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-**Describe the bug** -A clear and concise description of what the bug is. This could be anything from: - -1. A typo or text changes that make the lab better.... to -2. minor annoying code that still works.. to -3. breaking changes - -**To Reproduce** -Steps to reproduce the behavior (or some version of this): -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error - -**Expected behavior** -A clear and concise description of what you expected to happen. - -**Screenshots** -If applicable, add screenshots to help explain your problem. - -**Desktop (please complete the following information):** - - OS: [e.g. iOS] - - Browser [e.g. chrome, safari] - - Version [e.g. 22] - -**Smartphone (please complete the following information):** - - Device: [e.g. iPhone6] - - OS: [e.g. iOS8.1] - - Browser [e.g. stock browser, safari] - - Version [e.g. 22] - -**! Additional context !** -- Sagemaker Studio Kernel used -- Instance used -- commit link to the workshop if you're not using the latest diff --git a/03_Model_customization/00_setup.ipynb b/00-fine-tuning-setup.ipynb similarity index 100% rename from 03_Model_customization/00_setup.ipynb rename to 00-fine-tuning-setup.ipynb diff --git a/00_Prerequisites/README.md b/00_Prerequisites/README.md deleted file mode 100644 index 84e0c7c4..00000000 --- a/00_Prerequisites/README.md +++ /dev/null @@ -1,7 +0,0 @@ -# Lab 0 - Introduction to Bedrock - -This lab will walk you through the basics of connecting to the Amazon Bedrock service from Python with boto3. - -First, ensure you've completed the setup in the ['Getting Started' section of the root README](../README.md#Getting-started) - -Then, you'll be ready to walk through the notebook [bedrock_basics.ipynb](bedrock_basics.ipynb), which shows how to install the required packages, connect to Bedrock, and invoke models. Before running any of the labs ensure you've run the [Bedrock boto3 setup notebook](../00_Prerequisites/bedrock_basics.ipynb). diff --git a/00_Prerequisites/bedrock_basics.ipynb b/00_Prerequisites/bedrock_basics.ipynb deleted file mode 100644 index df4a2b89..00000000 --- a/00_Prerequisites/bedrock_basics.ipynb +++ /dev/null @@ -1,1449 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "88a5ab2f-d044-4956-b75b-7408d9c3e323", - "metadata": {}, - "source": [ - "# Amazon Bedrock boto3 Prerequisites\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*\n", - "\n", - "---\n", - "\n", - "In this demo notebook, we demonstrate how to use the [`boto3` Python SDK](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) to work with [Amazon Bedrock](https://aws.amazon.com/bedrock/) Foundation Models.\n", - "\n", - "---" - ] - }, - { - "cell_type": "markdown", - "id": "6aeedd9f-f0a3-4f8e-934d-22f6f7a89de5", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "Run the cells in this section to install the packages needed by the notebooks in this workshop. ⚠️ You will see pip dependency errors, you can safely ignore these errors. ⚠️\n", - "\n", - "IGNORE ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "108c611c-7246-45c4-9f1e-76888b5076eb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%pip install --no-build-isolation --force-reinstall \\\n", - " \"boto3>=1.28.57\" \\\n", - " \"awscli>=1.29.57\" \\\n", - " \"botocore>=1.31.57\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "27610c0f-7de6-4440-8f76-decf30e3c5ca", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Create the boto3 client\n", - "\n", - "Interaction with the Bedrock API is done via the AWS SDK for Python: [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html).\n", - "\n", - "#### Use different clients\n", - "The boto3 provides different clients for Amazon Bedrock to perform different actions. The actions for [`InvokeModel`](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) and [`InvokeModelWithResponseStream`](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html) are supported by Amazon Bedrock Runtime where as other operations, such as [ListFoundationModels](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html), are handled via [Amazon Bedrock client](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_Operations_Amazon_Bedrock.html).\n", - "\n", - "\n", - "#### Use the default credential chain\n", - "\n", - "If you are running this notebook from [Amazon Sagemaker Studio](https://aws.amazon.com/sagemaker/studio/) and your Sagemaker Studio [execution role](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html) has permissions to access Bedrock you can just run the cells below as-is. This is also the case if you are running these notebooks from a computer whose default AWS credentials have access to Bedrock.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae2b2a05-78a9-40ca-9b5e-121030f9ede1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "\n", - "boto3_bedrock = boto3.client('bedrock')" - ] - }, - { - "cell_type": "markdown", - "id": "9e9174c4-326a-463e-92e1-8c7e47111269", - "metadata": {}, - "source": [ - "#### Validate the connection\n", - "\n", - "We can check the client works by trying out the `list_foundation_models()` method, which will tell us all the models available for us to use " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f67b4466-12ff-4975-9811-7a19c6206604", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "boto3_bedrock.list_foundation_models()\n" - ] - }, - { - "cell_type": "markdown", - "id": "2f690043-df45-448f-8fa6-1ea8b06f1087", - "metadata": { - "tags": [] - }, - "source": [ - "---\n", - "\n", - "## `InvokeModel` body and output\n", - "\n", - "The `invoke_model()` method of the Amazon Bedrock runtime client (`InvokeModel` API) will be the primary method we use for most of our Text Generation and Processing tasks - whichever model we're using.\n", - "\n", - "Although the method is shared, the format of input and output varies depending on the foundation model used - as described below:" - ] - }, - { - "cell_type": "markdown", - "id": "a4650fa3-a831-4039-9fd6-749926d35979", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "### Amazon Titan Large\n", - "\n", - "#### Input\n", - "```json\n", - "{ \n", - " \"inputText\": \"\",\n", - " \"textGenerationConfig\" : { \n", - " \"maxTokenCount\": 512,\n", - " \"stopSequences\": [],\n", - " \"temperature\": 0.1, \n", - " \"topP\": 0.9\n", - " }\n", - "}\n", - "```\n", - "\n", - "#### Output\n", - "\n", - "```json\n", - "{\n", - " \"inputTextTokenCount\": 613,\n", - " \"results\": [{\n", - " \"tokenCount\": 219,\n", - " \"outputText\": \"\"\n", - " }]\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "7adb6bee-7654-4269-9127-9afa4e823454", - "metadata": {}, - "source": [ - "### Anthropic Claude\n", - "\n", - "#### Input\n", - "\n", - "```json\n", - "{\n", - " \"prompt\": \"\\n\\nHuman:\\n\\nAnswer:\",\n", - " \"max_tokens_to_sample\": 300,\n", - " \"temperature\": 0.5,\n", - " \"top_k\": 250,\n", - " \"top_p\": 1,\n", - " \"stop_sequences\": [\"\\n\\nHuman:\"]\n", - "}\n", - "```\n", - "\n", - "#### Output\n", - "\n", - "```json\n", - "{\n", - " \"completion\": \"\",\n", - " \"stop_reason\": \"stop_sequence\"\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "fb94aa2f-30da-499b-b3f5-02f102dbb1ea", - "metadata": {}, - "source": [ - "### Stability AI Stable Diffusion XL\n", - "\n", - "#### Input\n", - "\n", - "```json\n", - "{\n", - " \"text_prompts\": [\n", - " {\"text\": \"this is where you place your input text\"}\n", - " ],\n", - " \"cfg_scale\": 10,\n", - " \"seed\": 0,\n", - " \"steps\": 50\n", - "}\n", - "```\n", - "\n", - "#### Output\n", - "\n", - "```json\n", - "{ \n", - " \"result\": \"success\", \n", - " \"artifacts\": [\n", - " {\n", - " \"seed\": 123, \n", - " \"base64\": \"\",\n", - " \"finishReason\": \"SUCCESS\"\n", - " },\n", - " //...\n", - " ]\n", - "}\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "80f4adca-cfc4-439b-84b7-e528398684e3", - "metadata": { - "tags": [] - }, - "source": [ - "---\n", - "\n", - "## Common inference parameter definitions\n", - "\n", - "### Randomness and Diversity\n", - "\n", - "Foundation models generally support the following parameters to control randomness and diversity in the \n", - "response.\n", - "\n", - "**Temperature** – Large language models use probability to construct the words in a sequence. For any \n", - "given next word, there is a probability distribution of options for the next word in the sequence. When \n", - "you set the temperature closer to zero, the model tends to select the higher-probability words. When \n", - "you set the temperature further away from zero, the model may select a lower-probability word.\n", - "\n", - "In technical terms, the temperature modulates the probability density function for the next tokens, \n", - "implementing the temperature sampling technique. This parameter can deepen or flatten the density \n", - "function curve. A lower value results in a steeper curve with more deterministic responses, and a higher \n", - "value results in a flatter curve with more random responses.\n", - "\n", - "**Top K** – Temperature defines the probability distribution of potential words, and Top K defines the cut \n", - "off where the model no longer selects the words. For example, if K=50, the model selects from 50 of the \n", - "most probable words that could be next in a given sequence. This reduces the probability that an unusual \n", - "word gets selected next in a sequence.\n", - "In technical terms, Top K is the number of the highest-probability vocabulary tokens to keep for Top-\n", - "K-filtering - This limits the distribution of probable tokens, so the model chooses one of the highest-\n", - "probability tokens.\n", - "\n", - "**Top P** – Top P defines a cut off based on the sum of probabilities of the potential choices. If you set Top \n", - "P below 1.0, the model considers the most probable options and ignores less probable ones. Top P is \n", - "similar to Top K, but instead of capping the number of choices, it caps choices based on the sum of their \n", - "probabilities.\n", - "For the example prompt \"I hear the hoof beats of ,\" you may want the model to provide \"horses,\" \n", - "\"zebras\" or \"unicorns\" as the next word. If you set the temperature to its maximum, without capping \n", - "Top K or Top P, you increase the probability of getting unusual results such as \"unicorns.\" If you set the \n", - "temperature to 0, you increase the probability of \"horses.\" If you set a high temperature and set Top K or \n", - "Top P to the maximum, you increase the probability of \"horses\" or \"zebras,\" and decrease the probability \n", - "of \"unicorns.\"\n", - "\n", - "### Length\n", - "\n", - "The following parameters control the length of the generated response.\n", - "\n", - "**Response length** – Configures the minimum and maximum number of tokens to use in the generated \n", - "response.\n", - "\n", - "**Length penalty** – Length penalty optimizes the model to be more concise in its output by penalizing \n", - "longer responses. Length penalty differs from response length as the response length is a hard cut off for \n", - "the minimum or maximum response length.\n", - "\n", - "In technical terms, the length penalty penalizes the model exponentially for lengthy responses. 0.0 \n", - "means no penalty. Set a value less than 0.0 for the model to generate longer sequences, or set a value \n", - "greater than 0.0 for the model to produce shorter sequences.\n", - "\n", - "### Repetitions\n", - "\n", - "The following parameters help control repetition in the generated response.\n", - "\n", - "**Repetition penalty (presence penalty)** – Prevents repetitions of the same words (tokens) in responses. \n", - "1.0 means no penalty. Greater than 1.0 decreases repetition." - ] - }, - { - "cell_type": "markdown", - "id": "ce22c308-ebbf-4ef5-a823-832b7c236e31", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## Try out the models\n", - "\n", - "With some theory out of the way, let's see the models in action! Run the cells below to see basic, synchronous example invocations for each model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a0a79b9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import boto3\n", - "import botocore\n", - "import json \n", - "\n", - "bedrock_runtime = boto3.client('bedrock-runtime')\n" - ] - }, - { - "cell_type": "markdown", - "id": "893872fe-04fa-4f09-9736-6c6173ec1fc2", - "metadata": { - "tags": [] - }, - "source": [ - "### Amazon Titan Large" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7df55eed-a3cf-426c-95ea-ec60dade6477", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# If you'd like to try your own prompt, edit this parameter!\n", - "prompt_data = \"\"\"Command: Write me a blog about making strong business decisions as a leader.\n", - "\n", - "Blog:\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "174a8eb1-f9a4-4946-bfe2-550d21487f48", - "metadata": {}, - "source": [ - "Next, we will construct the body with the `prompt_data` above, and add a optional parameters like `topP` and `temperature`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dd2bb671-6b10-4948-9e5e-95d6ced3b86f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "try:\n", - "\n", - " body = json.dumps({\"inputText\": prompt_data, \"textGenerationConfig\" : {\"topP\":0.95, \"temperature\":0.2}})\n", - " modelId = \"amazon.titan-tg1-large\"\n", - " accept = \"application/json\"\n", - " contentType = \"application/json\"\n", - "\n", - " response = bedrock_runtime.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - "\n", - " print(response_body.get(\"results\")[0].get(\"outputText\"))\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "3d7c0fe6-576a-4380-89aa-726bab5d65ff", - "metadata": { - "tags": [] - }, - "source": [ - "### Anthropic Claude" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7a725de2-bdea-4d86-b12d-d1d7cdda010b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# If you'd like to try your own prompt, edit this parameter!\n", - "prompt_data = \"\"\"Human: Write me a blog about making strong business decisions as a leader.\n", - "\n", - "Assistant:\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ba33ac0-fa16-4c4f-b882-e838d0cb5830", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps({\"prompt\": prompt_data, \"max_tokens_to_sample\": 500})\n", - "modelId = \"anthropic.claude-instant-v1\" # change this to use a different version from the model provider\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "try:\n", - "\n", - " response = bedrock_runtime.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - "\n", - " print(response_body.get(\"completion\"))\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "bc498bea", - "metadata": { - "tags": [] - }, - "source": [ - "### Stability Stable Diffusion XL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "173e51a2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt_data = \"a landscape with trees\"\n", - "body = json.dumps({\n", - " \"text_prompts\": [{\"text\": prompt_data}],\n", - " \"cfg_scale\": 10,\n", - " \"seed\": 20,\n", - " \"steps\": 50\n", - "})\n", - "modelId = \"stability.stable-diffusion-xl-v1\"\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "try:\n", - "\n", - " response = bedrock_runtime.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - "\n", - " print(response_body[\"result\"])\n", - " print(f'{response_body.get(\"artifacts\")[0].get(\"base64\")[0:80]}...')\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "1a271fa6-13fd-480a-87a5-3702d29a5c43", - "metadata": {}, - "source": [ - "**Note:** The output is a [base64 encoded](https://docs.python.org/3/library/base64.html) string of the image data. You can use any image processing library (such as [Pillow](https://pillow.readthedocs.io/en/stable/)) to decode the image as in the example below:\n", - "\n", - "```python\n", - "import base64\n", - "import io\n", - "from PIL import Image\n", - "\n", - "base_64_img_str = response_body.get(\"artifacts\")[0].get(\"base64\")\n", - "image = Image.open(io.BytesIO(base64.decodebytes(bytes(base_64_img_str, \"utf-8\"))))\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45072848-000a-4c22-8f08-2647e5c2230e", - "metadata": {}, - "outputs": [], - "source": [ - "import base64\n", - "import io\n", - "from PIL import Image\n", - "\n", - "base_64_img_str = response_body.get(\"artifacts\")[0].get(\"base64\")\n", - "image = Image.open(io.BytesIO(base64.decodebytes(bytes(base_64_img_str, \"utf-8\"))))\n", - "image" - ] - }, - { - "cell_type": "markdown", - "id": "4621a301-53e4-4182-9fce-8ee422813e9d", - "metadata": {}, - "source": [ - "## Generate streaming output\n", - "\n", - "For large language models, it can take noticeable time to generate long output sequences. Rather than waiting for the entire response to be available, latency-sensitive applications may like to **stream** the response to users.\n", - "\n", - "Run the code below to see how you can achieve this with Bedrock's `invoke_model_with_response_stream()` method - returning the response body in separate chunks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c69627e3", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import clear_output, display, display_markdown, Markdown\n", - "prompt_data = \"\"\"Command: Write me a blog about making strong business decisions as a leader.\n", - "\n", - "Blog:\n", - "\"\"\"\n", - "\n", - "body = json.dumps({\"inputText\": prompt_data})\n", - "modelId = \"amazon.titan-tg1-large\" # (Change this, and the request body, to try different models)\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "try:\n", - "\n", - " response = bedrock_runtime.invoke_model_with_response_stream(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " stream = response.get('body')\n", - " output = []\n", - "\n", - " if stream:\n", - " for event in stream:\n", - " chunk = event.get('chunk')\n", - " if chunk:\n", - " chunk_obj = json.loads(chunk.get('bytes').decode())\n", - " if 'outputText' in chunk_obj:\n", - " text = chunk_obj.get('outputText', None)\n", - " print(text,end='')\n", - " if not text :\n", - " break\n", - " #text = chunk_obj['outputText']\n", - " clear_output(wait=True)\n", - " output.append(text)\n", - " display_markdown(Markdown(''.join(output)))\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "adf097eb-1bfa-41f1-8135-cdd4ad6e9983", - "metadata": {}, - "source": [ - "### Anthropic Claude (messages API)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bd800ef5-78f7-43d4-b73e-c37c7609cb40", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# If you'd like to try your own prompt, edit this parameter!\n", - "prompt_data = \"\"\"Human: Write me 500 word paragraph about making strong business decisions as a leader.\n", - "\n", - "Assistant:\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ef24e0c-dda8-4f5d-bbcb-db389e67e713", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "messages_API_body = {\n", - " \"anthropic_version\": \"bedrock-2023-05-31\", \n", - " \"max_tokens\": 512,\n", - " \"messages\": [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": [\n", - " {\n", - " \"type\": \"text\",\n", - " \"text\": prompt_data\n", - " }\n", - " ]\n", - " }\n", - " ]\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be7fbbe8-78b8-4071-b80c-3f954185fad8", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "from IPython.display import clear_output, display, display_markdown, Markdown\n", - "\n", - "body = json.dumps(messages_API_body)\n", - "modelId = \"anthropic.claude-v2\" # (Change this to try different model versions)\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "try:\n", - "\n", - " response = bedrock_runtime.invoke_model_with_response_stream(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " \n", - " stream = response.get('body')\n", - " \n", - " \n", - " output = []\n", - "\n", - " if stream:\n", - " for event in stream:\n", - " chunk = event.get('chunk')\n", - " if chunk:\n", - " chunk_obj = json.loads(chunk.get('bytes').decode())\n", - " if 'delta' in chunk_obj:\n", - " delta_obj = chunk_obj.get('delta', None)\n", - " if delta_obj:\n", - " text = delta_obj.get('text', None)\n", - " print(text,end='')\n", - " if not text :\n", - " break\n", - " # output.append(text[0]) if type(text) is list and len(text)>0 else output.append('')\n", - " # display_markdown(Markdown(text))\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "1ef3451d-b66a-4b11-a1ed-734bf9e7bbec", - "metadata": {}, - "source": [ - "## Generate embeddings\n", - "\n", - "Use text embeddings to convert text into meaningful vector representations. You input a body of text \n", - "and the output is a (1 x n) vector. You can use embedding vectors for a wide variety of applications. \n", - "Bedrock currently offers Titan Embeddings for text embedding that supports text similarity (finding the \n", - "semantic similarity between bodies of text) and text retrieval (such as search).\n", - "\n", - "At the time of writing you can use `amazon.titan-embed-text-v1` as embedding model via the API. The input text size is 8192 tokens and the output vector length is 1536.\n", - "\n", - "To use a text embeddings model, use the InvokeModel API operation or the Python SDK.\n", - "Use InvokeModel to retrieve the vector representation of the input text from the specified model.\n", - "\n", - "\n", - "\n", - "#### Input\n", - "\n", - "```json\n", - "{\n", - " \"inputText\": \"\"\n", - "}\n", - "```\n", - "\n", - "#### Output\n", - "\n", - "```json\n", - "{\n", - " \"embedding\": []\n", - "}\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "id": "9645dbd8", - "metadata": {}, - "source": [ - "Let's see how to generate embeddings of some text:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1085cc56", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt_data = \"Amazon Bedrock supports foundation models from industry-leading providers such as \\\n", - "AI21 Labs, Anthropic, Stability AI, and Amazon. Choose the model that is best suited to achieving \\\n", - "your unique goals.\"\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5c54b424", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps({\"inputText\": prompt_data})\n", - "modelId = \"amazon.titan-embed-text-v1\" # (Change this to try different embedding models)\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "try:\n", - "\n", - " response = bedrock_runtime.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - "\n", - " embedding = response_body.get(\"embedding\")\n", - " print(f\"The embedding vector has {len(embedding)} values\\n{embedding[0:3]+['...']+embedding[-3:]}\")\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - "\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - "\n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "markdown", - "id": "5a48a0e8-147d-4525-a6b2-68a09af1b2c4", - "metadata": {}, - "source": [ - "## Next steps\n", - "\n", - "In this notebook we showed some basic examples of invoking Amazon Bedrock models using the AWS Python SDK. You're now ready to explore the other labs to dive deeper on different use-cases and patterns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f8bb76df-4e99-4ebe-a954-53992ad317dc", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - 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"memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 57, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/03_Model_customization/02_fine-tuning_llama2.ipynb b/01-fine-tuning-llama.ipynb similarity index 100% rename from 03_Model_customization/02_fine-tuning_llama2.ipynb rename to 01-fine-tuning-llama.ipynb diff --git a/01_Text_generation/00_text_generation_w_bedrock.ipynb b/01_Text_generation/00_text_generation_w_bedrock.ipynb deleted file mode 100644 index 7811c05e..00000000 --- a/01_Text_generation/00_text_generation_w_bedrock.ipynb +++ /dev/null @@ -1,893 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dc40c48b-0c95-4757-a067-563cfccd51a5", - "metadata": { - "tags": [] - }, - "source": [ - "# Invoke Bedrock model for text generation using zero-shot prompt\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*" - ] - }, - { - "cell_type": "markdown", - "id": "c9a413e2-3c34-4073-9000-d8556537bb6a", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this notebook we show you how to use a LLM to generate an email response to a customer who provided negative feedback on the quality of customer service that they received from the support engineer. \n", - "\n", - "We will use Bedrock's Amazon Titan Text large model using the Boto3 API. \n", - "\n", - "The prompt used in this example is called a zero-shot prompt because we are not providing any examples of text alongside their classification other than the prompt.\n", - "\n", - "**Note:** *This notebook can be run within or outside of AWS environment.*\n", - "\n", - "#### Context\n", - "To demonstrate the text generation capability of Amazon Bedrock, we will explore the use of Boto3 client to communicate with Amazon Bedrock API. We will demonstrate different configurations available as well as how simple input can lead to desired outputs.\n", - "\n", - "#### Pattern\n", - "We will simply provide the Amazon Bedrock API with an input consisting of a task, an instruction and an input for the model under the hood to generate an output without providing any additional example. The purpose here is to demonstrate how the powerful LLMs easily understand the task at hand and generate compelling outputs.\n", - "\n", - "![](./images/bedrock.jpg)\n", - "\n", - "#### Use case\n", - "To demonstrate the generation capability of models in Amazon Bedrock, let's take the use case of email generation.\n", - "\n", - "#### Persona\n", - "You are Bob a Customer Service Manager at AnyCompany and some of your customers are not happy with the customer service and are providing negative feedbacks on the service provided by customer support engineers. Now, you would like to respond to those customers humbly aplogizing for the poor service and regain trust. You need the help of an LLM to generate a bulk of emails for you which are human friendly and personalized to the customer's sentiment from previous email correspondence.\n", - "\n", - "#### Implementation\n", - "To fulfill this use case, in this notebook we will show how to generate an email with a thank you note based on the customer's previous email.We will use the Amazon Titan Text Large model using the Amazon Bedrock API with Boto3 client. " - ] - }, - { - "cell_type": "markdown", - "id": "64baae27-2660-4a1e-b2e5-3de49d069362", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "⚠️ ⚠️ ⚠️ Before running this notebook, ensure you've run the [Bedrock basics notebook](../00_Prerequisites/bedrock_basics.ipynb) notebook. ⚠️ ⚠️ ⚠️\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "776fd083", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "id": "4f634211-3de1-4390-8c3f-367af5554c39", - "metadata": {}, - "source": [ - "## Generate text\n", - "\n", - "Following on the use case explained above, let's prepare an input for the Amazon Bedrock service to generate an email. Note that this prompt would need to be modified with [Human:/Assistant: formatting for Claude.](https://docs.anthropic.com/claude/docs/human-and-assistant-formatting)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45ee2bae-6415-4dba-af98-a19028305c98", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# create the prompt\n", - "prompt_data = \"\"\"\n", - "Command: Write an email from Bob, Customer Service Manager, to the customer \"John Doe\" \n", - "who provided negative feedback on the service provided by our customer support \n", - "engineer\"\"\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "cc9784e5-5e9d-472d-8ef1-34108ee4968b", - "metadata": {}, - "source": [ - "Let's start by using the Amazon Titan Large model. Amazon Titan Large supports a context window of ~4k tokens and accepts the following parameters:\n", - "- `inputText`: Prompt to the LLM\n", - "- `textGenerationConfig`: These are the parameters that model will take into account while generating the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8af670eb-ad02-40df-a19c-3ed835fac8d9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps({\n", - " \"inputText\": prompt_data, \n", - " \"textGenerationConfig\":{\n", - " \"maxTokenCount\":4096,\n", - " \"stopSequences\":[],\n", - " \"temperature\":0,\n", - " \"topP\":0.9\n", - " }\n", - " }) " - ] - }, - { - "cell_type": "markdown", - "id": "c4ca6751", - "metadata": {}, - "source": [ - "The Amazon Bedrock API provides you with an API `invoke_model` which accepts the following:\n", - "- `modelId`: This is the model ARN for the various foundation models available under Amazon Bedrock\n", - "- `accept`: The type of input request\n", - "- `contentType`: The content type of the output\n", - "- `body`: A json string consisting of the prompt and the configurations\n", - "\n", - "Check [documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html) for Available text generation model Ids" - ] - }, - { - "cell_type": "markdown", - "id": "088cf6bf-dd73-4710-a0cc-6c11d220c431", - "metadata": {}, - "source": [ - "#### Invoke the Amazon Titan Large language model" - ] - }, - { - "cell_type": "markdown", - "id": "379498f2", - "metadata": {}, - "source": [ - "First, we explore how the model generates an output based on the prompt created earlier.\n", - "\n", - "##### Complete Output Generation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecaceef1-0f7f-4ae5-8007-ff7c25335251", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "modelId = 'amazon.titan-tg1-large' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "outputText = \"\\n\"\n", - "try:\n", - "\n", - " response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - " response_body = json.loads(response.get('body').read())\n", - "\n", - " outputText = response_body.get('results')[0].get('outputText')\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - " \n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - " \n", - " else:\n", - " raise error\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3748383a-c140-407f-a7f6-8f140ad57680", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# The relevant portion of the response begins after the first newline character\n", - "# Below we print the response beginning after the first occurence of '\\n'.\n", - "\n", - "email = outputText[outputText.index('\\n')+1:]\n", - "print(email)\n" - ] - }, - { - "cell_type": "markdown", - "id": "2d69e1a0", - "metadata": {}, - "source": [ - "##### Streaming Output Generation\n", - "Above is an example email generated by the Amazon Titan Large model by understanding the input request and using its inherent understanding of the different modalities. This request to the API is synchronous and waits for the entire output to be generated by the model.\n", - "\n", - "Bedrock also supports that the output can be streamed as it is generated by the model in form of chunks. Below is an example of invoking the model with streaming option. `invoke_model_with_response_stream` returns a `ResponseStream` which you can read from." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ad073290", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "output = []\n", - "try:\n", - " \n", - " response = boto3_bedrock.invoke_model_with_response_stream(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - " stream = response.get('body')\n", - " \n", - " i = 1\n", - " if stream:\n", - " for event in stream:\n", - " chunk = event.get('chunk')\n", - " if chunk:\n", - " chunk_obj = json.loads(chunk.get('bytes').decode())\n", - " text = chunk_obj['outputText']\n", - " output.append(text)\n", - " print(f'\\t\\t\\x1b[31m**Chunk {i}**\\x1b[0m\\n{text}\\n')\n", - " i+=1\n", - " \n", - "except botocore.exceptions.ClientError as error:\n", - " \n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - " \n", - " else:\n", - " raise error" - ] - }, - { - "cell_type": "markdown", - "id": "9a788be5", - "metadata": {}, - "source": [ - "The above helps to quickly get output of the model and let the service complete it as you read. This assists in use-cases where there are longer pieces of text that you request the model to generate. You can later combine all the chunks generated to form the complete output and use it for your use-case" - ] - }, - { - "cell_type": "markdown", - "id": "64b08b3b", - "metadata": {}, - "source": [ - "## Conclusion\n", - "You have now experimented with using `boto3` SDK which provides a vanilla exposure to Amazon Bedrock API. Using this API you have seen the use case of generating an email responding to a customer due to their negative feedback.\n", - "\n", - "### Take aways\n", - "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Anthropic Claude and AI21 Labs Jurassic models.\n", - "- Change the prompts to your specific usecase and evaluate the output of different models.\n", - "- Play with the token length to understand the latency and responsiveness of the service.\n", - "- Apply different prompt engineering principles to get better outputs.\n", - "\n", - "## Thank You" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cdf9261-1002-4da7-9124-943f72b43486", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - 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}, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/01_Text_generation/01_code_generation_w_bedrock.ipynb b/01_Text_generation/01_code_generation_w_bedrock.ipynb deleted file mode 100644 index 845513b2..00000000 --- a/01_Text_generation/01_code_generation_w_bedrock.ipynb +++ /dev/null @@ -1,1071 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "dc40c48b-0c95-4757-a067-563cfccd51a5", - "metadata": { - "tags": [] - }, - "source": [ - "# Invoke Bedrock model for code generation\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*" - ] - }, - { - "cell_type": "markdown", - "id": "c9a413e2-3c34-4073-9000-d8556537bb6a", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "In this notebook we show you how to use a LLM to generate code based on the text prompt. We will use Bedrock's Claude v2 using the Boto3 API. \n", - "\n", - "The prompt used in this example is called a zero-shot prompt because we are not providing any examples of text other than the prompt.