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

"""
InterviewAgent：正式访谈 Specialist
负责状态感知回复、开场白，不负责 Redis 持久化（由 Orchestrator 统一处理）
"""

import time
from typing import Any, List, Optional

from langchain_core.messages import HumanMessage, SystemMessage

from app.agents.chat.agent_turn import AgentChatTurn
from app.agents.chat.helpers import format_history_string, get_history_with_window
from app.agents.chat.interview_state_hints import (
    apply_autobiographical_boundary_guard,
    apply_duplicate_question_guard,
    extract_recent_questions,
    segments_are_only_duplicate_guard_fallback,
    update_recent_questions,
)
from app.agents.chat.interview_turn_plan import plan_interview_turn
from app.agents.chat.personas import normalize_interview_persona
from app.agents.chat.prompt_context import ChatPromptContext
from app.agents.chat.prompts_conversation import (
    SLOT_NAME_MAP,
    get_opening_prompt,
    get_re_greeting_prompt,
)
from app.agents.chat.reply_limits import (
    nonempty_segments_or_fallback,
    segments_from_llm_response,
    truncate_chat_segments,
)
from app.agents.chat.reply_planner import maybe_refine_turn_plan_with_llm
from app.agents.chat.stage_detection import keyword_fallback_primary_stage
from app.agents.state_schema import (
    MemoirStateSchema,
    interview_control_state,
    narrative_coverage_state,
)
from app.core.agent_logging import (
    agent_span,
    log_agent_payload,
    log_agent_summary,
)
from app.core.config import settings
from app.core.llm_gateway import LlmGateway, LlmUseCase
from app.core.logging import get_logger
from app.features.conversation.input_normalize import normalize_chat_input_for_agent

logger = get_logger(__name__)

# LLM 不可用或调用失败时对用户展示（不暴露异常细节、不触发 TTS）
_FALLBACK_REPLY = "刚才网络不太稳，没接上。你可以再说一遍，或稍后再试。"

# 仅在「重复问句守卫」把正文削成单句兜底时追加二次 system，只多调一次模型。
_DUPLICATE_GUARD_LLM_RETRY_SYSTEM_APPENDIX = """## 二次生成（纠偏）
上一版模型输出因包含与「最近已问过的问题」或「已确认事实」重复的问句，已被系统弃用。请**重新写一整条回复**：
- 仍须遵守上文全部主规则；
- 先贴着用户本轮原话承接半句到一两句（可有画面感）；
- **禁止**再用与刚才同义、仅换说法的确认型问句；
- 若要提问，须换**全新角度**，并锚在用户刚说的具体细节里；也可以本轮**完全不提问**，只并肩承接；
- **禁止**整段只有「这一段我记住了」或同类无信息套话。"""


def _finalize_chat_segments_after_llm(
    response_text: str,
    *,
    max_segments: int,
    max_chars: int,
    memoir_state: MemoirStateSchema,
    recent_questions: list[str],
) -> tuple[list[str], bool]:
    raw_list = segments_from_llm_response(
        response_text,
        max_segments=max_segments,
    )
    if not raw_list:
        raw_list = [response_text.strip()]
    out = truncate_chat_segments(
        raw_list,
        max_segments=max_segments,
        max_chars_per_segment=max_chars,
    )
    if not out:
        out = [response_text.strip()[:max_chars]]
    out = nonempty_segments_or_fallback(out, fallback=_FALLBACK_REPLY)
    out, deduped = apply_duplicate_question_guard(
        out,
        state=memoir_state,
        recent_questions=recent_questions,
    )
    return out, deduped


def _get_langchain_llm():
    try:
        return LlmGateway().langchain_llm_for(LlmUseCase("chat.interview"))
    except Exception:
        return None


def _message_contents_char_count(messages: List[Any]) -> int:
    n = 0
    for m in messages:
        c = getattr(m, "content", None)
        if isinstance(c, str):
            n += len(c)
    return n


class InterviewAgent:
    """正式访谈 Specialist Agent"""

    def __init__(self):
        self.llm = _get_langchain_llm()

    def _detect_user_stage(self, user_message: str) -> str:
        """关键词回退：与 stage_detection 一致（多阶段打分）。"""
        return keyword_fallback_primary_stage(user_message)

    def _resolve_text_for_model(
        self,
        user_message: str,
        normalized_user_message: Optional[str],
    ) -> str:
        """模型侧净稿：编排层已归一则直接用；否则在本层补一次（含可选 LLM）。"""
        if normalized_user_message is not None:
            return (normalized_user_message or "").strip()
        llm_n = None
        if settings.chat_input_normalize_enabled and (
            (settings.chat_input_normalize_mode or "").strip().lower() == "llm"
        ):
            llm_n = self.llm
        return normalize_chat_input_for_agent(user_message or "", llm=llm_n)

