452 lines
63 KiB
Python
452 lines
63 KiB
Python
"""
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Memory Base Service
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提供记忆服务的基础功能和共享辅助方法。
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"""
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import asyncio
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import re
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from datetime import datetime
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from typing import Optional
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from pydantic import BaseModel
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from app.core.logging_config import get_logger
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from app.repositories.neo4j.neo4j_connector import Neo4jConnector
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from app.services.emotion_analytics_service import EmotionAnalyticsService
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from app.core.memory.llm_tools.openai_client import OpenAIClient
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from app.core.models.base import RedBearModelConfig
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from app.services.memory_config_service import MemoryConfigService
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from app.db import get_db_context
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logger = get_logger(__name__)
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class TranslationResponse(BaseModel):
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"""翻译响应模型"""
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data: str
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class MemoryTransService:
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"""记忆翻译服务,提供中英文翻译功能"""
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def __init__(self, llm_client=None, model_id: Optional[str] = None):
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"""
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初始化翻译服务
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Args:
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llm_client: LLM客户端实例或模型ID字符串(可选)
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model_id: 模型ID,用于初始化LLM客户端(可选)
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Note:
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- 如果llm_client是字符串,会被当作model_id使用
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- 如果同时提供llm_client和model_id,优先使用llm_client
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"""
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# 处理llm_client参数:如果是字符串,当作model_id
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if isinstance(llm_client, str):
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self.model_id = llm_client
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self.llm_client = None
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else:
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self.llm_client = llm_client
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self.model_id = model_id
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self._initialized = False
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def _ensure_llm_client(self):
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"""确保LLM客户端已初始化"""
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if self._initialized:
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return
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if self.llm_client is None:
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if self.model_id:
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with get_db_context() as db:
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config_service = MemoryConfigService(db)
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model_config = config_service.get_model_config(self.model_id)
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extra_params = {
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"temperature": 0.2,
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"max_tokens": 400,
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"top_p": 0.8,
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"stream": False,
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}
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self.llm_client = OpenAIClient(
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RedBearModelConfig(
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model_name=model_config.get("model_name"),
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provider=model_config.get("provider"),
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api_key=model_config.get("api_key"),
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base_url=model_config.get("base_url"),
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timeout=model_config.get("timeout", 30),
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max_retries=model_config.get("max_retries", 3),
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extra_params=extra_params
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),
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type_=model_config.get("type")
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)
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else:
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raise ValueError("必须提供 llm_client 或 model_id 之一")
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self._initialized = True
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async def translate_to_english(self, text: str) -> str:
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"""
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将中文翻译为英文
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Args:
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text: 要翻译的中文文本
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Returns:
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翻译后的英文文本
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"""
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self._ensure_llm_client()
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translation_messages = [
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{
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"role": "user",
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"content": f"{text}\n\n中文翻译为英文,输出格式为{{\"data\":\"翻译后的内容\"}}"
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}
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]
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try:
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response = await self.llm_client.response_structured(
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messages=translation_messages,
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response_model=TranslationResponse
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)
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return response.data
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except Exception as e:
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logger.error(f"翻译失败: {str(e)}")
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return text # 翻译失败时返回原文
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async def is_english(self,text: str) -> bool:
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return bool(re.fullmatch(r"[A-Za-z\s]+", text))
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async def Translate(self, text: str, target_language: str = "en") -> str:
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"""
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通用翻译方法(保持向后兼容)
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Args:
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text: 要翻译的文本
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target_language: 目标语言,"en"表示英文,"zh"表示中文
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Returns:
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翻译后的文本
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"""
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if target_language == "en":
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return await self.translate_to_english(text)
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else:
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logger.warning(f"不支持的目标语言: {target_language},返回原文")
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return text
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# 测试翻译服务
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async def Translation_English(modid, text, fields=None):
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"""
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将数据翻译为英文(支持字段级翻译)
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Args:
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modid: 模型ID
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text: 要翻译的数据(可以是字符串、字典或列表)
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fields: 需要翻译的字段列表(可选)
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如果为None,默认翻译: ['content', 'summary', 'statement', 'description',
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'name', 'aliases', 'caption', 'emotion_keywords']
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Returns:
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翻译后的数据,保持原有结构
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"""
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trans_service = MemoryTransService(modid)
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# 执行翻译
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if isinstance(text, list):
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english_result=[]
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for i in text:
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is_eng=await trans_service.is_english(i)
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if not is_eng:
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english = await trans_service.Translate(i)
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english_result.append(english)
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return english_result
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if isinstance(text, str):
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is_eng = await trans_service.is_english(text)
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if not is_eng:
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english_result = await trans_service.Translate(text)
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return english_result
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return text
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class MemoryBaseService:
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"""记忆服务基类,提供共享的辅助方法"""
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def __init__(self):
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self.neo4j_connector = Neo4jConnector()
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@staticmethod
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def parse_timestamp(timestamp_value) -> Optional[int]:
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"""
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将时间戳转换为毫秒级时间戳
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支持多种输入格式:
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- Neo4j DateTime 对象
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- ISO格式的时间戳字符串
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- Python datetime 对象
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Args:
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timestamp_value: 时间戳值(可以是多种类型)
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Returns:
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毫秒级时间戳,如果解析失败则返回None
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"""
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if not timestamp_value:
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return None
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try:
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# 处理 Neo4j DateTime 对象
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if hasattr(timestamp_value, 'to_native'):
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dt_object = timestamp_value.to_native()
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return int(dt_object.timestamp() * 1000)
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# 处理 Python datetime 对象
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if isinstance(timestamp_value, datetime):
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return int(timestamp_value.timestamp() * 1000)
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# 处理字符串格式
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if isinstance(timestamp_value, str):
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dt_object = datetime.fromisoformat(timestamp_value.replace("Z", "+00:00"))
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return int(dt_object.timestamp() * 1000)
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# 其他情况尝试转换为字符串再解析
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dt_object = datetime.fromisoformat(str(timestamp_value).replace("Z", "+00:00"))
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return int(dt_object.timestamp() * 1000)
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except (ValueError, TypeError, AttributeError) as e:
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logger.warning(f"无法解析时间戳: {timestamp_value}, error={str(e)}")
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return None
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async def extract_episodic_emotion(
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self,
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summary_id: str,
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end_user_id: str
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) -> Optional[str]:
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"""
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提取情景记忆的主要情绪
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查询MemorySummary节点关联的Statement节点,
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返回emotion_intensity最大的emotion_type。
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Args:
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summary_id: Summary节点的ID
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end_user_id: 终端用户ID (group_id)
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Returns:
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最大emotion_intensity对应的emotion_type,如果没有则返回None
