Mnemex

""" Entity extraction for natural language activation. This module re-exports the CortexGraph entity extractor for use in activation detection. The extractor uses spaCy NER when available, with fallback to regex patterns for technology-specific entities. Entity types extracted: - Technology names (frameworks, languages, protocols, databases) - People (PERSON) - Organizations (ORG) - Products/tools (PRODUCT) - Locations (GPE) - Events (EVENT) - URLs and email addresses """ from cortexgraph.preprocessing.entity_extractor import EntityExtractor __all__ = ["EntityExtractor", "extract_entities"] def extract_entities(text: str, max_entities: int = 10) -> list[str]: """Extract named entities from text using default extractor. Convenience function for one-off entity extraction without creating an extractor instance. Args: text: Natural language text to analyze max_entities: Maximum entities to return (default: 10) Returns: List of entity strings (lowercase, deduplicated) Example: >>> entities = extract_entities("I prefer PostgreSQL for databases") >>> "postgresql" in entities True Note: This creates a new EntityExtractor each call. For repeated extractions, create an EntityExtractor instance and reuse it. """ extractor = EntityExtractor() return extractor.extract(text, max_entities=max_entities)