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
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LiveKit RAG AssistantHow 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.
π¬ LiveKit RAG Assistant v2.0
Enterprise-grade AI semantic search + real-time web integration for LiveKit documentation
π― 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
π 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.pyApp opens at http://localhost:8501
ποΈ Architecture
Streamlit (app.py) β MCP Server β Dual Search:
ββ Pinecone: Semantic search on embeddings (384-dim)
ββ Tavily: Real-time web results
β
Groq LLM (2048 tokens, temp 0.3) β Response + Sourcesπ§ 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 |
π Usage
Choose mode: π Docs or οΏ½ Web
Ask naturally: "How do I set up LiveKit?"
Get instant answer with π sources
Copy messages or re-ask from history
β‘ Performance
First query: ~15-20s (model load)
Cached queries: 2-5s
Search latency: <500ms
π οΈ Configuration
GROQ_API_KEY=gsk_***
TAVILY_API_KEY=tvly_***
PINECONE_API_KEY=***
PINECONE_INDEX_NAME=livekit-docsπ Populate Docs
python ingest_docs_quick.py # Creates 3,000+ vector chunksπ Files
app.py- Streamlit UI with premium designmcp_server_standard.py- MCP server with toolsingest_docs_quick.py- Document ingestionrequirements.txt- Dependencies.env- API keys
π¨ 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 |
οΏ½ Features
β Real-time chat with 60+ animations β Semantic + keyword hybrid search β Copy-to-clipboard for messages β Recent query suggestions β System status dashboard β Chat history persistence β Query validation + error handling
Version: 2.0 | Status: β Production Ready | Created: November 2025
π¨βπ» By @THENABILMAN | οΏ½ Open Source | β€οΈ For Developers
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