e4bf0710c7
数据库 - 新增迁移 0003：timeline_events.memory_source_id 外键 → memory_sources，便于按 ingest 源做时间线幂等 后端 - 记忆 - 新增 ingest 后 LLM 富化（摘要/事实/时间线），可配置开关与最大字符数 - 新增证据包组装：合并 chunk、摘要、事实、时间线、故事等检索结果；支持空 query 时是否仍带 rolling 等开关 - repo/retriever/service/router/schemas/summarizer/timeline/extractor 等扩展；文档 memory-retrieval.md 更新 后端 - 对话 WS - 增加 PING/PONG；分段 ASR 日志与空音频处理；转写失败与「无助手回复」错误提示更明确 - 助手多段回复持久化使用统一分隔符，与分段逻辑一致 后端 - Agent - reply_limits：按 [SPLIT] 与段落拆段，并保证非空 fallback，供 WS 与 TTS 多段下发 后端 - 回忆录任务 - transcript ingest 记录 source_id；任务成功结?
449 lines
17 KiB
Python
449 lines
17 KiB
Python
"""
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对话 Agent 提示词模板和访谈问题库
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"""
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from enum import Enum
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from typing import Dict, List
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from app.core.config import settings
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class ConversationStage(str, Enum):
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"""对话阶段枚举"""
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CHILDHOOD = "childhood" # 童年
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EDUCATION = "education" # 教育
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CAREER = "career" # 事业
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FAMILY = "family" # 家庭
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BELIEFS = "beliefs" # 信念
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SUMMARY = "summary" # 人生总结
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# 访谈问题库（每阶段 2～3 条短问句，供参考；主流程仍由模型与槽位驱动）
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INTERVIEW_QUESTIONS: Dict[ConversationStage, List[str]] = {
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ConversationStage.CHILDHOOD: [
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"你是在哪儿长大的？小时候印象最深的一件事是什么？",
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"小时候家里是怎样的？父母对你影响大吗？",
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],
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ConversationStage.EDUCATION: [
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"求学阶段印象最深的是哪段经历？",
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"有没有哪位老师或同学对你影响特别大？毕业后最初想做什么？",
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],
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ConversationStage.CAREER: [
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"第一次工作还记得吗？当时心情怎样？",
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"事业里遇到过最大的挑战是什么？怎么挺过来的？有没有特别自豪的时刻？",
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],
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ConversationStage.FAMILY: [
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"和伴侣是怎么认识的？做父母时最难忘或最骄傲的瞬间？",
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"家庭在你的人生里扮演什么角色？",
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],
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ConversationStage.BELIEFS: [
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"有没有一直坚守的信念或座右铭？哪些价值观对你最重要？",
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"你怎样理解成功与幸福？低谷时是什么支撑你？",
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],
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ConversationStage.SUMMARY: [
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"回顾一生，最重要的经验或教训是什么？最感激的人与事有哪些？",
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"若能对年轻时的自己说一句话，你会说什么？",
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],
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}
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def get_system_prompt(
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current_stage: ConversationStage,
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covered_topics: List[str],
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user_latest_response: str,
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) -> str:
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"""
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生成对话 Agent 的系统提示词
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Args:
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current_stage: 当前对话阶段
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covered_topics: 已聊过的话题列表
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user_latest_response: 用户最新回答
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Returns:
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系统提示词字符串
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"""
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stage_name_map = {
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ConversationStage.CHILDHOOD: "童年",
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ConversationStage.EDUCATION: "教育",
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ConversationStage.CAREER: "事业",
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ConversationStage.FAMILY: "家庭",
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ConversationStage.BELIEFS: "信念",
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ConversationStage.SUMMARY: "人生总结",
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}
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covered_topics_str = "、".join(covered_topics) if covered_topics else "暂无"
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prompt = f"""你是「岁月知己」，像老朋友一样陪用户聊人生。**回复要短**，像微信聊天，不要长篇、不要文学腔。
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规则：先简短接住对方一句，**最多再问一个具体问题**；禁止括号与思考过程；禁止采访腔（如「我注意到」「我想了解」）；**不要重复确认**对方刚说过或上文已能推断的信息。
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当前阶段：{stage_name_map.get(current_stage, current_stage.value)}
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已聊话题：{covered_topics_str}
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直接输出对用户说的话。"""
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return prompt
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def get_questions_for_stage(stage: ConversationStage) -> List[str]:
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"""获取指定阶段的所有问题"""
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return INTERVIEW_QUESTIONS.get(stage, [])
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SLOT_NAME_MAP = {
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"place": "成长的地方",
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"people": "重要的人",
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"daily_life": "日常生活",
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"emotion": "童年感受",
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"turning_event": "难忘的事",
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"school": "学校经历",
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"city": "求学的城市",
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"motivation": "学习动力",
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"challenge": "遇到的挑战",
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"change": "成长变化",
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"job": "工作内容",
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"environment": "工作环境",
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"decision": "重要决定",
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"pressure": "压力与困难",
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"growth": "职业成长",
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"relationship": "家人关系",
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"conflict": "矛盾与化解",
