forked from cardosofelipe/fast-next-template
Felipe Cardoso
c9d8c0835c
Add SQLAlchemy models for the Agent Memory System: - WorkingMemory: Key-value storage with TTL for active sessions - Episode: Experiential memories from task executions - Fact: Semantic knowledge triples with confidence scores - Procedure: Learned skills and procedures with success tracking - MemoryConsolidationLog: Tracks consolidation jobs between memory tiers Create enums for memory system: - ScopeType: global, project, agent_type, agent_instance, session - EpisodeOutcome: success, failure, partial - ConsolidationType: working_to_episodic, episodic_to_semantic, etc. - ConsolidationStatus: pending, running, completed, failed Add Alembic migration (0005) for all memory tables with: - Foreign key relationships to projects, agent_instances, agent_types - Comprehensive indexes for query patterns - Unique constraints for key lookups and triple uniqueness - Vector embedding column placeholders (Text fallback until pgvector enabled) Fix timezone-naive datetime.now() in types.py TaskState (review feedback) Includes 30 unit tests for models and enums. Closes #88 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
104 lines
3.0 KiB
Python
104 lines
3.0 KiB
Python
# app/models/memory/fact.py
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"""
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Fact database model.
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Stores semantic memories - learned facts in subject-predicate-object
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triple format with confidence scores and source tracking.
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"""
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from sqlalchemy import (
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Column,
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DateTime,
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Float,
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ForeignKey,
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Index,
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Integer,
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String,
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Text,
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)
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from sqlalchemy.dialects.postgresql import (
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ARRAY,
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UUID as PGUUID,
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)
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from sqlalchemy.orm import relationship
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from app.models.base import Base, TimestampMixin, UUIDMixin
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# Import pgvector type
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try:
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from pgvector.sqlalchemy import Vector # type: ignore[import-not-found]
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except ImportError:
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Vector = None
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class Fact(Base, UUIDMixin, TimestampMixin):
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"""
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Semantic memory model.
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Stores learned facts as subject-predicate-object triples:
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- "FastAPI" - "uses" - "Starlette framework"
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- "Project Alpha" - "requires" - "OAuth authentication"
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Facts have confidence scores that decay over time and can be
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reinforced when the same fact is learned again.
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"""
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__tablename__ = "facts"
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# Scoping: project_id is NULL for global facts
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project_id = Column(
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PGUUID(as_uuid=True),
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ForeignKey("projects.id", ondelete="CASCADE"),
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nullable=True,
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index=True,
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)
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# Triple format
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subject = Column(String(500), nullable=False, index=True)
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predicate = Column(String(255), nullable=False, index=True)
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object = Column(Text, nullable=False)
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# Confidence score (0.0 to 1.0)
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confidence = Column(Float, nullable=False, default=0.8, index=True)
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# Source tracking: which episodes contributed to this fact
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source_episode_ids: Column[list] = Column(
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ARRAY(PGUUID(as_uuid=True)), default=list, nullable=False
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)
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# Learning history
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first_learned = Column(DateTime(timezone=True), nullable=False)
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last_reinforced = Column(DateTime(timezone=True), nullable=False)
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reinforcement_count = Column(Integer, nullable=False, default=1)
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# Vector embedding for semantic search
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embedding = Column(Vector(1536) if Vector else Text, nullable=True)
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# Relationships
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project = relationship("Project", foreign_keys=[project_id])
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__table_args__ = (
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# Unique constraint on triple within project scope
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Index(
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"ix_facts_unique_triple",
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"project_id",
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"subject",
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"predicate",
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"object",
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unique=True,
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postgresql_where="project_id IS NOT NULL",
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),
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# Query patterns
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Index("ix_facts_subject_predicate", "subject", "predicate"),
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Index("ix_facts_project_subject", "project_id", "subject"),
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Index("ix_facts_confidence_time", "confidence", "last_reinforced"),
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# For finding facts by entity (subject or object)
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Index("ix_facts_subject", "subject"),
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)
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def __repr__(self) -> str:
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return (
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f"<Fact {self.id} '{self.subject}' - '{self.predicate}' - "
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f"'{self.object[:50]}...' conf={self.confidence:.2f}>"
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)
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