\n", - "\n", - "**Note:** *This notebook can be run within or outside of AWS environment.*\n", - "\n", - "#### Context\n", - "To demonstrate the code generation capability of Amazon Bedrock, we will explore the use of Boto3 client to communicate with Amazon Bedrock API. We will demonstrate different configurations available as well as how simple input can lead to desired outputs. We will explore code generation for two use cases:\n", - "1. Python code generation for analytical QnA\n", - "2. SQL query generation\n", - "\n", - "#### Pattern\n", - "In both use cases, we will simply provide the Amazon Bedrock API with an input consisting of a task, an instruction and an input for the model under the hood to generate an output without providing any additional example. The purpose here is to demonstrate how the powerful LLMs easily understand the task at hand and generate compelling outputs.\n", - "\n", - "![](../imgs/bedrock-code-gen.png)\n", - "\n", - "## Use case 1 - Python code generation for Analytical QnA\n", - "To demonstrate the generation capability of models in Amazon Bedrock, let's take the use case of code generation with Python to do some basic analytical QnA.\n", - "\n", - "#### Persona\n", - "\n", - "You are Moe, a Data Analyst, at AnyCompany. The company wants to understand its sales performance for different products for different products over the past year. You have been provided a dataset named sales.csv. The dataset contains the following columns:\n", - "\n", - "- Date (YYYY-MM-DD) format\n", - "- Product_ID (unique identifer for each product)\n", - "- Price (price at which each product was sold)\n", - "\n", - "#### Implementation\n", - "To fulfill this use case, in this notebook we will show how to generate code for a given prompt. We will use the Anthropic Claude v2 using the Amazon Bedrock API with Boto3 client. " - ] - }, - { - "cell_type": "markdown", - "id": "64baae27-2660-4a1e-b2e5-3de49d069362", - "metadata": {}, - "source": [ - "## Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "776fd083", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "id": "4f634211-3de1-4390-8c3f-367af5554c39", - "metadata": {}, - "source": [ - "## Code Generation\n", - "\n", - "Following on the use case explained above, let's prepare an input for the Amazon Bedrock service to generate python program for our use-case." - ] - }, - { - "cell_type": "markdown", - "id": "e7656be8", - "metadata": {}, - "source": [ - "#### Lab setup - create sample sales.csv data for this lab.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89a0ad24", - "metadata": {}, - "outputs": [], - "source": [ - "# create sales.csv file\n", - "import csv\n", - "\n", - "data = [\n", - " [\"date\", \"product_id\", \"price\", \"units_sold\"],\n", - " [\"2023-01-01\", \"P001\", 50, 20],\n", - " [\"2023-01-02\", \"P002\", 60, 15],\n", - " [\"2023-01-03\", \"P001\", 50, 18],\n", - " [\"2023-01-04\", \"P003\", 70, 30],\n", - " [\"2023-01-05\", \"P001\", 50, 25],\n", - " [\"2023-01-06\", \"P002\", 60, 22],\n", - " [\"2023-01-07\", \"P003\", 70, 24],\n", - " [\"2023-01-08\", \"P001\", 50, 28],\n", - " [\"2023-01-09\", \"P002\", 60, 17],\n", - " [\"2023-01-10\", \"P003\", 70, 29],\n", - " [\"2023-02-11\", \"P001\", 50, 23],\n", - " [\"2023-02-12\", \"P002\", 60, 19],\n", - " [\"2023-02-13\", \"P001\", 50, 21],\n", - " [\"2023-02-14\", \"P003\", 70, 31],\n", - " [\"2023-03-15\", \"P001\", 50, 26],\n", - " [\"2023-03-16\", \"P002\", 60, 20],\n", - " [\"2023-03-17\", \"P003\", 70, 33],\n", - " [\"2023-04-18\", \"P001\", 50, 27],\n", - " [\"2023-04-19\", \"P002\", 60, 18],\n", - " [\"2023-04-20\", \"P003\", 70, 32],\n", - " [\"2023-04-21\", \"P001\", 50, 22],\n", - " [\"2023-04-22\", \"P002\", 60, 16],\n", - " [\"2023-04-23\", \"P003\", 70, 34],\n", - " [\"2023-05-24\", \"P001\", 50, 24],\n", - " [\"2023-05-25\", \"P002\", 60, 21]\n", - "]\n", - "\n", - "# Write data to sales.csv\n", - "with open('sales.csv', 'w', newline='') as csvfile:\n", - " writer = csv.writer(csvfile)\n", - " writer.writerows(data)\n", - "\n", - "print(\"sales.csv has been created!\")" - ] - }, - { - "cell_type": "markdown", - "id": "d68e8af6", - "metadata": {}, - "source": [ - "#### Analyzing sales with Amazon Bedrock generated Python program" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "45ee2bae-6415-4dba-af98-a19028305c98", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Create the prompt\n", - "# Analyzing sales\n", - "\n", - "prompt_data = \"\"\"\n", - "\n", - "Human: You have a CSV, sales.csv, with columns:\n", - "- date (YYYY-MM-DD)\n", - "- product_id\n", - "- price\n", - "- units_sold\n", - "\n", - "Create a python program to analyze the sales data from a CSV file. The program should be able to read the data, and determine below:\n", - "\n", - "- Total revenue for the year\n", - "- The product with the highest revenue\n", - "- The date with the highest revenue\n", - "- Visualize monthly sales using a bar chart\n", - "\n", - "Ensure the code is syntactically correct, bug-free, optimized, not span multiple lines unnessarily, and prefer to use standard libraries. Return only python code without any surrounding text, explanation or context.\n", - "\n", - "Assistant:\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "cc9784e5-5e9d-472d-8ef1-34108ee4968b", - "metadata": {}, - "source": [ - "Let's start by using the Anthropic Claude V2 model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8af670eb-ad02-40df-a19c-3ed835fac8d9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Claude - Body Syntex\n", - "body = json.dumps({\n", - " \"prompt\": prompt_data,\n", - " \"max_tokens_to_sample\":4096,\n", - " \"temperature\":0.5,\n", - " \"top_k\":250,\n", - " \"top_p\":0.5,\n", - " \"stop_sequences\": [\"\\n\\nHuman:\"]\n", - " }) " - ] - }, - { - "cell_type": "markdown", - "id": "088cf6bf-dd73-4710-a0cc-6c11d220c431", - "metadata": {}, - "source": [ - "#### Invoke the Anthropic Claude v2 model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "016a118a", - "metadata": {}, - "outputs": [], - "source": [ - "modelId = 'anthropic.claude-v2' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "\n", - "response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - "response_body = json.loads(response.get('body').read())\n", - "\n", - "response_body.get('completion')" - ] - }, - { - "cell_type": "markdown", - "id": "ddddd1ec", - "metadata": {}, - "source": [ - "#### (Optional) Execute the Bedrock generated code for validation. Go to text editor to copy the generated code as printed output can be trucncated. Replace the code in below cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77d9b428", - "metadata": {}, - "outputs": [], - "source": [ - "# Sample Generated Python Code ( Generated with Amazon Bedrock in previous step)\n", - "\n", - "import csv\n", - "from collections import defaultdict\n", - "import matplotlib.pyplot as plt\n", - "\n", - "revenue = 0\n", - "monthly_revenue = defaultdict(int)\n", - "product_revenue = defaultdict(int)\n", - "max_revenue = 0\n", - "max_revenue_date = ''\n", - "max_revenue_product = ''\n", - "\n", - "with open('sales.csv') as f:\n", - " reader = csv.reader(f)\n", - " next(reader)\n", - " for row in reader:\n", - " date = row[0]\n", - " product = row[1]\n", - " price = float(row[2])\n", - " units = int(row[3])\n", - "\n", - " revenue += price * units\n", - " product_revenue[product] += price * units\n", - " monthly_revenue[date[:7]] += price * units\n", - "\n", - " if revenue > max_revenue:\n", - " max_revenue = revenue\n", - " max_revenue_date = date\n", - " max_revenue_product = product\n", - "\n", - "months = list(monthly_revenue.keys())\n", - "values = list(monthly_revenue.values())\n", - "\n", - "plt.bar(months, values)\n", - "plt.xlabel('Month')\n", - "plt.ylabel('Revenue')\n", - "plt.title('Monthly Revenue')\n", - "plt.show()\n", - "\n", - "print('Total Revenue:', revenue)\n", - "print('Product with max revenue:', max_revenue_product)\n", - "print('Date with max revenue:', max_revenue_date)" - ] - }, - { - "cell_type": "markdown", - "id": "7520ebf6-bf5e-4d36-af25-fbe71a2440b7", - "metadata": {}, - "source": [ - "___" - ] - }, - { - "cell_type": "markdown", - "id": "0c094a43-f276-414d-98b9-fa658b67659d", - "metadata": {}, - "source": [ - "## Use case 2 - SQL query generation\n", - "\n", - "To demonstrate the generation capability of models in Amazon Bedrock, let's take the use case of code generation with Python to do some basic analytical QnA.\n", - "\n", - "In this section we show you how to use a LLM to generate SQL Query to analyze Sales data.\n", - "\n", - "We will use Bedrock's Claude V2 model using the Boto3 API. \n", - "\n", - "The prompt used in this example is called a zero-shot prompt because we are not providing any examples of text other than the prompt.\n", - "\n", - "**Note:** *This notebook can be run within or outside of AWS environment.*\n", - "\n", - "#### Context\n", - "To demonstrate the SQL code generation capability of Amazon Bedrock, we will explore the use of Boto3 client to communicate with Amazon Bedrock API. We will demonstrate different configurations available as well as how simple input can lead to desired outputs.\n", - "\n", - "#### Pattern\n", - "We will simply provide the Amazon Bedrock API with an input consisting of a task, an instruction and an input for the model under the hood to generate an output without providing any additional example. The purpose here is to demonstrate how the powerful LLMs easily understand the task at hand and generate compelling outputs.\n", - "\n", - "#### Use case\n", - "Let's take the use case to generate SQL queries to analyze sales data, focusing on trends, top products and average sales.\n", - "\n", - "#### Persona\n", - "Maya is a business analyst, at AnyCompany primarily focusing on sales and inventory data. She is transitioning from Speadsheet analysis to data-driven analysis and want to use SQL to fetch specific data points effectively. She wants to use LLMs to generate SQL queries for her analysis. \n", - "\n", - "#### Implementation\n", - "To fulfill this use case, in this notebook we will show how to generate SQL queries. We will use the Anthropic Claude v2 model using the Amazon Bedrock API with Boto3 client. " - ] - }, - { - "cell_type": "markdown", - "id": "5dab2f38-301c-486a-8587-cd7e6062c61e", - "metadata": {}, - "source": [ - "### Generate SQL Query\n", - "\n", - "Following on the use case explained above, let's prepare an input for the Amazon Bedrock service to generate SQL query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "479e7f28-230e-49ff-836f-801ffd4bdbe0", - "metadata": {}, - "outputs": [], - "source": [ - "# create the prompt to generate SQL query\n", - "prompt_data = \"\"\"\n", - "\n", - "Human: AnyCompany has a database with a table named sales_data containing sales records. The table has following columns:\n", - "- date (YYYY-MM-DD)\n", - "- product_id\n", - "- price\n", - "- units_sold\n", - "\n", - "Can you generate SQL queries for the below: \n", - "- Identify the top 5 best selling products by total sales for the year 2023\n", - "- Calculate the monthly average sales for the year 2023\n", - "\n", - "Assistant:\n", - "\"\"\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "6d8a80fc-95d8-467e-9016-e6bf244e8d4f", - "metadata": {}, - "source": [ - "Let's start by using the Anthorpic Claude v2 model. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fc4b2a1-d13d-44ba-89df-773fcbafb609", - "metadata": {}, - "outputs": [], - "source": [ - "# Claude - Body Syntex\n", - "body = json.dumps({\n", - " \"prompt\": prompt_data,\n", - " \"max_tokens_to_sample\":4096,\n", - " \"temperature\":0.5,\n", - " \"top_k\":250,\n", - " \"top_p\":0.5,\n", - " \"stop_sequences\": [\"\\n\\nHuman:\"]\n", - " }) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3d45353-2137-46f5-be93-6b7ecc21cd2c", - "metadata": {}, - "outputs": [], - "source": [ - "modelId = 'anthropic.claude-v2' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "\n", - "response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - "response_body = json.loads(response.get('body').read())\n", - "\n", - "print(response_body.get('completion'))" - ] - }, - { - "cell_type": "markdown", - "id": "64b08b3b", - "metadata": {}, - "source": [ - "## Conclusion\n", - "You have now experimented with using `boto3` SDK which provides a vanilla exposure to Amazon Bedrock API. Using this API you generate a python program to analyze and visualize given sales data, and generate SQL statements based on an input task and schema.\n", - "\n", - "### Take aways\n", - "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Amazon Titan and AI21 Labs Jurassic models!\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fe7ae02d-4b4e-470a-bfbe-9cbdd30b8db6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - 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"gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - }, - "vscode": { - "interpreter": { - "hash": "00878cbed564b904a98b4a19808853cb6b9988746b881ea025a8408713879bf5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/01_Text_generation/02_text-summarization-titan+claude.ipynb b/01_Text_generation/02_text-summarization-titan+claude.ipynb deleted file mode 100644 index e74d3644..00000000 --- a/01_Text_generation/02_text-summarization-titan+claude.ipynb +++ /dev/null @@ -1,921 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "fded102b", - "metadata": {}, - "source": [ - "# Text summarization with small files with Amazon Titan\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*" - ] - }, - { - "cell_type": "markdown", - "id": "fab8b2cf", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "In this example, you are going to ingest a small amount of data (String data) directly into Amazon Bedrock API (using Amazon Titan model) and give it an instruction to summarize the respective text.\n", - "\n", - "### Architecture\n", - "\n", - "![](../imgs/41-text-simple-1.png)\n", - "\n", - "In this architecture:\n", - "\n", - "1. A small piece of text (or small file) is loaded\n", - "1. A foundation model processes those data\n", - "1. Model returns a response with the summary of the ingested text\n", - "\n", - "### Use case\n", - "\n", - "This approach can be used to summarize call transcripts, meetings transcripts, books, articles, blog posts, and other relevant content.\n", - "\n", - "### Challenges\n", - "This approach can be used when the input text or file fits within the model context length. In notebook `06_OpenSource_examples/00_Langchain_TextGeneration_examples/04_long text summarization using LCEL chains on Langchain.ipynb`, we will explore an approach to address the challenge when users have large document(s) that exceed the token limit." - ] - }, - { - "cell_type": "markdown", - "id": "e9c888b8", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "⚠️ ⚠️ ⚠️ Before running this notebook, ensure you've run the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites) notebook. ⚠️ ⚠️ ⚠️" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e86d86b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "id": "342796d0", - "metadata": {}, - "source": [ - "## Summarizing a short text with boto3\n", - " \n", - "To learn detail of API request to Amazon Bedrock, this notebook introduces how to create API request and send the request via Boto3 rather than relying on langchain, which gives simpler API by wrapping Boto3 operation. " - ] - }, - { - "cell_type": "markdown", - "id": "9da4d9ee", - "metadata": {}, - "source": [ - "### Request Syntax of InvokeModel in Boto3\n", - "\n", - "\n", - "We use `InvokeModel` API for sending request to a foundation model. Here is an example of API request for sending text to Amazon Titan Text Large. Inference parameters in `textGenerationConfig` depends on the model that you are about to use. Inference paramerters of Amazon Titan Text are:\n", - "- **maxTokenCount** configures the max number of tokens to use in the generated response. (int, defaults to 512)\n", - "- **stopSequences** is used to make the model stop at a desired point, such as the end of a sentence or a list. The returned response will not contain the stop sequence.\n", - "- **temperature** modulates the probability density function for the next tokens, implementing the temperature sampling technique. This parameter can be used to deepen or flatten the density function curve. A lower value results in a steeper curve and more deterministic responses, whereas a higher value results in a flatter curve and more random responses. (float, defaults to 0, max value is 1.5)\n", - "- **topP** controls token choices, based on the probability of the potential choices. If you set Top P below 1.0, the model considers only the most probable options and ignores less probable options. The result is more stable and repetitive completions.\n", - "\n", - "```python\n", - "response = bedrock.invoke_model(body={\n", - " \"inputText\": \"this is where you place your input text\",\n", - " \"textGenerationConfig\": {\n", - " \"maxTokenCount\": 4096,\n", - " \"stopSequences\": [],\n", - " \"temperature\":0,\n", - " \"topP\":1\n", - " },\n", - " },\n", - " modelId=\"amazon.titan-tg1-large\", \n", - " accept=accept, \n", - " contentType=contentType)\n", - "\n", - "```\n", - "\n", - "### Writing prompt with text to be summarized\n", - "\n", - "In this notebook, you can use any short text whose tokens are less than the maximum token of a foundation model. As an exmple of short text, let's take one paragraph of an [AWS blog post](https://aws.amazon.com/jp/blogs/machine-learning/announcing-new-tools-for-building-with-generative-ai-on-aws/) about announcement of Amazon Bedrock.\n", - "\n", - "The prompt starts with an instruction `Please provide a summary of the following text.`, and includes text surrounded by `` tag. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ece0c069", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt = \"\"\"\n", - "Please provide a summary of the following text. Do not add any information that is not mentioned in the text below.\n", - "\n", - "\n", - "AWS took all of that feedback from customers, and today we are excited to announce Amazon Bedrock, \\\n", - "a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. \\\n", - "Bedrock is the easiest way for customers to build and scale generative AI-based applications using FMs, \\\n", - "democratizing access for all builders. Bedrock will offer the ability to access a range of powerful FMs \\\n", - "for text and images—including Amazons Titan FMs, which consist of two new LLMs we’re also announcing \\\n", - "today—through a scalable, reliable, and secure AWS managed service. With Bedrock’s serverless experience, \\\n", - "customers can easily find the right model for what they’re trying to get done, get started quickly, privately \\\n", - "customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS \\\n", - "tools and capabilities they are familiar with, without having to manage any infrastructure (including integrations \\\n", - "with Amazon SageMaker ML features like Experiments to test different models and Pipelines to manage their FMs at scale).\n", - "\n", - "\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "3efddbb0", - "metadata": {}, - "source": [ - "## Creating request body with prompt and inference parameters \n", - "\n", - "Following the request syntax of `invoke_model`, you create request body with the above prompt and inference parameters.\n", - "\n", - "### Titan:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "60d191eb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps({\"inputText\": prompt, \n", - " \"textGenerationConfig\":{\n", - " \"maxTokenCount\":4096,\n", - " \"stopSequences\":[],\n", - " \"temperature\":0,\n", - " \"topP\":1\n", - " },\n", - " }) " - ] - }, - { - "cell_type": "markdown", - "id": "cc9f3326", - "metadata": {}, - "source": [ - "## Invoke foundation model via Boto3\n", - "\n", - "Here sends the API request to Amazon Bedrock with specifying request parameters `modelId`, `accept`, and `contentType`. Following the prompt, the foundation model in Amazon Bedrock sumamrizes the text." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9f400d76", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "modelId = 'amazon.titan-tg1-large' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "\n", - "try:\n", - " \n", - " response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - " response_body = json.loads(response.get('body').read())\n", - "\n", - " print(response_body.get('results')[0].get('outputText'))\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - " \n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\")\n", - " \n", - " else:\n", - " raise error" - ] - }, - { - "cell_type": "markdown", - "id": "3c527882", - "metadata": {}, - "source": [ - "In the above the Bedrock service generates the entire summary for the given prompt in a single output. Note that this can be slow if the output contains large amount of tokens. \n" - ] - }, - { - "cell_type": "markdown", - "id": "0169392d-4db1-42f1-b110-a43a0ff16819", - "metadata": {}, - "source": [ - "___" - ] - }, - { - "cell_type": "markdown", - "id": "db1323e2-3790-47cc-84a6-512d6341c96a", - "metadata": {}, - "source": [ - "### Claude:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "239679b4-abb6-4ebc-8b20-8ee7f47ffa33", - "metadata": {}, - "outputs": [], - "source": [ - "prompt = \"\"\"\n", - "\n", - "Human: Please provide a summary of the following text.\n", - "\n", - "AWS took all of that feedback from customers, and today we are excited to announce Amazon Bedrock, \\\n", - "a new service that makes FMs from AI21 Labs, Anthropic, Stability AI, and Amazon accessible via an API. \\\n", - "Bedrock is the easiest way for customers to build and scale generative AI-based applications using FMs, \\\n", - "democratizing access for all builders. Bedrock will offer the ability to access a range of powerful FMs \\\n", - "for text and images—including Amazons Titan FMs, which consist of two new LLMs we’re also announcing \\\n", - "today—through a scalable, reliable, and secure AWS managed service. With Bedrock’s serverless experience, \\\n", - "customers can easily find the right model for what they’re trying to get done, get started quickly, privately \\\n", - "customize FMs with their own data, and easily integrate and deploy them into their applications using the AWS \\\n", - "tools and capabilities they are familiar with, without having to manage any infrastructure (including integrations \\\n", - "with Amazon SageMaker ML features like Experiments to test different models and Pipelines to manage their FMs at scale).\n", - "\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0b8c17b1-ac0c-4f59-862b-7ba9285f9355", - "metadata": {}, - "outputs": [], - "source": [ - "body = json.dumps({\"prompt\": prompt,\n", - " \"max_tokens_to_sample\":4096,\n", - " \"temperature\":0.5,\n", - " \"top_k\":250,\n", - " \"top_p\":0.5,\n", - " \"stop_sequences\":[]\n", - " }) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "81f66bd0-1d8f-48f7-abc9-21bae614f144", - "metadata": {}, - "outputs": [], - "source": [ - "modelId = 'anthropic.claude-v2'\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "\n", - "response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - "response_body = json.loads(response.get('body').read())\n", - "\n", - "print(response_body.get('completion'))" - ] - }, - { - "cell_type": "markdown", - "id": "62a93aeb", - "metadata": {}, - "source": [ - "## Conclusion\n", - "You have now experimented with using `boto3` SDK which provides a vanilla exposure to Amazon Bedrock API. Using this API you have seen the use case of generating a summary of AWS news about Amazon Bedrock.\n", - "\n", - "### Take aways\n", - "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Anthropic Claude and AI21 Labs Jurassic models.\n", - "- Change the prompts to your specific usecase and evaluate the output of different models.\n", - "- Play with the token length to understand the latency and responsiveness of the service.\n", - "- Apply different prompt engineering principles to get better outputs." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51c6883a-5896-4205-b710-e2eef6eed18a", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - 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"gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/01_Text_generation/03_qa_with_bedrock_titan.ipynb b/01_Text_generation/03_qa_with_bedrock_titan.ipynb deleted file mode 100644 index a659dfd3..00000000 --- a/01_Text_generation/03_qa_with_bedrock_titan.ipynb +++ /dev/null @@ -1,1005 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Question and answers with Bedrock\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*\n", - "\n", - "Question Answering (QA) is an important task that involves extracting answers to factual queries posed in natural language. Typically, a QA system processes a query against a knowledge base containing structured or unstructured data and generates a response with accurate information. Ensuring high accuracy is key to developing a useful, reliable and trustworthy question answering system, especially for enterprise use cases. \n", - "\n", - "Generative AI models like Titan and Claude use probability distributions to generate responses to questions. These models are trained on vast amounts of text data, which allows them to predict what comes next in a sequence or what word might follow a particular word. However, these models are not able to provide accurate or deterministic answers to every question because there is always some degree of uncertainty in the data. Enterprises need to query domain specific and proprietary data and use the information to answer questions, and more generally data on which the model has not been trained on. \n", - "\n", - "In this module, we will demonstrate how to use the Bedrock Titan model to provide information response to queries.\n", - "\n", - "In this example we will be running the Model with no context and then manually try and provide the context. There is no `RAG` augmentation happening here. This approach works with short documents or single-ton applications, it might not scale to enterprise level question answering where there could be large enterprise documents which cannot all be fit into the prompt sent to the model. \n", - "\n", - "### Challenges\n", - "- How to have the model return factual answers for question\n", - "\n", - "### Proposal\n", - "To the above challenges, this notebook proposes the following strategy\n", - "\n", - "#### Prepare documents\n", - "Before being able to answer the questions, the documents are **normally** processed and stored in a document store index. However, here we will send in the request with the full relevant context to the model and expect the response back. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "⚠️ ⚠️ ⚠️ Before running this notebook, ensure you've run the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites) notebook. ⚠️ ⚠️ ⚠️\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Section 1: Q&A with the knowledge of the model\n", - "In this section we try to use models provided by Bedrock service to answer questions based on the knowledge it gained during the training phase." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this Notebook we will be using the `invoke_model()` method of Amazon Bedrock client. The mandatory parameters required to use this method are `modelId` which represents the Amazon Bedrock model ARN, and `body` which is the prompt for our task. The `body` prompt changes depending on the foundation model provider selected. We walk through this in detail below\n", - "\n", - "```\n", - "{\n", - " modelId= model_id,\n", - " contentType= \"application/json\",\n", - " accept= \"application/json\",\n", - " body=body\n", - "}\n", - "\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## Scenario\n", - "\n", - "We are trying to model a situation where we are asking the model to provide information to change the tires. We will first ask the model based on the training data to provide us with an answer for our specific make and model of the car. This technique is called 'Zero Shot` . We will soon realize that even though the model seems to be returning the answers which seem relevant to our specific car, it is actually halucinating. The reason we can find that out is because we run through a fake car and we get almost similiar scenario and answer back\n", - "\n", - "This situation implies we need to augment the model's training with additional data about our specific make and model of the car and then the model will return us very specific answer. In this notebook we will not use any external sources to augment the data but simulate how a RAG based augmentation system would work. \n", - "\n", - "To run our final test we provide a full detailed section from our manual which goes and explains for our specific car how the tire changes work and then we will test to get a curated response back from the model\n", - "\n", - "## Task\n", - "\n", - "To start the process, you select one of the models provided by Bedrock. For this use case you select Titan. These models are able to answer generic questions about cars.\n", - "\n", - "For example you ask the Titan model to tell you how to change a flat tire on your Audi.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt_data = \"\"\"You are an helpful assistant. Answer questions in a concise way. If you are unsure about the\n", - "answer say 'I am unsure'\n", - "\n", - "Question: How can I fix a flat tire on my Audi A8?\n", - "Answer:\"\"\"\n", - "parameters = {\n", - " \"maxTokenCount\":512,\n", - " \"stopSequences\":[],\n", - " \"temperature\":0,\n", - " \"topP\":0.9\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's invoke of the model passing in the JSON body to generate the response" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps({\"inputText\": prompt_data, \"textGenerationConfig\": parameters})\n", - "modelId = \"amazon.titan-tg1-large\" # change this to use a different version from the model provider\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "try:\n", - " \n", - " response = boto3_bedrock.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - " answer = response_body.get(\"results\")[0].get(\"outputText\")\n", - " print(answer.strip())\n", - "\n", - "except botocore.exceptions.ClientError as error:\n", - " if error.response['Error']['Code'] == 'AccessDeniedException':\n", - " print(f\"\\x1b[41m{error.response['Error']['Message']}\\\n", - " \\nTo troubeshoot this issue please refer to the following resources.\\\n", - " \\nhttps://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_access-denied.html\\\n", - " \\nhttps://docs.aws.amazon.com/bedrock/latest/userguide/security-iam.html\\x1b[0m\\n\") \n", - " class StopExecution(ValueError):\n", - " def _render_traceback_(self):\n", - " pass\n", - " raise StopExecution \n", - " else:\n", - " raise error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the model may answer incorrectly, or use our instructions to say 'I am unsure'.\n", - "\n", - "Another issue can be seen by trying to ask the same question for a completely fake car brand and model, say a Amazon Tirana." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt_data = \"How can I fix a flat tire on my Amazon Tirana?\"\n", - "body = json.dumps({\"inputText\": prompt_data, \n", - " \"textGenerationConfig\": parameters})\n", - "modelId = \"amazon.titan-tg1-large\" # change this to use a different version from the model provider\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - ")\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "answer = response_body.get(\"results\")[0].get(\"outputText\")\n", - "print(answer.strip())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see the answer that the model provides is plausible even though this vehicle does not exist. The model is _hallucinating_.\n", - "\n", - "How can we fix this issue and have the model provide answers based on the specific instructions valid for my car model?\n", - "\n", - "Research by Facebook in 2020 found that LLM knowledge could be augmented on the fly by providing the additional knowledge base as part of the prompt. This approach is called Retrieval Augmented Generation, or RAG.\n", - "\n", - "Let's see how we can use this to improve our application.\n", - "\n", - "The following is an excerpt of the manual of the Audi A8 (in reality it is not the real manual, but let's assume so). This document is also conveniently short enough to fit entirely in the prompt of Titan Large. \n", - "\n", - "```\n", - "Tires and tire pressure:\n", - "\n", - "Tires are made of black rubber and are mounted on the wheels of your vehicle. They provide the necessary grip for driving, cornering, and braking. Two important factors to consider are tire pressure and tire wear, as they can affect the performance and handling of your car.\n", - "\n", - "Where to find recommended tire pressure:\n", - "\n", - "You can find the recommended tire pressure specifications on the inflation label located on the driver's side B-pillar of your vehicle. Alternatively, you can refer to your vehicle's manual for this information. The recommended tire pressure may vary depending on the speed and the number of occupants or maximum load in the vehicle.