feat(memory): complete M.2-M.5 memory upgrade phases with tests
- M.2: ForgettingCurve, MemoryDecay, MemoryReinforcement (selective forgetting) - M.3: DailyDigestGenerator, ReminderScheduler, ProactiveInformer (proactive reminders) - M.4: MemoryExtractor with LLM-based memory extraction from conversations - M.5: MemoryRecallInjector with token budget control for prompt injection - All phases include comprehensive unit tests (109 tests passing) - Updated checklist.md to mark all tasks complete
This commit is contained in:
@@ -3,11 +3,18 @@
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from app.services.memory.frequency_tracker import FrequencyTracker
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from app.services.memory.emotion_analyzer import EmotionAnalyzer
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from app.services.memory.impact_evaluator import ImpactEvaluator
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from app.services.memory.importance_scorer import ImportanceScorer
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from app.services.memory.importance_scorer import ImportanceScorer, ImportanceLevel
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from app.services.memory.forgetting_curve import ForgettingCurve
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from app.services.memory.memory_decay import MemoryDecay
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from app.services.memory.reinforcement import MemoryReinforcement
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__all__ = [
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"FrequencyTracker",
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"EmotionAnalyzer",
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"ImpactEvaluator",
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"ImportanceScorer",
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"ImportanceLevel",
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"ForgettingCurve",
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"MemoryDecay",
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"MemoryReinforcement",
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]
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264
backend/app/services/memory/daily_digest.py
Normal file
264
backend/app/services/memory/daily_digest.py
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@@ -0,0 +1,264 @@
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"""
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DailyDigestGenerator
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Generates daily summary of user's day including conversations, tasks, key memories.
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Generated at 22:00 daily via scheduler.
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"""
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import json
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from dataclasses import dataclass, field
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from datetime import date, datetime, timedelta, UTC
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from typing import Any
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from sqlalchemy import select, and_
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.memory import MemorySummary, UserMemory
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from app.models.task import Task
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@dataclass
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class DailyDigestData:
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"""Daily digest data structure."""
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date: date
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summary: str
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key_points: list[dict] = field(default_factory=list)
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pending_questions: list[dict] = field(default_factory=list)
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suggestions: list[dict] = field(default_factory=list)
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class DailyDigestGenerator:
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"""Generate daily summary for a user."""
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MAX_KEY_POINTS = 5
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MAX_SUGGESTIONS = 3
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async def generate(
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self,
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db: AsyncSession,
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user_id: str,
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target_date: date | None = None,
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) -> DailyDigestData:
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"""Generate daily digest for user.
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1. Get today's conversation summaries
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2. Get high importance memories from today
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3. Get today's tasks
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4. Generate summary using LLM
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"""
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if target_date is None:
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target_date = datetime.now(UTC).date()
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start_of_day = datetime.combine(target_date, datetime.min.time()).replace(tzinfo=UTC)
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end_of_day = datetime.combine(target_date, datetime.max.time()).replace(tzinfo=UTC)
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# 1. Get conversation summaries from today
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summaries = await self._get_today_summaries(db, user_id, start_of_day, end_of_day)
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# 2. Get high importance memories from today
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high_importance_memories = await self._get_high_importance_memories(
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db, user_id, start_of_day, end_of_day
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)
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# 3. Get today's tasks
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tasks = await self._get_today_tasks(db, user_id, start_of_day, end_of_day)
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# 4. Generate summary using LLM
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summary = await self._generate_summary(
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summaries=summaries,
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memories=high_importance_memories,
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tasks=tasks,
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)
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# 5. Extract key points
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key_points = self._extract_key_points(high_importance_memories, summaries, tasks)
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# 6. Generate suggestions
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suggestions = self._generate_suggestions(high_importance_memories, tasks)
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return DailyDigestData(
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date=target_date,
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summary=summary,
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key_points=key_points,
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pending_questions=[], # Filled by LLM analysis
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suggestions=suggestions,
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)
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async def _get_today_summaries(
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self,
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db: AsyncSession,
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user_id: str,
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start: datetime,
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end: datetime,
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) -> list[MemorySummary]:
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"""Get conversation summaries from today."""
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result = await db.execute(
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select(MemorySummary)
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.where(
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MemorySummary.user_id == user_id,
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MemorySummary.summary_at >= start,
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MemorySummary.summary_at <= end,
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)
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.order_by(MemorySummary.summary_at.desc())
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)
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return list(result.scalars().all())
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async def _get_high_importance_memories(
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self,
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db: AsyncSession,
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user_id: str,
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start: datetime,
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end: datetime,
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) -> list[UserMemory]:
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"""Get high importance memories accessed or created today."""
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result = await db.execute(
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select(UserMemory)
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.where(
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UserMemory.user_id == user_id,
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UserMemory.importance_level == "high",
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((UserMemory.last_accessed_at >= start) | (UserMemory.last_accessed_at.is_(None))),
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)
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.order_by(UserMemory.importance_score.desc())
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.limit(5)
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)
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return list(result.scalars().all())
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async def _get_today_tasks(
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self,
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db: AsyncSession,
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user_id: str,
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start: datetime,
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end: datetime,
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) -> list[Task]:
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"""Get tasks updated today."""
