Summary Lookup RAG

Introduction

In this project, we use summary lookup RAG to help narrow the focus of the retrieval from the vector database by first running a query on document summaries, and then querying relevant chunks of data from the selected documents.

Prerequisites

• Docker
• Python 3.11 (local setup)
• Pinecone account

Setup

1. Create a Pinecone Index:

Before running the application, you need to set up two Pinecone indices:

1. Log in to your Pinecone account at https://app.pinecone.io/
2. Navigate to the "Indexes" section and click "Create Index"
3. Fill in the following details:
   • Name: Choose a semantic name for your summary index (e.g., "summary-index")
   • Dimensions: Set to 1536
   • Metric: Choose cosine
4. For Environment and Region (free tier):
   • Cloud Provider: Select aws
   • Region: Choose us-east-1
5. Click "Create Index" to confirm
6. Create a second index for your chunks, following the above procedure
7. For the second index, give the index a semantic name (e.g., "chunk-index")

Remember the names you gave to your indices, as you'll need them for the .env file.

2. Set up environment variables:

• Copy the sample environment file:

  cp .env.sample .env
  

• Edit the .env file and add your API keys and other required variables:

   OPENAI_API_KEY=<your-openai-key>
   LANGCHAIN_API_KEY=<your-langsmith-key>
   LANGCHAIN_TRACING_V2=true
   LANGCHAIN_PROJECT=gauntlet_class_4
   PINECONE_API_KEY=<your-pinecone-key>
   PINECONE_SUMMARY_INDEX=<your-summary-index-name>
   PINECONE_CHUNK_INDEX=<your-chunk-index-name>
  

  Make sure to use the relevant index names you created for PINECONE_SUMMARY_INDEX and PINECONE_CHUNK_INDEX

Start Docker

1. Build the Docker image:

   docker build -t office-hours-two .
   

2. Run the upload scripts:

   docker compose run --rm upload_summaries
   docker compose run --rm upload_chunks
   

3. Run the main file:

   docker compose run --rm summary_lookup
   

4. Run a specific script (any new .py file you may add):

   docker compose run --rm summary_lookup python <script_name.py>
   

Note: The Docker setup will automatically use the environment variables from your .env file. You don't need to export them to your system environment when using Docker.

Running Different Scripts

You can use the provided run.sh script for easier execution. Make sure to make the script executable with chmod +x run.sh in the CLI before using:

./run.sh upload_summaries
./run.sh upload_chunks
./run.sh main
./run.sh <your_new_py_file>

Local Setup (Alternative to Docker)

If you prefer to run the examples locally:

1. Ensure you have Python 3.11.0 or higher installed.
2. Clone the repository:

   git clone [repository-url]
   cd [repository-name]
   

3. Set up the environment:

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`
   pip install -r requirements.txt
   

4. Configure environment variables as described in the Setup section.
5. Run the files:

   python3 upload_summaries.py
   python3 upload_chunks.py
   python3 main.py
   

Need Help?

Reach out to the course instructor or learning assistant