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"support": "相互支持",
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"responsibility": "家庭责任",
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"value": "核心价值观",
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"regret": "遗憾与释怀",
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"pride": "骄傲的事",
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"lesson": "人生经验",
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}
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STAGE_RELATED_TOPICS = {
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"childhood": ["family", "education"],
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"education": ["childhood", "career"],
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"career": ["education", "family", "belief"],
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"family": ["childhood", "career", "belief"],
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"belief": ["career", "family"],
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}
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LIGHT_TOPICS = [
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"有什么爱好或者特别喜欢的消遣方式吗？",
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"最近有什么让你开心的事吗？",
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"有没有什么趣事想分享？",
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"平时喜欢看什么书或者电影吗？",
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]
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RESPONSE_STYLES = [
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"empathy",
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"curious",
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"reflection",
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"lighthearted",
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"connection",
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]
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def get_opening_prompt(
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current_stage: str,
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empty_slots_readable: List[str],
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user_profile_context: str = "",
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) -> str:
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"""空对话时 AI 先开口的提示词"""
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stage_name_map = {
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"childhood": "童年时光",
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"education": "求学经历",
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"career": "职业生涯",
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"family": "家庭生活",
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"belief": "人生信念",
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}
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stage_name = stage_name_map.get(current_stage, current_stage)
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if empty_slots_readable:
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topics_str = "、".join(empty_slots_readable)
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topics_heading = (
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f"## 当前建议话题（{stage_name}）\n可以从中选一个来问：{topics_str}"
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)
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task_question = (
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"2. **必须问一个问题**：接着问一个**具体、好回答**的问题，引导用户开始分享；"
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"优先落在上述还未聊透的方向上。不要问太宽泛的「有什么想聊的」。"
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)
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_opening_examples = {
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"childhood": (
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"示例（仅供参考风格）：\n"
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'"你好呀～ 想听听你的人生故事。你小时候是在哪儿长大的？那边有什么特别让你怀念的？"\n或\n'
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'"在的！今天想聊聊你。你童年里印象最深的一件事是什么？"'
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),
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"education": (
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"示例（仅供参考风格）：\n"
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'"嗨～ 想听听你求学那段日子。你印象最深的是哪段学校时光？"\n或\n'
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'"在呢！你读书时有没有一位老师或同学，到现在还会想起？"'
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),
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"career": (
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"示例（仅供参考风格）：\n"
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'"你好呀～ 想听听你工作这条路上故事。你第一份工作还记得吗，当时什么心情？"\n或\n'
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'"在的！你现在或过去做过的工作里，哪一段你最想先聊聊？"'
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),
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"family": (
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"示例（仅供参考风格）：\n"
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'"嗨～ 想听听你家里的事。和家里人相处时，有没有特别暖或难忘的一刻？"\n或\n'
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'"在呢！如果用一个词形容你心里的「家」，你会想到什么？"'
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),
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"belief": (
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"示例（仅供参考风格）：\n"
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'"你好呀～ 想听听你心里看重的东西。有没有一句你一直信到现在的话？"\n或\n'
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'"在的！你觉得自己走到今天，最放不下或最骄傲的是什么？"'
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),
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}
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style_examples = _opening_examples.get(
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current_stage,
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_opening_examples["childhood"],
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)
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else:
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topics_heading = (
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f"## 当前阶段（{stage_name}）\n"
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"访谈结构化槽位里，这一阶段的主要问题在素材侧**已有覆盖**。"
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"开场要像老朋友重逢：接近况、接续上次聊过的事、或任何用户可能想提起的新片段；"
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"**禁止**为了凑问题而默认再从「童年在哪长大」等已覆盖模板重头盘问。"
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)
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task_question = (
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"2. **问候 + 轻巧引子**：用一句温暖的话接上对话；若自然，可以问一个与近况、"
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"想续上的回忆、或新冒出来的小事有关的问题。若不适合追问，问候 + 一句开放式引子即可。"
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)
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style_examples = (
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"示例（仅供参考风格）：\n"
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'"嘿，又见面啦～ 今天有没有哪件事突然从脑子里冒出来，想跟我说说？"\n或\n'
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'"在的！上次聊到那儿我还记着，你后来还有想起什么细节吗？"'
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)
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profile_section = (
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f"\n## 用户基本信息\n{user_profile_context}\n" if user_profile_context else ""
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)
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return f"""你是「岁月知己」。用户刚进对话，**还没说话**，请你先开口。**短、像微信**，一两句问候 + 一句具体问题即可，不要排比、不要文学描写。