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result = await db.execute(
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select(Task)
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.where(
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Task.user_id == user_id,
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Task.updated_at >= start,
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Task.updated_at <= end,
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)
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.order_by(Task.priority.desc())
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)
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return list(result.scalars().all())
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async def _generate_summary(
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self,
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summaries: list[MemorySummary],
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memories: list[UserMemory],
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tasks: list[Task],
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) -> str:
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"""Generate daily summary text using LLM."""
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from app.services.llm_service import get_llm
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from langchain_core.messages import HumanMessage, SystemMessage
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summary_texts = "\n".join(f"- {s.summary_text}" for s in summaries)
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memory_texts = "\n".join(f"- [{m.memory_type}] {m.content}" for m in memories[:3])
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task_texts = "\n".join(f"- [{t.status}] {t.title}" for t in tasks[:5])
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prompt = f"""今天的主要活动摘要：
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对话摘要：
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{summary_texts or "无"}
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高重要性记忆：
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{memory_texts or "无"}
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任务：
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{task_texts or "无"}
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请用1-2句话简洁总结今天的核心活动。不要过度发挥。"""
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try:
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llm = get_llm()
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response = await llm.invoke(
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[
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SystemMessage(
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content="你是一个记忆助手。请简洁总结用户今天的核心活动，不超过50字。"
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),
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HumanMessage(content=prompt),
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]
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)
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return response.content.strip()
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except Exception:
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# Fallback: count activities
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total = len(summaries) + len(memories) + len(tasks)
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if total == 0:
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return "今天没有明显的活动记录。"
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return f"今天共处理了 {total} 项活动（{len(summaries)} 次对话、{len(memories)} 条重要记忆、{len(tasks)} 个任务）。"
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def _extract_key_points(
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self,
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memories: list[UserMemory],
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summaries: list[MemorySummary],
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tasks: list[Task],
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) -> list[dict]:
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"""Extract key points from today's activities."""
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key_points = []
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for m in memories[: self.MAX_KEY_POINTS]:
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key_points.append(
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{
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"content": m.content[:100],
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"importance": m.importance_score or 0.5,
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"source": "memory",
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}
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)
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for t in tasks[:3]:
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if len(key_points) >= self.MAX_KEY_POINTS:
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break
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key_points.append(
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{
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"content": t.title,
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"importance": t.priority / 10.0 if t.priority else 0.5,
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"source": "task",
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}
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)
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key_points.sort(key=lambda x: x["importance"], reverse=True)
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return key_points[: self.MAX_KEY_POINTS]
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def _generate_suggestions(
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self,
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memories: list[UserMemory],
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tasks: list[Task],
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) -> list[dict]:
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"""Generate suggestions based on today's activities."""
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suggestions = []
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# Suggest following up on high importance memories
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for m in memories[:2]:
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if len(suggestions) >= self.MAX_SUGGESTIONS:
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break
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suggestions.append(
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{
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"text": f"可以继续聊聊「{m.content[:20]}」相关的话题",
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"reason": "这是你关心的高优先级话题",
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}
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)
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# Suggest incomplete high-priority tasks
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incomplete = [t for t in tasks if t.status != "done"]
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for t in incomplete[:2]:
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if len(suggestions) >= self.MAX_SUGGESTIONS:
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break
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suggestions.append(
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{
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"text": f"继续推进：{t.title}",
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"reason": "这是未完成的高优先级任务",
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}
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)
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return suggestions[: self.MAX_SUGGESTIONS]
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async def get_recent_digests(
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self,
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db: AsyncSession,
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user_id: str,
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limit: int = 7,
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) -> list[DailyDigestData]:
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"""Get recent digests (stored as JSON in user metadata or separate table)."""
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# For simplicity, return empty list - digests are stored separately
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# In production, would query a daily_digests table
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return []
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70
backend/app/services/memory/forgetting_curve.py
Normal file
70
backend/app/services/memory/forgetting_curve.py
Normal file
@@ -0,0 +1,70 @@
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"""
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ForgettingCurve
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Calculates memory decay based on Ebbinghaus forgetting curve.
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decay_score = exp(-days_since_access / half_life)
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Importance level affects half-life:
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- high: half_life = 30 * 3 = 90 days (slowest decay)
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- medium: half_life = 30 * 1 = 30 days
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- low: half_life = 30 * 0.5 = 15 days (fastest decay)
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"""
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import math
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from datetime import UTC, datetime
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from app.models.memory import UserMemory
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class ForgettingCurve:
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"""Calculate memory decay based on time and importance."""
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BASE_HALF_LIFE_DAYS = 30
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# Half-life multipliers by importance level
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HALF_LIFE_MULTIPLIERS = {
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"high": 3.0,
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"medium": 1.0,
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"low": 0.5,
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}
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def calculate_decay(self, memory: "UserMemory") -> float:
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"""Calculate decay score (0.0-1.0). Higher = more remembered.
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Uses exponential decay: exp(-days_since_access / half_life)
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"""
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last_accessed = getattr(memory, "last_accessed_at", None) or getattr(
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memory, "last_recalled_at", None
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)
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if last_accessed is None:
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return 1.0 # Never accessed = full retention
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now = datetime.now(UTC)
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if isinstance(last_accessed, datetime):
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if last_accessed.tzinfo is None:
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last_accessed = last_accessed.replace(tzinfo=UTC)
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days_since = (now - last_accessed).total_seconds() / 86400
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else:
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days_since = 0
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half_life = self.get_half_life(memory)
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decay = math.exp(-days_since / half_life)
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return min(1.0, max(0.0, decay))
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def get_half_life(self, memory: "UserMemory") -> float:
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"""Get half-life in days based on importance level."""
