Migrate all repos into monorepo context folders
Bahn: aisupport, Analyse-O2C-C2S, awesome-bahn-mcp-servers, beam-mcp,
Confluence_Bot, db-planet-mcp-server, O2C-Harness, project-audit,
Projekt-KIQ-HP, teamlandkarte-mcp
Dhive: Jury-Voting
Privat: CV, NoteGraph (NOTE: NoteGraph needs complete redo after consolidation)
Shared: AI-Orchestrator, OrgMyLife, power_skills_and_more
Shared/references: symphony (read-only)
Bahn repos remain available as independent remotes - this monorepo
pulls them in via subtree, the originals are untouched.
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# filepath: tests/test_bm25.py
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"""Unit tests for :mod:`teamlandkarte_mcp.matching.bm25`.
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Covers tokenization, :class:`Bm25Index` behaviour, and the
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:func:`bm25_rank_competences` convenience wrapper.
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"""
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from __future__ import annotations
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from teamlandkarte_mcp.matching.bm25 import (
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Bm25Index,
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_tokenize,
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bm25_rank_competences,
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)
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# ---------------------------------------------------------------------------
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# Tokenization
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# ---------------------------------------------------------------------------
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def test_tokenize_lowercase() -> None:
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"""Tokens are always lowercase."""
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assert _tokenize("Python") == ["python"]
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def test_tokenize_slash() -> None:
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"""Slashes split tokens."""
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assert _tokenize("CI/CD Pipeline") == ["ci", "cd", "pipeline"]
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def test_tokenize_parentheses_and_space() -> None:
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"""Parentheses and spaces split tokens; PWA is its own token."""
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assert _tokenize("Progressive Web App (PWA)") == [
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"progressive",
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"web",
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"app",
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"pwa",
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]
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def test_tokenize_dot() -> None:
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"""Dots split tokens (e.g. React.js → react, js)."""
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assert _tokenize("React.js") == ["react", "js"]
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def test_tokenize_hyphen() -> None:
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"""Hyphens split tokens."""
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assert _tokenize("Type-Script") == ["type", "script"]
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def test_tokenize_empty_string() -> None:
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"""Empty string produces empty token list."""
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assert _tokenize("") == []
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def test_tokenize_only_special_chars() -> None:
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"""String of only special characters produces empty list."""
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assert _tokenize("---///") == []
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# ---------------------------------------------------------------------------
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# Bm25Index — empty corpus
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# ---------------------------------------------------------------------------
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def test_bm25_index_empty_corpus_rank_returns_empty() -> None:
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"""Empty corpus → rank() returns empty list."""
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index = Bm25Index(corpus=[])
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assert index.rank("Python") == []
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# ---------------------------------------------------------------------------
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# Bm25Index — exact match
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# ---------------------------------------------------------------------------
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def test_bm25_index_exact_match_score_positive() -> None:
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"""Exact match produces a positive BM25 score in a realistic-sized corpus.
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A corpus of five distinct skills ensures each term's IDF is positive
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(no term appears in more than half the documents), so BM25 assigns a
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meaningful positive score to the matching document.
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"""
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corpus = ["Python", "JavaScript", "Java", "Go", "Ruby"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Python")
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py_score = next(s for d, s in result if d == "Python")
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assert py_score > 0.0
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def test_bm25_index_case_insensitive_match() -> None:
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"""BM25 matching is case-insensitive (tokenization lowercases both sides).
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Uses a realistic-sized corpus so IDF weights are positive.
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"""
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corpus = ["python", "JavaScript", "Java", "Go", "Ruby"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Python")
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py_score = next(s for d, s in result if d == "python")
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assert py_score > 0.0
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# ---------------------------------------------------------------------------
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# Bm25Index — no token overlap
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# ---------------------------------------------------------------------------
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def test_bm25_index_no_token_overlap_score_zero() -> None:
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"""No shared token between query and document → score 0.0."""
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index = Bm25Index(corpus=["JavaScript"])
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result = index.rank("Python")
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assert len(result) == 1
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assert result[0][0] == "JavaScript"
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assert result[0][1] == 0.0
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def test_bm25_index_partial_token_overlap() -> None:
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"""Partial token overlap gives positive score in a realistic-sized corpus.
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"Machine Learning" shares the "learning" token with "Deep Learning".
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A five-document corpus keeps "learning" IDF positive (it appears in 2/5
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docs, well below the 50 % threshold that would zero out the IDF).
