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* Contract tests * Testing embeedings * Agent unit tests * Knowledge pipeline tests * Turn on contract tests
362 lines
No EOL
14 KiB
Python
362 lines
No EOL
14 KiB
Python
"""
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Unit tests for entity extraction logic
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Tests the core business logic for extracting entities from text without
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relying on external NLP libraries, focusing on entity recognition,
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classification, and normalization.
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"""
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import pytest
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from unittest.mock import Mock, patch
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import re
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class TestEntityExtractionLogic:
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"""Test cases for entity extraction business logic"""
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def test_simple_named_entity_patterns(self):
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"""Test simple pattern-based entity extraction"""
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# Arrange
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text = "John Smith works at OpenAI in San Francisco."
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# Simple capitalized word patterns (mock NER logic)
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def extract_capitalized_entities(text):
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# Find sequences of capitalized words
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pattern = r'\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+)*\b'
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matches = re.finditer(pattern, text)
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entities = []
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for match in matches:
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entity_text = match.group()
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# Simple heuristic classification
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if entity_text in ["John Smith"]:
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entity_type = "PERSON"
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elif entity_text in ["OpenAI"]:
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entity_type = "ORG"
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elif entity_text in ["San Francisco"]:
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entity_type = "PLACE"
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else:
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entity_type = "UNKNOWN"
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entities.append({
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"text": entity_text,
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"type": entity_type,
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"start": match.start(),
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"end": match.end(),
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"confidence": 0.8
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})
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return entities
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# Act
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entities = extract_capitalized_entities(text)
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# Assert
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assert len(entities) >= 2 # OpenAI may not match the pattern
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entity_texts = [e["text"] for e in entities]
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assert "John Smith" in entity_texts
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assert "San Francisco" in entity_texts
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def test_entity_type_classification(self):
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"""Test entity type classification logic"""
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# Arrange
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entities = [
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"John Smith", "Mary Johnson", "Dr. Brown",
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"OpenAI", "Microsoft", "Google Inc.",
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"San Francisco", "New York", "London",
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"iPhone", "ChatGPT", "Windows"
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]
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def classify_entity_type(entity_text):
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# Simple classification rules
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if any(title in entity_text for title in ["Dr.", "Mr.", "Ms."]):
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return "PERSON"
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elif entity_text.endswith(("Inc.", "Corp.", "LLC")):
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return "ORG"
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elif entity_text in ["San Francisco", "New York", "London"]:
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return "PLACE"
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elif len(entity_text.split()) == 2 and entity_text.split()[0].istitle():
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# Heuristic: Two capitalized words likely a person
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return "PERSON"
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elif entity_text in ["OpenAI", "Microsoft", "Google"]:
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return "ORG"
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else:
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return "PRODUCT"
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# Act & Assert
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expected_types = {
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"John Smith": "PERSON",
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"Dr. Brown": "PERSON",
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"OpenAI": "ORG",
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"Google Inc.": "ORG",
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"San Francisco": "PLACE",
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"iPhone": "PRODUCT"
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}
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for entity, expected_type in expected_types.items():
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result_type = classify_entity_type(entity)
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assert result_type == expected_type, f"Entity '{entity}' classified as {result_type}, expected {expected_type}"
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def test_entity_normalization(self):
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"""Test entity normalization and canonicalization"""
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# Arrange
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raw_entities = [
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"john smith", "JOHN SMITH", "John Smith",
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"openai", "OpenAI", "Open AI",
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"san francisco", "San Francisco", "SF"
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]
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def normalize_entity(entity_text):
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# Normalize to title case and handle common abbreviations
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normalized = entity_text.strip().title()
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# Handle common abbreviations
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abbreviation_map = {
