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
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
"""
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ProactiveInformer
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Checks conversation context and proactively informs user of relevant memories.
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Only fires probabilistically based on trigger type.
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"""
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import random
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from sqlalchemy.ext.asyncio import AsyncSession
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# Trigger types and their firing probabilities
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TRIGGERS = {
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"high_importance_topic": {"probability": 0.8},
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"repeat_question": {"probability": 1.0},
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"forgotten_context": {"probability": 0.5},
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"pending_goal": {"probability": 0.3},
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}
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# Message style templates for proactive informing
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INFORM_STYLE = {
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"casual": "对了，你之前提到...",
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"gentle": "不知道你有没有注意到...",
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"helpful": "我记起你关心这个，要不看看...",
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}
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# Keywords that indicate different trigger types
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TRIGGER_KEYWORDS = {
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"high_importance_topic": ["关于", "提到", "说过", "记得"],
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"repeat_question": ["之前", "上次", "以前", "好像问过"],
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"forgotten_context": ["忘了", "不记得", "记不清", "之前聊过"],
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"pending_goal": ["目标", "计划", "想做", "打算", "想学"],
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}
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class ProactiveInformer:
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"""Proactively inform users of relevant memories based on conversation context."""
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def should_inform(self, trigger_type: str) -> bool:
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"""Check if should inform based on probability."""
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if trigger_type not in TRIGGERS:
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return False
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probability = TRIGGERS[trigger_type]["probability"]
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return random.random() < probability
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def detect_trigger(self, message: str) -> str | None:
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"""Detect which trigger type (if any) this message corresponds to."""
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msg_lower = message.lower()
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for trigger_type, keywords in TRIGGER_KEYWORDS.items():
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for keyword in keywords:
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if keyword in msg_lower:
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return trigger_type
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return None
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def get_inform_message(self, trigger_type: str, context: dict) -> str:
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"""Generate natural proactive message based on trigger type."""
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style = INFORM_STYLE.get(context.get("style", "casual"), INFORM_STYLE["casual"])
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if trigger_type == "high_importance_topic":
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memory_content = context.get("memory_content", "")
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return f"{style}「{memory_content[:30]}」这个话题你很关心，要深入聊聊吗？"
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elif trigger_type == "repeat_question":
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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。"
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elif trigger_type == "forgotten_context":
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memory_content = context.get("memory_content", "")
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return f"这个话题你一个月前聊过：「{memory_content[:30]}」。要恢复一下吗？"
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elif trigger_type == "pending_goal":
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goal_content = context.get("goal_content", "")
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return f"你之前说要「{goal_content[:30]}」，有进展了吗？"
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return ""
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async def check_and_inform(
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self,
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db: "AsyncSession",
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user_id: str,
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current_message: str,
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) -> str | None:
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"""Main entry point.
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Returns proactive message if should inform, else None.
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"""
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# 1. Detect trigger type
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trigger_type = self.detect_trigger(current_message)
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if not trigger_type:
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return None
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# 2. Check probability
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if not self.should_inform(trigger_type):
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return None
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# 3. Fetch relevant context
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context = await self._fetch_trigger_context(db, user_id, trigger_type)
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if not context:
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return None
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# 4. Generate message
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return self.get_inform_message(trigger_type, context)
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async def _fetch_trigger_context(
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self,
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db: "AsyncSession",
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user_id: str,
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trigger_type: str,
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) -> dict | None:
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"""Fetch relevant context for the trigger type."""
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from sqlalchemy import select
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from app.models.memory import UserMemory
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# Get most recent high-importance memory
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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.is_archived == False,
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)
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.order_by(UserMemory.last_accessed_at.desc())
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.limit(1)
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)
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memory = result.scalar_one_or_none()
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if not memory:
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return None
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return {
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"memory_content": memory.content,
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"style": "casual",
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"memory_id": str(memory.id),
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}
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