Which integrations are available for this server?

Provides async LangChain integration for building RAG (Retrieval-Augmented Generation) pipelines with semantic search and LLM-powered responses Offers a premium glassmorphism UI for interacting with the LiveKit documentation search system, featuring real-time chat, animations, and source attribution

How do I use LiveKit RAG Assistant?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LiveKit RAG Assistant How do I set up audio rooms in LiveKit?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

💬 LiveKit RAG Assistant v2.0

Enterprise-grade AI semantic search + real-time web integration for LiveKit documentation

🎯 Features

Dual Search: Pinecone docs (3,000+ vectors) + Tavily real-time web
Standard MCP: Async LangChain with Model Context Protocol
Ultra-Fast: Groq LLM (llama-3.3-70b) sub-5s responses
Premium UI: Glassmorphism design with 60+ animations
Source Attribution: Full transparency on every answer

Related MCP server: Semantic Search MCP Server

🚀 Quick Start

# Setup
conda create -n langmcp python=3.12
conda activate langmcp
pip install -r requirements.txt

# Configure .env
GROQ_API_KEY=your_key
TAVILY_API_KEY=your_key
PINECONE_API_KEY=your_key
PINECONE_INDEX_NAME=livekit-docs

# Terminal 1: Start MCP Server
python mcp_server_standard.py

# Terminal 2: Start UI
streamlit run app.py

App opens at http://localhost:8501

🏗️ Architecture

Streamlit (app.py) → MCP Server → Dual Search:
├─ Pinecone: Semantic search on embeddings (384-dim)
└─ Tavily: Real-time web results
    ↓
Groq LLM (2048 tokens, temp 0.3) → Response + Sources

🔧 Tech Stack

Layer	Tech	Purpose
Frontend	Streamlit	Premium glassmorphism UI
Backend	MCP Standard	Async subprocess
LLM	Groq API	Ultra-fast inference
Embeddings	HuggingFace	all-MiniLM-L6-v2 (384-dim)
Vector DB	Pinecone	Serverless similarity search
Web Search	Tavily	Real-time internet results

📚 Usage

Choose mode: 📚 Docs or � Web
Ask naturally: "How do I set up LiveKit?"
Get instant answer with 📄 sources
Copy messages or re-ask from history

⚡ Performance

First query: ~15-20s (model load)
Cached queries: 2-5s
Search latency: <500ms

🛠️ Configuration

GROQ_API_KEY=gsk_***
TAVILY_API_KEY=tvly_***
PINECONE_API_KEY=***
PINECONE_INDEX_NAME=livekit-docs

🔄 Populate Docs

python ingest_docs_quick.py  # Creates 3,000+ vector chunks

📊 Files

app.py - Streamlit UI with premium design
mcp_server_standard.py - MCP server with tools
ingest_docs_quick.py - Document ingestion
requirements.txt - Dependencies
.env - API keys

🚨 Troubleshooting

Issue	Solution
No results	Try web mode or different keywords
MCP not found	Start mcp_server_standard.py in Terminal 1
Slow first response	Normal (15-20s) - model initializes once
API errors	Verify all keys in .env file

� Features

✅ Real-time chat with 60+ animations ✅ Semantic + keyword hybrid search ✅ Copy-to-clipboard for messages ✅ Recent query suggestions ✅ System status dashboard ✅ Chat history persistence ✅ Query validation + error handling

Version: 2.0 | Status: ✅ Production Ready | Created: November 2025

👨‍💻 By @THENABILMAN | � Open Source | ❤️ For Developers

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Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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