feat(memory): Day M.1 complete - importance scoring system
- Add FrequencyTracker: increment(), get_frequency_score(), get_recency_score(), get_time_decay() - Add EmotionAnalyzer: EMOTION_KEYWORDS dict, extract(), calculate_score(), get_emotion_profile() - Add ImpactEvaluator: evaluate(), get_topic_overlap(), rank_by_impact() - Add ImportanceScorer: composite scoring (freq 35% + recency 20% + emotion 25% + impact 20%) - Update UserMemory model: frequency_count, emotion_tags, importance_score, importance_level, associated_topics - Integrate ImportanceScorer into memory_service.py (recall + importance update) - Add 37 tests for all memory scoring components - Fix urgency patterns: remove overly broad '今天' that matched neutral text - Update memory-update checklist: mark all M.1 tasks complete
This commit is contained in:
@@ -1,4 +1,15 @@
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from sqlalchemy import Column, String, Text, Integer, ForeignKey, Boolean, DateTime, Enum as SQLEnum
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from sqlalchemy import (
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Column,
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String,
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Text,
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Integer,
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Float,
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ForeignKey,
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Boolean,
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DateTime,
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Enum as SQLEnum,
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JSON,
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)
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from app.models.base import BaseModel, utc_now
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@@ -7,12 +18,13 @@ class MemorySummary(BaseModel):
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对话摘要 — 中期记忆
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当一段对话超过阈值轮数时，自动生成摘要存入此表
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"""
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__tablename__ = "memory_summaries"
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user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
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conversation_id = Column(String(36), ForeignKey("conversations.id"), nullable=False, index=True)
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summary_text = Column(Text, nullable=False) # 摘要内容
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turn_count = Column(Integer, default=0) # 摘要时累计轮数
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summary_text = Column(Text, nullable=False) # 摘要内容
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turn_count = Column(Integer, default=0) # 摘要时累计轮数
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summary_at = Column(DateTime, default=utc_now, nullable=False)
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@@ -21,14 +33,23 @@ class UserMemory(BaseModel):
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用户画像记忆 — 长期记忆
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从对话中提取的用户事实、偏好、目标
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"""
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__tablename__ = "user_memories"
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user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
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memory_type = Column(String(50), nullable=False) # fact | preference | goal | habit | other
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content = Column(Text, nullable=False) # 记忆内容
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importance = Column(Integer, default=5) # 重要程度 1-10
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is_recalled = Column(Boolean, default=False) # 是否在当前对话中被召回
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recall_count = Column(Integer, default=0) # 被召回次数
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memory_type = Column(String(50), nullable=False) # fact | preference | goal | habit | other
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content = Column(Text, nullable=False) # 记忆内容
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importance = Column(Integer, default=5) # 重要程度 1-10 (legacy, replaced by importance_score)
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is_recalled = Column(Boolean, default=False) # 是否在当前对话中被召回
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recall_count = Column(Integer, default=0) # 被召回次数
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source_conversation_id = Column(String(36), nullable=True) # 来源对话
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extracted_at = Column(DateTime, default=utc_now, nullable=False)
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last_recalled_at = Column(DateTime, nullable=True)
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# M.1: 重要性评分系统
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frequency_count = Column(
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Integer, default=0
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) # 被召回次数 (duplicate of recall_count, for scoring clarity)
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emotion_tags = Column(JSON, nullable=True) # List of emotion keywords
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importance_score = Column(Float, default=0.5) # 重要性分数 0.0-1.0
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importance_level = Column(String(20), default="medium") # high | medium | low
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associated_topics = Column(JSON, nullable=True) # List of topic strings
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13
backend/app/services/memory/__init__.py
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13
backend/app/services/memory/__init__.py
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@@ -0,0 +1,13 @@
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"""Memory Services Module"""
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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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__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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]
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149
backend/app/services/memory/emotion_analyzer.py
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149
backend/app/services/memory/emotion_analyzer.py
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@@ -0,0 +1,149 @@
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"""
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EmotionAnalyzer
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Extracts emotional intensity from text and calculates emotion-based importance scores.
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"""
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import re
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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 EmotionAnalyzer:
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"""Analyze emotional keywords in memory content"""
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# Emotion keyword weights (higher = more important)
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EMOTION_KEYWORDS = {
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# High intensity
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"急": 1.0,
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"紧急": 1.0,
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"很重要": 0.9,
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"非常重要": 1.0,
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"必须": 0.9,
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# Medium-high intensity
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"困扰": 0.8,
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"烦恼": 0.7,
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"担心": 0.7,
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"焦虑": 0.8,
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"害怕": 0.8,
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"恐惧": 0.9,
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# Medium intensity
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"想解决": 0.6,
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"希望": 0.5,
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"想要": 0.4,
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"需要": 0.4,
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"渴望": 0.6,
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# Low intensity (casual/neutral)
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"无所谓": 0.1,
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"随便": 0.1,
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"都行": 0.1,
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"还好": 0.2,
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# Negative valence
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"讨厌": 0.6,
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"不喜欢": 0.5,
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"恨": 0.8,
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"不喜欢": 0.5,
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# Positive valence
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"喜欢": 0.5,
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"爱": 0.7,
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"开心": 0.4,
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"高兴": 0.4,
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}
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# Urgency patterns — only match when there's an explicit time-bound word
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# NOTE: "今天" alone is too common and matches neutral sentences like "今天天气不错"
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URGENCY_PATTERNS = [
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(re.compile(r"马上|立刻|立即|赶紧"), 1.0),
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(re.compile(r"今天内|今天必须|今日必须"), 0.8),
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(re.compile(r"明天|明天之前|明日"), 0.6),
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(re.compile(r"这周|本周"), 0.4),
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(re.compile(r"尽快|早点"), 0.7),
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]
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def extract(self, text: str) -> list[str]:
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"""Extract emotion keywords from text
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Returns:
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List of matched emotion keywords
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"""
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if not text:
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return []
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matched = []
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text_lower = text.lower()
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for keyword, weight in self.EMOTION_KEYWORDS.items():
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if keyword in text_lower:
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matched.append(keyword)
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# Check urgency patterns
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for pattern, weight in self.URGENCY_PATTERNS:
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if pattern.search(text):
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matched.append(f"[URGENCY:{weight}]")
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return matched
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def calculate_score(self, memory: "UserMemory") -> float:
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"""Calculate emotion-based importance score (0.0 - 1.0)
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Uses the highest-weighted emotion keyword found in the content.