    async def generate_response_with_state(
        self,
        conversation_id: str,
        user_message: str,
        memoir_state: MemoirStateSchema,
        user_profile_context: str = "",
        detected_user_stage: Optional[str] = None,
        memory_evidence_text: str = "",
        memory_anchor_source: str = "",
        memory_planner_text: str = "",
        background_voice: str = "default",
        normalized_user_message: Optional[str] = None,
        occupation: str = "",
        profile_birth_year: int | None = None,
        profile_era_place: str = "",
        stage_switched_this_turn: bool = False,
        scene_cues_for_planner: Optional[list[str]] = None,
    ) -> AgentChatTurn:
        """生成状态感知的访谈回复，不持久化（由 Orchestrator 负责）"""
        if not self.llm:
            logger.warning("InterviewAgent: LLM 未配置，返回兜底文案")
            return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)
        try:
            text_for_model = self._resolve_text_for_model(
                user_message, normalized_user_message
            )
            narrative_state = narrative_coverage_state(memoir_state)
            control_state = interview_control_state(memoir_state)
            empty_slots = control_state.prompt_empty_slots_for_stage(
                narrative_state, memoir_state.current_stage
            )
            filled_slots = narrative_state.filled_slots_for_stage(
                memoir_state.current_stage
            )
            if detected_user_stage is not None:
                du = detected_user_stage
            else:
                du = self._detect_user_stage(text_for_model)
            hw = await get_history_with_window(
                conversation_id,
                max_pairs=settings.chat_history_max_pairs,
                max_chars=settings.chat_history_max_chars,
            )
            recent_questions = extract_recent_questions(hw.window)
            conversation_turn_total = hw.turn_total
            all_stages_coverage = narrative_state.all_stages_coverage()
            persona = normalize_interview_persona(settings.chat_interview_persona)
            max_segments = int(settings.chat_interview_max_segments)
            max_tokens = int(settings.chat_interview_max_tokens)
            max_chars = int(settings.chat_interview_max_chars_per_segment)

            turn_plan = plan_interview_turn(
                current_stage=memoir_state.current_stage,
                empty_slots=empty_slots,
                normalized_user_message=text_for_model,
                memory_evidence_text=(memory_anchor_source or "").strip(),
                stage_switched_this_turn=stage_switched_this_turn,
            )
            logger.info(
                "event=interview_turn_plan mode={} anchor_slot={} snippet_len={}",
                turn_plan.mode,
                turn_plan.anchor_slot_key or "-",
                len(turn_plan.anchor_snippet or ""),
            )

            reply_planner_raw = ""
            baseline_mode = turn_plan.mode
            baseline_primary_focus = turn_plan.primary_focus
            if settings.chat_reply_planner_llm_enabled:
                rq_preview = (
                    "\n".join(recent_questions[-4:])
                    if recent_questions
                    else ""
                )
                turn_plan, reply_planner_raw = await maybe_refine_turn_plan_with_llm(
                    self.llm,
                    plan=turn_plan,
                    text_for_model=text_for_model,
                    memory_evidence_text=(memory_planner_text or memory_evidence_text)
                    or "",
                    max_tokens=int(settings.chat_reply_planner_max_tokens),
                    temperature=float(settings.chat_reply_planner_temperature),
                    scene_cues_for_planner=scene_cues_for_planner or [],
                    recent_questions_preview=rq_preview,
                )
                if reply_planner_raw:
                    logger.info(
                        "event=reply_planner_applied memory_usage={} reply_shape={} "
                        "mode={} primary_focus={} focus_source={}",
                        turn_plan.memory_usage,
                        turn_plan.reply_shape,
                        turn_plan.mode,
                        turn_plan.primary_focus,
                        turn_plan.focus_source,
                    )

