update_insight_system_zh: |
  从下面的句子中提取出给定类别的{user_name}的资料信息，并判断与已有信息是否矛盾，若矛盾以新信息为准整合已有信息和新信息并输出。若不需要更改，则回答“无”。
  其中矛盾的形式可以有很多种，可以是逻辑上的矛盾，可以是属性上的变化导致的矛盾，比如不能同时在两个地方工作，同一个时刻不能在两个地点，同一个时刻不能干两件事情等等。
  请一步步思考，并按如下格式输出, 其中信息一定加<>:
  思考: 思考的依据和过程，150字以内。
  {user_name}的资料: <信息>

update_insight_system: |
  Extract the given category of {user_name}'s profile information from the following sentences and determine if it contradicts the existing information. If there is a contradiction, integrate the existing information and the new information, prioritizing the new information, and output the result. If no changes are needed, respond with 'None'.
  The contradictions can come in many forms, such as logical contradictions or changes in attributes leading to contradictions, for example, not being able to work in two places simultaneously, being unable to be in two locations at the same time, or being unable to perform two tasks at the same time, etc.
  Think step by step, and output in the following format, with information enclosed in <>:
  Thoughts: The basis and process of your thinking, within 150 words.
  {user_name}'s profile: <Information>

update_insight_few_shot_zh: |
  示例1:
  因为昨天成都下大雨，{user_name}全身都被淋湿了。
  {user_name}关心明天成都的天气预报。
  类别：{user_name}所在地区
  已有信息：{user_name}所在地区: 杭州
  思考：从第一句句子可以得出{user_name}在成都。第二句句子没有直接透露{user_name}所在地信息，但与第一句句子{user_name}在成都的信息吻合。这与已有信息（{user_name}在杭州）矛盾，输出更新的信息。
  {user_name}的资料：<成都>

  示例2:
  {user_name}最近养好了肠胃。
  {user_name}关注中医养生。
  类别：{user_name}健康状况
  已有信息：{user_name}健康状况: 肠胃不好，高血压
  思考：从第一句句子可以得出{user_name}最近养好了肠胃，与已有信息矛盾，以新信息为准。第二句句子与{user_name}健康状况无关。整合已有信息和新信息得到{user_name}健康状况是肠胃健康，高血压。
  {user_name}的资料：<肠胃健康，高血压>

  示例3:
  {user_name}刚刚毕业，第一份工作是银行前台。
  {user_name}的理想工作是职业游戏选手。
  类别：{user_name}职业
  已有信息：{user_name}职业：在招商银行工作
  思考：整合已有信息和第一句句子的信息可以得出{user_name}的现在的职业是招商银行前台。第二句句子说明了{user_name}的理想工作但并不是现在的职业。
  {user_name}的资料：<招商银行前台>

  示例4:
  {user_name}大学期间接触过优化算法的研究。
  类别：{user_name}学习专业
  已有信息：{user_name}学习专业：与人工智能相关
  思考：从句子可以得出{user_name}大学学习的专业与优化算法相关，这与已有信息（{user_name}学习专业与人工智能相关）不矛盾，整合可以得出{user_name}大学学习的专业与人工智能和优化算法相关。
  {user_name}的资料：<与人工智能和优化算法相关>

  示例5:
  {user_name}单身。
  {user_name}受到一名18岁男生的追求，但不想接受又不想伤害他。
  {user_name}喜欢成熟且情绪稳定的男生。
  类别：{user_name}情感状况
  已有信息：{user_name}情感状况：有男朋友
  思考：从第一句句子可以得出{user_name}现在单身，与已有信息矛盾，以新信息为准。从第二句句子得出{user_name}受到一名18岁男生的追求但并不喜欢他。第三句话表达了{user_name}理想的伴侣类型但与{user_name}情感状况无关。整合得出{user_name}情感状况为单身，受到一名18岁男生的追求但并不喜欢他。
  {user_name}的资料：<单身，受到一名18岁男生的追求但并不喜欢他。>

  示例6:
  {user_name}女朋友下个月过生日。
  {user_name}生日在7月15日。
  {user_name}的还在在学校经常逃课。
  {user_name}喜欢打篮球。
  类别：{user_name}生日
  已有信息：{user_name}生日：1987年7月15日。
  思考：第一句句子中提及生日，但并不是用户的生日，无法得出用户生日信息。从第二句句子可以得出用户生日在7月15日，与已有信息不矛盾，整合可以得出用户生日是1987年7月15日。
  {user_name}的资料：<1987年7月15日>

