RAG-Powered Chat Application

This project implements a Retrieval-Augmented Generation (RAG) system with OpenAI integration, featuring document upload capabilities and a streaming chat API.

Features

• Document upload and processing for RAG
• Streaming chat API with OpenAI integration
• FastAPI-based web server

Installation

1. Install tesseract-ocr and Clone the repository.

• For macOS, use Homebrew: brew install tesseract
• For ubuntu: sudo apt-get update && sudo apt-get install tesseract-ocr

2. Create a virtual environment and activate it:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install --no-build-isolation faiss-cpu==1.7.2

Click to expend: If you suffer an error: `Failed to build faiss-cpu` from this step:

The error is related to building the faiss-cpu package, which requires SWIG. Here's a quick guide to resolve this issue: 1. Install SWIG:

• For macOS, use Homebrew: brew install swig
• For ubuntu: sudo apt-get update && sudo apt-get install build-essential swig libopenblas-dev

2. or, SWIG does not work

• you can try using Anaconda/Miniconda. However, I dont want to put much tutorial here.

2. After installing SWIG, try installing the requirements again:

   pip install -r requirements.txt
   

3. If issues persist with faiss-cpu, try using a pre-built wheel:

   • In requirements.txt, replace faiss-cpu==1.7.2 with faiss-cpu==1.7.2 --only-binary :all:

4. Alternatively, use faiss-cpu from conda:

   • Install Miniconda or Anaconda
   • Create a new conda environment: conda create -n your_env_name python=3.8
   • Install faiss-cpu: conda install -c conda-forge faiss-cpu
   • Install other requirements: pip install -r requirements.txt

3. Start the server:

   Create a .env file: Create a file named .env in the root directory of the project and add the following content:

   OPENAI_API_BASE=https://XXXXX/v1
   OPENAI_API_KEY=your_api_key_here
   

Following that, run this command:

python -m venv venv
source venv/bin/activate
uvicorn app:app --host 0.0.0.0 --port 8000

The server will run on http://localhost:8000

API Endpoints:

• POST /upload: Upload a document for processing
  • Parameters:
    • file: The document file to upload
    • doc_type: (Optional) The type of the document
  • Returns:
    • lib_id: A unique identifier for the uploaded document's RAG instance
• POST /chat: Send a chat message and receive a streaming response
  • Parameters:
    • message: The chat message to process
    • lib_id: The unique identifier of the RAG instance to use for this chat

Usage Example:

1. Upload a document:

   curl -X POST -F "file=@your_document.pdf" -F "doc_type=pdf" http://localhost:8000/upload
   

   This will return a lib_id in the response.

2. Chat using the uploaded document:

   curl -X POST -F "message=Your question here" -F "lib_id=the_lib_id_from_upload" http://localhost:8000/chat
   

   Replace the_lib_id_from_upload with the actual lib_id received from the upload response.

Acknowledgements

• FastAPI
• OpenAI
• Uvicorn
• RAG (Retrieval-Augmented Generation)