4f2833873c
* mental models * DRAFT: refactor entity observations * fix db patch * agentic * agentic * reflect agent * new style * more * fix ci * fix * fix
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
"""
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Pydantic models for mental models.
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"""
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from datetime import datetime, timezone
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from enum import Enum
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from pydantic import BaseModel, Field
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class MentalModelSubtype(str, Enum):
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"""Subtype of mental model - how it was created."""
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STRUCTURAL = "structural" # Derived from mission, created upfront
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EMERGENT = "emergent" # Discovered from data patterns
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LEARNED = "learned" # Formed through reflection
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PINNED = "pinned" # User-defined, persists across refreshes
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class MentalModel(BaseModel):
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"""
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A mental model representing synthesized understanding.
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Mental models are the agent's consolidated knowledge. Unlike raw facts,
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mental models provide:
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- A one-liner description for quick scanning/retrieval
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- A full summary for deep understanding
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- Links to related mental models
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"""
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id: str = Field(description="Unique identifier within the bank")
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bank_id: str = Field(description="Bank this mental model belongs to")
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subtype: MentalModelSubtype = Field(description="How this model was created")
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name: str = Field(description="Human-readable name")
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description: str = Field(description="One-liner for quick scanning and retrieval matching")
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summary: str | None = Field(default=None, description="Full synthesized understanding")
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# References
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entity_id: str | None = Field(default=None, description="Reference to entities table when type=entity")
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source_facts: list[str] = Field(default_factory=list, description="Fact IDs used to generate summary")
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links: list[str] = Field(default_factory=list, description="Related mental model IDs")
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# Tags for scoped visibility (similar to document tags)
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tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility filtering")
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# Timestamps
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last_updated: datetime | None = Field(default=None, description="When summary was last regenerated")
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created_at: datetime = Field(
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default_factory=lambda: datetime.now(timezone.utc), description="When this model was created"
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)
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class StructuralModelTemplate(BaseModel):
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"""
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A template for a structural mental model.
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Generated by LLM based on the bank's mission. Represents what any agent
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with this role would need to track.
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"""
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id: str = Field(default="", description="Existing model ID to keep, or empty for new models")
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name: str = Field(description="Human-readable name")
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description: str = Field(description="What this model should track")
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initial_probes: list[str] = Field(default_factory=list, description="Initial search queries to populate this model")
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class StructuralModelDerivationResponse(BaseModel):
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"""Response from LLM for structural model derivation."""
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templates: list[StructuralModelTemplate] = Field(description="Structural model templates derived from the mission")
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class EmergentCandidate(BaseModel):
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"""
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A candidate for promotion to emergent mental model.
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Detected through pattern analysis of facts.
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"""
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name: str = Field(description="Name of the detected pattern/entity")
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detection_method: str = Field(description="How this candidate was detected")
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mention_count: int = Field(default=0, description="How many times referenced")
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entity_id: str | None = Field(default=None, description="Entity ID if detected as entity")
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relevance_score: float = Field(default=0.0, description="Score from mission filter (0-1)")
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class ResearchResult(BaseModel):
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"""
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Result from the research endpoint.
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Contains the answer along with the mental models and facts used.
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"""
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answer: str = Field(description="The synthesized answer")
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mental_models_used: list[str] = Field(default_factory=list, description="IDs of mental models that contributed")
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facts_used: list[str] = Field(default_factory=list, description="Fact IDs that contributed")
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question_type: str | None = Field(default=None, description="Detected question type (WHO, WHAT, HOW, etc.)")
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