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File Compass

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Performs semantic file search for AI workstations leveraging HNSW indexing and local embeddings via Ollama to efficiently locate relevant code and content.

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File Compass revolutionizes how developers and AI assistants interact with large codebases and project directories. Instead of relying on keyword-based searches that often miss semantic context, this tool employs vector embeddings and HNSW indexing to understand the meaning behind your queries. It enables users to find specific files, functions, or concepts by simply describing what they're looking for, offering sub-second search times even across tens of thousands of files. Beyond its powerful search capabilities, File Compass integrates seamlessly with AI assistants like Claude Code through its Model Context Protocol (MCP) server, providing a suite of tools for semantic search, code preview, and quick indexing, making it an indispensable asset for AI-powered development workflows.

Key Features

01Fast Search: HNSW indexing ensures sub-second query responses for large datasets
02MCP Server: Integrates with AI assistants like Claude Code for enhanced functionality
030 GitHub stars
04Local Embeddings: Uses Ollama (e.g., nomic-embed-text) for privacy-preserving, local AI processing
05Semantic Search: Find files by describing intent, not just keywords
06Multi-Language AST: Semantic chunking for Python, JS, TS, Rust, Go via Tree-sitter

Use Cases

01Quickly locate specific code logic or configuration files within a large codebase by semantic description.
02Empower AI assistants to semantically search and interact with project files without needing external APIs.
03Efficiently navigate and understand new or unfamiliar repositories by describing desired functionalities.