juricode-retrieval 0.1.0

Dense-retrieval core for JuriCode-JP: artefact loading, query embedding, cosine top-k, and article-level dedup as the single source of truth

juricode-retrieval

Dense-retrieval core for JuriCode-JP, packaged (MIT) as the single source of truth for the parity-critical primitives.

What it provides

primitive	purpose
_load_artefacts	Load the embedding matrix (.npy), records (.meta.jsonl), and provider state (.vec.json, .vec.pkl fallback). Missing artefacts fail loudly.
_encode_queries	Embed query strings via the artefact's provider (gemini / openai / tfidf). The openai and gemini clients are imported lazily.
_cosine_topk	Cosine-similarity top-k of a query matrix against the corpus matrix.
dedup_by_article	Collapse duplicate segments of the same article to the top-ranked one.

Why a package

tools/embed/retrieve.py re-imports these four objects, so every eval and service caller runs the identical function objects rather than a copy. The retrieval math is therefore unchanged by construction, and a test asserts the object identity (retrieve._load_artefacts is juricode_retrieval._load_artefacts).

Dependencies

numpy is required (imported lazily inside each primitive, so importing the package itself needs no numpy). google-genai is the production embedding provider; the openai and tfidf branches are optional and imported only when selected.