Semantic search for Git commit history, powered by TurboQuant vector compression (ICLR 2026).

Stop searching by keywords. Search by meaning.

# Current: keyword matching only
git log --grep="memory leak"     # Only finds commits with exact text "memory leak"
                                  # Misses: "fix kfree_skb double free"
                                  # Misses: "plug UAF in reset path"
                                  # Misses: "resolve dangling pointer"

# CommitMind: semantic search
commitmind search "memory leak"
# >> #1 [0.94] a3f2c1d  Fix kfree_skb double free in netfilter
# >> #2 [0.91] b7e4a2f  Plug use-after-free in device reset path
# >> #3 [0.87] c9d1b3e  Resolve dangling pointer in slab allocator

CommitMind understands the meaning of your query and finds semantically related commits - even when the exact words don't match.

Git commits --> Sentence embeddings --> TurboQuant compression --> Semantic search
                (all-MiniLM-L6-v2)      (7.6x compression)       (asymmetric scoring)

1. Extract commit messages + file change metadata from git history
2. Embed each commit into a 384-dimensional vector (local model, no API needed)
3. Compress vectors with TurboQuant (Google's ICLR 2026 algorithm) - 87% memory savings
4. Search using asymmetric inner-product estimation (no decompression needed)

pip install commitmind

Or install from source:

git clone https://github.com/wjddusrb03/commitmind.git
cd commitmind
pip install -e ".[dev]"

# 1. Index your repository
cd your-project
commitmind index

# Output:
# Indexing complete!
#   > 3,842 commits indexed
#   > Compressed: 18.2 MB -> 2.4 MB (7.6x)
#   > Saved to .commitmind/index.pkl

# 2. Search by meaning
commitmind search "authentication bug fix"

# 3. View stats
commitmind stats

# Index with options
commitmind index --max-commits 1000    # Limit to recent 1000 commits
commitmind index --branch main         # Index specific branch
commitmind index --bits 2              # Use 2-bit quantization (more compression)

# Search with options
commitmind search "query" -k 10        # Return top 10 results

• New team member: "What authentication changes were made recently?"
• Bug tracking: "Find commits related to network timeout issues"
• Security audit: "Show all SQL injection related fixes"
• Code archaeology: Search Linux kernel's 1M+ commits by meaning
• Cross-language: Search English commits with Korean queries (and vice versa)

Thanks to TurboQuant compression:

CommitMind uses TurboQuant (Google Research, ICLR 2026):

1. PolarQuant: Random orthogonal rotation + Lloyd-Max scalar quantization (3-bit)
2. QJL: Quantized Johnson-Lindenstrauss residual correction (1-bit)
3. Asymmetric scoring: Compute similarity WITHOUT decompressing vectors

This achieves ~7.6x compression with minimal accuracy loss.

• Python 3.9+
• Git repository
• CPU only (no GPU required)
• ~500 MB disk for embedding model (downloaded once)

Issues and pull requests are welcome! If you find a bug or have suggestions, please open an issue.

MIT License

If you use CommitMind in your research:

@software{commitmind2026,
  title={CommitMind: Semantic Git Commit Search with TurboQuant Compression},
  author={wjddusrb03},
  year={2026},
  url={https://github.com/wjddusrb03/commitmind}
}

• langchain-turboquant - LangChain VectorStore with TurboQuant compression
• TurboQuant paper - Original ICLR 2026 paper by Google Research