chore: capture semantic pipeline baseline
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"""AI semantic layer V1: fingerprint, KB, dedup, provider contract, budget guard, candidates."""
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import json
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from sqlalchemy import create_engine, select
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from sqlalchemy.orm import Session
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from central.app.models import Base, Dataset, NcmEntry, CestEntry, CestNcmEntry, TipiEntry
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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 _ncm_dataset(session, entries):
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dataset = Dataset(kind="NCM", provider="Siscomex", sha256="a" * 64, version_label="v", act="a", source_url="u", status="ACTIVE", record_count=len(entries))
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session.add(dataset); session.flush()
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for code, description in entries:
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session.add(NcmEntry(dataset_id=dataset.id, code=code, description=description, valid_from="", valid_to=""))
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session.flush()
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return dataset
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# ---------------- fingerprint / identity ----------------
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def test_fingerprint_is_stable_and_ignores_unstable_context():
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from central.app.ai.fingerprint import normalize_description, product_fingerprint, validate_gtin
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a = product_fingerprint("Coca-Cola 2LT", "COCA-COLA", "UN", "2")
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b = product_fingerprint("coca cola 2lt ", "coca-cola", "un", "2.0")
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assert a == b
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# fiscal/company context never participates in global identity
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c = product_fingerprint("Coca-Cola 2LT", "COCA-COLA", "UN", "2")
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assert a == c
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assert normalize_description("Água Mineral 500ml") == "AGUA MINERAL 500ML"
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assert validate_gtin("7891234567895") == "7891234567895" # valid GTIN-13
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assert validate_gtin("7891234567890") is None # bad checksum
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assert validate_gtin("123") is None
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# ---------------- knowledge base ----------------
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def test_kb_exact_fingerprint_hit_and_conflict_without_majority():
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from central.app.ai.kb import KnowledgeBase, EXACT, FINGERPRINT
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engine = _engine()
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with Session(engine) as session:
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kb = KnowledgeBase(session)
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p1 = kb.upsert_product(fingerprint="f1", gtin="7891234567895", normalized_description="AGUA MINERAL 500ML")
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# same GTIN arrives with a different fingerprint -> same product, exact hit
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p2, hit = kb.resolve(gtin="7891234567895", fingerprint="other")
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assert p2.id == p1.id and hit == EXACT
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# same fingerprint, no GTIN -> fingerprint hit
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p3, hit = kb.resolve(gtin=None, fingerprint="f2")
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assert p3 is None and hit is None
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p4 = kb.upsert_product(fingerprint="f2", normalized_description="OUTRO")
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_, hit = kb.resolve(gtin=None, fingerprint="f2")
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assert hit == FINGERPRINT
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kb.add_knowledge(p1.id, "candidate_ncm", "22011000", source="OFFICIAL_DATASET", confidence=1.0)
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kb.add_knowledge(p1.id, "candidate_ncm", "22019000", source="OFFICIAL_DATASET", confidence=1.0)
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current = kb.current_knowledge(p1.id)
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assert current["candidate_ncm"]["conflict"] is True # no majority auto-pick
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assert current["candidate_ncm"]["values"] == {"22011000", "22019000"}
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stats = kb.stats()
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assert stats["products"] == 2 and stats["gtin_products"] == 1 and stats["conflicts"] == 1
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def test_kb_aggregates_observations_across_tenants():
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from central.app.ai.kb import KnowledgeBase
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engine = _engine()
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with Session(engine) as session:
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kb = KnowledgeBase(session)
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p = kb.upsert_product(fingerprint="f1", normalized_description="REFRIGERANTE 2L")
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kb.record_observation(p.id, tenant_id=1, company_key="MMMODAS", erp_code="000001", database_label="MMMODAS", observed_fields={"ncm": "22021000"})
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kb.record_observation(p.id, tenant_id=2, company_key="UNIAO", erp_code="000009", database_label="UNIAO", observed_fields={"ncm": "22021000"})
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agg = kb.observation_summary(p.id)
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assert agg["observation_count"] == 2 and agg["tenant_count"] == 2
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assert agg["agreement"]["ncm"] == {"22021000": 2} and agg["conflicts"] == {}
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# ---------------- work item dedup ----------------
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def test_work_items_dedup_by_knowledge_needed():
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from central.app.ai.workitems import build_work_items
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questions = [
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{"QUESTION_ID": "Q1", "DATABASE": "MMMODAS", "COMPANY": "MMMODAS", "SCOPE": "NCM", "GROUP": "", "NCM": "33059000", "KEY": "ex_tipi", "CELLS": 10},
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{"QUESTION_ID": "Q2", "DATABASE": "UNIAO", "COMPANY": "UNIAO", "SCOPE": "NCM", "GROUP": "", "NCM": "33059000", "KEY": "ex_tipi", "CELLS": 5},
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{"QUESTION_ID": "Q3", "DATABASE": "RINCAO", "COMPANY": "RINCAO", "SCOPE": "GROUP", "GROUP": "000015", "KEY": "product_role", "CELLS": 40},
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{"QUESTION_ID": "Q4", "DATABASE": "CONSTRUCAO", "COMPANY": "CONSTRUCAO", "SCOPE": "PRODUCT", "GROUP": "", "NCM": "", "KEY": "ncm", "CELLS": 8, "QUESTION": "produto PRODUCT 000054", "DATABASE_CODE": "000054"},
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]
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items = build_work_items(questions, product_fingerprints={"CONSTRUCAO:000054": "fp-a"})
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# 4 raw questions -> 3 deduped work items (two ex_tipi questions for NCM 33059000 collapse)
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by_key = {i.dedup_key: i for i in items}
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assert len(items) == 3