            ctx = ChatPromptContext(
                current_stage=memoir_state.current_stage,
                empty_slots=empty_slots,
                filled_slots=filled_slots,
                all_stages_coverage=all_stages_coverage,
                detected_user_stage=du,
                user_profile_context=user_profile_context,
                persona=persona,
                memory_evidence_text=memory_evidence_text,
                background_voice=background_voice,
                occupation=occupation,
                profile_birth_year=profile_birth_year,
                profile_era_place=profile_era_place,
                known_facts=memoir_state.known_facts,
                persona_threads=memoir_state.persona_threads,
                recent_questions=recent_questions or memoir_state.recent_questions,
                turn_plan=turn_plan,
            )
            system_prompt = ctx.guided_system_prompt()
            messages: List[Any] = [SystemMessage(content=system_prompt)]
            messages.extend(hw.window)
            messages.append(HumanMessage(content=text_for_model))
            history_pairs_windowed = len(hw.window) // 2
            window_chars = sum(len(getattr(m, "content", "") or "") for m in hw.window)
            logger.info(
                "event=history_window_applied total={} windowed={} chars={}",
                conversation_turn_total,
                history_pairs_windowed,
                window_chars,
            )
            log_agent_payload(
                logger,
                "InterviewAgent.generate_response.prompt",
                format_history_string(
                    messages,
                    omit_system_body=settings.agent_log_omit_system_message_body,
                ),
            )
            chat_llm = self.llm.bind(
                max_tokens=max_tokens,
                temperature=float(settings.chat_interview_temperature),
            )
            prompt_chars = _message_contents_char_count(messages)
            llm_t0 = time.perf_counter()
            with agent_span(
                logger,
                "InterviewAgent.generate_response.llm",
                conversation_id=conversation_id,
                stage=memoir_state.current_stage,
            ):
                logger.info(
                    "event=chat_prompt_built agent=InterviewAgent.generate_response_with_state "
                    "prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
                    prompt_chars,
                    conversation_turn_total,
                    history_pairs_windowed,
                )
                response = await chat_llm.ainvoke(messages)
            response_ms = (time.perf_counter() - llm_t0) * 1000
            logger.info(
                "event=chat_llm_done agent=InterviewAgent.generate_response_with_state "
                "response_latency_ms={:.2f}",
                response_ms,
            )
            response_text = (
                response.content if hasattr(response, "content") else str(response)
            )
            log_agent_payload(
                logger, "InterviewAgent.generate_response.raw_response", response_text
            )
            rq_base = recent_questions or memoir_state.recent_questions
            out, deduped = _finalize_chat_segments_after_llm(
                response_text,
                max_segments=max_segments,
                max_chars=max_chars,
                memoir_state=memoir_state,
                recent_questions=rq_base,
            )
            retry_used = False
            if deduped and segments_are_only_duplicate_guard_fallback(out):
                retry_system = (
                    f"{system_prompt}\n\n{_DUPLICATE_GUARD_LLM_RETRY_SYSTEM_APPENDIX}"
                )
                retry_messages: List[Any] = [
                    SystemMessage(content=retry_system),
                    *hw.window,
                    HumanMessage(content=text_for_model),
                ]
                log_agent_payload(
                    logger,
                    "InterviewAgent.generate_response.retry_prompt",
                    format_history_string(
                        retry_messages,
                        omit_system_body=settings.agent_log_omit_system_message_body,
                    ),
                )
                llm_t1 = time.perf_counter()
                with agent_span(
                    logger,
                    "InterviewAgent.generate_response.llm_retry",
                    conversation_id=conversation_id,
                    stage=memoir_state.current_stage,
                ):
                    logger.info(
                        "event=chat_prompt_built agent=InterviewAgent.duplicate_guard_retry "
                        "prompt_chars={} conversation_id={}",
                        _message_contents_char_count(retry_messages),
                        conversation_id,
                    )
                    response_retry = await chat_llm.ainvoke(retry_messages)
                logger.info(
                    "event=chat_llm_done agent=InterviewAgent.duplicate_guard_retry "
                    "response_latency_ms={:.2f}",
                    (time.perf_counter() - llm_t1) * 1000,
                )
                response_text_retry = (
                    response_retry.content
                    if hasattr(response_retry, "content")
                    else str(response_retry)
                )
                log_agent_payload(
                    logger,
                    "InterviewAgent.generate_response.raw_response_retry",
                    response_text_retry,
                )
                out, deduped = _finalize_chat_segments_after_llm(
                    response_text_retry,
                    max_segments=max_segments,
                    max_chars=max_chars,
                    memoir_state=memoir_state,
                    recent_questions=rq_base,
                )
                retry_used = True
            out, auto_bio = apply_autobiographical_boundary_guard(out)
            updated_recent_questions = update_recent_questions(rq_base, out)
            log_agent_summary(
                logger,
                "InterviewAgent.generate_response segments={} conversation_id={} "
                "max_tokens={}",
                len(out),
                conversation_id,
                max_tokens,
            )
            return AgentChatTurn(
                messages=out,
                skip_tts=False,
                interview_state_meta={
                    "recent_questions": updated_recent_questions,
                    "duplicate_question_guard_triggered": deduped,
                    "duplicate_question_guard_llm_retry": retry_used,
                    "autobiographical_boundary_guard_triggered": auto_bio,
                    "reply_planner_llm_used": bool(
                        settings.chat_reply_planner_llm_enabled
                        and (reply_planner_raw or "").strip()
                    ),
                    "reply_planner_raw_preview": (reply_planner_raw or "")[:800],
                    "focus_planner_baseline_mode": baseline_mode,
                    "focus_planner_baseline_primary_focus": baseline_primary_focus,
                    "focus_planner_mode": turn_plan.mode,
                    "focus_planner_primary_focus": turn_plan.primary_focus,
                    "focus_planner_focus_source": turn_plan.focus_source,
                    "focus_planner_focus_summary": (turn_plan.focus_summary or "")[:200],
                },
            )
        except Exception as e:
            logger.error("生成回应失败: {}", e, exc_info=True)
            return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)

