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>
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# app/models/memory/consolidation.py
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"""
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Memory Consolidation Log database model.
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Tracks memory consolidation jobs that transfer knowledge
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between memory tiers.
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"""
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from sqlalchemy import Column, DateTime, Enum, Index, Integer, Text
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from app.models.base import Base, TimestampMixin, UUIDMixin
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from .enums import ConsolidationStatus, ConsolidationType
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class MemoryConsolidationLog(Base, UUIDMixin, TimestampMixin):
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"""
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Memory consolidation job log.
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Tracks consolidation operations:
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- Working -> Episodic (session end)
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- Episodic -> Semantic (fact extraction)
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- Episodic -> Procedural (procedure learning)
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- Pruning (removing low-value memories)
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"""
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__tablename__ = "memory_consolidation_log"
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# Consolidation type
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consolidation_type: Column[ConsolidationType] = Column(
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Enum(ConsolidationType),
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nullable=False,
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index=True,
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)
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# Counts
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source_count = Column(Integer, nullable=False, default=0)
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result_count = Column(Integer, nullable=False, default=0)
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# Timing
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started_at = Column(DateTime(timezone=True), nullable=False)
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completed_at = Column(DateTime(timezone=True), nullable=True)
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# Status
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status: Column[ConsolidationStatus] = Column(
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Enum(ConsolidationStatus),
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nullable=False,
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default=ConsolidationStatus.PENDING,
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index=True,
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)
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# Error details if failed
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error = Column(Text, nullable=True)
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__table_args__ = (
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# Query patterns
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Index("ix_consolidation_type_status", "consolidation_type", "status"),
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Index("ix_consolidation_started", "started_at"),
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)
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@property
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def duration_seconds(self) -> float | None:
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"""Calculate duration of the consolidation job."""
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if self.completed_at is None or self.started_at is None:
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return None
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return (self.completed_at - self.started_at).total_seconds()
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def __repr__(self) -> str:
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return (
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f"<MemoryConsolidationLog {self.id} "
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f"type={self.consolidation_type.value} status={self.status.value}>"
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)
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