🧠 Retrieval-Augmented Generation (RAG) Chatbot

This is a full-stack RAG application powered by LangChain, Groq LLM, FAISS vector store, and React frontend. It allows you to upload PDF documents, index them with semantic search, and query them via an interactive chat interface.

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📁 Project Structure

project-root/
├── backend/
│   ├── app.py                      # Flask API entry point
│   ├── connect_memory_with_llm.py # LangChain chain that connects vector DB with Groq LLM
│   ├── create_memory_for_llm.py   # PDF ingestion and vector store creation
│   ├── data/                       # Directory for input PDFs
│   ├── vectorstore/db_faiss/      # FAISS index storage
│   ├── .env                       # Environment variables
│   ├── requirements.txt           # Backend dependencies
│   ├── Pipfile / Pipfile.lock     # Optional: pipenv dependency management
│   └── vercel.json                # Optional: deployment config for Vercel
└── frontend/
    └── ...                        # React application (Chat UI)

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🛠️ Backend Setup (Python + Flask)

🔧 Install Dependencies

cd backend
pip install -r requirements.txt

Or use pipenv:

pipenv install
pipenv shell

🔐 Environment Variables

Create a .env file in the backend/ directory:

GROK_API=your_groq_api_key_here

📄 Step 1: Ingest PDF Documents

Run this to process and chunk PDF files and store in FAISS:

python create_memory_for_llm.py

Ensure your PDFs are placed in the backend/data/ directory.

🤖 Step 2: Start the Flask API

python app.py

The API will be live at: http://localhost:5001/api/chat

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💬 Frontend Setup (React)

📦 Install Dependencies

cd frontend
npm install

🌐 Environment Variables

Create a .env file in the frontend/ directory:

REACT_APP_API=http://localhost:5001

🚀 Run the App

npm start

Navigate to http://localhost:3000 in your browser to use the chat UI.

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✨ Features

• Upload and index PDFs using HuggingFace embeddings and FAISS
• Ask natural language questions and retrieve answers grounded in the uploaded documents
• Groq LLM handles question answering with a custom prompt
• Sources of each answer are returned for transparency
• React-based interactive chat interface

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🧠 Technologies Used

• Backend

  • Python, Flask
  • LangChain
  • Groq API (LLM)
  • HuggingFace Transformers (embeddings)
  • FAISS (vector store)

• Frontend

  • React
  • Axios
  • Tailwind CSS (optional for styling)

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🚀 Deployment

Backend on Vercel (Optional)

You can deploy app.py via vercel.json using Vercel’s Python support.

Frontend on Vercel/Netlify

Set your environment variable REACT_APP_API to your deployed backend API URL.

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📸 Demo Video

Screen.Recording.2025-06-28.at.5.37.43.PM.mp4

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📄 License

This project is licensed under the MIT License.

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Let me know if you'd like:

• Docker support
• Markdown formatting for source links
• Advanced RAG strategies like MapReduce or HyDE

I'm happy to help!