked response ID" - assert ( - response.model == "anthropic.claude-3-sonnet-20240229-v1:0" - ), "Response model should match the mocked response model" + assert response.model == "anthropic.claude-3-sonnet-20240229-v1:0", ( + "Response model should match the mocked response model" + ) assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" assert response.usage.completion_tokens == 20, "Response completion tokens should match the mocked response usage" @@ -228,7 +226,6 @@ def test_create_response_with_tool_call(mock_chat, bedrock_client): # Test message conversion from OpenAI to Bedrock format @pytest.mark.skipif(skip, reason=skip_reason) def test_oai_messages_to_bedrock_messages(bedrock_client): - # Test that the "name" key is removed and system messages converted to user message test_messages = [ {"role": "system", "content": "You are a helpful AI bot."}, @@ -274,9 +271,9 @@ def test_oai_messages_to_bedrock_messages(bedrock_client): {"role": "user", "content": [{"text": "Summarise the conversation."}]}, ] - assert ( - messages == expected_messages - ), "Final 'system' message was not changed to 'user' or continue messages not included" + assert messages == expected_messages, ( + "Final 'system' message was not changed to 'user' or continue messages not included" + ) # Test that the last message is a user or system message and if not, add a continue message test_messages = [ diff --git a/test/oai/test_cerebras.py b/test/oai/test_cerebras.py index b9dc2c786b..3cfe50fc8e 100644 --- a/test/oai/test_cerebras.py +++ b/test/oai/test_cerebras.py @@ -43,7 +43,6 @@ def cerebras_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason=skip_reason) def test_initialization(): - # Missing any api_key with pytest.raises(AssertionError) as assertinfo: CerebrasClient() # Should raise an AssertionError due to missing api_key @@ -181,9 +180,9 @@ def test_create_response(mock_chat, cerebras_client): response = cerebras_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Cerebras response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Cerebras response", ( + "Response content should match expected output" + ) assert response.id == "mock_cerebras_response_id", "Response ID should match the mocked response ID" assert response.model == "llama-3.3-70b", "Response model should match the mocked response model" assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" diff --git a/test/oai/test_client.py b/test/oai/test_client.py index 963f9b7752..7699236afc 100755 --- a/test/oai/test_client.py +++ b/test/oai/test_client.py @@ -12,36 +12,30 @@ import pytest -from autogen import OpenAIWrapper, config_list_from_json +from autogen import OpenAIWrapper from autogen.cache.cache import Cache from autogen.oai.client import LEGACY_CACHE_DIR, LEGACY_DEFAULT_CACHE_SEED -from ..conftest import skip_openai # noqa: E402 +from ..conftest import Credentials TOOL_ENABLED = False try: import openai - from openai import OpenAI + from openai import OpenAI # noqa: F401 if openai.__version__ >= "1.1.0": TOOL_ENABLED = True - from openai.types.chat.chat_completion import ChatCompletionMessage + from openai.types.chat.chat_completion import ChatCompletionMessage # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai - -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" + skip = False +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_aoai_chat_completion(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"api_type": ["azure"], "tags": ["gpt-3.5-turbo"]}, - ) +def test_aoai_chat_completion(credentials_azure_gpt_35_turbo: Credentials): + config_list = credentials_azure_gpt_35_turbo.config_list client = OpenAIWrapper(config_list=config_list) response = client.create(messages=[{"role": "user", "content": "2+2="}], cache_seed=None) print(response) @@ -57,14 +51,10 @@ def test_aoai_chat_completion(): print(client.extract_text_or_completion_object(response)) +@pytest.mark.openai @pytest.mark.skipif(skip or not TOOL_ENABLED, reason="openai>=1.1.0 not installed") -def test_oai_tool_calling_extraction(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"api_type": ["azure"], "tags": ["gpt-3.5-turbo"]}, - ) - client = OpenAIWrapper(config_list=config_list) +def test_oai_tool_calling_extraction(credentials_gpt_4o_mini: Credentials): + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create( messages=[ { @@ -94,31 +84,25 @@ def test_oai_tool_calling_extraction(): print(client.extract_text_or_completion_object(response)) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_chat_completion(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - ) - client = OpenAIWrapper(config_list=config_list) +def test_chat_completion(credentials_gpt_4o_mini: Credentials): + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create(messages=[{"role": "user", "content": "1+1="}]) print(response) print(client.extract_text_or_completion_object(response)) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_completion(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-35-turbo-instruct", "gpt-3.5-turbo-instruct"]}, - ) - client = OpenAIWrapper(config_list=config_list) +def test_completion(credentials_azure_gpt_35_turbo_instruct: Credentials): + client = OpenAIWrapper(config_list=credentials_azure_gpt_35_turbo_instruct.config_list) response = client.create(prompt="1+1=") print(response) print(client.extract_text_or_completion_object(response)) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") @pytest.mark.parametrize( "cache_seed", @@ -127,39 +111,29 @@ def test_completion(): 42, ], ) -def test_cost(cache_seed): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-35-turbo-instruct", "gpt-3.5-turbo-instruct"]}, - ) - client = OpenAIWrapper(config_list=config_list, cache_seed=cache_seed) +def test_cost(credentials_azure_gpt_35_turbo_instruct: Credentials, cache_seed): + client = OpenAIWrapper(config_list=credentials_azure_gpt_35_turbo_instruct.config_list, cache_seed=cache_seed) response = client.create(prompt="1+3=") print(response.cost) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_customized_cost(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, file_location=KEY_LOC, filter_dict={"tags": ["gpt-3.5-turbo-instruct"]} - ) +def test_customized_cost(credentials_azure_gpt_35_turbo_instruct: Credentials): + config_list = credentials_azure_gpt_35_turbo_instruct.config_list for config in config_list: config.update({"price": [1000, 1000]}) client = OpenAIWrapper(config_list=config_list, cache_seed=None) response = client.create(prompt="1+3=") - assert ( - response.cost >= 4 - ), f"Due to customized pricing, cost should be > 4. Message: {response.choices[0].message.content}" + assert response.cost >= 4, ( + f"Due to customized pricing, cost should be > 4. Message: {response.choices[0].message.content}" + ) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_usage_summary(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-35-turbo-instruct", "gpt-3.5-turbo-instruct"]}, - ) - client = OpenAIWrapper(config_list=config_list) +def test_usage_summary(credentials_azure_gpt_35_turbo_instruct: Credentials): + client = OpenAIWrapper(config_list=credentials_azure_gpt_35_turbo_instruct.config_list) response = client.create(prompt="1+3=", cache_seed=None) # usage should be recorded @@ -183,19 +157,14 @@ def test_usage_summary(): # check update response = client.create(prompt="1+3=", cache_seed=42) - assert ( - client.total_usage_summary["total_cost"] == response.cost * 2 - ), "total_cost should be equal to response.cost * 2" + assert client.total_usage_summary["total_cost"] == response.cost * 2, ( + "total_cost should be equal to response.cost * 2" + ) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_legacy_cache(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-3.5-turbo"]}, - ) - +def test_legacy_cache(credentials_gpt_4o_mini: Credentials): # Prompt to use for testing. prompt = "Write a 100 word summary on the topic of the history of human civilization." @@ -204,7 +173,7 @@ def test_legacy_cache(): shutil.rmtree(LEGACY_CACHE_DIR) # Test default cache seed. - client = OpenAIWrapper(config_list=config_list) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) start_time = time.time() cold_cache_response = client.create(messages=[{"role": "user", "content": prompt}]) end_time = time.time() @@ -219,7 +188,7 @@ def test_legacy_cache(): assert os.path.exists(os.path.join(LEGACY_CACHE_DIR, str(LEGACY_DEFAULT_CACHE_SEED))) # Test with cache seed set through constructor - client = OpenAIWrapper(config_list=config_list, cache_seed=13) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list, cache_seed=13) start_time = time.time() cold_cache_response = client.create(messages=[{"role": "user", "content": prompt}]) end_time = time.time() @@ -234,7 +203,7 @@ def test_legacy_cache(): assert os.path.exists(os.path.join(LEGACY_CACHE_DIR, str(13))) # Test with cache seed set through create method - client = OpenAIWrapper(config_list=config_list) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) start_time = time.time() cold_cache_response = client.create(messages=[{"role": "user", "content": prompt}], cache_seed=17) end_time = time.time() @@ -257,14 +226,9 @@ def test_legacy_cache(): assert os.path.exists(os.path.join(LEGACY_CACHE_DIR, str(21))) +@pytest.mark.openai @pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_cache(): - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-3.5-turbo"]}, - ) - +def test_cache(credentials_gpt_4o_mini: Credentials): # Prompt to use for testing. prompt = "Write a 100 word summary on the topic of the history of artificial intelligence." @@ -278,7 +242,7 @@ def test_cache(): # Test cache set through constructor. with Cache.disk(cache_seed=49, cache_path_root=cache_dir) as cache: - client = OpenAIWrapper(config_list=config_list, cache=cache) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list, cache=cache) start_time = time.time() cold_cache_response = client.create(messages=[{"role": "user", "content": prompt}]) end_time = time.time() @@ -296,7 +260,7 @@ def test_cache(): assert not os.path.exists(os.path.join(cache_dir, str(LEGACY_DEFAULT_CACHE_SEED))) # Test cache set through method. - client = OpenAIWrapper(config_list=config_list) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) with Cache.disk(cache_seed=312, cache_path_root=cache_dir) as cache: start_time = time.time() cold_cache_response = client.create(messages=[{"role": "user", "content": prompt}], cache=cache) diff --git a/test/oai/test_client_stream.py b/test/oai/test_client_stream.py index 125e1dfb46..a84edd1dd5 100755 --- a/test/oai/test_client_stream.py +++ b/test/oai/test_client_stream.py @@ -11,16 +11,16 @@ import pytest -from autogen import OpenAIWrapper, config_list_from_json +from autogen import OpenAIWrapper -from ..conftest import skip_openai # noqa: E402 +from ..conftest import Credentials, reason try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai + skip = False # raises exception if openai>=1 is installed and something is wrong with imports # otherwise the test will be skipped @@ -31,31 +31,20 @@ ChoiceDeltaToolCallFunction, ) -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" - -@pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_aoai_chat_completion_stream() -> None: - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"api_type": ["azure"], "tags": ["gpt-3.5-turbo"]}, - ) - client = OpenAIWrapper(config_list=config_list) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_aoai_chat_completion_stream(credentials_gpt_4o_mini: Credentials) -> None: + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create(messages=[{"role": "user", "content": "2+2="}], stream=True) print(response) print(client.extract_text_or_completion_object(response)) -@pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_chat_completion_stream() -> None: - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-3.5-turbo"]}, - ) - client = OpenAIWrapper(config_list=config_list) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_chat_completion_stream(credentials_gpt_4o_mini: Credentials) -> None: + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create(messages=[{"role": "user", "content": "1+1="}], stream=True) print(response) print(client.extract_text_or_completion_object(response)) @@ -95,7 +84,8 @@ def test__update_dict_from_chunk() -> None: assert d["s"] == "beginning and end" -@pytest.mark.skipif(skip, reason="openai>=1 not installed") +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) def test__update_function_call_from_chunk() -> None: function_call_chunks = [ ChoiceDeltaFunctionCall(arguments=None, name="get_current_weather"), @@ -127,7 +117,8 @@ def test__update_function_call_from_chunk() -> None: ChatCompletionMessage(role="assistant", function_call=full_function_call, content=None) -@pytest.mark.skipif(skip, reason="openai>=1 not installed") +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) def test__update_tool_calls_from_chunk() -> None: tool_calls_chunks = [ ChoiceDeltaToolCall( @@ -200,13 +191,9 @@ def test__update_tool_calls_from_chunk() -> None: # todo: remove when OpenAI removes functions from the API -@pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_chat_functions_stream() -> None: - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["gpt-3.5-turbo"]}, - ) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_chat_functions_stream(credentials_gpt_4o_mini: Credentials) -> None: functions = [ { "name": "get_current_weather", @@ -223,7 +210,7 @@ def test_chat_functions_stream() -> None: }, }, ] - client = OpenAIWrapper(config_list=config_list) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create( messages=[{"role": "user", "content": "What's the weather like today in San Francisco?"}], functions=functions, @@ -234,13 +221,9 @@ def test_chat_functions_stream() -> None: # test for tool support instead of the deprecated function calls -@pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_chat_tools_stream() -> None: - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, - file_location=KEY_LOC, - filter_dict={"tags": ["multitool"]}, - ) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_chat_tools_stream(credentials_gpt_4o_mini: Credentials) -> None: tools = [ { "type": "function", @@ -260,7 +243,7 @@ def test_chat_tools_stream() -> None: }, }, ] - client = OpenAIWrapper(config_list=config_list) + client = OpenAIWrapper(config_list=credentials_gpt_4o_mini.config_list) response = client.create( messages=[{"role": "user", "content": "What's the weather like today in San Francisco?"}], tools=tools, @@ -280,12 +263,10 @@ def test_chat_tools_stream() -> None: assert len(tool_calls) > 0 -@pytest.mark.skipif(skip, reason="openai>=1 not installed") -def test_completion_stream() -> None: - config_list = config_list_from_json( - env_or_file=OAI_CONFIG_LIST, file_location=KEY_LOC, filter_dict={"tags": ["gpt-3.5-turbo-instruct"]} - ) - client = OpenAIWrapper(config_list=config_list) +@pytest.mark.openai +@pytest.mark.skipif(skip, reason=reason) +def test_completion_stream(credentials_azure_gpt_35_turbo_instruct: Credentials) -> None: + client = OpenAIWrapper(config_list=credentials_azure_gpt_35_turbo_instruct.config_list) response = client.create(prompt="1+1=", stream=True) print(response) print(client.extract_text_or_completion_object(response)) diff --git a/test/oai/test_client_utils.py b/test/oai/test_client_utils.py index 8334b74f7f..57fb0d5186 100644 --- a/test/oai/test_client_utils.py +++ b/test/oai/test_client_utils.py @@ -8,7 +8,6 @@ import pytest -import autogen from autogen.oai.client_utils import should_hide_tools, validate_parameter @@ -56,7 +55,7 @@ def test_validate_parameter(): } # Should return default - assert 512 == validate_parameter(params, "max_tokens", int, False, 512, (0, None), None) + assert validate_parameter(params, "max_tokens", int, False, 512, (0, None), None) == 512 # Test invalid parameters params = { @@ -91,7 +90,7 @@ def test_validate_parameter(): } # Should all be set to defaults - assert 512 == validate_parameter(params, "max_tokens", int, True, 512, (0, None), None) + assert validate_parameter(params, "max_tokens", int, True, 512, (0, None), None) == 512 assert validate_parameter(params, "presence_penalty", (int, float), True, None, (-2, 2), None) is None assert validate_parameter(params, "frequency_penalty", (int, float), True, None, (-2, 2), None) is None assert validate_parameter(params, "min_p", (int, float), True, None, (0, 1), None) is None @@ -102,8 +101,11 @@ def test_validate_parameter(): } # Should all be set to defaults - assert "Meta-Llama/Llama-Guard-7b" == validate_parameter( - params, "safety_model", str, True, None, None, ["Meta-Llama/Llama-Guard-7b", "Meta-Llama/Llama-Guard-13b"] + assert ( + validate_parameter( + params, "safety_model", str, True, None, None, ["Meta-Llama/Llama-Guard-7b", "Meta-Llama/Llama-Guard-13b"] + ) + == "Meta-Llama/Llama-Guard-7b" ) # Test invalid list options diff --git a/test/oai/test_cohere.py b/test/oai/test_cohere.py index 34bb6f6114..0e40eff722 100644 --- a/test/oai/test_cohere.py +++ b/test/oai/test_cohere.py @@ -22,7 +22,7 @@ reason = "Cohere dependency not installed!" -@pytest.fixture() +@pytest.fixture def cohere_client(): return CohereClient(api_key="dummy_api_key") @@ -46,9 +46,9 @@ def test_intialization(cohere_client): @pytest.mark.skipif(skip, reason=reason) def test_calculate_cohere_cost(): - assert ( - calculate_cohere_cost(0, 0, model="command-r") == 0.0 - ), "Cost should be 0 for 0 input_tokens and 0 output_tokens" + assert calculate_cohere_cost(0, 0, model="command-r") == 0.0, ( + "Cost should be 0 for 0 input_tokens and 0 output_tokens" + ) assert calculate_cohere_cost(100, 200, model="command-r-plus") == 0.0033 diff --git a/test/oai/test_custom_client.py b/test/oai/test_custom_client.py index 0e7fea224d..3225084802 100644 --- a/test/oai/test_custom_client.py +++ b/test/oai/test_custom_client.py @@ -4,29 +4,27 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Dict import pytest from autogen import OpenAIWrapper -from autogen.oai import ModelClient try: - from openai import OpenAI + from openai import OpenAI # noqa: F401 except ImportError: skip = True else: skip = False +TEST_COST = 20000000 +TEST_CUSTOM_RESPONSE = "This is a custom response." +TEST_DEVICE = "cpu" +TEST_LOCAL_MODEL_NAME = "local_model_name" +TEST_OTHER_PARAMS_VAL = "other_params" +TEST_MAX_LENGTH = 1000 -def test_custom_model_client(): - TEST_COST = 20000000 - TEST_CUSTOM_RESPONSE = "This is a custom response." - TEST_DEVICE = "cpu" - TEST_LOCAL_MODEL_NAME = "local_model_name" - TEST_OTHER_PARAMS_VAL = "other_params" - TEST_MAX_LENGTH = 1000 +def test_custom_model_client(): class CustomModel: def __init__(self, config: dict, test_hook): self.test_hook = test_hook diff --git a/test/oai/test_gemini.py b/test/oai/test_gemini.py index 7bc834b3bd..93ee5f4cdd 100644 --- a/test/oai/test_gemini.py +++ b/test/oai/test_gemini.py @@ -1,19 +1,22 @@ -# Copyright (c) 2023 - 2024, Owners of https://github.com/autogenhub +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai # # SPDX-License-Identifier: Apache-2.0 # -# Portions derived from https://github.com/microsoft/autogen are under the MIT License. +# Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT + import os from unittest.mock import MagicMock, patch import pytest try: - import google.auth + import google.auth # noqa: F401 from google.api_core.exceptions import InternalServerError from google.auth.credentials import Credentials from google.cloud.aiplatform.initializer import global_config as vertexai_global_config + from google.generativeai.types import GenerateContentResponse + from vertexai.generative_models import GenerationResponse as VertexAIGenerationResponse from vertexai.generative_models import HarmBlockThreshold as VertexAIHarmBlockThreshold from vertexai.generative_models import HarmCategory as VertexAIHarmCategory from vertexai.generative_models import SafetySetting as VertexAISafetySetting @@ -93,9 +96,9 @@ def test_valid_initialization(gemini_client): @pytest.mark.skipif(skip, reason="Google GenAI dependency is not installed") def test_google_application_credentials_initialization(): GeminiClient(google_application_credentials="credentials.json", project_id="fake-project-id") - assert ( - os.environ["GOOGLE_APPLICATION_CREDENTIALS"] == "credentials.json" - ), "Incorrect Google Application Credentials initialization" + assert os.environ["GOOGLE_APPLICATION_CREDENTIALS"] == "credentials.json", ( + "Incorrect Google Application Credentials initialization" + ) @pytest.mark.skipif(skip, reason="Google GenAI dependency is not installed") @@ -187,9 +190,9 @@ def compare_safety_settings(converted_safety_settings, expected_safety_settings) converted_setting = converted_safety_settings[i] yield expected_setting.to_dict() == converted_setting.to_dict() - assert len(converted_safety_settings) == len( - expected_safety_settings - ), "The length of the safety settings is incorrect" + assert len(converted_safety_settings) == len(expected_safety_settings), ( + "The length of the safety settings is incorrect" + ) settings_comparison = compare_safety_settings(converted_safety_settings, expected_safety_settings) assert all(settings_comparison), "Converted safety settings are incorrect" @@ -204,9 +207,7 @@ def test_vertexai_default_safety_settings_dict(gemini_client): } converted_safety_settings = GeminiClient._to_vertexai_safety_settings(safety_settings) - expected_safety_settings = { - category: VertexAIHarmBlockThreshold.BLOCK_ONLY_HIGH for category in safety_settings.keys() - } + expected_safety_settings = {category: VertexAIHarmBlockThreshold.BLOCK_ONLY_HIGH for category in safety_settings} def compare_safety_settings(converted_safety_settings, expected_safety_settings): for expected_setting_key in expected_safety_settings.keys(): @@ -214,9 +215,9 @@ def compare_safety_settings(converted_safety_settings, expected_safety_settings) converted_setting = converted_safety_settings[expected_setting_key] yield expected_setting == converted_setting - assert len(converted_safety_settings) == len( - expected_safety_settings - ), "The length of the safety settings is incorrect" + assert