65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
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"""
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Data models for entity classification (L2 typed entities).
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Each entity is assigned an Entity Type (what kind of thing it is) and a
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VSM System (which control layer it inhabits). Both assignments come with
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a one-sentence rationale from the LLM, stored alongside the classification.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Any, Dict, Optional
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#: Controlled vocabulary for entity types.
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ENTITY_TYPES = ["Element", "Process", "Relation", "Principle", "Institution"]
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#: Controlled vocabulary for VSM system assignments.
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VSM_SYSTEMS = ["S1", "S2", "S3", "S3*", "S4", "S5"]
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@dataclass
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class EntityClassification:
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"""L2 classification for a single entity."""
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entity_slug: str
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entity_type: str # one of ENTITY_TYPES
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vsm_system: str # one of VSM_SYSTEMS
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type_rationale: str = "" # one sentence
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vsm_rationale: str = "" # one sentence
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classified_by: str = "" # model name
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classified_at: Optional[datetime] = None
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def to_dict(self) -> Dict[str, Any]:
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d: Dict[str, Any] = {
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"entity_slug": self.entity_slug,
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"entity_type": self.entity_type,
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"vsm_system": self.vsm_system,
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}
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if self.type_rationale:
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d["type_rationale"] = self.type_rationale
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if self.vsm_rationale:
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d["vsm_rationale"] = self.vsm_rationale
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if self.classified_by:
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d["classified_by"] = self.classified_by
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if self.classified_at is not None:
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d["classified_at"] = self.classified_at.isoformat()
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return d
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "EntityClassification":
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classified_at: Optional[datetime] = None
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if "classified_at" in data:
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classified_at = datetime.fromisoformat(data["classified_at"])
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return cls(
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entity_slug=data["entity_slug"],
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entity_type=data["entity_type"],
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vsm_system=data["vsm_system"],
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type_rationale=data.get("type_rationale", ""),
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vsm_rationale=data.get("vsm_rationale", ""),
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classified_by=data.get("classified_by", ""),
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classified_at=classified_at,
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)
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