40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
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"""AgentMemory model for persistent agent memory with pgvector embeddings."""
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from __future__ import annotations
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import uuid
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from sqlalchemy import Index, String, Text
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from sqlalchemy.dialects.postgresql import UUID as PGUUID
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from sqlalchemy.orm import Mapped, mapped_column
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from app.core.db import Base, TenantMixin
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from app.models.owned_mixin import OwnedMixin
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class AgentMemory(Base, TenantMixin, OwnedMixin):
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"""Persistent agent memory with semantic search via pgvector.
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Stores agent memories (facts, context, learned patterns) with
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vector embeddings for semantic retrieval.
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"""
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__tablename__ = "agent_memories"
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__table_args__ = (
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Index("ix_agent_memories_tenant_agent", "tenant_id", "agent_id"),
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Index("ix_agent_memories_tenant_type", "tenant_id", "memory_type"),
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)
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id: Mapped[uuid.UUID] = mapped_column(
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PGUUID(as_uuid=True), primary_key=True, default=uuid.uuid4
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)
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agent_id: Mapped[uuid.UUID] = mapped_column(
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PGUUID(as_uuid=True), nullable=False, index=True
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)
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memory_type: Mapped[str] = mapped_column(
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String(50), nullable=False, default="fact"
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)
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content: Mapped[str] = mapped_column(Text, nullable=False)
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# embedding column is managed via raw SQL (pgvector extension)
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# embedding vector(768) — see migration 0001_initial.sql
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