    async def generate_opening_message(
        self,
        conversation_id: str,
        memoir_state: MemoirStateSchema,
        user_profile_context: str = "",
        background_voice: str = "default",
        occupation: str = "",
        profile_birth_year: Optional[int] = None,
        profile_era_place: str = "",
    ) -> List[str]:
        """生成空对话开场白，不持久化（由 Orchestrator 负责）"""
        if not self.llm:
            return ["你好呀～ 又见面了。今天想从人生里哪一小段回忆开始聊聊？"]
        try:
            narrative_state = narrative_coverage_state(memoir_state)
            control_state = interview_control_state(memoir_state)
            empty_slots = control_state.prompt_empty_slots_for_stage(
                narrative_state, memoir_state.current_stage
            )
            empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
            persona = normalize_interview_persona(settings.chat_interview_persona)
            prompt = get_opening_prompt(
                current_stage=memoir_state.current_stage,
                empty_slots_readable=empty_slots_readable,
                user_profile_context=user_profile_context,
                persona=persona,
                background_voice=background_voice,
                occupation=occupation,
                profile_birth_year=profile_birth_year,
                profile_era_place=profile_era_place,
            )
            hw = await get_history_with_window(
                conversation_id,
                max_pairs=settings.chat_history_max_pairs,
                max_chars=settings.chat_history_max_chars,
            )
            messages: List[Any] = [SystemMessage(content=prompt)]
            messages.extend(hw.window)
            if not hw.window:
                messages.append(
                    HumanMessage(content="（对话刚开始，请自然地说出你的开场白。）")
                )
            else:
                messages.append(
                    HumanMessage(content="（请根据上文，自然接续并说出你的开场白。）")
                )
            log_agent_payload(
                logger,
                "InterviewAgent.opening.prompt",
                format_history_string(
                    messages,
                    omit_system_body=settings.agent_log_omit_system_message_body,
                ),
            )
            opening_llm = self.llm.bind(
                max_tokens=settings.chat_opening_max_tokens,
                temperature=float(settings.chat_interview_temperature),
            )
            prompt_chars = _message_contents_char_count(messages)
            llm_t0 = time.perf_counter()
            with agent_span(
                logger,
                "InterviewAgent.opening.llm",
                conversation_id=conversation_id,
            ):
                logger.info(
                    "event=chat_prompt_built agent=InterviewAgent.generate_opening_message "
                    "prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
                    prompt_chars,
                    hw.turn_total,
                    len(hw.window) // 2,
                )
                response = await opening_llm.ainvoke(messages)
            logger.info(
                "event=chat_llm_done agent=InterviewAgent.generate_opening_message "
                "response_latency_ms={:.2f}",
                (time.perf_counter() - llm_t0) * 1000,
            )
            response_text = (
                response.content if hasattr(response, "content") else str(response)
            )
            log_agent_payload(
                logger, "InterviewAgent.opening.raw_response", response_text
            )
            raw_list = segments_from_llm_response(response_text, max_segments=2)
            if not raw_list:
                raw_list = [response_text.strip()]
            max_chars = int(settings.chat_interview_max_chars_per_segment)
            out = truncate_chat_segments(
                raw_list,
                max_segments=2,
                max_chars_per_segment=max_chars,
            )
            log_agent_summary(
                logger,
                "InterviewAgent.opening segments={} conversation_id={}",
                len(out),
                conversation_id,
            )
            segments = out if out else [response_text.strip()[:max_chars]]
            return nonempty_segments_or_fallback(
                segments,
                fallback="你好呀～ 又见面了。今天想从人生里哪一小段回忆开始聊聊？",
            )
        except Exception as e:
            logger.error("生成开场白失败: {}", e, exc_info=True)
            return ["你好呀～ 又见面了。今天想从人生里哪一小段回忆开始聊聊？"]

