Fix/language unification (#283)
* [changes]add user_summary language unification * [add]Entity extraction, user memory, emotion suggestions, unified language type for writing * [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields. * [changes]add user_summary language unification * [add]Entity extraction, user memory, emotion suggestions, unified language type for writing * [add]Complete the switch between Chinese and English for the emotion labels and emotion suggestions fields. * [changes]Modify the code based on the AI review
This commit is contained in:
乐力齐
@@ -57,24 +57,57 @@ class EmotionAnalyticsService:
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self.emotion_repo = EmotionRepository(connector)
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logger.info("情绪分析服务初始化完成")
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# 情绪类型的中英文映射
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EMOTION_TYPE_TRANSLATIONS = {
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'joy': {'zh': '喜悦', 'en': 'Joy'},
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'sadness': {'zh': '悲伤', 'en': 'Sadness'},
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'anger': {'zh': '愤怒', 'en': 'Anger'},
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'fear': {'zh': '恐惧', 'en': 'Fear'},
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'surprise': {'zh': '惊讶', 'en': 'Surprise'},
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'neutral': {'zh': '中性', 'en': 'Neutral'}
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}
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def _translate_emotion_type(self, emotion_type: str, language: str = "zh") -> str:
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"""将情绪类型翻译成指定语言
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Args:
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emotion_type: 情绪类型（英文key）
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language: 目标语言 ("zh" 或 "en")
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Returns:
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翻译后的情绪类型名称
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"""
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if emotion_type in self.EMOTION_TYPE_TRANSLATIONS:
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return self.EMOTION_TYPE_TRANSLATIONS[emotion_type].get(language, emotion_type)
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return emotion_type
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async def get_emotion_tags(
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self,
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end_user_id: str,
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emotion_type: Optional[str] = None,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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limit: int = 10
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limit: int = 10,
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language: str = "zh"
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) -> Dict[str, Any]:
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"""获取情绪标签统计
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查询指定用户的情绪类型分布，包括计数、百分比和平均强度。
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确保返回所有6个情绪维度（joy、sadness、anger、fear、surprise、neutral），
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即使某些维度没有数据也会返回count=0的记录。
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Args:
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end_user_id: 用户ID
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emotion_type: 情绪类型过滤
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start_date: 开始日期
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end_date: 结束日期
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limit: 返回数量限制
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language: 输出语言 ("zh" 中文, "en" 英文)
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"""
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try:
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logger.info(f"获取情绪标签统计: user={end_user_id}, type={emotion_type}, "
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f"start={start_date}, end={end_date}, limit={limit}")
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f"start={start_date}, end={end_date}, limit={limit}, language={language}")
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# 调用仓储层查询
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tags = await self.emotion_repo.get_emotion_tags(
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@@ -91,15 +124,17 @@ class EmotionAnalyticsService:
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# 将查询结果转换为字典，方便查找
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tags_dict = {tag["emotion_type"]: tag for tag in tags}
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# 补全缺失的情绪维度
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# 补全缺失的情绪维度，并翻译 emotion_type
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complete_tags = []
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for emotion in all_emotion_types:
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if emotion in tags_dict:
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complete_tags.append(tags_dict[emotion])
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tag = tags_dict[emotion].copy()
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tag["emotion_type"] = self._translate_emotion_type(emotion, language)
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complete_tags.append(tag)
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else:
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# 如果该情绪类型不存在，添加默认值
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complete_tags.append({
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"emotion_type": emotion,
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"emotion_type": self._translate_emotion_type(emotion, language),
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"count": 0,
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"percentage": 0.0,
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"avg_intensity": 0.0
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@@ -475,6 +510,7 @@ class EmotionAnalyticsService:
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self,
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end_user_id: str,
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db: Session,
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language: str = "zh",
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) -> Dict[str, Any]:
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"""生成个性化情绪建议
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@@ -483,6 +519,7 @@ class EmotionAnalyticsService:
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Args:
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end_user_id: 宿主ID（用户组ID）
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db: 数据库会话
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language: 输出语言 ("zh" 中文, "en" 英文)
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Returns:
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Dict: 包含个性化建议的响应：
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@@ -533,7 +570,7 @@ class EmotionAnalyticsService:
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user_profile = await self._get_simple_user_profile(end_user_id)
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# 6. 构建LLM prompt
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prompt = await self._build_suggestion_prompt(health_data, patterns, user_profile)
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prompt = await self._build_suggestion_prompt(health_data, patterns, user_profile, language)
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# 7. 调用LLM生成建议（使用配置中的LLM）
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if llm_client is None:
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@@ -554,12 +591,12 @@ class EmotionAnalyticsService:
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except Exception as e:
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logger.error(f"LLM 结构化输出失败: {str(e)}")
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# 返回默认建议
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suggestions_response = self._get_default_suggestions(health_data)
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suggestions_response = self._get_default_suggestions(health_data, language)
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# 8. 验证建议数量（3-5条）
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if len(suggestions_response.suggestions) < 3:
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logger.warning(f"建议数量不足: {len(suggestions_response.suggestions)}")
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suggestions_response = self._get_default_suggestions(health_data)
