life-echo/api/app/agents/chat/prompts_conversation.py

"""
对话 Agent 提示词模板（场景化承接 + 细节深挖 + 人物串联）。
"""

from typing import Dict, List, Optional

from app.agents.chat.background_voice import (
    get_background_voice_tone_hint,
    normalize_background_voice,
)
from app.agents.chat.occupation_context import get_occupation_chat_hint
from app.agents.chat.output_rules import (
    chat_output_rules,
    chat_output_rules_en,
)
from app.agents.chat.personas import (
    AGENT_NAME_EN,
    AGENT_NAME_ZH,
    get_interview_persona_tone_hint,
    normalize_interview_persona,
)
from app.agents.chat.prompt_layers import (
    assemble_guided_prompt,
    build_absolute_donts_block,
    build_behavior_policy_block,
    build_context_block,
    build_question_outline_block,
    build_reply_strategy_block,
    build_style_profile_block,
)
from app.agents.stage_constants import (
    STAGE_ERA_HINTS,
    stage_display_name,
)
from app.agents.state_schema import KnownFact, PersonaThread
from app.core.config import settings

# 风格示例的单一事实源已迁至 `app.agents.style_profiles.ChatStyleProfile.reply_style_examples`；
# 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。

SLOT_NAME_MAP = {
    "place": "成长的地方",
    "people": "重要的人",
    "daily_life": "日常生活",
    "emotion": "童年感受",
    "turning_event": "难忘的事",
    "school": "学校经历",
    "city": "求学的城市",
    "motivation": "学习动力",
    "challenge": "遇到的挑战",
    "change": "成长变化",
    "job": "工作内容",
    "environment": "工作环境",
    "decision": "重要决定",
    "pressure": "压力与困难",
    "growth": "职业成长",
    "relationship": "家人关系",
    "conflict": "矛盾与化解",
    "support": "相互支持",
    "responsibility": "家庭责任",
    "value": "核心价值观",
    "regret": "遗憾与释怀",
    "pride": "骄傲的事",
    "lesson": "人生经验",
}

SLOT_NAME_MAP_EN = {
    "place": "where you grew up",
    "people": "important people",
    "daily_life": "everyday life",
    "emotion": "childhood feelings",
    "turning_event": "memorable moments",
    "school": "school experiences",
    "city": "the city you studied in",
    "motivation": "what drove you",
    "challenge": "challenges you faced",
    "change": "how you changed",
    "job": "what you did at work",
    "environment": "your work environment",
    "decision": "important decisions",
    "pressure": "pressure and hardship",
    "growth": "career growth",
    "relationship": "family relationships",
    "conflict": "conflicts and resolutions",
    "support": "mutual support",
    "responsibility": "family responsibilities",
    "value": "core values",
    "regret": "regrets and acceptance",
    "pride": "moments you're proud of",
    "lesson": "life lessons",
}


def slot_name_map_for(language: str) -> Dict[str, str]:
    return SLOT_NAME_MAP_EN if language == "en" else SLOT_NAME_MAP


def _compact_era_hint(
    current_stage: str,
    *,
    birth_year: int | None = None,
    era_place: str = "",
) -> str:
    if not birth_year:
        return ""

    birth_place = (era_place or "").strip()

    age_range = STAGE_ERA_HINTS.get(current_stage, (0, 30))
    era_start = birth_year + age_range[0]
    era_end = birth_year + age_range[1]

    era_events = []
    decade_events = {
        1950: "新中国成立初期、土地改革、抗美援朝",
        1960: "大跃进、三年自然灾害、中苏关系变化",
        1970: "文化大革命、知青上山下乡、中美建交",
        1980: "改革开放、恢复高考、个体经济兴起、电视普及",
        1990: "社会主义市场经济、下海潮、香港回归、互联网初期",
        2000: "加入WTO、房地产兴起、手机普及、北京奥运",
        2010: "移动互联网爆发、微信时代、共享经济、双创浪潮",
        2020: "新冠疫情、直播经济、人工智能崛起",
    }