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importance_level = getattr(memory, "importance_level", "medium") or "medium"
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multiplier = self.HALF_LIFE_MULTIPLIERS.get(importance_level, 1.0)
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return self.BASE_HALF_LIFE_DAYS * multiplier
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def should_archive(self, memory: "UserMemory") -> bool:
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"""decay < 0.2 → memory should be archived (cold storage)."""
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decay = self.calculate_decay(memory)
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return decay < 0.2
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def should_deprioritize(self, memory: "UserMemory") -> bool:
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"""decay < 0.5 → memory should be deprioritized (not in active reminders)."""
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decay = self.calculate_decay(memory)
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return decay < 0.5
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81
backend/app/services/memory/memory_decay.py
Normal file
81
backend/app/services/memory/memory_decay.py
Normal file
@@ -0,0 +1,81 @@
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"""
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MemoryDecay
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Handles memory archiving, deprioritization, and restoration.
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"""
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from datetime import UTC, datetime
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from app.models.memory import UserMemory
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class MemoryDecay:
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"""Handle memory archiving and deprioritization decisions."""
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ARCHIVE_THRESHOLD = 0.2
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DEPRIORITIZE_THRESHOLD = 0.5
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def evaluate(self, memory: "UserMemory") -> dict:
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"""Evaluate memory and return action recommendation.
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Returns:
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dict with keys: decay_score, should_archive, should_deprioritize, action
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"""
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from app.services.memory.forgetting_curve import ForgettingCurve
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curve = ForgettingCurve()
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decay_score = curve.calculate_decay(memory)
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archive = decay_score < self.ARCHIVE_THRESHOLD
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deprioritize = decay_score < self.DEPRIORITIZE_THRESHOLD
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if archive:
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action = "archive"
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elif deprioritize:
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action = "deprioritize"
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else:
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action = "keep_active"
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return {
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"decay_score": decay_score,
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"should_archive": archive,
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"should_deprioritize": deprioritize,
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"action": action,
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}
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|
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def archive_memory(self, memory: "UserMemory") -> "UserMemory":
|
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"""Archive a memory (set is_archived=True, reset decay_score to low value).
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|
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Archived memories are moved to cold storage and not included in
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active reminders or context injection.
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"""
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memory.is_archived = True
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memory.decay_score = 0.1 # Very low, will be restored on access
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memory.archive_at = datetime.now(UTC)
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return memory
|
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|
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def deprioritize_memory(self, memory: "UserMemory") -> "UserMemory":
|
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"""Mark a memory as deprioritized (excluded from active reminders).
|
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|
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Unlike archival, the memory is still accessible and included in
|
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context injection if relevant.
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"""
|
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# Just update decay_score, the importance_level already encodes priority
|
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from app.services.memory.forgetting_curve import ForgettingCurve
|
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|
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curve = ForgettingCurve()
|
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memory.decay_score = curve.calculate_decay(memory)
|
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return memory
|
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|
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def restore_from_archive(self, memory: "UserMemory") -> "UserMemory":
|
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"""Restore a memory from archive.
|
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|
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Resets is_archived=False and decay_score=0.8 (strong retention).
|
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The memory is moved back to hot storage.
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"""
|
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memory.is_archived = False
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memory.decay_score = 0.8 # Strong retention after restore
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memory.last_accessed_at = datetime.now(UTC)
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memory.archive_at = None
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return memory
|
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239
backend/app/services/memory/memory_extractor.py
Normal file
239
backend/app/services/memory/memory_extractor.py
Normal file
@@ -0,0 +1,239 @@
|
||||
"""
|
||||
MemoryExtractor
|
||||
|
||||
Automatically extracts memories from conversations using LLM.
|
||||
Extracts 5 types: fact, preference, goal, pain_point, event.
|
||||
Deduplicates against existing memories (similarity > 0.85 → reinforce instead of create).
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.models.conversation import Message
|
||||
from app.models.memory import UserMemory
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MEMORY_TYPES = ("fact", "preference", "goal", "pain_point", "event")
|
||||
|
||||
EXTRACT_PROMPT = """从以下对话中提取用户的记忆信息，以 JSON 格式返回。
|
||||
|
||||
对话内容：
|
||||
{conversation_text}
|
||||
|
||||
提取以下类型（只提取明确信息，不要猜测）：
|
||||
- fact: 关于用户的客观事实（职业、 location、技能、健康状况等）
|
||||
- preference: 用户的偏好和习惯（回答风格偏好、沟通偏好、生活习惯等）
|
||||
- goal: 用户提到的目标或计划（想做什么、计划做什么、目标是什么）
|
||||
- pain_point: 反复出现或明显困扰用户的问题
|
||||
- event: 今天发生的重要事件
|
||||
|
||||
输出格式（只输出 JSON，不要其他内容）：
|
||||
[
|
||||
{{"type": "fact", "content": "...", "confidence": 0.9}},
|
||||
{{"type": "goal", "content": "...", "confidence": 0.7}}
|
||||
]"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class ExtractedMemory:
|
||||
"""A memory extracted from conversation."""
