Cocktails RAG MCP Server

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.

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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

  1. Get Groq API key (free): https://console.groq.com/keys

  2. 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
    
  3. Pre-download models (required):

    Download embeddings and reranker models:

    uv run python -c "from src.rag.rag import RAG; RAG(); print('Models downloaed!')"
    
  4. Install for Claude Desktop:

    Automatic (Recommended)

    uv run fastmcp install claude-desktop fastmcp.json --name cocktails --env-file .env
    

    Manual

    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 and GROQ_API_KEY with your API key.

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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