    for decade, events in decade_events.items():
        if era_start <= decade + 9 and era_end >= decade:
            era_events.append(f"{decade}年代：{events}")

    parts: List[str] = []
    if era_events:
        place_hint = f" {birth_place}" if birth_place else ""
        parts.append(
            f"时代联想（口述里一两句带过即可）：约 {era_start}-{era_end} 年{place_hint}；"
            f"可提及 {era_events[0]}"
            + (f"；{era_events[1]}" if len(era_events) > 1 else "")
            + "。"
        )
    parts.append(
        "时代与流行文化（开放式，自然带入）：\n"
        "- 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"
        "- **优先开放式**问法；少用「你是不是也……」式半封闭逼认。\n"
        "- 与大事记呼应时点到为止，勿展开成长串史实。"
    )
    return "\n".join(parts) + "\n"


def _get_opening_prompt_en(
    current_stage: str,
    empty_slots_readable: List[str],
    user_profile_context: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
) -> str:
    """English-lite opening prompt; ignores persona/background-voice nuances."""
    stage_name = stage_display_name(current_stage, language="en")
    if empty_slots_readable:
        topics_str = ", ".join(empty_slots_readable)
        topics_heading = (
            f"## Suggested topics for this stage ({stage_name})\n"
            f"Pick one of these to ask about: {topics_str}"
        )
        task_question = (
            "2. You are a **warm, host-style confidant**: ask one **specific, "
            "easy-to-answer, vivid** question that pulls the user into telling a "
            "life memory; ideally land on one of the topics above. Avoid vague "
            "openers like \"How have you been?\" Open the door with one small "
            "anchor (a place, a person, an object, or a tiny scene from a day)."
        )
    else:
        topics_heading = (
            f"## Current stage ({stage_name})\n"
            "The main topics for this stage are largely covered. Open with "
            "something tied to a previous memory or a fresh small angle of this "
            "stage; do not interrogate from the start."
        )
        task_question = (
            "2. **Greeting + a memory hook**: after a warm acknowledgement, "
            "drop a light, concrete question tied to recollection — never "
            "small-talk filler."
        )

    profile_section = ""
    if user_profile_context.strip():
        profile_section = "## About the user\n" + user_profile_context.strip() + "\n"

    return f"""You are "{AGENT_NAME_EN}" — a warm host-style friend. The user just opened the chat and **has not said anything yet**; you speak first. Tone like an old friend, but your job is to help the user start telling their life story; in two or three short sentences, give a greeting plus **one vivid, recollection-oriented question** tied to the current stage or suggested topics. No flowery prose, no long literary descriptions, no generic small-talk.

{profile_section}{topics_heading}

## Task
1. Brief greeting.
{task_question}
3. Sound natural and warm.

## Format
- Use `[SPLIT]` to break into at most two short bubbles, or keep greeting + question in one short bubble.
- {chat_output_rules_en()} Do not write the user's answer for them.

Output (spoken-style English only, no Markdown):"""


def get_opening_prompt(
    current_stage: str,
    empty_slots_readable: List[str],
    user_profile_context: str = "",
    persona: str = "default",
    background_voice: str = "default",
    occupation: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
    language: str = "zh",
) -> str:
    """空对话时 AI 先开口的提示词"""
    if language == "en":
        return _get_opening_prompt_en(
            current_stage,
            empty_slots_readable,
            user_profile_context=user_profile_context,
            profile_birth_year=profile_birth_year,
            profile_era_place=profile_era_place,
        )
    stage_name = stage_display_name(current_stage, language="zh")
    bv_open = normalize_background_voice(background_voice)
    if empty_slots_readable:
        topics_str = "、".join(empty_slots_readable)
        topics_heading = (
            f"## 当前建议话题（{stage_name}）\n可以从中选一个来问：{topics_str}"
        )
        task_question = (
            "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；"
            "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」。"
            "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）。"
            "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？」"
        )
    else:
        topics_heading = (
            f"## 当前阶段（{stage_name}）\n"
            "这一阶段的主要话题在素材侧**已有覆盖**。"
            "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**；"
            "**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。"
        )
        task_question = (
            "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；"
            "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。"
        )

