55cfbc7f80
Two complementary changes to reduce conversation cold-start friction: A. Returning-user re-greeting (backend) - When WS reconnects to a non-empty conversation and last_message_at is older than chat_re_greeting_idle_hours (default 6h), the agent emits a warm continuation message that references prior history instead of staying silent. - Self-debouncing: the AI message updates last_message_at, so reconnects within the window will not re-trigger. - Skipped while profile collection is still pending. D. Topic suggestion chips (backend + Expo) - New WS message type topic_suggestions carries 3-4 quick-start chips derived from the current memoir stage's empty slots (deterministic, no extra LLM cost). Sent alongside opening / re-greeting / resume. - Expo chat screen renders a horizontally-scrollable chip row above the input bar; tapping a chip sends the chip's text as a user message and clears the row. Sending any text/voice also clears the chips.
481 lines
19 KiB
Python
481 lines
19 KiB
Python
"""
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对话 Agent 提示词模板（场景化承接 + 细节深挖 + 人物串联）。
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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_tone_hint,
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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.output_rules import chat_output_rules
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from app.agents.chat.personas import (
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get_interview_persona_tone_hint,
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normalize_interview_persona,
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)
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from app.agents.chat.prompt_layers import (
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assemble_guided_prompt,
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build_absolute_donts_block,
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build_behavior_policy_block,
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build_context_block,
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build_question_outline_block,
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build_reply_strategy_block,
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build_style_profile_block,
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)
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from app.agents.stage_constants import STAGE_DISPLAY_ZH, STAGE_ERA_HINTS
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from app.agents.state_schema import KnownFact, PersonaThread
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from app.core.config import settings
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# 风格示例的单一事实源已迁至 `app.agents.style_profiles.ChatStyleProfile.reply_style_examples`；
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# 这里**不再**维护具体字面示例，避免同一模块被当作 few-shot 锚点反复注入，导致风格过拟合。
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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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def _compact_era_hint(
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current_stage: str,
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*,
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birth_year: int | None = None,
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era_place: str = "",
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) -> str:
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if not birth_year:
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return ""
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birth_place = (era_place or "").strip()
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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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parts: List[str] = []
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if era_events:
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place_hint = f" {birth_place}" if birth_place else ""
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parts.append(
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f"时代联想（口述里一两句带过即可）：约 {era_start}-{era_end} 年{place_hint}；"
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f"可提及 {era_events[0]}"
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+ (f"；{era_events[1]}" if len(era_events) > 1 else "")
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+ "。"
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)
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parts.append(
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"时代与流行文化（开放式，自然带入）：\n"
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"- 可从当时的街景、媒介、校园与市井、年节习俗等**泛泛**起头，邀请用户讲自己的版本，勿替用户断言细节。\n"
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"- **优先开放式**问法；少用「你是不是也……」式半封闭逼认。\n"
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"- 与大事记呼应时点到为止，勿展开成长串史实。"
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)
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return "\n".join(parts) + "\n"
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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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profile_birth_year: Optional[int] = None,
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profile_era_place: 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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bv_open = normalize_background_voice(background_voice)
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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. 你是**主持式知己**：接着问一个**具体、好回答、有画面感**的问题，帮用户进入**人生回忆**叙述；"
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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」「最近怎么样」。"
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"像把门敞开请人讲自己的故事，不要像面试第一题；一句里带一个小锚（地方、人物、物件或一天里的片段即可）。"
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"不要用「下面我们聊聊…」类未承接的硬切。好问题举例：「说到童年，你脑海里最先蹦出来的是哪个画面？」"
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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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"这一阶段的主要话题在素材侧**已有覆盖**。"
