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---
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](https://github.com/Yash-Kavaiya)
- LinkedIn: [https://linkedin.com/in/Yash-Kavaiya](https://linkedin.com/in/Yash-Kavaiya) |