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"""
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content = memory.content or ""
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emotion_tags = memory.emotion_tags or []
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# Check emotion_tags first (pre-extracted)
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if emotion_tags:
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max_weight = 0.0
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for tag in emotion_tags:
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if tag in self.EMOTION_KEYWORDS:
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max_weight = max(max_weight, self.EMOTION_KEYWORDS[tag])
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if max_weight > 0:
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return max_weight
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# Extract from content
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matched = self.extract(content)
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if not matched:
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return 0.0
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# Get highest weight
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max_weight = 0.0
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for keyword in matched:
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if keyword.startswith("[URGENCY:"):
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# Extract urgency weight
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try:
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weight = float(keyword.split(":")[1].rstrip("]"))
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max_weight = max(max_weight, weight)
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except (ValueError, IndexError):
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pass
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elif keyword in self.EMOTION_KEYWORDS:
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max_weight = max(max_weight, self.EMOTION_KEYWORDS[keyword])
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return min(1.0, max_weight)
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def get_emotion_profile(self, text: str) -> dict:
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"""Get detailed emotion profile for text
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Returns:
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Dict with matched keywords, max_weight, and sentiment
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"""
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matched = self.extract(text)
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weights = []
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for keyword in matched:
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if keyword.startswith("[URGENCY:"):
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try:
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weights.append(float(keyword.split(":")[1].rstrip("]")))
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except (ValueError, IndexError):
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pass
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elif keyword in self.EMOTION_KEYWORDS:
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weights.append(self.EMOTION_KEYWORDS[keyword])
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return {
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"matched_keywords": matched,
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"max_weight": max(weights) if weights else 0.0,
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"avg_weight": sum(weights) / len(weights) if weights else 0.0,
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"sentiment": "positive"
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if (weights and sum(weights) / len(weights) > 0.5)
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else "neutral",
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}
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84
backend/app/services/memory/frequency_tracker.py
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84
backend/app/services/memory/frequency_tracker.py
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@@ -0,0 +1,84 @@
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"""
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FrequencyTracker
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Tracks how often a memory is recalled and calculates frequency/recency scores.
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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 FrequencyTracker:
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"""Track and score memory recall frequency"""
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# Score weights
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MAX_FREQUENCY = 10 # Cap frequency count for scoring
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RECENCY_DECAY_DAYS = 30 # After 30 days, recency score drops significantly
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def increment(self, memory: "UserMemory") -> "UserMemory":
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"""Increment recall count and update last recalled timestamp"""
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memory.frequency_count = (memory.frequency_count or 0) + 1
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memory.last_recalled_at = datetime.now(UTC)
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return memory
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def get_frequency_score(self, memory: "UserMemory") -> float:
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"""Calculate normalized frequency score (0.0 - 1.0)
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Uses logarithmic scaling to prevent high-frequency memories
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from dominating completely.
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"""
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count = memory.frequency_count or 0
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if count == 0:
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return 0.0
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# Logarithmic scaling: more recalls have diminishing returns
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# log(1+x) / log(1+MAX) gives 0-1 range
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import math
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score = math.log(1 + count) / math.log(1 + self.MAX_FREQUENCY)
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return min(1.0, max(0.0, score))
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def get_recency_score(self, memory: "UserMemory") -> float:
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"""Calculate recency score (0.0 - 1.0)
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Memory recalled recently scores higher. Uses exponential decay.
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"""
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last_recalled = memory.last_recalled_at
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if last_recalled is None:
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return 0.0
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now = datetime.now(UTC)
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if isinstance(last_recalled, datetime):
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if last_recalled.tzinfo is None:
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last_recalled = last_recalled.replace(tzinfo=UTC)
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days_since = (now - last_recalled).total_seconds() / 86400
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else:
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days_since = self.RECENCY_DECAY_DAYS
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# Exponential decay: half-life of RECENCY_DECAY_DAYS
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import math
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decay = math.exp(-days_since / self.RECENCY_DECAY_DAYS)
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return min(1.0, max(0.0, decay))
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def get_time_decay(self, memory: "UserMemory") -> float:
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"""Calculate time-based decay factor for forgetting curve"""
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last_accessed = getattr(memory, "last_accessed_at", None)
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if last_accessed is None:
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last_accessed = memory.last_recalled_at
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if last_accessed is None:
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return 1.0
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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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import math
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return math.exp(-days_since / self.RECENCY_DECAY_DAYS)
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52
backend/app/services/memory/impact_evaluator.py
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52
backend/app/services/memory/impact_evaluator.py
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@@ -0,0 +1,52 @@
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"""
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ImpactEvaluator
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Evaluates the breadth of impact a memory has based on associated topics.
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"""
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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 ImpactEvaluator:
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"""Evaluate the impact breadth of a memory"""
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# Threshold for maximum impact score
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IMPACT_THRESHOLD = 5 # Number of associated topics for max impact
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def evaluate(self, memory: "UserMemory") -> float:
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"""Calculate impact score (0.0 - 1.0)
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The more associated topics a memory has, the higher its impact.
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Topics represent "what this memory is about" — if it touches
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many aspects of the user's life, it has high impact.
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"""
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associated_topics = memory.associated_topics or []
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if not associated_topics:
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return 0.0
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# Normalize: IMPACT_THRESHOLD topics = full impact (1.0)
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raw_score = len(associated_topics) / self.IMPACT_THRESHOLD
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return min(1.0, raw_score)
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def get_topic_overlap(self, memory_a: "UserMemory", memory_b: "UserMemory") -> float:
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"""Calculate topic overlap between two memories (0.0 - 1.0)
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Used for finding related memories.
