# 💬 LiveKit RAG Assistant
**AI-powered semantic search and question-answering system for LiveKit documentation**
## 🎯 Features
- **Dual Search Modes**: Documentation (Pinecone) + Real-time Web Search (Tavily)
- **Standard MCP Server**: Async LangChain integration with Model Context Protocol
- **Fast Responses**: Groq LLM (llama-3.3-70b) with ultra-fast inference
- **Semantic Search**: HuggingFace embeddings (384-dim) with vector indexing
- **Source Attribution**: View exact sources for every answer
- **Chat History**: Persistent conversation tracking with recent query access
- **Query Validation**: Prevents invalid inputs with helpful error messages
- **Copy-to-Clipboard**: One-click message sharing
## 🚀 Quick Start
### Prerequisites
- Python 3.10+ with conda
- API Keys: GROQ, TAVILY, PINECONE, HuggingFace
### Installation
```bash
# 1. Clone and setup environment
cd c:\lg mcp ai
conda create -n langmcp python=3.12
conda activate langmcp
# 2. Install dependencies
pip install -r requirements.txt
# 3. Configure .env file
echo "GROQ_API_KEY=your_key" >> .env
echo "TAVILY_API_KEY=your_key" >> .env
echo "PINECONE_API_KEY=your_key" >> .env
echo "PINECONE_INDEX_NAME=livekit-docs" >> .env
```
### Running the Application
**Terminal 1** - Start MCP Server:
```bash
python mcp_server_standard.py
```
**Terminal 2** - Start Streamlit UI:
```bash
streamlit run app.py
```
The app opens at `http://localhost:8501`
## 📊 Architecture
```
Streamlit UI (app.py)
↓
Query Validation → Error Handling
↓
MCP Server (subprocess) → mcp_server_standard.py
↓
Dual Search Layer:
├─ Pinecone (3,007 vectors) - Semantic search
└─ Tavily API - Real-time web results
↓
LLM Layer (Groq):
├─ Temperature: 0.3 (detailed, focused)
├─ Max Tokens: 2048 (comprehensive answers)
└─ Model: llama-3.3-70b-versatile
↓
Response Display + Source Attribution
```
## 🔧 Tech Stack
| Component | Technology | Purpose |
|-----------|-----------|---------|
| **Frontend** | Streamlit 1.28+ | Premium glassmorphism UI |
| **Backend** | MCP Standard | Async subprocess server |
| **LLM** | Groq API | Ultra-fast inference (free tier) |
| **Embeddings** | HuggingFace | sentence-transformers/all-MiniLM-L6-v2 |
| **Vector DB** | Pinecone Serverless | Ultra-fast similarity search (AWS us-east-1) |
| **Web Search** | Tavily API | Real-time internet search |
| **Framework** | LangChain | LLM orchestration & tools |
| **Language** | Python 3.12 | Modern syntax & features |
## 📚 Project Structure
```
c:\lg mcp ai\
├── app.py # Main Streamlit application (enhanced)
├── mcp_server_standard.py # MCP server with LangChain tools
├── ingest_docs_quick.py # Document ingestion to Pinecone
├── requirements.txt # Python dependencies
├── .env # API keys & configuration
├── README.md # This file
└── ingest_docs.py # Legacy ingestion script
```
## 🎮 Usage
### Ask Questions
1. **Choose Search Mode**: Documentation (📚) or Web Search (🌐)
2. **Type Question**: Natural language queries work best
3. **Get Answer**: AI responds with detailed 3-5 sentence answers
4. **View Sources**: Click "View Sources" to see cited documents
### Features
- **Copy Messages**: 📋 Click button on any message to copy
- **Recent Queries**: ↻ Quick re-ask from history
- **Quick Help**: 💡 Expandable tips and usage guide
- **Performance Metrics**: 📊 Real-time statistics in System Status
- **Error Prevention**: ✅ Query validation with helpful feedback
## ⚡ Performance
- **First Query**: ~15-20s (model initialization)
- **Subsequent Queries**: 3-8s (cached LLM)
- **Copy/History**: Instant (client-side)
- **Metrics Update**: Real-time (no overhead)
## 📖 Example Queries
- "How do I set up LiveKit?"
- "What are the best practices for video conferencing?"
- "How do I implement real-time communication?"
- "What authentication methods are available?"
- "How do I handle bandwidth optimization?"
- "Deploy to Kubernetes - how does LiveKit handle it?"
## 🛠️ Configuration
Edit `.env` file:
```env
GROQ_API_KEY=gsk_your_groq_key
TAVILY_API_KEY=tvly_your_tavily_key
PINECONE_API_KEY=your_pinecone_key
PINECONE_INDEX_NAME=livekit-docs
HF_TOKEN=optional_huggingface_token
```
## 🔄 Ingestion
Populate Pinecone with LiveKit documentation:
```bash
python ingest_docs_quick.py
```
This creates 3,007 searchable vector chunks from LiveKit docs.
## 📊 Status Checks
View system status in Streamlit sidebar:
```
✅ MCP Server Ready - Status indicator
✅ Groq LLM - API connection
✅ Pinecone VectorDB - Index status
💬 Messages - Total message count
👤 Questions - User query count
🤖 Responses - AI response count
```
## 🚨 Troubleshooting
| Issue | Solution |
|-------|----------|
| "No relevant documentation found" | Try web search mode or different keywords |
| "MCP Server not found" | Ensure `mcp_server_standard.py` is running in Terminal 1 |
| Slow first response | Normal - model loads on first query (~15-20s) |
| API key errors | Check `.env` file and verify all keys are set |
| Empty Pinecone index | Run `python ingest_docs_quick.py` to populate |
## 📝 Notes
- All chat history saved in session state
- Supports semantic search with keyword fallback
- Responses stored with source attribution
- Query validation prevents invalid inputs
- Performance optimized for fast inference
## 👨💻 Created By
**@THENABILMAN** - [GitHub](https://github.com/THENABILMAN)
## 📄 License
Built with ❤️ for developers. Feel free to modify and extend!
---
**Version**: Enhanced v1.0 | **Status**: ✅ Production Ready | **Date**: November 1, 2025