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suggestions_response = self._get_default_suggestions(health_data, language)
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elif len(suggestions_response.suggestions) > 5:
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logger.warning(f"建议数量过多: {len(suggestions_response.suggestions)}")
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suggestions_response.suggestions = suggestions_response.suggestions[:5]
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@@ -624,7 +661,8 @@ class EmotionAnalyticsService:
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self,
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health_data: Dict[str, Any],
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patterns: Dict[str, Any],
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user_profile: Dict[str, Any]
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user_profile: Dict[str, Any],
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language: str = "zh"
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) -> str:
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"""构建情绪建议生成的prompt
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@@ -632,6 +670,7 @@ class EmotionAnalyticsService:
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health_data: 情绪健康数据
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patterns: 情绪模式分析结果
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user_profile: 用户画像数据
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language: 输出语言 ("zh" 中文, "en" 英文)
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Returns:
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str: LLM prompt
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@@ -643,66 +682,114 @@ class EmotionAnalyticsService:
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prompt = await render_emotion_suggestions_prompt(
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health_data=health_data,
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patterns=patterns,
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user_profile=user_profile
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user_profile=user_profile,
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language=language
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)
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return prompt
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def _get_default_suggestions(self, health_data: Dict[str, Any]) -> EmotionSuggestionsResponse:
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def _get_default_suggestions(self, health_data: Dict[str, Any], language: str = "zh") -> EmotionSuggestionsResponse:
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"""获取默认建议（当LLM调用失败时使用）
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Args:
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health_data: 情绪健康数据
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language: 输出语言 ("zh" 中文, "en" 英文)
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Returns:
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EmotionSuggestionsResponse: 默认建议
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"""
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health_score = health_data.get('health_score', 0)
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if health_score >= 80:
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summary = "您的情绪健康状况优秀，请继续保持积极的生活态度。"
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elif health_score >= 60:
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summary = "您的情绪健康状况良好，可以通过一些调整进一步提升。"
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elif health_score >= 40:
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summary = "您的情绪健康需要关注，建议采取一些改善措施。"
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else:
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summary = "您的情绪健康需要重点关注，建议寻求专业帮助。"
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if language == "en":
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if health_score >= 80:
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summary = "Your emotional health is excellent. Keep up the positive attitude."
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elif health_score >= 60:
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summary = "Your emotional health is good. Some adjustments can further improve it."
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elif health_score >= 40:
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summary = "Your emotional health needs attention. Consider taking improvement measures."
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else:
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summary = "Your emotional health needs serious attention. Consider seeking professional help."
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suggestions = [
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EmotionSuggestion(
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type="emotion_balance",
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title="保持情绪平衡",
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content="通过正念冥想和深呼吸练习，帮助您更好地管理情绪波动，提升情绪稳定性。",
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priority="high",
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actionable_steps=[
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"每天早晨进行5-10分钟的正念冥想",
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"感到情绪波动时，进行3次深呼吸",
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"记录每天的情绪变化，识别触发因素"
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]
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),
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EmotionSuggestion(
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type="activity_recommendation",
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title="增加户外活动",
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content="适度的户外运动可以有效改善情绪，增强身心健康。建议每周进行3-4次户外活动。",
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priority="medium",
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actionable_steps=[
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"每周安排2-3次30分钟的散步",
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"周末尝试户外运动如骑行或爬山",
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"在户外活动时关注周围环境，放松心情"
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]
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),
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EmotionSuggestion(
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type="social_connection",
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title="加强社交联系",
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content="与朋友和家人保持良好的社交联系，可以提供情感支持，改善情绪健康。",
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priority="medium",
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actionable_steps=[
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"每周至少与一位朋友或家人深入交流",
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"参加感兴趣的社交活动或兴趣小组",
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"主动分享自己的感受和想法"
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]
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)
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]
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suggestions = [
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EmotionSuggestion(
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type="Emotion Balance",
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title="Maintain Emotional Balance",
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content="Through mindfulness meditation and deep breathing exercises, help you better manage emotional fluctuations and improve emotional stability.",
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priority="High",
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actionable_steps=[
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"Practice 5-10 minutes of mindfulness meditation every morning",
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"Take 3 deep breaths when feeling emotional fluctuations",
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"Record daily emotional changes to identify triggers"
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]
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),
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EmotionSuggestion(
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type="Activity Recommendation",
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title="Increase Outdoor Activities",
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content="Moderate outdoor exercise can effectively improve mood and enhance physical and mental health. Recommend 3-4 outdoor activities per week.",
