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Semantic file search for AI workstations using HNSW vector indexing

Find files by describing what you're looking for, not just by name

# Install
git clone https://github.com/mcp-tool-shop-org/file-compass.git
cd file-compass && pip install -e .

# Pull embedding model
ollama pull nomic-embed-text

# Index your code
file-compass index -d "C:/Projects"

# Search semantically
file-compass search "authentication middleware"

• Semantic Search - Find files by describing what you're looking for
• Quick Search - Instant filename/symbol search (no embedding required)
• Multi-Language AST - Tree-sitter support for Python, JS, TS, Rust, Go
• Result Explanations - Understand why each result matched
• Local Embeddings - Uses Ollama (no API keys needed)
• Fast Search - HNSW indexing for sub-second queries
• Git-Aware - Optionally filter to git-tracked files only
• MCP Server - Integrates with Claude Code and other MCP clients
• Security Hardened - Input validation, path traversal protection

# Clone the repository
git clone https://github.com/mcp-tool-shop-org/file-compass.git
cd file-compass

# Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# or: source venv/bin/activate  # Linux/Mac

# Install dependencies
pip install -e .

# Pull the embedding model
ollama pull nomic-embed-text

• Python 3.10+
• Ollama with nomic-embed-text model

# Index a directory
file-compass index -d "C:/Projects"

# Index multiple directories
file-compass index -d "C:/Projects" "D:/Code"

# Semantic search
file-compass search "database connection handling"

# Filter by file type
file-compass search "training loop" --types python

# Git-tracked files only
file-compass search "API endpoints" --git-only

# Search by filename or symbol name
file-compass scan -d "C:/Projects"  # Build quick index

file-compass status

File Compass includes an MCP server for integration with Claude Code and other AI assistants.

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "file-compass": {
      "command": "python",
      "args": ["-m", "file_compass.gateway"],
      "cwd": "C:/path/to/file-compass"
    }
  }
}

1. Scanning - Discovers files matching configured extensions, respects .gitignore
2. Chunking - Splits files into semantic pieces:
   • Python/JS/TS/Rust/Go: AST-aware via tree-sitter (functions, classes)
   • Markdown: Heading-based sections
   • JSON/YAML: Top-level keys
   • Other: Sliding window with overlap
3. Embedding - Generates 768-dim vectors via Ollama
4. Indexing - Stores vectors in HNSW index, metadata in SQLite
5. Search - Embeds query, finds nearest neighbors, returns ranked results

file-compass/
├── file_compass/
│   ├── __init__.py      # Package init
│   ├── config.py        # Configuration
│   ├── embedder.py      # Ollama client with retry
│   ├── scanner.py       # File discovery
│   ├── chunker.py       # Multi-language AST chunking
│   ├── indexer.py       # HNSW + SQLite index
│   ├── quick_index.py   # Fast filename/symbol search
│   ├── explainer.py     # Result explanations
│   ├── merkle.py        # Incremental updates
│   ├── gateway.py       # MCP server
│   └── cli.py           # CLI
├── tests/               # 298 tests, 91% coverage
├── pyproject.toml
└── LICENSE

• Input Validation - All MCP inputs are validated
• Path Traversal Protection - Files outside allowed directories blocked
• SQL Injection Prevention - Parameterized queries only
• Error Sanitization - Internal errors not exposed

# Run tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=file_compass --cov-report=term-missing

# Type checking
mypy file_compass/

Part of MCP Tool Shop — the Compass Suite for AI-powered development:

• Tool Compass - Semantic MCP tool discovery
• Integradio - Vector-embedded Gradio components
• Backpropagate - Headless LLM fine-tuning
• Comfy Headless - ComfyUI without the complexity

• Questions / help: Discussions
• Bug reports: Issues

MIT License - see LICENSE for details.

• Ollama for local LLM inference
• hnswlib for fast vector search
• nomic-embed-text for embeddings
• tree-sitter for multi-language AST parsing