len(converted_safety_settings) == len(expected_safety_settings), ( + "The length of the safety settings is incorrect" + ) settings_comparison = compare_safety_settings(converted_safety_settings, expected_safety_settings) assert all(settings_comparison), "Converted safety settings are incorrect" @@ -244,9 +245,9 @@ def compare_safety_settings(converted_safety_settings, expected_safety_settings) converted_setting = converted_safety_settings[i] yield expected_setting.to_dict() == converted_setting.to_dict() - assert len(converted_safety_settings) == len( - expected_safety_settings - ), "The length of the safety settings is incorrect" + assert len(converted_safety_settings) == len(expected_safety_settings), ( + "The length of the safety settings is incorrect" + ) settings_comparison = compare_safety_settings(converted_safety_settings, expected_safety_settings) assert all(settings_comparison), "Converted safety settings are incorrect" @@ -296,13 +297,16 @@ def test_create_response_with_text(mock_calculate_cost, mock_generative_model, g mock_usage_metadata.prompt_token_count = 100 mock_usage_metadata.candidates_token_count = 50 - # Setup the mock to return a response with only text content mock_text_part = MagicMock() mock_text_part.text = "Example response" mock_text_part.function_call = None - mock_response = MagicMock() + mock_response = MagicMock(spec=GenerateContentResponse) + mock_response._done = True + mock_response._iterator = None + mock_response._result = None mock_response.parts = [mock_text_part] + mock_response.usage_metadata = mock_usage_metadata mock_chat.send_message.return_value = mock_response @@ -346,13 +350,15 @@ def test_vertexai_create_response( mock_usage_metadata.prompt_token_count = 100 mock_usage_metadata.candidates_token_count = 50 - # Setup the mock to return a response with only text content mock_text_part = MagicMock() mock_text_part.text = "Example response" mock_text_part.function_call = None - mock_response = MagicMock() - mock_response.parts = [mock_text_part] + mock_candidate = MagicMock() + mock_candidate.content.parts = [mock_text_part] + + mock_response = MagicMock(spec=VertexAIGenerationResponse) + mock_response.candidates = [mock_candidate] mock_response.usage_metadata = mock_usage_metadata mock_chat.send_message.return_value = mock_response diff --git a/test/oai/test_groq.py b/test/oai/test_groq.py index 5e331f85aa..1ca97ecdb9 100644 --- a/test/oai/test_groq.py +++ b/test/oai/test_groq.py @@ -43,7 +43,6 @@ def groq_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason=skip_reason) def test_initialization(): - # Missing any api_key with pytest.raises(AssertionError) as assertinfo: GroqClient() # Should raise an AssertionError due to missing api_key @@ -184,9 +183,9 @@ def test_create_response(mock_chat, groq_client): response = groq_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Groq response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Groq response", ( + "Response content should match expected output" + ) assert response.id == "mock_groq_response_id", "Response ID should match the mocked response ID" assert response.model == "llama3-70b-8192", "Response model should match the mocked response model" assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" diff --git a/test/oai/test_mistral.py b/test/oai/test_mistral.py index 588b24392a..a43e2b518b 100644 --- a/test/oai/test_mistral.py +++ b/test/oai/test_mistral.py @@ -10,14 +10,14 @@ try: from mistralai import ( - AssistantMessage, - Function, - FunctionCall, - Mistral, - SystemMessage, - ToolCall, - ToolMessage, - UserMessage, + AssistantMessage, # noqa: F401 + Function, # noqa: F401 + FunctionCall, # noqa: F401 + Mistral, # noqa: F401 + SystemMessage, # noqa: F401 + ToolCall, # noqa: F401 + ToolMessage, # noqa: F401 + UserMessage, # noqa: F401 ) from autogen.oai.mistral import MistralAIClient, calculate_mistral_cost @@ -51,7 +51,6 @@ def mistral_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason="Mistral.AI dependency is not installed") def test_initialization(): - # Missing any api_key with pytest.raises(AssertionError) as assertinfo: MistralAIClient() # Should raise an AssertionError due to missing api_key @@ -111,9 +110,9 @@ def test_create_response(mock_chat, mistral_client): response = mistral_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Mistral response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Mistral response", ( + "Response content should match expected output" + ) assert response.id == "mock_mistral_response_id", "Response ID should match the mocked response ID" assert response.model == "mistral-small-latest", "Response model should match the mocked response model" assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" diff --git a/test/oai/test_ollama.py b/test/oai/test_ollama.py index 0b25decde7..3267c11fac 100644 --- a/test/oai/test_ollama.py +++ b/test/oai/test_ollama.py @@ -34,7 +34,6 @@ def __init__(self, text, choices, usage, cost, model): @pytest.fixture def ollama_client(): - # Set Ollama client with some default values client = OllamaClient() @@ -50,7 +49,6 @@ def ollama_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason=skip_reason) def test_initialization(): - # Creation works without an api_key OllamaClient() @@ -144,9 +142,9 @@ def test_create_response(mock_chat, ollama_client): response = ollama_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Ollama response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Ollama response", ( + "Response content should match expected output" + ) assert response.id == "mock_ollama_response_id", "Response ID should match the mocked response ID" assert response.model == "llama3.1:8b", "Response model should match the mocked response model" assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" @@ -231,36 +229,36 @@ def test_manual_tool_calling_parsing(ollama_client): }, ] - assert ( - response_tool_calls == expected_tool_calls - ), "Manual Tool Calling Parsing of response did not yield correct tool_calls (full string match)" + assert response_tool_calls == expected_tool_calls, ( + "Manual Tool Calling Parsing of response did not yield correct tool_calls (full string match)" + ) # Test the parsing with a substring containing the response content (should still pass) response_content = """I will call two functions, weather_forecast and currency_calculator:\n[{"name": "weather_forecast", "arguments":{"location": "New York"}},{"name": "currency_calculator", "arguments":{"base_amount": 123.45, "quote_currency": "EUR", "base_currency": "USD"}}]""" response_tool_calls = response_to_tool_call(response_content) - assert ( - response_tool_calls == expected_tool_calls - ), "Manual Tool Calling Parsing of response did not yield correct tool_calls (partial string match)" + assert response_tool_calls == expected_tool_calls, ( + "Manual Tool Calling Parsing of response did not yield correct tool_calls (partial string match)" + ) # Test the parsing with an invalid function call response_content = """[{"function": "weather_forecast", "args":{"location": "New York"}},{"function": "currency_calculator", "args":{"base_amount": 123.45, "quote_currency": "EUR", "base_currency": "USD"}}]""" response_tool_calls = response_to_tool_call(response_content) - assert ( - response_tool_calls is None - ), "Manual Tool Calling Parsing of response did not yield correct tool_calls (invalid function call)" + assert response_tool_calls is None, ( + "Manual Tool Calling Parsing of response did not yield correct tool_calls (invalid function call)" + ) # Test the parsing with plain text response_content = """Call the weather_forecast function and pass in 'New York' as the 'location' argument.""" response_tool_calls = response_to_tool_call(response_content) - assert ( - response_tool_calls is None - ), "Manual Tool Calling Parsing of response did not yield correct tool_calls (no function in text)" + assert response_tool_calls is None, ( + "Manual Tool Calling Parsing of response did not yield correct tool_calls (no function in text)" + ) # Test message conversion from OpenAI to Ollama format diff --git a/test/oai/test_together.py b/test/oai/test_together.py index bff18d1b7a..b19d3ee8ca 100644 --- a/test/oai/test_together.py +++ b/test/oai/test_together.py @@ -9,7 +9,7 @@ import pytest try: - from openai.types.chat.chat_completion import ChatCompletionMessage, Choice + from openai.types.chat.chat_completion import ChatCompletionMessage, Choice # noqa: F401 from autogen.oai.together import TogetherClient, calculate_together_cost @@ -42,7 +42,6 @@ def together_client(): # Test initialization and configuration @pytest.mark.skipif(skip, reason="Together.AI dependency is not installed") def test_initialization(): - # Missing any api_key with pytest.raises(AssertionError) as assertinfo: TogetherClient() # Should raise an AssertionError due to missing api_key @@ -185,9 +184,9 @@ def test_create_response(mock_create, together_client): response = together_client.create(params) # Assertions to check if response is structured as expected - assert ( - response.choices[0].message.content == "Example Llama response" - ), "Response content should match expected output" + assert response.choices[0].message.content == "Example Llama response", ( + "Response content should match expected output" + ) assert response.id == "mock_together_response_id", "Response ID should match the mocked response ID" assert response.model == "meta-llama/Llama-3-8b-chat-hf", "Response model should match the mocked response model" assert response.usage.prompt_tokens == 10, "Response prompt tokens should match the mocked response usage" @@ -198,7 +197,6 @@ def test_create_response(mock_create, together_client): @pytest.mark.skipif(skip, reason="Together.AI dependency is not installed") @patch("autogen.oai.together.TogetherClient.create") def test_create_response_with_tool_call(mock_create, together_client): - # Define the mock response directly within the patch mock_function = MagicMock(name="currency_calculator") mock_function.name = "currency_calculator" diff --git a/test/oai/test_utils.py b/test/oai/test_utils.py index 8eb7c0dbc4..bd904d0433 100755 --- a/test/oai/test_utils.py +++ b/test/oai/test_utils.py @@ -10,13 +10,12 @@ import logging import os import tempfile -from typing import Dict, List from unittest import mock from unittest.mock import patch import pytest -import autogen # noqa: E402 +import autogen from autogen.oai.openai_utils import DEFAULT_AZURE_API_VERSION, filter_config, is_valid_api_key from ..conftest import MOCK_OPEN_AI_API_KEY @@ -338,12 +337,12 @@ def test_config_list_from_dotenv(mock_os_environ, caplog): # Call the function with the mixed validity map config_list = autogen.config_list_from_dotenv(model_api_key_map=invalid_model_api_key_map) assert config_list, "Expected configurations to be loaded" - assert any( - config["model"] == "gpt-3.5-turbo" for config in config_list - ), "gpt-3.5-turbo configuration not found" - assert all( - config["model"] != "gpt-4" for config in config_list - ), "gpt-4 configuration found, but was not expected" + assert any(config["model"] == "gpt-3.5-turbo" for config in config_list), ( + "gpt-3.5-turbo configuration not found" + ) + assert all(config["model"] != "gpt-4" for config in config_list), ( + "gpt-4 configuration found, but was not expected" + ) assert "API key not found or empty for model gpt-4" in caplog.text @@ -361,9 +360,9 @@ def test_get_config_list(): assert config_list, "The config_list should not be empty." # Check that the config_list has the correct length - assert len(config_list) == len( - api_keys - ), "The config_list should have the same number of items as the api_keys list." + assert len(config_list) == len(api_keys), ( + "The config_list should have the same number of items as the api_keys list." + ) # Check that each config in the config_list has the correct structure and data for i, config in enumerate(config_list): @@ -384,9 +383,9 @@ def test_get_config_list(): # Test with None base_urls config_list_without_base = autogen.get_config_list(api_keys, None, api_type, api_version) - assert all( - "base_url" not in config for config in config_list_without_base - ), "The configs should not have base_url when None is provided." + assert all("base_url" not in config for config in config_list_without_base), ( + "The configs should not have base_url when None is provided." + ) # Test with empty string in api_keys api_keys_with_empty = ["key1", "", "key3"] diff --git a/test/test_browser_utils.py b/test/test_browser_utils.py index 1bc5bf3914..6bf948f915 100755 --- a/test/test_browser_utils.py +++ b/test/test_browser_utils.py @@ -10,12 +10,11 @@ import math import os import re +from tempfile import TemporaryDirectory import pytest import requests -from .agentchat.test_assistant_agent import KEY_LOC # noqa: E402 - BLOG_POST_URL = "https://docs.ag2.ai/blog/2023-04-21-LLM-tuning-math" BLOG_POST_TITLE = "Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH - AG2" BLOG_POST_STRING = "Large language models (LLMs) are powerful tools that can generate natural language texts for various applications, such as chatbots, summarization, translation, and more. GPT-4 is currently the state of the art LLM in the world. Is model selection irrelevant? What about inference parameters?" @@ -49,26 +48,26 @@ skip_bing = False -def _rm_folder(path): - """Remove all the regular files in a folder, then deletes the folder. Assumes a flat file structure, with no subdirectories.""" - for fname in os.listdir(path): - fpath = os.path.join(path, fname) - if os.path.isfile(fpath): - os.unlink(fpath) - os.rmdir(path) +# def _rm_folder(path): +# """Remove all the regular files in a folder, then deletes the folder. Assumes a flat file structure, with no subdirectories.""" +# for fname in os.listdir(path): +# fpath = os.path.join(path, fname) +# if os.path.isfile(fpath): +# os.unlink(fpath) +# os.rmdir(path) + + +@pytest.fixture +def downloads_folder(): + with TemporaryDirectory() as downloads_folder: + yield downloads_folder @pytest.mark.skipif( skip_all, reason="do not run if dependency is not installed", ) -def test_simple_text_browser(): - # Create a downloads folder (removing any leftover ones from prior tests) - downloads_folder = os.path.join(KEY_LOC, "downloads") - if os.path.isdir(downloads_folder): - _rm_folder(downloads_folder) - os.mkdir(downloads_folder) - +def test_simple_text_browser(downloads_folder: str): # Instantiate the browser user_agent = "python-requests/" + requests.__version__ viewport_size = 1024 @@ -151,9 +150,6 @@ def test_simple_text_browser(): viewport = browser.visit_page(PDF_URL) assert PDF_STRING in viewport - # Clean up - _rm_folder(downloads_folder) - @pytest.mark.skipif( skip_all or skip_bing, @@ -171,7 +167,7 @@ def test_bing_search(): ) assert BING_STRING in browser.visit_page("bing: " + BING_QUERY) - assert BING_TITLE == browser.page_title + assert browser.page_title == BING_TITLE assert len(browser.viewport_pages) == 1 assert browser.viewport_pages[0] == (0, len(browser.page_content)) diff --git a/test/test_code_utils.py b/test/test_code_utils.py index 0e702bb441..6d1911c692 100755 --- a/test/test_code_utils.py +++ b/test/test_code_utils.py @@ -15,7 +15,6 @@ import pytest -import autogen from autogen.code_utils import ( UNKNOWN, check_can_use_docker_or_throw, @@ -32,10 +31,8 @@ is_docker_running, ) -from .conftest import skip_docker +from .conftest import Credentials, skip_docker -KEY_LOC = "notebook" -OAI_CONFIG_LIST = "OAI_CONFIG_LIST" here = os.path.abspath(os.path.dirname(__file__)) if skip_docker or not is_docker_running() or not decide_use_docker(use_docker=None): @@ -44,137 +41,6 @@ skip_docker_test = False -# def test_find_code(): -# try: -# import openai -# except ImportError: -# return -# # need gpt-4 for this task -# config_list = autogen.config_list_from_json( -# OAI_CONFIG_LIST, -# file_location=KEY_LOC, -# filter_dict={ -# "model": ["gpt-4o", "gpt4", "gpt-4-32k", "gpt-4-32k-0314"], -# }, -# ) -# # config_list = autogen.config_list_from_json( -# # OAI_CONFIG_LIST, -# # file_location=KEY_LOC, -# # filter_dict={ -# # "model": { -# # "gpt-3.5-turbo", -# # "gpt-3.5-turbo-16k", -# # "gpt-3.5-turbo-16k-0613", -# # "gpt-3.5-turbo-0301", -# # "chatgpt-35-turbo-0301", -# # "gpt-35-turbo-v0301", -# # }, -# # }, -# # ) -# seed = 42 -# messages = [ -# { -# "role": "user", -# "content": "Print hello world to a file called hello.txt", -# }, -# { -# "role": "user", -# "content": """ -# # filename: write_hello.py -# ``` -# with open('hello.txt', 'w') as f: -# f.write('Hello, World!') -# print('Hello, World! printed to hello.txt') -# ``` -# Please execute the above Python code to print "Hello, World!" to a file called hello.txt and print the success message. -# """, -# }, -# ] -# codeblocks, _ = find_code(messages, seed=seed, config_list=config_list) -# assert codeblocks[0][0] == "python", codeblocks -# messages += [ -# { -# "role": "user", -# "content": """ -# exitcode: 0 (execution succeeded) -# Code output: -# Hello, World! printed to hello.txt -# """, -# }, -# { -# "role": "assistant", -# "content": "Great! Can I help you with anything else?", -# }, -# ] -# codeblocks, content = find_code(messages, seed=seed, config_list=config_list) -# assert codeblocks[0][0] == "unknown", content -# messages += [ -# { -# "role": "user", -# "content": "Save a pandas df with 3 rows and 3 columns to disk.", -# }, -# { -# "role": "assistant", -# "content": """ -# ``` -# # filename: save_df.py -# import pandas as pd - -# df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}) -# df.to_csv('df.csv') -# print('df saved to df.csv') -# ``` -# Please execute the above Python code to save a pandas df with 3 rows and 3 columns to disk. -# Before you run the code above, run -# ``` -# pip install pandas -# ``` -# first to install pandas. -# """, -# }, -# ] -# codeblocks, content = find_code(messages, seed=seed, config_list=config_list) -# assert ( -# len(codeblocks) == 2 -# and (codeblocks[0][0] == "sh" -# and codeblocks[1][0] == "python" -# or codeblocks[0][0] == "python" -# and codeblocks[1][0] == "sh") -# ), content - -# messages += [ -# { -# "role": "user", -# "content": "The code is unsafe to execute in my environment.", -# }, -# { -# "role": "assistant", -# "content": "please run python write_hello.py", -# }, -# ] -# # codeblocks, content = find_code(messages, config_list=config_list) -# # assert codeblocks[0][0] != "unknown", content -# # I'm sorry, but I cannot execute code from earlier messages. Please provide the code again if you would like me to execute it. - -# messages[-1]["content"] = "please skip pip install pandas if you already have pandas installed" -# codeblocks, content = find_code(messages, seed=seed, config_list=config_list) -# assert codeblocks[0][0] != "sh", content - -# messages += [ -# { -# "role": "user", -# "content": "The code is still unsafe to execute in my environment.", -# }, -# { -# "role": "assistant", -# "content": "Let me try something else. Do you have docker installed?", -# }, -# ] -# codeblocks, content = find_code(messages, seed=seed, config_list=config_list) -# assert codeblocks[0][0] == "unknown", content -# print(content) - - def test_infer_lang(): assert infer_lang("print('hello world')") == "python" assert infer_lang("pip install autogen") == "sh" @@ -295,9 +161,7 @@ def scrape(url): text = soup.find("div", {"id": "bodyContent"}).text return title, text ``` -""".replace( - "\n", "\r\n" - ) +""".replace("\n", "\r\n") ) print(codeblocks) assert len(codeblocks) == 1 and codeblocks[0][0] == "python" @@ -522,12 +386,12 @@ def test_create_virtual_env_with_extra_args(): assert venv_context.env_name == os.path.split(temp_dir)[1] -def _test_improve(): +def _test_improve(credentials_all: Credentials): try: - import openai + import openai # noqa: F401 except ImportError: return - config_list = autogen.config_list_openai_aoai(KEY_LOC) + config_list = credentials_all.config_list improved, _ = improve_function( "autogen/math_utils.py", "solve_problem", diff --git a/test/test_graph_utils.py b/test/test_graph_utils.py index ea25a67f5c..9baa15ff08 100644 --- a/test/test_graph_utils.py +++ b/test/test_graph_utils.py @@ -5,7 +5,6 @@ # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT import logging -from typing import Any import pytest diff --git a/test/test_logging.py b/test/test_logging.py index 24c356e99f..d6a73f5022 100644 --- a/test/test_logging.py +++ b/test/test_logging.py @@ -171,9 +171,9 @@ def test_log_new_agent(db_connection): """ for row in cur.execute(query): - assert ( - row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"] - ), "session id is not valid uuid" + assert row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"], ( + "session id is not valid uuid" + ) assert row["name"] == agent_name assert row["class"] == "AssistantAgent" assert row["init_args"] == json.dumps(init_args) @@ -195,9 +195,9 @@ def test_log_oai_wrapper(db_connection): """ for row in cur.execute(query): - assert ( - row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"] - ), "session id is not valid uuid" + assert row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"], ( + "session id is not valid uuid" + ) saved_init_args = json.loads(row["init_args"]) assert "config_list" in saved_init_args assert "api_key" not in saved_init_args["config_list"][0] @@ -223,9 +223,9 @@ def test_log_oai_client(db_connection): """ for row in cur.execute(query): - assert ( - row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"] - ), "session id is not valid uuid" + assert row["session_id"] and str(uuid.UUID(row["session_id"], version=4)) == row["session_id"], ( + "session id is not valid uuid" + ) assert row["class"] == "AzureOpenAI" saved_init_args = json.loads(row["init_args"]) assert "api_version" in saved_init_args @@ -236,7 +236,7 @@ def test_to_dict(): from autogen import Agent from autogen.coding import LocalCommandLineCodeExecutor - agent_executor = LocalCommandLineCodeExecutor(work_dir=Path(".")) + agent_executor = LocalCommandLineCodeExecutor(work_dir=Path()) agent1 = autogen.ConversableAgent( "alice", diff --git a/test/test_notebook.py b/test/test_notebook.py index 533df8b623..e3268d2895 100755 --- a/test/test_notebook.py +++ b/test/test_notebook.py @@ -11,14 +11,12 @@ import pytest -from .conftest import skip_openai - try: - import openai + import openai # noqa: F401 except ImportError: skip = True else: - skip = False or skip_openai + skip = False here = os.path.abspath(os.path.dirname(__file__)) @@ -54,6 +52,7 @@ def run_notebook(input_nb, output_nb="executed_openai_notebook.ipynb", save=Fals nbformat.write(nb, nb_executed_file) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.13"), reason="do not run if openai is not installed or py!