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>
116 lines
3.1 KiB
Python
116 lines
3.1 KiB
Python
# app/models/memory/procedure.py
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"""
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Procedure database model.
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Stores procedural memories - learned skills and procedures
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derived from successful task execution patterns.
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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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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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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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# 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 Procedure(Base, UUIDMixin, TimestampMixin):
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"""
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Procedural memory model.
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Stores learned procedures (skills) extracted from successful
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task execution patterns:
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- Name and trigger pattern for matching
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- Step-by-step actions
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- Success/failure tracking
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"""
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__tablename__ = "procedures"
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# Scoping
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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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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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# Procedure identification
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name = Column(String(255), nullable=False, index=True)
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trigger_pattern = Column(Text, nullable=False)
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# Steps as JSON array of step objects
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# Each step: {order, action, parameters, expected_outcome, fallback_action}
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steps = Column(JSONB, default=list, nullable=False)
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# Success tracking
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success_count = Column(Integer, nullable=False, default=0)
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failure_count = Column(Integer, nullable=False, default=0)
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# Usage tracking
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last_used = Column(DateTime(timezone=True), nullable=True, index=True)
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# Vector embedding for semantic matching
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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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agent_type = relationship("AgentType", foreign_keys=[agent_type_id])
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__table_args__ = (
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# Unique procedure name within scope
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Index(
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"ix_procedures_unique_name",
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"project_id",
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"agent_type_id",
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"name",
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unique=True,
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),
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# Query patterns
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Index("ix_procedures_project_name", "project_id", "name"),
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Index("ix_procedures_agent_type", "agent_type_id"),
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# For finding best procedures
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Index("ix_procedures_success_rate", "success_count", "failure_count"),
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)
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@property
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def success_rate(self) -> float:
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"""Calculate the success rate of this procedure."""
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total = self.success_count + self.failure_count
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if total == 0:
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return 0.0
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return self.success_count / total
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@property
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def total_uses(self) -> int:
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"""Get total number of times this procedure was used."""
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return self.success_count + self.failure_count
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def __repr__(self) -> str:
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return (
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f"<Procedure {self.name} ({self.id}) success_rate={self.success_rate:.2%}>"
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)
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