\n", - "\n", - "Reinflating the tires:\n", - "\n", - "When checking tire pressure, it is important to do so when the tires are cold. This means allowing the vehicle to sit for at least three hours to ensure the tires are at the same temperature as the ambient temperature.\n", - "\n", - "To reinflate the tires:\n", - "\n", - " Check the recommended tire pressure for your vehicle.\n", - " Follow the instructions provided on the air pump and inflate the tire(s) to the correct pressure.\n", - " In the center display of your vehicle, open the \"Car status\" app.\n", - " Navigate to the \"Tire pressure\" tab.\n", - " Press the \"Calibrate pressure\" option and confirm the action.\n", - " Drive the car for a few minutes at a speed above 30 km/h to calibrate the tire pressure.\n", - "\n", - "Note: In some cases, it may be necessary to drive for more than 15 minutes to clear any warning symbols or messages related to tire pressure. If the warnings persist, allow the tires to cool down and repeat the above steps.\n", - "\n", - "Flat Tire:\n", - "\n", - "If you encounter a flat tire while driving, you can temporarily seal the puncture and reinflate the tire using a tire mobility kit. This kit is typically stored under the lining of the luggage area in your vehicle.\n", - "\n", - "Instructions for using the tire mobility kit:\n", - "\n", - " Open the tailgate or trunk of your vehicle.\n", - " Lift up the lining of the luggage area to access the tire mobility kit.\n", - " Follow the instructions provided with the tire mobility kit to seal the puncture in the tire.\n", - " After using the kit, make sure to securely put it back in its original location.\n", - " Contact Rivesla or an appropriate service for assistance with disposing of and replacing the used sealant bottle.\n", - "\n", - "Please note that the tire mobility kit is a temporary solution and is designed to allow you to drive for a maximum of 10 minutes or 8 km (whichever comes first) at a maximum speed of 80 km/h. It is advisable to replace the punctured tire or have it repaired by a professional as soon as possible.\n", - "```\n", - "\n", - " \n", - "Next, we take this text and \"embed\" it in the prompt together with the original question. The prompt is also build in such a way as to try to hint the model to only look at the information provided as context." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "context = \"\"\"Tires and tire pressure:\n", - "\n", - "Tires are made of black rubber and are mounted on the wheels of your vehicle. They provide the necessary grip for driving, cornering, and braking. Two important factors to consider are tire pressure and tire wear, as they can affect the performance and handling of your car.\n", - "\n", - "Where to find recommended tire pressure:\n", - "\n", - "You can find the recommended tire pressure specifications on the inflation label located on the driver's side B-pillar of your vehicle. Alternatively, you can refer to your vehicle's manual for this information. The recommended tire pressure may vary depending on the speed and the number of occupants or maximum load in the vehicle.\n", - "\n", - "Reinflating the tires:\n", - "\n", - "When checking tire pressure, it is important to do so when the tires are cold. This means allowing the vehicle to sit for at least three hours to ensure the tires are at the same temperature as the ambient temperature.\n", - "\n", - "To reinflate the tires:\n", - "\n", - " Check the recommended tire pressure for your vehicle.\n", - " Follow the instructions provided on the air pump and inflate the tire(s) to the correct pressure.\n", - " In the center display of your vehicle, open the \"Car status\" app.\n", - " Navigate to the \"Tire pressure\" tab.\n", - " Press the \"Calibrate pressure\" option and confirm the action.\n", - " Drive the car for a few minutes at a speed above 30 km/h to calibrate the tire pressure.\n", - "\n", - "Note: In some cases, it may be necessary to drive for more than 15 minutes to clear any warning symbols or messages related to tire pressure. If the warnings persist, allow the tires to cool down and repeat the above steps.\n", - "\n", - "Flat Tire:\n", - "\n", - "If you encounter a flat tire while driving, you can temporarily seal the puncture and reinflate the tire using a tire mobility kit. This kit is typically stored under the lining of the luggage area in your vehicle.\n", - "\n", - "Instructions for using the tire mobility kit:\n", - "\n", - " Open the tailgate or trunk of your vehicle.\n", - " Lift up the lining of the luggage area to access the tire mobility kit.\n", - " Follow the instructions provided with the tire mobility kit to seal the puncture in the tire.\n", - " After using the kit, make sure to securely put it back in its original location.\n", - " Contact Audi or an appropriate service for assistance with disposing of and replacing the used sealant bottle.\n", - "\n", - "Please note that the tire mobility kit is a temporary solution and is designed to allow you to drive for a maximum of 10 minutes or 8 km (whichever comes first) at a maximum speed of 80 km/h. It is advisable to replace the punctured tire or have it repaired by a professional as soon as possible.\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's take the whole excerpt and pass it to the model together with the question." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "question = \"How can I fix a flat tire on my Audi A8?\"\n", - "prompt_data = f\"\"\"Answer the question based only on the information provided between ## and give step by step guide.\n", - "#\n", - "{context}\n", - "#\n", - "\n", - "Question: {question}\n", - "Answer:\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Invoke the model via boto3 to generate the response" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "body = json.dumps({\"inputText\": prompt_data, \"textGenerationConfig\": parameters})\n", - "modelId = \"amazon.titan-tg1-large\" # change this to use a different version from the model provider\n", - "accept = \"application/json\"\n", - "contentType = \"application/json\"\n", - "\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body, modelId=modelId, accept=accept, contentType=contentType\n", - ")\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "answer = response_body.get(\"results\")[0].get(\"outputText\")\n", - "print(answer.strip())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the model takes a while to understand the context and generate relevant answer for you, this might lead to poor experience for the user since they have to wait for a response for some seconds.\n", - "\n", - "Bedrock also supports streaming capability where the service generates an output as the model is generating tokens. Here is an example of how you can do that." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import display_markdown,Markdown,clear_output" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "response = boto3_bedrock.invoke_model_with_response_stream(body=body, modelId=modelId, accept=accept, contentType=contentType)\n", - "stream = response.get('body')\n", - "output = []\n", - "i = 1\n", - "if stream:\n", - " for event in stream:\n", - " chunk = event.get('chunk')\n", - " if chunk:\n", - " chunk_obj = json.loads(chunk.get('bytes').decode())\n", - " text = chunk_obj['outputText']\n", - " clear_output(wait=True)\n", - " output.append(text)\n", - " display_markdown(Markdown(''.join(output)))\n", - " i+=1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "We see the response is a summarized and step by step instruction of how to change the tires . This simple example shows how you can leverage the `RAG` or the Augmentation process to generate a curated response back" - ] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - 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"gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/01_Text_generation/04_entity_extraction.ipynb b/01_Text_generation/04_entity_extraction.ipynb deleted file mode 100644 index 88148ed1..00000000 --- a/01_Text_generation/04_entity_extraction.ipynb +++ /dev/null @@ -1,1020 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "46fa37fd-e892-4504-ad32-edabb4760596", - "metadata": {}, - "source": [ - "# Entity Extraction with Claude\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*\n", - "\n", - "### Context\n", - "Entity extraction is an NLP technique that allows us to automatically extract specific data from naturally written text, such as news, emails, books, etc.\n", - "That data can then later be saved to a database, used for lookup or any other type of processing.\n", - "\n", - "Classic entity extraction programs usually limit you to pre-defined classes, such as name, address, price, etc. or require you to provide many examples of types of entities you are interested in.\n", - "By using a LLM for entity extraction in most cases you are only required to specify what you need to extract in natural language. This gives you flexibility and accuracy in your queries while saving time by removing necessity of data labeling.\n", - "\n", - "In addition, LLM entity extraction can be used to help you assemble a dataset to later create a customised solution for your use case, such as [Amazon Comprehend custom entity](https://docs.aws.amazon.com/comprehend/latest/dg/custom-entity-recognition.html) recognition." - ] - }, - { - "cell_type": "markdown", - "id": "373675b6-cdc4-437e-83b5-7d897516b8fc", - "metadata": {}, - "source": [ - "## Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3dcc1624-19bf-4a3d-857a-89776dc74c3c", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "%pip install langchain==0.1.11" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8ef0441-b424-403e-9394-d81b64e8332b", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import sys\n", - "\n", - "import boto3\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "id": "1fb9074b-d72e-4419-9165-421414d28f4b", - "metadata": {}, - "source": [ - "## Configure langchain\n", - "\n", - "We begin with instantiating the LLM. Here we are using Anthropic Claude v2 for text generation.\n", - "\n", - "Note: It is possible to choose other models available with Bedrock. You can replace the `model_id` as follows to change the model.\n", - "\n", - "`llm = Bedrock(model_id=\"amazon.titan-tg1-large\")`\n", - "\n", - "Check [documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html) for Available text generation model Ids under Amazon Bedrock." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "621790c0-332a-4bab-bf81-967a63cb52fa", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.llms.bedrock import Bedrock\n", - "\n", - "# - create the Anthropic Model\n", - "llm = Bedrock(\n", - " model_id=\"anthropic.claude-v2\",\n", - " client=boto3_bedrock,\n", - " model_kwargs={\n", - " \"max_tokens_to_sample\": 200,\n", - " \"temperature\": 0, # Using 0 to get reproducible results\n", - " \"stop_sequences\": [\"\\n\\nHuman:\"]\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5f85e961-3530-4bf4-ac28-12611965d408", - "metadata": {}, - "source": [ - "## Entity Extraction\n", - "Now that we have our LLM initialised, we can start extracting entities.\n", - "\n", - "For this exercise we will pretend to be an online bookstore that receives questions and orders by email.\n", - "Our task would be to extract relevant information from the email to process the order.\n", - "\n", - "Let's begin by taking a look at the sample email:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b958f4c7-0ded-4537-9939-d1623337317f", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "emails_dir = Path(\".\") / \"emails\"\n", - "with open(emails_dir / \"00_treasure_island.txt\") as f:\n", - " book_question_email = f.read()\n", - "\n", - "print(book_question_email)" - ] - }, - { - "cell_type": "markdown", - "id": "59f62564-cd46-4bff-bda3-c0f29a47dd9d", - "metadata": {}, - "source": [ - "### Basic approach\n", - "\n", - "For basic cases we can directly ask the model to return the result.\n", - "Let's try extracting the name of the book." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efdc9062-64e9-4634-855c-d06ccb5efb50", - "metadata": {}, - "outputs": [], - "source": [ - "query = f\"\"\"\n", - "\n", - "Human: Given the email inside triple-backticks, please read it and analyse the contents.\n", - "If a name of a book is mentioned, return it, otherwise return nothing.\n", - "\n", - "Email: ```\n", - "{book_question_email}\n", - "```\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4742618e-25e9-441e-a6f8-b47330a0bd05", - "metadata": {}, - "outputs": [], - "source": [ - "result = llm(query)\n", - "print(result.strip())" - ] - }, - { - "cell_type": "markdown", - "id": "e31a3407-caca-445a-bb1a-d62d40ddccd2", - "metadata": {}, - "source": [ - "### Model specific prompts\n", - "\n", - "While basic approach works, to achieve best results we recommend to customise your prompts for the particular model you will be using.\n", - "In this example we are using `anthropic.claude-v2`, [prompt guide for which can be found here](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design).\n", - "\n", - "Here is the a more optimised prompt for Claude v2." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5a461a9-4bad-4634-b568-a07769b1d349", - "metadata": {}, - "outputs": [], - "source": [ - "prompt = \"\"\"\n", - "\n", - "Human: Given the email provided, please read it and analyse the contents.\n", - "If a name of a book is mentioned, return it.\n", - "If no name is mentioned, return empty string.\n", - "The email will be given between XML tags.\n", - "\n", - "\n", - "{email}\n", - "\n", - "\n", - "Return the name of the book between XML tags.\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5142cff9-c2d3-451e-8d21-06ce8538adb5", - "metadata": {}, - "outputs": [], - "source": [ - "query = prompt.format(email=book_question_email)\n", - "result = llm(query)\n", - "print(result.strip())" - ] - }, - { - "cell_type": "markdown", - "id": "e87ee5ab-33d9-4def-a462-8e5992032bd0", - "metadata": {}, - "source": [ - "To extract results easier, we can use a helper function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfa9d2d0-2bc8-465c-b89b-73b2fd76d4b2", - "metadata": {}, - "outputs": [], - "source": [ - "from bs4 import BeautifulSoup\n", - "\n", - "def extract_by_tag(response: str, tag: str, extract_all=False) -> str | list[str] | None:\n", - " soup = BeautifulSoup(response)\n", - " results = soup.find_all(tag)\n", - " if not results:\n", - " return\n", - " \n", - " texts = [res.get_text() for res in results]\n", - " if extract_all:\n", - " return texts\n", - " return texts[-1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fef7b280-71be-41ad-9f21-8c87d09226ae", - "metadata": {}, - "outputs": [], - "source": [ - "extract_by_tag(result, \"book\")" - ] - }, - { - "cell_type": "markdown", - "id": "f19454e6-22cd-41ee-888c-39801cc72c74", - "metadata": {}, - "source": [ - "We can check that our model doesn't return arbitrary results when no appropriate information is given (also know as 'hallucination'), by running our prompt on other emails." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35fd1343-9b4b-4efd-846f-1312af18e15c", - "metadata": {}, - "outputs": [], - "source": [ - "with open(emails_dir / \"01_return.txt\") as f:\n", - " return_email = f.read()\n", - "\n", - "print(return_email)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ec8a1b2-beb4-4ddf-9935-1fc7a3b08729", - "metadata": {}, - "outputs": [], - "source": [ - "query = prompt.format(email=return_email)\n", - "result = llm(query)\n", - "print(result.strip())" - ] - }, - { - "cell_type": "markdown", - "id": "d154c270-41dc-4e58-bca2-f9fe5d021223", - "metadata": {}, - "source": [ - "Using tags also allows us to extract multiple pieces of information at the same time and makes extraction much easier.\n", - "In the following prompt we will extract not just the book name, but any questions, requests and customer name." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ea5b5c9b-b0c0-427d-a7fb-005253e9bbb3", - "metadata": {}, - "outputs": [], - "source": [ - "prompt = \"\"\"\n", - "\n", - "Human: Given email provided , please read it and analyse the contents.\n", - "\n", - "Please extract the following information from the email:\n", - "- Any questions the customer is asking, return it inside XML tags.\n", - "- The customer full name, return it inside XML tags.\n", - "- Any book names the customer mentions, return it inside XML tags.\n", - "\n", - "If a particular bit of information is not present, return an empty string.\n", - "Make sure that each question can be understoon by itself, incorporate context if requred.\n", - "Each returned question should be concise, remove extra information if possible.\n", - "The email will be given between XML tags.\n", - "\n", - "\n", - "{email}\n", - "\n", - "\n", - "Return each question inside XML tags.\n", - "Return the name of each book inside XML tags.\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac605eb5-2483-46ed-a205-6932051c8d2b", - "metadata": {}, - "outputs": [], - "source": [ - "query = prompt.format(email=book_question_email)\n", - "result = llm(query)\n", - "print(result.strip())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a7cc2cb-8036-44a5-9fb6-db2172f9b601", - "metadata": {}, - "outputs": [], - "source": [ - "extract_by_tag(result, \"question\", extract_all=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5617e0d-0923-45b6-8e91-03748ad76d31", - "metadata": {}, - "outputs": [], - "source": [ - "extract_by_tag(result, \"name\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "66852eb2-97a2-4041-a76f-3fb03e1aaef5", - "metadata": {}, - "outputs": [], - "source": [ - "extract_by_tag(result, \"book\", extract_all=True)" - ] - }, - { - "cell_type": "markdown", - "id": "d830e149-5c89-4f50-9833-b499ee70f3f3", - "metadata": {}, - "source": [ - "## Conclusion\n", - "\n", - "Entity extraction is a powerful technique using which you can extract arbitrary data using plain text descriptions.\n", - "\n", - "This is particularly useful when you need to extract specific data which doesn't have clear structure. In such cases regex and other traditional extraction techniques can be very difficult to implement.\n", - "\n", - "### Take aways\n", - "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Amazon Titan and AI21 Labs Jurassic models.\n", - "- Change the prompts to your specific usecase and evaluate the output of different models.\n", - "- Apply different prompt engineering principles to get better outputs. Refer to the prompt guide for your chosen model for recommendations, e.g. [here is the prompt guide for Claude](https://docs.anthropic.com/claude/docs/introduction-to-prompt-design)." - ] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - 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Text Generation - -## Overview - -In this lab, you will learn how to generate text using LLMs on Amazon Bedrock by using the Bedrock API. - -We will first generate text using a zero-shot prompt. The zero-shot prompt provides instruction to generate text content without providing a detailed context. We will explore zero-shot email generation using Bedrock API (BoTo3). Then we will show how to improve the quality of the generated text by providing additional context in the prompt. Additionally, we will also look at text summarization, name entity recognition, and code generation examples. - -## Audience - -Architects, data scientists, and developer who want to learn how to use Amazon Bedrock LLMs to generate text. -Some of the business use cases for text generation include: - -- Generating product descriptions based on product features and benefits for marketing teams -- Generation of media articles and marketing campaigns -- Email and reports generation -- Code Translation, code explain and reviews - - -## Workshop Notebooks - -We will generate an email response to a customer where the customer had provided negative feedback on service received from a customer support engineer. The text generation workshop includes the following three notebooks. -1. [Generate Email with Amazon Titan](./00_text_generation_w_bedrock.ipynb) - Invokes Amazon Titan large text model using Bedrock API to generate an email response to a customer. It uses a zero-shot prompt without context as instruction to the model. -2. [Zero-shot Text Generation with Anthropic Claude](01_code_generatation_w_bedrock.ipynb) - Invokes Anthropic's Claude Text model using Bedrock API to generate Python code using Natural language. It shows examples of prompting to generate simple functions, classes, and full programs in Python for Data Analyst to perform sales analysis on a given Sales CSV dataset. -3. [Text summarization with Amazon Titan and Anthropic Claude](./02_text-summarization-titan+claude.ipynb) - Invoke Amazon Titan large text model and Anthropic's Claude Text model using Bedrock API to generate summary of provided text. -4. [Question and answers with Bedrock using Amazon Titan](./03_qa_with_bedrock_titan.ipynb) - Invoke Amazon Titan large text model to answer questions using models knowledge, check an example of hallucination, and using prompt engineering to address hallcination. -5. [Entity Extraction with Anthropic Claude](./04_entity_extraction.ipynb) - Invoke Anthropic's Claude Text model to extract name of book from a given email text. - - -## Setup -Before running any of the labs in this section ensure you've run the [Bedrock boto3 setup notebook](../00_Prerequisites/bedrock_basics.ipynb#Prerequisites). - - -## Architecture - -![Bedrock](./images/bedrock.jpg) diff --git a/01_Text_generation/emails/00_treasure_island.txt b/01_Text_generation/emails/00_treasure_island.txt deleted file mode 100644 index dc47c1b8..00000000 --- a/01_Text_generation/emails/00_treasure_island.txt +++ /dev/null @@ -1,8 +0,0 @@ -Dear Sir or Madam, - -I would like to order Treasure Island, do you have it in stock? - -Also, is it possible to pay by cheque? - -Yours sincerely, -John Smith \ No newline at end of file diff --git a/01_Text_generation/emails/01_return.txt b/01_Text_generation/emails/01_return.txt deleted file mode 100644 index 758b7f78..00000000 --- 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z_vAnOO3nG}>`rDOydhLqZcR%pc#q+G7dAhMuzq)OcsMliBJ~k|&efx!e(vmL-}iJR zQMFyF!appzy1ic&^xU_f*xBlYs`mOyFRe8wU78y3i}Q=x$h87z4&*fXj6Mg<@%xrf zW33h4OrIL%G-Q$V!7hN6F`?$kZcy}EbL;}$*NpY;Xbt0N$y z-7t\n", - "Note: Please make sure to enable `Anthropic Claude 3` and `Anthropic Claude Instant` model access in Amazon Bedrock Console, as the notebook will use Anthropic Claude 3 Sonnet and Claude instant models for testing the knowledge base once its created.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "Before running the rest of this notebook, you'll need to run the cells below to (ensure necessary libraries are installed and) connect to Bedrock." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "%pip install -U opensearch-py==2.3.1\n", - "%pip install -U boto3==1.33.2\n", - "%pip install -U retrying==1.3.4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# restart kernel\n", - "from IPython.core.display import HTML\n", - "HTML(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import os\n", - "import boto3\n", - "from botocore.exceptions import ClientError\n", - "import pprint\n", - "from utility import create_bedrock_execution_role, create_oss_policy_attach_bedrock_execution_role, create_policies_in_oss, interactive_sleep\n", - "import random\n", - "from retrying import retry\n", - "suffix = random.randrange(200, 900)\n", - "\n", - "sts_client = boto3.client('sts')\n", - "boto3_session = boto3.session.Session()\n", - "region_name = boto3_session.region_name\n", - "bedrock_agent_client = boto3_session.client('bedrock-agent', region_name=region_name)\n", - "service = 'aoss'\n", - "s3_client = boto3.client('s3')\n", - "account_id = sts_client.get_caller_identity()[\"Account\"]\n", - "s3_suffix = f\"{region_name}-{account_id}\"\n", - "bucket_name = f'bedrock-kb-{s3_suffix}' # replace it with your bucket name.\n", - "pp = pprint.PrettyPrinter(indent=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# Check if bucket exists, and if not create S3 bucket for knowledge base data source\n", - "try:\n", - " s3_client.head_bucket(Bucket=bucket_name)\n", - " print(f'Bucket {bucket_name} Exists')\n", - "except ClientError as e:\n", - " print(f'Creating bucket {bucket_name}')\n", - " s3bucket = s3_client.create_bucket(\n", - " Bucket=bucket_name,\n", - " CreateBucketConfiguration={ 'LocationConstraint': region_name }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a vector store - OpenSearch Serverless index\n", - "\n", - "### Step 1 - Create OSS policies and collection\n", - "Firt of all we have to create a vector store. In this section we will use *Amazon OpenSerach serverless.*\n", - "\n", - "Amazon OpenSearch Serverless is a serverless option in Amazon OpenSearch Service. As a developer, you can use OpenSearch Serverless to run petabyte-scale workloads without configuring, managing, and scaling OpenSearch clusters. You get the same interactive millisecond response times as OpenSearch Service with the simplicity of a serverless environment. Pay only for what you use by automatically scaling resources to provide the right amount of capacity for your application—without impacting data ingestion." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "import time\n", - "vector_store_name = f'bedrock-sample-rag-{suffix}'\n", - "index_name = f\"bedrock-sample-rag-index-{suffix}\"\n", - "aoss_client = boto3_session.client('opensearchserverless')\n", - "bedrock_kb_execution_role = create_bedrock_execution_role(bucket_name=bucket_name)\n", - "bedrock_kb_execution_role_arn = bedrock_kb_execution_role['Role']['Arn']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# create security, network and data access policies within OSS\n", - "encryption_policy, network_policy, access_policy = create_policies_in_oss(vector_store_name=vector_store_name,\n", - " aoss_client=aoss_client,\n", - " bedrock_kb_execution_role_arn=bedrock_kb_execution_role_arn)\n", - "collection = aoss_client.create_collection(name=vector_store_name,type='VECTORSEARCH')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp.pprint(collection)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get the OpenSearch serverless collection URL\n", - "collection_id = collection['createCollectionDetail']['id']\n", - "host = collection_id + '.' + region_name + '.aoss.amazonaws.com'\n", - "print(host)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# wait for collection creation\n", - "# This can take couple of minutes to finish\n", - "response = aoss_client.batch_get_collection(names=[vector_store_name])\n", - "# Periodically check collection status\n", - "while (response['collectionDetails'][0]['status']) == 'CREATING':\n", - " print('Creating collection...')\n", - " interactive_sleep(30)\n", - " response = aoss_client.batch_get_collection(names=[vector_store_name])\n", - "print('\\nCollection successfully created:')\n", - "pp.pprint(response[\"collectionDetails\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# create opensearch serverless access policy and attach it to Bedrock execution role\n", - "try:\n", - " create_oss_policy_attach_bedrock_execution_role(collection_id=collection_id,\n", - " bedrock_kb_execution_role=bedrock_kb_execution_role)\n", - " # It can take up to a minute for data access rules to be enforced\n", - " interactive_sleep(60)\n", - "except Exception as e:\n", - " print(\"Policy already exists\")\n", - " pp.pprint(e)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2 - Create vector index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create the vector index in Opensearch serverless, with the knn_vector field index mapping, specifying the dimension size, name and engine.\n", - "from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, RequestError\n", - "credentials = boto3.Session().get_credentials()\n", - "awsauth = auth = AWSV4SignerAuth(credentials, region_name, service)\n", - "\n", - "index_name = f\"bedrock-sample-index-{suffix}\"\n", - "body_json = {\n", - " \"settings\": {\n", - " \"index.knn\": \"true\",\n", - " \"number_of_shards\": 1,\n", - " \"knn.algo_param.ef_search\": 512,\n", - " \"number_of_replicas\": 0,\n", - " },\n", - " \"mappings\": {\n", - " \"properties\": {\n", - " \"vector\": {\n", - " \"type\": \"knn_vector\",\n", - " \"dimension\": 1536,\n", - " \"method\": {\n", - " \"name\": \"hnsw\",\n", - " \"engine\": \"faiss\",\n", - " \"space_type\": \"l2\"\n", - " },\n", - " },\n", - " \"text\": {\n", - " \"type\": \"text\"\n", - " },\n", - " \"text-metadata\": {\n", - " \"type\": \"text\" }\n", - " }\n", - " }\n", - "}\n", - "\n", - "# Build the OpenSearch client\n", - "oss_client = OpenSearch(\n", - " hosts=[{'host': host, 'port': 443}],\n", - " http_auth=awsauth,\n", - " use_ssl=True,\n", - " verify_certs=True,\n", - " connection_class=RequestsHttpConnection,\n", - " timeout=300\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, - "pycharm": { - "is_executing": true - } - }, - "outputs": [], - "source": [ - "# Create index\n", - "try:\n", - " response = oss_client.indices.create(index=index_name, body=json.dumps(body_json))\n", - " print('\\nCreating index:')\n", - " pp.pprint(response)\n", - "\n", - " # index creation can take up to a minute\n", - " interactive_sleep(60)\n", - "except RequestError as e:\n", - " # you can delete the index if its already exists\n", - " # oss_client.indices.delete(index=index_name)\n", - " print(f'Error while trying to create the index, with error {e.error}\\nyou may unmark the delete above to delete, and recreate the index')\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download data to ingest into our knowledge base" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download and prepare dataset\n", - "!mkdir -p ./data\n", - "\n", - "from urllib.request import urlretrieve\n", - "urls = [\n", - " 'https://s2.q4cdn.com/299287126/files/doc_financials/2023/ar/2022-Shareholder-Letter.pdf',\n", - " 'https://s2.q4cdn.com/299287126/files/doc_financials/2022/ar/2021-Shareholder-Letter.pdf',\n", - " 'https://s2.q4cdn.com/299287126/files/doc_financials/2021/ar/Amazon-2020-Shareholder-Letter-and-1997-Shareholder-Letter.pdf',\n", - " 'https://s2.q4cdn.com/299287126/files/doc_financials/2020/ar/2019-Shareholder-Letter.pdf'\n", - "]\n", - "\n", - "filenames = [\n", - " 'AMZN-2022-Shareholder-Letter.pdf',\n", - " 'AMZN-2021-Shareholder-Letter.pdf',\n", - " 'AMZN-2020-Shareholder-Letter.pdf',\n", - " 'AMZN-2019-Shareholder-Letter.pdf'\n", - "]\n", - "\n", - "data_root = \"./data/\"\n", - "\n", - "for idx, url in enumerate(urls):\n", - " file_path = data_root + filenames[idx]\n", - " urlretrieve(url, file_path)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload data to S3 Bucket data source" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Upload data to s3 to the bucket that was configured as a data source to the knowledge base\n", - "s3_client = boto3.client(\"s3\")\n", - "def uploadDirectory(path,bucket_name):\n", - " for root,dirs,files in os.walk(path):\n", - " for file in files:\n", - " s3_client.upload_file(os.path.join(root,file),bucket_name,file)\n", - "\n", - "uploadDirectory(data_root, bucket_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Knowledge Base\n", - "Steps:\n", - "- initialize Open search serverless configuration which will include collection ARN, index name, vector field, text field and metadata field.\n", - "- initialize chunking strategy, based on which KB will split the documents into pieces of size equal to the chunk size mentioned in the `chunkingStrategyConfiguration`.\n", - "- initialize the s3 configuration, which will be used to create the data source object later.\n", - "- initialize the Titan embeddings model ARN, as this will be used to create the embeddings for each of the text chunks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "opensearchServerlessConfiguration = {\n", - " \"collectionArn\": collection[\"createCollectionDetail\"]['arn'],\n", - " \"vectorIndexName\": index_name,\n", - " \"fieldMapping\": {\n", - " \"vectorField\": \"vector\",\n", - " \"textField\": \"text\",\n", - " \"metadataField\": \"text-metadata\"\n", - " }\n", - " }\n", - "\n", - "# Ingest strategy - How to ingest data from the data source\n", - "chunkingStrategyConfiguration = {\n", - " \"chunkingStrategy\": \"FIXED_SIZE\",\n", - " \"fixedSizeChunkingConfiguration\": {\n", - " \"maxTokens\": 512,\n", - " \"overlapPercentage\": 20\n", - " }\n", - "}\n", - "\n", - "# The data source to ingest documents from, into the OpenSearch serverless knowledge base index\n", - "s3Configuration = {\n", - " \"bucketArn\": f\"arn:aws:s3:::{bucket_name}\",\n", - " # \"inclusionPrefixes\":[\"*.*\"] # you can use this if you want to create a KB using data within s3 prefixes.\n", - "}\n", - "\n", - "# The embedding model used by Bedrock to embed ingested documents, and realtime prompts\n", - "embeddingModelArn = f\"arn:aws:bedrock:{region_name}::foundation-model/amazon.titan-embed-text-v1\"\n", - "\n", - "name = f\"bedrock-sample-knowledge-base-{suffix}\"\n", - "description = \"Amazon shareholder letter knowledge base.\"\n", - "roleArn = bedrock_kb_execution_role_arn\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Provide the above configurations as input to the `create_knowledge_base` method, which will create the Knowledge base." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a KnowledgeBase\n", - "from retrying import retry\n", - "\n", - "@retry(wait_random_min=1000, wait_random_max=2000,stop_max_attempt_number=7)\n", - "def create_knowledge_base_func():\n", - " create_kb_response = bedrock_agent_client.create_knowledge_base(\n", - " name = name,\n", - " description = description,\n", - " roleArn = roleArn,\n", - " knowledgeBaseConfiguration = {\n", - " \"type\": \"VECTOR\",\n", - " \"vectorKnowledgeBaseConfiguration\": {\n", - " \"embeddingModelArn\": embeddingModelArn\n", - " }\n", - " },\n", - " storageConfiguration = {\n", - " \"type\": \"OPENSEARCH_SERVERLESS\",\n", - " \"opensearchServerlessConfiguration\":opensearchServerlessConfiguration\n", - " }\n", - " )\n", - " return create_kb_response[\"knowledgeBase\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "try:\n", - " kb = create_knowledge_base_func()\n", - "except Exception as err:\n", - " print(f\"{err=}, {type(err)=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pp.pprint(kb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get KnowledgeBase \n", - "get_kb_response = bedrock_agent_client.get_knowledge_base(knowledgeBaseId = kb['knowledgeBaseId'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we need to create a data source, which will be associated with the knowledge base created above. Once the data source is ready, we can then start to ingest the documents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Create a DataSource in KnowledgeBase \n", - "create_ds_response = bedrock_agent_client.create_data_source(\n", - " name = name,\n", - " description = description,\n", - " knowledgeBaseId = kb['knowledgeBaseId'],\n", - " dataSourceConfiguration = {\n", - " \"type\": \"S3\",\n", - " \"s3Configuration\":s3Configuration\n", - " },\n", - " vectorIngestionConfiguration = {\n", - " \"chunkingConfiguration\": chunkingStrategyConfiguration\n", - " }\n", - ")\n", - "ds = create_ds_response[\"dataSource\"]\n", - "pp.pprint(ds)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get DataSource \n", - "bedrock_agent_client.get_data_source(knowledgeBaseId = kb['knowledgeBaseId'], dataSourceId = ds[\"dataSourceId\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Start ingestion job\n", - "Once the KB and data source is created, we can start the ingestion job.\n", - "During the ingestion job, KB will fetch the documents in the data source, pre-process it to extract text, chunk it based on the chunking size provided, create embeddings of each chunk and then write it to the vector database, in this case OSS." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Start an ingestion job\n", - "start_job_response = bedrock_agent_client.start_ingestion_job(knowledgeBaseId = kb['knowledgeBaseId'], dataSourceId = ds[\"dataSourceId\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "job = start_job_response[\"ingestionJob\"]\n", - "pp.pprint(job)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get job \n", - "while(job['status']!='COMPLETE' ):\n", - " get_job_response = bedrock_agent_client.get_ingestion_job(\n", - " knowledgeBaseId = kb['knowledgeBaseId'],\n", - " dataSourceId = ds[\"dataSourceId\"],\n", - " ingestionJobId = job[\"ingestionJobId\"]\n", - " )\n", - " job = get_job_response[\"ingestionJob\"]\n", - "pp.pprint(job)\n", - "interactive_sleep(40)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Print the knowledge base Id in bedrock, that corresponds to the Opensearch index in the collection we created before, we will use it for the invocation later\n", - "kb_id = kb[\"knowledgeBaseId\"]\n", - "pp.pprint(kb_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# keep the kb_id for invocation later in the invoke request\n", - "%store kb_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Test the knowledge base\n", - "### Note: If you plan to run any following notebooks, you can skip this section\n", - "### Using RetrieveAndGenerate API\n", - "Behind the scenes, RetrieveAndGenerate API converts queries into embeddings, searches the knowledge base, and then augments the foundation model prompt with the search results as context information and returns the FM-generated response to the question. For multi-turn conversations, Knowledge Bases manage short-term memory of the conversation to provide more contextual results.\n", - "\n", - "The output of the RetrieveAndGenerate API includes the generated response, source attribution as well as the retrieved text chunks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# try out KB using RetrieveAndGenerate API\n", - "bedrock_agent_runtime_client = boto3.client(\"bedrock-agent-runtime\", region_name=region_name)\n", - "# Lets see how different Anthropic models responds to the input text we provide\n", - "claude_model_ids = [ [\"Claude 3 Sonnet\", \"anthropic.claude-3-sonnet-20240229-v1:0\"], [\"Claude Instant\", \"anthropic.claude-instant-v1\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "def ask_bedrock_llm_with_knowledge_base(query: str, model_arn: str, kb_id: str) -> str:\n", - " response = bedrock_agent_runtime_client.retrieve_and_generate(\n", - " input={\n", - " 'text': query\n", - " },\n", - " retrieveAndGenerateConfiguration={\n", - " 'type': 'KNOWLEDGE_BASE',\n", - " 'knowledgeBaseConfiguration': {\n", - " 'knowledgeBaseId': kb_id,\n", - " 'modelArn': model_arn\n", - " }\n", - " },\n", - " )\n", - "\n", - " return response" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query = \"What is Amazon's doing in the field of generative AI?\"\n", - "\n", - "for model_id in claude_model_ids:\n", - " model_arn = f'arn:aws:bedrock:{region_name}::foundation-model/{model_id[1]}'\n", - " response = ask_bedrock_llm_with_knowledge_base(query, model_arn, kb_id)\n", - " generated_text = response['output']['text']\n", - " citations = response[\"citations\"]\n", - " contexts = []\n", - " for citation in citations:\n", - " retrievedReferences = citation[\"retrievedReferences\"]\n", - " for reference in retrievedReferences:\n", - " contexts.append(reference[\"content\"][\"text\"])\n", - " print(f\"---------- Generated using {model_id[0]}:\")\n", - " pp.pprint(generated_text )\n", - " print(f'---------- The citations for the response generated by {model_id[0]}:')\n", - " pp.pprint(contexts)\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieve API\n", - "Retrieve API converts user queries into embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom workflows on top of the semantic search results. The output of the Retrieve API includes the the retrieved text chunks, the location type and URI of the source data, as well as the relevance scores of the retrievals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "# retrieve api for fetching only the relevant context.\n", - "relevant_documents = bedrock_agent_runtime_client.retrieve(\n", - " retrievalQuery= {\n", - " 'text': query\n", - " },\n", - " knowledgeBaseId=kb_id,\n", - " retrievalConfiguration= {\n", - " 'vectorSearchConfiguration': {\n", - " 'numberOfResults': 3 # will fetch top 3 documents which matches closely with the query.\n", - " }\n", - " }\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "pp.pprint(relevant_documents[\"retrievalResults\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Next steps: Proceed to the next labs to learn how to use Bedrock Knowledge bases. Remember to CLEAN_UP at the end of your session.\n", - "
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"gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/02_KnowledgeBases_and_RAG/1_managed-rag-kb-retrieve-generate-api.ipynb b/02_KnowledgeBases_and_RAG/1_managed-rag-kb-retrieve-generate-api.ipynb deleted file mode 100644 index 3b4da5a9..00000000 --- a/02_KnowledgeBases_and_RAG/1_managed-rag-kb-retrieve-generate-api.ipynb +++ /dev/null @@ -1,249 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Q&A application using Knowledge Bases for Amazon Bedrock - RetrieveAndGenerate API\n", - "### Context\n", - "\n", - "With knowledge bases, you can securely connect foundation models (FMs) in Amazon Bedrock to your company\n", - "data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant,\n", - "context-specific, and accurate responses without continuously retraining the FM. All information retrieved from\n", - "knowledge bases comes with source attribution to improve transparency and minimize hallucinations. For more information on creating a knowledge base using console, please refer to this [post](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html).\n", - "\n", - "In this notebook, we will dive deep into building Q&A application using `RetrieveAndGenerate` API provided by Knowledge Bases for Amazon Bedrock. This API will query the knowledge base to get the desired number of document chunks based on similarity search, integrate it with Large Language Model (LLM) for answering questions.\n", - "\n", - "\n", - "### Pattern\n", - "\n", - "We can implement the solution using Retreival Augmented Generation (RAG) pattern. RAG retrieves data from outside the language model (non-parametric) and augments the prompts by adding the relevant retrieved data in context. Here, we are performing RAG effectively on the knowledge base created in the previous notebook or using console. \n", - "\n", - "### Pre-requisite\n", - "\n", - "Before being able to answer the questions, the documents must be processed and stored in knowledge base.\n", - "\n", - "1. Load the documents into the knowledge base by connecting your s3 bucket (data source). \n", - "2. Ingestion - Knowledge base will split them into smaller chunks (based on the strategy selected), generate embeddings and store it in the associated vectore store and notebook [0_create_ingest_documents_test_kb.ipynb](./0\\_create_ingest_documents_test_kb.ipynb) takes care of it for you.\n", - "\n", - "![data_ingestion.png](./images/data_ingestion.png)\n", - "\n", - "\n", - "#### Notebook Walkthrough\n", - "\n", - "For our notebook we will use the `RetreiveAndGenerate API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into\n", - "embeddings, searches the knowledge base, get the relevant results, augment the prompt and then invoking a LLM to generate the response. \n", - "\n", - "We will use the following workflow for this notebook. \n", - "\n", - "![retrieveAndGenerate.png](./images/retrieveAndGenerate.png)\n", - "\n", - "\n", - "### USE CASE:\n", - "\n", - "#### Dataset\n", - "\n", - "In this example, you will use several years of Amazon's Letter to Shareholders as a text corpus to perform Q&A on. This data is already ingested into the knowledge base. You will need the `knowledge base id` and `model ARN` to run this example. We are using `Anthropic Claude Instant` model for generating responses to user questions.\n", - "\n", - "### Python 3.10\n", - "\n", - "⚠ For this lab we need to run the notebook based on a Python 3.10 runtime. ⚠\n", - "\n", - "### Setup\n", - "\n", - "Install following packages. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install --upgrade pip\n", - "%pip install boto3==1.33.2 --force-reinstall --quiet\n", - "%pip install botocore==1.33.2 --force-reinstall --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# restart kernel\n", - "from IPython.core.display import HTML\n", - "HTML(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "store -r kb_id" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "import pprint\n", - "from botocore.client import Config\n", - "\n", - "pp = pprint.PrettyPrinter(indent=2)\n", - "\n", - "bedrock_config = Config(connect_timeout=120, read_timeout=120, retries={'max_attempts': 0})\n", - "bedrock_client = boto3.client('bedrock-runtime')\n", - "bedrock_agent_client = boto3.client(\"bedrock-agent-runtime\",\n", - " config=bedrock_config)\n", - "boto3_session = boto3.session.Session()\n", - "region_name = boto3_session.region_name\n", - "\n", - "model_id = \"anthropic.claude-instant-v1\" # try with both claude instant as well as claude-v2. for claude v2 - \"anthropic.claude-v2\"\n", - "region_id = region_name # replace it with the region you're running sagemaker notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## RetreiveAndGenerate API\n", - "Behind the scenes, `RetrieveAndGenerate` API converts queries into embeddings, searches the knowledge base, and then augments the foundation model prompt with the search results as context information and returns the FM-generated response to the question. For multi-turn conversations, Knowledge Bases manage short-term memory of the conversation to provide more contextual results. \n", - "\n", - "The output of the `RetrieveAndGenerate` API includes the `generated response`, `source attribution` as well as the `retrieved text chunks`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def retrieveAndGenerate(input, kbId, sessionId=None, model_id = \"anthropic.claude-instant-v1\", region_id = \"us-east-1\"):\n", - " model_arn = f'arn:aws:bedrock:{region_id}::foundation-model/{model_id}'\n", - " if sessionId:\n", - " return bedrock_agent_client.retrieve_and_generate(\n", - " input={\n", - " 'text': input\n", - " },\n", - " retrieveAndGenerateConfiguration={\n", - " 'type': 'KNOWLEDGE_BASE',\n", - " 'knowledgeBaseConfiguration': {\n", - " 'knowledgeBaseId': kbId,\n", - " 'modelArn': model_arn\n", - " }\n", - " },\n", - " sessionId=sessionId\n", - " )\n", - " else:\n", - " return bedrock_agent_client.retrieve_and_generate(\n", - " input={\n", - " 'text': input\n", - " },\n", - " retrieveAndGenerateConfiguration={\n", - " 'type': 'KNOWLEDGE_BASE',\n", - " 'knowledgeBaseConfiguration': {\n", - " 'knowledgeBaseId': kbId,\n", - " 'modelArn': model_arn\n", - " }\n", - " }\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "query = \"What is Amazon's doing in the field of generative AI?\"\n", - "response = retrieveAndGenerate(query, kb_id,model_id=model_id,region_id=region_id)\n", - "generated_text = response['output']['text']\n", - "pp.pprint(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "citations = response[\"citations\"]\n", - "contexts = []\n", - "for citation in citations:\n", - " retrievedReferences = citation[\"retrievedReferences\"]\n", - " for reference in retrievedReferences:\n", - " contexts.append(reference[\"content\"][\"text\"])\n", - "\n", - "pp.pprint(contexts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Next Steps\n", - "\n", - "If you want more customized experience, you can use `Retrieve API`. This API converts user queries into embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom workflows on top of the semantic search results. \n", - "For sample code, try following notebooks: \n", - "- [2_customized-rag-retrieve-api-claude-v2.ipynb](./2\\_customized-rag-retrieve-api-claude-v2.ipynb) - it calls the `retrieve` API to get relevant contexts and then augment the context to the prompt, which you can provide as input to any text-text model provided by Amazon Bedrock. \n", - " \n", - "- You can use the RetrieveQA chain from LangChain and add Knowledge Base as retriever. For sample code, try notebook: [3_customized-rag-retrieve-api-langchain-claude-v2.ipynb](./3\\_customized-rag-retrieve-api-langchain-claude-v2.ipynb)\n", - "\n", - "- If you are interested in evaluating your RAG application, for sample code, try notebook:[4_customized-rag-retrieve-api-titan-lite-evaluation](https://github.com/aws-samples/amazon-bedrock-samples/blob/bedrock-kb-images-update/knowledge-bases/4_customized-rag-retrieve-api-titan-lite-evaluation.ipynb/) where we are using `Amazon Titan Lite` model for generating responses and `Anthropic Claude V2` for evaluating response. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Next steps: Proceed to the next labs to learn how to use Bedrock Knowledge bases with Langchain and Claude. Remember to CLEAN_UP at the end of your session.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "kb-agents", - "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": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/02_KnowledgeBases_and_RAG/2_Langchain-rag-retrieve-api-mistral-and-claude-v2.ipynb b/02_KnowledgeBases_and_RAG/2_Langchain-rag-retrieve-api-mistral-and-claude-v2.ipynb deleted file mode 100644 index 1a78599d..00000000 --- a/02_KnowledgeBases_and_RAG/2_Langchain-rag-retrieve-api-mistral-and-claude-v2.ipynb +++ /dev/null @@ -1,1108 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Q&A application using Knowledge Bases for Amazon Bedrock - Retrieve API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Context\n", - "\n", - "In this notebook, we will dive deep into building Q&A application using Knowledge Bases for Amazon Bedrock - Retrieve API. Here, we will query the knowledge base to get the desired number of document chunks based on similarity search. We will then augment the prompt with relevant documents and query which will go as input to Anthropic Claude V2 for generating response.\n", - "\n", - "With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company\n", - "data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant,\n", - "context-specific, and accurate responses without continuously retraining the FM. All information retrieved from\n", - "knowledge bases comes with source attribution to improve transparency and minimize hallucinations. For more information on creating a knowledge base using console, please refer to this [post](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html).\n", - "We will cover 2 parts in the notebook:\n", - "- Part 1, we will share how you can use `RetrieveAPI` with foundation models from Amazon Bedrock. We will use the `mistral.mistral-7b` model. \n", - "- Part 2, we will showcase the langchain integration using Anthropic Claude V2.1 model.\n", - "\n", - "### Pattern\n", - "\n", - "We can implement the solution using Retreival Augmented Generation (RAG) pattern. RAG retrieves data from outside the language model (non-parametric) and augments the prompts by adding the relevant retrieved data in context. Here, we are performing RAG effectively on the knowledge base created using console/sdk. \n", - "\n", - "### Pre-requisite\n", - "\n", - "Before being able to answer the questions, the documents must be processed and ingested in vector database.\n", - "\n", - "1. Load the documents into the knowledge base by connecting your s3 bucket (data source). \n", - "2. Ingestion - Knowledge bases will split them into smaller chunks (based on the strategy selected), generate embeddings and store it in the associated vectore store and notebook [0_create_ingest_documents_test_kb.ipynb](./0\\_create_ingest_documents_test_kb.ipynb) takes care of it for you.\n", - "\n", - "![data_ingestion](./images/data_ingestion.png)\n", - "\n", - "\n", - "#### Notebook Walkthrough\n", - "\n", - "\n", - "\n", - "For our notebook we will use the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into\n", - "embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom\n", - "workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals. \n", - "\n", - "\n", - "We will then use the text chunks being generated and augment it with the original prompt and pass it through the `anthropic.claude-v2` model using prompt engineering patterns based on your use case.\n", - " \n", - "\n", - "### USE CASE:\n", - "\n", - "#### Dataset\n", - "\n", - "In this example, you will use several years of Amazon's Letter to Shareholders as a text corpus to perform Q&A on. This data is already ingested into the Knowledge Bases for Amazon Bedrock. You will need the `knowledge base id` to run this example.\n", - "In your specific use case, you can sync different files for different domain topics and query this notebook in the same manner to evaluate model responses using the retrieve API from knowledge bases.\n", - "\n", - "\n", - "### Python 3.10\n", - "\n", - "⚠ For this lab we need to run the notebook based on a Python 3.10 runtime. ⚠\n", - "\n", - "If you carry out the workshop from your local environment outside of the Amazon SageMaker studio please make sure you are running a Python runtime > 3.10.\n", - "\n", - "### Setup\n", - "\n", - "To run this notebook you would need to install following packages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install --upgrade pip\n", - "%pip install boto3==1.34.55 --force-reinstall --quiet\n", - "%pip install botocore==1.34.55 --force-reinstall --quiet\n", - "%pip install langchain==0.1.10 --force-reinstall --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Restart the kernel with the updated packages that are installed through the dependencies above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# restart kernel\n", - "from IPython.core.display import HTML\n", - "HTML(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "store -r kb_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Follow the steps below to initiate the bedrock client:\n", - "\n", - "1. Import the necessary libraries, along with langchain for bedrock model selection, llama index to store the service context containing the llm and embedding model instances. We will use this service context later in the notebook for evaluating the responses from our Q&A application. \n", - "\n", - "2. Initialize `anthropic.claude-v2` as our large language model to perform query completions using the RAG pattern with the given knowledge base, once we get all text chunk searches through the `retrieve` API." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "import pprint\n", - "from botocore.client import Config\n", - "import json\n", - "\n", - "pp = pprint.PrettyPrinter(indent=2)\n", - "session = boto3.session.Session()\n", - "region = session.region_name\n", - "bedrock_config = Config(connect_timeout=120, read_timeout=120, retries={'max_attempts': 0})\n", - "bedrock_client = boto3.client('bedrock-runtime', region_name = region)\n", - "bedrock_agent_client = boto3.client(\"bedrock-agent-runtime\",\n", - " config=bedrock_config, region_name = region)\n", - "print(region)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 1 - Retrieve API with foundation models from Amazon Bedrock\n", - "\n", - "Define a retrieve function that calls the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into\n", - "embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom\n", - "workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals. You can also use the `overrideSearchType` option in `retrievalConfiguration` which offers the choice to use either `HYBRID` or `SEMANTIC`. By default, it will select the right strategy for you to give you most relevant results, and if you want to override the default option to use either hybrid or semantic search, you can set the value to `HYBRID/SEMANTIC`.\n", - "\n", - "![retrieveAPI](./images/retrieveAPI.png)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def retrieve(query, kbId, numberOfResults=5):\n", - " return bedrock_agent_client.retrieve(\n", - " retrievalQuery= {\n", - " 'text': query\n", - " },\n", - " knowledgeBaseId=kbId,\n", - " retrievalConfiguration= {\n", - " 'vectorSearchConfiguration': {\n", - " 'numberOfResults': numberOfResults,\n", - " 'overrideSearchType': \"HYBRID\", # optional\n", - " }\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize your Knowledge base id before querying responses from the initialized LLM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will call the `retreive API`, and pass `knowledge base id`, `number of results` and `query` as paramters. \n", - "\n", - "`score`: You can view the associated score of each of the text chunk that was returned which depicts its correlation to the query in terms of how closely it matches it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "query = \"What is Amazon doing in the field of generative AI?\"\n", - "response = retrieve(query, kb_id, 5)\n", - "retrievalResults = response['retrievalResults']\n", - "pp.pprint(retrievalResults)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract the text chunks from the retrieveAPI response\n", - "\n", - "In the cell below, we will fetch the context from the retrieval results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch context from the response\n", - "def get_contexts(retrievalResults):\n", - " contexts = []\n", - " for retrievedResult in retrievalResults: \n", - " contexts.append(retrievedResult['content']['text'])\n", - " return contexts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "contexts = get_contexts(retrievalResults)\n", - "pp.pprint(contexts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prompt specific to the model to personalize responses \n", - "\n", - "Here, we will use the specific prompt below for the model to act as a financial advisor AI system that will provide answers to questions by using fact based and statistical information when possible. We will provide the `Retrieve API` responses from above as a part of the `{contexts}` in the prompt for the model to refer to, along with the user `query`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prompt = f\"\"\"\n", - "Human: You are a financial advisor AI system, and provides answers to questions by using fact based and statistical information when possible. \n", - "Use the following pieces of information to provide a concise answer to the question enclosed in tags. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "\n", - "{contexts}\n", - "\n", - "\n", - "\n", - "{query}\n", - "\n", - "\n", - "The response should be specific and use statistics or numbers when possible.\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Invoke foundation model from Amazon Bedrock\n", - "In this example, we will use `mistral.mistral-7b` foundation model from Amazon Bedrock. \n", - "Its a 7B dense Transformer, fast-deployed and easily customizable. Small, yet powerful for a variety of use cases.\n", - "- Max tokens: 8K\n", - "- Languages: English\n", - "- Supported use cases: Text summarization, structuration, question answering, and code completion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# payload with model paramters\n", - "mistral_payload = json.dumps({\n", - " \"prompt\": prompt,\n", - " \"max_tokens\":512,\n", - " \"temperature\":0.5,\n", - " \"top_k\":50,\n", - " \"top_p\":0.9\n", - "})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note: Please make sure you have subscribed to the mistral-7B model on the model access page in the Amazon Bedrock console before proceeding.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modelId = 'mistral.mistral-7b-instruct-v0:2' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "response = bedrock_client.invoke_model(body=mistral_payload, modelId=modelId, accept=accept, contentType=contentType)\n", - "response_body = json.loads(response.get('body').read())\n", - "response_text = response_body.get('outputs')[0]['text']\n", - "\n", - "pp.pprint(response_text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 2 - LangChain integration\n", - "In this notebook, we will dive deep into building Q&A application using Retrieve API provided by Knowledge Bases for Amazon Bedrock and LangChain. We will query the knowledge base to get the desired number of document chunks based on similarity search, integrate it with LangChain retriever and use Anthropic Claude V2.1 model for answering questions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.llms.bedrock import Bedrock\n", - "from langchain.retrievers.bedrock import AmazonKnowledgeBasesRetriever\n", - "\n", - "model_kwargs_claude = {\n", - " \"temperature\": 0,\n", - " \"top_k\": 10,\n", - " \"max_tokens_to_sample\": 3000\n", - "}\n", - "\n", - "llm = Bedrock(model_id=\"anthropic.claude-v2:1\",\n", - " model_kwargs=model_kwargs_claude,\n", - " client = bedrock_client,)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a `AmazonKnowledgeBasesRetriever` object from LangChain which will call the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query = \"By what percentage did AWS revenue grow year-over-year in 2022?\"\n", - "retriever = AmazonKnowledgeBasesRetriever(\n", - " knowledge_base_id=kb_id,\n", - " retrieval_config={\"vectorSearchConfiguration\": \n", - " {\"numberOfResults\": 4,\n", - " 'overrideSearchType': \"SEMANTIC\", # optional\n", - " }\n", - " },\n", - " # endpoint_url=endpoint_url,\n", - " # region_name=region,\n", - " # credentials_profile_name=\"\",\n", - " )\n", - "docs = retriever.get_relevant_documents(\n", - " query=query\n", - " )\n", - "pp.pprint(docs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prompt specific to the model to personalize responses\n", - "Here, we will use the specific prompt below for the model to act as a financial advisor AI system that will provide answers to questions by using fact based and statistical information when possible. We will provide the Retrieve API responses from above as a part of the `{context}` in the prompt for the model to refer to, along with the user `query`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.prompts import PromptTemplate\n", - "\n", - "PROMPT_TEMPLATE = \"\"\"\n", - "Human: You are a financial advisor AI system, and provides answers to questions by using fact based and statistical information when possible. \n", - "Use the following pieces of information to provide a concise answer to the question enclosed in tags. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "\n", - "{context}\n", - "\n", - "\n", - "\n", - "{question}\n", - "\n", - "\n", - "The response should be specific and use statistics or numbers when possible.\n", - "\n", - "Assistant:\"\"\"\n", - "claude_prompt = PromptTemplate(template=PROMPT_TEMPLATE, \n", - " input_variables=[\"context\",\"question\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integrating the retriever and the LLM defined above with RetrievalQA Chain to build the Q&A application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.chains import RetrievalQA\n", - "\n", - "qa = RetrievalQA.from_chain_type(\n", - " llm=llm,\n", - " chain_type=\"stuff\",\n", - " retriever=retriever,\n", - " return_source_documents=True,\n", - " chain_type_kwargs={\"prompt\": claude_prompt}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "answer = qa.invoke(query)\n", - "pp.pprint(answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "You can use Retrieve API for customizing your RAG based application, using either `InvokeModel` API from Bedrock, or you can integrate with LangChain using `AmazonKnowledgeBaseRetriever`.\n", - "Retrieve API provides you with the flexibility of using any foundation model provided by Amazon Bedrock, and choosing the right search type, either HYBRID or SEMANTIC, based on your use case. \n", - "Here is the [blog](#https://aws.amazon.com/blogs/machine-learning/knowledge-bases-for-amazon-bedrock-now-supports-hybrid-search/) for Hybrid Search feature, for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Remember to CLEAN_UP at the end of your session. \n", - "