=3.13", @@ -62,6 +61,7 @@ def test_agentchat_auto_feedback_from_code(save=False): run_notebook("agentchat_auto_feedback_from_code_execution.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.11"), reason="do not run if openai is not installed or py!=3.11", @@ -70,6 +70,7 @@ def _test_oai_completion(save=False): run_notebook("oai_completion.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.12"), reason="do not run if openai is not installed or py!=3.12", @@ -78,6 +79,7 @@ def test_agentchat_function_call(save=False): run_notebook("agentchat_function_call.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.10"), reason="do not run if openai is not installed or py!=3.10", @@ -86,6 +88,7 @@ def test_agentchat_function_call_currency_calculator(save=False): run_notebook("agentchat_function_call_currency_calculator.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.13"), reason="do not run if openai is not installed or py!=3.13", @@ -94,14 +97,16 @@ def test_agentchat_function_call_async(save=False): run_notebook("agentchat_function_call_async.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.12"), reason="do not run if openai is not installed or py!=3.12", ) -def _test_agentchat_MathChat(save=False): +def _test_agentchat_MathChat(save=False): # noqa: N802 run_notebook("agentchat_MathChat.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.10"), reason="do not run if openai is not installed or py!=3.10", @@ -110,6 +115,7 @@ def _test_oai_chatgpt_gpt4(save=False): run_notebook("oai_chatgpt_gpt4.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.12"), reason="do not run if openai is not installed or py!=3.12", @@ -118,6 +124,7 @@ def test_agentchat_groupchat_finite_state_machine(save=False): run_notebook("agentchat_groupchat_finite_state_machine.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.11"), reason="do not run if openai is not installed or py!=3.11", @@ -126,6 +133,7 @@ def test_agentchat_cost_token_tracking(save=False): run_notebook("agentchat_cost_token_tracking.ipynb", save=save) +@pytest.mark.openai @pytest.mark.skipif( skip or not sys.version.startswith("3.11"), reason="do not run if openai is not installed or py!=3.11", diff --git a/test/test_pydantic.py b/test/test_pydantic.py index 0006a605b0..5d58163d71 100644 --- a/test/test_pydantic.py +++ b/test/test_pydantic.py @@ -4,9 +4,9 @@ # # Portions derived from https://github.com/microsoft/autogen are under the MIT License. # SPDX-License-Identifier: MIT -from typing import Annotated, Dict, List, Optional, Tuple, Union +from typing import Annotated, Optional, Union -from pydantic import BaseModel, Field +from pydantic import BaseModel from autogen._pydantic import model_dump, model_dump_json, type2schema diff --git a/test/test_retrieve_utils.py b/test/test_retrieve_utils.py index f9e8d71b74..774b4163ca 100755 --- a/test/test_retrieve_utils.py +++ b/test/test_retrieve_utils.py @@ -6,9 +6,8 @@ # SPDX-License-Identifier: MIT #!/usr/bin/env python3 -m pytest -""" -Unit test for retrieve_utils.py -""" +"""Unit test for retrieve_utils.py""" + import pytest try: @@ -32,7 +31,7 @@ import os try: - from unstructured.partition.auto import partition + from unstructured.partition.auto import partition # noqa: F401 HAS_UNSTRUCTURED = True except ImportError: diff --git a/test/test_token_count.py b/test/test_token_count.py index de25b8a49d..6aeb5844c7 100755 --- a/test/test_token_count.py +++ b/test/test_token_count.py @@ -9,7 +9,7 @@ import pytest try: - from autogen.agentchat.contrib.img_utils import num_tokens_from_gpt_image + from autogen.agentchat.contrib.img_utils import num_tokens_from_gpt_image # noqa: F401 img_util_imported = True except ImportError: diff --git a/test/tools/test_dependency_injection.py b/test/tools/test_dependency_injection.py index e504c810ff..303498b8e6 100644 --- a/test/tools/test_dependency_injection.py +++ b/test/tools/test_dependency_injection.py @@ -10,12 +10,14 @@ from autogen.tools.dependency_injection import ( BaseContext, + ChatContext, Depends, Field, - _is_base_context_param, + _is_context_param, _is_depends_param, _remove_injected_params_from_signature, _string_metadata_to_description_field, + get_context_params, ) @@ -24,29 +26,37 @@ class MyContext(BaseContext, BaseModel): b: int def f_with_annotated( # type: ignore[misc] - a: int, + a: int, # noqa: N805 ctx: Annotated[MyContext, Depends(MyContext(b=2))], + chat_ctx: Annotated[ChatContext, "Chat context"], ) -> int: + assert isinstance(chat_ctx, ChatContext) return a + ctx.b async def f_with_annotated_async( # type: ignore[misc] - a: int, + a: int, # noqa: N805 ctx: Annotated[MyContext, Depends(MyContext(b=2))], + chat_ctx: Annotated[ChatContext, "Chat context"], ) -> int: + assert isinstance(chat_ctx, ChatContext) return a + ctx.b @staticmethod def f_without_annotated( a: int, + chat_ctx: ChatContext, ctx: MyContext = Depends(MyContext(b=3)), ) -> int: + assert isinstance(chat_ctx, ChatContext) return a + ctx.b @staticmethod async def f_without_annotated_async( a: int, + chat_ctx: ChatContext, ctx: MyContext = Depends(MyContext(b=3)), ) -> int: + assert isinstance(chat_ctx, ChatContext) return a + ctx.b @staticmethod @@ -64,14 +74,14 @@ async def f_without_annotated_and_depends_async( return a + ctx.b @staticmethod - def f_without_MyContext( + def f_without_MyContext( # noqa: N802 a: int, ctx: Annotated[int, Depends(lambda a: a + 2)], ) -> int: return a + ctx @staticmethod - def f_without_MyContext_async( + def f_without_MyContext_async( # noqa: N802 a: int, ctx: Annotated[int, Depends(lambda a: a + 2)], ) -> int: @@ -114,6 +124,8 @@ def f_all_params( b: Annotated[int, "b description"], ctx1: Annotated[MyContext, Depends(MyContext(b=2))], ctx2: Annotated[int, Depends(lambda a: a + 2)], + ctx6: ChatContext, + ctx7: Annotated[ChatContext, "ctx7 description"], ctx3: MyContext = Depends(MyContext(b=3)), ctx4: MyContext = MyContext(b=4), ctx5: int = Depends(lambda a: a + 2), @@ -122,13 +134,27 @@ def f_all_params( def test_is_base_context_param(self) -> None: sig = inspect.signature(self.f_all_params) - assert _is_base_context_param(sig.parameters["a"]) is False - assert _is_base_context_param(sig.parameters["b"]) is False - assert _is_base_context_param(sig.parameters["ctx1"]) is True - assert _is_base_context_param(sig.parameters["ctx2"]) is False - assert _is_base_context_param(sig.parameters["ctx3"]) is True - assert _is_base_context_param(sig.parameters["ctx4"]) is True - assert _is_base_context_param(sig.parameters["ctx5"]) is False + assert _is_context_param(sig.parameters["a"]) is False + assert _is_context_param(sig.parameters["b"]) is False + assert _is_context_param(sig.parameters["ctx1"]) is True + assert _is_context_param(sig.parameters["ctx2"]) is False + assert _is_context_param(sig.parameters["ctx3"]) is True + assert _is_context_param(sig.parameters["ctx4"]) is True + assert _is_context_param(sig.parameters["ctx5"]) is False + assert _is_context_param(sig.parameters["ctx6"]) is True + assert _is_context_param(sig.parameters["ctx7"]) is True + + def test_is_chat_context_param(self) -> None: + sig = inspect.signature(self.f_all_params) + assert _is_context_param(sig.parameters["ctx1"], subclass=ChatContext) is False + assert _is_context_param(sig.parameters["ctx3"], subclass=ChatContext) is False + assert _is_context_param(sig.parameters["ctx4"], subclass=ChatContext) is False + assert _is_context_param(sig.parameters["ctx6"], subclass=ChatContext) is True + assert _is_context_param(sig.parameters["ctx7"], subclass=ChatContext) is True + + def test_get_chat_context_params(self) -> None: + chat_context_params = get_context_params(self.f_all_params, subclass=ChatContext) + assert chat_context_params == ["ctx6", "ctx7"] def test_is_depends_param(self) -> None: sig = inspect.signature(self.f_all_params) diff --git a/test/tools/test_function_utils.py b/test/tools/test_function_utils.py index 7bcdbb0cfe..708bf21856 100644 --- a/test/tools/test_function_utils.py +++ b/test/tools/test_function_utils.py @@ -133,7 +133,7 @@ def _f1(a: str, b=2): # type: ignore[no-untyped-def] assert unannotated_with_default == {"b"} def _f2(a: str, b) -> str: # type: ignore[empty-body,no-untyped-def] - "ok" + """Ok""" missing, unannotated_with_default = get_missing_annotations(get_typed_signature(_f2), ["a", "b"]) assert missing == {"b"} @@ -148,7 +148,11 @@ def _f3() -> None: def test_get_parameters() -> None: - def f(a: Annotated[str, AG2Field(description="Parameter a")], b=1, c: Annotated[float, AG2Field(description="Parameter c")] = 1.0): # type: ignore[no-untyped-def] + def f( # type: ignore[no-untyped-def] + a: Annotated[str, AG2Field(description="Parameter a")], + b=1, # type: ignore[no-untyped-def] + c: Annotated[float, AG2Field(description="Parameter c")] = 1.0, + ): pass typed_signature = get_typed_signature(f) diff --git a/test/website/__init__.py b/test/website/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/test/website/test_process_api_reference.py b/test/website/test_process_api_reference.py new file mode 100644 index 0000000000..adc0e5f399 --- /dev/null +++ b/test/website/test_process_api_reference.py @@ -0,0 +1,105 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +import json +import sys +import tempfile +from pathlib import Path + +import pytest + +# Add the ../../website directory to sys.path +sys.path.append(str(Path(__file__).resolve().parent.parent.parent / "website")) +from process_api_reference import generate_mint_json_from_template + + +@pytest.fixture +def template_content(): + """Fixture providing the template JSON content.""" + return { + "name": "AG2", + "logo": {"dark": "/logo/ag2-white.svg", "light": "/logo/ag2.svg"}, + "navigation": [ + {"group": "", "pages": ["docs/Home", "docs/Getting-Started"]}, + { + "group": "Installation", + "pages": [ + "docs/installation/Installation", + "docs/installation/Docker", + "docs/installation/Optional-Dependencies", + ], + }, + {"group": "API Reference", "pages": ["PLACEHOLDER"]}, + { + "group": "AutoGen Studio", + "pages": [ + "docs/autogen-studio/getting-started", + "docs/autogen-studio/usage", + "docs/autogen-studio/faqs", + ], + }, + ], + } + + +@pytest.fixture +def temp_dir(): + """Fixture providing a temporary directory.""" + with tempfile.TemporaryDirectory() as tmp_dir: + yield Path(tmp_dir) + + +@pytest.fixture +def template_file(temp_dir, template_content): + """Fixture creating a template file in a temporary directory.""" + template_path = temp_dir / "mint-json-template.json" + with open(template_path, "w") as f: + json.dump(template_content, f, indent=2) + return template_path + + +@pytest.fixture +def target_file(temp_dir): + """Fixture providing the target mint.json path.""" + return temp_dir / "mint.json" + + +def test_generate_mint_json_from_template(template_file, target_file, template_content): + """Test that mint.json is generated correctly from template.""" + # Run the function + generate_mint_json_from_template(template_file, target_file) + + # Verify the file exists + assert target_file.exists() + + # Verify the contents + with open(target_file) as f: + generated_content = json.load(f) + + assert generated_content == template_content + + +def test_generate_mint_json_existing_file(template_file, target_file, template_content): + """Test that function works when mint.json already exists.""" + # Create an existing mint.json with different content + existing_content = {"name": "existing"} + with open(target_file, "w") as f: + json.dump(existing_content, f) + + # Run the function + generate_mint_json_from_template(template_file, target_file) + + # Verify the contents were overwritten + with open(target_file) as f: + generated_content = json.load(f) + + assert generated_content == template_content + + +def test_generate_mint_json_missing_template(target_file): + """Test handling of missing template file.""" + with tempfile.TemporaryDirectory() as tmp_dir: + nonexistent_template = Path(tmp_dir) / "nonexistent.template" + with pytest.raises(FileNotFoundError): + generate_mint_json_from_template(nonexistent_template, target_file) diff --git a/test/website/test_process_notebooks.py b/test/website/test_process_notebooks.py new file mode 100644 index 0000000000..1bc0ea8016 --- /dev/null +++ b/test/website/test_process_notebooks.py @@ -0,0 +1,344 @@ +# Copyright (c) 2023 - 2024, Owners of https://github.com/ag2ai +# +# SPDX-License-Identifier: Apache-2.0 + +import json +import sys +import tempfile +import textwrap +from pathlib import Path + +import pytest + +# Add the ../../website directory to sys.path +sys.path.append(str(Path(__file__).resolve().parent.parent.parent / "website")) +from process_notebooks import ( + add_authors_and_social_img_to_blog_posts, + ensure_mint_json_exists, + extract_example_group, + generate_nav_group, + get_sorted_files, +) + + +def test_ensure_mint_json(): + # Test with empty temp directory - should raise SystemExit + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + with pytest.raises(SystemExit): + ensure_mint_json_exists(tmp_path) + + # Now create mint.json - should not raise error + (tmp_path / "mint.json").touch() + ensure_mint_json_exists(tmp_path) # Should not raise any exception + + +class TestAddBlogsToNavigation: + @pytest.fixture + def test_dir(self): + """Create a temporary directory with test files.""" + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + + # Create test directory structure + files = [ + "2023-04-21-LLM-tuning-math/index.mdx", + "2023-05-18-GPT-adaptive-humaneval/index.mdx", + "2023-10-26-TeachableAgent/index.mdx", + "2023-10-18-RetrieveChat/index.mdx", + "2024-12-20-RetrieveChat/index.mdx", + "2024-12-20-Tools-interoperability/index.mdx", + "2024-12-20-RealtimeAgent/index.mdx", + "2024-08-26/index.mdx", + "2024-11-11/index.mdx", + "2024-11-12/index.mdx", + "2024-12-20-Another-RealtimeAgent/index.mdx", + ] + + for file_path in files: + full_path = tmp_path / file_path + full_path.parent.mkdir(parents=True, exist_ok=True) + full_path.touch() + + yield tmp_path + + @pytest.fixture + def expected(self): + return [ + "blog/2024-12-20-Tools-interoperability/index", + "blog/2024-12-20-RetrieveChat/index", + "blog/2024-12-20-RealtimeAgent/index", + "blog/2024-12-20-Another-RealtimeAgent/index", + "blog/2024-11-12/index", + "blog/2024-11-11/index", + "blog/2024-08-26/index", + "blog/2023-10-26-TeachableAgent/index", + "blog/2023-10-18-RetrieveChat/index", + "blog/2023-05-18-GPT-adaptive-humaneval/index", + "blog/2023-04-21-LLM-tuning-math/index", + ] + + def test_get_sorted_files(self, test_dir, expected): + actual = get_sorted_files(test_dir, "blog") + assert actual == expected, actual + + def test_add_blogs_to_navigation(self): + with tempfile.TemporaryDirectory() as tmp_dir: + website_dir = Path(tmp_dir) + blog_dir = website_dir / "blog" + + # Create test blog structure + test_files = [ + "2023-04-21-LLM-tuning-math/index.mdx", + "2024-12-20-RealtimeAgent/index.mdx", + "2024-12-20-RetrieveChat/index.mdx", + ] + + for file_path in test_files: + full_path = blog_dir / file_path + full_path.parent.mkdir(parents=True, exist_ok=True) + full_path.touch() + + # Expected result after processing + expected = { + "group": "Blog", + "pages": [ + "blog/2024-12-20-RetrieveChat/index", + "blog/2024-12-20-RealtimeAgent/index", + "blog/2023-04-21-LLM-tuning-math/index", + ], + } + + # Run function and check result + actual = generate_nav_group(blog_dir, "Blog", "blog") + assert actual == expected, actual + + # Expected result after processing + expected = { + "group": "Talks", + "pages": [ + "talks/2024-12-20-RetrieveChat/index", + "talks/2024-12-20-RealtimeAgent/index", + "talks/2023-04-21-LLM-tuning-math/index", + ], + } + + # Run function and check result + actual = generate_nav_group(blog_dir, "Talks", "talks") + assert actual == expected, actual + + +class TestUpdateNavigation: + def setup(self, temp_dir: Path) -> None: + """Set up test files in the temporary directory.""" + + # Create directories + snippets_dir = temp_dir / "snippets" / "data" + snippets_dir.mkdir(parents=True, exist_ok=True) + + # Create mint.json content + mint_json_content = { + "name": "AG2", + "logo": {"dark": "/logo/ag2-white.svg", "light": "/logo/ag2.svg"}, + "navigation": [ + {"group": "", "pages": ["docs/Home", "docs/Getting-Started"]}, + { + "group": "Installation", + "pages": [ + "docs/installation/Installation", + "docs/installation/Docker", + "docs/installation/Optional-Dependencies", + ], + }, + {"group": "API Reference", "pages": ["PLACEHOLDER"]}, + { + "group": "AutoGen Studio", + "pages": [ + "docs/autogen-studio/getting-started", + "docs/autogen-studio/usage", + "docs/autogen-studio/faqs", + ], + }, + ], + } + + # Create NotebooksMetadata.mdx content + notebooks_metadata_content = """{/* + Auto-generated file - DO NOT EDIT + Please edit the add_front_matter_to_metadata_mdx function in process_notebooks.py + */} + + export const notebooksMetadata = [ + { + "title": "Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering", + "link": "/notebooks/agentchat_RetrieveChat_mongodb", + "description": "Explore the use of AutoGen's RetrieveChat for tasks like code generation from docstrings, answering complex questions with human feedback, and exploiting features like Update Context, custom prompts, and few-shot learning.", + "image": null, + "tags": [ + "MongoDB", + "integration", + "RAG" + ], + "source": "/notebook/agentchat_RetrieveChat_mongodb.ipynb" + }, + { + "title": "Mitigating Prompt hacking with JSON Mode in Autogen", + "link": "/notebooks/JSON_mode_example", + "description": "Use JSON mode and Agent Descriptions to mitigate prompt manipulation and control speaker transition.", + "image": null, + "tags": [ + "JSON", + "description", + "prompt hacking", + "group chat", + "orchestration" + ], + "source": "/notebook/JSON_mode_example.ipynb" + }];""" + + # Write files + mint_json_path = temp_dir / "mint.json" + with open(mint_json_path, "w", encoding="utf-8") as f: + json.dump(mint_json_content, f, indent=2) + f.write("\n") + + metadata_path = snippets_dir / "NotebooksMetadata.mdx" + with open(metadata_path, "w", encoding="utf-8") as f: + f.write(notebooks_metadata_content) + + def test_extract_example_group(self): + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_path = Path(tmp_dir) + self.setup(tmp_path) + + # Run the function + metadata_path = tmp_path / "snippets" / "data" / "NotebooksMetadata.mdx" + actual = extract_example_group(metadata_path) + + expected = { + "group": "Examples", + "pages": [ + "notebooks/Examples", + { + "group": "Examples by Notebook", + "pages": [ + "notebooks/Notebooks", + "notebooks/agentchat_RetrieveChat_mongodb", + "notebooks/JSON_mode_example", + ], + }, + "notebooks/Gallery", + ], + } + + assert actual == expected, actual + + +class TestAddAuthorsAndSocialImgToBlogPosts: + @pytest.fixture + def test_dir(self): + """Create temporary test directory with blog posts and authors file.""" + with tempfile.TemporaryDirectory() as tmp_dir: + website_dir = Path(tmp_dir) + blog_dir = website_dir / "_blogs" + blog_dir.mkdir() + + # Create first blog post + post1_dir = blog_dir / "2023-04-21-LLM-tuning-math" + post1_dir.mkdir() + post1_content = textwrap.dedent(""" + --- + title: Does Model and Inference Parameter Matter in LLM Applications? - A Case Study for MATH + authors: sonichi + tags: [LLM, GPT, research] + --- + + lorem ipsum""").lstrip() + (post1_dir / "index.mdx").write_text(post1_content) + + # Create second blog post + post2_dir = blog_dir / "2023-06-28-MathChat" + post2_dir.mkdir() + post2_content = textwrap.dedent(""" + --- + title: Introducing RealtimeAgent Capabilities in AG2 + authors: + - marklysze + - sternakt + - davorrunje + - davorinrusevljan + tags: [Realtime API, Voice Agents, Swarm Teams, Twilio, AI Tools] + --- + + lorem ipsum""").lstrip() + (post2_dir / "index.mdx").write_text(post2_content) + + # Create authors.yml + authors_content = textwrap.dedent(""" + sonichi: + name: Chi Wang + title: Founder of AutoGen (now AG2) & FLAML + url: https://www.linkedin.com/in/chi-wang-autogen/ + image_url: https://github.com/sonichi.png + + marklysze: + name: Mark Sze + title: Software Engineer at AG2.ai + url: https://github.com/marklysze + image_url: https://github.com/marklysze.png + + sternakt: + name: Tvrtko Sternak + title: Machine Learning Engineer at Airt + url: https://github.com/sternakt + image_url: https://github.com/sternakt.png + + davorrunje: + name: Davor Runje + title: CTO at Airt + url: https://github.com/davorrunje + image_url: https://github.com/davorrunje.png + + davorinrusevljan: + name: Davorin + title: Developer + url: https://github.com/davorinrusevljan + image_url: https://github.com/davorinrusevljan.png + """).lstrip() + (blog_dir / "authors.yml").write_text(authors_content) + + yield website_dir + + def test_add_authors_and_social_img(self, test_dir): + # Run the function + add_authors_and_social_img_to_blog_posts(test_dir) + + # Get directory paths + generated_blog_dir = test_dir / "blog" + blog_dir = test_dir / "_blogs" + + # Verify directory structure matches + blog_files = set(p.relative_to(blog_dir) for p in blog_dir.glob("**/*.mdx")) + generated_files = set(p.relative_to(generated_blog_dir) for p in generated_blog_dir.glob("**/*.mdx")) + assert blog_files == generated_files + + # Verify number of files matches + assert len(list(blog_dir.glob("**/*.mdx"))) == len(list(generated_blog_dir.glob("**/*.mdx"))) + + # Verify content of first blog post + post1_path = generated_blog_dir / "2023-04-21-LLM-tuning-math" / "index.mdx" + actual = post1_path.read_text() + assert 'Chi Wang

