metadata
title: RAGBot
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false
license: mit
🤖 RAGBOT
A cloud-native, multi-tenant AI chatbot that instantly learns the content of any website and answers questions in real-time. Built for the Cloud Computing final project.
🚀 Key Features
Universal Ingestion: Crawls and indexes any provided URL (e.g., University sites, NGOs, Government portals) on-demand.
SaaS Architecture: Uses Multi-Tenancy via Vector Database Namespacing to isolate customer data within a single index.
RAG Pipeline: Combines Semantic Search (Pinecone) with LLM Generation (Gemini 2.0 Flash) for hallucination-free answers.
Client-Server Model: Decoupled FastAPI Backend and Chrome Extension Frontend.
Asynchronous Processing: Background workers handle heavy scraping tasks without blocking the UI.
🛠️ Tech Stack
Backend (Cloud Engine)
- Framework: FastAPI (Python)
- Vector Database: Pinecone (Serverless AWS)
- LLM: Google Gemini 2.0 Flash
- Crawler: Trafilatura (Sitemap & Content Discovery)
- Embeddings: Sentence-Transformers (
all-MiniLM-L6-v2)
Frontend (Client)
- Interface: Google Chrome Extension (Manifest V3)
- Interaction: Real-time Polling & Dynamic UI
📂 Project Structure
ragbot/
├── README.md # Documentation
├── api/ # 🐍 Backend Logic
│ ├── config.py # Environment & Logging setup
│ ├── crawler.py # Logic for sitemap parsing & scraping
│ ├── schemas.py # Pydantic data models
│ ├── server.py # Main FastAPI entry point
│ ├── utils.py # Security & URL validation
│ └── vectorstore.py # Pinecone batching & management
├── chrome-extension/ # 🧩 Frontend Client
│ ├── background.js # On-click sidepanel logic
│ ├── icons/ # Chrome Extension icons
│ │ ├── icon128.png
│ │ ├── icon16.png
│ │ ├── icon32.png
│ │ └── icon48.png
│ ├── manifest.json # Setup for Chrome Extension
│ ├── sidepanel.css # UI
│ ├── sidepanel.html # HTML Contents
│ └── sidepanel.js # Main UI logic
└── requirements.txt # Python dependencies