https://github.com/aalexuser/rag-chatbot

AI application that utilizes LLP to provide users with relevant information and responses.
https://github.com/aalexuser/rag-chatbot

Last synced: 21 days ago
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AI application that utilizes LLP to provide users with relevant information and responses.

• Host: GitHub
• URL: https://github.com/aalexuser/rag-chatbot
• Owner: AaLexUser
• License: mit
• Created: 2024-07-08T08:39:13.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-08T09:32:57.000Z (over 1 year ago)
• Last Synced: 2025-11-03T17:11:27.779Z (4 months ago)
• Language: JavaScript
• Size: 629 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          


    
RAG Chatbot










    

     

     

    

    

    

    

    

    



## Overview

The RAG Chatbot is a conversational AI application that utilizes LLM to provide users with relevant information and responses.

## Components

- `query.py`: Contains the Python script responsible for querying the RAG model and generating responses.

- `populate_db.py`: A Python script used to populate the database with relevant data and sources.

- `web.py`: The Flask web framework code that handles HTTP requests and serves as the backend for the chatbot's interface.

- `index.html`: The HTML template for the chatbot's user interface.

- `index.js`: A JavaScript file that contains the client-side logic.

## Features

- The chatbot responds with relevant information and sources, which are displayed below the input field.

- Users can view detailed information about each source by hovering over the source card.

- The chatbot also supports uploading files through web interface.

## Screenshots

![Asking the question](docs/imgs/1.png)

![Мiew source information](docs/imgs/2.png)

## Getting Started

1. Clone this repository using `git clone https://github.com/AaLexUser/Rag-chatbot.git`

2. Install dependencies by running pip `install -r requirements.txt` in the project root directory.

3. Run the Flask application using `python web.py` to start the chatbot's backend.

4. Open a web browser and navigate to `http://localhost:5000/` to access the chatbot's interface.

## License

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.