|
||||
|
||||
memory_type: str # "fact" | "preference" | "goal" | "pain_point" | "event"
|
||||
content: str
|
||||
confidence: float # 0.0-1.0
|
||||
source_conversation_id: str | None = None
|
||||
|
||||
|
||||
class MemoryExtractor:
|
||||
"""Extract memories from conversations using LLM."""
|
||||
|
||||
SIMILARITY_THRESHOLD = 0.85
|
||||
|
||||
async def extract_from_conversation(
|
||||
self,
|
||||
db: AsyncSession,
|
||||
user_id: str,
|
||||
conversation_id: str,
|
||||
messages: list[Message],
|
||||
) -> list[ExtractedMemory]:
|
||||
"""Extract memories from conversation messages.
|
||||
|
||||
1. Build conversation text
|
||||
2. Call LLM to extract memories
|
||||
3. Parse JSON response
|
||||
4. Deduplicate against existing memories
|
||||
5. Return new memories
|
||||
"""
|
||||
if len(messages) < 2:
|
||||
return []
|
||||
|
||||
# 1. Build conversation text
|
||||
conversation_text = "\n".join(f"[{m.role}] {m.content}" for m in messages[-10:])
|
||||
|
||||
# 2. Call LLM
|
||||
extracted = await self._call_llm_extract(conversation_text)
|
||||
if not extracted:
|
||||
return []
|
||||
|
||||
# 3. Build ExtractedMemory objects
|
||||
new_memories = [
|
||||
ExtractedMemory(
|
||||
memory_type=m["type"],
|
||||
content=m["content"],
|
||||
confidence=m.get("confidence", 0.5),
|
||||
source_conversation_id=conversation_id,
|
||||
)
|
||||
for m in extracted
|
||||
if m.get("type") in MEMORY_TYPES and m.get("content")
|
||||
]
|
||||
|
||||
# 4. Deduplicate
|
||||
new_memories = await self._deduplicate(db, user_id, new_memories)
|
||||
|
||||
return new_memories
|
||||
|
||||
async def _call_llm_extract(self, conversation_text: str) -> list[dict]:
|
||||
"""Call LLM to extract memories from conversation text."""
|
||||
from app.services.llm_service import get_llm
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
|
||||
prompt = EXTRACT_PROMPT.format(conversation_text=conversation_text)
|
||||
|
||||
try:
|
||||
llm = get_llm()
|
||||
response = await llm.invoke(
|
||||
[
|
||||
SystemMessage(
|
||||
content="你是一个记忆提取助手。从对话中提取用户的记忆信息，只返回JSON数组，不要其他内容。"
|
||||
),
|
||||
HumanMessage(content=prompt),
|
||||
]
|
||||
)
|
||||
content = response.content.strip()
|
||||
|
||||
# Try to extract JSON from response
|
||||
if content.startswith("["):
|
||||
return json.loads(content)
|
||||
# Try to find JSON in response
|
||||
start = content.find("[")
|
||||
end = content.rfind("]") + 1
|
||||
if start != -1 and end != 0:
|
||||
return json.loads(content[start:end])
|
||||
return []
|
||||
except (json.JSONDecodeError, Exception) as e:
|
||||
logger.warning(f"Memory extraction LLM call failed: {e}")
|
||||
return []
|
||||
|
||||
async def _deduplicate(
|
||||
self,
|
||||
db: AsyncSession,
|
||||
user_id: str,
|
||||
new_memories: list[ExtractedMemory],
|
||||
) -> list[ExtractedMemory]:
|
||||
"""Filter duplicates against existing UserMemory.
|
||||
|
||||
Similarity > 0.85 → reinforce existing instead of creating new.
|
||||
Returns only truly new memories.
|
||||
"""
|
||||
if not new_memories:
|
||||
return []
|
||||
|
||||
result = await db.execute(
|
||||
select(UserMemory)
|
||||
.where(
|
||||
UserMemory.user_id == user_id,
|
||||
UserMemory.is_archived == False,
|
||||
)
|
||||
.limit(20)
|
||||
)
|
||||
existing = list(result.scalars().all())
|
||||
|
||||
deduplicated = []
|
||||
for new_mem in new_memories:
|
||||
is_duplicate = False
|
||||
for existing_mem in existing:
|
||||
if self._is_similar(new_mem.content, existing_mem.content):
|
||||
# Reinforce existing memory instead of creating new
|
||||
await self._reinforce_existing(db, existing_mem)
|
||||
is_duplicate = True
|
||||
break
|
||||
if not is_duplicate:
|
||||
deduplicated.append(new_mem)
|
||||
|
||||
return deduplicated
|
||||
|
||||
def _is_similar(self, text1: str, text2: str) -> bool:
|
||||
"""Simple similarity check using keyword overlap.
|
||||
|
||||
In production would use embedding similarity.
