📚 WikiBot AI: Professional Wikipedia RAG Assistant

WikiBot AI is a sophisticated Retrieval-Augmented Generation (RAG) system that allows users to chat with Wikipedia content through a premium, modern web interface. It combines the power of vector databases with local LLMs (Large Language Models) to provide accurate, context-aware answers.

✨ Key Features

• 🔍 Advanced RAG: Intelligent retrieval from the Wikipedia database using ChromaDB and Sentence Transformers.
• 🧠 Context-Aware Chat: Remembers previous interactions for seamless multi-turn conversations.
• 🛑 Real-time Control: Stop generation mid-stream with a dedicated abort button.
• 🖼️ Premium UI: State-of-the-art glassmorphism design with responsive layouts and micro-animations.
• 🖨️ Professional Print: Export formatted chat histories directly to PDF or paper.
• ⚡ Streaming Responses: Watch the bot think and type in real-time.

🛠️ Technology Stack

• Backend: Python, FastAPI, Uvicorn
• Vector Database: ChromaDB
• Embeddings: all-MiniLM-L6-v2 (Sentence-Transformers)
• LLM Connection: OpenAI-compatible API (designed for LM Studio/Llama.cpp)
• Frontend: Vanilla HTML5, CSS3 (Glassmorphism), Modern JavaScript (ES6+)

🚀 Getting Started

1. Prerequisites

• Python 3.9+
• LM Studio or a local LLM server running at http://localhost:1234

2. Installation

# Clone the repository
git clone https://github.com/yourusername/wikipediarag.git
cd wikipediarag

# Install dependencies
pip install -r requirements.txt

3. Ingesting Knowledge

To populate the vector database with Wikipedia articles:

1. Place your simplewiki-latest-pages-articles.xml.bz2 in the root folder.
2. Run the ingestion script:

python ingest.py

4. Running the Application

python server.py

Open your browser and navigate to http://localhost:8000.

📁 Project Structure

├── static/
│   ├── index.html    # semantic UI structure
│   ├── index.css     # Premium glassmorphism styles
│   └── script.js    # Logic for streaming & history
├── ingest.py         # Wikipedia XML processor & embedder
├── query.py          # CLI testing tool for queries
├── server.py         # FastAPI backend with streaming
└── requirements.txt  # Python dependencies

📜 Documentation

API Endpoints

• GET /: Serves the frontend application.
• POST /chat: Accepts {query: string, history: Array} and returns a text stream.

Frontend Features

• Stop Button: Uses AbortController to terminate server requests safely.
• Print Utility: Uses @media print queries to strip UI elements and format text for documentation.
• History Management: Local state tracking to ensure the LLM understands pronouns and follow-up context.

📄 License

MIT License - feel free to use this project for your own learning or internal tools!