  示例7:
  今天{user_name}和同学去打球了。
  明天{user_name}和女朋友一起去杭州旅游。
  今天{user_name}买入了100股阿里巴巴股票。
  类别：{user_name}公司地址
  已有信息：{user_name}公司地址：
  思考：和公司地址都没有关联，没有新提取的信息。
  {user_name}的资料：<无>

update_insight_few_shot: |
  Example 1:
  Because it rained heavily in Chengdu yesterday, {user_name} got completely soaked.
  {user_name} is concerned about Chengdu's weather forecast for tomorrow.
  Category: {user_name}'s location
  Existing information: {user_name}'s location: Hangzhou
  Thought: From the first sentence, it can be inferred that {user_name} is in Chengdu. The second sentence does not directly reveal {user_name}'s location but matches the information that {user_name} is in Chengdu from the first sentence. This contradicts the existing information (that {user_name} is in Hangzhou), so we output the updated information.
  {user_name}'s profile: <Chengdu>

  Example 2:
  {user_name} recently recovered from stomach issues.
  {user_name} is interested in traditional Chinese medicine.
  Category: {user_name}'s health status
  Existing information: {user_name}'s health status: Stomach issues, high blood pressure
  Thought: From the first sentence, it can be inferred that {user_name} recently recovered from stomach issues, which contradicts the existing information. Therefore, the new information should take precedence. The second sentence is not related to {user_name}'s health status. Integrating the existing information and the new information, we get that {user_name}'s health status is healthy stomach and high blood pressure.
  {user_name}'s profile: <Healthy stomach, high blood pressure>

  Example 3:
  {user_name} just graduated, and their first job is as a bank receptionist.
  {user_name}'s dream job is to be a professional gamer.
  Category: {user_name}'s occupation
  Existing information: {user_name}'s occupation: Works at China Merchants Bank
  Thought: Integrating the existing information and the information from the first sentence, it can be inferred that {user_name}'s current occupation is a receptionist at China Merchants Bank. The second sentence explains {user_name}'s dream job but not the current occupation.
  {user_name}'s profile: <Receptionist at China Merchants Bank>

  Example 4:
  {user_name} was exposed to optimization algorithm research during university.
  Category: {user_name}'s field of study
  Existing information: {user_name}'s field of study: Related to artificial intelligence
  Thought: From the sentence, it can be inferred that {user_name}'s university major is related to optimization algorithms. This does not contradict the existing information (that {user_name}'s major is related to artificial intelligence). Integrating both, we can conclude that {user_name}'s university major is related to artificial intelligence and optimization algorithms.
  {user_name}'s profile: <Related to artificial intelligence and optimization algorithms>

  Example 5:
  {user_name} is single.
  {user_name} is pursued by an 18-year-old male but doesn't want to accept his advances or hurt him.
  {user_name} prefers mature and emotionally stable men.
  Category: {user_name}'s relationship status
  Existing information: {user_name}'s relationship status: Has a boyfriend
  Thought: From the first sentence, it can be inferred that {user_name} is currently single, which contradicts the existing information. Therefore, the new information should take precedence. From the second sentence, it can be inferred that {user_name} is being pursued by an 18-year-old male but does not like him. The third sentence expresses {user_name}'s ideal partner type but is not related to {user_name}'s relationship status. Integrating this, we conclude that {user_name}'s relationship status is single and being pursued by an 18-year-old male but does not like him.
  {user_name}'s profile: <Single, pursued by an 18-year-old male but does not like him>

  Example 6:
  {user_name}'s girlfriend's birthday is next month.
  {user_name}'s birthday is on July 15th.
  {user_name} often skips classes at school.
  {user_name} likes playing basketball.
  Category: {user_name}'s Birthday
  Existing Information: {user_name}'s Birthday: July 15, 1987.
  Thoughts: The first sentence mentions a birthday, but it is not the user's birthday, so it does not provide information about the user's birthday. From the second sentence, we know that the user's birthday is on July 15th, which is consistent with the existing information. We can conclude that the user's birthday is July 15, 1987.
  {user_name}'s profile: <July 15, 1987>

  Example 7:
  Today, {user_name} played basketball with classmates.
  Tomorrow, {user_name} is going to Hangzhou with his girlfriend.
  Today, {user_name} bought 100 shares of Alibaba stock.
  Category: {user_name}'s Company Address
  Existing Information: {user_name}'s Company Address:
  Thoughts: There is no information related to the company address, no new information extracted.
  {user_name}'s profile: <None>

update_insight_user_query_zh: |
  {user_query}
  类别：{insight_key}
  已有信息：{insight_key_value}

update_insight_user_query: |
  {user_query}
  Category: {insight_key}
  Existing information: {insight_key_value}

insight_string_format_zh: |
  {name}的{key}

insight_string_format: |
  The {key} of {name}