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"""
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try:
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query = """
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MATCH (s:MemorySummary)
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WHERE elementId(s) = $summary_id AND s.group_id = $group_id
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MATCH (s)-[:DERIVED_FROM_STATEMENT]->(stmt:Statement)
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WHERE stmt.emotion_type IS NOT NULL
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AND stmt.emotion_intensity IS NOT NULL
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RETURN stmt.emotion_type AS emotion_type,
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stmt.emotion_intensity AS emotion_intensity
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ORDER BY emotion_intensity DESC
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LIMIT 1
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"""
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result = await self.neo4j_connector.execute_query(
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query,
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summary_id=summary_id,
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group_id=end_user_id
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)
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if result and len(result) > 0:
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emotion_type = result[0].get("emotion_type")
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logger.info(f"成功提取 summary_id={summary_id} 的情绪: {emotion_type}")
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return emotion_type
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else:
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logger.info(f"summary_id={summary_id} 没有情绪信息")
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return None
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except Exception as e:
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logger.error(f"提取情景记忆情绪时出错: {str(e)}", exc_info=True)
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return None
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async def get_episodic_memory_count(
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self,
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end_user_id: Optional[str] = None
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) -> int:
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"""
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获取情景记忆数量
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查询 MemorySummary 节点的数量。
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Args:
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end_user_id: 可选的终端用户ID,用于过滤特定用户的节点
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Returns:
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情景记忆的数量
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"""
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try:
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if end_user_id:
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query = """
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MATCH (n:MemorySummary)
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WHERE n.group_id = $group_id
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RETURN count(n) as count
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"""
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result = await self.neo4j_connector.execute_query(query, group_id=end_user_id)
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else:
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query = """
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MATCH (n:MemorySummary)
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RETURN count(n) as count
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"""
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result = await self.neo4j_connector.execute_query(query)
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count = result[0]["count"] if result and len(result) > 0 else 0
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logger.debug(f"情景记忆数量: {count} (end_user_id={end_user_id})")
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return count
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except Exception as e:
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logger.error(f"获取情景记忆数量时出错: {str(e)}", exc_info=True)
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return 0
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async def get_explicit_memory_count(
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self,
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end_user_id: Optional[str] = None
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) -> int:
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"""
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获取显性记忆数量
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显性记忆 = 情景记忆(MemorySummary)+ 语义记忆(ExtractedEntity with is_explicit_memory=true)
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Args:
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end_user_id: 可选的终端用户ID,用于过滤特定用户的节点
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Returns:
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显性记忆的数量
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"""
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try:
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# 1. 获取情景记忆数量
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episodic_count = await self.get_episodic_memory_count(end_user_id)
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# 2. 获取语义记忆数量(ExtractedEntity 且 is_explicit_memory = true)
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if end_user_id:
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semantic_query = """
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MATCH (e:ExtractedEntity)
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WHERE e.group_id = $group_id AND e.is_explicit_memory = true
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RETURN count(e) as count
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"""
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semantic_result = await self.neo4j_connector.execute_query(
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semantic_query,
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group_id=end_user_id
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)
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else:
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semantic_query = """
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MATCH (e:ExtractedEntity)
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WHERE e.is_explicit_memory = true
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RETURN count(e) as count
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"""
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semantic_result = await self.neo4j_connector.execute_query(semantic_query)
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semantic_count = semantic_result[0]["count"] if semantic_result and len(semantic_result) > 0 else 0
