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>
126 lines
3.6 KiB
Python
126 lines
3.6 KiB
Python
# app/models/memory/episode.py
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"""
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Episode database model.
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Stores experiential memories - records of past task executions
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with context, actions, outcomes, and lessons learned.
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"""
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from sqlalchemy import (
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BigInteger,
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Column,
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DateTime,
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Enum,
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Float,
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ForeignKey,
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Index,
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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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JSONB,
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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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from .enums import EpisodeOutcome
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# Import pgvector type - will be available after migration enables extension
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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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# Fallback for environments without pgvector
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Vector = None
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class Episode(Base, UUIDMixin, TimestampMixin):
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"""
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Episodic memory model.
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Records experiential memories from agent task execution:
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- What task was performed
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- What actions were taken
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- What was the outcome
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- What lessons were learned
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"""
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__tablename__ = "episodes"
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# Foreign keys
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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=False,
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index=True,
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)
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agent_instance_id = Column(
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PGUUID(as_uuid=True),
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ForeignKey("agent_instances.id", ondelete="SET NULL"),
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nullable=True,
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index=True,
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)
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agent_type_id = Column(
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PGUUID(as_uuid=True),
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ForeignKey("agent_types.id", ondelete="SET NULL"),
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nullable=True,
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index=True,
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)
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# Session reference
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session_id = Column(String(255), nullable=False, index=True)
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# Task information
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task_type = Column(String(100), nullable=False, index=True)
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task_description = Column(Text, nullable=False)
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# Actions taken (list of action dictionaries)
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actions = Column(JSONB, default=list, nullable=False)
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# Context summary
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context_summary = Column(Text, nullable=False)
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# Outcome
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outcome: Column[EpisodeOutcome] = Column(
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Enum(EpisodeOutcome),
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nullable=False,
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index=True,
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)
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outcome_details = Column(Text, nullable=True)
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# Metrics
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duration_seconds = Column(Float, nullable=False, default=0.0)
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tokens_used = Column(BigInteger, nullable=False, default=0)
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# Learning
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lessons_learned = Column(JSONB, default=list, nullable=False)
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importance_score = Column(Float, nullable=False, default=0.5, index=True)
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# Vector embedding for semantic search
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# Using 1536 dimensions for OpenAI text-embedding-3-small
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embedding = Column(Vector(1536) if Vector else Text, nullable=True)
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# When the episode occurred
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occurred_at = Column(DateTime(timezone=True), nullable=False, index=True)
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# Relationships
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project = relationship("Project", foreign_keys=[project_id])
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agent_instance = relationship("AgentInstance", foreign_keys=[agent_instance_id])
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agent_type = relationship("AgentType", foreign_keys=[agent_type_id])
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__table_args__ = (
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# Primary query patterns
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Index("ix_episodes_project_task", "project_id", "task_type"),
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Index("ix_episodes_project_outcome", "project_id", "outcome"),
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Index("ix_episodes_agent_task", "agent_instance_id", "task_type"),
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Index("ix_episodes_project_time", "project_id", "occurred_at"),
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# For importance-based pruning
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Index("ix_episodes_importance_time", "importance_score", "occurred_at"),
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)
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def __repr__(self) -> str:
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return f"<Episode {self.id} task={self.task_type} outcome={self.outcome.value}>"
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