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"hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/02_KnowledgeBases_and_RAG/3_Langchain-rag-retrieve-api-claude-3.ipynb b/02_KnowledgeBases_and_RAG/3_Langchain-rag-retrieve-api-claude-3.ipynb deleted file mode 100644 index e688c5d6..00000000 --- a/02_KnowledgeBases_and_RAG/3_Langchain-rag-retrieve-api-claude-3.ipynb +++ /dev/null @@ -1,1095 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Building Q&A application using Knowledge Bases for Amazon Bedrock - Retrieve API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note: This lab uses the recently announced Claude v3, which is not available in AWS Workshop Studio yet. You may\n", - " continue with this lab if the account you are running this in has access to Claude V3.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Context\n", - "\n", - "In this notebook, we will dive deep into building Q&A application using Knowledge Bases for Amazon Bedrock - Retrieve API. Here, we will query the knowledge base to get the desired number of document chunks based on similarity search. We will then augment the prompt with relevant documents and query which will go as input to Anthropic Claude V2 for generating response.\n", - "\n", - "With a knowledge base, you can securely connect foundation models (FMs) in Amazon Bedrock to your company\n", - "data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant,\n", - "context-specific, and accurate responses without continuously retraining the FM. All information retrieved from\n", - "knowledge bases comes with source attribution to improve transparency and minimize hallucinations. For more information on creating a knowledge base using console, please refer to this [post](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html).\n", - "We will cover 2 parts in the notebook:\n", - "- Part 1, we will share how you can use `RetrieveAPI` with foundation models from Amazon Bedrock. We will use the `anthropic.claude-3-sonnet-20240229-v1:0` model. \n", - "- Part 2, we will showcase the langchain integration.\n", - "\n", - "### Pattern\n", - "\n", - "We can implement the solution using Retreival Augmented Generation (RAG) pattern. RAG retrieves data from outside the language model (non-parametric) and augments the prompts by adding the relevant retrieved data in context. Here, we are performing RAG effectively on the knowledge base created using console/sdk. \n", - "\n", - "### Pre-requisite\n", - "\n", - "Before being able to answer the questions, the documents must be processed and ingested in vector database.\n", - "\n", - "1. Load the documents into the knowledge base by connecting your s3 bucket (data source). \n", - "2. Ingestion - Knowledge bases will split them into smaller chunks (based on the strategy selected), generate embeddings and store it in the associated vectore store and notebook [0_create_ingest_documents_test_kb.ipynb](./0\\_create_ingest_documents_test_kb.ipynb) takes care of it for you.\n", - "\n", - "![data_ingestion](./images/data_ingestion.png)\n", - "\n", - "\n", - "#### Notebook Walkthrough\n", - "\n", - "\n", - "\n", - "For our notebook we will use the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into\n", - "embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom\n", - "workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals. \n", - "\n", - "\n", - "We will then use the text chunks being generated and augment it with the original prompt and pass it through the `anthropic.claude-3-sonnet-20240229-v1:0` model using prompt engineering patterns based on your use case.\n", - " \n", - "\n", - "### USE CASE:\n", - "\n", - "#### Dataset\n", - "\n", - "In this example, you will use several years of Amazon's Letter to Shareholders as a text corpus to perform Q&A on. This data is already ingested into the Knowledge Bases for Amazon Bedrock. You will need the `knowledge base id` to run this example.\n", - "In your specific use case, you can sync different files for different domain topics and query this notebook in the same manner to evaluate model responses using the retrieve API from knowledge bases.\n", - "\n", - "\n", - "### Python 3.10\n", - "\n", - "⚠ For this lab we need to run the notebook based on a Python 3.10 runtime. ⚠\n", - "\n", - "If you carry out the workshop from your local environment outside of the Amazon SageMaker studio please make sure you are running a Python runtime > 3.10.\n", - "\n", - "### Setup\n", - "\n", - "To run this notebook you would need to install following packages.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%pip install --upgrade pip\n", - "%pip install boto3 --force-reinstall --quiet\n", - "%pip install botocore --force-reinstall --quiet\n", - "%pip install langchain --force-reinstall --quiet" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Restart the kernel with the updated packages that are installed through the dependencies above" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# restart kernel\n", - "from IPython.core.display import HTML\n", - "HTML(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "store -r kb_id\n", - "# kb_id = \"\" If you have already created knowledge base, comment the `store -r kb_id` and provide knowledge base id here." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Follow the steps below to initiate the bedrock client:\n", - "\n", - "1. Import the necessary libraries, along with langchain for bedrock model selection, llama index to store the service context containing the llm and embedding model instances. We will use this service context later in the notebook for evaluating the responses from our Q&A application. \n", - "\n", - "2. Initialize `anthropic.claude-3-sonnet-20240229-v1:0` as our large language model to perform query completions using the RAG pattern with the given knowledge base, once we get all text chunk searches through the `retrieve` API." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import boto3\n", - "import pprint\n", - "from botocore.client import Config\n", - "import json\n", - "\n", - "pp = pprint.PrettyPrinter(indent=2)\n", - "session = boto3.session.Session()\n", - "region = session.region_name\n", - "bedrock_config = Config(connect_timeout=120, read_timeout=120, retries={'max_attempts': 0})\n", - "bedrock_client = boto3.client('bedrock-runtime', region_name = region)\n", - "bedrock_agent_client = boto3.client(\"bedrock-agent-runtime\",\n", - " config=bedrock_config, region_name = region)\n", - "print(region)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Part 1 - Retrieve API with foundation models from Amazon Bedrock\n", - "\n", - "Define a retrieve function that calls the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into\n", - "embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom\n", - "workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals. You can also use the `overrideSearchType` option in `retrievalConfiguration` which offers the choice to use either `HYBRID` or `SEMANTIC`. By default, it will select the right strategy for you to give you most relevant results, and if you want to override the default option to use either hybrid or semantic search, you can set the value to `HYBRID/SEMANTIC`.\n", - "\n", - "![retrieveAPI](./images/retrieveAPI.png)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def retrieve(query, kbId, numberOfResults=5):\n", - " return bedrock_agent_client.retrieve(\n", - " retrievalQuery= {\n", - " 'text': query\n", - " },\n", - " knowledgeBaseId=kbId,\n", - " retrievalConfiguration= {\n", - " 'vectorSearchConfiguration': {\n", - " 'numberOfResults': numberOfResults,\n", - " 'overrideSearchType': \"HYBRID\", # optional\n", - " }\n", - " }\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Initialize your Knowledge base id before querying responses from the initialized LLM" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will call the `retreive API`, and pass `knowledge base id`, `number of results` and `query` as paramters. \n", - "\n", - "`score`: You can view the associated score of each of the text chunk that was returned which depicts its correlation to the query in terms of how closely it matches it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "query = \"What is Amazon doing in the field of Generative AI?\"\n", - "response = retrieve(query, kb_id, 5)\n", - "retrievalResults = response['retrievalResults']\n", - "pp.pprint(retrievalResults)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Extract the text chunks from the retrieveAPI response\n", - "\n", - "In the cell below, we will fetch the context from the retrieval results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fetch context from the response\n", - "def get_contexts(retrievalResults):\n", - " contexts = []\n", - " for retrievedResult in retrievalResults: \n", - " contexts.append(retrievedResult['content']['text'])\n", - " return contexts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "contexts = get_contexts(retrievalResults)\n", - "pp.pprint(contexts)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Prompt specific to the model to personalize responses \n", - "\n", - "Here, we will use the specific prompt below for the model to act as a financial advisor AI system that will provide answers to questions by using fact based and statistical information when possible. We will provide the `Retrieve API` responses from above as a part of the `{contexts}` in the prompt for the model to refer to, along with the user `query`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "prompt = f\"\"\"\n", - "Human: You are a financial advisor AI system, and provides answers to questions by using fact based and statistical information when possible. \n", - "Use the following pieces of information to provide a concise answer to the question enclosed in tags. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "\n", - "{contexts}\n", - "\n", - "\n", - "\n", - "{query}\n", - "\n", - "\n", - "The response should be specific and use statistics or numbers when possible.\n", - "\n", - "Assistant:\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Invoke foundation model from Amazon Bedrock\n", - "In this example, we will use `anthropic.claude-3-sonnet-20240229-v1:0` foundation model from Amazon Bedrock. \n", - "- It offers maximum utility at a lower price than competitors, and is engineered to be the dependable, high-endurance workhorse for scaled AI deployments. Claude 3 Sonnet can process images and return text outputs, and features a 200K context window.\n", - "- Model attributes\n", - " - Image to text & code, multilingual conversation, complex reasoning & analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# payload with model paramters\n", - "messages=[{ \"role\":'user', \"content\":[{'type':'text','text': prompt.format(contexts, query)}]}]\n", - "sonnet_payload = json.dumps({\n", - " \"anthropic_version\": \"bedrock-2023-05-31\",\n", - " \"max_tokens\": 512,\n", - " \"messages\": messages,\n", - " \"temperature\": 0.5,\n", - " \"top_p\": 1\n", - " } )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "modelId = 'anthropic.claude-3-sonnet-20240229-v1:0' # change this to use a different version from the model provider\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "response = bedrock_client.invoke_model(body=sonnet_payload, modelId=modelId, accept=accept, contentType=contentType)\n", - "response_body = json.loads(response.get('body').read())\n", - "response_text = response_body.get('content')[0]['text']\n", - "\n", - "pp.pprint(response_text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Part 2 - LangChain integration\n", - "In this notebook, we will dive deep into building Q&A application using Retrieve API provided by Knowledge Bases for Amazon Bedrock and LangChain. We will query the knowledge base to get the desired number of document chunks based on similarity search, integrate it with LangChain retriever and use `Anthropic Claude 3 Sonnet` model for answering questions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# from langchain.llms.bedrock import Bedrock\n", - "from langchain_community.chat_models.bedrock import BedrockChat\n", - "from langchain.retrievers.bedrock import AmazonKnowledgeBasesRetriever\n", - "\n", - "llm = BedrockChat(model_id=modelId, \n", - " client=bedrock_client)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a `AmazonKnowledgeBasesRetriever` object from LangChain which will call the `Retreive API` provided by Knowledge Bases for Amazon Bedrock which converts user queries into embeddings, searches the knowledge base, and returns the relevant results, giving you more control to build custom workflows on top of the semantic search results. The output of the `Retrieve API` includes the the `retrieved text chunks`, the `location type` and `URI` of the source data, as well as the relevance `scores` of the retrievals." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query = \"What is Amazon doing in the field of Generative AI?\"\n", - "retriever = AmazonKnowledgeBasesRetriever(\n", - " knowledge_base_id=kb_id,\n", - " retrieval_config={\"vectorSearchConfiguration\": \n", - " {\"numberOfResults\": 4,\n", - " 'overrideSearchType': \"SEMANTIC\", # optional\n", - " }\n", - " },\n", - " # endpoint_url=endpoint_url,\n", - " # region_name=region,\n", - " # credentials_profile_name=\"\",\n", - " )\n", - "docs = retriever.get_relevant_documents(\n", - " query=query\n", - " )\n", - "pp.pprint(docs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prompt specific to the model to personalize responses\n", - "Here, we will use the specific prompt below for the model to act as a financial advisor AI system that will provide answers to questions by using fact based and statistical information when possible. We will provide the Retrieve API responses from above as a part of the `{context}` in the prompt for the model to refer to, along with the user `query`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.prompts import PromptTemplate\n", - "\n", - "PROMPT_TEMPLATE = \"\"\"\n", - "Human: You are a financial advisor AI system, and provides answers to questions by using fact based and statistical information when possible. \n", - "Use the following pieces of information to provide a concise answer to the question enclosed in tags. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - "\n", - "{context}\n", - "\n", - "\n", - "\n", - "{question}\n", - "\n", - "\n", - "The response should be specific and use statistics or numbers when possible.\n", - "\n", - "Assistant:\"\"\"\n", - "claude_prompt = PromptTemplate(template=PROMPT_TEMPLATE, \n", - " input_variables=[\"context\",\"question\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integrating the retriever and the LLM defined above with RetrievalQA Chain to build the Q&A application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.chains import RetrievalQA\n", - "\n", - "qa = RetrievalQA.from_chain_type(\n", - " llm=llm,\n", - " chain_type=\"stuff\",\n", - " retriever=retriever,\n", - " return_source_documents=True,\n", - " chain_type_kwargs={\"prompt\": claude_prompt}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "answer = qa.invoke(query)\n", - "pp.pprint(answer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "You can use Retrieve API for customizing your RAG based application, using either `InvokeModel` API from Bedrock, or you can integrate with LangChain using `AmazonKnowledgeBaseRetriever`.\n", - "Retrieve API provides you with the flexibility of using any foundation model provided by Amazon Bedrock, and choosing the right search type, either HYBRID or SEMANTIC, based on your use case. \n", - "Here is the [blog](#https://aws.amazon.com/blogs/machine-learning/knowledge-bases-for-amazon-bedrock-now-supports-hybrid-search/) for Hybrid Search feature, for more details." - ] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - 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}, - { - "_defaultOrder": 48, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 49, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 50, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 51, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 52, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 53, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 57, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "kb-env", - "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": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/02_KnowledgeBases_and_RAG/4_CLEAN_UP.ipynb b/02_KnowledgeBases_and_RAG/4_CLEAN_UP.ipynb deleted file mode 100644 index 176466aa..00000000 --- a/02_KnowledgeBases_and_RAG/4_CLEAN_UP.ipynb +++ /dev/null @@ -1,720 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e6284875-f03e-4311-8946-077be779297a", - "metadata": {}, - "source": [ - "## Clean up\n", - "Please make sure to comment the below section if you are planning to use the Knowledge Base that you created above for building your RAG application.\n", - "If you only wanted to try out creating the KB using SDK, then please make sure to delete all the resources that were created as you will be incurred cost for storing documents in OSS index." - ] - }, - { - "cell_type": "markdown", - "id": "cbdb8e7f-03ee-4187-a23f-5ce802d95969", - "metadata": {}, - "source": [ - "#### Delete KnowledgeBase - uncomment and delete resources after completing all the notebooks.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "61507ff2-74cb-4c7d-802f-0678bdd3da98", - "metadata": {}, - "outputs": [], - "source": [ - "bedrock_agent_client = boto3_session.client('bedrock-agent', region_name=region_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5daa9c7a-623b-4d55-a275-0e98e992c755", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock_agent_client.delete_data_source(dataSourceId = ds[\"dataSourceId\"], knowledgeBaseId=kb['knowledgeBaseId'])\n", - "bedrock_agent_client.delete_knowledge_base(knowledgeBaseId=kb['knowledgeBaseId'])\n", - "oss_client.indices.delete(index=index_name)\n", - "aoss_client.delete_collection(id=collection_id)\n", - "aoss_client.delete_access_policy(type=\"data\", name=access_policy['accessPolicyDetail']['name'])\n", - "aoss_client.delete_security_policy(type=\"network\", name=network_policy['securityPolicyDetail']['name'])\n", - "aoss_client.delete_security_policy(type=\"encryption\", name=encryption_policy['securityPolicyDetail']['name'])" - ] - }, - { - "cell_type": "markdown", - "id": "f809292a-59a5-4e0e-91f6-a07d4e7056bb", - "metadata": {}, - "source": [ - "#### Delete role and policies\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4afc20b8-b174-4512-875e-a16e0b0ff882", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from utility import delete_iam_role_and_policies\n", - "delete_iam_role_and_policies()" - ] - }, - { - "cell_type": "markdown", - "id": "988b109b-b750-4e00-a168-70e4af7bdd46", - "metadata": {}, - "source": [ - "#### Delete S3 objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48cc53f4-783d-4074-b285-1b62a81fda8e", - "metadata": {}, - "outputs": [], - "source": [ - "objects = s3_client.list_objects(Bucket=bucket_name)\n", - "if 'Contents' in objects:\n", - " for obj in objects['Contents']:\n", - " s3_client.delete_object(Bucket=bucket_name, Key=obj['Key'])\n", - "s3_client.delete_bucket(Bucket=bucket_name)" - ] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5d.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 13, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5d.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 14, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5d.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 15, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5d.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 16, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5d.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 17, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5d.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 18, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5d.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 19, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 20, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": true, - "memoryGiB": 0, - "name": "ml.geospatial.interactive", - "supportedImageNames": [ - "sagemaker-geospatial-v1-0" - ], - "vcpuNum": 0 - }, - { - "_defaultOrder": 21, - "_isFastLaunch": true, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.c5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 22, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.c5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 23, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.c5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 24, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.c5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 25, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 72, - "name": "ml.c5.9xlarge", - "vcpuNum": 36 - }, - { - "_defaultOrder": 26, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 96, - "name": "ml.c5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 27, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 144, - "name": "ml.c5.18xlarge", - "vcpuNum": 72 - }, - { - "_defaultOrder": 28, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.c5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 29, - "_isFastLaunch": true, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g4dn.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 30, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g4dn.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 31, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g4dn.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 32, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g4dn.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 33, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g4dn.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 34, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g4dn.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 35, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 61, - "name": "ml.p3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 36, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 244, - "name": "ml.p3.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 37, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 488, - "name": "ml.p3.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 38, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.p3dn.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 39, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.r5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 40, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.r5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 41, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.r5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 42, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.r5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 43, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.r5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 44, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.r5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 45, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.r5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 46, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.r5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 47, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 48, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 49, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 50, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 51, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 52, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 53, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 57, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/02_KnowledgeBases_and_RAG/README.md b/02_KnowledgeBases_and_RAG/README.md deleted file mode 100644 index e7f6e9ca..00000000 --- a/02_KnowledgeBases_and_RAG/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# Amazon Bedrock Knowledge Base - Samples for building RAG workflows - -## Contents -- [0_create_ingest_documents_test_kb.ipynb](./0\_create_ingest_documents_test_kb.ipynb) - creates necessary role and policies required using the `utility.py` file. It uses the roles and policies to create Open Search Serverless vector index, knowledge base, data source, and then ingests the documents to the vector store. Once the documents are ingested it will then test the knowledge base using `RetrieveAndGenerate` API for question answering, and `Retrieve` API for fetching relevant documents. Finally, it deletes all the resources. If you want to continue with other notebooks, you can choose not to delete the resources and move to other notebooks. Please note, that if you do not delete the resources, you may be incurred cost of storing data in OpenSearch Serverless, even if you are not using it. Therefore, once you are done with trying out the sample code, make sure to delete all the resources. - -- [1_managed-rag-kb-retrieve-generate-api.ipynb](./1\_managed-rag-kb-retrieve-generate-api.ipynb) - Code sample for managed retrieval augmented generation (RAG) using `RetrieveAndGenerate` API from Knowledge Bases for Amazon Bedrock. - -- [2_customized-rag-retrieve-api-claude-v2.ipynb](./2\_customized-rag-retrieve-api-claude-v2.ipynb) - If you want to customize your RAG workflow, you can use the `retrieve` API provided by Knowledge Bases for Amazon Bedrock. Use this code sample as a starting point. - -- [3_customized-rag-retrieve-api-langchain-claude-v2.ipynb](./3\_customized-rag-retrieve-api-langchain-claude-v2.ipynb) - Code sample for using the `RetrieveQA` chain from LangChain and Amazon Knowledge Base as the retriever. - - -Remember to use the [4_CLEAN_UP.ipynb](./4\_CLEAN_UP.ipynb) - -*** - -### Note -If you use the notebook - [0_create_ingest_documents_test_kb.ipynb](./0\_create_ingest_documents_test_kb.ipynb) for creating the knowledge bases and do not delete the resources, you may be incurred cost of storing data in OpenSearch Serverless, even if you are not using it. Therefore, once you are done with trying out the sample code, make sure to delete all the resources. - -## Contributing - -We welcome community contributions! 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- -suffix = random.randrange(200, 900) -boto3_session = boto3.session.Session() -region_name = boto3_session.region_name -iam_client = boto3_session.client('iam') -account_number = boto3.client('sts').get_caller_identity().get('Account') -identity = boto3.client('sts').get_caller_identity()['Arn'] - -encryption_policy_name = f"bedrock-sample-rag-sp-{suffix}" -network_policy_name = f"bedrock-sample-rag-np-{suffix}" -access_policy_name = f'bedrock-sample-rag-ap-{suffix}' -bedrock_execution_role_name = f'AmazonBedrockExecutionRoleForKnowledgeBase_{suffix}' -fm_policy_name = f'AmazonBedrockFoundationModelPolicyForKnowledgeBase_{suffix}' -s3_policy_name = f'AmazonBedrockS3PolicyForKnowledgeBase_{suffix}' -oss_policy_name = f'AmazonBedrockOSSPolicyForKnowledgeBase_{suffix}' - - -def create_bedrock_execution_role(bucket_name): - foundation_model_policy_document = { - "Version": "2012-10-17", - "Statement": [ - { - "Effect": "Allow", - "Action": [ - "bedrock:InvokeModel", - ], - "Resource": [ - f"arn:aws:bedrock:{region_name}::foundation-model/amazon.titan-embed-text-v1" - ] - } - ] - } - - s3_policy_document = { - "Version": "2012-10-17", - "Statement": [ - { - "Effect": "Allow", - "Action": [ - "s3:GetObject", - "s3:ListBucket" - ], - "Resource": [ - f"arn:aws:s3:::{bucket_name}", - f"arn:aws:s3:::{bucket_name}/*" - ], - "Condition": { - "StringEquals": { - "aws:ResourceAccount": f"{account_number}" - } - } - } - ] - } - - assume_role_policy_document = { - "Version": "2012-10-17", - "Statement": [ - { - "Effect": "Allow", - "Principal": { - "Service": "bedrock.amazonaws.com" - }, - "Action": "sts:AssumeRole" - } - ] - } - # create policies based on the policy documents - fm_policy = iam_client.create_policy( - PolicyName=fm_policy_name, - PolicyDocument=json.dumps(foundation_model_policy_document), - Description='Policy for accessing foundation model', - ) - - s3_policy = iam_client.create_policy( - PolicyName=s3_policy_name, - PolicyDocument=json.dumps(s3_policy_document), - Description='Policy for reading documents from s3') - - # create bedrock execution role - bedrock_kb_execution_role = iam_client.create_role( - RoleName=bedrock_execution_role_name, - AssumeRolePolicyDocument=json.dumps(assume_role_policy_document), - Description='Amazon Bedrock Knowledge Base Execution Role for accessing OSS and S3', - MaxSessionDuration=3600 - ) - - # fetch arn of the policies and role created above - bedrock_kb_execution_role_arn = bedrock_kb_execution_role['Role']['Arn'] - s3_policy_arn = s3_policy["Policy"]["Arn"] - fm_policy_arn = fm_policy["Policy"]["Arn"] - - # attach policies to Amazon Bedrock execution role - iam_client.attach_role_policy( - RoleName=bedrock_kb_execution_role["Role"]["RoleName"], - PolicyArn=fm_policy_arn - ) - iam_client.attach_role_policy( - RoleName=bedrock_kb_execution_role["Role"]["RoleName"], - PolicyArn=s3_policy_arn - ) - return bedrock_kb_execution_role - - -def create_oss_policy_attach_bedrock_execution_role(collection_id, bedrock_kb_execution_role): - # define oss policy document - oss_policy_document = { - "Version": "2012-10-17", - "Statement": [ - { - "Effect": "Allow", - "Action": [ - "aoss:APIAccessAll" - ], - "Resource": [ - f"arn:aws:aoss:{region_name}:{account_number}:collection/{collection_id}" - ] - } - ] - } - oss_policy = iam_client.create_policy( - PolicyName=oss_policy_name, - PolicyDocument=json.dumps(oss_policy_document), - Description='Policy for accessing opensearch serverless', - ) - oss_policy_arn = oss_policy["Policy"]["Arn"] - print("Opensearch serverless arn: ", oss_policy_arn) - - iam_client.attach_role_policy( - RoleName=bedrock_kb_execution_role["Role"]["RoleName"], - PolicyArn=oss_policy_arn - ) - return None - - -def create_policies_in_oss(vector_store_name, aoss_client, bedrock_kb_execution_role_arn): - encryption_policy = aoss_client.create_security_policy( - name=encryption_policy_name, - policy=json.dumps( - { - 'Rules': [{'Resource': ['collection/' + vector_store_name], - 'ResourceType': 'collection'}], - 'AWSOwnedKey': True - }), - type='encryption' - ) - - network_policy = aoss_client.create_security_policy( - name=network_policy_name, - policy=json.dumps( - [ - {'Rules': [{'Resource': ['collection/' + vector_store_name], - 'ResourceType': 'collection'}], - 'AllowFromPublic': True} - ]), - type='network' - ) - access_policy = aoss_client.create_access_policy( - name=access_policy_name, - policy=json.dumps( - [ - { - 'Rules': [ - { - 'Resource': ['collection/' + vector_store_name], - 'Permission': [ - 'aoss:CreateCollectionItems', - 'aoss:DeleteCollectionItems', - 'aoss:UpdateCollectionItems', - 'aoss:DescribeCollectionItems'], - 'ResourceType': 'collection' - }, - { - 'Resource': ['index/' + vector_store_name + '/*'], - 'Permission': [ - 'aoss:CreateIndex', - 'aoss:DeleteIndex', - 'aoss:UpdateIndex', - 'aoss:DescribeIndex', - 'aoss:ReadDocument', - 'aoss:WriteDocument'], - 'ResourceType': 'index' - }], - 'Principal': [identity, bedrock_kb_execution_role_arn], - 'Description': 'Easy data policy'} - ]), - type='data' - ) - return encryption_policy, network_policy, access_policy - - -def delete_iam_role_and_policies(): - fm_policy_arn = f"arn:aws:iam::{account_number}:policy/{fm_policy_name}" - s3_policy_arn = f"arn:aws:iam::{account_number}:policy/{s3_policy_name}" - oss_policy_arn = f"arn:aws:iam::{account_number}:policy/{oss_policy_name}" - iam_client.detach_role_policy( - RoleName=bedrock_execution_role_name, - PolicyArn=s3_policy_arn - ) - iam_client.detach_role_policy( - RoleName=bedrock_execution_role_name, - PolicyArn=fm_policy_arn - ) - iam_client.detach_role_policy( - RoleName=bedrock_execution_role_name, - PolicyArn=oss_policy_arn - ) - iam_client.delete_role(RoleName=bedrock_execution_role_name) - iam_client.delete_policy(PolicyArn=s3_policy_arn) - iam_client.delete_policy(PolicyArn=fm_policy_arn) - iam_client.delete_policy(PolicyArn=oss_policy_arn) - return 0 - - -def interactive_sleep(seconds: int): - dots = '' - for i in range(seconds): - dots += '.' - print(dots, end='\r') - time.sleep(1) - print('Done!') diff --git a/05_Agents/insurance_claims_agent/without_kb/create_and_invoke_agent.ipynb b/03-agent-insurance-claims.ipynb similarity index 100% rename from 05_Agents/insurance_claims_agent/without_kb/create_and_invoke_agent.ipynb rename to 03-agent-insurance-claims.ipynb diff --git a/03_Model_customization/01_fine-tuning-titan-lite.ipynb b/03_Model_customization/01_fine-tuning-titan-lite.ipynb deleted file mode 100644 index 93be87d9..00000000 --- a/03_Model_customization/01_fine-tuning-titan-lite.ipynb +++ /dev/null @@ -1,1242 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Fine-tune Amazon Titan Text Express provided by Amazon Bedrock\n", - "\n", - "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio. Also use ml.c5.2xlarge due to memory resources required*\n", - "\n", - "In this notebook, we will fine-tune [Amazon Titan Text Lite](#https://docs.aws.amazon.com/bedrock/latest/userguide/titan-text-models.html) model provided by Amazon Bedrock for summarization use case.\n", - "You can choose from list of base models or fine-tune one of your previously fine tuned model.\n", - "\n", - "## Prerequisites\n", - "\n", - " - Make sure you have executed `00_setup.ipynb` notebook.\n", - " - Make sure you are using the same kernel and instance as `00_setup.ipynb` notebook.\n", - "\n", - "

\n", - "Warning: This notebook will create provisioned throughput for testing the fine-tuned model. Therefore, please make sure to delete the provisioned throughput as mentioned in the last section of the notebook, otherwise you will be charged for it, even if you are not using it.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install -qU bert_score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# restart kernel for packages to take effect\n", - "from IPython.core.display import HTML\n", - "HTML(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## Fetching varialbes from `00_setup.ipynb` notebook. \n", - "%store -r role_arn\n", - "%store -r s3_train_uri\n", - "%store -r s3_validation_uri\n", - "%store -r s3_test_uri\n", - "%store -r bucket_name" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import pprint\n", - "pprint.pp(role_arn)\n", - "pprint.pp(s3_train_uri)\n", - "pprint.pp(s3_validation_uri)\n", - "pprint.pp(s3_test_uri)\n", - "pprint.pp(bucket_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "warnings.filterwarnings('ignore')\n", - "import json\n", - "import os\n", - "import sys\n", - "import boto3\n", - "import time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "session = boto3.session.Session()\n", - "region = session.region_name\n", - "sts_client = boto3.client('sts')\n", - "s3_client = boto3.client('s3')\n", - "aws_account_id = sts_client.get_caller_identity()[\"Account\"]\n", - "bedrock = boto3.client(service_name=\"bedrock\")\n", - "bedrock_runtime = boto3.client(service_name=\"bedrock-runtime\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test_file_name = \"test-cnn-10.jsonl\"\n", - "data_folder = \"fine-tuning-datasets\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Select the model you would like to fine-tune\n", - "You will have to provide the `base_model_id` for the model you are planning to fine-tune. You can get that using `list_foundation_models` API as follows: \n", - "```\n", - "for model in bedrock.list_foundation_models(\n", - " byCustomizationType=\"FINE_TUNING\")[\"modelSummaries\"]:\n", - " for key, value in model.items():\n", - " print(key, \":\", value)\n", - " print(\"-----\\n\")\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "base_model_id = \"amazon.titan-text-lite-v1:0:4k\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next you will need to provide the `customization_job_name`, `custom_model_name` and `customization_role` which will be used to create the fine-tuning job. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "ts = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n", - "\n", - "customization_job_name = f\"model-finetune-job-{ts}\"\n", - "custom_model_name = f\"finetuned-model-{ts}\"\n", - "customization_role = role_arn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create fine-tuning job\n", - "
\n", - "Note: Fine-tuning job will take around 40mins to complete.