' in actual + assert '

Davor Runje

' not in actual + + # Verify content of second blog post + post2_path = generated_blog_dir / "2023-06-28-MathChat" / "index.mdx" + actual = post2_path.read_text() + + assert 'Mark Sze

' in actual + assert '

Tvrtko Sternak

' in actual + assert '

Davor Runje

' in actual + assert '

Davorin

' in actual + assert '

Chi Wang

' not in actual diff --git a/website/.gitignore b/website/.gitignore index 59441e2942..f50e629dbf 100644 --- a/website/.gitignore +++ b/website/.gitignore @@ -9,6 +9,8 @@ .cache-loader docs/reference /notebooks +/blog +mint.json docs/tutorial/*.mdx docs/tutorial/**/*.png diff --git a/website/README.md b/website/README.md index 7bdd03be4e..b38480cd6b 100644 --- a/website/README.md +++ b/website/README.md @@ -31,24 +31,16 @@ pip install pydoc-markdown pyyaml termcolor nbclient The last command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. -## Build with Docker +## Build with devcontainer -To build and test documentation within a docker container, run the following commands from your project root directory: +To build and test documentation using devcontainer, open the project using [VSCode](https://code.visualstudio.com/), press `Ctrl+Shift+P` and select `Dev Containers: Reopen in Container`. -```bash -docker build -f .devcontainer/dev/Dockerfile -t ag2ai_dev_img https://github.com/ag2ai/ag2.git#main -``` - -Then start the container like so, this will log you in and ensure that Docker port 3000 is mapped to port 8081 on your local machine: - -```bash -docker run -it -p 8081:3000 -v $(pwd):/home/autogen/ag2 ag2ai_dev_img bash -``` +This will open the project in a devcontainer with all the required dependencies installed. -Once at the CLI in Docker run the following commands: +Open a terminal and run the following command to build and serve the documentation: ```console ./scripts/docs_serve.sh ``` -Once done you should be able to access the documentation at `http://127.0.0.1:8081` +Once done you should be able to access the documentation at `http://localhost:3000/`. diff --git a/website/blog/2023-04-21-LLM-tuning-math/img/level2algebra.png b/website/_blogs/2023-04-21-LLM-tuning-math/img/level2algebra.png similarity index 100% rename from website/blog/2023-04-21-LLM-tuning-math/img/level2algebra.png rename to website/_blogs/2023-04-21-LLM-tuning-math/img/level2algebra.png diff --git a/website/blog/2023-04-21-LLM-tuning-math/img/level3algebra.png b/website/_blogs/2023-04-21-LLM-tuning-math/img/level3algebra.png similarity index 100% rename from website/blog/2023-04-21-LLM-tuning-math/img/level3algebra.png rename to website/_blogs/2023-04-21-LLM-tuning-math/img/level3algebra.png diff --git a/website/blog/2023-04-21-LLM-tuning-math/img/level4algebra.png b/website/_blogs/2023-04-21-LLM-tuning-math/img/level4algebra.png similarity index 100% rename from website/blog/2023-04-21-LLM-tuning-math/img/level4algebra.png rename to website/_blogs/2023-04-21-LLM-tuning-math/img/level4algebra.png diff --git a/website/blog/2023-04-21-LLM-tuning-math/img/level5algebra.png b/website/_blogs/2023-04-21-LLM-tuning-math/img/level5algebra.png similarity index 100% rename from website/blog/2023-04-21-LLM-tuning-math/img/level5algebra.png rename to website/_blogs/2023-04-21-LLM-tuning-math/img/level5algebra.png diff --git a/website/blog/2023-04-21-LLM-tuning-math/index.mdx b/website/_blogs/2023-04-21-LLM-tuning-math/index.mdx similarity index 93% rename from website/blog/2023-04-21-LLM-tuning-math/index.mdx rename to website/_blogs/2023-04-21-LLM-tuning-math/index.mdx index f75ff3b59f..c8c97f9308 100644 --- a/website/blog/2023-04-21-LLM-tuning-math/index.mdx +++ b/website/_blogs/2023-04-21-LLM-tuning-math/index.mdx @@ -4,23 +4,6 @@ authors: sonichi tags: [LLM, GPT, research] --- -
-

Author:

- - -
-
- -
-
-

Chi Wang

-

Founder of AutoGen (now AG2) & FLAML

-
-
- - -
- ![level 2 algebra](img/level2algebra.png) **TL;DR:** diff --git a/website/blog/2023-05-18-GPT-adaptive-humaneval/img/design.png b/website/_blogs/2023-05-18-GPT-adaptive-humaneval/img/design.png similarity index 100% rename from website/blog/2023-05-18-GPT-adaptive-humaneval/img/design.png rename to website/_blogs/2023-05-18-GPT-adaptive-humaneval/img/design.png diff --git a/website/blog/2023-05-18-GPT-adaptive-humaneval/img/humaneval.png b/website/_blogs/2023-05-18-GPT-adaptive-humaneval/img/humaneval.png similarity index 100% rename from website/blog/2023-05-18-GPT-adaptive-humaneval/img/humaneval.png rename to website/_blogs/2023-05-18-GPT-adaptive-humaneval/img/humaneval.png diff --git a/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx b/website/_blogs/2023-05-18-GPT-adaptive-humaneval/index.mdx similarity index 95% rename from website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx rename to website/_blogs/2023-05-18-GPT-adaptive-humaneval/index.mdx index c3f7762fc5..8bab0152f1 100644 --- a/website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx +++ b/website/_blogs/2023-05-18-GPT-adaptive-humaneval/index.mdx @@ -4,23 +4,6 @@ authors: sonichi tags: [LLM, GPT, research] --- -
-

Author:

- - -
-
- -
-
-

Chi Wang

-

Founder of AutoGen (now AG2) & FLAML

-
-
- - -
- ![An adaptive way of using GPT-3.5 and GPT-4 outperforms GPT-4 in both coding success rate and inference cost](img/humaneval.png) **TL;DR:** diff --git a/website/blog/2023-06-28-MathChat/img/mathchatflow.png b/website/_blogs/2023-06-28-MathChat/img/mathchatflow.png similarity index 100% rename from website/blog/2023-06-28-MathChat/img/mathchatflow.png rename to website/_blogs/2023-06-28-MathChat/img/mathchatflow.png diff --git a/website/blog/2023-06-28-MathChat/img/result.png b/website/_blogs/2023-06-28-MathChat/img/result.png similarity index 100% rename from website/blog/2023-06-28-MathChat/img/result.png rename to website/_blogs/2023-06-28-MathChat/img/result.png diff --git a/website/blog/2023-06-28-MathChat/index.mdx b/website/_blogs/2023-06-28-MathChat/index.mdx similarity index 96% rename from website/blog/2023-06-28-MathChat/index.mdx rename to website/_blogs/2023-06-28-MathChat/index.mdx index c6476051c4..527b8facc1 100644 --- a/website/blog/2023-06-28-MathChat/index.mdx +++ b/website/_blogs/2023-06-28-MathChat/index.mdx @@ -4,23 +4,6 @@ authors: yiranwu tags: [LLM, GPT, research] --- -
-

Author:

- - -
-
- -
-
-

Yiran Wu

-

PhD student at Pennsylvania State University

-
-
- - -
- ![MathChat WorkFlow](img/mathchatflow.png) **TL;DR:** diff --git a/website/blog/2023-07-14-Local-LLMs/index.mdx b/website/_blogs/2023-07-14-Local-LLMs/index.mdx similarity index 90% rename from website/blog/2023-07-14-Local-LLMs/index.mdx rename to website/_blogs/2023-07-14-Local-LLMs/index.mdx index 147a896709..3ae21d0c20 100644 --- a/website/blog/2023-07-14-Local-LLMs/index.mdx +++ b/website/_blogs/2023-07-14-Local-LLMs/index.mdx @@ -4,23 +4,6 @@ authors: jialeliu tags: [LLM] --- -
-

Author:

- - -
-
- -
-
-

Jiale Liu

-

PhD student at Pennsylvania State University

-
-
- - -
- **TL;DR:** We demonstrate how to use autogen for local LLM application. As an example, we will initiate an endpoint using [FastChat](https://github.com/lm-sys/FastChat) and perform inference on [ChatGLMv2-6b](https://github.com/THUDM/ChatGLM2-6B). diff --git a/website/blog/2023-10-18-RetrieveChat/img/autogen-rag.gif b/website/_blogs/2023-10-18-RetrieveChat/img/autogen-rag.gif similarity index 100% rename from website/blog/2023-10-18-RetrieveChat/img/autogen-rag.gif rename to website/_blogs/2023-10-18-RetrieveChat/img/autogen-rag.gif diff --git a/website/blog/2023-10-18-RetrieveChat/img/retrievechat-arch.png b/website/_blogs/2023-10-18-RetrieveChat/img/retrievechat-arch.png similarity index 100% rename from website/blog/2023-10-18-RetrieveChat/img/retrievechat-arch.png rename to website/_blogs/2023-10-18-RetrieveChat/img/retrievechat-arch.png diff --git a/website/blog/2023-10-18-RetrieveChat/index.mdx b/website/_blogs/2023-10-18-RetrieveChat/index.mdx similarity index 97% rename from website/blog/2023-10-18-RetrieveChat/index.mdx rename to website/_blogs/2023-10-18-RetrieveChat/index.mdx index 24637e23d1..8ff236e650 100644 --- a/website/blog/2023-10-18-RetrieveChat/index.mdx +++ b/website/_blogs/2023-10-18-RetrieveChat/index.mdx @@ -4,23 +4,6 @@ authors: thinkall tags: [LLM, RAG] --- -
-

Author:

- - -
-
- -
-
-

Li Jiang

-

Senior Software Engineer at Microsoft

-
-
- - -
- *Last update: August 14, 2024; AutoGen version: v0.2.35* ![RAG Architecture](img/retrievechat-arch.png) diff --git a/website/blog/2023-10-26-TeachableAgent/img/teachable-arch.png b/website/_blogs/2023-10-26-TeachableAgent/img/teachable-arch.png similarity index 100% rename from website/blog/2023-10-26-TeachableAgent/img/teachable-arch.png rename to website/_blogs/2023-10-26-TeachableAgent/img/teachable-arch.png diff --git a/website/blog/2023-10-26-TeachableAgent/index.mdx b/website/_blogs/2023-10-26-TeachableAgent/index.mdx similarity index 98% rename from website/blog/2023-10-26-TeachableAgent/index.mdx rename to website/_blogs/2023-10-26-TeachableAgent/index.mdx index 7cfe7fc841..c04d2904eb 100644 --- a/website/blog/2023-10-26-TeachableAgent/index.mdx +++ b/website/_blogs/2023-10-26-TeachableAgent/index.mdx @@ -4,23 +4,6 @@ authors: rickyloynd-microsoft tags: [LLM, teach] --- -
-

Author:

- - -
-
- -
-
-

Ricky Loynd

-

Senior Research Engineer at Microsoft

-
-
- - -
- ![Teachable Agent Architecture](img/teachable-arch.png) **TL;DR:** diff --git a/website/blog/2023-11-06-LMM-Agent/img/teaser.png b/website/_blogs/2023-11-06-LMM-Agent/img/teaser.png similarity index 100% rename from website/blog/2023-11-06-LMM-Agent/img/teaser.png rename to website/_blogs/2023-11-06-LMM-Agent/img/teaser.png diff --git a/website/blog/2023-11-06-LMM-Agent/index.mdx b/website/_blogs/2023-11-06-LMM-Agent/index.mdx similarity index 88% rename from website/blog/2023-11-06-LMM-Agent/index.mdx rename to website/_blogs/2023-11-06-LMM-Agent/index.mdx index 7649b18b13..7c14be1f94 100644 --- a/website/blog/2023-11-06-LMM-Agent/index.mdx +++ b/website/_blogs/2023-11-06-LMM-Agent/index.mdx @@ -4,23 +4,6 @@ authors: beibinli tags: [LMM, multimodal] --- -
-

Author:

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- -
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Beibin Li

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Senior Research Engineer at Microsoft

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-
- - -
- ![LMM Teaser](img/teaser.png) **In Brief:** diff --git a/website/blog/2023-11-09-EcoAssistant/img/chat.webp b/website/_blogs/2023-11-09-EcoAssistant/img/chat.webp similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/img/chat.webp rename to website/_blogs/2023-11-09-EcoAssistant/img/chat.webp diff --git a/website/blog/2023-11-09-EcoAssistant/img/results.png b/website/_blogs/2023-11-09-EcoAssistant/img/results.png similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/img/results.png rename to website/_blogs/2023-11-09-EcoAssistant/img/results.png diff --git a/website/blog/2023-11-09-EcoAssistant/img/system.webp b/website/_blogs/2023-11-09-EcoAssistant/img/system.webp similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/img/system.webp rename to website/_blogs/2023-11-09-EcoAssistant/img/system.webp diff --git a/website/blog/2023-11-09-EcoAssistant/img/template-demo.png b/website/_blogs/2023-11-09-EcoAssistant/img/template-demo.png similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/img/template-demo.png rename to website/_blogs/2023-11-09-EcoAssistant/img/template-demo.png diff --git a/website/blog/2023-11-09-EcoAssistant/img/template.png b/website/_blogs/2023-11-09-EcoAssistant/img/template.png similarity index 100% rename from website/blog/2023-11-09-EcoAssistant/img/template.png rename to website/_blogs/2023-11-09-EcoAssistant/img/template.png diff --git a/website/blog/2023-11-09-EcoAssistant/index.mdx b/website/_blogs/2023-11-09-EcoAssistant/index.mdx similarity index 93% rename from website/blog/2023-11-09-EcoAssistant/index.mdx rename to website/_blogs/2023-11-09-EcoAssistant/index.mdx index ddbcd41046..607ffedb2d 100644 --- a/website/blog/2023-11-09-EcoAssistant/index.mdx +++ b/website/_blogs/2023-11-09-EcoAssistant/index.mdx @@ -4,23 +4,6 @@ authors: jieyuz2 tags: [LLM, RAG, cost-effectiveness] --- -
-

Author:

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-
-

Jieyu Zhang

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PhD student at University of Washington

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-
- - -
- ![system](img/system.webp) **TL;DR:** diff --git a/website/blog/2023-11-13-OAI-assistants/img/teaser.jpg b/website/_blogs/2023-11-13-OAI-assistants/img/teaser.jpg similarity index 100% rename from website/blog/2023-11-13-OAI-assistants/img/teaser.jpg rename to website/_blogs/2023-11-13-OAI-assistants/img/teaser.jpg diff --git a/website/blog/2023-11-13-OAI-assistants/index.mdx b/website/_blogs/2023-11-13-OAI-assistants/index.mdx similarity index 89% rename from website/blog/2023-11-13-OAI-assistants/index.mdx rename to website/_blogs/2023-11-13-OAI-assistants/index.mdx index 83d58b2042..bc632a8846 100644 --- a/website/blog/2023-11-13-OAI-assistants/index.mdx +++ b/website/_blogs/2023-11-13-OAI-assistants/index.mdx @@ -4,23 +4,6 @@ authors: gagb tags: [openai-assistant] --- -
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Author:

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Gagan Bansal

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Senior Researcher at Microsoft Research

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-
- - -
- ![OpenAI Assistant](img/teaser.jpg)

AutoGen enables collaboration among multiple ChatGPTs for complex tasks.

diff --git a/website/blog/2023-11-20-AgentEval/img/agenteval-CQ.webp b/website/_blogs/2023-11-20-AgentEval/img/agenteval-CQ.webp similarity index 100% rename from website/blog/2023-11-20-AgentEval/img/agenteval-CQ.webp rename to website/_blogs/2023-11-20-AgentEval/img/agenteval-CQ.webp diff --git a/website/blog/2023-11-20-AgentEval/img/math-problems-plot.png b/website/_blogs/2023-11-20-AgentEval/img/math-problems-plot.png similarity index 100% rename from website/blog/2023-11-20-AgentEval/img/math-problems-plot.png rename to website/_blogs/2023-11-20-AgentEval/img/math-problems-plot.png diff --git a/website/blog/2023-11-20-AgentEval/img/tasks-taxonomy.webp b/website/_blogs/2023-11-20-AgentEval/img/tasks-taxonomy.webp similarity index 100% rename from website/blog/2023-11-20-AgentEval/img/tasks-taxonomy.webp rename to website/_blogs/2023-11-20-AgentEval/img/tasks-taxonomy.webp diff --git a/website/blog/2023-11-20-AgentEval/index.mdx b/website/_blogs/2023-11-20-AgentEval/index.mdx similarity index 93% rename from website/blog/2023-11-20-AgentEval/index.mdx rename to website/_blogs/2023-11-20-AgentEval/index.mdx index 5894ee9a02..5c57428dd3 100644 --- a/website/blog/2023-11-20-AgentEval/index.mdx +++ b/website/_blogs/2023-11-20-AgentEval/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM, GPT, evaluation, task utility] --- -
-

Authors:

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-
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Julia Kiseleva

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Senior Researcher at Microsoft Research

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-
- - -
-
- -
-
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Negar Arabzadeh

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PhD student at the University of Waterloo

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-
- - -
- ![Fig.1: A verification framework](img/agenteval-CQ.webp)

Fig.1 illustrates the general flow of AgentEval

diff --git a/website/blog/2023-11-26-Agent-AutoBuild/img/agent_autobuild.webp b/website/_blogs/2023-11-26-Agent-AutoBuild/img/agent_autobuild.webp similarity index 100% rename from website/blog/2023-11-26-Agent-AutoBuild/img/agent_autobuild.webp rename to website/_blogs/2023-11-26-Agent-AutoBuild/img/agent_autobuild.webp diff --git a/website/blog/2023-11-26-Agent-AutoBuild/index.mdx b/website/_blogs/2023-11-26-Agent-AutoBuild/index.mdx similarity index 92% rename from website/blog/2023-11-26-Agent-AutoBuild/index.mdx rename to website/_blogs/2023-11-26-Agent-AutoBuild/index.mdx index 6f51849631..dc55a940cf 100644 --- a/website/blog/2023-11-26-Agent-AutoBuild/index.mdx +++ b/website/_blogs/2023-11-26-Agent-AutoBuild/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM, research] --- -
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Authors:

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-
- -
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Linxin Song

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PhD student at the University of Southern California

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-
- - -
-
- -
-
-

Jieyu Zhang

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PhD student at University of Washington

-
-
- - -
- ![Overall structure of AutoBuild](img/agent_autobuild.webp) **TL;DR:** diff --git a/website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_config.png b/website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_config.png similarity index 100% rename from website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_config.png rename to website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_config.png diff --git a/website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_home.png b/website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_home.png similarity index 100% rename from website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_home.png rename to website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_home.png diff --git a/website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_skills.png b/website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_skills.png similarity index 100% rename from website/blog/2023-12-01-AutoGenStudio/img/autogenstudio_skills.png rename to website/_blogs/2023-12-01-AutoGenStudio/img/autogenstudio_skills.png diff --git a/website/blog/2023-12-01-AutoGenStudio/index.mdx b/website/_blogs/2023-12-01-AutoGenStudio/index.mdx similarity index 91% rename from website/blog/2023-12-01-AutoGenStudio/index.mdx rename to website/_blogs/2023-12-01-AutoGenStudio/index.mdx index d1bcc7d819..f5ef581a85 100644 --- a/website/blog/2023-12-01-AutoGenStudio/index.mdx +++ b/website/_blogs/2023-12-01-AutoGenStudio/index.mdx @@ -7,45 +7,6 @@ authors: tags: [AutoGen, UI, web, UX] --- -
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Authors:

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- -
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Victor Dibia

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Principal RSDE at Microsoft Research

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-
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-
- -
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Gagan Bansal

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Senior Researcher at Microsoft Research

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-
- - -
-
- -
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Saleema Amershi

-

Senior Principal Research Manager at Microsoft Research

-
-
- - -
- ![AutoGen Studio Playground View: Solving a task with multiple agents that generate a pdf document with images.](img/autogenstudio_home.png)

diff --git a/website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.webp b/website/_blogs/2023-12-23-AgentOptimizer/img/agentoptimizer.webp similarity index 100% rename from website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.webp rename to website/_blogs/2023-12-23-AgentOptimizer/img/agentoptimizer.webp diff --git a/website/blog/2023-12-23-AgentOptimizer/index.mdx b/website/_blogs/2023-12-23-AgentOptimizer/index.mdx similarity index 92% rename from website/blog/2023-12-23-AgentOptimizer/index.mdx rename to website/_blogs/2023-12-23-AgentOptimizer/index.mdx index 101a53650c..0067a87c9c 100644 --- a/website/blog/2023-12-23-AgentOptimizer/index.mdx +++ b/website/_blogs/2023-12-23-AgentOptimizer/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM, research] --- -

-

Authors:

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-
- -
-
-

Shaokun Zhang

-

PhD student at the Pennsylvania State University

-
-
- - -
-
- -
-
-

Jieyu Zhang

-

PhD student at University of Washington

-
-
- - -
- ![Overall structure of AgentOptimizer](img/agentoptimizer.webp) diff --git a/website/blog/2023-12-29-AgentDescriptions/index.mdx b/website/_blogs/2023-12-29-AgentDescriptions/index.mdx similarity index 96% rename from website/blog/2023-12-29-AgentDescriptions/index.mdx rename to website/_blogs/2023-12-29-AgentDescriptions/index.mdx index 53c0a264c0..6af11f0459 100644 --- a/website/blog/2023-12-29-AgentDescriptions/index.mdx +++ b/website/_blogs/2023-12-29-AgentDescriptions/index.mdx @@ -5,23 +5,6 @@ authors: tags: [AutoGen] --- -
-

Author:

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-
- -
-
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Adam Fourney

-

Principal Researcher Microsoft Research

-
-
- - -
- ## TL;DR AutoGen 0.2.2 introduces a [description](https://docs.ag2.ai/docs/reference/agentchat/conversable_agent#init) field to ConversableAgent (and all subclasses), and changes GroupChat so that it uses agent `description`s rather than `system_message`s when choosing which agents should speak next. diff --git a/website/blog/2024-01-23-Code-execution-in-docker/index.mdx b/website/_blogs/2024-01-23-Code-execution-in-docker/index.mdx similarity index 88% rename from website/blog/2024-01-23-Code-execution-in-docker/index.mdx rename to website/_blogs/2024-01-23-Code-execution-in-docker/index.mdx index a433aae11c..33ed74ec98 100644 --- a/website/blog/2024-01-23-Code-execution-in-docker/index.mdx +++ b/website/_blogs/2024-01-23-Code-execution-in-docker/index.mdx @@ -5,23 +5,6 @@ authors: tags: [AutoGen] --- -
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Author:

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- -
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Olga Vrousgou

-

Senior Software Engineer at Microsoft Research

-
-
- - -
- ## TL;DR AutoGen 0.2.8 enhances operational safety by making 'code execution inside a Docker container' the default setting, focusing on informing users about its operations and empowering them to make informed decisions regarding code execution. diff --git a/website/blog/2024-01-25-AutoGenBench/img/teaser.jpg b/website/_blogs/2024-01-25-AutoGenBench/img/teaser.jpg similarity index 100% rename from website/blog/2024-01-25-AutoGenBench/img/teaser.jpg rename to website/_blogs/2024-01-25-AutoGenBench/img/teaser.jpg diff --git a/website/blog/2024-01-25-AutoGenBench/index.mdx b/website/_blogs/2024-01-25-AutoGenBench/index.mdx similarity index 92% rename from website/blog/2024-01-25-AutoGenBench/index.mdx rename to website/_blogs/2024-01-25-AutoGenBench/index.mdx index 8b18c1e998..70e0699e98 100644 --- a/website/blog/2024-01-25-AutoGenBench/index.mdx +++ b/website/_blogs/2024-01-25-AutoGenBench/index.mdx @@ -6,34 +6,6 @@ authors: tags: [AutoGen] --- -
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Authors:

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-
- -
-
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Adam Fourney

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Principal Researcher Microsoft Research

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-
- - -
-
- -
-
-

Qingyun Wu

-

Co-Founder of AutoGen/AG2 & FLAML, Assistant Professor at Penn State University

-
-
- - -
- ![AutoGenBench](img/teaser.jpg)

diff --git a/website/blog/2024-01-26-Custom-Models/index.mdx b/website/_blogs/2024-01-26-Custom-Models/index.mdx similarity index 94% rename from website/blog/2024-01-26-Custom-Models/index.mdx rename to website/_blogs/2024-01-26-Custom-Models/index.mdx index 8c9d3e2a57..d97685ebe4 100644 --- a/website/blog/2024-01-26-Custom-Models/index.mdx +++ b/website/_blogs/2024-01-26-Custom-Models/index.mdx @@ -5,23 +5,6 @@ authors: tags: [AutoGen] --- -

-

Author:

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-
- -
-
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Olga Vrousgou

-

Senior Software Engineer at Microsoft Research

-
-
- - -
- ## TL;DR AutoGen now supports custom models! This feature empowers users to define and load their own models, allowing for a more flexible and personalized inference mechanism. By adhering to a specific protocol, you can integrate your custom model for use with AutoGen and respond to prompts any way needed by using any model/API call/hardcoded response you want. diff --git a/website/blog/2024-02-02-AutoAnny/img/AutoAnnyLogo.jpg b/website/_blogs/2024-02-02-AutoAnny/img/AutoAnnyLogo.jpg similarity index 100% rename from website/blog/2024-02-02-AutoAnny/img/AutoAnnyLogo.jpg rename to website/_blogs/2024-02-02-AutoAnny/img/AutoAnnyLogo.jpg diff --git a/website/blog/2024-02-02-AutoAnny/index.mdx b/website/_blogs/2024-02-02-AutoAnny/index.mdx similarity index 83% rename from website/blog/2024-02-02-AutoAnny/index.mdx rename to website/_blogs/2024-02-02-AutoAnny/index.mdx index ca4a239bab..fb03d00964 100644 --- a/website/blog/2024-02-02-AutoAnny/index.mdx +++ b/website/_blogs/2024-02-02-AutoAnny/index.mdx @@ -5,23 +5,6 @@ authors: tags: [AutoGen] --- -
-

Author:

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-
- -
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Gagan Bansal

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Senior Researcher at Microsoft Research

-
-
- - -
-
AutoAnny Logo
diff --git a/website/blog/2024-02-11-FSM-GroupChat/img/FSM_logic.webp b/website/_blogs/2024-02-11-FSM-GroupChat/img/FSM_logic.webp similarity index 100% rename from website/blog/2024-02-11-FSM-GroupChat/img/FSM_logic.webp rename to website/_blogs/2024-02-11-FSM-GroupChat/img/FSM_logic.webp diff --git a/website/blog/2024-02-11-FSM-GroupChat/img/FSM_of_multi-agents.webp b/website/_blogs/2024-02-11-FSM-GroupChat/img/FSM_of_multi-agents.webp similarity index 100% rename from website/blog/2024-02-11-FSM-GroupChat/img/FSM_of_multi-agents.webp rename to website/_blogs/2024-02-11-FSM-GroupChat/img/FSM_of_multi-agents.webp diff --git a/website/blog/2024-02-11-FSM-GroupChat/img/teaser.webp b/website/_blogs/2024-02-11-FSM-GroupChat/img/teaser.webp similarity index 100% rename from website/blog/2024-02-11-FSM-GroupChat/img/teaser.webp rename to website/_blogs/2024-02-11-FSM-GroupChat/img/teaser.webp diff --git a/website/blog/2024-02-11-FSM-GroupChat/index.mdx b/website/_blogs/2024-02-11-FSM-GroupChat/index.mdx similarity index 93% rename from website/blog/2024-02-11-FSM-GroupChat/index.mdx rename to website/_blogs/2024-02-11-FSM-GroupChat/index.mdx index f7a4c32a4e..38c41f7bfe 100644 --- a/website/blog/2024-02-11-FSM-GroupChat/index.mdx +++ b/website/_blogs/2024-02-11-FSM-GroupChat/index.mdx @@ -6,34 +6,6 @@ authors: tags: [AutoGen] --- -
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Authors:

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Joshua Kim

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AI Freelancer at SpectData

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- - -
-
- -
-
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Yishen Sun

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Data Scientist at PingCAP LAB

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-
- - -
- ![FSM Group Chat](img/teaser.webp)

Finite State Machine (FSM) Group Chat allows the user to constrain agent transitions.