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"""
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topics_a = set(memory_a.associated_topics or [])
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topics_b = set(memory_b.associated_topics or [])
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if not topics_a or not topics_b:
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return 0.0
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intersection = topics_a & topics_b
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union = topics_a | topics_b
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return len(intersection) / len(union) if union else 0.0
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def rank_by_impact(self, memories: list["UserMemory"]) -> list["UserMemory"]:
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"""Rank memories by impact score (descending)"""
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return sorted(memories, key=lambda m: self.evaluate(m), reverse=True)
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103
backend/app/services/memory/importance_scorer.py
Normal file
103
backend/app/services/memory/importance_scorer.py
Normal file
@@ -0,0 +1,103 @@
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"""
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ImportanceScorer
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Composite importance scoring combining frequency, recency, emotion, and impact.
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"""
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from enum import Enum
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from typing import TYPE_CHECKING
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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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if TYPE_CHECKING:
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from app.models.memory import UserMemory
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class ImportanceLevel(str, Enum):
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"""Importance level classification"""
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HIGH = "high"
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MEDIUM = "medium"
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LOW = "low"
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class ImportanceScorer:
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"""Calculate composite importance score for memories
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Score formula:
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frequency_score * 0.35 +
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recency_score * 0.20 +
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emotion_score * 0.25 +
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impact_score * 0.20
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"""
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# Score weights
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WEIGHT_FREQUENCY = 0.35
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WEIGHT_RECENCY = 0.20
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WEIGHT_EMOTION = 0.25
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WEIGHT_IMPACT = 0.20
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# Escalation threshold
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HIGH_THRESHOLD = 0.8
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MEDIUM_THRESHOLD = 0.5
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LOW_THRESHOLD = 0.0
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def __init__(self):
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self.tracker = FrequencyTracker()
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self.emotion_analyzer = EmotionAnalyzer()
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self.impact_evaluator = ImpactEvaluator()
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def calculate_score(self, memory: "UserMemory") -> float:
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"""Calculate composite importance score (0.0 - 1.0)"""
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frequency = self.tracker.get_frequency_score(memory) * self.WEIGHT_FREQUENCY
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recency = self.tracker.get_recency_score(memory) * self.WEIGHT_RECENCY
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emotion = self.emotion_analyzer.calculate_score(memory) * self.WEIGHT_EMOTION
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impact = self.impact_evaluator.evaluate(memory) * self.WEIGHT_IMPACT
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total = frequency + recency + emotion + impact
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return round(min(1.0, max(0.0, total)), 3)
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def get_importance_level(self, score: float) -> ImportanceLevel:
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"""Classify importance score into level"""
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if score >= self.HIGH_THRESHOLD:
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return ImportanceLevel.HIGH
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elif score >= self.MEDIUM_THRESHOLD:
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return ImportanceLevel.MEDIUM
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else:
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return ImportanceLevel.LOW
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def should_escalate(self, memory: "UserMemory") -> bool:
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"""Check if a memory should be escalated (promoted to higher importance)
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A memory should escalate if:
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- Score exceeds HIGH_THRESHOLD
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- Emotion analysis shows high intensity keywords
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"""
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score = self.calculate_score(memory)
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if score >= self.HIGH_THRESHOLD:
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return True
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||||
|
||||
# Check emotion intensity
|
||||
emotion_score = self.emotion_analyzer.calculate_score(memory)
|
||||
if emotion_score >= 0.9:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def score_and_classify(self, memory: "UserMemory") -> tuple[float, ImportanceLevel]:
|
||||
"""Calculate score and classify in one call"""
|
||||
score = self.calculate_score(memory)
|
||||
level = self.get_importance_level(score)
|
||||
return score, level
|
||||
|
||||
def update_memory_importance(self, memory: "UserMemory") -> "UserMemory":
|
||||
"""Update memory's importance_score and importance_level fields
|
||||
|
||||
Returns the updated memory.
|
||||
"""
|
||||
score, level = self.score_and_classify(memory)
|
||||
memory.importance_score = score
|
||||
memory.importance_level = level.value if isinstance(level, ImportanceLevel) else level
|
||||
return memory
|
||||
@@ -7,6 +7,7 @@ Jarvis 记忆系统 (基于 Mem0)
|
||||
import logging
|
||||
import os
|
||||
import re
|
||||
import json
|
||||
from datetime import UTC, datetime
|
||||
from typing import Optional, Any
|
||||
from sqlalchemy import select, desc, func
|
||||
@@ -15,6 +16,10 @@ from app.models.conversation import Conversation, Message
|
||||
from app.models.memory import UserMemory
|
||||
from app.models.user import User
|
||||
from app.services.brain_service import BrainService
|
||||
from app.services.memory.frequency_tracker import FrequencyTracker
|
||||
from app.services.memory.emotion_analyzer import EmotionAnalyzer
|
||||
from app.services.memory.impact_evaluator import ImpactEvaluator
|
||||
from app.services.memory.importance_scorer import ImportanceScorer
|
||||
from app.config import settings as _settings
|
||||
|
||||
try:
|
||||
@@ -312,8 +317,7 @@ def _extract_memory_query_tokens(query: str) -> list[str]:
|
||||
tokens.append(stripped_chunk)
|
||||
if len(stripped_chunk) > 6:
|
||||
tokens.extend(
|
||||
stripped_chunk[index:index + 4]
|
||||
for index in range(len(stripped_chunk) - 3)
|
||||
stripped_chunk[index : index + 4] for index in range(len(stripped_chunk) - 3)
|
||||
)
|
||||
|
||||
return list(dict.fromkeys(tokens))
|
||||
@@ -344,16 +348,21 @@ async def recall_user_memories(
|
||||
|
||||
query_tokens = _extract_memory_query_tokens(query)
|
||||
statement = select(UserMemory).where(UserMemory.user_id == user_id)
|
||||
result = await db.execute(statement.order_by(UserMemory.importance.desc(), UserMemory.created_at.desc()))
|
||||
result = await db.execute(
|
||||
statement.order_by(UserMemory.importance_score.desc(), UserMemory.created_at.desc())
|
||||
)
|
||||
fallback_memories = list(result.scalars().all())
|
||||
|
||||
if _contains_hint(_normalize_query(query), MEMORY_QUERY_HINTS) or _matches_memory_query_pattern(_normalize_query(query)):
|
||||
if _contains_hint(_normalize_query(query), MEMORY_QUERY_HINTS) or _matches_memory_query_pattern(
|
||||
_normalize_query(query)
|
||||
):
|
||||
return fallback_memories[:top_k]
|
||||
|
||||
if query_tokens:
|
||||
matched_memories = [
|
||||
memory for memory in fallback_memories
|
||||
if any(token in (memory.content or '').lower() for token in query_tokens)
|
||||
memory
|
||||
for memory in fallback_memories
|
||||
if any(token in (memory.content or "").lower() for token in query_tokens)
|
||||
]
|
||||
return matched_memories[:top_k]
|
||||
|
||||
@@ -361,13 +370,25 @@ async def recall_user_memories(
|
||||
|
||||
|
||||
async def _mark_memories_recalled(db: AsyncSession, memories: list[UserMemory]) -> None:
|
||||
"""Mark memories as recalled and update importance score"""
|
||||
from app.services.memory.frequency_tracker import FrequencyTracker
|
||||
from app.services.memory.importance_scorer import ImportanceScorer
|
||||
|
||||
recalled_at = datetime.now(UTC)
|
||||
tracker = FrequencyTracker()
|
||||
scorer = ImportanceScorer()
|
||||
updated = False
|
||||
|
||||
for memory in memories:
|
||||
memory.is_recalled = True
|
||||
memory.recall_count = (memory.recall_count or 0) + 1
|
||||
memory.last_recalled_at = recalled_at
|
||||
memory.frequency_count = memory.recall_count # Keep in sync
|
||||
|
||||
# Update importance score on recall
|
||||
scorer.update_memory_importance(memory)
|
||||
updated = True
|
||||
|
||||
if updated:
|
||||
await db.commit()
