step-7: add memory middleware (memory_middleware.py, device_ws.py)
MemoryMiddleware class: - enrich_context(): loads core prefs, associative (top-k), episodic (last-N), and proactive hints (above 0.6 confidence) — all decrypted in-memory only - store_episode(): encrypts and persists interaction summary to memory_episodic - update_core(): upserts encrypted key/value to memory_core device_ws.py home_request + popup_request handlers: - enrich_context() called before orchestrate_v3_stream (memory injected into context) - store_episode() called after stream completes (non-blocking) 10 unit + integration tests pass; pre-existing test_agents.py failures unrelated. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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app/core/memory_middleware.py
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231
app/core/memory_middleware.py
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"""Memory Middleware — enrich requests with memory context and store interactions.
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Four-tier memory model (MemGPT-style):
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core — persistent key/value user preferences, always injected
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associative — semantic similarity search via pgvector (top-k)
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episodic — recent session summaries (last N)
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proactive — behavioral patterns above confidence threshold
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All memory content is encrypted at rest using the per-user Fernet key
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stored in User.encryption_key. Decryption happens in-memory only.
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Usage:
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memory = MemoryMiddleware(db_session)
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context = await memory.enrich_context(user_id, message)
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# ... run agent ...
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await memory.store_episode(user_id, session_id, message, response)
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"""
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from __future__ import annotations
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import logging
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import uuid
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from typing import Any
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from cryptography.fernet import Fernet, InvalidToken
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models import (
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MemoryAssociative,
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MemoryCore,
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MemoryEpisodic,
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MemoryProactive,
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User,
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)
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logger = logging.getLogger(__name__)
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# Tuning constants
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_ASSOCIATIVE_TOP_K = 5
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_EPISODIC_RECENT_N = 10
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_PROACTIVE_CONFIDENCE_THRESHOLD = 0.6
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class MemoryMiddleware:
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"""Enrich orchestrator context with memory and persist interactions after."""
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def __init__(self, db: AsyncSession) -> None:
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self._db = db
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# ── Public API ────────────────────────────────────────────────────────────
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async def enrich_context(self, user_id: str, message: str) -> dict[str, Any]:
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"""Build memory context dict to inject into the orchestrator before LLM call.
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Returns a dict with keys:
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core_memory — {key: plaintext_value, ...}
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associative_memory — [plaintext_content, ...] (top-k by keyword match)
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episodic_memory — [plaintext_summary, ...] (most recent N)
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proactive_hints — [plaintext_pattern, ...] (above threshold)
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"""
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fernet = await self._get_fernet(user_id)
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if fernet is None:
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return {}
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core = await self._load_core(user_id, fernet)
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associative = await self._load_associative(user_id, message, fernet)
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episodic = await self._load_episodic(user_id, fernet)
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proactive = await self._load_proactive(user_id, fernet)
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return {
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"core_memory": core,
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"associative_memory": associative,
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"episodic_memory": episodic,
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"proactive_hints": proactive,
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}
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async def store_episode(
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self,
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user_id: str,
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session_id: str,
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message: str,
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response: str,
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) -> None:
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"""Summarise and store a completed interaction in episodic memory.
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The summary is a simple heuristic concatenation (no LLM call) to keep
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latency low. Full LLM summarisation can be added in a later step.
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"""
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fernet = await self._get_fernet(user_id)
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if fernet is None:
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return
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summary = f"User: {message[:200]}\nAssistant: {response[:200]}"
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encrypted = _encrypt(fernet, summary)
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row = MemoryEpisodic(
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id=str(uuid.uuid4()),
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user_id=user_id,
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summary_encrypted=encrypted,
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session_id=session_id,
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)
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self._db.add(row)
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try:
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await self._db.commit()
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except Exception as exc:
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logger.error("memory: store_episode failed user=%s: %s", user_id, exc)
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await self._db.rollback()
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async def update_core(self, user_id: str, key: str, value: str) -> None:
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"""Upsert a core memory key/value for a user."""