    if bv_open == "cadre":
        opening_style_rules = (
            "## 语境与语气（干部/机关）\n"
            "- 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"
        )
    elif bv_open == "military":
        opening_style_rules = (
            "## 语境与语气（军队相关口述常见交流方式）\n"
            "- 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"
        )
    else:
        opening_style_rules = (
            "## 风格\n"
            "- 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；"
            "可轻快，允许一点画面感，不要排比和长段文学描写。\n"
        )

    profile_lines: List[str] = []
    if user_profile_context.strip():
        profile_lines.append(user_profile_context.strip())
    occ = get_occupation_chat_hint(occupation, background_voice)
    if occ:
        profile_lines.append(occ)
    profile_section = ""
    if profile_lines:
        profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"

    persona_key = normalize_interview_persona(persona)
    persona_tone = get_interview_persona_tone_hint(persona_key)
    voice_tone = get_background_voice_tone_hint(background_voice)
    tone_bits = [t for t in (persona_tone, voice_tone) if t]
    tone_paragraph = ""
    if tone_bits:
        tone_paragraph = " " + " ".join(tone_bits) + "\n\n"

    opening_head = (
        f"你是「{AGENT_NAME_ZH}」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
        "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**；"
        "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"
    )
    if bv_open != "default":
        opening_head = (
            f"你是「{AGENT_NAME_ZH}」——主持式知己：用户刚进对话，**还没说话**，请你先开口。"
            "**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"
        )

    era_opening_line = ""
    if (
        settings.chat_era_context_enabled
        and profile_birth_year is not None
        and _compact_era_hint(
            current_stage,
            birth_year=profile_birth_year,
            era_place=profile_era_place,
        )
    ):
        era_opening_line = (
            "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），"
            "勿写成长段描写或排比。\n"
        )

    return f"""{opening_head}{tone_paragraph}{profile_section}{topics_heading}
## 任务
1. 简短问候。
{task_question}
3. 自然、温暖。
{era_opening_line}
## 格式
- 可用 [SPLIT] 分成最多 2 条；或一条里「问候 + 问题」。
- {chat_output_rules()} 不要替用户编回答。

{opening_style_rules}
直接输出（仅自然口语，无 Markdown）："""


def _get_guided_conversation_prompt_en(
    current_stage: str,
    empty_slots: List[str],
    filled_slots: Dict[str, str],
    detected_user_stage: str = "",
    user_profile_context: str = "",
    memory_evidence_text: str = "",
    recent_questions: list[str] | None = None,
    turn_directive_block: str = "",
) -> str:
    """English-lite guided interview prompt (no persona/voice nuances)."""
    stage_name = stage_display_name(current_stage, language="en")
    detected_name = (
        stage_display_name(detected_user_stage, language="en")
        if detected_user_stage and detected_user_stage != current_stage
        else ""
    )
    empty_readable = [SLOT_NAME_MAP_EN.get(s, s) for s in empty_slots]
    filled_lines = []
    for k, v in (filled_slots or {}).items():
        name = SLOT_NAME_MAP_EN.get(k, k)
        if v:
            filled_lines.append(f"- {name}: {v}")
    filled_block = "\n".join(filled_lines) if filled_lines else "(none yet)"

    suggested_block = (
        "Suggested still-open angles for this stage: " + ", ".join(empty_readable)
        if empty_readable
        else "Main angles for this stage are largely covered."
    )

    detected_line = (
        f"\nThe user is currently talking about: **{detected_name}** (system was tracking **{stage_name}**)."
        if detected_name
        else ""
    )