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"开场仍要**回到人生故事线**：优先接续上次聊过的片段、（若有）记忆线索里出现过的事，或当前阶段里**新鲜的一小角**；"
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"**禁止**为了凑问题而从「童年在哪长大」等已覆盖模板重头盘问；**也不要**把泛泛近况（「今天忙吗」「最近好吗」）当成默认主线。"
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)
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task_question = (
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"2. **问候 + 回忆向勾子**：温暖接话后，带一个与**口述回忆**有关的轻巧引子或具体问题；"
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"若接不上具体事，就用当前阶段的一个**有画面的开放式起头**，仍落在人生经历上，而非纯社交寒暄。"
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)
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if bv_open == "cadre":
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opening_style_rules = (
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"## 语境与语气（干部/机关）\n"
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"- 问候稳重、敬语适度；避免官样排比与过轻佻的网络撒娇语气。\n"
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)
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elif bv_open == "military":
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opening_style_rules = (
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"## 语境与语气（军队相关口述常见交流方式）\n"
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"- 简洁、得体；不用「嗨～」类过轻佻起势；不堆军事辞藻、不编军旅细节。\n"
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)
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else:
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opening_style_rules = (
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"## 风格\n"
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"- 像**温暖的谈话场主持人**：口语、自然、能接住人，但默认把用户带进**人生回忆**叙述；"
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"可轻快，允许一点画面感，不要排比和长段文学描写。\n"
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)
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profile_lines: List[str] = []
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if user_profile_context.strip():
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profile_lines.append(user_profile_context.strip())
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occ = get_occupation_chat_hint(occupation, background_voice)
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if occ:
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profile_lines.append(occ)
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profile_section = ""
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if profile_lines:
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profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"
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persona_key = normalize_interview_persona(persona)
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persona_tone = get_interview_persona_tone_hint(persona_key)
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voice_tone = get_background_voice_tone_hint(background_voice)
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tone_bits = [t for t in (persona_tone, voice_tone) if t]
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tone_paragraph = ""
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if tone_bits:
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tone_paragraph = " " + " ".join(tone_bits) + "\n\n"
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opening_head = (
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"你是「岁月知己」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
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"语气像老朋友，但**职责是帮对方开口讲人生故事**；两三句内问候 + **一个落在当前阶段或建议话题上的、有画面感的问题**；"
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"不要排比、不要长段文学描写，**不要**把泛泛问近况当主菜。\n\n"
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)
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if bv_open != "default":
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opening_head = (
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"你是「岁月知己」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
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"**短**；两三句内问候 + **一个回忆向的具体问题**；不要排比、不要文学描写。\n\n"
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)
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era_opening_line = ""
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if (
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settings.chat_era_context_enabled
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and profile_birth_year is not None
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and _compact_era_hint(
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current_stage,
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birth_year=profile_birth_year,
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era_place=profile_era_place,
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)
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):
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era_opening_line = (
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"4. 用户资料里已有出生年份与时代参考时，问候里的具体问题可**轻轻带一点年代氛围**（点到为止），"
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"勿写成长段描写或排比。\n"
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)
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return f"""{opening_head}{tone_paragraph}{profile_section}{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。
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{era_opening_line}
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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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{opening_style_rules}
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直接输出（仅自然口语，无 Markdown）："""
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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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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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background_voice: str = "default",
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occupation: str = "",
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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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known_facts: list[KnownFact] | None = None,
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persona_threads: list[PersonaThread] | None = None,
