Retrieval Augmented Generation based Application using FastAPI and LangChain

This project focuses on developing an application using the Retrieval-Augmented Generation (RAG) framework combined with FastAPI. The application is designed to handle user queries by leveraging the content of a provided PDF file. By accepting a file path as input, it processes and extracts relevant information from the PDF to generate accurate and contextually relevant answers.

Setup Guide

Prerequisites

• Python 3.10+
• Mistral API Access Key
• HuggingFace API Access Token

Installation Steps

1. Clone the repository:

   git clone https://github.com/suhail-chand/rag-fastapi.git
   cd rag-fastapi

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate   # Linux/Mac
   venv\Scripts\activate      # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file and add the following variables:

   HF_TOKEN='<HuggingFace_Access_Token>'
   MISTRAL_API_KEY='<Mistral_API_Key>'

5. Run the FastAPI server:

   fastapi dev .\src\main.py

FastAPI application will be running at http://127.0.0.1:8000 and to access the Swagger UI navigate to http://127.0.0.1:8000/docs.