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{profile_section}
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{topics_heading}
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## 任务
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1. 简短问候。
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{task_question}
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3. 自然、温暖，但**字数要少**。
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## 格式
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- 可用 [SPLIT] 分成最多 2 条；或一条里「问候 + 问题」。
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- 禁止括号、思考过程；不要替用户编回答。
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{style_examples}
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直接输出："""
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def _build_era_context(current_stage: str, user_profile_context: str) -> str:
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"""根据用户的人生阶段和出生年份，生成对应时代的历史/政治/文化背景提示"""
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if not user_profile_context:
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return ""
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birth_year = None
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birth_place = ""
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for line in user_profile_context.split("\n"):
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if "出生年份" in line:
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try:
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birth_year = int(line.split("：")[1].strip().replace("年", ""))
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except (ValueError, IndexError):
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pass
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if "出生地" in line or "成长地" in line:
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birth_place = line.split("：")[1].strip() if "：" in line else ""
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if not birth_year:
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return ""
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stage_era_map = {
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"childhood": (0, 12),
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"education": (6, 22),
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"career": (18, 50),
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"family": (20, 50),
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"belief": (30, 60),
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}
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age_range = stage_era_map.get(current_stage, (0, 30))
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era_start = birth_year + age_range[0]
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era_end = birth_year + age_range[1]
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era_events = []
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decade_events = {
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1950: "新中国成立初期、土地改革、抗美援朝",
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1960: "大跃进、三年自然灾害、中苏关系变化",
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1970: "文化大革命、知青上山下乡、中美建交",
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1980: "改革开放、恢复高考、个体经济兴起、电视普及",
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1990: "社会主义市场经济、下海潮、香港回归、互联网初期",
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2000: "加入WTO、房地产兴起、手机普及、北京奥运",
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2010: "移动互联网爆发、微信时代、共享经济、双创浪潮",
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2020: "新冠疫情、直播经济、人工智能崛起",
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}
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for decade, events in decade_events.items():
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if era_start <= decade + 9 and era_end >= decade:
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era_events.append(f"{decade}年代：{events}")
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if not era_events:
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return ""
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place_hint = f" {birth_place}" if birth_place else ""
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# 短提示即可，避免模型写长段「时代散文」
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return (
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f"\n## 时代参考（一两句带过即可，勿长篇）\n"
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f"约 {era_start}-{era_end} 年{place_hint}；可联想：{era_events[0]}"
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+ (f"；{era_events[1]}" if len(era_events) > 1 else "")
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+ "\n"
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)
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def get_guided_conversation_prompt(
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current_stage: str,
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empty_slots: List[str],
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filled_slots: Dict[str, str],
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user_message: str,
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conversation_turn: int = 0,
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same_topic_turns: int = 0,
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all_stages_coverage: Dict[str, Dict] = None,
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detected_user_stage: str = "",
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user_profile_context: str = "",
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) -> str:
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"""生成状态感知的对话提示词"""
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stage_name_map = {
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"childhood": "童年时光",
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"education": "求学经历",
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"career": "职业生涯",
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"family": "家庭生活",
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"belief": "人生信念",
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}
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current_stage_name = stage_name_map.get(current_stage, current_stage)
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user_stage_name = (
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stage_name_map.get(detected_user_stage, "") if detected_user_stage else ""
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)
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user_jumped = detected_user_stage and detected_user_stage != current_stage
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empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
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empty_slots_str = (
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"、".join(empty_slots_readable) if empty_slots_readable else "已聊得很充分"
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)
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filled_info = []
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for key, value in filled_slots.items():
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readable_key = SLOT_NAME_MAP.get(key, key)
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filled_info.append(
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f"{readable_key}: {value[:50]}..."