    profile_section = ""
    if user_profile_context.strip():
        profile_section = "\n## About the user\n" + user_profile_context.strip()

    memory_section = ""
    if (memory_evidence_text or "").strip():
        memory_section = (
            "\n## Reference memory snippets (for continuity only — do NOT write them as the user's first-person experience this turn)\n"
            + memory_evidence_text.strip()
        )

    recent_q_section = ""
    if recent_questions:
        last = recent_questions[-4:]
        recent_q_section = (
            "\n## Recently asked questions (do NOT repeat these; offer a new angle)\n"
            + "\n".join(f"- {q}" for q in last)
        )

    directive_block = (turn_directive_block or "").strip()
    directive_section = (
        f"\n## This turn's plan\n{directive_block}\n" if directive_block else ""
    )

    return f"""{directive_section}You are "{AGENT_NAME_EN}," a warm host-style friend helping the user record a memoir. Reply in conversational English.

## Stage context
Currently tracking life stage: **{stage_name}**.{detected_line}
{suggested_block}

## Already gathered for this stage
{filled_block}{profile_section}{memory_section}{recent_q_section}

## Behaviour
- Pick up the **specific** detail the user just said (one tangible noun or short phrase) and gently push one step deeper before asking your next question.
- Prefer ONE clear, specific question per reply. Open-ended over forced A/B options.
- If the user is in the middle of a story, follow that thread; do not switch topics for the sake of coverage.
- If you previously asked about something and the user already answered, do not re-ask.
- Stay short and precise. One acknowledgement sentence + one question is the default shape.

## Strict rules
- {chat_output_rules_en()}

## Format
- Use `[SPLIT]` to split into at most two short bubbles when natural.

Reply in English only. Do not output Markdown headings."""


def get_guided_conversation_prompt(
    current_stage: str,
    empty_slots: List[str],
    filled_slots: Dict[str, str],
    all_stages_coverage: Optional[Dict[str, Dict]] = None,
    detected_user_stage: str = "",
    user_profile_context: str = "",
    persona: str = "default",
    memory_evidence_text: str = "",
    background_voice: str = "default",
    occupation: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
    known_facts: list[KnownFact] | None = None,
    persona_threads: list[PersonaThread] | None = None,
    recent_questions: list[str] | None = None,
    turn_directive_block: str = "",
    language: str = "zh",
) -> str:
    """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。"""
    if language == "en":
        return _get_guided_conversation_prompt_en(
            current_stage=current_stage,
            empty_slots=empty_slots,
            filled_slots=filled_slots,
            detected_user_stage=detected_user_stage,
            user_profile_context=user_profile_context,
            memory_evidence_text=memory_evidence_text,
            recent_questions=recent_questions,
            turn_directive_block=turn_directive_block,
        )
    persona_key = normalize_interview_persona(persona)
    persona_tone = get_interview_persona_tone_hint(persona_key)
    voice_tone = get_background_voice_tone_hint(background_voice)
    tone_bits = [t for t in (persona_tone, voice_tone) if t]
    tone_line = ""
    if tone_bits:
        tone_line = " " + " ".join(tone_bits)

    user_jumped = bool(detected_user_stage and detected_user_stage != current_stage)
    active_stage = (
        detected_user_stage if user_jumped and detected_user_stage else current_stage
    )

    era_line = ""
    if settings.chat_era_context_enabled:
        era_line = _compact_era_hint(
            active_stage,
            birth_year=profile_birth_year,
            era_place=profile_era_place,
        )

    empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]

    # ---- Context 层：纯状态与素材 ----
    topic_and_context_block = build_context_block(
        current_stage=current_stage,
        detected_user_stage=detected_user_stage,
        empty_slots_readable=empty_slots_readable,
        filled_slots=filled_slots,
        slot_name_map=SLOT_NAME_MAP,
        all_stages_coverage=all_stages_coverage,
        user_profile_context=user_profile_context,
        occupation=occupation,
        background_voice=background_voice,
        known_facts=known_facts,
        persona_threads=persona_threads,
        recent_questions=recent_questions,
        memory_evidence_text=memory_evidence_text,
        era_line=era_line,
    )

    question_outline_block = build_question_outline_block(current_stage, empty_slots)

