README.md

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!

• GitHub: https://github.com/Yash-Kavaiya
• LinkedIn: https://linkedin.com/in/Yash-Kavaiya