461 lines
19 KiB
Python
461 lines
19 KiB
Python
"""
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对话 Agent 提示词模板
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"""
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from typing import Dict, List, Optional
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from app.agents.chat.background_voice import (
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get_background_voice_chat_block,
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normalize_background_voice,
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)
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from app.agents.chat.occupation_context import get_occupation_chat_hint
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from app.agents.chat.interview_reply_length import (
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heuristic_likely_chit_chat,
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heuristic_likely_emotional,
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heuristic_likely_new_detail,
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)
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from app.agents.chat.personas import (
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get_interview_persona_block,
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get_opening_persona_line,
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normalize_interview_persona,
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)
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from app.agents.chat.output_rules import chat_output_rules
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from app.agents.stage_constants import CHAT_STAGES, STAGE_DISPLAY_ZH, STAGE_ERA_HINTS
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from app.core.config import settings
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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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def _guided_voice_intro_line(background_voice: str) -> str:
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"""顶部角色描述（具体「接住」写法集中在 ## 你要做的）。"""
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return (
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"你是「岁月知己」，像老朋友陪用户聊人生。"
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"短句为主，遵守下方「本轮回复长度」档位。"
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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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persona: str = "default",
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background_voice: str = "default",
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occupation: 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 = STAGE_DISPLAY_ZH.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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bv = normalize_background_voice(background_voice)
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if bv == "cadre":
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style_examples += (
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"\n（干部/机关语境：问候稳重、不用「嗨～」；示例可参考）\n"
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'"您好，想听听您的人生故事。您小时候是在哪儿长大的？哪一段印象最深？"\n或\n'
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'"您好。今天想从您印象最深的一件事聊起，可以吗？"'
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)
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elif bv == "military":
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style_examples += (
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"\n（军队语境：简洁、得体；不用「嗨～」；示例可参考）\n"
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'"您好。想听听您的经历。您童年印象最深的一件事是什么？"\n或\n'
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'"您好，有空的话想聊聊您的人生故事。您小时候在哪儿长大？"'
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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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persona_key = normalize_interview_persona(persona)
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opening_persona = get_opening_persona_line(persona_key)
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persona_extra = f"\n## 访谈性格\n{opening_persona}\n" if opening_persona else ""
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voice_block = get_background_voice_chat_block(background_voice)
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voice_section = f"\n{voice_block}\n" if voice_block else ""
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occ_hint = get_occupation_chat_hint(occupation, background_voice)
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occ_section = f"\n{occ_hint}\n" if occ_hint else ""
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bv = normalize_background_voice(background_voice)
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if bv == "default":
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opening_head = (
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"你是「岁月知己」。用户刚进对话，**还没说话**，请你先开口。"
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"**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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)
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else:
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opening_head = (
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"你是「岁月知己」。用户刚进对话，**还没说话**，请你先开口。"
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"**短；两三句内完成问候 + 一个具体问题**；不要排比、不要文学描写。"
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)
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return f"""{opening_head}
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{profile_section}
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{topics_heading}
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{persona_extra}{voice_section}{occ_section}
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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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- {chat_output_rules()} 不要替用户编回答。
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{style_examples}
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直接输出（仅自然口语）："""
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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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age_range = STAGE_ERA_HINTS.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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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 _format_reply_length_section(current_mode: str) -> str:
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"""仅输出当前档位说明，减少重复 tokens。"""
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safe = (
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current_mode
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if current_mode in ("brief", "standard", "expanded")
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else "standard"
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)
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mode_desc = {
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"brief": "一两句话，简短温暖；可带一个小问题也可以不带。",
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"standard": "承接对方 + 最多一个具体问题；像朋友聊天，不写长段。",
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"expanded": "用户本轮内容或情绪较浓——可多一两句承接核心点，再自然追问；仍控制在两段以内。",
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}
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desc = mode_desc[safe]
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return f"""## 本轮回复长度
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**当前档位：{safe}**
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{desc}
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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_total: int = 0,
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same_topic_turns: int = 0,
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all_stages_coverage: Optional[Dict[str, Dict]] = None,
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detected_user_stage: str = "",
