step-6: add memory models and migration (models.py, alembic)
- User.encryption_key: per-user Fernet key generated on registration - MemoryCore: encrypted key/value preferences - MemoryAssociative: encrypted semantic memory + pgvector(1536) embedding - MemoryEpisodic: encrypted session summaries - MemoryProactive: encrypted behavioral patterns with confidence score - Migration 004: enables pgvector extension, creates all 4 tables + ivfflat index - auth.py register: generates Fernet key for new users - 8 unit tests pass (SQLite in-memory, JSON embedding fallback) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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144
alembic/versions/004_add_memory_tables.py
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144
alembic/versions/004_add_memory_tables.py
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"""Add memory tables and user encryption_key column.
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Memory tables:
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memory_core — per-user key/value preferences (encrypted)
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memory_associative — semantic memory with pgvector embedding (encrypted)
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memory_episodic — session summaries (encrypted)
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memory_proactive — behavioral patterns (encrypted)
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Also adds encryption_key column to users table.
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Revision ID: 004
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Revises: 003
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Create Date: 2026-03-08
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"""
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from __future__ import annotations
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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revision: str = "004"
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down_revision: Union[str, None] = "003"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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# ── Enable pgvector extension (idempotent) ────────────────────────────────
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op.execute("CREATE EXTENSION IF NOT EXISTS vector;")
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# ── Add encryption_key to users ───────────────────────────────────────────
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op.add_column(
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"users",
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sa.Column("encryption_key", sa.String(64), nullable=True),
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)
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# ── memory_core ───────────────────────────────────────────────────────────
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op.create_table(
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"memory_core",
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sa.Column("id", sa.String(36), primary_key=True),
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sa.Column(
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"user_id",
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sa.String(36),
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sa.ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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),
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sa.Column("key", sa.String(255), nullable=False),
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sa.Column("value_encrypted", sa.Text, nullable=False),
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sa.Column(
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"updated_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.func.now(),
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),
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)
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op.create_index("ix_memory_core_user_id", "memory_core", ["user_id"])
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# ── memory_associative ────────────────────────────────────────────────────
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# The embedding column uses pgvector's vector(1536) type.
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op.create_table(
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"memory_associative",
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sa.Column("id", sa.String(36), primary_key=True),
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sa.Column(
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"user_id",
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sa.String(36),
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sa.ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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),
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sa.Column("content_encrypted", sa.Text, nullable=False),
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sa.Column("entity_type", sa.String(100), nullable=True),
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sa.Column("entity_id", sa.String(255), nullable=True),
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sa.Column(
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"updated_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.func.now(),
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),
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)
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# Add the pgvector column separately (not supported by generic sa types)
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op.execute(
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"ALTER TABLE memory_associative ADD COLUMN embedding vector(1536);"
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)
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op.create_index("ix_memory_associative_user_id", "memory_associative", ["user_id"])
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# IVFFlat index for approximate nearest-neighbour search
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op.execute(
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"CREATE INDEX ix_memory_associative_embedding "
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"ON memory_associative USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);"
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)
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# ── memory_episodic ───────────────────────────────────────────────────────
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op.create_table(
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"memory_episodic",
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sa.Column("id", sa.String(36), primary_key=True),
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sa.Column(
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"user_id",
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sa.String(36),
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sa.ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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),
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sa.Column("summary_encrypted", sa.Text, nullable=False),
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sa.Column("session_id", sa.String(255), nullable=False),
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sa.Column(
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"created_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.func.now(),
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),
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)
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op.create_index("ix_memory_episodic_user_id", "memory_episodic", ["user_id"])
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op.create_index("ix_memory_episodic_session_id", "memory_episodic", ["session_id"])
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# ── memory_proactive ──────────────────────────────────────────────────────
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op.create_table(
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"memory_proactive",
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sa.Column("id", sa.String(36), primary_key=True),
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sa.Column(
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"user_id",
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sa.String(36),
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sa.ForeignKey("users.id", ondelete="CASCADE"),
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nullable=False,
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),
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sa.Column("pattern_encrypted", sa.Text, nullable=False),
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sa.Column("confidence", sa.Float, nullable=False, server_default="0.5"),
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sa.Column("source", sa.String(50), nullable=False, server_default="inferred"),
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sa.Column(
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"created_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.func.now(),
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),
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)
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op.create_index("ix_memory_proactive_user_id", "memory_proactive", ["user_id"])
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def downgrade() -> None:
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op.drop_table("memory_proactive")
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op.drop_table("memory_episodic")
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op.drop_index("ix_memory_associative_embedding", "memory_associative")
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op.drop_table("memory_associative")
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op.drop_table("memory_core")
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op.drop_column("users", "encryption_key")
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