axm-echo

Neural similarity & echo detection over code corpora (MiniLM + scikit-learn).

Overview

Neural similarity & echo detection over code corpora (MiniLM + scikit-learn).

Features

• Neural by default — the st (MiniLM) backend ships in the base install (torch + sentence-transformers) and runs in-process; no extra to enable.
• tfidf opt-out — the pure-CPU numpy + scikit-learn backend stays available (--backend tfidf) for callers that want to avoid loading torch.
• Built on axm-ast for code-corpus extraction — the corpus feeding both echo_code (cross-package dedup) and echo_check (reuse retrieval).

Installation

# echo is neural by default — the install ships torch + sentence-transformers.
uv add axm-echo

Or as a workspace dependency in pyproject.toml:

[project]
dependencies = ["axm-echo"]

[tool.uv.sources]
axm-echo = { workspace = true }

Quick Start

from axm_echo import embed, extract_monorepo, neighbors

symbols = extract_monorepo()  # public symbols across the configured scope
matrix = embed([s["embed_text"] for s in symbols], backend="tfidf")
for idx, score in neighbors(matrix[0], matrix, k=5):
    print(f"{score:.3f}  {symbols[idx]['qualname']}")

See the documentation site for the echo_code / echo_check tools and the scope how-to.

Development

This package is part of the axm-forge-workspace uv workspace.

# Run tests for this package
uv run --package axm-echo pytest

# From workspace root
make test-axm-echo

License

MIT — © 2026 Gabriel Jarry