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priority="Medium",
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actionable_steps=[
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"Schedule 2-3 30-minute walks per week",
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"Try outdoor sports like cycling or hiking on weekends",
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"Focus on surroundings and relax during outdoor activities"
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]
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),
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EmotionSuggestion(
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type="Social Connection",
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title="Strengthen Social Connections",
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content="Maintaining good social connections with friends and family can provide emotional support and improve emotional health.",
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priority="Medium",
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actionable_steps=[
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"Have a deep conversation with at least one friend or family member weekly",
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"Join social activities or interest groups you enjoy",
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"Actively share your feelings and thoughts"
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]
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)
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]
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else:
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if health_score >= 80:
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summary = "您的情绪健康状况优秀，请继续保持积极的生活态度。"
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elif health_score >= 60:
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summary = "您的情绪健康状况良好，可以通过一些调整进一步提升。"
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elif health_score >= 40:
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summary = "您的情绪健康需要关注，建议采取一些改善措施。"
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else:
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summary = "您的情绪健康需要重点关注，建议寻求专业帮助。"
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suggestions = [
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EmotionSuggestion(
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type="情绪平衡",
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title="保持情绪平衡",
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content="通过正念冥想和深呼吸练习，帮助您更好地管理情绪波动，提升情绪稳定性。",
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priority="高",
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actionable_steps=[
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"每天早晨进行5-10分钟的正念冥想",
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"感到情绪波动时，进行3次深呼吸",
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"记录每天的情绪变化，识别触发因素"
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]
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),
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EmotionSuggestion(
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type="活动建议",
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title="增加户外活动",
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content="适度的户外运动可以有效改善情绪，增强身心健康。建议每周进行3-4次户外活动。",
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priority="中",
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actionable_steps=[
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"每周安排2-3次30分钟的散步",
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"周末尝试户外运动如骑行或爬山",
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"在户外活动时关注周围环境，放松心情"
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]
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),
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EmotionSuggestion(
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type="社交联系",
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title="加强社交联系",
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content="与朋友和家人保持良好的社交联系，可以提供情感支持，改善情绪健康。",
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priority="中",
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actionable_steps=[
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"每周至少与一位朋友或家人深入交流",
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"参加感兴趣的社交活动或兴趣小组",
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"主动分享自己的感受和想法"
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]
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)
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]
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return EmotionSuggestionsResponse(
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health_summary=summary,
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@@ -32,7 +32,6 @@ from app.repositories.memory_short_repository import ShortTermMemoryRepository
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.schemas.memory_agent_schema import Write_UserInput
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from app.schemas.memory_config_schema import ConfigurationError
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from app.services.memory_base_service import Translation_English
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from app.services.memory_config_service import MemoryConfigService
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from app.services.memory_konwledges_server import (
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write_rag,
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@@ -265,7 +264,7 @@ class MemoryAgentService:
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logger.info("Log streaming completed, cleaning up resources")
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# LogStreamer uses context manager for file handling, so cleanup is automatic
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async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str) -> str:
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async def write_memory(self, end_user_id: str, messages: list[dict], config_id: Optional[uuid.UUID]|int, db: Session, storage_type: str, user_rag_memory_id: str, language: str = "zh") -> str:
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"""
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Process write operation with config_id
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@@ -276,6 +275,7 @@ class MemoryAgentService:
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db: SQLAlchemy database session
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storage_type: Storage type (neo4j or rag)
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user_rag_memory_id: User RAG memory ID
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language: 语言类型 ("zh" 中文, "en" 英文)
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Returns:
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Write operation result status
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@@ -341,7 +341,8 @@ class MemoryAgentService:
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initial_state = {
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"messages": langchain_messages,
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"end_user_id": end_user_id,
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"memory_config": memory_config
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"memory_config": memory_config,
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"language": language
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}
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# 获取节点更新信息
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@@ -890,9 +891,7 @@ class MemoryAgentService:
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async def get_hot_memory_tags_by_user(
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self,
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end_user_id: Optional[str] = None,
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limit: int = 20,
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model_id: Optional[str] = None,
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language_type: Optional[str] = "zh"
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limit: int = 20
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) -> List[Dict[str, Any]]:
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"""
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获取指定用户的热门记忆标签
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@@ -906,17 +905,15 @@ class MemoryAgentService:
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{"name": "标签名", "frequency": 频次},
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...