\n", - "\n", - "Amazon Titan text model customization hyperparameters: \n", - "- `epochs`: The number of iterations through the entire training dataset and can take up any integer values in the range of 1-10, with a default value of 5.\n", - "- `batchSize`: The number of samples processed before updating model parameters and can take up any integer values in the range of 1-64, with a default value of 1.\n", - "- `learningRate`:\tThe rate at which model parameters are updated after each batch\twhich can take up a float value betweek 0.0-1.0 with a default value set to\t1.00E-5.\n", - "- `learningRateWarmupSteps`: The number of iterations over which the learning rate is gradually increased to the specified rate and can take any integer value between 0-250 with a default value of 5.\n", - "\n", - "For guidelines on setting hyper-parameters refer to the guidelines provided [here](#https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-guidelines.html)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Select the customization type from \"FINE_TUNING\" or \"CONTINUED_PRE_TRAINING\". \n", - "customization_type = \"FINE_TUNING\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define the hyperparameters for fine-tuning Titan text model\n", - "hyper_parameters = {\n", - " \"epochCount\": \"2\",\n", - " \"batchSize\": \"1\",\n", - " \"learningRate\": \"0.00003\",\n", - " }\n", - "\n", - "\n", - "s3_bucket_config=f's3://{bucket_name}/outputs/output-{custom_model_name}'\n", - "# Specify your data path for training, validation(optional) and output\n", - "training_data_config = {\"s3Uri\": s3_train_uri}\n", - "\n", - "validation_data_config = {\n", - " \"validators\": [{\n", - " # \"name\": \"validation\",\n", - " \"s3Uri\": s3_validation_uri\n", - " }]\n", - " }\n", - "\n", - "output_data_config = {\"s3Uri\": s3_bucket_config}\n", - "\n", - "\n", - "# Create the customization job\n", - "training_job_response = bedrock.create_model_customization_job(\n", - " customizationType=customization_type,\n", - " jobName=customization_job_name,\n", - " customModelName=custom_model_name,\n", - " roleArn=customization_role,\n", - " baseModelIdentifier=base_model_id,\n", - " hyperParameters=hyper_parameters,\n", - " trainingDataConfig=training_data_config,\n", - " validationDataConfig=validation_data_config,\n", - " outputDataConfig=output_data_config\n", - ")\n", - "training_job_response" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check fine-tuning job status" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]\n", - "print(fine_tune_job)\n", - "\n", - "while fine_tune_job == \"InProgress\":\n", - " time.sleep(60)\n", - " fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]\n", - " print (fine_tune_job)\n", - " time.sleep(60)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "pprint.pp(fine_tune_job)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "output_job_name = \"model-customization-job-\"+fine_tune_job['jobArn'].split('/')[-1]\n", - "output_job_name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are ready to create [`provisioned throughput`](#) which is needed before you can do the inference on the fine-tuned model.\n", - "\n", - "### Overview of Provisioned throughput\n", - "You specify Provisioned Throughput in Model Units (MU). A model unit delivers a specific throughput level for the specified model. The throughput level of a MU for a given Text model specifies the following:\n", - "\n", - "- The total number of input tokens per minute – The number of input tokens that an MU can process across all requests within a span of one minute.\n", - "\n", - "- The total number of output tokens per minute – The number of output tokens that an MU can generate across all requests within a span of one minute.\n", - "\n", - "Model unit quotas depend on the level of commitment you specify for the Provisioned Throughput.\n", - "\n", - "- For custom models with no commitment, a quota of one model unit is available for each Provisioned Throughput. You can create up to two Provisioned Throughputs per account.\n", - "\n", - "- For base or custom models with commitment, there is a default quota of 0 model units. To request an increase, use the [limit increase form](#https://support.console.aws.amazon.com/support/home#/case/create?issueType=service-limit-increase)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve Custom Model\n", - "Once the customization job is finished, you can check your existing custom model(s) and retrieve the modelArn of your fine-tuned model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# List your custom models\n", - "bedrock.list_custom_models()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "model_id = bedrock.get_custom_model(modelIdentifier=custom_model_name)['modelArn']\n", - "model_id" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create Provisioned Throughput\n", - "
\n", - "Note: Creating provisioned throughput will take around 20-30mins to complete.
\n", - "\n", - "You will need to create provisioned throughput to be able to evaluate the model performance. You can do so through the [console].(https://docs.aws.amazon.com/bedrock/latest/userguide/prov-cap-console.html) or use the following api call:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import boto3 \n", - "boto3.client(service_name='bedrock')\n", - "provisioned_model_id = bedrock.create_provisioned_model_throughput(\n", - " modelUnits=1,\n", - " provisionedModelName='test-model', \n", - " modelId=model_id\n", - ")['provisionedModelArn'] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "status_provisioning = bedrock.get_provisioned_model_throughput(provisionedModelId = provisioned_model_id)['status']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import time\n", - "while status_provisioning == 'Creating':\n", - " time.sleep(60)\n", - " status_provisioning = bedrock.get_provisioned_model_throughput(provisionedModelId=provisioned_model_id)['status']\n", - " print(status_provisioning)\n", - " time.sleep(60)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Invoke the Custom Model\n", - "\n", - "Before invoking lets get the sample prompt from our test data. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Provide the prompt text \n", - "test_file_path = f'{data_folder}/{test_file_name}'\n", - "with open(test_file_path) as f:\n", - " lines = f.read().splitlines()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "test_prompt = json.loads(lines[3])['prompt']\n", - "reference_summary = json.loads(lines[3])['completion']\n", - "pprint.pp(test_prompt)\n", - "print(reference_summary)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt = f\"\"\"\n", - "{test_prompt}\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "base_model_arn = f'arn:aws:bedrock:{region}::foundation-model/amazon.titan-text-lite-v1'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure to construct model input following the format needed by titan text model following instructions [here](#https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-text.html). \n", - "Please pay attention to the \"Model invocation request body field\" section in the `body` variable, which we will pass as payload to the custom model trained above. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "body = json.dumps(\n", - " {\n", - " \"inputText\": prompt,\n", - " \"textGenerationConfig\": {\n", - " \"maxTokenCount\": 2048,\n", - " \"stopSequences\": ['User:'],\n", - " \"temperature\": 0,\n", - " \"topP\": 0.9\n", - " }\n", - " }\n", - " )\n", - "\n", - "accept = 'application/json'\n", - "contentType = 'application/json'\n", - "\n", - "fine_tuned_response = bedrock_runtime.invoke_model(body=body, \n", - " modelId=provisioned_model_id, \n", - " accept=accept, \n", - " contentType=contentType)\n", - "\n", - "base_model_response = bedrock_runtime.invoke_model(body=body, \n", - " modelId=base_model_arn, \n", - " accept=accept, \n", - " contentType=contentType)\n", - "\n", - "fine_tuned_response_body = json.loads(fine_tuned_response.get('body').read())\n", - "base_model_response_body = json.loads(base_model_response.get('body').read())\n", - "\n", - "print(\"Base model response: \", base_model_response_body[\"results\"][0][\"outputText\"] + '\\n')\n", - "print(\"Fine tuned model response:\", fine_tuned_response_body[\"results\"][0][\"outputText\"]+'\\n')\n", - "print(\"Reference summary from test data: \" , reference_summary)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the performance of the model \n", - "In this section, we will use `BertScore` metrics to evaluate the performance of the fine-tuned model as compared to base model to check if fine-tuning has improved the results.\n", - "\n", - "- `BERTScore`: calculates the similarity between a summary and reference texts based on the outputs of BERT (Bidirectional Encoder Representations from Transformers), a powerful language model. [Medium article link](#https://haticeozbolat17.medium.com/bertscore-and-rouge-two-metrics-for-evaluating-text-summarization-systems-6337b1d98917)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "base_model_generated_response = [base_model_response_body[\"results\"][0][\"outputText\"]]\n", - "fine_tuned_generated_response = [fine_tuned_response_body[\"results\"][0][\"outputText\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from bert_score import score\n", - "reference_summary = [reference_summary]\n", - "fine_tuned_model_P, fine_tuned_R, fine_tuned_F1 = score(fine_tuned_generated_response, reference_summary, lang=\"en\")\n", - "base_model_P, base_model_R, base_model_F1 = score(base_model_generated_response, reference_summary, lang=\"en\")\n", - "print(\"F1 score: base model \", base_model_F1)\n", - "print(\"F1 score: fine-tuned model\", fine_tuned_F1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion\n", - "From the scores above and looking at the base model summary, fine-tuned model summary and reference summary, it clearly indicates that fine-tuning the model tends to improve the results on the task its trained on. We only used 1K records for training with 100 validation records and 2 epochs, and were able to get better results. \n", - "You may want to adjust the learning_rate first, visualize training and validation metrics to understand the performance of training job, before increase the size of your data. \n", - "\n", - "
\n", - "Tip: \n", - " Please refer to the guidelines provided for fine-tuning the model based on your task.
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Delete provisioned througput\n", - "
\n", - "Warning: Please make sure to delete providsioned throughput as there will cost incurred if its left in running state, even if you are not using it. \n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock.delete_provisioned_model_throughput(provisionedModelId=provisioned_model_id)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5d.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 13, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5d.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 14, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5d.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 15, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5d.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 16, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5d.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 17, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5d.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 18, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5d.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 19, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 20, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": true, - "memoryGiB": 0, - "name": "ml.geospatial.interactive", - "supportedImageNames": [ - "sagemaker-geospatial-v1-0" - ], - "vcpuNum": 0 - }, - { - "_defaultOrder": 21, - "_isFastLaunch": true, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.c5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 22, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.c5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 23, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.c5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 24, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.c5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 25, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 72, - "name": "ml.c5.9xlarge", - "vcpuNum": 36 - }, - { - "_defaultOrder": 26, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 96, - "name": "ml.c5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 27, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 144, - "name": "ml.c5.18xlarge", - "vcpuNum": 72 - }, - { - "_defaultOrder": 28, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.c5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 29, - "_isFastLaunch": true, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g4dn.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 30, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g4dn.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 31, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g4dn.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 32, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g4dn.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 33, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g4dn.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 34, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g4dn.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 35, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 61, - "name": "ml.p3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 36, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 244, - "name": "ml.p3.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 37, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 488, - "name": "ml.p3.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 38, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.p3dn.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 39, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.r5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 40, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.r5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 41, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.r5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 42, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.r5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 43, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.r5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 44, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.r5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 45, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.r5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 46, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.r5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 47, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 48, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 49, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 50, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 51, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 52, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 53, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 57, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/03_Model_customization/03_continued_pretraining_titan_text.ipynb b/03_Model_customization/03_continued_pretraining_titan_text.ipynb deleted file mode 100644 index c2bebce8..00000000 --- a/03_Model_customization/03_continued_pretraining_titan_text.ipynb +++ /dev/null @@ -1,1334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# Continued Pre-training Foundation Models in Amazon Bedrock\n", - "\n", - "> *This notebook has been tested to work with the **`SageMaker Distribution 1.3`** kernel in SageMaker Studio*\n", - "\n", - "In this notebook, we will build the end-to-end workflow for continous pre-training and evaluating the Foundation Models (FMs) in Amazon Bedrock. \n", - "\n", - "- Prerequisite: Before running this notebook, please make sure you have created Bedrock Service role for customization jobs following [instructions on managing permissions for customization jobs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-iam-role.html)\n", - "- In this notebook we demonstrate using boto3 sdk for conintuous pre-training of the Amazon Titan Text model. You can also do this in the Bedrock console following the instructions [here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-console.html).\n", - "\n", - "
\n", - "Warning: This notebook will create provisioned throughput for testing the fine-tuned model. Therefore, please make sure to delete the provisioned throughput as mentioned in the last section of the notebook, otherwise you will be charged for it, even if you are not using it.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "Install and import all the needed libraries and dependencies to complete this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install --upgrade pip\n", - "!pip install -qU --force-reinstall boto3 langchain datasets typing_extensions pypdf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install ipywidgets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!pip install jsonlines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "%store -r role_arn\n", - "%store -r bucket_name" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import warnings\n", - "import json\n", - "import os\n", - "import sys\n", - "import boto3\n", - "import logging\n", - "from botocore.exceptions import ClientError\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", - "from langchain.document_loaders import PyPDFLoader\n", - "from urllib.request import urlretrieve\n", - "warnings.filterwarnings('ignore')\n", - "import random\n", - "import jsonlines" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Check the available models in Amazon Bedrock\n", - "Retrieve the modelId's available of base models for Continued Pre-training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock = boto3.client(service_name=\"bedrock\")\n", - "boto3_session = boto3.session.Session()\n", - "s3_client = boto3.client('s3')\n", - "sts_client = boto3.client('sts')\n", - "account_id = sts_client.get_caller_identity()[\"Account\"]\n", - "region_name = boto3_session.region_name\n", - "s3_suffix = f\"{region_name}-{account_id}\"\n", - "\n", - "print(\"s3 bucket name: \", bucket_name)\n", - "\n", - "for model in bedrock.list_foundation_models(\n", - " byCustomizationType=\"CONTINUED_PRE_TRAINING\")[\"modelSummaries\"]:\n", - " print(\"-----------------------------------\")\n", - " print(\"{} -- {}\".format(model[\"providerName\"], model[\"modelName\"]))\n", - " print(\"-----------------------------------\")\n", - " for key, value in model.items():\n", - " print(key, \":\", value)\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preparing a Continued Pre-training dataset\n", - "\n", - "To carry out Continued Pre-training on a text-to-text model, prepare a training and optional validation dataset by creating a JSONL file with multiple JSON lines. Because Continued Pre-training involves unlabeled data, each JSON line is a sample containing only an input field. Use 6 characters per token as an approximation for the number of tokens. The format is as follows.\n", - "\n", - " {\"input\": \"\"}\n", - " \n", - " {\"input\": \"\"}\n", - " \n", - " {\"input\": \"\"} \n", - "\n", - "The following is an example item that could be in the training data:\n", - " \n", - " {\"input\": \"AWS stands for Amazon Web Services\"}\n", - " \n", - "See more guidance on how to [prepare your Bedrock continued pre-training dataset](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-prereq.html). \n", - "\n", - "Once your Continued Pre-training dataset is ready, upload it to Amazon S3 and save the s3Uri to be used for creating a Continued Pre-training job. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sample Dataset\n", - "Create a dataset using a PDF file.\n", - "Make sure that your dataset is propotional to the model. Since, the foundation models are big in size, continued pre-training will require bigger dataset. If you use a small dataset for example a PDF file with few pages, you will not be able to see significant difference in the model reponses.\n", - "\n", - "For this workshop, we are using [`aws-cli user guide`](#https://docs.aws.amazon.com/pdfs/cli/latest/userguide/aws-cli.pdf#cli-services-s3).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Download the file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "Note: Downloading the dataset will take about 20mins as dataset contains 5M rows and is 22.3 GB in size. However, for training the model we will only use a subset of the data.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "!mkdir data\n", - "url = 'https://docs.aws.amazon.com/pdfs/cli/latest/userguide/aws-cli.pdf'\n", - "file_name = \"./data/aws-cli.pdf\"\n", - "urlretrieve(url, file_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Please note the following [quotas for the continued pretraining](#https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html#model-customization-quotas) customization job. \n", - "\n", - "\n", - " \t\t\n", - " \n", - " \n", - " \t\t\n", - " \t\t\n", - "\t\n", - "\n", - "\t\t\n", - "
DescriptionMaximum (continued pre-training)Adjustable
Sum of input and output tokens when batch size is 2 4,096No
Sum of input and output tokens when batch size is between 3 and 62,048No
Character quota per sample in datasetToken quota x 6No
Training records in a dataset100,000Yes
Validation records in a dataset1,000 Yes
Training dataset file size\t10 GB Yes
Validation dataset file size100 MBYes
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the above quotas, we will first load the pdf file, chunk it based on the above quotas, and transform into the format as needed for continued pre-training job. \n", - " \n", - " {\"input\": \"\"}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Split the file text" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "loader = PyPDFLoader(file_name)\n", - "document = loader.load()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "document[368].page_content" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# - in our testing Character split works better with this PDF data set\n", - "text_splitter = RecursiveCharacterTextSplitter(\n", - " # Set a really small chunk size, just to show.\n", - " chunk_size = 20000, # 4096 tokens * 6 chars per token = 24,576 \n", - " chunk_overlap = 2000, # overlap for continuity across chunks\n", - ")\n", - "\n", - "docs = text_splitter.split_documents(document)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create the dataset file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "contents = \"\"\n", - "for doc in docs:\n", - " content = {\"input\": doc.page_content}\n", - " contents += (json.dumps(content) + \"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "dataset_folder = \"data\"\n", - "train_file_name = \"aws-cli-dataset.jsonl\"\n", - "train_dataset_filename = f\"./{dataset_folder}/{train_file_name}\"\n", - "\n", - "with open(train_dataset_filename, \"w\") as file:\n", - " file.writelines(contents)\n", - " file.close()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Upload the file to your Amazon S3 bucket" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "path = f'{dataset_folder}'\n", - "folder_name = \"continued-pretraining\" #Your folder name\n", - "# Upload data to s3\n", - "s3_client = boto3.client(\"s3\")\n", - "s3_client.upload_file(f'{path}/{train_file_name}', bucket_name, f'{folder_name}/train/{train_file_name}')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "s3_train_uri=f's3://{bucket_name}/{folder_name}/train/{train_file_name}'\n", - "s3_train_uri" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create the Continued Pre-training job\n", - "Now you have the dataset prepared and uploaded it is time to launch a new Continued Pre-training job. Complete the following fields required for the create_model_customization_job() API call. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "ts = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n", - "\n", - "# Select the foundation model you want to customize (you can find this from the \"modelId\" from listed foundation model list above)\n", - "base_model_id = \"amazon.titan-text-lite-v1:0:4k\"\n", - "\n", - "# Select the \"CONTINUED_PRE_TRAINING\" customization type. \n", - "customization_type = \"CONTINUED_PRE_TRAINING\"\n", - "\n", - "# Specify the roleArn for your customization job\n", - "customization_role = role_arn\n", - "\n", - "# Create a customization job name\n", - "customization_job_name = f\"cpt-titan-lite-books-{ts}\"\n", - "\n", - "# Create a customized model name for your continued pre-trained model\n", - "custom_model_name = f\"cpt-titan-lite-books-{ts}\"\n", - "\n", - "# Define the hyperparameters for continued pre-trained model\n", - "hyper_parameters = {\n", - " \"epochCount\": \"1\",\n", - " \"batchSize\": \"1\",\n", - " \"learningRate\": \"0.00005\",\n", - " }\n", - "\n", - "\n", - "# Specify your data path for training, validation(optional) and output\n", - "training_data_config = {\"s3Uri\": s3_train_uri}\n", - "\n", - "'''\n", - "# REMOVE COMMENT IF YOU WANT TO USE A VALIDATION DATASET\n", - "validation_data_config = {\n", - " \"validators\": [{\n", - " # \"name\": \"validation\",\n", - " \"s3Uri\": s3_validation_uri\n", - " }]\n", - " }\n", - "'''\n", - "\n", - "output_data_config = {\"s3Uri\": \"s3://{}/{}/output/\".format(bucket_name, folder_name)}\n", - "\n", - "# Create the customization job\n", - "bedrock.create_model_customization_job(\n", - " customizationType=customization_type,\n", - " jobName=customization_job_name,\n", - " customModelName=custom_model_name,\n", - " roleArn=customization_role,\n", - " baseModelIdentifier=base_model_id,\n", - " hyperParameters=hyper_parameters,\n", - " trainingDataConfig=training_data_config,\n", - " # validationDataConfig=validation_data_config,\n", - " outputDataConfig=output_data_config\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check Customization Job Status\n", - "Continued Pre-training a model will require some time. The following code will help you get the status of the training job. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "training_job_status = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import time\n", - "\n", - "while training_job_status == \"InProgress\":\n", - " time.sleep(60)\n", - " fine_tune_job = bedrock.get_model_customization_job(jobIdentifier=customization_job_name)[\"status\"]\n", - " print (training_job_status)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Retrieve your customized model \n", - "Once the customization job is Fisnihed, you can check your existing custom model(s) and retrieve the modelArn of your continually pre-trained model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# List your custom models\n", - "bedrock.list_custom_models()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "custom_model_arn = bedrock.get_custom_model(modelIdentifier=custom_model_name)['modelArn']\n", - "custom_model_arn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare the customization output\n", - "Provision the customized model and compare the answer against the base model to evaluate the improvement" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Provision the customized model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock_runtime = boto3.client(service_name=\"bedrock-runtime\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import boto3\n", - "boto3.client(service_name='bedrock')\n", - "provisioned_model_id = bedrock.create_provisioned_model_throughput(\n", - " modelUnits=1,\n", - " provisionedModelName='custom_model_name', \n", - " modelId=bedrock.get_custom_model(modelIdentifier=custom_model_name)['modelArn']\n", - ")['provisionedModelArn'] " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "status_provisioning = bedrock.get_provisioned_model_throughput(provisionedModelId = provisioned_model_id)['status']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import time\n", - "while status_provisioning == 'Creating':\n", - " time.sleep(60)\n", - " status_provisioning = bedrock.get_provisioned_model_throughput(provisionedModelId=provisioned_model_id)['status']\n", - " print(status_provisioning)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define models to compare" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "provider = \"Amazon\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "for model in bedrock.list_foundation_models(\n", - " byProvider=provider)[\"modelSummaries\"]:\n", - " print(\"-----------------------------------\")\n", - " print(\"{} -- {}\".format(model[\"providerName\"], model[\"modelName\"]))\n", - " print(\"-----------------------------------\")\n", - " for key, value in model.items():\n", - " print(key, \":\", value)\n", - " print(\"\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock.list_provisioned_model_throughputs()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "provisioned_model_arn=bedrock.list_provisioned_model_throughputs()[\"provisionedModelSummaries\"][0][\"provisionedModelArn\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "model_ids = [f\"arn:aws:bedrock:{region_name}::foundation-model/amazon.titan-text-lite-v1\", provisioned_model_arn] #Include your custom model and base models to test against" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compare outputs for all models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def compare_model_outputs(model_ids, prompt):\n", - " for model in model_ids:\n", - " response = bedrock_runtime.invoke_model(\n", - " modelId=model,\n", - " body = json.dumps({\n", - " \"inputText\": prompt,\n", - " \"textGenerationConfig\": {\n", - " \"maxTokenCount\": 300,\n", - " \"stopSequences\": [],\n", - " \"temperature\": 0,\n", - " \"topP\": 0.3\n", - " }\n", - " })\n", - " )\n", - " response_body = json.loads(response.get(\"body\").read())\n", - " print(\"-----------------------------------\")\n", - " print(model)\n", - " print(response_body[\"results\"][0][\"outputText\"])\n", - " print(\"-----------------------------------\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt = \"\"\"\n", - "Write aws-cli bash script to create a dynamoDB table. \n", - "Do not repeat answer.\n", - "Do not add any preamble. \n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "compare_model_outputs(model_ids, prompt)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Clean up resources" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "bedrock.delete_provisioned_model_throughput(provisionedModelId=provisioned_model_id)" - ] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5d.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 13, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5d.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 14, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5d.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 15, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5d.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 16, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5d.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 17, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5d.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 18, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5d.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 19, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 20, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": true, - "memoryGiB": 0, - "name": "ml.geospatial.interactive", - "supportedImageNames": [ - "sagemaker-geospatial-v1-0" - ], - "vcpuNum": 0 - }, - { - "_defaultOrder": 21, - "_isFastLaunch": true, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.c5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 22, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.c5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 23, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.c5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 24, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.c5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 25, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 72, - "name": "ml.c5.9xlarge", - "vcpuNum": 36 - }, - { - "_defaultOrder": 26, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 96, - "name": "ml.c5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 27, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 144, - "name": "ml.c5.18xlarge", - "vcpuNum": 72 - }, - { - "_defaultOrder": 28, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.c5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 29, - "_isFastLaunch": true, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g4dn.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 30, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g4dn.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 31, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g4dn.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 32, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g4dn.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 33, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g4dn.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 34, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g4dn.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 35, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 61, - "name": "ml.p3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 36, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 244, - "name": "ml.p3.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 37, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 488, - "name": "ml.p3.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 38, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.p3dn.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 39, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.r5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 40, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.r5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 41, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.r5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 42, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.r5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 43, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.r5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 44, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.r5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 45, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.r5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 46, - "_isFastLaunch": false, - "category": "Memory Optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.r5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 47, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.g5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 48, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.g5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 49, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.g5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 50, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.g5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 51, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 1, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.g5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 52, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.g5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 53, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 4, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.g5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 54, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 768, - "name": "ml.g5.48xlarge", - "vcpuNum": 192 - }, - { - "_defaultOrder": 55, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 56, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 8, - "hideHardwareSpecs": false, - "memoryGiB": 1152, - "name": "ml.p4de.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 57, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.trn1.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 58, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1.32xlarge", - "vcpuNum": 128 - }, - { - "_defaultOrder": 59, - "_isFastLaunch": false, - "category": "Accelerated computing", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 512, - "name": "ml.trn1n.32xlarge", - "vcpuNum": 128 - } - ], - "instance_type": "ml.t3.medium", - "kernelspec": { - "display_name": "Python 3 (Data Science 3.0)", - "language": "python", - "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/03_Model_customization/README.md b/03_Model_customization/README.md deleted file mode 100644 index 772eea56..00000000 --- a/03_Model_customization/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Lab 10 - Custom Models - - -