diff --git a/website/blog/2024-02-29-StateFlow/img/alfworld.png b/website/_blogs/2024-02-29-StateFlow/img/alfworld.png similarity index 100% rename from website/blog/2024-02-29-StateFlow/img/alfworld.png rename to website/_blogs/2024-02-29-StateFlow/img/alfworld.png diff --git a/website/blog/2024-02-29-StateFlow/img/bash_result.png b/website/_blogs/2024-02-29-StateFlow/img/bash_result.png similarity index 100% rename from website/blog/2024-02-29-StateFlow/img/bash_result.png rename to website/_blogs/2024-02-29-StateFlow/img/bash_result.png diff --git a/website/blog/2024-02-29-StateFlow/img/intercode.webp b/website/_blogs/2024-02-29-StateFlow/img/intercode.webp similarity index 100% rename from website/blog/2024-02-29-StateFlow/img/intercode.webp rename to website/_blogs/2024-02-29-StateFlow/img/intercode.webp diff --git a/website/blog/2024-02-29-StateFlow/img/sf_example_1.webp b/website/_blogs/2024-02-29-StateFlow/img/sf_example_1.webp similarity index 100% rename from website/blog/2024-02-29-StateFlow/img/sf_example_1.webp rename to website/_blogs/2024-02-29-StateFlow/img/sf_example_1.webp diff --git a/website/blog/2024-02-29-StateFlow/index.mdx b/website/_blogs/2024-02-29-StateFlow/index.mdx similarity index 95% rename from website/blog/2024-02-29-StateFlow/index.mdx rename to website/_blogs/2024-02-29-StateFlow/index.mdx index 4382ac5574..43d7e700a3 100644 --- a/website/blog/2024-02-29-StateFlow/index.mdx +++ b/website/_blogs/2024-02-29-StateFlow/index.mdx @@ -4,23 +4,6 @@ authors: yiranwu tags: [LLM, research] --- -
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Author:

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-
- -
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Yiran Wu

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PhD student at Pennsylvania State University

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-
- - -
- **TL;DR:** Introduce Stateflow, a task-solving paradigm that conceptualizes complex task-solving processes backed by LLMs as state machines. Introduce how to use GroupChat to realize such an idea with a customized speaker selection function. diff --git a/website/blog/2024-03-03-AutoGen-Update/img/contributors.png b/website/_blogs/2024-03-03-AutoGen-Update/img/contributors.png similarity index 100% rename from website/blog/2024-03-03-AutoGen-Update/img/contributors.png rename to website/_blogs/2024-03-03-AutoGen-Update/img/contributors.png diff --git a/website/blog/2024-03-03-AutoGen-Update/img/dalle_gpt4v.png b/website/_blogs/2024-03-03-AutoGen-Update/img/dalle_gpt4v.png similarity index 100% rename from website/blog/2024-03-03-AutoGen-Update/img/dalle_gpt4v.png rename to website/_blogs/2024-03-03-AutoGen-Update/img/dalle_gpt4v.png diff --git a/website/blog/2024-03-03-AutoGen-Update/img/gaia.png b/website/_blogs/2024-03-03-AutoGen-Update/img/gaia.png similarity index 100% rename from website/blog/2024-03-03-AutoGen-Update/img/gaia.png rename to website/_blogs/2024-03-03-AutoGen-Update/img/gaia.png diff --git a/website/blog/2024-03-03-AutoGen-Update/img/love.png b/website/_blogs/2024-03-03-AutoGen-Update/img/love.png similarity index 100% rename from website/blog/2024-03-03-AutoGen-Update/img/love.png rename to website/_blogs/2024-03-03-AutoGen-Update/img/love.png diff --git a/website/blog/2024-03-03-AutoGen-Update/img/teach.png b/website/_blogs/2024-03-03-AutoGen-Update/img/teach.png similarity index 100% rename from website/blog/2024-03-03-AutoGen-Update/img/teach.png rename to website/_blogs/2024-03-03-AutoGen-Update/img/teach.png diff --git a/website/blog/2024-03-03-AutoGen-Update/index.mdx b/website/_blogs/2024-03-03-AutoGen-Update/index.mdx similarity index 96% rename from website/blog/2024-03-03-AutoGen-Update/index.mdx rename to website/_blogs/2024-03-03-AutoGen-Update/index.mdx index 233ff52e76..f0170d5c84 100644 --- a/website/blog/2024-03-03-AutoGen-Update/index.mdx +++ b/website/_blogs/2024-03-03-AutoGen-Update/index.mdx @@ -4,23 +4,6 @@ authors: sonichi tags: [news, summary, roadmap] --- -
-

Author:

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- -
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Chi Wang

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Founder of AutoGen (now AG2) & FLAML

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- ![autogen is loved](img/love.png) **TL;DR** diff --git a/website/blog/2024-03-11-AutoDefense/img/architecture.webp b/website/_blogs/2024-03-11-AutoDefense/img/architecture.webp similarity index 100% rename from website/blog/2024-03-11-AutoDefense/img/architecture.webp rename to website/_blogs/2024-03-11-AutoDefense/img/architecture.webp diff --git a/website/blog/2024-03-11-AutoDefense/img/defense-agency-design.webp b/website/_blogs/2024-03-11-AutoDefense/img/defense-agency-design.webp similarity index 100% rename from website/blog/2024-03-11-AutoDefense/img/defense-agency-design.webp rename to website/_blogs/2024-03-11-AutoDefense/img/defense-agency-design.webp diff --git a/website/blog/2024-03-11-AutoDefense/img/table-4agents.png b/website/_blogs/2024-03-11-AutoDefense/img/table-4agents.png similarity index 100% rename from website/blog/2024-03-11-AutoDefense/img/table-4agents.png rename to website/_blogs/2024-03-11-AutoDefense/img/table-4agents.png diff --git a/website/blog/2024-03-11-AutoDefense/img/table-agents.png b/website/_blogs/2024-03-11-AutoDefense/img/table-agents.png similarity index 100% rename from website/blog/2024-03-11-AutoDefense/img/table-agents.png rename to website/_blogs/2024-03-11-AutoDefense/img/table-agents.png diff --git a/website/blog/2024-03-11-AutoDefense/img/table-compared-methods.png b/website/_blogs/2024-03-11-AutoDefense/img/table-compared-methods.png similarity index 100% rename from website/blog/2024-03-11-AutoDefense/img/table-compared-methods.png rename to website/_blogs/2024-03-11-AutoDefense/img/table-compared-methods.png diff --git a/website/blog/2024-03-11-AutoDefense/index.mdx b/website/_blogs/2024-03-11-AutoDefense/index.mdx similarity index 91% rename from website/blog/2024-03-11-AutoDefense/index.mdx rename to website/_blogs/2024-03-11-AutoDefense/index.mdx index 1cef402845..8b21a7e6fb 100644 --- a/website/blog/2024-03-11-AutoDefense/index.mdx +++ b/website/_blogs/2024-03-11-AutoDefense/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM, GPT, research] --- -
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Yifan Zeng

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PhD student at Oregon State University

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Yiran Wu

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PhD student at Pennsylvania State University

-
-
- - -
- ![architecture](img/architecture.webp) ## TL;DR diff --git a/website/blog/2024-05-24-Agent/img/agents.png b/website/_blogs/2024-05-24-Agent/img/agents.png similarity index 100% rename from website/blog/2024-05-24-Agent/img/agents.png rename to website/_blogs/2024-05-24-Agent/img/agents.png diff --git a/website/blog/2024-05-24-Agent/img/leadership.png b/website/_blogs/2024-05-24-Agent/img/leadership.png similarity index 100% rename from website/blog/2024-05-24-Agent/img/leadership.png rename to website/_blogs/2024-05-24-Agent/img/leadership.png diff --git a/website/blog/2024-05-24-Agent/index.mdx b/website/_blogs/2024-05-24-Agent/index.mdx similarity index 96% rename from website/blog/2024-05-24-Agent/index.mdx rename to website/_blogs/2024-05-24-Agent/index.mdx index 27e081c909..2f7354298c 100644 --- a/website/blog/2024-05-24-Agent/index.mdx +++ b/website/_blogs/2024-05-24-Agent/index.mdx @@ -4,23 +4,6 @@ authors: sonichi tags: [thoughts, interview notes] --- -
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Author:

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Chi Wang

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Founder of AutoGen (now AG2) & FLAML

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- ![agents](img/agents.png) **TL;DR** diff --git a/website/blog/2024-06-21-AgentEval/img/agenteval_ov_v3.webp b/website/_blogs/2024-06-21-AgentEval/img/agenteval_ov_v3.webp similarity index 100% rename from website/blog/2024-06-21-AgentEval/img/agenteval_ov_v3.webp rename to website/_blogs/2024-06-21-AgentEval/img/agenteval_ov_v3.webp diff --git a/website/blog/2024-06-21-AgentEval/index.mdx b/website/_blogs/2024-06-21-AgentEval/index.mdx similarity index 92% rename from website/blog/2024-06-21-AgentEval/index.mdx rename to website/_blogs/2024-06-21-AgentEval/index.mdx index 7d6417d298..65d2ad45e7 100644 --- a/website/blog/2024-06-21-AgentEval/index.mdx +++ b/website/_blogs/2024-06-21-AgentEval/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM, GPT, evaluation, task utility] --- -
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Authors:

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James Woffinden-Luey

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Senior Research Engineer at Microsoft Research

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Julia Kiseleva

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Senior Researcher at Microsoft Research

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- ![Fig.1: An AgentEval framework with verification step](img/agenteval_ov_v3.webp)

Fig.1 illustrates the general flow of AgentEval with verification step

diff --git a/website/blog/2024-06-24-AltModels-Classes/img/agentstogether.jpeg b/website/_blogs/2024-06-24-AltModels-Classes/img/agentstogether.jpeg similarity index 100% rename from website/blog/2024-06-24-AltModels-Classes/img/agentstogether.jpeg rename to website/_blogs/2024-06-24-AltModels-Classes/img/agentstogether.jpeg diff --git a/website/blog/2024-06-24-AltModels-Classes/index.mdx b/website/_blogs/2024-06-24-AltModels-Classes/index.mdx similarity index 95% rename from website/blog/2024-06-24-AltModels-Classes/index.mdx rename to website/_blogs/2024-06-24-AltModels-Classes/index.mdx index f2ef0657d4..a09a388eed 100644 --- a/website/blog/2024-06-24-AltModels-Classes/index.mdx +++ b/website/_blogs/2024-06-24-AltModels-Classes/index.mdx @@ -6,34 +6,6 @@ authors: tags: [mistral ai,anthropic,together.ai,gemini] --- -
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Authors:

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Mark Sze

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Software Engineer at AG2.ai

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Hrushikesh Dokala

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CS Undergraduate Based in India

-
-
- - -
- ![agents](img/agentstogether.jpeg) ## TL;DR diff --git a/website/blog/2024-07-25-AgentOps/img/autogen-integration.png b/website/_blogs/2024-07-25-AgentOps/img/autogen-integration.png similarity index 100% rename from website/blog/2024-07-25-AgentOps/img/autogen-integration.png rename to website/_blogs/2024-07-25-AgentOps/img/autogen-integration.png diff --git a/website/blog/2024-07-25-AgentOps/img/dashboard.png b/website/_blogs/2024-07-25-AgentOps/img/dashboard.png similarity index 100% rename from website/blog/2024-07-25-AgentOps/img/dashboard.png rename to website/_blogs/2024-07-25-AgentOps/img/dashboard.png diff --git a/website/blog/2024-07-25-AgentOps/img/flow.png b/website/_blogs/2024-07-25-AgentOps/img/flow.png similarity index 100% rename from website/blog/2024-07-25-AgentOps/img/flow.png rename to website/_blogs/2024-07-25-AgentOps/img/flow.png diff --git a/website/blog/2024-07-25-AgentOps/img/session-replay.png b/website/_blogs/2024-07-25-AgentOps/img/session-replay.png similarity index 100% rename from website/blog/2024-07-25-AgentOps/img/session-replay.png rename to website/_blogs/2024-07-25-AgentOps/img/session-replay.png diff --git a/website/blog/2024-07-25-AgentOps/index.mdx b/website/_blogs/2024-07-25-AgentOps/index.mdx similarity index 90% rename from website/blog/2024-07-25-AgentOps/index.mdx rename to website/_blogs/2024-07-25-AgentOps/index.mdx index 53990f24b3..f624350105 100644 --- a/website/blog/2024-07-25-AgentOps/index.mdx +++ b/website/_blogs/2024-07-25-AgentOps/index.mdx @@ -6,34 +6,6 @@ authors: tags: [LLM,Agent,Observability,AutoGen,AgentOps] --- -
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Authors:

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Alex Reibman

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Co-founder/CEO at AgentOps

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Braelyn Boynton

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AI Engineer at AgentOps

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- AgentOps and AutoGen ## TL;DR diff --git a/website/blog/2024-10-23-NOVA/img/nexla_autogen.webp b/website/_blogs/2024-10-23-NOVA/img/nexla_autogen.webp similarity index 100% rename from website/blog/2024-10-23-NOVA/img/nexla_autogen.webp rename to website/_blogs/2024-10-23-NOVA/img/nexla_autogen.webp diff --git a/website/blog/2024-10-23-NOVA/img/nova_architecture.webp b/website/_blogs/2024-10-23-NOVA/img/nova_architecture.webp similarity index 100% rename from website/blog/2024-10-23-NOVA/img/nova_architecture.webp rename to website/_blogs/2024-10-23-NOVA/img/nova_architecture.webp diff --git a/website/blog/2024-10-23-NOVA/index.mdx b/website/_blogs/2024-10-23-NOVA/index.mdx similarity index 90% rename from website/blog/2024-10-23-NOVA/index.mdx rename to website/_blogs/2024-10-23-NOVA/index.mdx index ee15646b18..f968c8d917 100644 --- a/website/blog/2024-10-23-NOVA/index.mdx +++ b/website/_blogs/2024-10-23-NOVA/index.mdx @@ -6,34 +6,6 @@ authors: tags: [data automation, agents, Autogen, Nexla] --- -
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Authors:

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Noel Nebu Panicker

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AI Engineer at Nexla

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Amey Desai

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Head of AI at Nexla

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-
- - -
- ![nexla_autogen](img/nexla_autogen.webp) In today’s fast-paced GenAI landscape, organizations are constantly searching for smarter, more efficient ways to manage and transform data. [Nexla](https://nexla.com/) is a platform dedicated to the automation of data engineering, enabling users to get ready-to-use data with minimal hassle. Central to Nexla’s approach are [Nexsets](https://nexla.com/nexsets-modern-data-building-blocks/)—data products that streamline the process of integrating, transforming, delivering, and monitoring data. Our mission is to make data ready-to-use for everyone, eliminating the complexities traditionally associated with data workflows. diff --git a/website/blog/2024-11-15-CaptainAgent/img/build.webp b/website/_blogs/2024-11-15-CaptainAgent/img/build.webp similarity index 100% rename from website/blog/2024-11-15-CaptainAgent/img/build.webp rename to website/_blogs/2024-11-15-CaptainAgent/img/build.webp diff --git a/website/blog/2024-11-15-CaptainAgent/img/chat.webp b/website/_blogs/2024-11-15-CaptainAgent/img/chat.webp similarity index 100% rename from website/blog/2024-11-15-CaptainAgent/img/chat.webp rename to website/_blogs/2024-11-15-CaptainAgent/img/chat.webp diff --git a/website/blog/2024-11-15-CaptainAgent/img/overall.webp b/website/_blogs/2024-11-15-CaptainAgent/img/overall.webp similarity index 100% rename from website/blog/2024-11-15-CaptainAgent/img/overall.webp rename to website/_blogs/2024-11-15-CaptainAgent/img/overall.webp diff --git a/website/blog/2024-11-15-CaptainAgent/index.mdx b/website/_blogs/2024-11-15-CaptainAgent/index.mdx similarity index 73% rename from website/blog/2024-11-15-CaptainAgent/index.mdx rename to website/_blogs/2024-11-15-CaptainAgent/index.mdx index 7f1aba3fe3..cbb777155c 100644 --- a/website/blog/2024-11-15-CaptainAgent/index.mdx +++ b/website/_blogs/2024-11-15-CaptainAgent/index.mdx @@ -8,66 +8,6 @@ authors: - qingyunwu tags: [LLM, GPT, AutoBuild] --- -
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Authors:

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Jiale Liu

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PhD student at Pennsylvania State University

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Linxin Song

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PhD student at the University of Southern California

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-
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-
- -
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Jieyu Zhang

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PhD student at University of Washington

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Shaokun Zhang

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PhD student at the Pennsylvania State University

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- -
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Qingyun Wu

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Co-Founder of AutoGen/AG2 & FLAML, Assistant Professor at Penn State University

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-
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diff --git a/website/blog/2024-11-17-Swarm/index.mdx b/website/_blogs/2024-11-17-Swarm/index.mdx similarity index 94% rename from website/blog/2024-11-17-Swarm/index.mdx rename to website/_blogs/2024-11-17-Swarm/index.mdx index f9b3599d60..ebcd16c672 100644 --- a/website/blog/2024-11-17-Swarm/index.mdx +++ b/website/_blogs/2024-11-17-Swarm/index.mdx @@ -6,34 +6,6 @@ authors: tags: [groupchat, swarm] --- -
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Authors:

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Yiran Wu

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PhD student at Pennsylvania State University

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-
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- -
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Mark Sze

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Software Engineer at AG2.ai

-
-
- - -
- AG2 now provides an implementation of the swarm orchestration from OpenAI's [Swarm](https://github.com/openai/swarm) framework, with some additional features! diff --git a/website/blog/2024-11-27-Prompt-Leakage-Probing/img/probing_flow.webp b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/probing_flow.webp similarity index 100% rename from website/blog/2024-11-27-Prompt-Leakage-Probing/img/probing_flow.webp rename to website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/probing_flow.webp diff --git a/website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_report.png b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_report.png similarity index 100% rename from website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_report.png rename to website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_report.png diff --git a/website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_social_img.webp b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_social_img.webp similarity index 100% rename from website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_social_img.webp rename to website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_leakage_social_img.webp diff --git a/website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_test_chat.webp b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_test_chat.webp similarity index 100% rename from website/blog/2024-11-27-Prompt-Leakage-Probing/img/prompt_test_chat.webp rename to website/_blogs/2024-11-27-Prompt-Leakage-Probing/img/prompt_test_chat.webp diff --git a/website/blog/2024-11-27-Prompt-Leakage-Probing/index.mdx b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/index.mdx similarity index 92% rename from website/blog/2024-11-27-Prompt-Leakage-Probing/index.mdx rename to website/_blogs/2024-11-27-Prompt-Leakage-Probing/index.mdx index 036221a647..7feea8cca6 100644 --- a/website/blog/2024-11-27-Prompt-Leakage-Probing/index.mdx +++ b/website/_blogs/2024-11-27-Prompt-Leakage-Probing/index.mdx @@ -7,45 +7,6 @@ authors: tags: [LLM, security] --- -
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Authors:

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Tvrtko Sternak

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Machine Learning Engineer at Airt

