RAG Chatbot with FastAPI Backend and Streamlit Frontend

Overview

This project demonstrates how to build a multi-user RAG chatbot that answers questions based on your own documents. The system utilizes LangChain for the RAG (Retrieval-Augmented Generation) component, FastAPI for the backend API, and Streamlit for the frontend interface.

This project covers:

• Implementing a RAG system using LangChain to combine document retrieval and response generation.
• Processing and storing documents for efficient retrieval in the RAG system.
• Building a conversational AI that handles multi-turn interactions.
• Modularizing the code for integration with FastAPI, enabling production-grade deployment.
• Creating an interactive Streamlit frontend that communicates with the FastAPI backend for real-time data management.

What’s Built

• RAG System: Fundamentals of RAG and how to use LangChain’s models, prompts, and retrievers to create a system that answers document-based questions.

• FastAPI Backend: API endpoints for managing document uploads, processing queries, and delivering responses to the frontend.

• Streamlit Frontend: An intuitive interface that allows users to interact with the backend for uploading documents and asking questions.

Prerequisites

Before starting, ensure the following:

• Python 3.8+ installed on the system.
• pip for managing dependencies.
• Basic understanding of Python, FastAPI, Streamlit, and RESTful APIs.
• Knowledge of RAG systems and LangChain.

Required Packages

The following Python packages are required:

• fastapi
• uvicorn
• streamlit
• langchain
• langchain-openai
• langchain-chroma
• python-multipart
• docx2txt
• pypdf