files-db-mcp

[](https://mseep.ai/app/randomm-files-db-mcp) # Files-DB-MCP: Vector Search for Code Projects A local vector database system that provides LLM coding agents with fast, efficient search capabilities for software projects via the Message Control Protocol (MCP). ## Features - **Zero Configuration** - Auto-detects project structure with sensible defaults - **Real-Time Monitoring** - Continuously watches for file changes - **Vector Search** - Semantic search for finding relevant code - **MCP Interface** - Compatible with Claude Code and other LLM tools - **Open Source Models** - Uses Hugging Face models for code embeddings ## Installation ### Option 1: Clone and Setup (Recommended) ```bash # Using SSH (recommended if you have SSH keys set up with GitHub) git clone git@github.com:randomm/files-db-mcp.git ~/.files-db-mcp && bash ~/.files-db-mcp/install/setup.sh # Using HTTPS (if you don't have SSH keys set up) git clone https://github.com/randomm/files-db-mcp.git ~/.files-db-mcp && bash ~/.files-db-mcp/install/setup.sh ``` ### Option 2: Automated Installation Script ```bash curl -fsSL https://raw.githubusercontent.com/randomm/files-db-mcp/main/install/install.sh | bash ``` ## Usage After installation, run in any project directory: ```bash files-db-mcp ``` The service will: 1. Detect your project files 2. Start indexing in the background 3. Begin responding to MCP search queries immediately ## Requirements - Docker - Docker Compose ## Configuration Files-DB-MCP works without configuration, but you can customize it with environment variables: - `EMBEDDING_MODEL` - Change the embedding model (default: 'jinaai/jina-embeddings-v2-base-code' or project-specific model) - `FAST_STARTUP` - Set to 'true' to use a smaller model for faster startup (default: 'false') - `QUANTIZATION` - Enable/disable quantization (default: 'true') - `BINARY_EMBEDDINGS` - Enable/disable binary embeddings (default: 'false') - `IGNORE_PATTERNS` - Comma-separated list of files/dirs to ignore ### First-Time Startup On first run, Files-DB-MCP will download embedding models which may take several minutes depending on: - The size of the selected model (300-500MB for high-quality models) - Your internet connection speed Subsequent startups will be much faster as models are cached in a persistent Docker volume. For faster initial startup, you can: ```bash # Use a smaller, faster model (90MB) EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 files-db-mcp # Or enable fast startup mode FAST_STARTUP=true files-db-mcp ``` ### Model Caching Files-DB-MCP automatically persists downloaded embedding models, so you only need to download them once: - Models are stored in a Docker volume called `model_cache` - This volume persists between container restarts and across different projects - The cache is shared for all projects using Files-DB-MCP on your machine - You don't need to download the model again for each project ## Claude Code Integration Add to your Claude Code configuration: ```json { "mcpServers": { "files-db-mcp": { "command": "python", "args": ["/path/to/src/claude_mcp_server.py", "--host", "localhost", "--port", "6333"] } } } ``` For details, see [Claude MCP Integration](docs/claude_mcp_integration.md). ## Documentation - [Installation Guide](docs/installation_guide.md) - Detailed setup instructions - [API Reference](docs/api_reference.md) - Complete API documentation - [Configuration Guide](docs/configuration_reference.md) - Configuration options ## Repository Structure - `/src` - Source code - `/tests` - Unit and integration tests - `/docs` - Documentation - `/scripts` - Utility scripts - `/install` - Installation scripts - `/.docker` - Docker configuration - `/config` - Configuration files - `/ai-assist` - AI assistance files ## License [MIT License](LICENSE) ## Contributing Contributions welcome! Please feel free to submit a pull request.