file-compass 1.0.1

Semantic file search for AI workstations using HNSW indexing

日本語 | 中文 | Español | Français | हिन्दी | Italiano | Português (BR)

Semantic file search for AI workstations using HNSW vector indexing

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

---

Why File Compass?

Problem	Solution
"Where's that database connection file?"	file-compass search "database connection handling"
Keyword search misses semantic matches	Vector embeddings understand meaning
Slow search across large codebases	HNSW index: <100ms for 10K+ files
Need to integrate with AI assistants	MCP server for Claude Code

Quick Start

# 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"

Features

• 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

Installation

# 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

Requirements

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

Usage

Build the Index

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

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

Search Files

# 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

Quick Search (No Embeddings)

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

Check Status

file-compass status

MCP Server

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

Available Tools

Tool	Description
file_search	Semantic search with explanations
file_preview	Code preview with syntax highlighting
file_quick_search	Fast filename/symbol search
file_quick_index_build	Build the quick search index
file_actions	Context, usages, related, history, symbols
file_index_status	Check index statistics
file_index_scan	Build or rebuild the full index

Claude Code Integration

Add to your claude_desktop_config.json:

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

Configuration

Variable	Default	Description
FILE_COMPASS_DIRECTORIES	F:/AI	Comma-separated directories
FILE_COMPASS_OLLAMA_URL	http://localhost:11434	Ollama server URL
FILE_COMPASS_EMBEDDING_MODEL	nomic-embed-text	Embedding model

How It Works

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

Performance

Metric	Value
Index Size	~1KB per chunk
Search Latency	<100ms for 10K+ chunks
Quick Search	<10ms for filename/symbol
Embedding Speed	~3-4s per chunk (local)

Architecture

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

Security

• 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

Development

# Run tests
pytest tests/ -v

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

# Type checking
mypy file_compass/

Related Projects

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

Security & Data Scope

File Compass is a local-first semantic file search tool and MCP server.

• Data accessed: Local file contents for indexing, HNSW vector index, SQLite metadata, Ollama embeddings (local inference)
• Data NOT accessed: No cloud sync. No telemetry. No analytics. No external API calls beyond local Ollama
• Permissions: File system read for indexing, write for index storage. Path traversal protection enforced

Full policy: SECURITY.md

---

Scorecard

Category	Score
A. Security	10/10
B. Error Handling	10/10
C. Operator Docs	10/10
D. Shipping Hygiene	10/10
E. Identity (soft)	10/10
Overall	50/50

---

Support

• Questions / help: Discussions
• Bug reports: Issues

License

MIT License - see LICENSE for details.

Acknowledgments

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

---

Built by MCP Tool Shop