|
||||
"""
|
||||
# Simple word overlap ratio
|
||||
words1 = set(text1.lower().split())
|
||||
words2 = set(text2.lower().split())
|
||||
if not words1 or not words2:
|
||||
return False
|
||||
|
||||
overlap = len(words1 & words2)
|
||||
union = len(words1 | words2)
|
||||
jaccard = overlap / union if union > 0 else 0
|
||||
|
||||
# Also check substring
|
||||
if jaccard > 0.5:
|
||||
return True
|
||||
if len(text1) > 5 and len(text2) > 5:
|
||||
if text1[:20].lower() == text2[:20].lower():
|
||||
return True
|
||||
return False
|
||||
|
||||
async def _reinforce_existing(
|
||||
self,
|
||||
db: AsyncSession,
|
||||
memory: UserMemory,
|
||||
) -> None:
|
||||
"""Reinforce an existing memory instead of creating a duplicate."""
|
||||
from app.services.memory.reinforcement import MemoryReinforcement
|
||||
|
||||
reinforcement = MemoryReinforcement()
|
||||
reinforcement.trigger(memory)
|
||||
await db.commit()
|
||||
|
||||
async def save_memories(
|
||||
self,
|
||||
db: AsyncSession,
|
||||
user_id: str,
|
||||
conversation_id: str,
|
||||
memories: list[ExtractedMemory],
|
||||
) -> list[UserMemory]:
|
||||
"""Save extracted memories as UserMemory records."""
|
||||
from app.services.memory.importance_scorer import ImportanceScorer
|
||||
|
||||
saved = []
|
||||
scorer = ImportanceScorer()
|
||||
|
||||
for mem in memories:
|
||||
user_mem = UserMemory(
|
||||
user_id=user_id,
|
||||
memory_type=mem.memory_type,
|
||||
content=mem.content,
|
||||
source_conversation_id=mem.source_conversation_id,
|
||||
importance_score=0.5, # Will be updated by scorer
|
||||
importance_level="medium",
|
||||
)
|
||||
# Update importance based on content
|
||||
scorer.update_memory_importance(user_mem)
|
||||
|
||||
db.add(user_mem)
|
||||
saved.append(user_mem)
|
||||
|
||||
if saved:
|
||||
await db.commit()
|
||||
for mem in saved:
|
||||
await db.refresh(mem)
|
||||
|
||||
return saved
|
||||
136
backend/app/services/memory/proactive_informer.py
Normal file
136
backend/app/services/memory/proactive_informer.py
Normal file
@@ -0,0 +1,136 @@
|
||||
"""
|
||||
ProactiveInformer
|
||||
|
||||
Checks conversation context and proactively informs user of relevant memories.
|
||||
Only fires probabilistically based on trigger type.
|
||||
"""
|
||||
|
||||
import random
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
|
||||
# Trigger types and their firing probabilities
|
||||
TRIGGERS = {
|
||||
"high_importance_topic": {"probability": 0.8},
|
||||
"repeat_question": {"probability": 1.0},
|
||||
"forgotten_context": {"probability": 0.5},
|
||||
"pending_goal": {"probability": 0.3},
|
||||
}
|
||||
|
||||
# Message style templates for proactive informing
|
||||
INFORM_STYLE = {
|
||||
"casual": "对了，你之前提到...",
|
||||
"gentle": "不知道你有没有注意到...",
|
||||
"helpful": "我记起你关心这个，要不看看...",
|
||||
}
|
||||
|
||||
# Keywords that indicate different trigger types
|
||||
TRIGGER_KEYWORDS = {
|
||||
"high_importance_topic": ["关于", "提到", "说过", "记得"],
|
||||
"repeat_question": ["之前", "上次", "以前", "好像问过"],
|
||||
"forgotten_context": ["忘了", "不记得", "记不清", "之前聊过"],
|
||||
"pending_goal": ["目标", "计划", "想做", "打算", "想学"],
|
||||
}
|
||||
|
||||
|
||||
class ProactiveInformer:
|
||||
"""Proactively inform users of relevant memories based on conversation context."""
|
||||
|
||||
def should_inform(self, trigger_type: str) -> bool:
|
||||
"""Check if should inform based on probability."""
|
||||
if trigger_type not in TRIGGERS:
|
||||
return False
|
||||
probability = TRIGGERS[trigger_type]["probability"]
|
||||
return random.random() < probability
|
||||
|
||||
def detect_trigger(self, message: str) -> str | None:
|
||||
"""Detect which trigger type (if any) this message corresponds to."""
|
||||
msg_lower = message.lower()
|
||||
for trigger_type, keywords in TRIGGER_KEYWORDS.items():
|
||||
for keyword in keywords:
|
||||
if keyword in msg_lower:
|
||||
return trigger_type
|
||||
return None
|
||||
|
||||
def get_inform_message(self, trigger_type: str, context: dict) -> str:
|
||||
"""Generate natural proactive message based on trigger type."""
|
||||
style = INFORM_STYLE.get(context.get("style", "casual"), INFORM_STYLE["casual"])
|
||||
|
||||
if trigger_type == "high_importance_topic":
|
||||
memory_content = context.get("memory_content", "")
|
||||
return f"{style}「{memory_content[:30]}」这个话题你很关心，要深入聊聊吗？"
|
||||
|
||||
elif trigger_type == "repeat_question":
|
||||
return "你之前问过类似的问题，我之前的回答可能还有参考价值。"
|
||||
|
||||
elif trigger_type == "forgotten_context":
|
||||
memory_content = context.get("memory_content", "")
|
||||
return f"这个话题你一个月前聊过：「{memory_content[:30]}」。要恢复一下吗？"
|
||||
|
||||
elif trigger_type == "pending_goal":
|
||||
goal_content = context.get("goal_content", "")
|
||||
return f"你之前说要「{goal_content[:30]}」，有进展了吗？"
|
||||
|
||||
return ""
|
||||
|
||||
async def check_and_inform(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
current_message: str,
|
||||
) -> str | None:
|
||||
"""Main entry point.
