semcode-search 0.1.0

Semantic code search CLI — find code by meaning, not just text. Powered by bge-small-code-v1.

semantic-codesearch

Semantic code search CLI — find code by meaning, not just text.

Powered by bge-small-code-v1, a 33M parameter code embedding model trained on 200K CoRNStack triplets across Python, JavaScript, Java, and Go.

Install

pip install semantic-codesearch

Usage

Index a codebase

codesearch index .

Walks the directory, chunks code files (30 lines with 5-line overlap), embeds each chunk with the ONNX model, and stores everything in a local SQLite database (.codesearch.db).

Search by meaning

codesearch search "function that sorts users by date"
codesearch search "authentication middleware" -n 10
codesearch search "database connection pool" -d /path/to/repo

Results show file path, line range, similarity score, and a code preview:

────────────────────────────────────────────────────────────
  #1 src/auth.py:26-32  (71.8% match)
────────────────────────────────────────────────────────────
    26 if AUTH_TOKEN:
    27     auth = request.headers.get("authorization", "")
    28     if auth != f"Bearer {AUTH_TOKEN}":
    29         return JSONResponse({"error": "unauthorized"}, ...)

View index stats

codesearch stats

Features

• Semantic search — finds code by meaning, not keywords. "sort by date" finds sorted(users, key=lambda u: u.created_at).
• Fast — ONNX model runs on CPU. Indexing ~50 files takes ~15 seconds. Searches are instant (cosine similarity on cached embeddings).
• Local & private — everything runs locally. No API calls, no data leaves your machine.
• Auto-downloads model — fetches bge-small-code-v1 ONNX from HuggingFace on first run (~130MB).
• 50+ file types — Python, JS, TS, Java, Go, Rust, C/C++, SQL, YAML, and more.
• Smart directory skipping — ignores .git, node_modules, __pycache__, .venv, dist, etc.

How it works

1. Chunking — splits each file into overlapping 30-line chunks
2. Embedding — runs each chunk through bge-small-code-v1 (ONNX, 384-dim output)
3. Storage — stores embeddings + metadata in SQLite (.codesearch.db)
4. Search — embeds your query, computes cosine similarity against all chunks, returns top-k

Model

Built on BAAI/bge-small-en-v1.5 (33M params), fine-tuned on CoRNStack code search triplets with Matryoshka loss for flexible embedding dimensions (384/256/128/64).

• Accuracy@1: 72.6% | Accuracy@10: 91.8% | NDCG@10: 82.5%
• ONNX INT8: 33.8MB — small enough to run in a browser

Requirements

• Python 3.10-3.13 (onnxruntime doesn't support 3.14 yet)
• No GPU needed — runs on CPU

License

MIT