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metadata
license: apache-2.0
title: '# Website RAG Chatbot'
sdk: gradio
emoji: 📊
colorFrom: green
colorTo: yellow
pinned: false

Website RAG Chatbot

This project implements a Retrieval-Augmented Generation (RAG) system using Langchain, Google's Generative AI, and Gradio. It creates a chatbot interface that answers questions based on information retrieved from a pre-processed set of web pages.

Features

  • Web-based chatbot interface using Gradio
  • RAG system powered by Langchain and Google's Generative AI
  • Local FAISS vector store for efficient information retrieval
  • Customizable UI with CSS styling

Prerequisites

  • Python 3.9 or higher
  • Google API key for access to Generative AI models
  • Langchain API key
  • Pre-processed FAISS index (stored in ./Vector_DB/faiss_index)

Installation

  1. Clone this repository:

    git clone 
cd website-rag-chatbot

  2. Install the required packages:

    pip install -r requirements.txt

  3. Set up your environment variables: Create a .env file in the project root and add the following:

    GOOGLE_API_KEY=your_google_api_key
LANGCHAIN_API_KEY=your_langchain_api_key

Usage

To run the chatbot locally:

python app.py

This will start the Gradio interface, typically accessible at http://localhost:7860.

Deployment

This project is configured for deployment to Google Cloud Run.

  1. Build the Docker image:

    docker build -t website-rag-chatbot .

  2. Tag and push the image to Google Container Registry:

    docker tag website-rag-chatbot gcr.io/your-project-id/website-rag-chatbot
docker push gcr.io/your-project-id/website-rag-chatbot

  3. Deploy to Cloud Run:

    gcloud run deploy website-rag-chatbot --image gcr.io/your-project-id/website-rag-chatbot --platform managed

    Remember to set the necessary environment variables in the Cloud Run configuration.

Project Structure

  • app.py: Main application file containing the Gradio interface and RAG logic
  • Dockerfile: Configuration for containerizing the application
  • requirements.txt: List of Python package dependencies
  • Vector_DB/faiss_index: Pre-processed FAISS index (not included in repository)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Langchain for the RAG framework
  • Google for the Generative AI models
  • Gradio for the easy-to-use interface building

Contact

Created by Yash Kavaiya - feel free to contact me!