code-rag-mcp 0.1.2

MCP server for efficient code search

Code-RAG

Semantic code search for your entire codebase. Ask questions in plain English, get relevant code snippets with source locations.

Instead of grepping for function names, ask "authentication logic" and find all related auth code across your project.

Quick Start

# Create virtual environment, optional but recommended
python -m venv .venv
source .venv/bin/activate

# Install
pip install -e .

# Start the MCP server (default)
code-rag

# Or use the interactive CLI search
code-rag-cli

Example (CLI)

$ code-rag-cli --path /home/user/myproject

Processing codebase... Found 247 files
Indexed 1,234 chunks in 12s

Query: authentication logic

Result 1 | Similarity: 0.85
File: src/code_rag/auth/authenticator.py (lines 45-78)

class Authenticator:
    """Handle user authentication and session management."""

    def authenticate(self, username: str, password: str) -> bool:
        ...

Why Use Code-RAG?

• Understand unfamiliar codebases - Ask questions instead of reading everything
• Find examples - "error handling with retries" finds all relevant patterns
• Refactoring aid - Locate all code related to a feature you're changing
• Documentation - Extract context for writing docs or onboarding

Use with Claude Code (MCP Integration)

Code-RAG works as an MCP server, letting Claude automatically search your codebase during conversations.

Quick Setup

Option 1: Using uvx (after publishing to PyPI)

# Claude Desktop (config.json)
claude mcp add code-rag

# Or with Claude Code
claude mcp add code-rag --transport stdio uvx code-rag-mcp

Option 2: Local installation

# Install in virtual environment
pip install -e .

# Register with Claude Code
claude mcp add code-rag --transport stdio path/to/venv/bin/code-rag-mcp

Configuration

The MCP server reads configuration from environment variables or config files. Configure via your MCP client's settings:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "code-rag": {
      "command": "uvx",
      "args": ["code-rag-mcp"],
      "env": {
        "CODE_RAG_EMBEDDING_MODEL": "nomic-ai/CodeRankEmbed",
        "CODE_RAG_DATABASE_TYPE": "chroma",
        "CODE_RAG_RERANKER_ENABLED": "true"
      }
    }
  }
}

Claude Code (via claude mcp add):

# Basic setup
claude mcp add code-rag --transport stdio uvx code-rag-mcp

# Then configure via environment variables or config files

Common Configuration Options:

• CODE_RAG_EMBEDDING_MODEL - Embedding model (default: nomic-ai/CodeRankEmbed)
  • nomic-ai/CodeRankEmbed - Code-optimized, runs locally
  • text-embedding-3-small - OpenAI embeddings (requires OPENAI_API_KEY)
• CODE_RAG_DATABASE_TYPE - Database backend: chroma or qdrant (default: chroma)
• CODE_RAG_CHUNK_SIZE - Chunk size in characters (default: 1024)
• CODE_RAG_RERANKER_ENABLED - Enable result reranking (default: false)
• CODE_RAG_SHARED_SERVER - Share embedding server across instances (default: true)

Example with OpenAI embeddings:

{
  "mcpServers": {
    "code-rag": {
      "command": "uvx",
      "args": ["code-rag-mcp"],
      "env": {
        "CODE_RAG_EMBEDDING_MODEL": "text-embedding-3-small",
        "OPENAI_API_KEY": "sk-...",
        "CODE_RAG_RERANKER_ENABLED": "true"
      }
    }
  }
}

Usage

Once configured, Claude can automatically search your codebase:

You: "Find the database connection logic"

Claude: [Automatically searches and finds the code]
        "I found the database connection logic in src/code_rag/db/connection.py..."

See docs/mcp.md for detailed setup and troubleshooting.

Basic Usage

# Different codebase
code-rag-cli --path /path/to/repo

# Force reindex
code-rag-cli --reindex

# More results
code-rag-cli --results 10

# Different embedding model (better for code)
code-rag-cli --model text-embedding-3-small # need to set OPENAI_API_KEY env

# Use Qdrant instead of ChromaDB
code-rag-cli --database qdrant

Configuration

Priority Order

Configuration is loaded in this order (higher priority overrides lower):

1. Environment variables (highest priority)
2. Custom config file via CODE_RAG_CONFIG_FILE environment variable
3. Project config: ./code-rag.config
4. User config: ~/.config/code-rag/config (auto-created with defaults)

For MCP servers: Set environment variables in your MCP client config (see MCP Integration section above).

For CLI usage: Use environment variables or config files.

Environment Variables

# Use code-optimized embeddings (recommended)
export CODE_RAG_EMBEDDING_MODEL="nomic-ai/CodeRankEmbed"

# Or OpenAI embeddings
export OPENAI_API_KEY="sk-..."
export CODE_RAG_EMBEDDING_MODEL="text-embedding-3-small"

# Use Qdrant
export CODE_RAG_DATABASE_TYPE="qdrant"

# Adjust chunk size
export CODE_RAG_CHUNK_SIZE="2048"

# Enable reranking for better results
export CODE_RAG_RERANKER_ENABLED="true"

# Add custom ignore patterns (comma-separated)
export CODE_RAG_ADDITIONAL_IGNORE_PATTERNS="*.tmp,*.bak,logs/"

Config File Format

Config files use the same format (key=value):

# ~/.config/code-rag/config or ./code-rag.config
CODE_RAG_EMBEDDING_MODEL=nomic-ai/CodeRankEmbed
CODE_RAG_DATABASE_TYPE=chroma
CODE_RAG_CHUNK_SIZE=1024
CODE_RAG_RERANKER_ENABLED=false

Full configuration options in docs/IMPLEMENTATION.md.

How It Works

1. Scans your codebase (respects .gitignore)
2. Chunks code intelligently (syntax-aware for Python, JS, Go, Rust, Java, C/C++)
3. Embeds chunks as vectors using ML models
4. Stores in vector database (ChromaDB or Qdrant)
5. Searches semantically when you query

Pluggable architecture - swap databases, embedding models, or add new ones.

API Usage

Use programmatically:

from code_rag.api import CodeRAGAPI

api = CodeRAGAPI(database_type="chroma", embedding_model="all-MiniLM-L6-v2")
api.initialize_collection("myproject")

# Index
chunks = api.index_codebase("/path/to/project")

# Search
results = api.search("authentication logic", n_results=5)
for r in results:
    print(f"{r['file_path']} - {r['similarity']:.2f}")

Documentation

• AGENTS.md - Developer onboarding and architecture overview
• docs/IMPLEMENTATION.md - Detailed implementation reference
• docs/mcp.md - MCP server setup guide

Supported Languages

Syntax-aware chunking for: Python, JavaScript, TypeScript, Go, Rust, Java, C, C++

Other languages use line-aware chunking (still works, just less context-aware).

Requirements

• Python 3.8+
• Minimal dependencies (ChromaDB + sentence-transformers by default)
• Optional: OpenAI API key, Qdrant server

Troubleshooting

Import errors? pip install -e . --force-reinstall

Database issues? code-rag --reindex

Memory issues? export CODE_RAG_BATCH_SIZE="16"

Development

Setup

# Install with dev dependencies
pip install -e ".[dev]"

Testing & Linting

# Run tests
pytest

# Format code
black .
isort .

# Linting
flake8

Contributing

1. Fork the repo
2. Create feature branch
3. Make changes
4. Add tests
5. Submit PR

See AGENTS.md for architecture and docs/IMPLEMENTATION.md for internals.

License

MIT License. See LICENSE for details.

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Built with ChromaDB, Qdrant, sentence-transformers, and Tree-sitter