egutierrez
63a9cb5273
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
"""Genera la seccion del system prompt que describe los entity types disponibles para extraccion."""
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def build_entity_schema_prompt(entity_presets: list[dict]) -> str:
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"""Genera texto legible para el LLM describiendo los entity types disponibles.
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Formatea los presets del registry en una seccion del system prompt que indica
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al LLM que tipos de entidades puede extraer y que atributos tiene cada uno.
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Args:
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entity_presets: Lista de presets con campos 'label', 'type_ref' y
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opcionalmente 'metadata_fields'. Ejemplo:
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[{"type_ref": "osint_person_go_cybersecurity",
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"label": "Person",
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"metadata_fields": ["full_name", "alias"]}]
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Returns:
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String formateado con la seccion del prompt. Retorna string vacio si
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la lista de presets esta vacia.
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"""
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if not entity_presets:
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return ""
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lines = ["Entity types available for extraction:", ""]
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for i, preset in enumerate(entity_presets, start=1):
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label = preset.get("label", "Unknown")
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type_ref = preset.get("type_ref", "")
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metadata_fields = preset.get("metadata_fields", [])
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lines.append(f"{i}. {label} (type_ref: {type_ref})")
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if metadata_fields:
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attrs = ", ".join(metadata_fields)
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lines.append(f" Attributes: {attrs}")
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lines.append("")
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# Remove trailing blank line
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if lines and lines[-1] == "":
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lines.pop()
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return "\n".join(lines)
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