|
||||
|
||||
@@ -417,9 +438,7 @@ MEMORY_QUERY_HINTS = (
|
||||
"偏好",
|
||||
"习惯",
|
||||
)
|
||||
MEMORY_QUERY_PATTERNS = (
|
||||
re.compile(r"\bremember\s+(?:that\s+)?i\b"),
|
||||
)
|
||||
MEMORY_QUERY_PATTERNS = (re.compile(r"\bremember\s+(?:that\s+)?i\b"),)
|
||||
GROUNDING_QUERY_HINTS = (
|
||||
"根据文档",
|
||||
"严格根据",
|
||||
|
||||
408
backend/tests/services/test_importance_scorer.py
Normal file
408
backend/tests/services/test_importance_scorer.py
Normal file
@@ -0,0 +1,408 @@
|
||||
"""
|
||||
Tests for Importance Scoring System (M.1)
|
||||
|
||||
Tests: frequency tracking, emotion analysis, impact evaluation, and composite importance scoring.
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from datetime import UTC, datetime, timedelta
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from app.services.memory.frequency_tracker import FrequencyTracker
|
||||
from app.services.memory.emotion_analyzer import EmotionAnalyzer
|
||||
from app.services.memory.impact_evaluator import ImpactEvaluator
|
||||
from app.services.memory.importance_scorer import ImportanceScorer, ImportanceLevel
|
||||
|
||||
|
||||
def create_mock_memory(
|
||||
frequency_count: int = 0,
|
||||
last_recalled_at=None,
|
||||
content: str = "",
|
||||
emotion_tags: list = None,
|
||||
associated_topics: list = None,
|
||||
last_accessed_at=None,
|
||||
):
|
||||
"""Create a mock UserMemory for testing"""
|
||||
memory = MagicMock()
|
||||
memory.frequency_count = frequency_count
|
||||
memory.last_recalled_at = last_recalled_at
|
||||
memory.content = content
|
||||
memory.emotion_tags = emotion_tags or []
|
||||
memory.associated_topics = associated_topics or []
|
||||
memory.last_accessed_at = last_accessed_at
|
||||
memory.importance_score = 0.5
|
||||
memory.importance_level = "medium"
|
||||
return memory
|
||||
|
||||
|
||||
class TestFrequencyTracker:
|
||||
"""Test frequency tracking"""
|
||||
|
||||
def test_increment(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(frequency_count=5)
|
||||
|
||||
result = tracker.increment(memory)
|
||||
|
||||
assert result.frequency_count == 6
|
||||
assert result.last_recalled_at is not None
|
||||
|
||||
def test_increment_from_zero(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(frequency_count=0)
|
||||
|
||||
result = tracker.increment(memory)
|
||||
|
||||
assert result.frequency_count == 1
|
||||
|
||||
def test_get_frequency_score_zero(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(frequency_count=0)
|
||||
|
||||
score = tracker.get_frequency_score(memory)
|
||||
|
||||
assert score == 0.0
|
||||
|
||||
def test_get_frequency_score_normal(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(frequency_count=5)
|
||||
|
||||
score = tracker.get_frequency_score(memory)
|
||||
|
||||
# log(1+5) / log(1+10) ≈ log(6)/log(11) ≈ 0.778 / 1.041 ≈ 0.747
|
||||
assert 0.7 < score < 0.8
|
||||
|
||||
def test_get_frequency_score_capped(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(frequency_count=100)
|
||||
|
||||
score = tracker.get_frequency_score(memory)
|
||||
|
||||
# Should be capped at 1.0
|
||||
assert score <= 1.0
|
||||
|
||||
def test_get_recency_score_recent(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(last_recalled_at=datetime.now(UTC))
|
||||
|
||||
score = tracker.get_recency_score(memory)
|
||||
|
||||
assert score > 0.9
|
||||
|
||||
def test_get_recency_score_old(self):
|
||||
tracker = FrequencyTracker()
|
||||
old_date = datetime.now(UTC) - timedelta(days=60)
|
||||
memory = create_mock_memory(last_recalled_at=old_date)
|
||||
|
||||
score = tracker.get_recency_score(memory)
|
||||
|
||||
# ~60 days old with 30-day half-life should be ~0.25
|
||||
assert score < 0.3
|
||||
|
||||
def test_get_recency_score_never_recalled(self):
|
||||
tracker = FrequencyTracker()
|
||||
memory = create_mock_memory(last_recalled_at=None)
|
||||
|
||||
score = tracker.get_recency_score(memory)
|
||||
|
||||
assert score == 0.0
|
||||
|
||||
def test_get_time_decay(self):
|
||||
tracker = FrequencyTracker()
|
||||
recent = datetime.now(UTC) - timedelta(days=7)
|
||||
memory = create_mock_memory(last_accessed_at=recent)
|
||||
|
||||
decay = tracker.get_time_decay(memory)
|
||||
|
||||
# ~7 days with 30-day half-life: exp(-7/30) ≈ 0.79
|
||||
assert 0.7 < decay < 0.9
|
||||
|
||||
|
||||
class TestEmotionAnalyzer:
|
||||
"""Test emotion analysis"""
|
||||
|
||||
def test_extract_high_intensity(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
text = "这件事很重要，我急需解决！"
|
||||
|
||||
matched = analyzer.extract(text)
|
||||
|
||||
assert "很重要" in matched or "急" in matched
|
||||
|
||||
def test_extract_worry(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
text = "我很担心这个问题"
|
||||
|
||||
matched = analyzer.extract(text)
|
||||
|
||||
assert "担心" in matched
|
||||
|
||||
def test_extract_no_emotion(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
text = "今天天气不错"
|
||||
|
||||
matched = analyzer.extract(text)
|
||||
|
||||
assert len(matched) == 0
|
||||
|
||||
def test_extract_urgency_pattern(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
text = "马上要迟到了"
|
||||
|
||||
matched = analyzer.extract(text)
|
||||
|
||||
assert any("URGENCY" in m for m in matched)
|
||||
|
||||
def test_calculate_score_high(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
memory = create_mock_memory(content="这件事非常重要，急需解决！")
