IETM_FIXE\ietm6
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Mise en place de tests unitaires coté Python et Angular Mise en place de la couverture de test directement dans le workflow : le programme ne build pas si jamais un test échoue Passage en v0.16.2 en conséquence
120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
"""Tests du stockage vectoriel fichier + recherche hybride (app.infrastructure.vector_store).
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Le répertoire de stockage est redirigé vers un tmp_path et le cache mémoire est
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vidé avant chaque test pour une isolation totale.
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"""
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from __future__ import annotations
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import pytest
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from app.infrastructure import vector_store
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@pytest.fixture(autouse=True)
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def isolated_store(tmp_path, monkeypatch):
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monkeypatch.setattr(vector_store, "_STORE_DIR", tmp_path)
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vector_store._CACHE.clear()
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yield
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vector_store._CACHE.clear()
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# --- cosinus -------------------------------------------------------------------
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def test_cosine_identical_is_one():
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assert vector_store._cosine([1.0, 0.0], [2.0, 0.0]) == pytest.approx(1.0)
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def test_cosine_orthogonal_is_zero():
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assert vector_store._cosine([1.0, 0.0], [0.0, 1.0]) == 0.0
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def test_cosine_mismatched_or_zero_is_zero():
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assert vector_store._cosine([1.0], [1.0, 2.0]) == 0.0
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assert vector_store._cosine([0.0, 0.0], [1.0, 1.0]) == 0.0
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assert vector_store.cosine_similarity([], [1.0]) == 0.0 # alias public
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# --- mots significatifs --------------------------------------------------------
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def test_significant_words_filters_stopwords_and_short():
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words = vector_store._significant_words("Le dragon DORT dans la caverne avec les gobelins")
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assert "dragon" in words
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assert "caverne" in words
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assert "gobelins" in words
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assert "les" not in words and "avec" not in words and "la" not in words
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# --- save / exists / delete ----------------------------------------------------
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def test_save_then_exists_and_delete():
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vector_store.save("src1", ["chunk a"], [[1.0, 0.0]])
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assert vector_store.exists("src1") is True
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vector_store.delete("src1")
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assert vector_store.exists("src1") is False
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def test_save_rejects_mismatched_lengths():
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with pytest.raises(ValueError):
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vector_store.save("s", ["a", "b"], [[1.0]])
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with pytest.raises(ValueError):
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vector_store.save("s", ["a"], [[1.0]], pages=[1, 2])
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def test_all_chunks_returns_text_and_page():
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vector_store.save("s", ["t1", "t2"], [[1.0], [2.0]], pages=[3, 7])
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chunks = vector_store.all_chunks("s")
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assert chunks == [{"text": "t1", "page": 3}, {"text": "t2", "page": 7}]
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# --- recherche -----------------------------------------------------------------
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def test_search_ranks_by_cosine():
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vector_store.save("s", ["proche", "loin"], [[1.0, 0.0], [0.0, 1.0]])
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results = vector_store.search(["s"], [1.0, 0.0], top_k=2)
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assert [r["text"] for r in results] == ["proche", "loin"]
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assert results[0]["score"] > results[1]["score"]
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def test_search_respects_top_k():
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vector_store.save("s", ["a", "b", "c"], [[1.0], [0.9], [0.8]])
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assert len(vector_store.search(["s"], [1.0], top_k=2)) == 2
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def test_search_min_score_filters_out_weak_matches():
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vector_store.save("s", ["proche", "orthogonal"], [[1.0, 0.0], [0.0, 1.0]])
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results = vector_store.search(["s"], [1.0, 0.0], top_k=5, min_score=0.5)
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assert [r["text"] for r in results] == ["proche"]
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def test_search_lexical_bonus_promotes_exact_term_match():
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# Deux extraits de cosinus IDENTIQUE : le bonus lexical départage celui qui
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# contient le mot exact de la question.
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vector_store.save(
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"s",
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["Strahd règne sur Barovia", "un texte neutre sans rapport"],
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[[1.0, 0.0], [1.0, 0.0]],
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)
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results = vector_store.search(["s"], [1.0, 0.0], top_k=2, query_text="Strahd")
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assert results[0]["text"] == "Strahd règne sur Barovia"
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assert results[0]["score"] > results[1]["score"]
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def test_search_includes_source_id_and_page():
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vector_store.save("livre", ["extrait"], [[1.0]], pages=[42])
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[res] = vector_store.search(["livre"], [1.0], top_k=1)
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assert res["source_id"] == "livre"
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assert res["page"] == 42
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# --- résumés (analyse approfondie) ---------------------------------------------
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def test_summaries_roundtrip_keyed_by_batch_tokens():
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vector_store.save_summaries("s", 1000, [{"summary": "résumé", "vector": [1.0]}])
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assert vector_store.load_summaries("s", 1000) == [{"summary": "résumé", "vector": [1.0]}]
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# Taille de lot différente → invalidé (le découpage ne correspondrait plus).
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assert vector_store.load_summaries("s", 2000) is None
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def test_load_summaries_absent_returns_none():
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assert vector_store.load_summaries("inconnu", 1000) is None
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