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user_profile_context: str = "",
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persona: str = "default",
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memory_evidence_text: str = "",
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reply_length_mode: str = "standard",
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background_voice: str = "default",
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occupation: 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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``user_message`` 仅用于启发式（新细节/闲聊/情绪），其原文**不会**写入本提示，用户话仅以最终 HumanMessage 传入模型。
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``conversation_turn_total`` 为 Redis 全量历史的轮次数，不受窗口截断影响。
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"""
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persona_key = normalize_interview_persona(persona)
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persona_block = get_interview_persona_block(persona_key)
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likely_new = heuristic_likely_new_detail(user_message)
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likely_chit = heuristic_likely_chit_chat(user_message)
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reply_length_section = _format_reply_length_section(reply_length_mode)
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current_stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
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user_stage_name = (
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STAGE_DISPLAY_ZH.get(detected_user_stage, "") if detected_user_stage else ""
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)
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user_jumped = bool(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)
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if empty_slots_readable
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else "本阶段暂无明显缺口"
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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: List[str] = []
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uncovered_stages: List[str] = []
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if all_stages_coverage:
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cur_cn = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
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progress_lines.append(f"当前阶段：{cur_cn}")
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for stage in CHAT_STAGES:
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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_DISPLAY_ZH.get(stage, stage)
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if total_n <= 0:
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continue
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if filled_n == 0:
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progress_lines.append(f" {sname}：未聊")
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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}：{filled_n}/{total_n}")
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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 >= 5 or (
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filled_count >= 3 and same_topic_turns >= 4
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)
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should_lighten_mood = (
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conversation_turn_total > 0 and conversation_turn_total % 7 == 0
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)
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should_try_new_stage = filled_count >= 4 and len(empty_slots) <= 1
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related_stages = STAGE_RELATED_TOPICS.get(current_stage, [])
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related_stages_str = "、".join([STAGE_DISPLAY_ZH.get(s, s) for s in related_stages])
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emotional = heuristic_likely_emotional(user_message)
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tone_section = f"{persona_block}\n" if persona_block else ""
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followup_trigger_block = "## 本轮追问判定\n"
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followup_trigger_block += (
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"总体原则见「对话方向」与「你要做的」；以下为仅本轮生效的判定：\n"
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)
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if likely_new:
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followup_trigger_block += (
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"**【本轮判定】用户补充了新细节 → 承接后须追问 1 句。**\n"
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)
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elif emotional:
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followup_trigger_block += (
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"**【本轮判定】用户情绪较浓 → 先好好共情承接，不必急着追问。**\n"
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)
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else:
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followup_trigger_block += (
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"（无特殊判定时按惯例：新线头追问一句，否则可只承接。）\n"
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)
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memoir_orientation_lines = [
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"## 对话方向",
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"追问与承接**优先服务于人生故事与回忆录素材**，但不要让对方觉得你在走流程。",
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"若用户**明显在闲聊**，以陪聊为主，**不要**用回忆录式问题打断。",
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"若用户一边回忆一边开玩笑，先接情绪，再轻轻带回一个与经历相关的小问题。",
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]
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if likely_chit:
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memoir_orientation_lines.append(
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"**【本轮偏闲聊】** → 以承接与陪聊为主；若用户自然带回经历，再追问。"
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)
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memoir_orientation_block = "\n".join(memoir_orientation_lines) + "\n"
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memory_section = ""
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mem_trim = (memory_evidence_text or "").strip()
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if mem_trim:
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memory_section = (
|
||
"## 相关记忆摘录（仅供衔接，禁止编造）\n"
|
||
"以下为系统从用户**过往口述**中检索到的摘录，**不是**用户本轮亲口新说的内容。\n"
|
||
"承接时可自然用「你之前提过……」「上次你说到……」等口语，不要把摘录里的细节写成本轮用户新告诉你的事实；禁止编造摘录未出现的内容。\n\n"
|
||
f"{mem_trim}\n\n"
|
||
)
|
||
|
||
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:
|
||
if likely_new:
|
||
dynamic_guidance += f"\n- 若用户本轮**刚补充**新细节，请先就这一点追问一句，再自然转到未聊方向：{empty_slots_str}"
|
||
else:
|
||
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 ""
|
||
)
|
||
|
||
voice_block = get_background_voice_chat_block(background_voice)
|
||
voice_section = f"\n{voice_block}\n" if voice_block else ""
|
||
occ_hint = get_occupation_chat_hint(occupation, background_voice)
|
||
occ_section = f"\n{occ_hint}\n" if occ_hint else ""
|
||
intro_line = _guided_voice_intro_line(background_voice)
|
||
|
||
prompt = f"""{intro_line}
|
||
{topic_desc}
|
||
{reply_length_section}
|
||
{profile_section}
|
||
{voice_section}{occ_section}
|
||
## 本阶段已聊
|
||
{filled_slots_str}
|
||
|
||
## 还可聊的方向
|
||
{empty_slots_str}
|
||
|
||
## 进度
|
||
{progress_str}
|
||
{era_context}
|
||
{memoir_orientation_block}{memory_section}{followup_trigger_block}
|
||
{tone_section}
|
||
|
||
## 你要做的
|
||
1. **先接住对方**——一句真诚回应，不要写成总结或讲评。
|
||
2. 用户跳到别的人生阶段，跟着聊，别硬拉回。
|
||
3. **最多追问一个**具体、好答的问题（参照上方「本轮追问判定」）；无需追问时，只承接就好。
|
||
4. 用户回「嗯」「对」之类，结合上文理解，承接或换个新角度，不要重复上一轮问过的事。
|
||
5. 可用 [SPLIT] 分成**最多 2 条**消息。
|
||
{dynamic_guidance}{uncovered_hint}
|
||
|
||
## 不要做的
|
||
{chat_output_rules()}
|
||
|
||
直接输出（仅自然口语，无任何括号前缀或旁白）："""
|
||
|
||
return prompt
|