    # ---- BehaviorPolicy 层：通用行为规则（本轮模式由 TurnPlan 单独注入） ----
    behavior_policy_block = build_behavior_policy_block()
    reply_strategy_block = build_reply_strategy_block()
    absolute_donts_block = build_absolute_donts_block(chat_output_rules())

    # ---- StyleProfile 层：口吻 + 文采密度 + 成稿质量导向 ----
    style_profile_block = build_style_profile_block(
        persona=persona, background_voice=background_voice
    )

    return assemble_guided_prompt(
        turn_directive_block=turn_directive_block,
        topic_and_context_block=topic_and_context_block,
        question_outline_block=question_outline_block,
        behavior_policy_block=behavior_policy_block,
        style_profile_block=style_profile_block,
        reply_strategy_block=reply_strategy_block,
        absolute_donts_block=absolute_donts_block,
        intro_tone_line=tone_line,
    )


# 运行时 prompt 生成走 `prompt_layers.assemble_guided_prompt`。
# 旧的超大 system prompt 已拆入 BehaviorPolicy / Context / StyleProfile 三层，此处不再保留快照。


def _get_re_greeting_prompt_en(
    current_stage: str,
    empty_slots_readable: List[str],
    user_profile_context: str = "",
    background_voice: str = "default",
    occupation: str = "",
    idle_hours: float = 6.0,
) -> str:
    """English re-greeting; mirrors Chinese structure with lighter persona nuance."""
    stage_name = stage_display_name(current_stage, language="en")
    bv = normalize_background_voice(background_voice)

    if idle_hours >= 168:
        idle_phrase = "it's been quite a while"
    elif idle_hours >= 48:
        idle_phrase = "it's been several days"
    elif idle_hours >= 20:
        idle_phrase = "about a day has passed"
    else:
        idle_phrase = "only a little time has passed"

    if empty_slots_readable:
        topics_str = ", ".join(empty_slots_readable[:4])
        topic_hint = (
            f"## You can still explore ({stage_name})\n"
            f"If picking up last time feels hard, gently land on one of these: {topics_str}."
        )
    else:
        topic_hint = (
            f"## Current focus ({stage_name})\n"
            "Most beats here are covered; prefer returning to a concrete person, place, or moment from before."
        )

    if bv == "cadre":
        style_note = "## Tone\nSteady and respectful; no slick slogans or stacked parallelisms."
    elif bv == "military":
        style_note = "## Tone\nCrisp and appropriate; no dramatic military flourishes."
    else:
        style_note = "## Tone\nLike an old friend you have not seen in a bit: warm, restrained, no preachy lists."

    profile_lines: List[str] = []
    if user_profile_context.strip():
        profile_lines.append(user_profile_context.strip())
    occ = get_occupation_chat_hint(occupation, background_voice)
    if occ:
        profile_lines.append(occ)
    profile_section = ""
    if profile_lines:
        profile_section = "## About the user\n" + "\n".join(profile_lines) + "\n"

    head = (
        f'You are "{AGENT_NAME_EN}" — a host-style confidant. The user returns with **existing chat history** and has **not spoken yet** — you speak first. '
        f"Context: {idle_phrase} since their last message.\n\n"
        "**Job**: offer a warm reopening that shows you remember something specific they shared, then a light, memory-oriented hook you hand back to them.\n\n"
        "## Requirements\n"
        "1. **Must** reference one or two concrete details from the history (a person, place, object, or beat) — do not genericize with \"we had a great chat last time.\"\n"
        "2. **Do not** reuse a brand-new-chat hello; something like \"Last time you mentioned X — want to continue?\" fits better.\n"
        "3. The hook should be **specific, easy to answer, and visual**, tied to life memory — not \"how have you been\" small talk.\n"
        "4. If history offers no usable threads, pick a small landing from the stage hints; still avoid vague interrogation.\n"
        "5. Keep it short: two or three sentences, no long paragraphs.\n"
    )

    return f"""{head}{profile_section}{topic_hint}
{style_note}
## Format
- Use `[SPLIT]` for at most two short bubbles, or one bubble with reopening + hook.
- {chat_output_rules_en()} Do not write the user's answer for them.

