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
from app.agents.chat.personas import (
    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_DISPLAY_ZH, STAGE_ERA_HINTS
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",
}


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(
    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 = "",
) -> str:
    """空对话时 AI 先开口的提示词"""
    stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
    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 = (
        "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。"
        "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 = (
            "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。"
            "**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(
    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 = "",
) -> 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。"""
    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。


__all__ = [
    "SLOT_NAME_MAP",
    "get_guided_conversation_prompt",
    "get_opening_prompt",
]