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"""
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corpus = ["Deep Learning", "Python", "JavaScript", "Java", "Go"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Machine Learning")
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dl_score = next(s for d, s in result if d == "Deep Learning")
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assert dl_score > 0.0
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# ---------------------------------------------------------------------------
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# Bm25Index — multiple documents, ranking order
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# ---------------------------------------------------------------------------
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def test_bm25_index_best_match_ranked_first() -> None:
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"""The document with the highest score appears first.
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Uses five documents so all query tokens have positive IDF and the
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exact-match document outscores partial-match documents.
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"""
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corpus = ["Machine Learning", "Deep Learning", "JavaScript", "Python", "Java"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Machine Learning")
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assert result[0][0] == "Machine Learning"
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assert result[0][1] > result[1][1]
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def test_bm25_index_zero_score_documents_included() -> None:
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"""Documents with score 0.0 are included in the ranked list.
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Uses a realistic-sized corpus so the Python document itself gets a
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positive score, while the unrelated JavaScript document correctly
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scores 0.0 (no token overlap).
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"""
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corpus = ["Python", "JavaScript", "Java", "Go", "Ruby"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Python")
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docs = [r[0] for r in result]
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assert "JavaScript" in docs
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js_score = next(s for d, s in result if d == "JavaScript")
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assert js_score == 0.0
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def test_bm25_index_result_sorted_descending() -> None:
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"""Ranked list is sorted by score descending.
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Uses a realistic-sized corpus to ensure stable positive IDF values.
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"""
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corpus = ["Python", "Python Advanced", "JavaScript", "Java", "Go"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("Python")
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scores = [s for _, s in result]
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assert scores == sorted(scores, reverse=True)
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# ---------------------------------------------------------------------------
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# bm25_rank_competences convenience wrapper
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# ---------------------------------------------------------------------------
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def test_bm25_rank_competences_empty_candidates() -> None:
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"""Empty candidate list returns empty list."""
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assert bm25_rank_competences("Python", []) == []
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def test_bm25_rank_competences_consistent_with_index() -> None:
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"""Convenience wrapper produces same result as Bm25Index.rank()."""
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candidates = ["Python", "JavaScript", "Machine Learning", "Java", "Go"]
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index_result = Bm25Index(corpus=candidates).rank("Python")
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wrapper_result = bm25_rank_competences("Python", candidates)
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assert index_result == wrapper_result
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# ---------------------------------------------------------------------------
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# Global-index filtering
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# ---------------------------------------------------------------------------
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def test_bm25_index_rank_filters_to_candidate_subset() -> None:
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"""Scores from a global index can be filtered to a candidate's own skills.
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This mirrors the production path in ``_bm25_rrf_similarity`` where a
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global ``Bm25Index`` is built over all candidates and then the result is
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filtered to only include the current candidate's competences.
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"""
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global_corpus = [
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"Python",
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"Machine Learning",
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"JavaScript",
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"TypeScript",
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"Java",
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"Go",
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"Ruby",
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]
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global_index = Bm25Index(corpus=global_corpus)
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# Simulate one candidate who only knows Python + Machine Learning
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candidate_skills = {"Python", "Machine Learning"}
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all_ranked = global_index.rank("Python")
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filtered = [(doc, score) for doc, score in all_ranked if doc in candidate_skills]
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assert len(filtered) == 2
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docs = {d for d, _ in filtered}
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assert docs == candidate_skills
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# Python should score higher than Machine Learning for query "Python"
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py_score = next(s for d, s in filtered if d == "Python")
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ml_score = next(s for d, s in filtered if d == "Machine Learning")
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assert py_score > ml_score
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def test_bm25_ubiquitous_term_clamped_to_zero() -> None:
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"""A term that appears in more than half the corpus gets IDF ≤ 0.
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``rank()`` clamps the resulting negative BM25 score to 0.0 — the term
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is non-discriminative and should not contribute positively to any match.
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"""
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# "dev" appears in 4 out of 5 documents → IDF < 0
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corpus = ["dev Python", "dev Java", "dev Go", "dev Ruby", "JavaScript"]
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index = Bm25Index(corpus=corpus)
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result = index.rank("dev")
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scores = [s for _, s in result]
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# All scores must be ≥ 0 (negative raw scores clamped to 0.0)
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assert all(s >= 0.0 for s in scores)
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