Output (spoken English only, no Markdown):"""


def get_re_greeting_prompt(
    current_stage: str,
    empty_slots_readable: List[str],
    user_profile_context: str = "",
    persona: str = "default",
    background_voice: str = "default",
    occupation: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
    idle_hours: float = 6.0,
    language: str = "zh",
) -> str:
    """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。"""
    if language == "en":
        return _get_re_greeting_prompt_en(
            current_stage=current_stage,
            empty_slots_readable=empty_slots_readable,
            user_profile_context=user_profile_context,
            background_voice=background_voice,
            occupation=occupation,
            idle_hours=idle_hours,
        )
    stage_name = stage_display_name(current_stage, language="zh")
    bv = normalize_background_voice(background_voice)

    if idle_hours >= 168:
        idle_phrase = "好一阵子没聊了"
    elif idle_hours >= 48:
        idle_phrase = "好几天没聊了"
    elif idle_hours >= 20:
        idle_phrase = "隔了一天"
    else:
        idle_phrase = "今天又见面"

    if empty_slots_readable:
        topics_str = "、".join(empty_slots_readable[:4])
        topic_hint = (
            f"## 当前阶段（{stage_name}）还可以聊\n"
            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}。"
        )
    else:
        topic_hint = (
            f"## 当前阶段（{stage_name}）\n"
            "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。"
        )

    if bv == "cadre":
        style_note = "## 语气\n稳重、敬语适度；问候不油滑、不堆排比。"
    elif bv == "military":
        style_note = "## 语气\n简洁、得体；不过度起势、不堆军事辞藻。"
    else:
        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。"

    profile_lines: List[str] = []
    if user_profile_context.strip():
        profile_lines.append(user_profile_context.strip())
    occ = get_occupation_chat_hint(occupation, background_voice)
    if occ:
        profile_lines.append(occ)
    profile_section = ""
    if profile_lines:
        profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"

    persona_key = normalize_interview_persona(persona)
    persona_tone = get_interview_persona_tone_hint(persona_key)
    voice_tone = get_background_voice_tone_hint(background_voice)
    tone_bits = [t for t in (persona_tone, voice_tone) if t]
    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,
    language: str = "zh",
) -> 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}]
    """
    slot_labels = slot_name_map_for(language)
    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, zh_label in stage_bank:
        if slot_key in empty_set and slot_key not in seen:
            label = (
                zh_label if language != "en" else slot_labels.get(slot_key, zh_label)
            )
            text = (
                f"I'd like to talk about {label}."
                if language == "en"
                else f"我想聊聊{label}"
            )
            chips.append({"id": slot_key, "label": label, "text": text})
            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, zh_label in stage_bank:
        if slot_key in seen:
            continue
        label = (
            zh_label if language != "en" else slot_labels.get(slot_key, zh_label)
        )
        text = (
            f"I'd like to talk about {label}."
            if language == "en"
            else f"我想聊聊{label}"
        )
        chips.append({"id": slot_key, "label": label, "text": text})
        seen.add(slot_key)
        if len(chips) >= max_chips:
            return chips

    return chips


__all__ = [
    "SLOT_NAME_MAP",
    "SLOT_NAME_MAP_EN",
    "slot_name_map_for",
    "build_topic_chips",
    "get_guided_conversation_prompt",
    "get_opening_prompt",
    "get_re_greeting_prompt",
]