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Davor Runje

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CTO at Airt

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-
- -
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Chi Wang

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Founder of AutoGen (now AG2) & FLAML

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-
- - -
- ![Prompt leakage social img](img/prompt_leakage_social_img.webp) ## Introduction diff --git a/website/blog/2024-12-02-ReasoningAgent2/img/reasoningagent_1.webp b/website/_blogs/2024-12-02-ReasoningAgent2/img/reasoningagent_1.webp similarity index 100% rename from website/blog/2024-12-02-ReasoningAgent2/img/reasoningagent_1.webp rename to website/_blogs/2024-12-02-ReasoningAgent2/img/reasoningagent_1.webp diff --git a/website/blog/2024-12-02-ReasoningAgent2/img/reasoningagent_2.webp b/website/_blogs/2024-12-02-ReasoningAgent2/img/reasoningagent_2.webp similarity index 100% rename from website/blog/2024-12-02-ReasoningAgent2/img/reasoningagent_2.webp rename to website/_blogs/2024-12-02-ReasoningAgent2/img/reasoningagent_2.webp diff --git a/website/blog/2024-12-02-ReasoningAgent2/img/tree-of-thoughts.png b/website/_blogs/2024-12-02-ReasoningAgent2/img/tree-of-thoughts.png similarity index 100% rename from website/blog/2024-12-02-ReasoningAgent2/img/tree-of-thoughts.png rename to website/_blogs/2024-12-02-ReasoningAgent2/img/tree-of-thoughts.png diff --git a/website/blog/2024-12-02-ReasoningAgent2/index.mdx b/website/_blogs/2024-12-02-ReasoningAgent2/index.mdx similarity index 83% rename from website/blog/2024-12-02-ReasoningAgent2/index.mdx rename to website/_blogs/2024-12-02-ReasoningAgent2/index.mdx index 20cc6c398e..0d8d28dc80 100644 --- a/website/blog/2024-12-02-ReasoningAgent2/index.mdx +++ b/website/_blogs/2024-12-02-ReasoningAgent2/index.mdx @@ -9,67 +9,6 @@ authors: tags: [LLM, GPT, research] --- -
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Authors:

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Hrushikesh Dokala

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CS Undergraduate Based in India

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BabyCNM

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AG2 Contributor

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Shaokun Zhang

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PhD student at the Pennsylvania State University

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Chi Wang

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Founder of AutoGen (now AG2) & FLAML

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Qingyun Wu

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Co-Founder of AutoGen/AG2 & FLAML, Assistant Professor at Penn State University

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- **TL;DR:** diff --git a/website/blog/2024-12-06-FalkorDB-Structured/img/falkordb.png b/website/_blogs/2024-12-06-FalkorDB-Structured/img/falkordb.png similarity index 100% rename from website/blog/2024-12-06-FalkorDB-Structured/img/falkordb.png rename to website/_blogs/2024-12-06-FalkorDB-Structured/img/falkordb.png diff --git a/website/blog/2024-12-06-FalkorDB-Structured/img/tripplanner.webp b/website/_blogs/2024-12-06-FalkorDB-Structured/img/tripplanner.webp similarity index 100% rename from website/blog/2024-12-06-FalkorDB-Structured/img/tripplanner.webp rename to website/_blogs/2024-12-06-FalkorDB-Structured/img/tripplanner.webp diff --git a/website/blog/2024-12-06-FalkorDB-Structured/index.mdx b/website/_blogs/2024-12-06-FalkorDB-Structured/index.mdx similarity index 83% rename from website/blog/2024-12-06-FalkorDB-Structured/index.mdx rename to website/_blogs/2024-12-06-FalkorDB-Structured/index.mdx index bb0d22d962..6cde227f36 100644 --- a/website/blog/2024-12-06-FalkorDB-Structured/index.mdx +++ b/website/_blogs/2024-12-06-FalkorDB-Structured/index.mdx @@ -9,67 +9,6 @@ authors: tags: [RAG, Graph RAG, Structured Outputs, swarm, nested chat] --- -
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Authors:

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Mark Sze

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Software Engineer at AG2.ai

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Tvrtko Sternak

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Machine Learning Engineer at Airt

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Davor Runje

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CTO at Airt

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AgentGenie

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AG2 Contributor

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Qingyun Wu

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Co-Founder of AutoGen/AG2 & FLAML, Assistant Professor at Penn State University

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- ![FalkorDB Web](img/falkordb.png) diff --git a/website/blog/2024-12-20-RealtimeAgent/img/1_service_running.png b/website/_blogs/2024-12-20-RealtimeAgent/img/1_service_running.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/1_service_running.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/1_service_running.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/2_incoming_call.png b/website/_blogs/2024-12-20-RealtimeAgent/img/2_incoming_call.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/2_incoming_call.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/2_incoming_call.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png b/website/_blogs/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/3_request_for_flight_cancellation.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/4_flight_number_name.png b/website/_blogs/2024-12-20-RealtimeAgent/img/4_flight_number_name.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/4_flight_number_name.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/4_flight_number_name.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/5_refund_policy.png b/website/_blogs/2024-12-20-RealtimeAgent/img/5_refund_policy.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/5_refund_policy.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/5_refund_policy.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/6_flight_refunded.png b/website/_blogs/2024-12-20-RealtimeAgent/img/6_flight_refunded.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/6_flight_refunded.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/6_flight_refunded.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png b/website/_blogs/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/realtime_agent_swarm.png diff --git a/website/blog/2024-12-20-RealtimeAgent/img/twilio_endpoint_config.png b/website/_blogs/2024-12-20-RealtimeAgent/img/twilio_endpoint_config.png similarity index 100% rename from website/blog/2024-12-20-RealtimeAgent/img/twilio_endpoint_config.png rename to website/_blogs/2024-12-20-RealtimeAgent/img/twilio_endpoint_config.png diff --git a/website/blog/2024-12-20-RealtimeAgent/index.mdx b/website/_blogs/2024-12-20-RealtimeAgent/index.mdx similarity index 93% rename from website/blog/2024-12-20-RealtimeAgent/index.mdx rename to website/_blogs/2024-12-20-RealtimeAgent/index.mdx index 0ba5802a50..559d1c0b95 100644 --- a/website/blog/2024-12-20-RealtimeAgent/index.mdx +++ b/website/_blogs/2024-12-20-RealtimeAgent/index.mdx @@ -9,56 +9,6 @@ tags: [Realtime API, Voice Agents, Swarm Teams, Twilio, AI Tools] --- -
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Mark Sze

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Software Engineer at AG2.ai

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Tvrtko Sternak

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Machine Learning Engineer at Airt

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Davor Runje

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CTO at Airt

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Davorin Ruševljan

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Developer

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- **TL;DR:** diff --git a/website/_blogs/2024-12-20-Reasoning-Update/img/mcts_example.png b/website/_blogs/2024-12-20-Reasoning-Update/img/mcts_example.png new file mode 100644 index 0000000000..1fb00844e8 --- /dev/null +++ b/website/_blogs/2024-12-20-Reasoning-Update/img/mcts_example.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf664edc82f026900bbd5e886c511d55f4c0d8e6cd1f75d8ea7fc6a07f80046c +size 366499 diff --git a/website/blog/2024-12-20-Reasoning-Update/img/reasoningagent_1.png b/website/_blogs/2024-12-20-Reasoning-Update/img/reasoningagent_1.png similarity index 100% rename from website/blog/2024-12-20-Reasoning-Update/img/reasoningagent_1.png rename to website/_blogs/2024-12-20-Reasoning-Update/img/reasoningagent_1.png diff --git a/website/blog/2024-12-20-Reasoning-Update/index.mdx b/website/_blogs/2024-12-20-Reasoning-Update/index.mdx similarity index 86% rename from website/blog/2024-12-20-Reasoning-Update/index.mdx rename to website/_blogs/2024-12-20-Reasoning-Update/index.mdx index a296a01e74..ba534ced41 100644 --- a/website/blog/2024-12-20-Reasoning-Update/index.mdx +++ b/website/_blogs/2024-12-20-Reasoning-Update/index.mdx @@ -8,56 +8,6 @@ authors: tags: [LLM, GPT, research, tutorial] --- -
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BabyCNM

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AG2 Contributor

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Hrushikesh Dokala

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CS Undergraduate Based in India

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Chi Wang

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Founder of AutoGen (now AG2) & FLAML

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Qingyun Wu

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Co-Founder of AutoGen/AG2 & FLAML, Assistant Professor at Penn State University

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- **Key Updates in this Release:** diff --git a/website/blog/2024-12-20-Tools-interoperability/index.mdx b/website/_blogs/2024-12-20-Tools-interoperability/index.mdx similarity index 98% rename from website/blog/2024-12-20-Tools-interoperability/index.mdx rename to website/_blogs/2024-12-20-Tools-interoperability/index.mdx index 0746c5e69c..a01dd5826b 100644 --- a/website/blog/2024-12-20-Tools-interoperability/index.mdx +++ b/website/_blogs/2024-12-20-Tools-interoperability/index.mdx @@ -5,23 +5,6 @@ authors: tags: [LLM, tools, langchain, crewai, pydanticai] --- -
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Robert Jambrecic

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Machine Learning Engineer at Airt

