gti
d21a9d1140
git-subtree-dir: fiscal git-subtree-mainline:d95165fc8fgit-subtree-split:102ba84500
117 lines
5.5 KiB
Python
117 lines
5.5 KiB
Python
"""Semantic candidate retrieval V2: hierarchical official corpus + KB-neighbor bridge + hybrid ranking."""
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from sqlalchemy import create_engine
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from sqlalchemy.orm import Session
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from central.app.models import Base, Dataset, KbKnowledge, KbObservation, KbProduct, NcmEntry, Tenant
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def _engine():
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engine = create_engine("sqlite://")
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Base.metadata.create_all(engine)
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return engine
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def _seed(session):
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ncm = Dataset(kind="NCM", provider="Siscomex", sha256="n" * 64, version_label="v", act="a", source_url="u", status="ACTIVE", record_count=7)
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session.add(ncm); session.flush()
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rows = [
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("22", "BEBIDAS, LIQUIDOS ALCOOLICOS E VINAGRES"),
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("2202", "AGUAS, INCLUIDAS AS AGUAS GASEIFICADAS E MINERAIS"),
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("2202.10", "AGUAS MINERAIS E GASEIFICADAS"),
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("22021000", "AGUAS MINERAIS NAO GASEIFICADAS"),
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("61", "VESTUARIO E SEUS ACESSORIOS, DE MALHA"),
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("6105", "CAMISAS DE MALHA, DE USO MASCULINO"),
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("61051000", "DE ALGODAO"),
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]
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for code, description in rows:
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session.add(NcmEntry(dataset_id=ncm.id, code=code.replace(".", ""), description=description, valid_from="", valid_to=""))
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session.flush()
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# two tenants already selling mineral water with a validated NCM
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tenant_a, tenant_b = Tenant(name="A"), Tenant(name="B")
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session.add_all([tenant_a, tenant_b]); session.flush()
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p1 = KbProduct(fingerprint="f1", normalized_description="AGUA MINERAL SEM GAS 500ML")
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p2 = KbProduct(fingerprint="f2", normalized_description="AGUA MINERAL GARRAFA 1L")
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p3 = KbProduct(fingerprint="f3", normalized_description="CAMISA MASCULINA MALHA ALGODAO")
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session.add_all([p1, p2, p3]); session.flush()
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for product, ncm_value, tenant in ((p1, "22021000", tenant_a), (p2, "22021000", tenant_b), (p3, "61051000", tenant_a)):
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session.add(KbKnowledge(kb_product_id=product.id, knowledge_key="validated_ncm", value=ncm_value, source="OFFICIAL_DATASET", confidence=1.0))
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session.add(KbObservation(kb_product_id=p1.id, tenant_id=tenant_a.id, company_key="A", erp_code="1", database_label="A", observed_fields={}))
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session.add(KbObservation(kb_product_id=p2.id, tenant_id=tenant_b.id, company_key="B", erp_code="2", database_label="B", observed_fields={}))
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session.add(KbObservation(kb_product_id=p3.id, tenant_id=tenant_a.id, company_key="A", erp_code="3", database_label="A", observed_fields={}))
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session.commit()
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return ncm.sha256
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def test_enriched_corpus_carries_ancestor_context():
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from central.app.ai.retrieval import EnrichedNcmCorpus
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engine = _engine()
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with Session(engine) as session:
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_seed(session)
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corpus = EnrichedNcmCorpus.build(session)
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doc = corpus.document("22021000")
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# chapter + position + subposition context enriches the very short legal text (stemmed)
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assert "BEBIDA" in doc and "AGUA" in doc
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assert "VESTUARIO" not in doc
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def test_hierarchical_retrieval_prefers_right_positions():
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from central.app.ai.retrieval import EnrichedNcmCorpus, hierarchical_candidates
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engine = _engine()
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with Session(engine) as session:
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_seed(session)
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corpus = EnrichedNcmCorpus.build(session)
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codes = [code for code, _, _ in hierarchical_candidates(corpus, "AGUA MINERAL", k=2)]
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assert codes[0] == "22021000"
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def test_kb_neighbor_bridge_maps_commercial_to_ncm():
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from central.app.ai.retrieval import KbNeighborIndex, kb_neighbor_candidates
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engine = _engine()
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with Session(engine) as session:
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_seed(session)
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index = KbNeighborIndex.build(session)
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neighbors = kb_neighbor_candidates(index, "AGUA MINERAL PET 500ML", k=5)
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assert neighbors and neighbors[0][0] == "22021000"
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# cross-tenant agreement is reported, never auto-majority
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assert neighbors[0][3] >= 2
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def test_hybrid_merges_official_and_kb_rankings():
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from central.app.ai.retrieval import EnrichedNcmCorpus, KbNeighborIndex, hybrid_candidates
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engine = _engine()
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with Session(engine) as session:
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_seed(session)
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corpus = EnrichedNcmCorpus.build(session)
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index = KbNeighborIndex.build(session)
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hybrid = hybrid_candidates(corpus, index, "CAMISA MASCULINA ALGODAO", k=2)
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assert hybrid[0][0] == "61051000"
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scores = hybrid_candidates(corpus, index, "AGUA MINERAL SEM GAS", k=2)
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assert scores[0][0] == "22021000"
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def test_lexicon_expands_abbreviations_supported_by_kb():
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from central.app.ai.retrieval import derive_abbreviation_lexicon, expand_stems
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corpus = {
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"REFRIGERANTE COCA 2L": "22021000",
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"REFRI GUARANA 1L": "22021000",
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"REFRI LARANJA 350ML": "22021000",
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"CAMISA POLO ALGODAO": "61051000",
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"CAM POLO MASC": "61051000",
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"CAM POLO AZUL": "61051000",
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"BOTA COURO MASC": "64039900",
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}
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lexicon = derive_abbreviation_lexicon(corpus, min_support=2)
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assert lexicon.get("REFRI") == "REFRIGERANTE"
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assert lexicon.get("CAM") == "CAMISA"
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expanded = expand_stems({"REFRI", "COLA"}, lexicon)
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assert "REFRIGERANTE" in expanded
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def test_commercial_tokens_strip_units_and_sizes():
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from central.app.ai.retrieval import commercial_tokens
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tokens = commercial_tokens("CREME TRATAMENTO NOVEX 1KG CX 12 COR AZUL TAM M REF 453")
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assert "NOVEX" in tokens and "CREME" in tokens and "TRATAMENTO" in tokens
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assert "1KG" not in tokens and "CX" not in tokens and "COR" not in tokens and "TAM" not in tokens
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assert "AZUL" not in tokens and "REF" not in tokens and "453" not in tokens
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assert "12" not in tokens
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