222 lines
8.6 KiB
Python
222 lines
8.6 KiB
Python
"""
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Core response models for Hindsight memory system.
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These models define the structure of data returned by the core MemoryEngine class.
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API response models should be kept separate and convert from these core models to maintain
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API stability even if internal models change.
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"""
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from typing import Optional, List, Dict, Any
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from pydantic import BaseModel, Field, ConfigDict
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class PersonalityTraits(BaseModel):
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"""
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Personality traits for a bank using the Big Five model.
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All traits are scored 0.0-1.0 where higher values indicate stronger presence of the trait.
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"""
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openness: float = Field(description="Openness to experience (0.0-1.0)")
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conscientiousness: float = Field(description="Conscientiousness and organization (0.0-1.0)")
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extraversion: float = Field(description="Extraversion and sociability (0.0-1.0)")
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agreeableness: float = Field(description="Agreeableness and cooperation (0.0-1.0)")
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neuroticism: float = Field(description="Emotional sensitivity and neuroticism (0.0-1.0)")
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bias_strength: float = Field(description="How strongly personality influences thinking (0.0-1.0)")
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"openness": 0.8,
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"conscientiousness": 0.6,
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"extraversion": 0.4,
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"agreeableness": 0.7,
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"neuroticism": 0.3,
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"bias_strength": 0.5
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}
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})
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class MemoryFact(BaseModel):
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"""
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A single memory fact returned by search or think operations.
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This represents a unit of information stored in the memory system,
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including both the content and metadata.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"id": "123e4567-e89b-12d3-a456-426614174000",
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"text": "Alice works at Google on the AI team",
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"fact_type": "world",
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"entities": ["Alice", "Google"],
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"context": "work info",
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"occurred_start": "2024-01-15T10:30:00Z",
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"occurred_end": "2024-01-15T10:30:00Z",
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"mentioned_at": "2024-01-15T10:30:00Z",
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"document_id": "session_abc123",
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"metadata": {"source": "slack"},
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"chunk_id": "bank123_session_abc123_0",
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"activation": 0.95
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}
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})
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id: str = Field(description="Unique identifier for the memory fact")
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text: str = Field(description="The actual text content of the memory")
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fact_type: str = Field(description="Type of fact: 'world', 'bank', 'opinion', or 'observation'")
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entities: Optional[List[str]] = Field(None, description="Entity names mentioned in this fact")
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context: Optional[str] = Field(None, description="Additional context for the memory")
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occurred_start: Optional[str] = Field(None, description="ISO format date when the event started occurring")
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occurred_end: Optional[str] = Field(None, description="ISO format date when the event ended occurring")
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mentioned_at: Optional[str] = Field(None, description="ISO format date when the fact was mentioned/learned")
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document_id: Optional[str] = Field(None, description="ID of the document this memory belongs to")
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metadata: Optional[Dict[str, str]] = Field(None, description="User-defined metadata")
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chunk_id: Optional[str] = Field(None, description="ID of the chunk this fact was extracted from (format: bank_id_document_id_chunk_index)")
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# Internal metrics (used by system but may not be exposed in API)
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activation: Optional[float] = Field(None, description="Internal activation score")
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class ChunkInfo(BaseModel):
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"""Information about a chunk."""
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chunk_text: str = Field(description="The raw chunk text")
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chunk_index: int = Field(description="Index of the chunk within the document")
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truncated: bool = Field(default=False, description="Whether the chunk was truncated due to token limits")
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class RecallResult(BaseModel):
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"""
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Result from a recall operation.
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Contains a list of matching memory facts and optional trace information
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for debugging and transparency.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"results": [
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{
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"id": "123e4567-e89b-12d3-a456-426614174000",
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"text": "Alice works at Google on the AI team",
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"fact_type": "world",
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"context": "work info",
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"occurred_start": "2024-01-15T10:30:00Z",
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"occurred_end": "2024-01-15T10:30:00Z",
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"activation": 0.95
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}
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],
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"trace": {
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"query": "What did Alice say about machine learning?",
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"num_results": 1
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}
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}
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})
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results: List[MemoryFact] = Field(description="List of memory facts matching the query")
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trace: Optional[Dict[str, Any]] = Field(None, description="Trace information for debugging")
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entities: Optional[Dict[str, "EntityState"]] = Field(
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None,
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description="Entity states for entities mentioned in results (keyed by canonical name)"
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)
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chunks: Optional[Dict[str, ChunkInfo]] = Field(
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None,
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description="Chunks for facts, keyed by '{document_id}_{chunk_index}'"
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)
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class ReflectResult(BaseModel):
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"""
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Result from a reflect operation.
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Contains the formulated answer, the facts it was based on (organized by type),
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and any new opinions that were formed during the reflection process.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"text": "Based on my knowledge, machine learning is being actively used in healthcare...",
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"based_on": {
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"world": [
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{
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"id": "123e4567-e89b-12d3-a456-426614174000",
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"text": "Machine learning is used in medical diagnosis",
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"fact_type": "world",
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"context": "healthcare",
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"occurred_start": "2024-01-15T10:30:00Z",
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"occurred_end": "2024-01-15T10:30:00Z"
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}
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],
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"agent": [],
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"opinion": []
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},
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"new_opinions": [
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"Machine learning has great potential in healthcare"
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]
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}
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})
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text: str = Field(description="The formulated answer text")
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based_on: Dict[str, List[MemoryFact]] = Field(
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description="Facts used to formulate the answer, organized by type (world, agent, opinion)"
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)
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new_opinions: List[str] = Field(
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default_factory=list,
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description="List of newly formed opinions during reflection"
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)
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class Opinion(BaseModel):
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"""
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An opinion with confidence score.
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Opinions represent the bank's formed perspectives on topics,
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with a confidence level indicating strength of belief.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"text": "Machine learning has great potential in healthcare",
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"confidence": 0.85
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}
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})
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text: str = Field(description="The opinion text")
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confidence: float = Field(description="Confidence score between 0.0 and 1.0")
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class EntityObservation(BaseModel):
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"""
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An observation about an entity.
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Observations are objective facts synthesized from multiple memory facts
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about an entity, without personality influence.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"text": "John is detail-oriented and works at Google",
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"mentioned_at": "2024-01-15T10:30:00Z"
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}
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})
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text: str = Field(description="The observation text")
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mentioned_at: Optional[str] = Field(None, description="ISO format date when this observation was created")
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class EntityState(BaseModel):
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"""
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Current mental model of an entity.
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Contains observations synthesized from facts about the entity.
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"""
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model_config = ConfigDict(json_schema_extra={
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"example": {
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"entity_id": "123e4567-e89b-12d3-a456-426614174000",
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"canonical_name": "John",
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"observations": [
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{"text": "John is detail-oriented", "mentioned_at": "2024-01-15T10:30:00Z"},
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{"text": "John works at Google on the AI team", "mentioned_at": "2024-01-14T09:00:00Z"}
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]
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}
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})
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entity_id: str = Field(description="Unique identifier for the entity")
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canonical_name: str = Field(description="Canonical name of the entity")
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observations: List[EntityObservation] = Field(
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default_factory=list,
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description="List of observations about this entity"
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)
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