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if len(value) > 50
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else f"{readable_key}: {value}"
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)
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filled_slots_str = "\n".join(filled_info) if filled_info else "刚开始聊"
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progress_lines = []
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uncovered_stages = []
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if all_stages_coverage:
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for stage in ["childhood", "education", "career", "family", "belief"]:
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cov = all_stages_coverage.get(stage, {})
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filled_n = cov.get("filled", 0)
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total_n = cov.get("total", 0)
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sname = stage_name_map.get(stage, stage)
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if filled_n == 0:
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progress_lines.append(f" {sname}：还没聊到")
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uncovered_stages.append(sname)
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elif filled_n < total_n:
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progress_lines.append(f" {sname}：聊了一些（{filled_n}/{total_n}）")
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else:
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progress_lines.append(f" {sname}：已聊得很充分 ✓")
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progress_str = "\n".join(progress_lines) if progress_lines else ""
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filled_count = len(filled_slots)
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should_switch_topic = same_topic_turns >= 3 or (
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filled_count >= 2 and same_topic_turns >= 2
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)
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should_lighten_mood = conversation_turn > 0 and conversation_turn % 5 == 0
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should_try_new_stage = filled_count >= 3 and len(empty_slots) <= 2
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related_stages = STAGE_RELATED_TOPICS.get(current_stage, [])
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related_stages_str = "、".join([stage_name_map.get(s, s) for s in related_stages])
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style = RESPONSE_STYLES[conversation_turn % len(RESPONSE_STYLES)]
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style_guidance = {
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"empathy": "共情一两句即可",
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"curious": "若还有未展开的细节可好奇问一个点；若上文已说清或可自然推断，只承接或换角度，**勿为凑问题而追问**",
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"reflection": "可一句简短感慨，勿讲大道理",
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"lighthearted": "轻松一点，别讲段子太长",
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"connection": "可提「我也有过类似感受」一句，勿编造具体经历细节",
|
||
}.get(style, "")
|
||
|
||
dynamic_guidance = ""
|
||
if user_jumped:
|
||
dynamic_guidance += f"""
|
||
- **用户正在聊「{user_stage_name}」的话题，跟着他/她的节奏走，不要试图拉回「{current_stage_name}」**
|
||
- 顺着用户的思路，帮他/她把这个话题聊深聊透
|
||
- 这是很自然的事情，人回忆往事经常会跳跃，你要做的是陪伴和倾听"""
|
||
else:
|
||
if should_lighten_mood:
|
||
dynamic_guidance += "\n- 聊了一会儿了，可以适当轻松一下，聊点有趣的"
|
||
if should_switch_topic and empty_slots_readable:
|
||
dynamic_guidance += (
|
||
f"\n- 这个话题聊得差不多了，可以自然转到：{empty_slots_str}"
|
||
)
|
||
if should_try_new_stage and related_stages:
|
||
dynamic_guidance += (
|
||
f"\n- 如果自然的话，可以尝试聊聊相关的话题，比如{related_stages_str}"
|
||
)
|
||
|
||
uncovered_hint = ""
|
||
if not user_jumped and uncovered_stages and should_try_new_stage:
|
||
uncovered_hint = f"\n- 还没聊到的人生阶段有：{'、'.join(uncovered_stages)}，如果聊天中有自然的契机，可以轻轻带一句，但不要刻意"
|
||
|
||
if user_jumped:
|
||
topic_desc = f"你们原本在聊「{current_stage_name}」，但用户自然地聊到了「{user_stage_name}」的内容"
|
||
else:
|
||
topic_desc = f"你们聊到了「{current_stage_name}」这个话题"
|
||
|
||
profile_section = ""
|
||
if user_profile_context:
|
||
profile_section = f"\n## 用户基本信息\n{user_profile_context}\n"
|
||
|
||
active_stage = (
|
||
detected_user_stage if user_jumped and detected_user_stage else current_stage
|
||
)
|
||
era_context = (
|
||
_build_era_context(active_stage, user_profile_context)
|
||
if settings.chat_era_context_enabled
|
||
else ""
|
||
)
|
||
|
||
prompt = f"""你是「岁月知己」，陪用户聊人生。**像微信：短句、少修辞、别写小作文。**
|
||
{topic_desc}
|
||
{profile_section}
|
||
## 本阶段已聊
|
||
{filled_slots_str}
|
||
|
||
## 还可聊的方向
|
||
{empty_slots_str}
|
||
|
||
## 进度
|
||
{progress_str}
|
||
{era_context}
|
||
## 用户刚才说
|
||
"{user_message}"
|
||
|
||
## 本轮语气
|
||
{style_guidance}
|
||
|
||
## 任务（短）
|
||
1. 先简短回应一句，不要总结成长文。
|
||
2. 用户若跳到别的人生阶段，跟着他聊，别硬拉回。
|
||
3. 需要追问时**只问一个**具体小问题；**不必每轮都问**；若用户已说明或语境已能推出（如谁买的、和谁），**别再为同一件事做 yes/no 确认**。
|
||
4. 用户只回简短肯定/否定（如「是的」「对」）时，**结合上文**理解，承接即可或问**新**角度，勿重复上一句已问过的事。
|
||
5. 可用 [SPLIT] 分成**最多 2 条**消息，每条都很短。
|
||
{dynamic_guidance}{uncovered_hint}
|
||
|
||
## 禁止
|
||
括号/思考过程；采访腔；**重复确认**用户档案、**上文已说**或**强暗示下已可知**的事实（包括无信息量的「是不是他/她…」式追问）；别编用户没说的细节。
|
||
|
||
直接输出（[SPLIT] 可选，最多 2 段）："""
|
||
|
||
return prompt
|
||
|
||
|
||
# 别名：访谈对话专用；勿与回忆录 `app.agents.memoir.prompts.get_memoir_editor_system_prompt` 混淆
|
||
get_interview_system_prompt = get_system_prompt
|