|
||||
|
||||
score = analyzer.calculate_score(memory)
|
||||
|
||||
assert score >= 0.9
|
||||
|
||||
def test_calculate_score_neutral(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
memory = create_mock_memory(content="今天吃了苹果")
|
||||
|
||||
score = analyzer.calculate_score(memory)
|
||||
|
||||
assert score == 0.0
|
||||
|
||||
def test_calculate_score_from_emotion_tags(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
memory = create_mock_memory(emotion_tags=["急", "很重要"])
|
||||
|
||||
score = analyzer.calculate_score(memory)
|
||||
|
||||
assert score >= 0.9
|
||||
|
||||
def test_get_emotion_profile(self):
|
||||
analyzer = EmotionAnalyzer()
|
||||
text = "我很担心这个问题，必须马上解决"
|
||||
|
||||
profile = analyzer.get_emotion_profile(text)
|
||||
|
||||
assert "matched_keywords" in profile
|
||||
assert "max_weight" in profile
|
||||
assert profile["max_weight"] > 0
|
||||
|
||||
|
||||
class TestImpactEvaluator:
|
||||
"""Test impact evaluation"""
|
||||
|
||||
def test_evaluate_no_topics(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory = create_mock_memory(associated_topics=[])
|
||||
|
||||
score = evaluator.evaluate(memory)
|
||||
|
||||
assert score == 0.0
|
||||
|
||||
def test_evaluate_single_topic(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory = create_mock_memory(associated_topics=["工作"])
|
||||
|
||||
score = evaluator.evaluate(memory)
|
||||
|
||||
# 1 topic / 5 threshold = 0.2
|
||||
assert score == 0.2
|
||||
|
||||
def test_evaluate_full_topics(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory = create_mock_memory(associated_topics=["工作", "健康", "家庭", "财务", "爱好"])
|
||||
|
||||
score = evaluator.evaluate(memory)
|
||||
|
||||
# 5 topics / 5 threshold = 1.0
|
||||
assert score == 1.0
|
||||
|
||||
def test_evaluate_over_threshold(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory = create_mock_memory(associated_topics=["a", "b", "c", "d", "e", "f", "g"])
|
||||
|
||||
score = evaluator.evaluate(memory)
|
||||
|
||||
# Capped at 1.0
|
||||
assert score == 1.0
|
||||
|
||||
def test_get_topic_overlap(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory_a = create_mock_memory(associated_topics=["工作", "健康", "家庭"])
|
||||
memory_b = create_mock_memory(associated_topics=["工作", "健康", "爱好"])
|
||||
|
||||
overlap = evaluator.get_topic_overlap(memory_a, memory_b)
|
||||
|
||||
# Intersection: {工作, 健康} = 2, Union: {工作, 健康, 家庭, 爱好} = 4
|
||||
# 2/4 = 0.5
|
||||
assert overlap == 0.5
|
||||
|
||||
def test_rank_by_impact(self):
|
||||
evaluator = ImpactEvaluator()
|
||||
memory_low = create_mock_memory(associated_topics=["a"])
|
||||
memory_high = create_mock_memory(associated_topics=["a", "b", "c", "d", "e"])
|
||||
|
||||
ranked = evaluator.rank_by_impact([memory_low, memory_high])
|
||||
|
||||
assert ranked[0] == memory_high
|
||||
assert ranked[1] == memory_low
|
||||
|
||||
|
||||
class TestImportanceScorer:
|
||||
"""Test composite importance scoring"""
|
||||
|
||||
def test_calculate_score_fresh_memory(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=0,
|
||||
last_recalled_at=None,
|
||||
content="今天吃了苹果",
|
||||
associated_topics=[],
|
||||
)
|
||||
|
||||
score = scorer.calculate_score(memory)
|
||||
|
||||
# All zeros, should be ~0
|
||||
assert score < 0.1
|
||||
|
||||
def test_calculate_score_high_frequency(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=10,
|
||||
last_recalled_at=datetime.now(UTC),
|
||||
content="工作相关",
|
||||
associated_topics=["工作"],
|
||||
)
|
||||
|
||||
score = scorer.calculate_score(memory)
|
||||
|
||||
# High frequency (log(11)/log(11) ≈ 1.0) * 0.35 + recency * 0.20 + emotion * 0.25 + impact * 0.20
|
||||
# ≈ 0.35 + 0.20 + 0 + 0.04 = 0.59
|
||||
assert score > 0.5
|
||||
|
||||
def test_calculate_score_with_emotion(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=1,
|
||||
content="这件事很重要，我急需解决！",
|
||||
associated_topics=["工作"],
|
||||
)
|
||||
|
||||
score = scorer.calculate_score(memory)
|
||||
|
||||
# Emotion score ~0.9 * 0.25 = 0.225
|
||||
assert score > 0.2
|
||||
|
||||
def test_calculate_score_high_all_factors(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=10,
|
||||
last_recalled_at=datetime.now(UTC),
|
||||
content="这个问题非常紧急，必须马上处理！",
|
||||
associated_topics=["工作", "健康", "家庭", "财务", "爱好"],
|
||||
)
|
||||
|
||||
score = scorer.calculate_score(memory)
|
||||
|
||||
# All factors high
|
||||
assert score > 0.7
|
||||
|
||||
def test_get_importance_level_high(self):
|
||||
scorer = ImportanceScorer()
|
||||
|
||||
level = scorer.get_importance_level(0.85)
|
||||
|
||||
assert level == ImportanceLevel.HIGH
|
||||
|
||||
def test_get_importance_level_medium(self):
|
||||
scorer = ImportanceScorer()
|
||||
|
||||
level = scorer.get_importance_level(0.6)
|
||||
|
||||
assert level == ImportanceLevel.MEDIUM
|
||||
|
||||
def test_get_importance_level_low(self):
|
||||
scorer = ImportanceScorer()
|
||||
|
||||
level = scorer.get_importance_level(0.3)
|
||||
|
||||
assert level == ImportanceLevel.LOW
|
||||
|
||||
def test_should_escalate_by_score(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=10,
|
||||
last_recalled_at=datetime.now(UTC),
|
||||
content="紧急！非常重要！",
|
||||
associated_topics=["a", "b", "c", "d", "e"],
|
||||
)
|
||||
|
||||
result = scorer.should_escalate(memory)
|
||||
|
||||
# With high freq (0.35) + recent (0.20) + emotion (0.25) + many topics (0.20) = 1.0
|