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recent_questions: list[str] | None = None,
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turn_directive_block: str = "",
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) -> str:
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"""生成状态感知的对话提示词；用户原话仅以 HumanMessage 传入，不写入本 system 文本。"""
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persona_key = normalize_interview_persona(persona)
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persona_tone = get_interview_persona_tone_hint(persona_key)
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voice_tone = get_background_voice_tone_hint(background_voice)
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tone_bits = [t for t in (persona_tone, voice_tone) if t]
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tone_line = ""
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if tone_bits:
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tone_line = " " + " ".join(tone_bits)
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user_jumped = bool(detected_user_stage and detected_user_stage != current_stage)
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active_stage = (
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detected_user_stage if user_jumped and detected_user_stage else current_stage
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)
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era_line = ""
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if settings.chat_era_context_enabled:
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era_line = _compact_era_hint(
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active_stage,
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birth_year=profile_birth_year,
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era_place=profile_era_place,
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)
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empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
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# ---- Context 层：纯状态与素材 ----
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topic_and_context_block = build_context_block(
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current_stage=current_stage,
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detected_user_stage=detected_user_stage,
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empty_slots_readable=empty_slots_readable,
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filled_slots=filled_slots,
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slot_name_map=SLOT_NAME_MAP,
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all_stages_coverage=all_stages_coverage,
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user_profile_context=user_profile_context,
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occupation=occupation,
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background_voice=background_voice,
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known_facts=known_facts,
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persona_threads=persona_threads,
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recent_questions=recent_questions,
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memory_evidence_text=memory_evidence_text,
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era_line=era_line,
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)
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question_outline_block = build_question_outline_block(current_stage, empty_slots)
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# ---- BehaviorPolicy 层：通用行为规则（本轮模式由 TurnPlan 单独注入） ----
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behavior_policy_block = build_behavior_policy_block()
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reply_strategy_block = build_reply_strategy_block()
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absolute_donts_block = build_absolute_donts_block(chat_output_rules())
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# ---- StyleProfile 层：口吻 + 文采密度 + 成稿质量导向 ----
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style_profile_block = build_style_profile_block(
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persona=persona, background_voice=background_voice
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)
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return assemble_guided_prompt(
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turn_directive_block=turn_directive_block,
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topic_and_context_block=topic_and_context_block,
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question_outline_block=question_outline_block,
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behavior_policy_block=behavior_policy_block,
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style_profile_block=style_profile_block,
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reply_strategy_block=reply_strategy_block,
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absolute_donts_block=absolute_donts_block,
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intro_tone_line=tone_line,
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)
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# 运行时 prompt 生成走 `prompt_layers.assemble_guided_prompt`。
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# 旧的超大 system prompt 已拆入 BehaviorPolicy / Context / StyleProfile 三层，此处不再保留快照。
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def get_re_greeting_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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profile_birth_year: Optional[int] = None,
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profile_era_place: str = "",
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idle_hours: float = 6.0,
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) -> str:
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"""老对话回访问候提示词：用户带着已有历史回到对话，AI 先开口做承接式问候。"""
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stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