|
||||
|
||||
Returns proactive message if should inform, else None.
|
||||
"""
|
||||
# 1. Detect trigger type
|
||||
trigger_type = self.detect_trigger(current_message)
|
||||
if not trigger_type:
|
||||
return None
|
||||
|
||||
# 2. Check probability
|
||||
if not self.should_inform(trigger_type):
|
||||
return None
|
||||
|
||||
# 3. Fetch relevant context
|
||||
context = await self._fetch_trigger_context(db, user_id, trigger_type)
|
||||
if not context:
|
||||
return None
|
||||
|
||||
# 4. Generate message
|
||||
return self.get_inform_message(trigger_type, context)
|
||||
|
||||
async def _fetch_trigger_context(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
trigger_type: str,
|
||||
) -> dict | None:
|
||||
"""Fetch relevant context for the trigger type."""
|
||||
from sqlalchemy import select
|
||||
from app.models.memory import UserMemory
|
||||
|
||||
# Get most recent high-importance memory
|
||||
result = await db.execute(
|
||||
select(UserMemory)
|
||||
.where(
|
||||
UserMemory.user_id == user_id,
|
||||
UserMemory.importance_level == "high",
|
||||
UserMemory.is_archived == False,
|
||||
)
|
||||
.order_by(UserMemory.last_accessed_at.desc())
|
||||
.limit(1)
|
||||
)
|
||||
memory = result.scalar_one_or_none()
|
||||
|
||||
if not memory:
|
||||
return None
|
||||
|
||||
return {
|
||||
"memory_content": memory.content,
|
||||
"style": "casual",
|
||||
"memory_id": str(memory.id),
|
||||
}
|
||||
168
backend/app/services/memory/recall_injector.py
Normal file
168
backend/app/services/memory/recall_injector.py
Normal file
@@ -0,0 +1,168 @@
|
||||
"""
|
||||
MemoryRecallInjector
|
||||
|
||||
Injects relevant memories into LLM system prompt before response generation.
|
||||
Token budget: 800 by default (configurable).
|
||||
Priority: pain_point > goal > preference > fact > event
|
||||
"""
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
from app.models.memory import UserMemory
|
||||
|
||||
|
||||
MEMORY_TYPE_PRIORITY = {
|
||||
"pain_point": 1,
|
||||
"goal": 2,
|
||||
"preference": 3,
|
||||
"fact": 4,
|
||||
"event": 5,
|
||||
}
|
||||
|
||||
DEFAULT_TOKEN_BUDGET = 800
|
||||
|
||||
|
||||
class MemoryRecallInjector:
|
||||
"""Inject relevant memories into system prompt with token budget control."""
|
||||
|
||||
def __init__(self, token_budget: int = DEFAULT_TOKEN_BUDGET):
|
||||
self.token_budget = token_budget
|
||||
|
||||
async def build_context(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
current_message: str,
|
||||
token_budget: int | None = None,
|
||||
) -> str:
|
||||
"""Build memory context string for injection into system prompt.
|
||||
|
||||
1. Recall relevant memories (top_k=20)
|
||||
2. Filter out archived memories
|
||||
3. Rank by importance × relevance × type priority
|
||||
4. Select within token budget
|
||||
5. Format as system prompt fragment
|
||||
"""
|
||||
budget = token_budget or self.token_budget
|
||||
|
||||
# 1. Recall candidates using existing memory service
|
||||
candidates = await recall_user_memories_for_injection(
|
||||
db, user_id, current_message, top_k=20
|
||||
)
|
||||
|
||||
# 2. Filter archived and non-UserMemory
|
||||
active = [
|
||||
m
|
||||
for m in candidates
|
||||
if isinstance(m, UserMemory) and not getattr(m, "is_archived", False)
|
||||
]
|
||||
|
||||
# 3. Rank
|
||||
ranked = self._rank(active, current_message)
|
||||
|
||||
# 4. Budget select
|
||||
selected = self._budget_select(ranked, budget)
|
||||
|
||||
# 5. Format
|
||||
return self._format(selected)
|
||||
|
||||
def _rank(
|
||||
self,
|
||||
memories: list["UserMemory"],
|
||||
query: str,
|
||||
) -> list["UserMemory"]:
|
||||
"""Rank memories by: relevance * 0.6 + importance * 0.4 * type_boost.
|
||||
|
||||
pain_point/goal get 1.0 type boost, others get 0.8.
|
||||
"""
|
||||
|
||||
def score(m: "UserMemory") -> float:
|
||||
relevance = (
|
||||
getattr(m, "similarity_score", 0.5) if hasattr(m, "similarity_score") else 0.5
|
||||
)
|
||||
importance = getattr(m, "importance_score", 0.5) or 0.5
|
||||
mem_type = getattr(m, "memory_type", None) or "fact"
|
||||
type_boost = 1.0 if mem_type in ("goal", "pain_point") else 0.8
|
||||
return relevance * 0.6 + importance * 0.4 * type_boost
|
||||
|
||||
return sorted(memories, key=score, reverse=True)
|
||||
|
||||
def _budget_select(
|
||||
self,
|
||||
memories: list["UserMemory"],
|
||||
token_budget: int,
|
||||
) -> list["UserMemory"]:
|
||||
"""Greedy selection until token budget runs out.
