LangChain-RAG-Chatbot

A modern Retrieval-Augmented Generation (RAG) chatbot powered by LangChain, FastAPI, and Streamlit. Upload documents, chat with your data, and get AI-powered answers—all in a user-friendly web app.

Live Preview:
👉 https://langchain-rag-chatbot-frontend.onrender.com/

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🚀 Features

• Document Upload: Upload PDF, Word, or HTML documents.
• AI-Powered Chat: Chat with your documents using Google’s Generative AI and LangChain.
• Semantic Search: Fast vector search powered by Chroma and Google embeddings.
• Responsive UI: Simple, intuitive Streamlit web interface.
• Cloud-Ready: Easy deployment to Render, AWS, or other platforms.

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🏗️ Architecture Overview

• Backend: FastAPI (Python) for RESTful API, document processing, and chat logic.
• Frontend: Streamlit (Python) for the web UI.
• Vector Database: Chroma for local vector storage and semantic search.
• LLM: Google Generative AI for generating answers.
• Deployment: Docker and Render for containerized and cloud deployment.

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🛠️ How to Run Locally

Prerequisites

• Python 3.10+
• Docker (optional, for containerized setup)
• Google API Key (for Google Generative AI)

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1. Clone the Repository

git clone https://github.com/MohammedMusharraf11/LangChain-RAG-Chatbot.git
cd LangChain-RAG-Chatbot

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2. Set Up Environment Variables

Create a .env file in the root directory:

GOOGLE_API_KEY=your_api_key_here

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3. Install Dependencies

For the Backend

cd api
pip install -r requirements.txt

For the Frontend

cd app
pip install -r requirements.txt

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4. Run the Backend

cd api
uvicorn main:app --host 0.0.0.0 --port 8000

5. Run the Frontend

cd app
streamlit run streamlit_app.py --server.port=8501 --server.address=0.0.0.0

🔒 Privacy Note

By default, all users share the same document and chat storage. For production or private use, add user authentication and filter data by user or session.

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Project Structure

langchain-rag-chatbot/
├── api/
│   ├── main.py                # FastAPI backend
│   ├── requirements.txt       # Backend dependencies
│   ├── chroma_db/             # Vector database (ignored in git)
│   ├── rag_app.db             # SQLite database (ignored in git)
│   └── ...                    # Other backend files
├── app/
│   ├── streamlit_app.py       # Streamlit frontend
│   ├── requirements.txt       # Frontend dependencies
│   └── ...                    # Other frontend files
├── docker-compose.yml         # Docker Compose config
├── Dockerfile                 # (Optional) Frontend Dockerfile
├── .env                       # Environment variables (ignored in git)
└── README.md                  # This file

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first.