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bv = normalize_background_voice(background_voice)
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if idle_hours >= 168:
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idle_phrase = "好一阵子没聊了"
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elif idle_hours >= 48:
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idle_phrase = "好几天没聊了"
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elif idle_hours >= 20:
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idle_phrase = "隔了一天"
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else:
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idle_phrase = "今天又见面"
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if empty_slots_readable:
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topics_str = "、".join(empty_slots_readable[:4])
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topic_hint = (
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f"## 当前阶段（{stage_name}）还可以聊\n"
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f"如果上次聊过的事不便直接接续，可从这些方向里挑一个落点：{topics_str}。"
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)
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else:
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topic_hint = (
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f"## 当前阶段（{stage_name}）\n"
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"这一阶段主要话题已有覆盖；优先回到上次聊过的人/事/地方，做温和的承接。"
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)
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if bv == "cadre":
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style_note = "## 语气\n稳重、敬语适度；问候不油滑、不堆排比。"
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elif bv == "military":
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style_note = "## 语气\n简洁、得体；不过度起势、不堆军事辞藻。"
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else:
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style_note = "## 语气\n像许久未见的老朋友，温暖而克制；不要排比、不要长段文学描写。"
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profile_lines: List[str] = []
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if user_profile_context.strip():
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profile_lines.append(user_profile_context.strip())
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occ = get_occupation_chat_hint(occupation, background_voice)
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if occ:
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profile_lines.append(occ)
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profile_section = ""
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if profile_lines:
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profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"
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persona_key = normalize_interview_persona(persona)
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persona_tone = get_interview_persona_tone_hint(persona_key)
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voice_tone = get_background_voice_tone_hint(background_voice)
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tone_bits = [t for t in (persona_tone, voice_tone) if t]
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tone_paragraph = ""
|
||
if tone_bits:
|
||
tone_paragraph = " " + " ".join(tone_bits) + "\n\n"
|
||
|
||
head = (
|
||
"你是「岁月知己」——主持式知己。用户带着**已有的对话历史**回到这里，**还没说话**，请你先开口。"
|
||
f"语境：距上次消息已经{idle_phrase}。"
|
||
"**职责**：用一句温暖的承接打招呼，让对方感到「我记得你上次说过的事」，再轻轻递上一个**回忆向**的钩子，把话头交还给他。\n\n"
|
||
"## 要求\n"
|
||
"1. **必须**轻轻引用历史里的具体人/事/地方/物件做承接（一两个细节即可，不要罗列），不要空喊「上次聊得很好」。\n"
|
||
"2. **不要**用与刚开新对话相同的「您好/你好呀」式硬开场；像「上次你说到 X，今天想接着讲讲吗？」更合适。\n"
|
||
"3. 钩子要**具体、好回答、有画面感**，落在人生回忆里；不要问「最近怎么样」「今天忙吗」这种纯社交寒暄。\n"
|
||
"4. 若历史里没有可用细节，可从「当前阶段还可以聊」里挑一个轻巧落点；仍要避免泛泛盘问。\n"
|
||
"5. 简短：两三句内，不要排比、不要长段。\n"
|
||
)
|
||
|
||
return f"""{head}{tone_paragraph}{profile_section}{topic_hint}
|
||
{style_note}
|
||
## 格式
|
||
- 可用 [SPLIT] 分成最多 2 条；或一条里「承接 + 钩子」。
|
||
- {chat_output_rules()} 不要替用户编回答。
|
||
|
||
直接输出（仅自然口语，无 Markdown）："""
|
||
|
||
|
||
_STAGE_TOPIC_CHIP_BANK: Dict[str, List[tuple[str, str]]] = {
|
||
"childhood": [
|
||
("place", "童年长大的地方"),
|
||
("people", "童年里重要的人"),
|
||
("daily_life", "童年的一天"),
|
||
("turning_event", "童年最难忘的一件事"),
|
||
("emotion", "童年最深的感受"),
|
||
],
|
||
"education": [
|
||
("school", "学生时代的学校"),
|
||
("city", "求学的城市"),
|
||
("motivation", "读书时的动力"),
|
||
("challenge", "求学路上的难关"),
|
||
("change", "求学带来的变化"),
|
||
],
|
||
"career": [
|
||
("job", "做过的工作"),
|
||
("environment", "工作的环境"),
|
||
("decision", "职业里的关键决定"),
|
||
("pressure", "工作中的压力"),
|
||
("growth", "职业上的成长"),
|
||
],
|
||
"family": [
|
||
("relationship", "家人之间的关系"),
|
||
("conflict", "家里的矛盾与化解"),
|
||
("support", "家人之间的相互支持"),
|
||
("responsibility", "肩上的家庭责任"),
|
||
],
|
||
"later_life": [
|
||
("value", "现在最看重的事"),
|
||
("regret", "心里的遗憾"),
|
||
("pride", "最骄傲的事"),
|
||
("lesson", "想留下的人生经验"),
|
||
],
|
||
}
|
||
|
||
|
||
def build_topic_chips(
|
||
current_stage: str,
|
||
empty_slots: List[str],
|
||
*,
|
||
max_chips: int = 4,
|
||
) -> List[Dict[str, str]]:
|
||
"""根据当前阶段与空 slot 列表生成 quick-start 话题 chips。
|
||
|
||
返回结构：[{"id": slot_key, "label": 短标签, "text": 用户点击后发出的句子}]
|
||
"""
|
||
stage_bank = _STAGE_TOPIC_CHIP_BANK.get(current_stage) or []
|
||
seen: set[str] = set()
|
||
chips: List[Dict[str, str]] = []
|
||
|
||
# 优先从「当前阶段空 slot」挑选（与开场提问方向一致）
|
||
empty_set = {s for s in empty_slots if s}
|
||
for slot_key, label in stage_bank:
|
||
if slot_key in empty_set and slot_key not in seen:
|
||
chips.append(
|
||
{
|
||
"id": slot_key,
|
||
"label": label,
|
||
"text": f"我想聊聊{label}",
|
||
}
|
||
)
|
||
seen.add(slot_key)
|
||
if len(chips) >= max_chips:
|
||
return chips
|
||
|
||
# 不足则用阶段默认话题补齐
|
||
for slot_key, label in stage_bank:
|
||
if slot_key in seen:
|
||
continue
|
||
chips.append(
|
||
{
|
||
"id": slot_key,
|
||
"label": label,
|
||
"text": f"我想聊聊{label}",
|
||
}
|
||
)
|
||
seen.add(slot_key)
|
||
if len(chips) >= max_chips:
|
||
return chips
|
||
|
||
return chips
|
||
|
||
|
||
__all__ = [
|
||
"SLOT_NAME_MAP",
|
||
"build_topic_chips",
|
||
"get_guided_conversation_prompt",
|
||
"get_opening_prompt",
|
||
"get_re_greeting_prompt",
|
||
]
|