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- **TL;DR** diff --git a/website/_blogs/2025-01-07-Tools-Dependency-Injection/index.mdx b/website/_blogs/2025-01-07-Tools-Dependency-Injection/index.mdx new file mode 100644 index 0000000000..402cce3ad6 --- /dev/null +++ b/website/_blogs/2025-01-07-Tools-Dependency-Injection/index.mdx @@ -0,0 +1,387 @@ +--- +title: Tools Dependency Injection +authors: + - rjambrecic +tags: [tools, tool calling, dependency injection] +--- + + +[Dependency Injection](https://en.wikipedia.org/wiki/Dependency_injection) is a secure way to connect external functions to agents without exposing sensitive data such as passwords, tokens, or personal information. This approach ensures that sensitive information remains protected while still allowing agents to perform their tasks effectively, even when working with large language models (LLMs). + +In this guide, we’ll explore how to build secure workflows that handle sensitive data safely. + +As an example, we’ll create an agent that retrieves user's account balance. The best part is that sensitive data like username and password are never shared with the LLM. Instead, it’s securely injected directly into the function at runtime, keeping it safe while maintaining seamless functionality. + +## Why Dependency Injection Is Essential + +Here’s why dependency injection is a game-changer for secure LLM workflows: + +- **Enhanced Security**: Your sensitive data is never directly exposed to the LLM. +- **Simplified Development**: Secure data can be seamlessly accessed by functions without requiring complex configurations. +- **Unmatched Flexibility**: It supports safe integration of diverse workflows, allowing you to scale and adapt with ease. + +In this guide, we’ll explore how to set up dependency injection and build secure workflows. Let’s dive in! + +**Note:** This blog builds upon the concepts covered in the following [notebook](/notebooks/tools_dependency_injection). + +## Installation + +To install `AG2`, simply run the following command: + +```bash +pip install ag2 +``` + + +## Imports + +The functionality demonstrated in this guide is located in the [`autogen.tools.dependency_injection`](/docs/reference/tools/dependency_injection) module. This module provides key components for dependency injection: + +- [`BaseContext`](/docs/reference/tools/dependency_injection#basecontext): abstract base class used to define and encapsulate data contexts, such as user account information, which can then be injected into functions or agents securely. +- [`Depends`](/docs/reference/tools/dependency_injection#depends): a function used to declare and inject dependencies, either from a context (like [`BaseContext`](/docs/reference/tools/dependency_injection#basecontext)) or a function, ensuring sensitive data is provided securely without direct exposure. + +```python +import os +from typing import Annotated, Literal + +from pydantic import BaseModel + +from autogen import GroupChat, GroupChatManager +from autogen.agentchat import ConversableAgent, UserProxyAgent +from autogen.tools.dependency_injection import BaseContext, Depends +``` + +## Define a BaseContext Class +We start by defining a [`BaseContext`](/docs/reference/tools/dependency_injection#basecontext) class for accounts. This will act as the base structure for dependency injection. By using this approach, sensitive information like usernames and passwords is never exposed to the LLM. + +```python +class Account(BaseContext, BaseModel): + username: str + password: str + currency: Literal["USD", "EUR"] = "USD" + + +alice_account = Account(username="alice", password="password123") +bob_account = Account(username="bob", password="password456") + +account_ballace_dict = { + (alice_account.username, alice_account.password): 300, + (bob_account.username, bob_account.password): 200, +} +``` + +## Helper Functions +To ensure that the provided account is valid and retrieve its balance, we create two helper functions. + +```python +def _verify_account(account: Account): + if (account.username, account.password) not in account_ballace_dict: + raise ValueError("Invalid username or password") + + +def _get_balance(account: Account): + _verify_account(account) + return f"Your balance is {account_ballace_dict[(account.username, account.password)]}{account.currency}" +``` + +## Injecting BaseContext Parameter + +Dependency injection simplifies passing data to a function. Here, we'll inject an `Account` instance into a function automatically. + +### Agent Configuration + +Configure the agents for the interaction. + +- `config_list` defines the LLM configurations, including the model and API key. +- [`UserProxyAgent`](/docs/reference/agentchat/user_proxy_agent) simulates user inputs without requiring actual human interaction (set to `NEVER`). +- [`AssistantAgent`](/docs/reference/agentchat/assistant_agent) represents the AI agent, configured with the LLM settings. + + +```python +config_list = [{"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]}] +assistant = ConversableAgent( + name="assistant", + llm_config={"config_list": config_list}, +) +user_proxy = UserProxyAgent( + name="user_proxy_1", + human_input_mode="NEVER", + llm_config=False, +) +``` + +### Register the Function with Dependency Injection +We register a function where the account information for `bob` is injected as a dependency. + +**Note:** You can also use `account: Account = Depends(bob_account)` as an alternative syntax. + +```python +@user_proxy.register_for_execution() +@assistant.register_for_llm(description="Get the balance of the account") +def get_balance_1( + # Account which will be injected to the function + account: Annotated[Account, Depends(bob_account)], + # It is also possible to use the following syntax to define the dependency + # account: Account = Depends(bob_account), +) -> str: + return _get_balance(account) +``` + +### Initiate the Chat +Finally, we initiate a chat to retrieve the balance. + +```python +user_proxy.initiate_chat(assistant, message="Get users balance", max_turns=2) +``` + +```console +user_proxy_1 (to assistant): + +Get users balance + +-------------------------------------------------------------------------------- + +>>>>>>>> USING AUTO REPLY... +assistant (to user_proxy_1): + +***** Suggested tool call (call_ognvIidhVCUdxvH0vnJEPxzk): get_balance_1 ***** +Arguments: +{} +****************************************************************************** + +-------------------------------------------------------------------------------- + +>>>>>>>> EXECUTING FUNCTION get_balance_1... +user_proxy_1 (to assistant): + +***** Response from calling tool (call_ognvIidhVCUdxvH0vnJEPxzk) ***** +Your balance is 200USD +********************************************************************** + +-------------------------------------------------------------------------------- + +>>>>>>>> USING AUTO REPLY... +assistant (to user_proxy_1): + +Your balance is 200 USD. + +-------------------------------------------------------------------------------- +``` + +## Injecting Parameters Without BaseContext + +Sometimes, you might not want to use [`BaseContext`](/docs/reference/tools/dependency_injection#basecontext). Here's how to inject simple parameters directly. + +### Agent Configuration + +Configure the agents for the interaction. + +- `config_list` defines the LLM configurations, including the model and API key. +- [`UserProxyAgent`](/docs/reference/agentchat/user_proxy_agent) simulates user inputs without requiring actual human interaction (set to `NEVER`). +- [`AssistantAgent`](/docs/reference/agentchat/assistant_agent) represents the AI agent, configured with the LLM settings. + +```python +config_list = [{"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]}] +assistant = ConversableAgent( + name="assistant", + llm_config={"config_list": config_list}, +) +user_proxy = UserProxyAgent( + name="user_proxy_1", + human_input_mode="NEVER", + llm_config=False, +) +``` + +### Register the Function with Direct Parameter Injection +Instead of injecting a full context like `Account`, you can directly inject individual parameters, such as the username and password, into a function. This allows for more granular control over the data injected into the function, and still ensures that sensitive information is managed securely. + +Here’s how you can set it up: + +```python +def get_username() -> str: + return "bob" + +def get_password() -> str: + return "password456" + +@user_proxy.register_for_execution() +@assistant.register_for_llm(description="Get the balance of the account") +def get_balance_2( + username: Annotated[str, Depends(get_username)], + password: Annotated[str, Depends(get_password)], + # or use lambdas + # username: Annotated[str, Depends(lambda: "bob")], + # password: Annotated[str, Depends(lambda: "password456")], +) -> str: + account = Account(username=username, password=password) + return _get_balance(account) +``` + + +### Initiate the Chat +As before, initiate a chat to test the function. + +```python +user_proxy.initiate_chat(assistant, message="Get users balance", max_turns=2) +``` + +```console +user_proxy_1 (to assistant): + +Get users balance + +-------------------------------------------------------------------------------- + +>>>>>>>> USING AUTO REPLY... +assistant (to user_proxy_1): + +***** Suggested tool call (call_REyBiQkznsd2JzNr4i7Z2N7q): get_balance_2 ***** +Arguments: +{} +****************************************************************************** + +-------------------------------------------------------------------------------- + +>>>>>>>> EXECUTING FUNCTION get_balance_2... +user_proxy_1 (to assistant): + +***** Response from calling tool (call_REyBiQkznsd2JzNr4i7Z2N7q) ***** +Your balance is 200USD +********************************************************************** + +-------------------------------------------------------------------------------- + +>>>>>>>> USING AUTO REPLY... +assistant (to user_proxy_1): + +Your balance is 200 USD. + +-------------------------------------------------------------------------------- +``` + +## Assigning Different Contexts to Multiple Agents + +You can assign different contexts, such as distinct account data, to different agents within the same group chat. This ensures that each assistant works with its own unique set of data—e.g., one assistant can use `alice_account`, while another can use `bob_account`. + +### GroupChat Configuration +Let's configure a `GroupChat` with two assistant agents. + +```python +config_list = [{"model": "gpt-4o-mini", "api_key": os.environ["OPENAI_API_KEY"]}] +llm_config = {"config_list": config_list} +assistant_1 = ConversableAgent( + name="assistant_1", + llm_config={"config_list": config_list}, +) +assistant_2 = ConversableAgent( + name="assistant_2", + llm_config={"config_list": config_list}, +) +user_proxy = UserProxyAgent( + name="user_proxy_1", + human_input_mode="NEVER", + llm_config=False, +) + +groupchat = GroupChat(agents=[user_proxy, assistant_1, assistant_2], messages=[], max_round=5) +manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config) +``` + + +### Register Functions for Each Assistant + +- For `assistant_1`, we inject the `alice_account` context using `Depends(alice_account)`, ensuring that it retrieves the balance for Alice’s account. +- For `assistant_2`, we inject the `bob_account` context using `Depends(bob_account)`, ensuring that it retrieves the balance for Bob’s account. + +```python +@user_proxy.register_for_execution() +@assistant_1.register_for_llm(description="Get the balance of the account") +def get_balance_for_assistant_1( + account: Annotated[Account, Depends(alice_account)], +) -> str: + return _get_balance(account) + + +@user_proxy.register_for_execution() +@assistant_2.register_for_llm(description="Get the balance of the account") +def get_balance_for_assistant_2( + account: Annotated[Account, Depends(bob_account)], +) -> str: + return _get_balance(account) +``` + +### Initiate the Chat +Finally, initiate the group chat where both assistants respond by using their respective contexts. Each assistant will handle its own task — one will retrieve Alice’s balance, and the other will retrieve Bob’s balance + +```python +message = "Both assistants, please get the balance of the account" +user_proxy.initiate_chat(manager, message=message, max_turns=1) +``` + +```console +user_proxy_1 (to chat_manager): + +Both assistants, please get the balance of the account + +-------------------------------------------------------------------------------- + +Next speaker: assistant_1 + + +>>>>>>>> USING AUTO REPLY... +assistant_1 (to chat_manager): + +***** Suggested tool call (call_wfTGOY4O9mEDBuIOrajJDSNj): get_balance_for_assistant_1 ***** +Arguments: +{} +******************************************************************************************** + +-------------------------------------------------------------------------------- + +Next speaker: user_proxy_1 + + +>>>>>>>> EXECUTING FUNCTION get_balance_for_assistant_1... +user_proxy_1 (to chat_manager): + +***** Response from calling tool (call_wfTGOY4O9mEDBuIOrajJDSNj) ***** +Your balance is 300USD +********************************************************************** + +-------------------------------------------------------------------------------- + +Next speaker: assistant_2 + + +>>>>>>>> USING AUTO REPLY... +assistant_2 (to chat_manager): + +***** Suggested tool call (call_QNO5v9vGRUfRsmUAjL9yV318): get_balance_for_assistant_2 ***** +Arguments: +{} +******************************************************************************************** + +-------------------------------------------------------------------------------- + +Next speaker: user_proxy_1 + + +>>>>>>>> EXECUTING FUNCTION get_balance_for_assistant_2... +user_proxy_1 (to chat_manager): + +***** Response from calling tool (call_QNO5v9vGRUfRsmUAjL9yV318) ***** +Your balance is 200USD +********************************************************************** + +-------------------------------------------------------------------------------- +``` + + +## Conclusion + +In this blog post, we explore **Dependency Injection (DI)** as a secure and effective way to manage sensitive data in workflows involving LLMs. Dependency Injection ensures that sensitive data, such as passwords or personal information, remains protected by injecting necessary details at runtime instead of exposing them directly to the LLM. + +The post provides a comprehensive guide on setting up and using DI with agents, illustrating how to securely retrieve account balances without sharing sensitive data. It includes step-by-step instructions on configuring agents, defining contexts, and using the Depends function to inject account details directly into the functions. Various methods are demonstrated, such as injecting contexts, passing simple parameters, and even managing multiple contexts for different agents in group chats. + +By following this guide, developers can create secure and flexible workflows that prevent unauthorized access to sensitive data while leveraging LLMs' full potential. diff --git a/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/img/websocket_communication_diagram.png b/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/img/websocket_communication_diagram.png new file mode 100644 index 0000000000..99c3e482c6 --- /dev/null +++ b/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/img/websocket_communication_diagram.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1fc93941357add8d4c6db1b4675f9a04dda6bd43394b8b591e073b33d97297e6 +size 152437 diff --git a/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/index.mdx b/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/index.mdx new file mode 100644 index 0000000000..a49dc23b53 --- /dev/null +++ b/website/_blogs/2025-01-08-RealtimeAgent-over-websocket/index.mdx @@ -0,0 +1,261 @@ +--- +title: Real-Time Voice Interactions with the WebSocket Audio Adapter +authors: + - marklysze + - sternakt + - davorrunje + - davorinrusevljan +tags: [Realtime API, Voice Agents, AI Tools] + +--- + +![Realtime agent communication over websocket](img/websocket_communication_diagram.png) + +**TL;DR:** +- **Demo implementation**: Implement a website using websockets and communicate using voice with the [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent) +- **Introducing [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter)**: Stream audio directly from your browser using [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/). +- **Simplified Development**: Connect to real-time agents quickly and effortlessly with minimal setup. + +# **Realtime over WebSockets** + +In our [previous blog post](/blog/2024-12-20-RealtimeAgent/index), we introduced a way to interact with the [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent) using [**`TwilioAudioAdapter`**](/docs/reference/agentchat/realtime_agent/twilio_audio_adapter#twilioaudioadapter). While effective, this approach required a setup-intensive process involving [Twilio](https://www.twilio.com/) integration, account configuration, number forwarding, and other complexities. Today, we're excited to introduce the[**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter), a streamlined approach to real-time audio streaming directly via a web browser. + +This post explores the features, benefits, and implementation of the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter), showing how it transforms the way we connect with real-time agents. + +## **Why We Built the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter)** +### **Challenges with Existing Solutions** +Previously introduced [**`TwilioAudioAdapter`**](/docs/reference/agentchat/realtime_agent/twilio_audio_adapter#twilioaudioadapter) provides a robust way to cennect to your [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent), it comes with challenges: +- **Browser Limitations**: For teams building web-first applications, integrating with a telephony platform can feel redundant. +- **Complex Setup**: Configuring Twilio accounts, verifying numbers, and setting up forwarding can be time-consuming. +- **Platform Dependency**: This solution requires developers to rely on external API, which adds latency and costs. + +### **Our Solution** +The [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) eliminates these challenges by allowing direct audio streaming over [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/). It integrates seamlessly with modern web technologies, enabling real-time voice interactions without external telephony platforms. + +## **How It Works** +At its core, the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) leverages [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) to handle real-time audio streaming. This means your browser becomes the communication bridge, sending audio packets to a server where a [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent) agent processes them. + +Here’s a quick overview of its components and how they fit together: + +1. **WebSocket Connection**: + - The adapter establishes a [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) connection between the client (browser) and the server. + - Audio packets are streamed in real time through this connection. + +2. **Integration with FastAPI**: + - Using Python's [FastAPI](https://fastapi.tiangolo.com/) framework, developers can easily set up endpoints for handling [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) traffic. + +3. **Powered by Realtime Agents**: + - The audio adapter integrates with an AI-powered [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent), allowing the agent to process audio inputs and respond intelligently. + +## **Key Features** +### **1. Simplified Setup** +Unlike [**`TwilioAudioAdapter`**](/docs/reference/agentchat/realtime_agent/twilio_audio_adapter#twilioaudioadapter), the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) requires no phone numbers, no telephony configuration, and no external accounts. It's a plug-and-play solution. + +### **2. Real-Time Performance** +By streaming audio over [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/), the adapter ensures low latency, making conversations feel natural and seamless. + +### **3. Browser-Based** +Everything happens within the user's browser, meaning no additional software is required. This makes it ideal for web applications. + +### **4. Flexible Integration** +Whether you're building a chatbot, a voice assistant, or an interactive application, the adapter can integrate easily with existing frameworks and AI systems. + +## **Example: Build a Voice-Enabled Weather Bot** +Let’s walk through a practical example where we use the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) to create a voice-enabled weather bot. +You can find the full example [here](https://github.com/ag2ai/realtime-agent-over-websockets/tree/main). + +To run the demo example, follow these steps: + +### **1. Clone the Repository** +```bash +git clone https://github.com/ag2ai/realtime-agent-over-websockets.git +cd realtime-agent-over-websockets +``` + +### **2. Set Up Environment Variables** +Create a `OAI_CONFIG_LIST` file based on the provided `OAI_CONFIG_LIST_sample`: +```bash +cp OAI_CONFIG_LIST_sample OAI_CONFIG_LIST +``` +In the OAI_CONFIG_LIST file, update the `api_key` to your OpenAI API key. + +### (Optional) Create and use a virtual environment + +To reduce cluttering your global Python environment on your machine, you can create a virtual environment. On your command line, enter: + +``` +python3 -m venv env +source env/bin/activate +``` + +### **3. Install Dependencies** +Install the required Python packages using `pip`: +```bash +pip install -r requirements.txt +``` + +### **4. Start the Server** +Run the application with Uvicorn: +```bash +uvicorn realtime_over_websockets.main:app --port 5050 +``` + +After you start the server you should see your application running in the logs: + +```bash +INFO: Started server process [64425] +INFO: Waiting for application startup. +INFO: Application startup complete. +INFO: Uvicorn running on http://0.0.0.0:5050 (Press CTRL+C to quit) +``` + +### Ready to Chat? 🚀 +Now you can simply open [**localhost:5050/start-chat**](http://localhost:5050/start-chat) in your browser, and dive into an interactive conversation with the [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent)! 🎤✨ + +To get started, simply speak into your microphone and ask a question. For example, you can say: + +**"What's the weather like in Seattle?"** + +This initial question will activate the agent, and it will respond, showcasing its ability to understand and interact with you in real time. + +## Code review +Let’s dive in and break down how this example works—from setting up the server to handling real-time audio streaming with [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/). + +### **Set Up the FastAPI app** +We use [FastAPI](https://fastapi.tiangolo.com/) to serve the chat interface and handle WebSocket connections. A key part is configuring the server to load and render HTML templates dynamically for the user interface. + +- **Template Loading**: Use `Jinja2Templates` to load `chat.html` from the `templates` directory. The template is dynamically rendered with variables like the server's `port`. +- **Static Files**: Serve assets (e.g., JavaScript, CSS) from the `static` directory. + +```python +app = FastAPI() + + +@app.get("/", response_class=JSONResponse) +async def index_page() -> dict[str, str]: + return {"message": "WebSocket Audio Stream Server is running!"} + + +website_files_path = Path(__file__).parent / "website_files" + +app.mount( + "/static", StaticFiles(directory=website_files_path / "static"), name="static" +) + +templates = Jinja2Templates(directory=website_files_path / "templates") + + +@app.get("/start-chat/", response_class=HTMLResponse) +async def start_chat(request: Request) -> HTMLResponse: + """Endpoint to return the HTML page for audio chat.""" + port = request.url.port + return templates.TemplateResponse("chat.html", {"request": request, "port": port}) +``` + +### Defining the WebSocket Endpoint + +The `/media-stream` WebSocket route is where real-time audio interaction is processed and streamed to the AI assistant. Let’s break it down step-by-step: + +1. **Accept the WebSocket Connection** + The WebSocket connection is established when a client connects to `/media-stream`. Using `await websocket.accept()`, we ensure the connection is live and ready for communication. + +```python +@app.websocket("/media-stream") +async def handle_media_stream(websocket: WebSocket) -> None: + """Handle WebSocket connections providing audio stream and OpenAI.""" + await websocket.accept() +``` + +2. **Initialize Logging** + A logger instance (`getLogger("uvicorn.error")`) is set up to monitor and debug the server's activities, helping track events during the connection and interaction process. + +```python + logger = getLogger("uvicorn.error") +``` +3. **Set Up the `WebSocketAudioAdapter`** + The [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) bridges the client’s audio stream with the [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent). It streams audio data over [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) in real time, ensuring seamless communication between the browser and the agent. + +```python + audio_adapter = WebSocketAudioAdapter(websocket, logger=logger) +``` + +4. **Configure the Realtime Agent** + The `RealtimeAgent` is the AI assistant driving the interaction. Key parameters include: + - **Name**: The agent identity, here called `"Weather Bot"`. + - **System Message**: System message for the agent. + - **Language Model Configuration**: Defined by `realtime_llm_config` for LLM settings. + - **Audio Adapter**: Connects the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) for handling audio. + - **Logger**: Logs the agent's activities for better observability. + +```python + realtime_agent = RealtimeAgent( + name="Weather Bot", + system_message="Hello there! I am an AI voice assistant powered by Autogen and the OpenAI Realtime API. You can ask me about weather, jokes, or anything you can imagine. Start by saying 'How can I help you'?", + llm_config=realtime_llm_config, + audio_adapter=audio_adapter, + logger=logger, + ) +``` + +5. **Define a Custom Realtime Function** + The `get_weather` function is registered as a realtime callable function. When the user asks about the weather, the agent can call the function to get an accurate weather report and respond based on the provided information: + - Returns `"The weather is cloudy."` for `"Seattle"`. + - Returns `"The weather is sunny."` for other locations. + +```python + @realtime_agent.register_realtime_function( # type: ignore [misc] + name="get_weather", description="Get the current weather" + ) + def get_weather(location: Annotated[str, "city"]) -> str: + return ( + "The weather is cloudy." + if location == "Seattle" + else "The weather is sunny." + ) +``` + +6. **Run the Realtime Agent** + The `await realtime_agent.run()` method starts the agent, handling incoming audio streams, processing user queries, and responding in real time. + +Here is the full code for the `/media-stream` endpoint: + +```python +@app.websocket("/media-stream") +async def handle_media_stream(websocket: WebSocket) -> None: + """Handle WebSocket connections providing audio stream and OpenAI.""" + await websocket.accept() + + logger = getLogger("uvicorn.error") + + audio_adapter = WebSocketAudioAdapter(websocket, logger=logger) + + realtime_agent = RealtimeAgent( + name="Weather Bot", + system_message="Hello there! I am an AI voice assistant powered by Autogen and the OpenAI Realtime API. You can ask me about weather, jokes, or anything you can imagine. Start by saying 'How can I help you'?", + llm_config=realtime_llm_config, + audio_adapter=audio_adapter, + logger=logger, + ) + + @realtime_agent.register_realtime_function( # type: ignore [misc] + name="get_weather", description="Get the current weather" + ) + def get_weather(location: Annotated[str, "city"]) -> str: + return ( + "The weather is cloudy." + if location == "Seattle" + else "The weather is sunny." + ) + + await realtime_agent.run() +``` + +## **Benefits in Action** +- **Quick Prototyping**: Spin up a real-time voice application in minutes. +- **Cost Efficiency**: Eliminate third-party telephony costs. +- **User-Friendly**: Runs in the browser, making it accessible to anyone with a microphone. + +## **Conclusion** +The [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter) marks a shift toward simpler, more accessible real-time audio solutions. It empowers developers to build and deploy voice applications faster and more efficiently. Whether you're creating an AI assistant, a voice-enabled app, or an experimental project, this adapter is your go-to tool for real-time audio streaming. + +Try it out and bring your voice-enabled ideas to life! diff --git a/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_communication_diagram.png b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_communication_diagram.png new file mode 100644 index 0000000000..c9efe92d60 --- /dev/null +++ b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_communication_diagram.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8692f66986a467ea15fccdd314f3fe5e5a8de1d89efa343e4c67253adaf05f81 +size 160477 diff --git a/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_connection_diagram.png b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_connection_diagram.png new file mode 100644 index 0000000000..42b0adb965 --- /dev/null +++ b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/img/webrtc_connection_diagram.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:be16c1e7701e6249ab031c406dc19046f03b15a2156ee33af5cb513c19eda826 +size 164538 diff --git a/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/index.mdx b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/index.mdx new file mode 100644 index 0000000000..3be5485e81 --- /dev/null +++ b/website/_blogs/2025-01-09-RealtimeAgent-over-WebRTC/index.mdx @@ -0,0 +1,307 @@ +--- +title: Real-Time Voice Interactions over WebRTC +authors: + - marklysze + - sternakt + - davorrunje + - davorinrusevljan +tags: [Realtime API, Voice Agents, AI Tools, WebRTC] + +--- + +![Realtime agent communication over WebRTC](img/webrtc_communication_diagram.png) + +**TL;DR:** +- Build a real-time voice application using [WebRTC](https://webrtc.org/) and connect it with the [**`RealtimeAgent`**](/docs/reference/agentchat/realtime_agent/realtime_agent). [Demo implementation](https://github.com/ag2ai/realtime-agent-over-webrtc). +- **Optimized for Real-Time Interactions**: Experience seamless voice communication with minimal latency and enhanced reliability. + +# **Realtime Voice Applications with WebRTC** + +In our [previous blog post](/blog/2025-01-08-RealtimeAgent-over-websocket), we introduced the [**`WebSocketAudioAdapter`**](/docs/reference/agentchat/realtime_agent/websocket_audio_adapter#websocketaudioadapter), a simple way to stream real-time audio using [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/). While effective, [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) can face challenges with quality and reliability in high-latency or network-variable scenarios. Enter [WebRTC](https://webrtc.org/). + +Today, we’re excited to showcase the integration with [OpenAI Realtime API with WebRTC](https://platform.openai.com/docs/guides/realtime-webrtc), leveraging WebRTC’s peer-to-peer communication capabilities to provide a robust, low-latency, high-quality audio streaming experience directly from the browser. + +## **Why WebRTC?** +[WebRTC](https://webrtc.org/) (Web Real-Time Communication) is a powerful technology for enabling direct peer-to-peer communication between browsers and servers. It was built with real-time audio, video, and data transfer in mind, making it an ideal choice for real-time voice applications. Here are some key benefits: + +### **1. Low Latency** +[WebRTC's](https://webrtc.org/) peer-to-peer design minimizes latency, ensuring natural, fluid conversations. + +### **2. Adaptive Quality** +[WebRTC](https://webrtc.org/) dynamically adjusts audio quality based on network conditions, maintaining a seamless user experience even in suboptimal environments. + +### **3. Secure by Design** +With encryption (DTLS and SRTP) baked into its architecture, [WebRTC](https://webrtc.org/) ensures secure communication between peers. + +### **4. Widely Supported** +[WebRTC](https://webrtc.org/) is supported by all major modern browsers, making it highly accessible for end users. + +## **How It Works** + +This example demonstrates using [WebRTC](https://webrtc.org/) to establish low-latency, real-time interactions with [OpenAI Realtime API with WebRTC](https://platform.openai.com/docs/guides/realtime-webrtc) from a web browser. Here's how it works: + +![Realtime agent communication over WebRTC](img/webrtc_connection_diagram.png) + +1. **Request an Ephemeral API Key** + - The browser connects to your backend via [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) to exchange configuration details, such as the ephemeral key and model information. + - [WebSockets](https://fastapi.tiangolo.com/advanced/websockets/) handle signaling to bootstrap the [WebRTC](https://webrtc.org/) session. + - The browser requests a short-lived API key from your server. + +2. **Generate an Ephemeral API Key** + - Your backend generates an ephemeral key via the OpenAI REST API and returns it. These keys expire after one minute to enhance security. + +3. **Initialize the WebRTC Connection** + - **Audio Streaming**: The browser captures microphone input and streams it to OpenAI while playing audio responses via an `