Llama Chat Bot on Amazon Reviews RAG Pipeline

This project implements a Retrieval-Augmented Generation (RAG) pipeline using FastAPI to create a chat bot that answers questions based on Amazon reviews.

Features

• FastAPI: A modern, fast (high-performance), web framework for building APIs with Python 3.6+.
• RAG Pipeline: Combines retrieval and generation to provide accurate and contextually relevant answers.
• Environment Variables: Uses dotenv to manage environment variables.
• Logging: Integrated logging for better traceability and debugging.

Installation

1. Clone the repository:

   git clone https://github.com/rajtulluri/Llama-chat-bot-on-Amazon-reviews-RAG-pipeline.git
   cd Llama-chat-bot-on-Amazon-reviews-RAG-pipeline

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Usage

1. Run the server:

   uvicorn main:app --host 0.0.0.0 --port 8080

2. Send a POST request to /query endpoint:

   • Endpoint: /query
   • Method: POST
   • Request Body:

     {
         "question": "Your question here",
         "product_name": "Product name here"
     }

Project Structure

• main.py: The main entry point of the application.
• rag/rag_pipeline.py: Contains the implementation of the RAG pipeline.
• templates/request.py: Defines the request model.
• templates/response.py: Defines the response model.