Rag-template

This template demonstrates a FastAPI server with Retrieval-Augmented Generation (RAG). It uses LlamaIndex for efficient retrieval of relevant documents, enabling the generation of accurate and context-aware responses based on indexed data using the OpenAI API.

Features

• Query Endpoint: Accepts a user query, retrieves relevant information from the LlamaIndex, and generates a GPT-based response.
• Rebuild Index: Allows rebuilding the document index to reflect updated data.
• LlamaIndex Integration: Uses llama_index for efficient document storage and retrieval.
• GPT Response Generation: Leverages OpenAI's GPT API to generate contextually relevant answers.

Installation

1. Clone the repository:

   git clone <repository_url>
   cd <repository_name>

2. Install dependencies: Ensure you have Python 3.8+ and pip installed, then run:

   pip install -r requirements.txt

3. Set up OpenAI API key: Add your OpenAI API key to the environment variable OPENAI_API_KEY. This can be done by adding the following line to your .bashrc, .zshrc, or creating a .env file:

   export OPENAI_API_KEY="your_openai_api_key"

Usage

Start the Server

Run the FastAPI app with uvicorn:

uvicorn src.app:app --reload

The server will be available at http://127.0.0.1:8000.