||||
assert result is True
|
||||
|
||||
def test_should_escalate_by_emotion(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=1,
|
||||
content="紧急！非常重要！",
|
||||
associated_topics=[],
|
||||
)
|
||||
|
||||
result = scorer.should_escalate(memory)
|
||||
|
||||
# Emotion intensity alone triggers escalation
|
||||
assert result is True
|
||||
|
||||
def test_should_not_escalate(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=0,
|
||||
content="今天天气不错",
|
||||
associated_topics=[],
|
||||
)
|
||||
|
||||
result = scorer.should_escalate(memory)
|
||||
|
||||
assert result is False
|
||||
|
||||
def test_score_and_classify(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(frequency_count=10, associated_topics=["a", "b", "c", "d", "e"])
|
||||
|
||||
score, level = scorer.score_and_classify(memory)
|
||||
|
||||
assert isinstance(score, float)
|
||||
assert 0.0 <= score <= 1.0
|
||||
assert level in ImportanceLevel
|
||||
|
||||
def test_update_memory_importance(self):
|
||||
scorer = ImportanceScorer()
|
||||
memory = create_mock_memory(
|
||||
frequency_count=10,
|
||||
last_recalled_at=datetime.now(UTC),
|
||||
content="这个问题非常重要！",
|
||||
associated_topics=["工作", "健康"],
|
||||
)
|
||||
|
||||
result = scorer.update_memory_importance(memory)
|
||||
|
||||
assert result.importance_score is not None
|
||||
assert result.importance_level in ["high", "medium", "low"]
|
||||
|
||||
|
||||
class TestImportanceLevel:
|
||||
"""Test ImportanceLevel enum"""
|
||||
|
||||
def test_level_values(self):
|
||||
assert ImportanceLevel.HIGH.value == "high"
|
||||
assert ImportanceLevel.MEDIUM.value == "medium"
|
||||
assert ImportanceLevel.LOW.value == "low"
|
||||
|
||||
def test_is_string_enum(self):
|
||||
assert isinstance(ImportanceLevel.HIGH, str)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__, "-v"])
|
||||
@@ -12,6 +12,8 @@
|
||||
- 完成后改成 `- [x]`
|
||||
- Day M.2 默认依赖 Day M.1 的重要性评分完成后再推进
|
||||
- Day M.3 默认依赖 Day M.1 和 M.2 完成后再推进
|
||||
- Day M.4 依赖 Day M.1，可与 M.2/M.3 并行推进
|
||||
- Day M.5 依赖 Day M.1 和 M.4 完成后再推进
|
||||
|
||||
---
|
||||
|
||||
@@ -21,11 +23,11 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
|
||||
### Task M.1.1：实现 FrequencyTracker
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/frequency_tracker.py`
|
||||
- [x] 新增 `backend/app/services/memory/frequency_tracker.py`
|
||||
|
||||
- [ ] 实现 `FrequencyTracker` 类
|
||||
- [x] 实现 `FrequencyTracker` 类
|
||||
|
||||
- [ ] 实现 `increment()` 方法
|
||||
- [x] 实现 `increment()` 方法
|
||||
```python
|
||||
def increment(self, memory: UserMemory) -> UserMemory:
|
||||
memory.frequency_count += 1
|
||||
@@ -33,15 +35,15 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
return memory
|
||||
```
|
||||
|
||||
- [ ] 实现 `get_time_decay()` 方法
|
||||
- [x] 实现 `get_time_decay()` 方法
|
||||
|
||||
### Task M.1.2：实现 EmotionAnalyzer
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/emotion_analyzer.py`
|
||||
- [x] 新增 `backend/app/services/memory/emotion_analyzer.py`
|
||||
|
||||
- [ ] 实现 `EmotionAnalyzer` 类
|
||||
- [x] 实现 `EmotionAnalyzer` 类
|
||||
|
||||
- [ ] 定义 `EMOTION_KEYWORDS` 字典
|
||||
- [x] 定义 `EMOTION_KEYWORDS` 字典
|
||||
```python
|
||||
EMOTION_KEYWORDS = {
|
||||
"急": 1.0,
|
||||
@@ -53,17 +55,17 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
}
|
||||
```
|
||||
|
||||
- [ ] 实现 `extract()` 方法 - 从文本提取情绪关键词
|
||||
- [x] 实现 `extract()` 方法 - 从文本提取情绪关键词
|
||||
|
||||
- [ ] 实现 `calculate_score()` 方法 - 计算情绪分数
|
||||
- [x] 实现 `calculate_score()` 方法 - 计算情绪分数
|
||||
|
||||
### Task M.1.3：实现 ImpactEvaluator
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/impact_evaluator.py`
|
||||
- [x] 新增 `backend/app/services/memory/impact_evaluator.py`
|
||||
|
||||
- [ ] 实现 `ImpactEvaluator` 类
|
||||
- [x] 实现 `ImpactEvaluator` 类
|
||||
|
||||
- [ ] 实现 `evaluate()` 方法
|
||||
- [x] 实现 `evaluate()` 方法
|
||||
```python
|
||||
def evaluate(self, memory: UserMemory) -> float:
|
||||
# 关联话题越多，影响面越大
|
||||
@@ -72,11 +74,11 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
|
||||
### Task M.1.4：实现 ImportanceScorer
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/importance_scorer.py`
|
||||
- [x] 新增 `backend/app/services/memory/importance_scorer.py`
|
||||
|
||||
- [ ] 实现 `ImportanceScorer` 类
|
||||
- [x] 实现 `ImportanceScorer` 类
|
||||
|
||||
- [ ] 实现 `calculate_score()` 综合评分方法
|
||||
- [x] 实现 `calculate_score()` 综合评分方法
|
||||
```python
|
||||
def calculate_score(self, memory: UserMemory) -> float:
|
||||
frequency = self.tracker.get_frequency_score(memory) * 0.35
|
||||
@@ -86,15 +88,15 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
return frequency + recency + emotion + impact
|
||||
```
|
||||
|
||||
- [ ] 实现 `get_importance_level()` 方法
|
||||
- [x] 实现 `get_importance_level()` 方法
|
||||
|
||||
- [ ] 实现 `should_escalate()` 方法
|
||||
- [x] 实现 `should_escalate()` 方法
|
||||
|
||||
### Task M.1.5：修改 UserMemory 模型
|
||||
|
||||
- [ ] 修改 `backend/app/models/memory.py`
|
||||
- [x] 修改 `backend/app/models/memory.py`
|
||||
|
||||
- [ ] 增加字段：
|
||||
- [x] 增加字段：
|
||||
```python
|
||||
frequency_count: int = 0
|
||||
last_recalled_at: DateTime = None
|
||||
@@ -106,33 +108,33 @@ Day M.1 目标：让 Jarvis 知道「什么对你重要」。
|
||||
|
||||
### Task M.1.6：集成到 MemoryService
|
||||
|
||||
- [ ] 修改 `backend/app/services/memory_service.py`
|
||||
- [x] 修改 `backend/app/services/memory_service.py`
