Cocktails RAG MCP Server
Provides cocktail recommendations using a Retrieval-Augmented Generation (RAG) pipeline powered by LangChain, FAISS, and Groq. It enables users to search for cocktail recipes and receive personalized drink suggestions through natural language.
README
Cocktails RAG MCP Server
MCP tool for cocktail recommendations using RAG (Retrieval-Augmented Generation).
Requirements
- Python 3.11+
- uv package manager - https://docs.astral.sh/uv/getting-started/installation/
Quick Start
-
Get Groq API key (free): https://console.groq.com/keys
-
Setup:
# Clone the repository git clone https://github.com/00200200/cocktails-rag-mcp.git cd cocktails-rag-mcp # Copy environment template cp .env.example .env # Edit .env and add your GROQ_API_KEY nano .env # Install dependencies uv sync -
Pre-download models (required):
Download embeddings and reranker models:
uv run python -c "from src.rag.rag import RAG; RAG(); print('Models downloaed!')" -
Install for Claude Desktop:
Automatic (Recommended)
uv run fastmcp install claude-desktop fastmcp.json --name cocktails --env-file .envManual
Edit config file:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
{ "mcpServers": { "cocktails": { "command": "uv", "args": [ "run", "--with","faiss-cpu", "--with","fastmcp", "--with","jq", "--with","langchain", "--with","langchain-community", "--with","langchain-groq", "--with","langchain-huggingface", "--with","pandas", "--with","python-dotenv", "--with","sentence-transformers", "fastmcp", "run", "/ABSOLUTE/PATH/TO/src/mcp/server.py:mcp" ], "env": { "GROQ_API_KEY": "your_groq_api_key_here" } } } }Replace
/ABSOLUTE/PATH/TO/with your project path andGROQ_API_KEYwith your API key. - macOS:
Example Usage
<p align="center"> <img src="assets/claude_example_1.png" width="600"> </p>
<p align="center"> <img src="assets/claude_example_2.png" width="600"> </p>
Local Testing
# Test RAG pipeline directly
uv run python -m src.rag.rag
# Test MCP server locally
uv run python src/mcp/server.py
Development
Code Formatting
# Format code with black
uv tool run black .
# Sort imports with isort
uv tool run isort .
Project Structure
RAG/
├── src/
│ ├── mcp/ # MCP server implementation (FastMCP)
│ ├── rag/ # RAG pipeline (retrieve, rerank, generate)
│ ├── db/ # FAISS vector database handler
│ └── data/ # Data loading utilities
├── data/ # Cocktail dataset
├── faiss_index/ # Generated FAISS index (auto-created on first run)
├── notebooks/ # EDA notebook
├── fastmcp.json # FastMCP configuration
├── pyproject.toml # Project dependencies
└── .env.example # Environment template
Tech Stack
- MCP Framework: FastMCP
- RAG Framework: LangChain
- Embeddings: BAAI/bge-m3 (local via HuggingFace)
- Vector DB: FAISS (local)
- Reranker: BAAI/bge-reranker-v2-m3 (local via HuggingFace)
- LLM: Groq API (llama-3.1-8b-instant)
- Package Manager: uv
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