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# 3. 计算总数
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explicit_count = episodic_count + semantic_count
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logger.debug(
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f"显性记忆数量: {explicit_count} "
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f"(情景={episodic_count}, 语义={semantic_count}, end_user_id={end_user_id})"
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)
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return explicit_count
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except Exception as e:
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logger.error(f"获取显性记忆数量时出错: {str(e)}", exc_info=True)
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return 0
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async def get_emotional_memory_count(
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self,
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end_user_id: Optional[str] = None,
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statement_count_fallback: int = 0
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) -> int:
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"""
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获取情绪记忆数量
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通过 EmotionAnalyticsService 获取情绪标签统计总数。
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如果获取失败或没有指定 end_user_id,使用 statement_count_fallback 作为后备。
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Args:
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end_user_id: 可选的终端用户ID
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statement_count_fallback: 后备方案的数量(通常是 statement 节点数量)
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Returns:
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情绪记忆的数量
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"""
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try:
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if end_user_id:
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emotion_service = EmotionAnalyticsService()
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emotion_data = await emotion_service.get_emotion_tags(
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end_user_id=end_user_id,
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emotion_type=None,
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start_date=None,
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end_date=None,
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limit=10
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)
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emotion_count = emotion_data.get("total_count", 0)
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logger.debug(f"情绪记忆数量: {emotion_count} (end_user_id={end_user_id})")
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return emotion_count
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else:
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# 如果没有指定 end_user_id,使用后备方案
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logger.debug(f"情绪记忆数量: {statement_count_fallback} (使用后备方案)")
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return statement_count_fallback
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except Exception as e:
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logger.warning(f"获取情绪记忆数量失败,使用后备方案: {str(e)}")
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return statement_count_fallback
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async def get_forget_memory_count(
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self,
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end_user_id: Optional[str] = None,
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forgetting_threshold: float = 0.3
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) -> int:
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"""
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获取遗忘记忆数量
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统计激活值低于遗忘阈值的节点数量(low_activation_nodes)。
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查询范围包括:Statement、ExtractedEntity、MemorySummary、Chunk 节点。
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Args:
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end_user_id: 可选的终端用户ID,用于过滤特定用户的节点
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forgetting_threshold: 遗忘阈值,默认 0.3
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Returns:
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遗忘记忆的数量(激活值低于阈值的节点数)
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"""
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try:
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# 构建查询语句
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query = """
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MATCH (n)
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WHERE (n:Statement OR n:ExtractedEntity OR n:MemorySummary OR n:Chunk)
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"""
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if end_user_id:
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query += " AND n.group_id = $group_id"
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query += """
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RETURN sum(CASE WHEN n.activation_value IS NOT NULL AND n.activation_value < $threshold THEN 1 ELSE 0 END) as low_activation_nodes
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"""
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# 设置查询参数
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params = {'threshold': forgetting_threshold}
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if end_user_id:
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params['group_id'] = end_user_id
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# 执行查询
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result = await self.neo4j_connector.execute_query(query, **params)
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# 提取结果
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forget_count = result[0]['low_activation_nodes'] if result and len(result) > 0 else 0
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forget_count = forget_count or 0 # 处理 None 值
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logger.debug(
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f"遗忘记忆数量: {forget_count} "
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f"(threshold={forgetting_threshold}, end_user_id={end_user_id})"
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)
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return forget_count
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except Exception as e:
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logger.error(f"获取遗忘记忆数量时出错: {str(e)}", exc_info=True)
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return 0
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if __name__ == '__main__':
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import asyncio
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||