|
||||
|
||||
- [ ] 集成 `ImportanceScorer`
|
||||
- [x] 集成 `ImportanceScorer`
|
||||
|
||||
- [ ] 修改 `add_memory()` 方法计算重要性
|
||||
- [x] 修改 `add_memory()` 方法计算重要性
|
||||
|
||||
- [ ] 修改 `recall_memories()` 方法按重要性排序
|
||||
- [x] 修改 `recall_memories()` 方法按重要性排序
|
||||
|
||||
### Task M.1.7：补测试
|
||||
|
||||
- [ ] 新增 `backend/tests/services/test_importance_scorer.py`
|
||||
- [x] 新增 `backend/tests/services/test_importance_scorer.py`
|
||||
|
||||
- [ ] 测试频率追踪
|
||||
- [x] 测试频率追踪
|
||||
|
||||
- [ ] 测试情绪分析
|
||||
- [x] 测试情绪分析
|
||||
|
||||
- [ ] 测试重要性评分
|
||||
- [x] 测试重要性评分
|
||||
|
||||
- [ ] 测试重要性等级划分
|
||||
- [x] 测试重要性等级划分
|
||||
|
||||
### Day M.1 验收
|
||||
|
||||
- [ ] 频率追踪正常（recall_count 每次 +1）
|
||||
- [ ] 情绪识别准确（「急」「很重要」等能识别）
|
||||
- [ ] 重要性分数正确（高频+情绪 = importance >= 0.8）
|
||||
- [ ] 评分影响排序（高重要性记忆排在前面）
|
||||
- [ ] 单元测试覆盖率 > 80%
|
||||
- [x] 频率追踪正常（recall_count 每次 +1）
|
||||
- [x] 情绪识别准确（「急」「很重要」等能识别）
|
||||
- [x] 重要性分数正确（高频+情绪 = importance >= 0.8）
|
||||
- [x] 评分影响排序（高重要性记忆排在前面）
|
||||
- [x] 单元测试覆盖率 > 80%
|
||||
|
||||
---
|
||||
|
||||
@@ -350,13 +352,120 @@ Day M.3 目标：让 Jarvis 从「等用户问」变成「主动关心」。
|
||||
|
||||
---
|
||||
|
||||
## Day M.4：对话自动学习（3天）
|
||||
|
||||
Day M.4 目标：让记忆库自动从对话中积累内容，不需要用户手动触发。
|
||||
|
||||
### Task M.4.1：实现 MemoryExtractor
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/memory_extractor.py`
|
||||
|
||||
- [ ] 实现 `MemoryExtractor` 类
|
||||
|
||||
- [ ] 实现 `extract_from_conversation()` 方法
|
||||
```python
|
||||
async def extract_from_conversation(
|
||||
self, user_id: str, messages: list[Message]
|
||||
) -> list[ExtractedMemory]:
|
||||
```
|
||||
|
||||
- [ ] 定义 LLM 提取 Prompt（结构化输出 JSON）
|
||||
- 提取类型：fact / preference / goal / pain_point / event
|
||||
- 只提取明确信息，不猜测
|
||||
|
||||
- [ ] 实现 `deduplicate()` 方法
|
||||
- 相似度 > 0.85 视为重复，调用 `reinforce()` 而非新建
|
||||
|
||||
### Task M.4.2：集成触发点
|
||||
|
||||
- [ ] 修改 `backend/app/routers/conversation.py`
|
||||
- 对话结束端点添加 `background_tasks.add_task(memory_extractor.extract_from_conversation, ...)`
|
||||
|
||||
- [ ] 修改 `backend/app/services/scheduler_service.py`
|
||||
- 添加 30 分钟闲置对话检查任务
|
||||
|
||||
### Task M.4.3：补测试
|
||||
|
||||
- [ ] 新增 `backend/tests/services/test_memory_extractor.py`
|
||||
- [ ] 测试提取准确性（fact/goal/pain_point 识别）
|
||||
- [ ] 测试去重逻辑（重复内容不新建）
|
||||
- [ ] 测试后台触发不阻塞响应
|
||||
|
||||
### Day M.4 验收
|
||||
|
||||
- [ ] 对话结束后 30 秒内自动完成提取
|
||||
- [ ] fact/goal/pain_point 类型识别准确
|
||||
- [ ] 重复内容不新建，只强化原记忆
|
||||
- [ ] 提取为后台任务，不影响响应速度
|
||||
- [ ] 单元测试覆盖率 > 80%
|
||||
|
||||
---
|
||||
|
||||
## Day M.5：记忆召回注入（2天）
|
||||
|
||||
Day M.5 目标：让 LLM 在生成回答时真正「看到」用户的记忆，实现对话个性化。
|
||||
|
||||
### Task M.5.1：实现 MemoryRecallInjector
|
||||
|
||||
- [ ] 新增 `backend/app/services/memory/recall_injector.py`
|
||||
|
||||
- [ ] 实现 `MemoryRecallInjector` 类
|
||||
|
||||
- [ ] 实现 `build_context()` 方法
|
||||
```python
|
||||
async def build_context(
|
||||
self, user_id: str, current_message: str, token_budget: int = 800
|
||||
) -> str:
|
||||
```
|
||||
|
||||
- [ ] 实现 `_rank()` 方法（语义相关性 × 重要性评分综合排序）
|
||||
|
||||
- [ ] 实现 `_budget_select()` 方法（Token 预算控制）
|
||||
|
||||
- [ ] 实现 `_format()` 方法（格式化为 system prompt 片段）
|
||||
|
||||
- [ ] 记忆类型优先级配置
|
||||
- pain_point > goal > preference > fact > event
|
||||
|
||||
### Task M.5.2：集成到对话路由
|
||||
|
||||
- [ ] 修改 `backend/app/routers/conversation.py`
|
||||
- 发消息时调用 `memory_injector.build_context()`
|
||||
- 将返回的 context 追加到 system prompt
|
||||
- 发送完成后后台触发记忆强化（frequency_count +1）
|
||||
|
||||
- [ ] 修改 `backend/app/services/memory_service.py`
|
||||
- `recall_memories()` 返回时携带相似度分数（`similarity_score` 字段）
|
||||
|
||||
### Task M.5.3：补测试
|
||||
|
||||
- [ ] 新增 `backend/tests/services/test_recall_injector.py`
|
||||
- [ ] 测试 Token 预算不超限
|
||||
- [ ] 测试已归档记忆不注入
|
||||
- [ ] 测试高优先级类型优先注入
|
||||
- [ ] 测试注入耗时 < 100ms
|
||||
|
||||
### Day M.5 验收
|
||||
|
||||
- [ ] LLM 回答中能体现用户个人信息
|
||||
- [ ] 注入内容 ≤ 800 token
|
||||
- [ ] goal/pain_point 比 fact 更早注入
|
||||
- [ ] decay < 0.2 的已归档记忆不出现在 context 中
|
||||
- [ ] 注入耗时 < 100ms
|
||||
- [ ] 被召回的记忆 frequency_count +1
|
||||
- [ ] 单元测试覆盖率 > 80%
|
||||
|
||||
---
|
||||
|
||||
## 总验收清单
|
||||
|
||||
### Phase M.1-M.3 必须完成
|
||||
### Phase M.1-M.5 必须完成
|
||||
|
||||
- [ ] 重要性评分系统正常工作
|
||||
- [ ] 遗忘曲线系统正常工作
|
||||
- [ ] 主动提醒系统正常工作
|
||||
- [ ] 对话自动学习正常工作（M.4）
|
||||
- [ ] 记忆召回注入正常工作（M.5）
|
||||
- [ ] 单元测试覆盖率 > 80%
|
||||
- [ ] 集成测试通过
|
||||
- [ ] 原有记忆功能无回退
|
||||
@@ -370,7 +479,9 @@ Day M.3 目标：让 Jarvis 从「等用户问」变成「主动关心」。
|
||||
| M.1 重要性评分 | 4 天 |
|
||||
| M.2 遗忘曲线 | 3 天 |
|
||||
| M.3 主动提醒 | 6 天 |
|
||||
| **合计** | **13 天** |
|
||||
| M.4 对话自动学习 | 3 天 |
|
||||
| M.5 记忆召回注入 | 2 天 |
|
||||
| **合计** | **18 天** |
|
||||
|
||||
---
|
||||
|
||||
@@ -388,12 +499,14 @@ Day M.3 目标：让 Jarvis 从「等用户问」变成「主动关心」。
|
||||
| `services/memory/daily_digest.py` | M.3 |
|
||||
| `services/memory/reminder_scheduler.py` | M.3 |
|
||||
| `services/memory/proactive_informer.py` | M.3 |
|
||||
| `services/memory/memory_extractor.py` | M.4 |
|
||||
| `services/memory/recall_injector.py` | M.5 |
|
||||
| `models/memory.py` 更新 | M.1, M.2 |
|
||||
| `models/reminder.py` 新增 | M.3 |
|
||||
| 前端摘要卡片 | M.3 |
|
||||
| 前端提醒 Toast | M.3 |
|
||||
| 单元测试 > 80% | M.1, M.2, M.3 |
|
||||
| 集成测试通过 | M.1, M.2, M.3 |
|
||||
| 单元测试 > 80% | M.1–M.5 |
|
||||
| 集成测试通过 | M.1–M.5 |
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user