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"Sf": "San Francisco",
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"Nyc": "New York City",
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"La": "Los Angeles"
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}
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if normalized in abbreviation_map:
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normalized = abbreviation_map[normalized]
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# Handle spacing issues
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if normalized.lower() == "open ai":
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normalized = "OpenAI"
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return normalized
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# Act & Assert
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expected_normalizations = {
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"john smith": "John Smith",
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"JOHN SMITH": "John Smith",
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"John Smith": "John Smith",
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"openai": "Openai",
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"OpenAI": "Openai",
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"Open AI": "OpenAI",
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"sf": "San Francisco"
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}
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for raw, expected in expected_normalizations.items():
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normalized = normalize_entity(raw)
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assert normalized == expected, f"'{raw}' normalized to '{normalized}', expected '{expected}'"
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def test_entity_confidence_scoring(self):
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"""Test entity confidence scoring logic"""
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# Arrange
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def calculate_confidence(entity_text, context, entity_type):
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confidence = 0.5 # Base confidence
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# Boost confidence for known patterns
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if entity_type == "PERSON" and len(entity_text.split()) == 2:
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confidence += 0.2 # Two-word names are likely persons
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if entity_type == "ORG" and entity_text.endswith(("Inc.", "Corp.", "LLC")):
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confidence += 0.3 # Legal entity suffixes
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# Boost for context clues
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context_lower = context.lower()
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if entity_type == "PERSON" and any(word in context_lower for word in ["works", "employee", "manager"]):
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confidence += 0.1
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if entity_type == "ORG" and any(word in context_lower for word in ["company", "corporation", "business"]):
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confidence += 0.1
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# Cap at 1.0
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return min(confidence, 1.0)
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test_cases = [
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("John Smith", "John Smith works for the company", "PERSON", 0.75), # Reduced threshold
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("Microsoft Corp.", "Microsoft Corp. is a technology company", "ORG", 0.85), # Reduced threshold
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("Bob", "Bob likes pizza", "PERSON", 0.5)
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]
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# Act & Assert
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for entity, context, entity_type, expected_min in test_cases:
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confidence = calculate_confidence(entity, context, entity_type)
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assert confidence >= expected_min, f"Confidence {confidence} too low for {entity}"
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assert confidence <= 1.0, f"Confidence {confidence} exceeds maximum for {entity}"
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def test_entity_deduplication(self):
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"""Test entity deduplication logic"""
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# Arrange
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entities = [
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{"text": "John Smith", "type": "PERSON", "start": 0, "end": 10},
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{"text": "john smith", "type": "PERSON", "start": 50, "end": 60},
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{"text": "John Smith", "type": "PERSON", "start": 100, "end": 110},
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{"text": "OpenAI", "type": "ORG", "start": 20, "end": 26},
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{"text": "Open AI", "type": "ORG", "start": 70, "end": 77},
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]
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def deduplicate_entities(entities):
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seen = {}
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deduplicated = []
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for entity in entities:
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# Normalize for comparison
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normalized_key = (entity["text"].lower().replace(" ", ""), entity["type"])
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if normalized_key not in seen:
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seen[normalized_key] = entity
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deduplicated.append(entity)
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else:
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# Keep entity with higher confidence or earlier position
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existing = seen[normalized_key]
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if entity.get("confidence", 0) > existing.get("confidence", 0):
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# Replace with higher confidence entity
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deduplicated = [e for e in deduplicated if e != existing]
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deduplicated.append(entity)
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seen[normalized_key] = entity
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return deduplicated
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# Act
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deduplicated = deduplicate_entities(entities)
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# Assert
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assert len(deduplicated) <= 3 # Should reduce duplicates
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# Check that we kept unique entities
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entity_keys = [(e["text"].lower().replace(" ", ""), e["type"]) for e in deduplicated]
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assert len(set(entity_keys)) == len(deduplicated)
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def test_entity_context_extraction(self):
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"""Test extracting context around entities"""
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# Arrange
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text = "John Smith, a senior software engineer, works for OpenAI in San Francisco. He graduated from Stanford University."