    async def generate_re_greeting_message(
        self,
        conversation_id: str,
        memoir_state: MemoirStateSchema,
        idle_hours: float,
        user_profile_context: str = "",
        background_voice: str = "default",
        occupation: str = "",
        profile_birth_year: Optional[int] = None,
        profile_era_place: str = "",
    ) -> List[str]:
        """老对话回访问候：用户带着已有历史回到对话时，AI 主动做承接式开场。

        与 generate_opening_message 的差异：prompt 明确告知有历史 + 距上次的时间感受，
        要求轻轻引用历史里的具体细节，不能用首次见面式硬开场。
        """
        if not self.llm:
            return ["上次聊到的事我还记着，今天想继续往下讲讲吗？"]
        try:
            narrative_state = narrative_coverage_state(memoir_state)
            control_state = interview_control_state(memoir_state)
            empty_slots = control_state.prompt_empty_slots_for_stage(
                narrative_state, memoir_state.current_stage
            )
            empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
            persona = normalize_interview_persona(settings.chat_interview_persona)
            prompt = get_re_greeting_prompt(
                current_stage=memoir_state.current_stage,
                empty_slots_readable=empty_slots_readable,
                user_profile_context=user_profile_context,
                persona=persona,
                background_voice=background_voice,
                occupation=occupation,
                profile_birth_year=profile_birth_year,
                profile_era_place=profile_era_place,
                idle_hours=idle_hours,
            )
            hw = await get_history_with_window(
                conversation_id,
                max_pairs=settings.chat_history_max_pairs,
                max_chars=settings.chat_history_max_chars,
            )
            messages: List[Any] = [SystemMessage(content=prompt)]
            messages.extend(hw.window)
            messages.append(
                HumanMessage(
                    content=(
                        "（用户回到这个已有历史的对话，还没说话。"
                        "请基于上文做温和的承接式回访问候。）"
                    )
                )
            )
            log_agent_payload(
                logger,
                "InterviewAgent.re_greeting.prompt",
                format_history_string(
                    messages,
                    omit_system_body=settings.agent_log_omit_system_message_body,
                ),
            )
            re_greet_llm = self.llm.bind(
                max_tokens=settings.chat_opening_max_tokens,
                temperature=float(settings.chat_interview_temperature),
            )
            llm_t0 = time.perf_counter()
            with agent_span(
                logger,
                "InterviewAgent.re_greeting.llm",
                conversation_id=conversation_id,
            ):
                logger.info(
                    "event=chat_prompt_built agent=InterviewAgent.generate_re_greeting_message "
                    "prompt_chars={} history_pairs_total={} history_pairs_windowed={} idle_hours={:.2f}",
                    _message_contents_char_count(messages),
                    hw.turn_total,
                    len(hw.window) // 2,
                    idle_hours,
                )
                response = await re_greet_llm.ainvoke(messages)
            logger.info(
                "event=chat_llm_done agent=InterviewAgent.generate_re_greeting_message "
                "response_latency_ms={:.2f}",
                (time.perf_counter() - llm_t0) * 1000,
            )
            response_text = (
                response.content if hasattr(response, "content") else str(response)
            )
            log_agent_payload(
                logger, "InterviewAgent.re_greeting.raw_response", response_text
            )
            raw_list = segments_from_llm_response(response_text, max_segments=2)
            if not raw_list:
                raw_list = [response_text.strip()]
            max_chars = int(settings.chat_interview_max_chars_per_segment)
            out = truncate_chat_segments(
                raw_list,
                max_segments=2,
                max_chars_per_segment=max_chars,
            )
            log_agent_summary(
                logger,
                "InterviewAgent.re_greeting segments={} conversation_id={} idle_hours={:.2f}",
                len(out),
                conversation_id,
                idle_hours,
            )
            segments = out if out else [response_text.strip()[:max_chars]]
            return nonempty_segments_or_fallback(
                segments,
                fallback="上次聊到的事我还记着，今天想继续往下讲讲吗？",
            )
        except Exception as e:
            logger.error("生成回访问候失败: {}", e, exc_info=True)
            return ["上次聊到的事我还记着，今天想继续往下讲讲吗？"]