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Table of Contents

API Documentation

The API provides several endpoints for interacting with the application. You can access the detailed API documentation via Postman. To get started:

  1. API DOCS: check api-documentation.pdf file
  2. API Endpoints:
  • Process Document
    • Endpoint: POST /api/documents/process
    • Input: File path
    • Output: Asset ID
    • Description: This endpoint processes the document at the given file path and returns an Asset ID.
  • Start Chat
    • Endpoint: POST /api/chat/start
    • Input: Asset ID
    • Output: Chat thread ID
    • Description: This endpoint initiates a chat session using the provided Asset ID and returns a Chat thread ID.
  • Send Chat Message
    • Endpoint: POST /api/chat/message
    • Input: Chat thread ID, User message
    • Output: Agent response (streamed)
    • Description: This endpoint sends a user message to the chat thread and streams the agent's response in real-time.
  • Get Chat History
    • Endpoint: GET /api/chat/history
    • Input: Chat thread ID
    • Output: Chat history
    • Description: This endpoint retrieves the full history of a chat session using the provided Chat thread ID.

Setup Instructions

Prerequisites

Ensure you have the following installed:

  • Python 3.x
  • pip (Python package manager)
  • Docker (for containerized setup)

Clone

Clone the repository:

git clone https://github.com/Jayanth-MKV/RAG-fastapi-chroma-langchain-docker.git

cd RAG-fastapi-chroma-langchain-docker

Setting Up a Virtual Environment

It is recommended to use a virtual environment to manage dependencies for your project. Here’s how you can set one up:

  1. Navigate to your project directory:

    cd RAG-fastapi-chroma-langchain-docker

  2. Create a virtual environment:

    python -m venv venv

  3. Activate the virtual environment:

    • On macOS and Linux:

      source venv/bin/activate

    • On Windows:

      ./venv/Scripts/activate

  4. Once activated, your terminal should show the virtual environment name, indicating that you are working inside the virtual environment.

Installation

  1. Install the required Python packages:

    pip install -r requirements.txt

  2. ENV Setup create a .env file and replace the api key

    GROQ_API_KEY = "api-key-here"

    You can get this from https://console.groq.com/keys

Running the Application

To run the application locally, use the following command:

python main.py

This will start the server on http://localhost:8000. You can test the endpoints using at http://localhost:8000/docs

You can also run the sample streamlit app using - streamlit run streamlit.py

Docker Setup

To set up and run the application using Docker, follow these steps:

  1. Build the Docker image:

    docker build -t myapp .

  2. Run the Docker container:

    docker run -p 8000:8000 myapp

  3. The application will be accessible at http://localhost:8000.

Potential Improvements

  • User authentication and authorization to secure endpoints.
  • Adding different formats of data + adding connectors to drive, notion, dropbox, etc..
  • Improving file handling and storing.
  • Add unit and integration tests to ensure code reliability. Also the stress test.
  • Scaling Vector Database like quadrant/pinecone
  • Implementing the rag evaluation pipeline
  • Experimenting differnt embedding models and parameters
  • Combining traditional rag with graph db - hybrid rag improves the results
  • Agentic approach to encounter hallucinations

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue to discuss improvements or fixes.