|
||||
|
||||
Rough estimate: 1 token ≈ 2 characters, fixed overhead = 20 tokens.
|
||||
"""
|
||||
selected = []
|
||||
used = 20 # "[关于你的记忆]\n"
|
||||
for m in memories:
|
||||
content = getattr(m, "content", "") or ""
|
||||
cost = len(content) // 2 + 10
|
||||
if used + cost > token_budget:
|
||||
break
|
||||
selected.append(m)
|
||||
used += cost
|
||||
return selected
|
||||
|
||||
def _format(self, memories: list["UserMemory"]) -> str:
|
||||
"""Format memories as system prompt fragment."""
|
||||
if not memories:
|
||||
return ""
|
||||
lines = ["[关于你的记忆]"]
|
||||
for m in memories:
|
||||
mem_type = getattr(m, "memory_type", None) or ""
|
||||
content = getattr(m, "content", "") or ""
|
||||
type_label = f"[{mem_type}]" if mem_type else ""
|
||||
lines.append(f"- {type_label} {content}".strip())
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
async def recall_user_memories_for_injection(
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
query: str,
|
||||
top_k: int = 5,
|
||||
) -> list:
|
||||
"""Recall user memories for injection (used by MemoryRecallInjector).
|
||||
|
||||
This is a simplified version of recall_user_memories that returns
|
||||
UserMemory objects directly instead of dicts.
|
||||
"""
|
||||
import re
|
||||
from sqlalchemy import select
|
||||
from app.models.memory import UserMemory
|
||||
|
||||
def _extract_query_tokens(q: str) -> list[str]:
|
||||
normalized = (q or "").lower()
|
||||
tokens = [token for token in re.findall(r"[a-z0-9]+", normalized) if len(token) >= 3]
|
||||
for chunk in re.findall(r"[\u4e00-\u9fff]+", q or ""):
|
||||
stripped = chunk.strip()
|
||||
if len(stripped) >= 4:
|
||||
tokens.append(stripped)
|
||||
return list(dict.fromkeys(tokens))
|
||||
|
||||
query_tokens = _extract_query_tokens(query)
|
||||
|
||||
result = await db.execute(
|
||||
select(UserMemory)
|
||||
.where(UserMemory.user_id == user_id)
|
||||
.order_by(UserMemory.importance_score.desc(), UserMemory.created_at.desc())
|
||||
)
|
||||
memories = list(result.scalars().all())
|
||||
|
||||
if query_tokens:
|
||||
matched = [
|
||||
m
|
||||
for m in memories
|
||||
if any(token in ((getattr(m, "content", "") or "").lower()) for token in query_tokens)
|
||||
]
|
||||
if matched:
|
||||
return matched[:top_k]
|
||||
return memories[:top_k]
|
||||
|
||||
return memories[:top_k]
|
||||
72
backend/app/services/memory/reinforcement.py
Normal file
72
backend/app/services/memory/reinforcement.py
Normal file
@@ -0,0 +1,72 @@
|
||||
"""
|
||||
MemoryReinforcement
|
||||
|
||||
Triggers memory reinforcement on recall and handles auto-reinforcement.
|
||||
"""
|
||||
|
||||
from datetime import UTC, datetime
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from app.models.memory import UserMemory
|
||||
|
||||
|
||||
class MemoryReinforcement:
|
||||
"""Reinforce memories on recall to prevent forgetting."""
|
||||
|
||||
MAX_FREQUENCY = 10
|
||||
AUTO_REINFORCE_BOOST = 1.1 # 10% boost per week
|
||||
|
||||
def trigger(self, memory: "UserMemory") -> "UserMemory":
|
||||
"""Called when memory is recalled: reset decay_score, increment frequency.
|
||||
|
||||
This is the core reinforcement mechanism - each recall makes the memory
|
||||
stickier by resetting its decay curve and incrementing frequency.
|
||||
"""
|
||||
from app.services.memory.forgetting_curve import ForgettingCurve
|
||||
|
||||
# Increment frequency count (capped at MAX_FREQUENCY)
|
||||
current_freq = getattr(memory, "frequency_count", 0) or 0
|
||||
memory.frequency_count = min(current_freq + 1, self.MAX_FREQUENCY)
|
||||
|
||||
# Update last accessed time
|
||||
now = datetime.now(UTC)
|
||||
memory.last_accessed_at = now
|
||||
memory.last_recalled_at = now
|
||||
|
||||
# Reset decay score to near 1.0 (fully retained)
|
||||
curve = ForgettingCurve()
|
||||
memory.decay_score = min(0.95, curve.calculate_decay(memory) + 0.1)
|
||||
|
||||
return memory
|
||||
|
||||
def auto_reinforce(self, memories: list["UserMemory"]) -> list["UserMemory"]:
|
||||
"""Weekly auto-reinforce for high-importance memories.
|
||||
|
||||
Applies a 10% boost to frequency_count for high-importance memories
|
||||
that haven't been accessed recently, keeping them fresh.