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]
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注意：标签语言由写入时的 X-Language-Type 决定，查询时不进行翻译
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"""
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try:
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# by_user=False 表示按 end_user_id 查询（在Neo4j中，end_user_id就是用户维度）
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tags = await get_hot_memory_tags(end_user_id, limit=limit, by_user=False)
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payload=[]
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payload = []
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for tag, freq in tags:
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if language_type!="zh":
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tag=await Translation_English(model_id, tag)
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payload.append({"name": tag, "frequency": freq})
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else:
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payload.append({"name": tag, "frequency": freq})
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payload.append({"name": tag, "frequency": freq})
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return payload
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except Exception as e:
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logger.error(f"热门记忆标签查询失败: {e}")
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@@ -16,7 +16,6 @@ import json
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from datetime import datetime
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from app.schemas.memory_episodic_schema import EmotionType
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from app.services.memory_base_service import Translation_English
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logger = logging.getLogger(__name__)
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@@ -374,119 +373,6 @@ class MemoryEntityService:
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logger.warning(f"转换时间格式失败: {e}, 原始值: {dt}")
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return str(dt) if dt is not None else None
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async def _translate_list(
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self,
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data_list: List[Dict[str, Any]],
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model_id: str,
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fields: List[str]
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) -> List[Dict[str, Any]]:
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"""
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翻译列表中每个字典的指定字段（并发有限度以降低整体延迟）
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|
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Args:
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data_list: 要翻译的字典列表
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model_id: 模型ID
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fields: 需要翻译的字段列表
|
||||
|
||||
Returns:
|
||||
翻译后的字典列表
|
||||
"""
|
||||
# 空列表或无字段时直接返回
|
||||
if not data_list or not fields:
|
||||
return data_list
|
||||
|
||||
import asyncio
|
||||
|
||||
# 并发限制，避免一次性发起过多请求
|
||||
# 可根据实际情况调整（建议 5-10）
|
||||
concurrency_limit = 5
|
||||
semaphore = asyncio.Semaphore(concurrency_limit)
|
||||
|
||||
async def translate_single_field(
|
||||
index: int,
|
||||
field: str,
|
||||
value: Any,
|
||||
) -> Optional[tuple]:
|
||||
"""
|
||||
翻译单个字段并返回 (索引, 字段名, 翻译结果)
|
||||
|
||||
Returns:
|
||||
(index, field, translated_value) 或 None（如果跳过）
|
||||
"""
|
||||
# 跳过空值
|
||||
if value is None or value == "":
|
||||
return None
|
||||
|
||||
# 统一转成字符串再翻译，防止非字符串类型导致错误
|
||||
text = str(value)
|
||||
|
||||
try:
|
||||
async with semaphore:
|
||||
# 调用 Translation_English 进行翻译
|
||||
# 注意：Translation_English 的参数顺序是 (model_id, text)
|
||||
translated = await Translation_English(model_id, text)
|
||||
|
||||
# 如果翻译结果为空，保留原值
|
||||
if translated is None or translated == "":
|