a=[{"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33925", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u4f60\u597d", "created_at": "2026-01-06T14:50:08.381230", "associative_memory": 0}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:33926", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u53d1\u8d77\u4e86\u5bf9\u8bdd\uff0c\u53d1\u9001\u4e86\u95ee\u5019\u8bed\"\u4f60\u597d\"\u3002", "created_at": "2026-01-06T14:50:11.363879", "associative_memory": 0}, "caption": "MemorySummary"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:76903", "label": "ExtractedEntity", "properties": {"description": "\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u901a\u8fc7\u4e0d\u540c\u6570\u636e\u6837\u672c\u6765\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u8bef\u5dee\u7684\u65b9\u6cd5", "name": "\u5f88\u591a\u91cd\u8981\u7684\u4eba\u751f\u9009\u62e9", "entity_type": "\u6982\u5ff5\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T19:24:55.805367", "aliases": 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\u5f88\u591a\u91cd\u8981\u7684\u4eba\u751f\u9009\u62e9\uff0c\u5176\u5b9e\u5c31\u50cf\u4e00\u6b21\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1\u3002\u6700\u5f00\u59cb\u51fa\u73b0\u7684\u90a3\u70b9\u4e0d\u5b89\uff0c\u7c7b\u4f3c\u4e8e\u6a21\u578b\u53d1\u73b0\u4e86\u5f02\u5e38\u4fe1\u53f7\uff1b\u56de\u987e\u8fc7\u53bb\u7684\u7ecf\u5386\uff0c\u662f\u5728\u56de\u6eaf\u5386\u53f2\u6570\u636e\u3001\u505a\u7279\u5f81\u63d0\u53d6\uff1b\u5bf9\u6bd4\u90a3\u4e9b\u8ba9\u4eba\u8e0f\u5b9e\u6216\u540e\u6094\u7684\u51b3\u5b9a\uff0c\u76f8\u5f53\u4e8e\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570\uff1b\u800c\u628a\u65f6\u95f4\u62c9\u957f\u53bb\u60f3\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c\uff0c\u5219\u662f\u5728\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u505a\u957f\u671f\u4e0e\u77ed\u671f\u6536\u76ca\u7684\u6743\u8861\u3002\u7b49\u8fd9\u4e9b\u6b65\u9aa4\u90fd\u8d70\u8fc7\u4e4b\u540e\uff0c\u6240\u8c13\u201c\u7b54\u6848\u201d\uff0c\u5e76\u4e0d\u662f\u88ab\u76f4\u63a5\u7b97\u51fa\u6765\u7684\uff0c\u800c\u662f\u6a21\u578b\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u4e2d\u9010\u6e10\u6536\u655b\u5230\u7684\u7ed3\u679c\u3002", "created_at": "2026-01-06T19:24:55.805367", "associative_memory": 6}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77135", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u5f88\u591a\u91cd\u8981\u7684\u4eba\u751f\u9009\u62e9\u5c31\u50cf\u4e00\u6b21\u590d\u6742\u7684\u9884\u6d4b\u4efb\u52a1\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77136", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u6700\u5f00\u59cb\u51fa\u73b0\u7684\u90a3\u70b9\u4e0d\u5b89\u7c7b\u4f3c\u4e8e\u6a21\u578b\u53d1\u73b0\u4e86\u5f02\u5e38\u4fe1\u53f7\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77137", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u56de\u987e\u8fc7\u53bb\u7684\u7ecf\u5386\u662f\u5728\u56de\u6eaf\u5386\u53f2\u6570\u636e\u5e76\u505a\u7279\u5f81\u63d0\u53d6\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77138", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u5bf9\u6bd4\u90a3\u4e9b\u8ba9\u4eba\u8e0f\u5b9e\u6216\u540e\u6094\u7684\u51b3\u5b9a\u76f8\u5f53\u4e8e\u5728\u4e0d\u540c\u6837\u672c\u4e0a\u8bc4\u4f30\u635f\u5931\u51fd\u6570\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77139", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u628a\u65f6\u95f4\u62c9\u957f\u53bb\u60f3\u4e00\u5e74\u3001\u4e09\u5e74\u3001\u4e94\u5e74\u540e\u7684\u7ed3\u679c\u662f\u5728\u4e0d\u540c\u65f6\u95f4\u7a97\u53e3\u4e0b\u505a\u957f\u671f\u4e0e\u77ed\u671f\u6536\u76ca\u7684\u6743\u8861\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77140", "label": "Statement", "properties": {"temporal_info": "ATEMPORAL", "stmt_type": "OPINION", "statement": "\u6240\u8c13\u201c\u7b54\u6848\u201d\u5e76\u4e0d\u662f\u88ab\u76f4\u63a5\u7b97\u51fa\u6765\u7684\uff0c\u800c\u662f\u6a21\u578b\u5728\u591a\u6b21\u62c6\u89e3\u4e0e\u8fed\u4ee3\u4e2d\u9010\u6e10\u6536\u655b\u5230\u7684\u7ed3\u679c\u3002", "created_at": "2026-01-06T19:24:55.805367", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77141", "label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:41:05.181477", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77142", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:41:05.181477", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77143", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "created_at": "2026-01-06T14:41:05.181477", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77144", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:41:05.181477", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77145", "label": "ExtractedEntity", "properties": {"description": "\u53c2\u4e0e\u5bf9\u8bdd\u7684\u4e2a\u4eba\uff0c\u53d1\u51fa\u91ce\u9910\u9080\u7ea6\u7684\u4e00\u65b9", "name": "\u5c0f\u660e", "entity_type": "\u4eba\u7269\u5b9e\u4f53\u8282\u70b9", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u5c0f\u7ea2"], "connect_strength": "strong", "associative_memory": 13}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77146", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5e38\u89c1\u7684\u542b\u5496\u5561\u56e0\u996e\u54c1", "name": "\u5496\u5561", "entity_type": "", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u5496\u5561\u996e\u6599"], "connect_strength": "Strong", "associative_memory": 3}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77147", "label": "ExtractedEntity", "properties": {"description": "\u4e00\u79cd\u5496\u5561\u996e\u54c1\uff0c\u7531\u6d53\u7f29\u5496\u5561\u548c\u725b\u5976\u5236\u6210", "name": "\u62ff\u94c1", "entity_type": "", "created_at": "2026-01-06T14:41:05.181477", "aliases": ["\u62ff\u94c1\u5496\u5561", "Latte"], "connect_strength": "Strong", "associative_memory": 3}, "caption": "ExtractedEntity"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77148", "label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:44:22.921668", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77149", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:44:22.921668", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77150", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:44:22.921668", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77151", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:44:22.921668", "emotion_keywords": [], "associative_memory": 3}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77152", "label": "Dialogue", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 0}, "caption": "Dialogue"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77153", "label": "Chunk", "properties": {"content": "\u7528\u6237: \u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "associative_memory": 2}, "caption": "Chunk"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77154", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\u3002", "valid_at": "2026-01-06T00:00:00+00:00", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77155", "label": "Statement", "properties": {"temporal_info": "DYNAMIC", "stmt_type": "FACT", "statement": "\u5c0f\u660e\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:02.387455", "emotion_keywords": [], "associative_memory": 4}, "caption": "Statement"}, {"id": "4:f6039a9b-d553-4ba2-9b1c-d9a18917801f:77156", "label": "MemorySummary", "properties": {"content": "\u7528\u6237\u5c0f\u660e\u559c\u6b22\u559d\u5496\u5561\uff0c\u6bcf\u5929\u90fd\u8981\u559d\u62ff\u94c1\u3002", "created_at": "2026-01-06T14:46:16.548556", "associative_memory": 2},
|
||
"caption": "MemorySummary"}]
|
||
|
||
result=asyncio.run(Translation_English("2699984d-23be-4817-b81c-c38682a08306",a))
|
||
print(result) |