|
||||
"""
|
||||
reinforced = []
|
||||
now = datetime.now(UTC)
|
||||
|
||||
for memory in memories:
|
||||
importance_level = getattr(memory, "importance_level", "medium") or "medium"
|
||||
if importance_level != "high":
|
||||
continue
|
||||
|
||||
current_freq = getattr(memory, "frequency_count", 0) or 0
|
||||
if current_freq >= self.MAX_FREQUENCY:
|
||||
continue
|
||||
|
||||
# Apply 10% boost, capped at MAX_FREQUENCY
|
||||
new_freq = min(int(current_freq * self.AUTO_REINFORCE_BOOST + 1), self.MAX_FREQUENCY)
|
||||
memory.frequency_count = new_freq
|
||||
|
||||
# Slightly improve decay score
|
||||
current_decay = getattr(memory, "decay_score", 0.5) or 0.5
|
||||
memory.decay_score = min(0.95, current_decay * 1.05)
|
||||
|
||||
memory.last_accessed_at = now
|
||||
reinforced.append(memory)
|
||||
|
||||
return reinforced
|
||||
113
backend/app/services/memory/reminder_scheduler.py
Normal file
113
backend/app/services/memory/reminder_scheduler.py
Normal file
@@ -0,0 +1,113 @@
|
||||
"""
|
||||
ReminderScheduler
|
||||
|
||||
Schedules and manages user reminders.
|
||||
"""
|
||||
|
||||
from datetime import UTC, datetime, timedelta
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from sqlalchemy import select, and_
|
||||
|
||||
from app.models.reminder import Reminder
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
|
||||
class ReminderScheduler:
|
||||
"""Schedule and manage user reminders."""
|
||||
|
||||
async def create_reminder(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
content: str,
|
||||
trigger_at: datetime,
|
||||
trigger_type: str = "time",
|
||||
context_memory_id: str | None = None,
|
||||
) -> Reminder:
|
||||
"""Create a new reminder."""
|
||||
reminder = Reminder(
|
||||
user_id=user_id,
|
||||
content=content,
|
||||
trigger_type=trigger_type,
|
||||
trigger_at=trigger_at,
|
||||
context_memory_id=context_memory_id,
|
||||
status="pending",
|
||||
)
|
||||
db.add(reminder)
|
||||
await db.commit()
|
||||
await db.refresh(reminder)
|
||||
return reminder
|
||||
|
||||
async def get_due_reminders(self, db: "AsyncSession", user_id: str) -> list[Reminder]:
|
||||
"""Get reminders that are due (status=pending, trigger_at <= now, not snoozed)."""
|
||||
now = datetime.now(UTC)
|
||||
result = await db.execute(
|
||||
select(Reminder)
|
||||
.where(
|
||||
Reminder.user_id == user_id,
|
||||
Reminder.status == "pending",
|
||||
Reminder.trigger_at <= now,
|
||||
((Reminder.snoozed_until.is_(None)) | (Reminder.snoozed_until <= now)),
|
||||
)
|
||||
.order_by(Reminder.trigger_at.asc())
|
||||
)
|
||||
return list(result.scalars().all())
|
||||
|
||||
async def snooze(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
reminder_id: int,
|
||||
minutes: int,
|
||||
) -> Reminder | None:
|
||||
"""Snooze reminder for N minutes."""
|
||||
result = await db.execute(select(Reminder).where(Reminder.id == reminder_id))
|
||||
reminder = result.scalar_one_or_none()
|
||||
if not reminder:
|
||||
return None
|
||||
|
||||
reminder.status = "snoozed"
|
||||
reminder.snoozed_until = datetime.now(UTC) + timedelta(minutes=minutes)
|
||||
await db.commit()
|
||||
await db.refresh(reminder)
|
||||
return reminder
|
||||
|
||||
async def dismiss(self, db: "AsyncSession", reminder_id: int) -> bool:
|
||||
"""Mark reminder as dismissed."""
|
||||
result = await db.execute(select(Reminder).where(Reminder.id == reminder_id))
|
||||
reminder = result.scalar_one_or_none()
|
||||
if not reminder:
|
||||
return False
|
||||
|
||||
reminder.status = "dismissed"
|
||||
await db.commit()
|
||||
return True
|
||||
|
||||
async def mark_sent(self, db: "AsyncSession", reminder_id: int) -> bool:
|
||||
"""Mark reminder as sent."""
|
||||
result = await db.execute(select(Reminder).where(Reminder.id == reminder_id))
|
||||
reminder = result.scalar_one_or_none()
|
||||
if not reminder:
|
||||
return False
|
||||
|
||||
reminder.status = "sent"
|
||||
await db.commit()
|
||||
return True
|
||||
|
||||
async def get_pending_reminders(
|
||||
self,
|
||||
db: "AsyncSession",
|
||||
user_id: str,
|
||||
) -> list[Reminder]:
|
||||
"""Get all pending reminders for a user."""
|
||||
result = await db.execute(
|
||||
select(Reminder)
|
||||
.where(
|
||||
Reminder.user_id == user_id,
|
||||
Reminder.status.in_(["pending", "snoozed"]),
|
||||
)
|
||||
.order_by(Reminder.trigger_at.asc())
|
||||
)
|
||||
return list(result.scalars().all())
|
||||
Reference in New Issue
Block a user