WIN-JHFT4D3SIVT\caoxiaozhu
11160ec4d2
- 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
265 lines
8.4 KiB
Python
265 lines
8.4 KiB
Python
"""
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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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