||||
return None
|
||||
|
||||
return index, field, translated
|
||||
except Exception as e:
|
||||
logger.warning(f"翻译字段 {field} (索引 {index}) 失败: {e}")
|
||||
return None
|
||||
|
||||
# 构造所有需要翻译的任务
|
||||
tasks = []
|
||||
for idx, item in enumerate(data_list):
|
||||
# 防御性检查：确保 item 是字典
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
for field in fields:
|
||||
if field not in item:
|
||||
continue
|
||||
|
||||
value = item.get(field)
|
||||
|
||||
# 对于 None 或空字符串的值，直接跳过，不创建任务
|
||||
if value is None or value == "":
|
||||
continue
|
||||
|
||||
tasks.append(
|
||||
asyncio.create_task(
|
||||
translate_single_field(idx, field, value)
|
||||
)
|
||||
)
|
||||
|
||||
# 如果没有需要翻译的任务，直接返回原列表
|
||||
if not tasks:
|
||||
return data_list
|
||||
|
||||
# 使用 gather 并发执行翻译任务（受 semaphore 限制）
|
||||
# return_exceptions=True 可以防止单个任务失败导致整体失败
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
# 创建深拷贝以避免修改原始数据
|
||||
translated_list = [item.copy() if isinstance(item, dict) else item for item in data_list]
|
||||
|
||||
# 将翻译结果回填到列表
|
||||
for result in results:
|
||||
# 跳过 None 结果和异常
|
||||
if result is None or isinstance(result, Exception):
|
||||
if isinstance(result, Exception):
|
||||
logger.warning(f"翻译任务异常: {result}")
|
||||
continue
|
||||
|
||||
idx, field, translated = result
|
||||
|
||||
# 防御性检查索引范围
|
||||
if 0 <= idx < len(translated_list) and isinstance(translated_list[idx], dict):
|
||||
translated_list[idx][field] = translated
|
||||
|
||||
return translated_list
|
||||
|
||||
|
||||
|
||||
|
||||
async def close(self):
|
||||
"""关闭数据库连接"""
|
||||
|
||||
@@ -236,12 +236,13 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
|
||||
return self._convert_timestamps_to_format(data_list)
|
||||
|
||||
|
||||
async def pilot_run_stream(self, payload: ConfigPilotRun) -> AsyncGenerator[str, None]:
|
||||
async def pilot_run_stream(self, payload: ConfigPilotRun, language: str = "zh") -> AsyncGenerator[str, None]:
|
||||
"""
|
||||
流式执行试运行，产生 SSE 格式的进度事件
|
||||
|
||||
Args:
|
||||
payload: 试运行配置和对话文本
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Yields:
|
||||
SSE 格式的字符串，包含以下事件类型：
|
||||
@@ -315,6 +316,7 @@ class DataConfigService: # 数据配置服务类（PostgreSQL）
|
||||
dialogue_text=dialogue_text,
|
||||
db=self.db,
|
||||
progress_callback=progress_callback,
|
||||
language=language,
|
||||
)
|
||||
logger.info("[PILOT_RUN_STREAM] pipeline_main completed")
|
||||
|
||||
|
||||
@@ -78,7 +78,8 @@ class OntologyService:
|
||||
scenario: str,
|
||||
domain: Optional[str] = None,
|
||||
scene_id: Optional[Any] = None,
|
||||
workspace_id: Optional[Any] = None
|
||||
workspace_id: Optional[Any] = None,
|
||||
language: str = "zh"
|
||||
) -> OntologyExtractionResponse:
|
||||
"""执行本体提取
|
||||
|
||||
@@ -91,6 +92,7 @@ class OntologyService:
|
||||
domain: 可选的领域提示
|
||||
scene_id: 可选的场景ID,用于权限验证（不再用于自动保存）
|
||||
workspace_id: 可选的工作空间ID,用于权限验证
|
||||
language: 输出语言 ("zh" 中文, "en" 英文)
|
||||
|
||||
Returns:
|
||||
OntologyExtractionResponse: 提取结果
|
||||
@@ -155,6 +157,7 @@ class OntologyService:
|
||||
llm_max_tokens=self.DEFAULT_LLM_MAX_TOKENS,
|
||||
max_description_length=self.DEFAULT_MAX_DESCRIPTION_LENGTH,
|
||||
timeout=self.DEFAULT_LLM_TIMEOUT,
|
||||
language=language,
|
||||
)
|
||||
|
||||
extraction_duration = time.time() - extraction_start_time
|
||||
|
||||
@@ -36,6 +36,7 @@ async def run_pilot_extraction(
|
||||
dialogue_text: str,
|
||||
db: Session,
|
||||
progress_callback: Optional[Callable[[str, str, Optional[dict]], Awaitable[None]]] = None,
|
||||
language: str = "zh",
|
||||
) -> None:
|
||||
"""
|
||||
执行试运行模式的知识提取流水线。
|
||||
@@ -43,10 +44,12 @@ async def run_pilot_extraction(
|
||||
Args:
|
||||
memory_config: 从数据库加载的内存配置对象
|
||||
dialogue_text: 输入的对话文本
|
||||
db: 数据库会话
|
||||
progress_callback: 可选的进度回调函数
|
||||
- 参数1 (stage): 当前处理阶段标识符
|
||||
- 参数2 (message): 人类可读的进度消息
|
||||
- 参数3 (data): 可选的附加数据字典
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
"""
|
||||
log_file = "logs/time.log"
|
||||
os.makedirs(os.path.dirname(log_file), exist_ok=True)
|
||||
@@ -146,6 +149,7 @@ async def run_pilot_extraction(
|
||||
config=config,
|
||||
progress_callback=progress_callback,
