"""
Pydantic models for mental models.
"""

from datetime import datetime, timezone
from enum import Enum

from pydantic import BaseModel, Field


class MentalModelSubtype(str, Enum):
    """Subtype of mental model - how it was created."""

    STRUCTURAL = "structural"  # Derived from mission, created upfront
    EMERGENT = "emergent"  # Discovered from data patterns
    LEARNED = "learned"  # Formed through reflection
    PINNED = "pinned"  # User-defined topic, observations LLM-generated
    DIRECTIVE = "directive"  # User-defined hard rules, observations user-provided


class MentalModel(BaseModel):
    """
    A mental model representing synthesized understanding.

    Mental models are the agent's consolidated knowledge. Unlike raw facts,
    mental models provide:
    - A one-liner description for quick scanning/retrieval
    - A full summary for deep understanding
    - Links to related mental models
    """

    id: str = Field(description="Unique identifier within the bank")
    bank_id: str = Field(description="Bank this mental model belongs to")
    subtype: MentalModelSubtype = Field(description="How this model was created")
    name: str = Field(description="Human-readable name")
    description: str = Field(description="One-liner for quick scanning and retrieval matching")
    summary: str | None = Field(default=None, description="Full synthesized understanding")

    # References
    entity_id: str | None = Field(default=None, description="Reference to entities table when type=entity")
    source_facts: list[str] = Field(default_factory=list, description="Fact IDs used to generate summary")
    links: list[str] = Field(default_factory=list, description="Related mental model IDs")

    # Tags for scoped visibility (similar to document tags)
    tags: list[str] = Field(default_factory=list, description="Tags for scoped visibility filtering")

    # Timestamps
    last_updated: datetime | None = Field(default=None, description="When summary was last regenerated")
    created_at: datetime = Field(
        default_factory=lambda: datetime.now(timezone.utc), description="When this model was created"
    )


class StructuralModelTemplate(BaseModel):
    """
    A template for a structural mental model.

    Generated by LLM based on the bank's mission. Represents what any agent
    with this role would need to track.
    """

    id: str = Field(default="", description="Existing model ID to keep, or empty for new models")
    name: str = Field(description="Human-readable name")
    description: str = Field(description="What this model should track")
    initial_probes: list[str] = Field(default_factory=list, description="Initial search queries to populate this model")


class StructuralModelDerivationResponse(BaseModel):
    """Response from LLM for structural model derivation."""

    templates: list[StructuralModelTemplate] = Field(description="Structural model templates derived from the mission")


class EmergentCandidate(BaseModel):
    """
    A candidate for promotion to emergent mental model.

    Detected through pattern analysis of facts.
    """

    name: str = Field(description="Name of the detected pattern/entity")
    detection_method: str = Field(description="How this candidate was detected")
    mention_count: int = Field(default=0, description="How many times referenced")
    entity_id: str | None = Field(default=None, description="Entity ID if detected as entity")
    relevance_score: float = Field(default=0.0, description="Score from mission filter (0-1)")


class ResearchResult(BaseModel):
    """
    Result from the research endpoint.

    Contains the answer along with the mental models and facts used.
    """

    answer: str = Field(description="The synthesized answer")
    mental_models_used: list[str] = Field(default_factory=list, description="IDs of mental models that contributed")
    facts_used: list[str] = Field(default_factory=list, description="Fact IDs that contributed")
    question_type: str | None = Field(default=None, description="Detected question type (WHO, WHAT, HOW, etc.)")