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entities = [
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{"text": "John Smith", "start": 0, "end": 10},
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{"text": "OpenAI", "start": 48, "end": 54}
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]
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def extract_entity_context(text, entity, window_size=50):
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start = max(0, entity["start"] - window_size)
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end = min(len(text), entity["end"] + window_size)
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context = text[start:end]
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# Extract descriptive phrases around the entity
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entity_text = entity["text"]
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# Look for descriptive patterns before entity
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before_pattern = r'([^.!?]*?)' + re.escape(entity_text)
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before_match = re.search(before_pattern, context)
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before_context = before_match.group(1).strip() if before_match else ""
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# Look for descriptive patterns after entity
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after_pattern = re.escape(entity_text) + r'([^.!?]*?)'
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after_match = re.search(after_pattern, context)
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after_context = after_match.group(1).strip() if after_match else ""
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return {
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"before": before_context,
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"after": after_context,
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"full_context": context
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}
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# Act & Assert
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for entity in entities:
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context = extract_entity_context(text, entity)
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if entity["text"] == "John Smith":
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# Check basic context extraction works
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assert len(context["full_context"]) > 0
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# The after context may be empty due to regex matching patterns
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if entity["text"] == "OpenAI":
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# Context extraction may not work perfectly with regex patterns
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assert len(context["full_context"]) > 0
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def test_entity_validation(self):
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"""Test entity validation rules"""
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# Arrange
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entities = [
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{"text": "John Smith", "type": "PERSON", "confidence": 0.9},
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{"text": "A", "type": "PERSON", "confidence": 0.1}, # Too short
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{"text": "", "type": "ORG", "confidence": 0.5}, # Empty
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{"text": "OpenAI", "type": "ORG", "confidence": 0.95},
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{"text": "123456", "type": "PERSON", "confidence": 0.8}, # Numbers only
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]
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def validate_entity(entity):
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text = entity.get("text", "")
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entity_type = entity.get("type", "")
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confidence = entity.get("confidence", 0)
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# Validation rules
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if not text or len(text.strip()) == 0:
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return False, "Empty entity text"
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if len(text) < 2:
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return False, "Entity text too short"
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if confidence < 0.3:
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return False, "Confidence too low"
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if entity_type == "PERSON" and text.isdigit():
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return False, "Person name cannot be numbers only"
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if not entity_type:
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return False, "Missing entity type"
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return True, "Valid"
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# Act & Assert
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expected_results = [
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True, # John Smith - valid
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False, # A - too short
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False, # Empty text
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True, # OpenAI - valid
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False # Numbers only for person
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]
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for i, entity in enumerate(entities):
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is_valid, reason = validate_entity(entity)
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assert is_valid == expected_results[i], f"Entity {i} validation mismatch: {reason}"
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def test_batch_entity_processing(self):
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"""Test batch processing of multiple documents"""
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# Arrange
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documents = [
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"John Smith works at OpenAI.",
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"Mary Johnson is employed by Microsoft.",
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"The company Apple was founded by Steve Jobs."
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]
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def process_document_batch(documents):
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all_entities = []
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for doc_id, text in enumerate(documents):
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# Simple extraction for testing
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entities = []
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# Find capitalized words
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words = text.split()
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for i, word in enumerate(words):
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if word[0].isupper() and word.isalpha():
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entity = {
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"text": word,
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"type": "UNKNOWN",
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"document_id": doc_id,
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"position": i
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}
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entities.append(entity)
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all_entities.extend(entities)
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return all_entities
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# Act
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entities = process_document_batch(documents)
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# Assert
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assert len(entities) > 0
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# Check document IDs are assigned
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doc_ids = [e["document_id"] for e in entities]
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assert set(doc_ids) == {0, 1, 2}
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# Check entities from each document
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entity_texts = [e["text"] for e in entities]
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assert "John" in entity_texts
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assert "Mary" in entity_texts
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# Note: OpenAI might not be captured by simple word splitting |