-Warning: This module cannot be executed in Workshop Studio Accounts, and you will have to run this notebook in your own account. -
- - -## Overview -Model customization is the process of providing training data to a model in order to improve its performance for specific use-cases. You can customize Amazon Bedrock foundation models in order to improve their performance and create a better customer experience. Amazon Bedrock currently provides the following customization methods. - -- Fine-tuning - - Provide labeled data in order to train a model to improve performance on specific tasks. By providing a training dataset of labeled examples, the model learns to associate what types of outputs should be generated for certain types of inputs. The model parameters are adjusted in the process and the model's performance is improved for the tasks represented by the training dataset. - -- Continued Pre-training - - Provide unlabeled data to pre-train a foundation model by familiarizing it with certain types of inputs. You can provide data from specific topics in order to expose a model to those areas. The Continued Pre-training process will tweak the model parameters to accommodate the input data and improve its domain knowledge. For example, you can train a model with private data, such as business documents, that are not publically available for training large language models. Additionally, you can continue to improve the model by retraining the model with more unlabeled data as it becomes available. - -## Relevance -Using your own data, you can privately and securely customize foundation models (FMs) in Amazon Bedrock to build applications that are specific to your domain, organization, and use case. Custom models enable you to create unique user experiences that reflect your company’s style, voice, and services. - -- With fine-tuning, you can increase model accuracy by providing your own task-specific labeled training dataset and further specialize your FMs. -- With continued pre-training, you can train models using your own unlabeled data in a secure and managed environment with customer managed keys. Continued pre-training helps models become more domain-specific by accumulating more robust knowledge and adaptability—beyond their original training. - -This module walks you through how to customize models through fine-tuning and continued pre-training, how to provision the custom models with provisioned throughput, and how to compare and evaluate model performance. - -## Target Audience - -This module can be executed by any developer familiar with Python, also by data scientists and other technical people who aspire to customize FMs in Bedrock. - -## Setup -- In this module, please run the [00_setup.ipynb](./00_setup.ipynb) first to make sure resources are properly set up for the following notebooks in this lab. -- At the end of the module, please run the [04_cleanup.ipynb](./04_cleanup.ipynb) to make sure resources are removed to avoid unnecessary costs. - - -## Patterns - -On this workshop, you will be able to learn following patterns on customizing FMs in Bedrock: - -2. [Fine-tune and Evaluate Llama2 in Bedrock for Summarization](./02_fine-tune_and_evaluate_llama2_bedrock_summarization.ipynb): Demonstrates an end-to-end workflow for fine-tuning, provisioning and evaluating a Meta Llama2 in Amazon Bedrock. \ No newline at end of file diff --git a/06_OpenSource_examples/00_Langchain_TextGeneration_examples/04_long text summarization using LCEL chains on Langchain.ipynb b/04-langchain-lcel-summary.ipynb similarity index 100% rename from 06_OpenSource_examples/00_Langchain_TextGeneration_examples/04_long text summarization using LCEL chains on Langchain.ipynb rename to 04-langchain-lcel-summary.ipynb diff --git a/04_Image_and_Multimodal/README.md b/04_Image_and_Multimodal/README.md deleted file mode 100644 index 91aa9b32..00000000 --- a/04_Image_and_Multimodal/README.md +++ /dev/null @@ -1,71 +0,0 @@ -# Lab 3 - Image Generation and Multimodal Embeddings - -## Overview - -Image generation can be a tedious task for artists, designers and content creators who illustrate their thoughts with the help of images. With the help of Foundation Models (FMs) this tedious task can be streamlined to just a single line of text that expresses the thoughts of the artist, FMs can be used for creating realistic and artistic images of various subjects, environments, and scenes from language prompts. - -Image indexing and searching is another tedious enterprise task. With the help of FMs, enterprise can build multimodal image indexing, searching and recommendation applications quickly. - -In this lab, we will explore how to use FMs available in Amazon Bedrock to generate images as well as modify existing images, and how to use FMs to do multimodal image indexing and searching. - - -## Prompt Engineering for Images - -Writing a good prompt can sometimes be an art. It is often difficult to predict whether a certain prompt will yield a satisfactory image with a given model. However, there are certain templates that have been observed to work. Broadly speaking, a prompt can be roughly broken down into three pieces: - -* type of image (photograph/sketch/painting etc.), and -* description (subject/object/environment/scene etc.), and -* the style of the image (realistic/artistic/type of art etc.). - -You can change each of the three parts individually, to generate variations of an image. Adjectives have been known to play a significant role in the image generation process. Also, adding more details help in the generation process.To generate a realistic image, you can use phrases such as "a photo of", "a photograph of", "realistic" or "hyper realistic". - -To generate images by artists you can use phrases like "by Pablo Picasso" or "oil painting by Rembrandt" or "landscape art by Frederic Edwin Church" or "pencil drawing by Albrecht Dürer". You can also combine different artists as well. To generate artistic image by category, you can add the art category in the prompt such as "lion on a beach, abstract". Some other categories include "oil painting", "pencil drawing", "pop art", "digital art", "anime", "cartoon", "futurism", "watercolor", "manga" etc. You can also include details such as lighting or camera lens, such as 35mm wide lens or 85mm wide lens and details about the framing (portrait/landscape/close up etc.). - -Note that the model generates different images even if same prompt is given multiple times. So, you can generate multiple images and select the image that suits your application best. - -## Foundation Models - -To provide these capabilities, Amazon Bedrock supports [Stable Diffusion XL](https://stability.ai/stablediffusion) from Stability AI and [Titan Image Generator](https://aws.amazon.com/bedrock/titan/) from Amazon for image generation, and [Titan Multimodal Embeddings](https://aws.amazon.com/bedrock/titan/) for multimodal image indexing and searching. - -### Stable Diffusion - -Stable Diffusion works on the principle of diffusion and is composed of multiple models each having different purpose: - -1. The CLIP text encoder; -2. The VAE decoder; -3. The UNet, and -4. The VAE_post_quant_conv - -The workings can be explained with this architecture: -![Stable Diffusion Architecture](./images/sd.png) - -### Titan Image Generator - -Titan Image Generator G1 is an image generation model. It generates images from text, and allows users to upload and edit an existing image. Users can edit an image with a text prompt (without a mask) or parts of an image with an image mask, or extend the boundaries of an image with outpainting. It can also generate variations of an image. - -### Titan Multimodal Embeddings - -Titan Multimodal Embeddings Generation 1 (G1) is a multimodal embeddings model for use cases like searching images by text, image, or a combination of text and image. Designed for high accuracy and fast responses, this model is an ideal choice for search and recommendations use cases. - -## Target Audience - -Marketing companies, agencies, web-designers, and general companies can take advantage of this feature to generate brand new images, from scratch. - -## Patterns - -In this workshop, you will be able to learn about Image Generation using Amazon Bedrock starting with text or image input. Use Stable Diffusion as an example in the below graph, and Titan Image Generator can also be used for the same purpose. You will also learn about multimodal image indexing and searching. Note that until the time of preparing this workshop, only Titan Image Generator supports outpainting. - -1. Text to Image - ![Text to Image](./images/71-txt-2-img.png) -2. Image to Image (Inpainting and Outpainting) - ![Text to Image](./images/72-img-2-img.png) -3. Multimodal Embeddings - ![Multimodal Embeddings](./images/multimodal-embeddings.png) - -## Setup -Before running any of the labs in this section ensure you've run the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites). - -## Helper -To facilitate image generation, there is a utility class `Bedrock` implementation in `./utils/bedrock.py`. This helps you to generate images easily. - -You can also explore different `style_preset` options [here](https://platform.stability.ai/docs/features/animation/parameters#available-styles). diff --git a/04_Image_and_Multimodal/bedrock-stable-diffusionXL.ipynb b/04_Image_and_Multimodal/bedrock-stable-diffusionXL.ipynb deleted file mode 100644 index 4c86917c..00000000 --- a/04_Image_and_Multimodal/bedrock-stable-diffusionXL.ipynb +++ /dev/null @@ -1,1188 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generating images using Stable Diffusion\n", - "\n", - "> This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio and with the **`conda_python3`** in a SageMaker Notebook Instance.\n", - "\n", - "---\n", - "\n", - "In this demo notebook, we show how to use [Stable Diffusion XL](https://stability.ai/stablediffusion) (SDXL) on [Amazon Bedrock](https://aws.amazon.com/bedrock/) for image generation (text-to-image) and image editing (image-to-image).\n", - "\n", - "Images in Stable Diffusion are generated by these 4 main models below\n", - "1. The CLIP text encoder;\n", - "2. The VAE decoder;\n", - "3. The UNet, and\n", - "4. The VAE_post_quant_conv\n", - "\n", - "These blocks are chosen because they represent the bulk of the compute in the pipeline\n", - "\n", - "see this diagram below\n", - "\n", - "![SD Architecture](./images/sd.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image prompting\n", - "\n", - "Writing a good prompt can be somewhat of an art. It's often difficult to predict whether a certain prompt will yield a satisfactory image with a given model. However, there are certain templates that have been observed to work. Broadly, a prompt can be roughly broken down into three pieces:\n", - "\n", - "1. **Type** of image (photograph/sketch/painting etc.)\n", - "2. **Description** of the content (subject/object/environment/scene etc.), and\n", - "3. **Style** of the image (realistic/artistic/type of art etc.).\n", - "\n", - "You can change each of the three parts individually to generate variations of an image. Adjectives have been known to play a significant role in the image generation process. Also, adding more details help in the generation process.\n", - "\n", - "To generate a realistic image, you can use phrases such as “a photo of”, “a photograph of”, “realistic” or “hyper realistic”. To generate images by artists you can use phrases like “by Pablo Piccaso” or “oil painting by Rembrandt” or “landscape art by Frederic Edwin Church” or “pencil drawing by Albrecht Dürer”. You can also combine different artists as well. To generate artistic image by category, you can add the art category in the prompt such as “lion on a beach, abstract”. Some other categories include “oil painting”, “pencil drawing, “pop art”, “digital art”, “anime”, “cartoon”, “futurism”, “watercolor”, “manga” etc. You can also include details such as lighting or camera lens such as 35mm wide lens or 85mm wide lens and details about the framing (portrait/landscape/close up etc.).\n", - "\n", - "Note that model generates different images even if same prompt is given multiple times. So, you can generate multiple images and select the image that suits your application best." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "⚠️ ⚠️ ⚠️ Before running this notebook, ensure you've run the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites) notebook. ⚠️ ⚠️ ⚠️\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import base64\n", - "import io\n", - "import json\n", - "import os\n", - "import sys\n", - "\n", - "# External dependencies\n", - "import boto3\n", - "from PIL import Image\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Text to Image\n", - "\n", - "In text-to-image mode, we'll provide a text description of what image **should** be generated, called a `prompt`.\n", - "\n", - "With Stable Diffusion XL (SDXL) we can also specify certain [style presets](https://platform.stability.ai/docs/release-notes#style-presets) to help influence the generation.\n", - "\n", - "To further influence image generation, we make use of [clip guidance presets](https://platform.stability.ai/docs/features/api-parameters#clip_guidance) and [samplers](https://platform.stability.ai/docs/features/api-parameters#sampler) to get more desirable results. \n", - "\n", - "Although the current SDXL model defaults to a square [resolution](https://platform.stability.ai/docs/features/api-parameters#about-dimensions) of 512px x 512px, it is capable of generating images at higher resolutions and non-squared aspect ratios. As shown below, the `width` of the image was set to 768px and the `height` remains at its default value of 512px. \n", - "\n", - "But what if we want to nudge the model to ***avoid*** specific content or style choices? Because image generation models are typically trained from *image descriptions*, trying to directly specify what you **don't** want in the prompt (for example `man without a beard`) doesn't usually work well: It would be very unusual to describe an image by the things it isn't!\n", - "\n", - "Instead, SDXL lets us specify a `weight` for each prompt, which can be negative. We'll use this to provide `negative_prompts` as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt = \"a beautiful mountain landscape\"\n", - "negative_prompts = [\n", - " \"poorly rendered\",\n", - " \"poor background details\",\n", - " \"poorly drawn mountains\",\n", - " \"disfigured mountain features\",\n", - "]\n", - "style_preset = \"photographic\" # (e.g. photographic, digital-art, cinematic, ...)\n", - "clip_guidance_preset = \"FAST_GREEN\" # (e.g. FAST_BLUE FAST_GREEN NONE SIMPLE SLOW SLOWER SLOWEST)\n", - "sampler = \"K_DPMPP_2S_ANCESTRAL\" # (e.g. DDIM, DDPM, K_DPMPP_SDE, K_DPMPP_2M, K_DPMPP_2S_ANCESTRAL, K_DPM_2, K_DPM_2_ANCESTRAL, K_EULER, K_EULER_ANCESTRAL, K_HEUN, K_LMS)\n", - "width = 768" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Amazon Bedrock `InvokeModel` provides access to SDXL by setting the right model ID, and returns a JSON response including a [Base64 encoded string](https://en.wikipedia.org/wiki/Base64) that represents the (PNG) image.\n", - "\n", - "For more information on available input parameters for the model, refer to the [Stability AI docs](https://platform.stability.ai/docs/api-reference#tag/v1generation/operation/textToImage).\n", - "\n", - "The cell below invokes the SDXL model through Amazon Bedrock to create an initial image string:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "request = json.dumps({\n", - " \"text_prompts\": (\n", - " [{\"text\": prompt, \"weight\": 1.0}]\n", - " + [{\"text\": negprompt, \"weight\": -1.0} for negprompt in negative_prompts]\n", - " ),\n", - " \"cfg_scale\": 5,\n", - " \"seed\": 42,\n", - " \"steps\": 60,\n", - " \"style_preset\": style_preset,\n", - " \"clip_guidance_preset\": clip_guidance_preset,\n", - " \"sampler\": sampler,\n", - " \"width\": width,\n", - "})\n", - "modelId = \"stability.stable-diffusion-xl-v1\"\n", - "\n", - "response = boto3_bedrock.invoke_model(body=request, modelId=modelId)\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "\n", - "print(response_body[\"result\"])\n", - "base_64_img_str = response_body[\"artifacts\"][0].get(\"base64\")\n", - "print(f\"{base_64_img_str[0:80]}...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By decoding our Base64 string to binary, and loading it with an image processing library like [Pillow](https://pillow.readthedocs.io/en/stable/) that can read PNG files, we can display and manipulate the image here in the notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data\", exist_ok=True)\n", - "image_1 = Image.open(io.BytesIO(base64.decodebytes(bytes(base_64_img_str, \"utf-8\"))))\n", - "image_1.save(\"data/image_1.png\")\n", - "image_1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Image to Image\n", - "\n", - "Generating images from text is powerful, but in some cases could need many rounds of prompt refinement to get an image \"just right\".\n", - "\n", - "Rather than starting from scratch with text each time, image-to-image generation lets us **modify an existing image** to make the specific changes we'd like.\n", - "\n", - "We'll have to pass our initial image in to the API in base64 encoding, so first let's prepare that. You can use either the initial image from the previous section, or a different one if you'd prefer:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def image_to_base64(img) -> str:\n", - " \"\"\"Convert a PIL Image or local image file path to a base64 string for Amazon Bedrock\"\"\"\n", - " if isinstance(img, str):\n", - " if os.path.isfile(img):\n", - " print(f\"Reading image from file: {img}\")\n", - " with open(img, \"rb\") as f:\n", - " return base64.b64encode(f.read()).decode(\"utf-8\")\n", - " else:\n", - " raise FileNotFoundError(f\"File {img} does not exist\")\n", - " elif isinstance(img, Image.Image):\n", - " print(\"Converting PIL Image to base64 string\")\n", - " buffer = io.BytesIO()\n", - " img.save(buffer, format=\"PNG\")\n", - " return base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n", - " else:\n", - " raise ValueError(f\"Expected str (filename) or PIL Image. Got {type(img)}\")\n", - "\n", - "\n", - "init_image_b64 = image_to_base64(image_1)\n", - "print(init_image_b64[:80] + \"...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A new guiding prompt can then help the model to act on the intial image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "change_prompt = \"add denser number of trees, extend lake\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The existing image is then passed through to the Stable Diffusion model via the `init_image` parameter.\n", - "\n", - "Again, you can refer to the [Stable Diffusion API docs](https://platform.stability.ai/docs/api-reference#tag/v1generation/operation/imageToImage) for more tips on how to use the different parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "request = json.dumps({\n", - " \"text_prompts\": (\n", - " [{\"text\": change_prompt, \"weight\": 1.0}]\n", - " + [{\"text\": negprompt, \"weight\": -1.0} for negprompt in negative_prompts]\n", - " ),\n", - " \"cfg_scale\": 10,\n", - " \"init_image\": init_image_b64,\n", - " \"seed\": 321,\n", - " \"start_schedule\": 0.6,\n", - " \"steps\": 50,\n", - " \"style_preset\": style_preset,\n", - " \"clip_guidance_preset\": clip_guidance_preset,\n", - " \"sampler\": sampler,\n", - "})\n", - "modelId = \"stability.stable-diffusion-xl-v1\"\n", - "\n", - "response = boto3_bedrock.invoke_model(body=request, modelId=modelId)\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "\n", - "print(response_body[\"result\"])\n", - "image_2_b64_str = response_body[\"artifacts\"][0].get(\"base64\")\n", - "print(f\"{image_2_b64_str[0:80]}...\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "image_2 = Image.open(io.BytesIO(base64.decodebytes(bytes(image_2_b64_str, \"utf-8\"))))\n", - "image_2.save(\"data/image_2.png\")\n", - "image_2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image Inpainting\n", - "\n", - "Yet another alternative to modify images is by using \"inpainting\". Inpainting refers to the process of replacing a portion of an image with another image based on a textual prompt. By providing a mask image that outlines the portion to be replaced, a textual prompt, and an image, the Stable Diffusion model can produce a new image that replaces the masked area with the object, subject, or environment described in the textual prompt.\n", - "\n", - "You can use the mask provided in the `images/mask.png` file.\n", - "\n", - "**Note**: The mask image is required to be the same resolution and aspect ratio as the image being inpainted upon. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from PIL import ImageOps\n", - "\n", - "def inpaint_mask(img, box):\n", - " \"\"\"Generates a segmentation mask for inpainting\"\"\"\n", - " img_size = img.size\n", - " assert len(box) == 4 # (left, top, right, bottom)\n", - " assert box[0] < box[2]\n", - " assert box[1] < box[3]\n", - " return ImageOps.expand(\n", - " Image.new(\n", - " mode = \"RGB\",\n", - " size = (\n", - " box[2] - box[0],\n", - " box[3] - box[1]\n", - " ),\n", - " color = 'black'\n", - " ),\n", - " border=(\n", - " box[0],\n", - " box[1],\n", - " img_size[0] - box[2],\n", - " img_size[1] - box[3]\n", - " ),\n", - " fill='white'\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "img2_size = image_2.size\n", - "box = (\n", - " (0),\n", - " (img2_size[1] - 900) ,\n", - " (img2_size[0]),\n", - " img2_size[1] - 700\n", - " )\n", - "\n", - "# Mask\n", - "mask = inpaint_mask(\n", - " image_2,\n", - " box\n", - ")\n", - "\n", - "# Debug\n", - "mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now define what we want to change in the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inpaint_prompt = \"add a helicopter\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly to what we did before, we will the pass the previously generated image through to the Stable Diffusion model via the `init_image` parameter.\n", - "\n", - "This time, we will also specify the `mask_source` parameter to pass the mask. \n", - "\n", - "You can refer to the [Stable Diffusion API docs](https://platform.stability.ai/docs/api-reference#tag/v1generation/operation/imageToImage) for more tips on how to use the different parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "request = json.dumps({\n", - " \"text_prompts\":[{\"text\": inpaint_prompt}],\n", - " \"init_image\": image_to_base64(image_2),\n", - " \"mask_source\": \"MASK_IMAGE_BLACK\",\n", - " \"mask_image\": image_to_base64(mask),\n", - " \"cfg_scale\": 10,\n", - " \"seed\": 32123,\n", - " \"style_preset\": style_preset,\n", - "})\n", - "modelId = \"stability.stable-diffusion-xl-v1\"\n", - "\n", - "response = boto3_bedrock.invoke_model(body=request, modelId=modelId)\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "\n", - "print(response_body[\"result\"])\n", - "image_3_b64_str = response_body[\"artifacts\"][0].get(\"base64\")\n", - "print(f\"{image_2_b64_str[0:80]}...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets show the image we just modified:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data\", exist_ok=True)\n", - "inpaint = Image.open(io.BytesIO(base64.decodebytes(bytes(image_3_b64_str, \"utf-8\"))))\n", - "inpaint.save(\"data/inpaint.png\")\n", - "inpaint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this lab we demonstrated how to generate new images from text, and transform existing images with text instructions - using [Stable Diffusion XL](https://stability.ai/stablediffusion) on [Amazon Bedrock](https://aws.amazon.com/bedrock/).\n", - "\n", - "Through the Bedrock API, we can provide a range of parameters to influence image generation which generally correspond to those listed in the [Stable Diffusion API docs](https://platform.stability.ai/docs/api-reference#tag/v1generation).\n", - "\n", - "One key point to note when using Bedrock is that output image PNG/JPEG data is returned as a [Base64 encoded string](https://en.wikipedia.org/wiki/Base64) within the JSON API response: You can use the Python built-in [base64 library](https://docs.python.org/3/library/base64.html) to decode this image data - for example to save a `.png` file. We also showed that image processing libraries like [Pillow](https://pillow.readthedocs.io/en/stable/) can be used to load (and perhaps edit) the images within Python.\n", - "\n", - "From here you can explore more advanced image generation options - or combine GenAI with traditional image processing tools - to build the best creative workflow for your use-case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5d.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 13, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5d.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 14, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5d.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 15, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5d.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 16, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5d.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 17, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5d.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 18, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5d.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 19, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5d.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 20, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": true, - "memoryGiB": 0, - "name": "ml.geospatial.interactive", - "supportedImageNames": [ - "sagemaker-geospatial-v1-0" - ], - "vcpuNum": 0 - }, - { - "_defaultOrder": 21, - "_isFastLaunch": true, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.c5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 22, - "_isFastLaunch": false, - "category": "Compute optimized", - "gpuNum": 0, - "hideHardwareSpecs": false, - 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"vscode": { - "interpreter": { - "hash": "00878cbed564b904a98b4a19808853cb6b9988746b881ea025a8408713879bf5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/04_Image_and_Multimodal/bedrock-titan-image-generator.ipynb b/04_Image_and_Multimodal/bedrock-titan-image-generator.ipynb deleted file mode 100644 index 3ae6cd99..00000000 --- a/04_Image_and_Multimodal/bedrock-titan-image-generator.ipynb +++ /dev/null @@ -1,1700 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generating images using Amazon Titan Image Generator\n", - "\n", - "> ☝️ This notebook should work well with the **`Data Science 3.0`** kernel in Amazon SageMaker Studio and with the **`conda_python3`** in a Amazon SageMaker Notebook Instance.\n", - "\n", - "---\n", - "\n", - "In this tutorial, we will show how to use the new [Amazon Titan Image Generator](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-image-models.html) on [Amazon Bedrock](https://aws.amazon.com/bedrock/) model to generate (text-to-image) and edit (image-to-image) images.\n", - "\n", - "![](images/titan_image_generator_playground.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Image Prompting\n", - "\n", - "Writing a good prompt can be somewhat of an art.\n", - "\n", - "It is often difficult to predict whether a given prompt will yield a satisfactory result with a certain model. \n", - "\n", - "However, there are certain templates that have been known to work.\n", - "\n", - "Broadly, a prompt can be broken down into three pieces:\n", - "\n", - "1. **Type** of image (photograph/sketch/painting/&c.)\n", - "2. **Description** of the content (subject/object/environment/scene/&c.), and\n", - "3. **Style** of the image (realistic/artistic/&c.).\n", - "\n", - "You can change each of the three parts individually to generate variations of an image. \n", - "\n", - "Adjectives have been known to play a significant role in the image generation process. \n", - "\n", - "Also, adding more details help in the generation process.\n", - "\n", - "In order to generate a **realistic** image, you can use phrases such as\n", - "\n", - "```\n", - "a photo of\n", - "a photograph of\n", - "realistic\n", - "hyper realistic\n", - "```\n", - "\n", - "To generate something more **artistic**, you can use phrases like\n", - "\n", - "```\n", - "by Pablo Picasso\n", - "oil painting by Rembrandt\n", - "landscape art by Frederic Edwin Church\n", - "pencil drawing by Albrecht Dürer\n", - "```\n", - "\n", - "You can also combine different artists as well.\n", - "\n", - "To generate artistic images by category, you can add the art category in the prompt such as\n", - "\n", - "```\n", - "lion on a beach, abstract\n", - "```\n", - "\n", - "Some other categories include\n", - "\n", - "```\n", - "oil painting\n", - "pencil drawing\n", - "pop art\n", - "digital art\n", - "anime\n", - "cartoon\n", - "futurism\n", - "watercolor\n", - "manga\n", - "&c.\n", - "```\n", - "\n", - "You can also include details such as lighting or camera lens such as\n", - "\n", - "```\n", - "35mm wide lens\n", - "85mm wide lens\n", - "```\n", - "\n", - "and details about the framing\n", - "\n", - "```\n", - "portrait\n", - "landscape\n", - "close up\n", - "&c.\n", - "```\n", - "\n", - "Note that models can generate different images even if same prompt is given multiple times. \n", - "\n", - "So, you can generate multiple images and select the image that suits your application best.\n", - "\n", - "> ☝️ For more information on Amazon Titan Image Generator prompt engineering, see [Amazon Titan Image Generator Prompt Engineering Best Practices](https://d2eo22ngex1n9g.cloudfront.net/Documentation/User+Guides/Titan/Amazon+Titan+Image+Generator+Prompt+Engineering+Guidelines.pdf). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "⚠️ ⚠️ ⚠️ Before running this notebook, make sure you've executed the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites). ⚠️ ⚠️ ⚠️\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Built-in libraries\n", - "import base64\n", - "import io\n", - "import json\n", - "import os\n", - "import sys\n", - "\n", - "# External dependencies\n", - "import boto3\n", - "from PIL import Image\n", - "import botocore\n", - "\n", - "boto3_bedrock = boto3.client('bedrock-runtime')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use Cases" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Text to Image\n", - "\n", - "In text-to-image mode, we provide a text description (prompt) of the image that **should** be generated.\n", - "\n", - "What if we want to ***avoid*** specific content or stylistic choices? Because image generation models are typically trained from *image descriptions*, trying to directly specify what you **don't** want in the prompt (e.g. `man without a beard`) doesn't usually work well: it would be very unusual to describe an image by what it is not!\n", - "\n", - "In the case of Amazon Titan Image Generator, we can specify a negative prompt to steer the model away from unwanted elements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "prompt = \"a beautiful lake surrounded by trees with a mountain range at the distance\"\n", - "negative_prompts = \"poorly rendered, poor background details, poorly drawn mountains, disfigured mountain features\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Amazon Bedrock `InvokeModel` provides access to Amazon Titan Image Generator by setting the right model ID, and returns a JSON response including a [Base64 encoded string](https://en.wikipedia.org/wiki/Base64) that represents the (PNG) image.\n", - "\n", - "When making an `InvokeModel` request, we need to fill the `body` field with a JSON object that varies depending on the task (`taskType`) you wish to perform viz. text to image, image variation, inpainting or outpainting. The Amazon Titan models supports the following parameters:\n", - "* `cfgscale` - determines how much the final image reflects the prompt\n", - "* `seed` - a number used to initialize the generation, using the same seed with the same prompt + settings combination will produce the same results\n", - "* `numberOfImages` - the number of times the image is sampled and produced\n", - "* `quality` - determines the output image quality (`standard` or `premium`)\n", - "\n", - "> ☝️ For more information on available input parameters for the model, refer to the [Amazon Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html#model-parameters-titan-img-request-body) (Inference parameters > Amazon Titan image models > Model invocation request body fields).\n", - "\n", - "The cell below invokes the Amazon Titan Image Generator model through Amazon Bedrock to create an initial image:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Create payload\n", - "body = json.dumps(\n", - " {\n", - " \"taskType\": \"TEXT_IMAGE\",\n", - " \"textToImageParams\": {\n", - " \"text\": prompt, # Required\n", - " \"negativeText\": negative_prompts # Optional\n", - " },\n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1, # Range: 1 to 5 \n", - " \"quality\": \"standard\", # Options: standard or premium\n", - " \"height\": 1024, # Supported height list in the docs \n", - " \"width\": 1024, # Supported width list in the docs\n", - " \"cfgScale\": 7.5, # Range: 1.0 (exclusive) to 10.0\n", - " \"seed\": 42 # Range: 0 to 214783647\n", - " }\n", - " }\n", - ")\n", - "\n", - "# Make model request\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body,\n", - " modelId=\"amazon.titan-image-generator-v1\",\n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - ")\n", - "\n", - "# Process the image\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img1_b64 = response_body[\"images\"][0]\n", - "\n", - "# Debug\n", - "print(f\"Output: {img1_b64[0:80]}...