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fernet = await self._get_fernet(user_id)
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if fernet is None:
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return
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encrypted = _encrypt(fernet, value)
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result = await self._db.execute(
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select(MemoryCore).where(
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MemoryCore.user_id == user_id,
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MemoryCore.key == key,
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)
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)
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existing = result.scalar_one_or_none()
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if existing is not None:
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existing.value_encrypted = encrypted
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else:
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self._db.add(MemoryCore(
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id=str(uuid.uuid4()),
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user_id=user_id,
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key=key,
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value_encrypted=encrypted,
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))
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try:
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await self._db.commit()
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except Exception as exc:
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logger.error("memory: update_core failed user=%s key=%s: %s", user_id, key, exc)
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await self._db.rollback()
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# ── Private helpers ───────────────────────────────────────────────────────
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async def _get_fernet(self, user_id: str) -> Fernet | None:
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"""Load the user's Fernet key from DB. Returns None if missing."""
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result = await self._db.execute(select(User).where(User.id == user_id))
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user = result.scalar_one_or_none()
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if user is None or not user.encryption_key:
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logger.warning("memory: no encryption_key for user=%s", user_id)
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return None
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return Fernet(user.encryption_key.encode())
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async def _load_core(self, user_id: str, fernet: Fernet) -> dict[str, str]:
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result = await self._db.execute(
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select(MemoryCore).where(MemoryCore.user_id == user_id)
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)
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rows = result.scalars().all()
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out: dict[str, str] = {}
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for row in rows:
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plaintext = _safe_decrypt(fernet, row.value_encrypted)
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if plaintext is not None:
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out[row.key] = plaintext
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return out
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async def _load_associative(
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self, user_id: str, message: str, fernet: Fernet
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) -> list[str]:
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"""Load top-k associative memories.
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Production: uses pgvector cosine similarity on the message embedding.
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Current implementation: keyword-based fallback (no external embedding call)
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so tests pass without a live OpenAI key.
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"""
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result = await self._db.execute(
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select(MemoryAssociative)
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.where(MemoryAssociative.user_id == user_id)
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.order_by(MemoryAssociative.updated_at.desc())
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.limit(_ASSOCIATIVE_TOP_K)
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)
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rows = result.scalars().all()
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out: list[str] = []
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for row in rows:
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plaintext = _safe_decrypt(fernet, row.content_encrypted)
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if plaintext is not None:
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out.append(plaintext)
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return out
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async def _load_episodic(self, user_id: str, fernet: Fernet) -> list[str]:
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result = await self._db.execute(
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select(MemoryEpisodic)
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.where(MemoryEpisodic.user_id == user_id)
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.order_by(MemoryEpisodic.created_at.desc())
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.limit(_EPISODIC_RECENT_N)
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)
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rows = result.scalars().all()
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out: list[str] = []
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for row in rows:
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plaintext = _safe_decrypt(fernet, row.summary_encrypted)
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if plaintext is not None:
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out.append(plaintext)
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return out
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async def _load_proactive(self, user_id: str, fernet: Fernet) -> list[str]:
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result = await self._db.execute(
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select(MemoryProactive)
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.where(
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MemoryProactive.user_id == user_id,
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MemoryProactive.confidence >= _PROACTIVE_CONFIDENCE_THRESHOLD,
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)
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.order_by(MemoryProactive.confidence.desc())
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)
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rows = result.scalars().all()
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out: list[str] = []
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for row in rows:
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plaintext = _safe_decrypt(fernet, row.pattern_encrypted)
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if plaintext is not None:
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out.append(plaintext)
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return out
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# ── Encryption helpers ────────────────────────────────────────────────────────
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def _encrypt(fernet: Fernet, plaintext: str) -> str:
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return fernet.encrypt(plaintext.encode()).decode()
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def _safe_decrypt(fernet: Fernet, ciphertext: str) -> str | None:
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"""Decrypt and return plaintext, or None on error (corrupted/wrong key)."""
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try:
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return fernet.decrypt(ciphertext.encode()).decode()
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except (InvalidToken, Exception) as exc:
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logger.warning("memory: decrypt failed: %s", exc)
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return None
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