115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
"""Unit tests for ECP models, schema validation, and structured error handling."""
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import pytest
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from src.models import (
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ECPSnapshot,
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RelatedEntity,
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ClassificationResult,
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ClassificationError,
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DecisionCategory,
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ErrorCode,
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)
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from src.parser import strip_markdown, extract_sentences, extract_evidence_snippets
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def test_ecp_snapshot_valid():
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data = {
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"target_entity_id": "ent_123",
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"target_name": "Petrobras",
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"aliases": ["Petróleo Brasileiro S.A.", "Petrobras"],
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"domain": "Oil & Gas",
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"anchors": ["pré-sal", "refinaria", "combustíveis"],
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"negative_anchors": ["petrobras posto pirata"],
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"graph_version": "1.0.0",
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"related_entities": [
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{
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"entity_id": "ent_456",
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"name": "Transpetro",
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"relation_type": "SUBSIDIARY_OF",
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"weight": 0.9,
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"aliases": ["Transpetro Logística"],
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"scope": "logistics",
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"confidence": 0.95
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}
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]
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}
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snapshot = ECPSnapshot.from_dict(data)
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assert snapshot.target_entity_id == "ent_123"
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assert snapshot.target_name == "Petrobras"
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assert len(snapshot.aliases) == 2
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assert len(snapshot.related_entities) == 1
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assert snapshot.related_entities[0].name == "Transpetro"
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assert snapshot.related_entities[0].weight == 0.9
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def test_ecp_snapshot_defaults():
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data = {
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"target_entity_id": "ent_123",
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"target_name": "Petrobras",
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"aliases": ["Petrobras"],
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"domain": "Oil & Gas",
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"anchors": ["energia"]
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}
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snapshot = ECPSnapshot.from_dict(data)
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assert snapshot.negative_anchors == []
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assert snapshot.graph_version == "1.0.0"
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assert snapshot.related_entities == []
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def test_ecp_snapshot_missing_required():
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data = {
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"target_entity_id": "ent_123",
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"aliases": ["Petrobras"],
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"domain": "Oil & Gas",
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"anchors": ["energia"]
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}
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with pytest.raises(ValueError, match="Missing required field"):
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ECPSnapshot.from_dict(data)
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def test_classification_result_serialization():
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res = ClassificationResult(
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decision=DecisionCategory.DIRECT_INHERENT,
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is_inherent=True,
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confidence=0.95,
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detected_language="pt",
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matched_anchors=["Petrobras"],
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evidence=["Petrobras anunciou investimentos no pré-sal."],
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rationale="Match forte da entidade alvo.",
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)
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d = res.to_dict()
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assert d["decision"] == "DIRECT_INHERENT"
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assert d["is_inherent"] is True
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assert d["confidence"] == 0.95
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assert d["detected_language"] == "pt"
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assert "Petrobras" in d["matched_anchors"]
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def test_classification_error_serialization():
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err = ClassificationError(
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error_code=ErrorCode.INVALID_ECP_JSON,
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message="Malformed JSON syntax",
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details={"path": "snapshot.json"}
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)
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d = err.to_dict()
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assert d["error_code"] == "invalid_ecp_json"
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assert d["message"] == "Malformed JSON syntax"
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assert d["details"]["path"] == "snapshot.json"
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def test_parser_strip_markdown():
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md = "# Title\n\nThis is **bold** text and [link](https://example.com).\n- item 1\n- item 2"
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plain = strip_markdown(md)
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assert "Title" in plain
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assert "bold text" in plain
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assert "link" in plain
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assert "[" not in plain
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assert "*" not in plain
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def test_extract_evidence_snippets():
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md = "O pré-sal brasileiro é uma das maiores reservas de petróleo. A Petrobras lidera a exploração técnica."
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snippets = extract_evidence_snippets(md, ["Petrobras"])
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assert len(snippets) > 0
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assert "Petrobras lidera" in snippets[0]
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