|
||||
embedding_id=str(memory_config.embedding_model_id),
|
||||
language=language,
|
||||
)
|
||||
|
||||
log_time("Orchestrator Initialization", time.time() - step_start, log_file)
|
||||
@@ -191,6 +195,7 @@ async def run_pilot_extraction(
|
||||
chunked_dialogs,
|
||||
llm_client=llm_client,
|
||||
embedder_client=embedder_client,
|
||||
language=language,
|
||||
)
|
||||
|
||||
log_time("Memory Summary Generation", time.time() - step_start, log_file)
|
||||
|
||||
@@ -18,7 +18,7 @@ from app.repositories.end_user_repository import EndUserRepository
|
||||
from app.repositories.neo4j.neo4j_connector import Neo4jConnector
|
||||
from app.schemas.memory_episodic_schema import EmotionSubject, EmotionType, type_mapping
|
||||
from app.services.implicit_memory_service import ImplicitMemoryService
|
||||
from app.services.memory_base_service import MemoryBaseService, MemoryTransService, Translation_English
|
||||
from app.services.memory_base_service import MemoryBaseService, MemoryTransService
|
||||
from app.services.memory_config_service import MemoryConfigService
|
||||
from app.services.memory_perceptual_service import MemoryPerceptualService
|
||||
from app.services.memory_short_service import ShortService
|
||||
@@ -455,9 +455,7 @@ class UserMemoryService:
|
||||
async def get_cached_memory_insight(
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
model_id: str,
|
||||
language_type: str
|
||||
end_user_id: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
从数据库获取缓存的记忆洞察（四个维度）
|
||||
@@ -465,7 +463,7 @@ class UserMemoryService:
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID (UUID)
|
||||
|
||||
|
||||
Returns:
|
||||
{
|
||||
"memory_insight": str, # 总体概述
|
||||
@@ -519,10 +517,6 @@ class UserMemoryService:
|
||||
memory_insight=end_user.memory_insight
|
||||
behavior_pattern=end_user.behavior_pattern
|
||||
growth_trajectory=end_user.growth_trajectory
|
||||
if language_type!='zh':
|
||||
memory_insight=await Translation_English(model_id,memory_insight)
|
||||
behavior_pattern=await Translation_English(model_id,behavior_pattern)
|
||||
growth_trajectory=await Translation_English(model_id,growth_trajectory)
|
||||
return {
|
||||
"memory_insight":memory_insight, # 总体概述存储在 memory_insight
|
||||
"behavior_pattern":behavior_pattern,
|
||||
@@ -571,6 +565,8 @@ class UserMemoryService:
|
||||
Args:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID (UUID)
|
||||
model_id: 模型ID（用于翻译）
|
||||
language_type: 语言类型 ("zh" 中文, "en" 英文)
|
||||
|
||||
Returns:
|
||||
{
|
||||
@@ -604,11 +600,10 @@ class UserMemoryService:
|
||||
personality_traits=end_user.personality_traits
|
||||
core_values=end_user.core_values
|
||||
one_sentence_summary=end_user.one_sentence_summary
|
||||
if language_type!='zh':
|
||||
user_summary=await Translation_English(model_id, user_summary)
|
||||
personality_traits = await Translation_English(model_id, personality_traits)
|
||||
core_values = await Translation_English(model_id, core_values)
|
||||
one_sentence_summary = await Translation_English(model_id, one_sentence_summary)
|
||||
|
||||
# 直接返回数据库中的数据，不进行二次翻译
|
||||
# 语言由生成时的 X-Language-Type 决定
|
||||
|
||||
has_cache = any([
|
||||
user_summary,
|
||||
personality_traits,
|
||||
@@ -658,7 +653,8 @@ class UserMemoryService:
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
workspace_id: Optional[uuid.UUID] = None
|
||||
workspace_id: Optional[uuid.UUID] = None,
|
||||
language: str = "zh"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
生成并缓存记忆洞察
|
||||
@@ -667,6 +663,7 @@ class UserMemoryService:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID (UUID)
|
||||
workspace_id: 工作空间ID (可选)
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Returns:
|
||||
{
|
||||
@@ -679,7 +676,7 @@ class UserMemoryService:
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始为 end_user_id {end_user_id} 生成记忆洞察")
|
||||
logger.info(f"开始为 end_user_id {end_user_id} 生成记忆洞察, language={language}")
|
||||
|
||||
# 转换为UUID并查询用户
|
||||
user_uuid = uuid.UUID(end_user_id)
|
||||
@@ -700,7 +697,7 @@ class UserMemoryService:
|
||||
# 使用 end_user_id 调用分析函数
|
||||
try:
|
||||
logger.info(f"使用 end_user_id={end_user_id} 生成记忆洞察")