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assert by_key["ex_tipi:33059000"].cells_covered == 15
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assert by_key["ex_tipi:33059000"].question_refs == ["Q1", "Q2"]
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assert by_key["product_role:RINCAO:000015"].cells_covered == 40
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assert by_key["invalid_ncm:fp-a"].cells_covered == 8
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# AI-ineligible keys never become work items
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assert all(i.task_type in ("INVALID_NCM", "CEST", "EX_TIPI", "PRODUCT_ROLE") for i in items)
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# ---------------- provider contract / structured output / budget ----------------
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def test_structured_output_schema_and_baseline_provider():
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from central.app.ai.provider import BaselineProvider, result_from_dict, SemanticOutputError
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provider = BaselineProvider()
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item = type("W", (), {"task_type": "INVALID_NCM", "dedup_key": "invalid_ncm:fp-a", "payload": {"description": "AGUA MINERAL NATURAL 500ML"}})()
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candidates = [("22011000", "AGUAS MINERAIS NATURAIS", 0.75), ("22019000", "AGUAS ... OUTRAS", 0.25)]
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result = provider.select(item, candidates, dataset_versions={"ncm": "sha-ncm"})
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assert result.task_type == "INVALID_NCM" and result.selected_candidate == "22011000"
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assert result.candidate_ids_seen == ["22011000", "22019000"]
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assert result.model == "baseline-deterministic-v1" and result.prompt_version == "ai-semantic-v1"
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assert result.dataset_versions and result.engine_version
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as_dict = result.to_dict()
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assert result_from_dict(as_dict).selected_candidate == "22011000"
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# schema fail-closed: a provider answer outside the candidate list is invalid
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try:
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result_from_dict({**as_dict, "selected_candidate": "99999999"})
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raise AssertionError("must reject")
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except SemanticOutputError:
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pass
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# no candidates at all -> NONE, never invented
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none_result = provider.select(item, [])
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assert none_result.selected_candidate == "NONE"
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def test_budget_guard_blocks_run_over_limit():
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from central.app.ai.provider import BudgetGuard
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guard = BudgetGuard(max_run_cost_usd=1.0, price_in_per_m=1.0, price_out_per_m=2.0)
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plan = guard.estimate(items_in_tokens=600_000, items_out_tokens=300_000)
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assert plan["estimated_cost"] > 0
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assert guard.authorize(plan) is False # (600k*1 + 300k*2)/1M = 1.2 > 1.0
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assert guard.authorize(guard.estimate(100_000, 50_000)) is True
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# ---------------- deterministic candidates ----------------
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def test_ncm_top_k_and_ex_tipi_candidates():
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from central.app.ai.candidates import ncm_top_k, ex_tipi_candidates_for_ncm, cest_candidates_for_ncm
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engine = _engine()
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with Session(engine) as session:
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_ncm_dataset(session, [("22011000", "AGUAS MINERAIS NATURAIS"), ("22019000", "AGUAS MINERAIS GASEIFICADAS OUTRAS"), ("08043000", "ABACAXIS FRESCOS")])
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top = ncm_top_k(session, "AGUA MINERAL NATURAL", k=2)
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assert [c[0] for c in top] == ["22011000", "22019000"]
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dataset = Dataset(kind="TIPI_IPI", provider="RFB", sha256="b" * 64, version_label="v", act="a", source_url="u", status="ACTIVE", record_count=2)
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session.add(dataset); session.flush()
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session.add(TipiEntry(dataset_id=dataset.id, ncm="33059000", ex=None, description="outros", ipi_rate=None, ipi_rate_text="NT", source_row=1))
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session.add(TipiEntry(dataset_id=dataset.id, ncm="33059000", ex="01", description="preparacoes para alisar", ipi_rate=None, ipi_rate_text="NT", source_row=2))
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cest_dataset = Dataset(kind="CEST", provider="CONFAZ", sha256="c" * 64, version_label="v", act="a", source_url="u", status="ACTIVE", record_count=1)
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session.add(cest_dataset); session.flush()
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cest = CestEntry(dataset_id=cest_dataset.id, code="03.999.99", description="x", annex="II")
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session.add(cest); session.flush()
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session.add(CestNcmEntry(cest_entry_id=cest.id, ncm_code="22011000"))
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session.commit()
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assert [e.ex for e in ex_tipi_candidates_for_ncm(session, "33059000")] == ["01"]
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assert [c.code for c in cest_candidates_for_ncm(session, "22011000")] == ["03.999.99"]
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def test_kb_persist_roundtrip_models():
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from central.app.models import KbProduct, KbKnowledge, KbObservation, TenantProductContext, AiWorkItem, AiRun
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engine = _engine()
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with Session(engine) as session:
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p = KbProduct(fingerprint="f", normalized_description="D")
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session.add(p); session.flush()
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session.add(KbKnowledge(kb_product_id=p.id, knowledge_key="candidate_ncm", value="22011000", source="OFFICIAL_DATASET", confidence=1.0))
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session.add(KbObservation(kb_product_id=p.id, tenant_id=1, company_key="C", erp_code="1", database_label="C", observed_fields={}))
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session.add(TenantProductContext(tenant_id=1, company_key="C", erp_code="1", kb_product_id=p.id))
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session.add(AiWorkItem(task_type="CEST", dedup_key="cest:1", payload={}, question_refs=[], cells_covered=1))
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session.add(AiRun(provider="baseline", model="m", prompt_version="v1", input_tokens=1, output_tokens=1, cost_usd=0.0, status="DONE", results=[]))
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session.commit()
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assert session.query(KbProduct).count() == 1 and session.query(AiWorkItem).count() == 1
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