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By decoding our image string and loading it with an image processing library like [Pillow](https://pillow.readthedocs.io/en/stable/), we can display and manipulate the image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data/titan\", exist_ok=True)\n", - "\n", - "# Decode + save\n", - "img1 = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img1_b64, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "img1.save(f\"data/titan/image_1.png\")\n", - "\n", - "# Display\n", - "img1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image Variation\n", - "\n", - "Generating images from text is powerful but, in some cases, you will need many rounds of prompt refinement to get just the right image.\n", - "\n", - "Rather than starting from scratch, image-to-image generation lets us **modify** an existing image to make specific changes.\n", - "\n", - "We'll have to pass our initial image in base64 encoding to API, so let's get that out of the way.\n", - "\n", - "(Feel free to use image created in the previous section or a different one)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def image_to_base64(img) -> str:\n", - " \"\"\"Converts a PIL Image or local image file path to a base64 string\"\"\"\n", - " if isinstance(img, str):\n", - " if os.path.isfile(img):\n", - " print(f\"Reading image from file: {img}\")\n", - " with open(img, \"rb\") as f:\n", - " return base64.b64encode(f.read()).decode(\"utf-8\")\n", - " else:\n", - " raise FileNotFoundError(f\"File {img} does not exist\")\n", - " elif isinstance(img, Image.Image):\n", - " buffer = io.BytesIO()\n", - " img.save(buffer, format=\"PNG\")\n", - " return base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n", - " else:\n", - " raise ValueError(f\"Expected str (filename) or PIL Image. Got {type(img)}\")\n", - "\n", - "img1_b64 = image_to_base64(img1)\n", - "print(f\"Input: {img1_b64[:80]}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will need a new prompt to guide the model when acting on the base image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "change_prompt = \"add a house on the lake shore\"\n", - "negative_prompt = \"bad quality, low resolution, cartoon\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The existing image is then passed through to the Titan model via the `images` parameter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Payload creation\n", - "body = json.dumps({\n", - " \"taskType\": \"IMAGE_VARIATION\",\n", - " \"imageVariationParams\": {\n", - " \"text\": change_prompt, # Optional\n", - " \"negativeText\": negative_prompts, # Optional\n", - " \"images\": [img1_b64], # One image is required\n", - " },\n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1,\n", - " \"quality\": \"premium\",\n", - " \"height\": 1024,\n", - " \"width\": 1024,\n", - " \"cfgScale\": 10,\n", - " \"seed\": 42\n", - " }\n", - " })\n", - "\n", - "# Model invocation\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body,\n", - " modelId=\"amazon.titan-image-generator-v1\",\n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - ")\n", - "\n", - "# Output processing\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img2_b64 = response_body[\"images\"][0]\n", - "\n", - "# Debug\n", - "print(f\"Output: {img2_b64[0:80]}...\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data/titan\", exist_ok=True)\n", - "\n", - "# Decode + save\n", - "img2 = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img2_b64, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "img2.save(\"data/titan/image_2.png\")\n", - "\n", - "# Display\n", - "img2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inpainting\n", - "\n", - "Another way to modify images is by using **inpainting**.\n", - "\n", - "Inpainting refers to the process of replacing a portion of an image with another image based on a textual prompt.\n", - "\n", - "By providing a mask image that outlines the portion to be replaced, a textual prompt, and the original image, the model can produce a new image that replaces the masked area with the object, subject, or environment described in the textual prompt.\n", - "\n", - "Let's start by creating a function that generates a mask from the original image and a set of box coordinates `(top, left, right, bottom)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from PIL import ImageOps\n", - "\n", - "def inpaint_mask(img, box):\n", - " \"\"\"Generates a segmentation mask for inpainting\"\"\"\n", - " img_size = img.size\n", - " assert len(box) == 4 # (left, top, right, bottom)\n", - " assert box[0] < box[2]\n", - " assert box[1] < box[3]\n", - " return ImageOps.expand(\n", - " Image.new(\n", - " mode = \"RGB\",\n", - " size = (\n", - " box[2] - box[0],\n", - " box[3] - box[1]\n", - " ),\n", - " color = 'black'\n", - " ),\n", - " border=(\n", - " box[0],\n", - " box[1],\n", - " img_size[0] - box[2],\n", - " img_size[1] - box[3]\n", - " ),\n", - " fill='white'\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll remove a single patch at the center of the image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "img2_size = img2.size\n", - "box = (\n", - " (img2_size[0] - 300) // 2,\n", - " img2_size[1] - 300,\n", - " (img2_size[0] + 300) // 2,\n", - " img2_size[1] - 200\n", - " )\n", - "\n", - "# Mask\n", - "mask = inpaint_mask(\n", - " img2,\n", - " box\n", - ")\n", - "\n", - "# Debug\n", - "mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will now define what we want to change in the image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inpaint_prompt = \"add a fishing boat\"\n", - "negative_prompts = \"bad quality, low res\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly to what we did before, we will the pass the previously generated image through to the Stable Diffusion model via the `image` parameter.\n", - "\n", - "This time, we will also specify the `maskImage` parameter to pass the mask." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Payload creation\n", - "body = json.dumps({\n", - " \"taskType\": \"INPAINTING\",\n", - " \"inPaintingParams\": {\n", - " \"text\": inpaint_prompt, # Optional\n", - " \"negativeText\": negative_prompts, # Optional\n", - " \"image\": image_to_base64(img2), # Required\n", - " # \"maskPrompt\": \"sky\", # One of \"maskImage\" or \"maskPrompt\" is required\n", - " \"maskImage\": image_to_base64(mask), # Input maskImage based on the values 0 (black) or 255 (white) only\n", - " }, \n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1,\n", - " \"quality\": \"premium\",\n", - " \"height\": 1024,\n", - " \"width\": 1024,\n", - " \"cfgScale\": 7.5,\n", - " \"seed\": 42\n", - " }\n", - "})\n", - "\n", - "# Model invocation\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body,\n", - " modelId=\"amazon.titan-image-generator-v1\",\n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - ")\n", - "\n", - "# Output processing\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img3_b64 = response_body[\"images\"][0]\n", - "print(f\"Output: {img3_b64[0:80]}...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets show the image we just modified:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data/titan\", exist_ok=True)\n", - "inpaint = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img3_b64, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "inpaint.save(\"data/titan/inpaint.png\")\n", - "inpaint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apart from using \"maskImage\", we can use \"maskPrompt\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inpaint_prompt = \"add a roller coaster\"\n", - "negative_prompts = \"bad quality, low res\"\n", - "mask_prompt = \"house\" # replace house in img1 with a roller coaster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Payload creation\n", - "body = json.dumps({\n", - " \"taskType\": \"INPAINTING\",\n", - " \"inPaintingParams\": {\n", - " \"text\": inpaint_prompt, # Optional\n", - " \"negativeText\": negative_prompts, # Optional\n", - " \"image\": image_to_base64(img1), # Required\n", - " \"maskPrompt\": mask_prompt, # One of \"maskImage\" or \"maskPrompt\" is required\n", - " # \"maskImage\": image_to_base64(mask), # Input maskImage based on the values 0 (black) or 255 (white) only\n", - " }, \n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1,\n", - " \"quality\": \"premium\",\n", - " \"height\": 1024,\n", - " \"width\": 1024,\n", - " \"cfgScale\": 7.5,\n", - " \"seed\": 42\n", - " }\n", - "})\n", - "\n", - "# Model invocation\n", - "response = boto3_bedrock.invoke_model(\n", - " body=body,\n", - " modelId=\"amazon.titan-image-generator-v1\",\n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - ")\n", - "\n", - "# Output processing\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img4_b64 = response_body[\"images\"][0]\n", - "print(f\"Output: {img4_b64[0:80]}...\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data/titan\", exist_ok=True)\n", - "inpaint2 = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img4_b64, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "inpaint2.save(\"data/titan/inpaint2.png\")\n", - "inpaint2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Outpainting\n", - "\n", - "In this final section, we are going to *extend* the image.\n", - "\n", - "This process, known as **outpainting**, involves generating new pixels that *seamlessly* extend an image's existing boundaries.\n", - "\n", - "We can do this by providing the original image and a segmentation mask, which can either be an image or a prompt." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start by creating a function that takes in the original image and the target size `(width, height)` and returns a mask image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def outpaint_mask(img, new_size):\n", - " \"\"\"Generates a segmentation mask for outpainting\"\"\"\n", - " # Image size must be a multiple of 64\n", - " old_size = img.size\n", - " assert len(new_size) == 2\n", - " assert new_size[0] >= old_size[0]\n", - " assert new_size[1] >= old_size[1]\n", - " assert new_size[0] % 64 == 0\n", - " assert new_size[1] % 64 == 0\n", - " # Create a mask and expand it\n", - " border = (\n", - " (new_size[0] - old_size[0]) // 2,\n", - " (new_size[1] - old_size[1]) // 2\n", - " )\n", - " return ImageOps.expand(\n", - " Image.new(\n", - " mode = \"RGB\",\n", - " size = img.size,\n", - " color = 'black'\n", - " ),\n", - " border=border,\n", - " fill='white'\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're going to crop the image created in the **Image Inpainting** section and then expand the cropped area" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "inpaint_crop = ImageOps.crop(inpaint, border=(300, 400, 300, 200))\n", - "print(f\"Crop size: {inpaint_crop.size}\")\n", - "inpaint_crop" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Define border\n", - "old_size = inpaint_crop.size\n", - "new_size = (1024, 1024)\n", - "border = (\n", - " (new_size[0] - old_size[0]) // 2,\n", - " (new_size[1] - old_size[1]) // 2\n", - ")\n", - "\n", - "# Generate a mask\n", - "mask = outpaint_mask(inpaint_crop, new_size)\n", - "\n", - "# Debug\n", - "print(f\"Original Image: {old_size}\")\n", - "print(f\"Outpaint Mask: {mask.size}\")\n", - "mask" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we define our prompts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "outpaint_prompt = \"add some trees and houses near the lake\"\n", - "# negative_prompt = \"bad quality, low res\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can add everything to our payload and make the request" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Payload creation\n", - "body = json.dumps(\n", - " {\n", - " \"taskType\": \"OUTPAINTING\",\n", - " \"outPaintingParams\": {\n", - " \"text\": outpaint_prompt,\n", - " # \"negativeText\": negative_prompts,\n", - " \"image\": image_to_base64(\n", - " ImageOps.expand(\n", - " inpaint_crop,\n", - " border=border,\n", - " fill='white'\n", - " )),\n", - " #\"maskPrompt\": mask_prompt,\n", - " \"maskImage\": image_to_base64(mask),\n", - " \"outPaintingMode\": \"DEFAULT\"\n", - " },\n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1,\n", - " \"quality\": \"standard\",\n", - " \"cfgScale\": 1.5,\n", - " \"seed\": 321,\n", - " }\n", - " }\n", - ")\n", - "\n", - "# Model invocation\n", - "response = boto3_bedrock.invoke_model(\n", - " body = body, \n", - " modelId = \"amazon.titan-image-generator-v1\",\n", - " accept = \"application/json\", \n", - " contentType = \"application/json\"\n", - ")\n", - "\n", - "# Output processing\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img_5_b64_str = response_body[\"images\"][0]\n", - "print(f\"Output: {img_5_b64_str[0:80]}...\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once it finishes, we can display the image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data\", exist_ok=True)\n", - "\n", - "# Decode + save\n", - "outpaint = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img_5_b64_str, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "outpaint.save(\"data/titan/outpaint.png\")\n", - "\n", - "# Display\n", - "outpaint" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apart from using \"maskImage\", we can use \"maskPrompt\"." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "outpaint_prompt = \"add some trees and houses near the lake\"\n", - "# negative_prompt = \"bad quality, low res\"\n", - "\n", - "# try different mask_prompt to see the difference in generated images\n", - "mask_prompt = \"lake\"\n", - "# mask_prompt = \"forest\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Payload creation\n", - "body = json.dumps(\n", - " {\n", - " \"taskType\": \"OUTPAINTING\",\n", - " \"outPaintingParams\": {\n", - " \"text\": outpaint_prompt,\n", - " # \"negativeText\": negative_prompts,\n", - " \"image\": image_to_base64(\n", - " ImageOps.expand(\n", - " inpaint_crop,\n", - " border=border,\n", - " fill='white'\n", - " )),\n", - " # \"image\": image_to_base64(inpaint_crop),\n", - " \"maskPrompt\": mask_prompt,\n", - " # \"maskImage\": image_to_base64(mask),\n", - " \"outPaintingMode\": \"DEFAULT\"\n", - " },\n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": 1,\n", - " \"quality\": \"standard\",\n", - " \"cfgScale\": 1.5,\n", - " \"seed\": 321,\n", - " }\n", - " }\n", - ")\n", - "\n", - "# Model invocation\n", - "response = boto3_bedrock.invoke_model(\n", - " body = body, \n", - " modelId = \"amazon.titan-image-generator-v1\",\n", - " accept = \"application/json\", \n", - " contentType = \"application/json\"\n", - ")\n", - "\n", - "# Output processing\n", - "response_body = json.loads(response.get(\"body\").read())\n", - "img_6_b64_str = response_body[\"images\"][0]\n", - "print(f\"Output: {img_6_b64_str[0:80]}...\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "os.makedirs(\"data\", exist_ok=True)\n", - "\n", - "# Decode + save\n", - "outpaint2 = Image.open(\n", - " io.BytesIO(\n", - " base64.decodebytes(\n", - " bytes(img_6_b64_str, \"utf-8\")\n", - " )\n", - " )\n", - ")\n", - "outpaint2.save(\"data/titan/outpaint2.png\")\n", - "\n", - "# Display\n", - "outpaint2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "In this lab, we demonstrated how to generate new images from text and transform existing images with text instructions using [Amazon Titan Image Generator](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-image-models.html) on [Amazon Bedrock](https://aws.amazon.com/bedrock/).\n", - "\n", - "Through the Bedrock API, we can provide a range of parameters to influence image generation (`text`, `negativeText`, `maskImage`, &c.)\n", - "\n", - "One key point to note when using Amazon Bedrock is that the output image data is returned as a [Base64 encoded string](https://en.wikipedia.org/wiki/Base64) within the JSON API response. You can use the Python built-in [`base64` library](https://docs.python.org/3/library/base64.html) to decode this image data and then save a `.png` file. We also showed that image processing libraries like [Pillow](https://pillow.readthedocs.io/en/stable/) can be used to load (and edit) images within Python.\n", - "\n", - "From here, you can explore more advanced image generation options or combine GenAI with traditional image processing tools to build the best creative workflow for your use case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "availableInstances": [ - { - "_defaultOrder": 0, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 4, - "name": "ml.t3.medium", - "vcpuNum": 2 - }, - { - "_defaultOrder": 1, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.t3.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 2, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.t3.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 3, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.t3.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 4, - "_isFastLaunch": true, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - "name": "ml.m5.large", - "vcpuNum": 2 - }, - { - "_defaultOrder": 5, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 16, - "name": "ml.m5.xlarge", - "vcpuNum": 4 - }, - { - "_defaultOrder": 6, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 32, - "name": "ml.m5.2xlarge", - "vcpuNum": 8 - }, - { - "_defaultOrder": 7, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 64, - "name": "ml.m5.4xlarge", - "vcpuNum": 16 - }, - { - "_defaultOrder": 8, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 128, - "name": "ml.m5.8xlarge", - "vcpuNum": 32 - }, - { - "_defaultOrder": 9, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 192, - "name": "ml.m5.12xlarge", - "vcpuNum": 48 - }, - { - "_defaultOrder": 10, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 256, - "name": "ml.m5.16xlarge", - "vcpuNum": 64 - }, - { - "_defaultOrder": 11, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 384, - "name": "ml.m5.24xlarge", - "vcpuNum": 96 - }, - { - "_defaultOrder": 12, - "_isFastLaunch": false, - "category": "General purpose", - "gpuNum": 0, - "hideHardwareSpecs": false, - "memoryGiB": 8, - 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"vscode": { - "interpreter": { - "hash": "00878cbed564b904a98b4a19808853cb6b9988746b881ea025a8408713879bf5" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/04_Image_and_Multimodal/bedrock-titan-multimodal-embeddings.ipynb b/04_Image_and_Multimodal/bedrock-titan-multimodal-embeddings.ipynb deleted file mode 100644 index d1505f0d..00000000 --- a/04_Image_and_Multimodal/bedrock-titan-multimodal-embeddings.ipynb +++ /dev/null @@ -1,1191 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "5cda9807-dbdd-496c-b663-af3d77f9bb70", - "metadata": {}, - "source": [ - "# Multimodal Embedding and Searching" - ] - }, - { - "cell_type": "markdown", - "id": "2d48ef88-4f8f-4cbe-b272-22f6805be03b", - "metadata": {}, - "source": [ - "Amazon Titan Multimodal Embedding Models can be used for enterprise tasks such as image search and similarity based recommendation, and has built-in mitigation that helps reduce bias in searching results. There are multiple embedding dimension sizes for best latency/accuracy tradeoffs for different needs, and all can be customized with a simple API to adapt to your own data while persists data security and privacy. Amazon Titan Multimodal Embedding models are provided as simple APIs for real-time or asynchronous batch transform searching and recommendation applications, and can be connected to different vector databases, including Amazon OpenSearch Service." - ] - }, - { - "cell_type": "markdown", - "id": "03c4f218-2e14-4a33-a13a-410bb548cd33", - "metadata": {}, - "source": [ - "In this example notebook, we will demo to you how to generate embeddings for images and optionally texts using Amazon Titan Multimodal Embedding Models, then search the embeddings with a query." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8ef07a00-9f50-44d2-bdf4-f0a09dfd7152", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "import re\n", - "import boto3\n", - "import json\n", - "import base64\n", - "import numpy as np\n", - "import seaborn as sns\n", - "from PIL import Image\n", - "from io import BytesIO\n", - "from scipy.spatial.distance import cdist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "29bfa65a-b27f-4fa6-abca-2e2523df6ae6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "boto3_session = boto3.session.Session()\n", - "region_name = boto3_session.region_name\n", - "bedrock_client = boto3.client(\n", - " \"bedrock-runtime\",\n", - " region_name,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "7d3d4673-f1ca-437d-825f-0f7fe06ace0d", - "metadata": { - "tags": [] - }, - "source": [ - "## Synthetic Dataset" - ] - }, - { - "cell_type": "markdown", - "id": "037173d2-5747-4e0c-ade9-c5c3ecf868ef", - "metadata": {}, - "source": [ - "We can leverage Amazon Bedrock Language Models to randomly generate 7 different products, each with 3 variants, using prompt:\n", - "\n", - "```\n", - "Generate a list of 7 items description for an online e-commerce shop, each comes with 3 variants of color or type. All with separate full sentence description.\n", - "```\n", - "\n", - "Note that when using different language models, the reponses might be different. For illustration purpose, suppose we get the below response." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a535a5a5-11eb-4857-953e-0005ebbca646", - "metadata": {}, - "outputs": [], - "source": [ - "response = 'Here is a list of 7 items with 3 variants each for an online e-commerce shop, with separate full sentence descriptions:\\n\\n1. T-shirt\\n- A red cotton t-shirt with a crew neck and short sleeves. \\n- A blue cotton t-shirt with a v-neck and short sleeves.\\n- A black polyester t-shirt with a scoop neck and cap sleeves.\\n\\n2. Jeans\\n- Classic blue relaxed fit denim jeans with a mid-rise waist. \\n- Black skinny fit denim jeans with a high-rise waist and ripped details at the knees. \\n- Stonewash straight leg denim jeans with a standard waist and front pockets.\\n\\n3. Sneakers \\n- White leather low-top sneakers with an almond toe cap and thick rubber outsole.\\n- Gray mesh high-top sneakers with neon green laces and a padded ankle collar. \\n- Tan suede mid-top sneakers with a round toe and ivory rubber cupsole. \\n\\n4. Backpack\\n- A purple nylon backpack with padded shoulder straps, front zipper pocket and laptop sleeve.\\n- A gray canvas backpack with brown leather trims, side water bottle pockets and drawstring top closure. \\n- A black leather backpack with multiple interior pockets, top carry handle and adjustable padded straps.\\n\\n5. Smartwatch\\n- A silver stainless steel smartwatch with heart rate monitor, GPS tracker and sleep analysis. \\n- A space gray aluminum smartwatch with step counter, phone notifications and calendar syncing. \\n- A rose gold smartwatch with activity tracking, music controls and customizable watch faces. \\n\\n6. Coffee maker\\n- A 12-cup programmable coffee maker in brushed steel with removable water tank and keep warm plate. \\n- A compact 5-cup single serve coffee maker in matt black with travel mug auto-dispensing feature.\\n- A retro style stovetop percolator coffee pot in speckled enamel with stay-cool handle and glass knob lid. \\n\\n7. Yoga mat \\n- A teal 4mm thick yoga mat made of natural tree rubber with moisture-wicking microfiber top.\\n- A purple 6mm thick yoga mat made of eco-friendly TPE material with integrated carrying strap. \\n- A patterned 5mm thick yoga mat made of PVC-free material with towel cover included.'\n", - "print(response)" - ] - }, - { - "cell_type": "markdown", - "id": "600e05f3-fd2a-4ba7-941e-9d77d7e489ca", - "metadata": {}, - "source": [ - "The following function converts the response to a list of descriptions. You may need to write your own function depending on the real response." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aea9f3df-c55e-4a88-af13-d777ad768ac7", - "metadata": {}, - "outputs": [], - "source": [ - "def extract_text(input_string):\n", - " pattern = r\"- (.*?)($|\\n)\"\n", - " matches = re.findall(pattern, input_string)\n", - " extracted_texts = [match[0] for match in matches]\n", - " return extracted_texts" - ] - }, - { - "cell_type": "markdown", - "id": "0e70d6bc-7616-4e73-9d05-611b56212b01", - "metadata": {}, - "source": [ - "Convert the response to a list of product descriptions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cd7996cd-2ef2-42ae-8144-5fcb312ad236", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "product_descriptions = extract_text(response)\n", - "product_descriptions" - ] - }, - { - "cell_type": "markdown", - "id": "f5685bdf-9fcb-47dd-9e8c-40b568b8a4a6", - "metadata": {}, - "source": [ - "The following function calls bedrock to generated images using \"amazon.titan-image-generator-v1\" model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90e4395e-da68-4c4c-bd99-609a3b12741f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def titan_image(\n", - " payload:dict, \n", - " num_image:int=2, \n", - " cfg:float=10.0, \n", - " seed:int=2024\n", - ") -> list:\n", - "\n", - " body = json.dumps(\n", - " {\n", - " **payload,\n", - " \"imageGenerationConfig\": {\n", - " \"numberOfImages\": num_image, # Number of images to be generated. Range: 1 to 5 \n", - " \"quality\": \"premium\", # Quality of generated images. Can be standard or premium.\n", - " \"height\": 1024, # Height of output image(s)\n", - " \"width\": 1024, # Width of output image(s)\n", - " \"cfgScale\": cfg, # Scale for classifier-free guidance. Range: 1.0 (exclusive) to 10.0\n", - " \"seed\": seed # The seed to use for re-producibility. Range: 0 to 214783647\n", - " }\n", - " }\n", - " )\n", - "\n", - " response = bedrock_client.invoke_model(\n", - " body=body, \n", - " modelId=\"amazon.titan-image-generator-v1\", \n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - " )\n", - "\n", - " response_body = json.loads(response.get(\"body\").read())\n", - " images = [\n", - " Image.open(\n", - " BytesIO(base64.b64decode(base64_image))\n", - " ) for base64_image in response_body.get(\"images\")\n", - " ]\n", - "\n", - " return images" - ] - }, - { - "cell_type": "markdown", - "id": "fdf9d81e-64e1-4939-a83f-3cbaf78b09fe", - "metadata": {}, - "source": [ - "Then we leverage the Titan Image Generation models to create product images for each of the descriptions. The following cell may take a few minutes to run." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "dded2456-e2fd-400a-bb96-88868c1f8db5", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "embed_dir = \"data/titan-embed\"\n", - "os.makedirs(embed_dir, exist_ok=True)\n", - "\n", - "titles = []\n", - "for prompt in product_descriptions:\n", - " images = titan_image(\n", - " {\n", - " \"taskType\": \"TEXT_IMAGE\",\n", - " \"textToImageParams\": {\n", - " \"text\": prompt, # Required\n", - " }\n", - " },\n", - " num_image=1\n", - " )\n", - " title = \"_\".join(prompt.split()[:4]).lower()\n", - " title = f\"{embed_dir}/{title}.png\"\n", - " titles.append(title)\n", - " images[0].save(title, format=\"png\")\n", - " print(f\"generated {title}\")" - ] - }, - { - "cell_type": "markdown", - "id": "900115e4-5be3-40c4-9982-b579c3f3f863", - "metadata": {}, - "source": [ - "# Multimodal Dataset Indexing" - ] - }, - { - "cell_type": "markdown", - "id": "bcac42d5-810e-4ad4-bd09-5ed89991e9d9", - "metadata": {}, - "source": [ - "The following function converts image, and optionally, text, into multimodal embeddings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "801aa752-afe0-47bc-b73a-ec2667c9559a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def titan_multimodal_embedding(\n", - " image_path:str=None, # maximum 2048 x 2048 pixels\n", - " description:str=None, # English only and max input tokens 128\n", - " dimension:int=1024, # 1,024 (default), 384, 256\n", - " model_id:str=\"amazon.titan-embed-image-v1\"\n", - "):\n", - " payload_body = {}\n", - " embedding_config = {\n", - " \"embeddingConfig\": { \n", - " \"outputEmbeddingLength\": dimension\n", - " }\n", - " }\n", - "\n", - " # You can specify either text or image or both\n", - " if image_path:\n", - " with open(image_path, \"rb\") as image_file:\n", - " input_image = base64.b64encode(image_file.read()).decode('utf8')\n", - " payload_body[\"inputImage\"] = input_image\n", - " if description:\n", - " payload_body[\"inputText\"] = description\n", - "\n", - " assert payload_body, \"please provide either an image and/or a text description\"\n", - " print(\"\\n\".join(payload_body.keys()))\n", - "\n", - " response = bedrock_client.invoke_model(\n", - " body=json.dumps({**payload_body, **embedding_config}), \n", - " modelId=model_id,\n", - " accept=\"application/json\", \n", - " contentType=\"application/json\"\n", - " )\n", - "\n", - " return json.loads(response.get(\"body\").read())" - ] - }, - { - "cell_type": "markdown", - "id": "32306437-4931-4450-bc84-999dc8f478a4", - "metadata": {}, - "source": [ - "Now we can create embeddings for the generated images, together with the product descriptions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d3eb696-b5b0-41e5-982b-8da18b9e3978", - "metadata": {}, - "outputs": [], - "source": [ - "multimodal_embeddings = []\n", - "for title in titles:\n", - " embedding = titan_multimodal_embedding(image_path=title, dimension=1024)[\"embedding\"]\n", - " multimodal_embeddings.append(embedding)\n", - " print(f\"generated embedding for {title}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "43f08305-caa0-43ed-abc5-d7ad71cefb55", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "np.array(multimodal_embeddings).shape, np.array(multimodal_embeddings)" - ] - }, - { - "cell_type": "markdown", - "id": "2c8b9942-ca89-4d7c-912f-2e9352a4b12c", - "metadata": { - "tags": [] - }, - "source": [ - "The following function produces a heatmap to display the inner product of the embeddings." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4da941c-2e89-452b-bae6-acff49f41cd2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def plot_similarity_heatmap(embeddings_a, embeddings_b):\n", - " inner_product = np.inner(embeddings_a, embeddings_b)\n", - " sns.set(font_scale=1.1)\n", - " graph = sns.heatmap(\n", - " inner_product,\n", - " vmin=np.min(inner_product),\n", - " vmax=1,\n", - " cmap=\"OrRd\",\n", - " )" - ] - }, - { - "cell_type": "markdown", - "id": "de7e129d-f11b-4b83-b6aa-4533383de0d5", - "metadata": {}, - "source": [ - "Generate a heatmap to display the inner product of the embeddings. You can see that the diagonal is dark red, which means one embedding is closely related to itself. Then you can notice that there are 3X3 squares which are lighter than the diagonal, but darker than the rest. It means those 3 embeddings are less closely related to each other, than to itself, but more closely related to the rest embeddings. This makes sense, as we generated 3 variants of each product. Products are more closely related if they are of the same type. Products are less closely related if they are of different types." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "faac6ff7-2aa9-4af7-af3f-9171f442ed6c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "plot_similarity_heatmap(multimodal_embeddings, multimodal_embeddings)" - ] - }, - { - "cell_type": "markdown", - "id": "409f2a79-337e-4994-a505-835819ab03a4", - "metadata": {}, - "source": [ - "# Multimodal Searching" - ] - }, - { - "cell_type": "markdown", - "id": "231cd8fa-75be-4062-9f50-5b3f91743db8", - "metadata": {}, - "source": [ - "The following function returns the top similar multimodal embeddings given a query multimodal embedding. Note in practise you can leverage managed vector database, e.g. Amazon OpenSearch Service, and here is for illustration purpose." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91ec2feb-436d-425d-9d54-6822dc66ad90", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def search(query_emb:np.array, indexes:np.array, top_k:int=1):\n", - " dist = cdist(query_emb, indexes, metric=\"cosine\")\n", - " return dist.argsort(axis=-1)[0,:top_k], np.sort(dist, axis=-1)[:top_k]" - ] - }, - { - "cell_type": "markdown", - "id": "0f3a8951-53b9-4807-951b-ffafa9380fc0", - "metadata": {}, - "source": [ - "Now we have created the embeddings, we can search the list with a query, to find the product which the query best describes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4aece41e-40fa-46dc-8edc-f366f5d4136b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "query_prompt = \"suede sneaker\"\n", - "query_emb = titan_multimodal_embedding(description=query_prompt, dimension=1024)[\"embedding\"]\n", - "len(query_emb)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ca6aa36-ed0b-4aad-b102-aad340118e88", - "metadata": {}, - "outputs": [], - "source": [ - "idx_returned, dist = search(\n", - " np.array(query_emb)[None], \n", - " np.array(multimodal_embeddings)\n", - ")\n", - "idx_returned, dist" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "86138c8e-fdd0-4cf1-ab16-393690828064", - "metadata": {}, - "outputs": [], - "source": [ - "for idx in idx_returned[:1]:\n", - " display(Image.open(f\"{titles[idx]}\"))" - ] - }, - { - "cell_type": "markdown", - "id": "b276f6a8-59ce-42a0-b511-e905b5e551a1", - "metadata": {}, - "source": [ - "Let's convert the above cells to a helper function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3fc9cfde-b51c-40dc-ba38-c4809d264d10", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "def multimodal_search(description:str, dimension:int):\n", - " query_emb = titan_multimodal_embedding(description=description, dimension=dimension)[\"embedding\"]\n", - "\n", - " idx_returned, dist = search(\n", - " np.array(query_emb)[None], \n", - " np.array(multimodal_embeddings)\n", - " )\n", - "\n", - " for idx in idx_returned[:1]:\n", - " display(Image.open(f\"{titles[idx]}\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a711b6b-edd2-49b1-8c57-08f4d1c3214b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "multimodal_search(description=\"white sneaker\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cecb9b78-c4e6-4087-9487-55fdd2e914b6", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "multimodal_search(description=\"mesh sneaker\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6db23053-dec8-48fd-a281-bf00865ccf08", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "multimodal_search(description=\"leather backpack\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d73627b-6680-4380-813b-6fb4e9080455", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "multimodal_search(description=\"nylon backpack\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "625e66dd-e7c2-43c8-9dcc-78d73183ca90", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "multimodal_search(description=\"canvas backpack\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "95eec24b-073d-4ce8-ad88-f7ed49c41632", - "metadata": {}, - "outputs": [], - "source": [ - "multimodal_search(description=\"running shoes\", dimension=1024)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": 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