|
||||
result = await analytics_memory_insight_report(end_user_id)
|
||||
result = await analytics_memory_insight_report(end_user_id, language=language)
|
||||
|
||||
memory_insight = result.get("memory_insight", "")
|
||||
behavior_pattern = result.get("behavior_pattern", "")
|
||||
@@ -789,7 +786,8 @@ class UserMemoryService:
|
||||
self,
|
||||
db: Session,
|
||||
end_user_id: str,
|
||||
workspace_id: Optional[uuid.UUID] = None
|
||||
workspace_id: Optional[uuid.UUID] = None,
|
||||
language: str = "zh"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
生成并缓存用户摘要（四个部分）
|
||||
@@ -798,6 +796,7 @@ class UserMemoryService:
|
||||
db: 数据库会话
|
||||
end_user_id: 终端用户ID (UUID)
|
||||
workspace_id: 工作空间ID (可选)
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Returns:
|
||||
{
|
||||
@@ -810,7 +809,7 @@ class UserMemoryService:
|
||||
}
|
||||
"""
|
||||
try:
|
||||
logger.info(f"开始为 end_user_id {end_user_id} 生成用户摘要")
|
||||
logger.info(f"开始为 end_user_id {end_user_id} 生成用户摘要, language={language}")
|
||||
|
||||
# 转换为UUID并查询用户
|
||||
user_uuid = uuid.UUID(end_user_id)
|
||||
@@ -831,7 +830,7 @@ class UserMemoryService:
|
||||
# 使用 end_user_id 调用分析函数
|
||||
try:
|
||||
logger.info(f"使用 end_user_id={end_user_id} 生成用户摘要")
|
||||
result = await analytics_user_summary(end_user_id)
|
||||
result = await analytics_user_summary(end_user_id, language=language)
|
||||
|
||||
user_summary = result.get("user_summary", "")
|
||||
personality = result.get("personality", "")
|
||||
@@ -915,7 +914,8 @@ class UserMemoryService:
|
||||
async def generate_cache_for_workspace(
|
||||
self,
|
||||
db: Session,
|
||||
workspace_id: uuid.UUID
|
||||
workspace_id: uuid.UUID,
|
||||
language: str = "zh"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
为整个工作空间生成缓存
|
||||
@@ -923,6 +923,7 @@ class UserMemoryService:
|
||||
Args:
|
||||
db: 数据库会话
|
||||
workspace_id: 工作空间ID
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Returns:
|
||||
{
|
||||
@@ -932,7 +933,7 @@ class UserMemoryService:
|
||||
"errors": List[Dict]
|
||||
}
|
||||
"""
|
||||
logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存")
|
||||
logger.info(f"开始为工作空间 {workspace_id} 批量生成缓存, language={language}")
|
||||
|
||||
total_users = 0
|
||||
successful = 0
|
||||
@@ -953,10 +954,10 @@ class UserMemoryService:
|
||||
|
||||
try:
|
||||
# 生成记忆洞察
|
||||
insight_result = await self.generate_and_cache_insight(db, end_user_id)
|
||||
insight_result = await self.generate_and_cache_insight(db, end_user_id, language=language)
|
||||
|
||||
# 生成用户摘要
|
||||
summary_result = await self.generate_and_cache_summary(db, end_user_id)
|
||||
summary_result = await self.generate_and_cache_summary(db, end_user_id, language=language)
|
||||
|
||||
# 检查是否都成功
|
||||
if insight_result["success"] and summary_result["success"]:
|
||||
@@ -1007,7 +1008,7 @@ class UserMemoryService:
|
||||
|
||||
# 独立的分析函数
|
||||
|
||||
async def analytics_memory_insight_report(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
async def analytics_memory_insight_report(end_user_id: Optional[str] = None, language: str = "zh") -> Dict[str, Any]:
|
||||
"""
|
||||
生成记忆洞察报告（四个维度）
|
||||
|
||||
@@ -1019,6 +1020,7 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Returns:
|
||||
包含四个维度报告的字典: {
|
||||
@@ -1029,8 +1031,12 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
|
||||
}
|
||||
"""
|
||||
from app.core.memory.utils.prompt.prompt_utils import render_memory_insight_prompt
|
||||
from app.core.language_utils import validate_language
|
||||
import re
|
||||
|
||||
# 验证语言参数
|
||||
language = validate_language(language)
|
||||
|
||||
insight = MemoryInsightHelper(end_user_id)
|
||||
|
||||
try:
|
||||
@@ -1070,7 +1076,8 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
|
||||
user_prompt = await render_memory_insight_prompt(
|
||||
domain_distribution=domain_distribution_str,
|
||||
active_periods=active_periods_str,
|
||||
social_connections=social_connections_str
|
||||
social_connections=social_connections_str,
|
||||
language=language
|
||||
)
|
||||
|
||||
messages = [
|
||||
@@ -1097,11 +1104,11 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
|
||||
full_response = str(content) if content is not None else ""
|
||||
|
||||
# 7. 解析四个部分
|
||||
# 使用正则表达式提取四个部分
|
||||
memory_insight_match = re.search(r'【总体概述】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
behavior_match = re.search(r'【行为模式】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
findings_match = re.search(r'【关键发现】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
trajectory_match = re.search(r'【成长轨迹】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
# 使用正则表达式提取四个部分（支持中英文双语标题）
|
||||
memory_insight_match = re.search(r'【(?:总体概述|Overview)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
behavior_match = re.search(r'【(?:行为模式|Behavior Pattern)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
findings_match = re.search(r'【(?:关键发现|Key Findings)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
trajectory_match = re.search(r'【(?:成长轨迹|Growth Trajectory)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
|
||||
memory_insight = memory_insight_match.group(1).strip() if memory_insight_match else ""
|
||||
behavior_pattern = behavior_match.group(1).strip() if behavior_match else ""
|
||||
@@ -1128,7 +1135,7 @@ async def analytics_memory_insight_report(end_user_id: Optional[str] = None) ->
|
||||
await insight.close()
|
||||
|
||||
|
||||
async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str, Any]:
|
||||
async def analytics_user_summary(end_user_id: Optional[str] = None, language: str = "zh") -> Dict[str, Any]:
|
||||
"""
|
||||
生成用户摘要（包含四个部分）
|
||||
|
||||
@@ -1139,6 +1146,7 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
|
||||
|
||||
Args:
|
||||
end_user_id: 可选的终端用户ID
|
||||
language: 语言类型 ("zh" 中文, "en" 英文)，默认中文
|
||||
|
||||
Returns:
|
||||
包含四部分摘要的字典: {
|
||||
@@ -1149,8 +1157,12 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
|
||||
}
|
||||
"""
|
||||
from app.core.memory.utils.prompt.prompt_utils import render_user_summary_prompt
|
||||
from app.core.language_utils import validate_language
|
||||
import re
|
||||
|
||||
# 验证语言参数
|
||||
language = validate_language(language)
|
||||
|
||||
# 创建 UserSummaryHelper 实例
|
||||
user_summary_tool = UserSummaryHelper(end_user_id or os.getenv("SELECTED_end_user_id", "group_123"))
|
||||
|
||||
@@ -1165,8 +1177,9 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
|
||||
# 2) 使用 prompt_utils 渲染提示词
|
||||
user_prompt = await render_user_summary_prompt(
|
||||
user_id=user_summary_tool.user_id,
|
||||
entities=", ".join(entity_lines) if entity_lines else "(空)",
|
||||
statements=" | ".join(statement_samples) if statement_samples else "(空)"
|
||||
entities=", ".join(entity_lines) if entity_lines else "(空)" if language == "zh" else "(empty)",
|
||||
statements=" | ".join(statement_samples) if statement_samples else "(空)" if language == "zh" else "(empty)",
|
||||
language=language
|
||||
)
|
||||
|
||||
messages = [
|
||||
@@ -1193,11 +1206,11 @@ async def analytics_user_summary(end_user_id: Optional[str] = None) -> Dict[str,
|
||||
full_response = str(content) if content is not None else ""
|
||||
|
||||
# 5) 解析四个部分
|
||||
# 使用正则表达式提取四个部分
|
||||
user_summary_match = re.search(r'【基本介绍】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
personality_match = re.search(r'【性格特点】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
core_values_match = re.search(r'【核心价值观】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
one_sentence_match = re.search(r'【一句话总结】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
# 使用正则表达式提取四个部分（支持中英文标题）
|
||||
user_summary_match = re.search(r'【(?:基本介绍|Basic Introduction)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
personality_match = re.search(r'【(?:性格特点|Personality Traits)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
core_values_match = re.search(r'【(?:核心价值观|Core Values)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
one_sentence_match = re.search(r'【(?:一句话总结|One-Sentence Summary)】\s*\n(.*?)(?=\n【|$)', full_response, re.DOTALL)
|
||||
|
||||
user_summary = user_summary_match.group(1).strip() if user_summary_match else ""
|
||||
personality = personality_match.group(1).strip() if personality_match else ""
|
||||
|
||||
Reference in New Issue
Block a user