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_duplicate_question_guard,
    extract_recent_questions,
    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,
)
from app.agents.chat.reply_limits import (
    nonempty_segments_or_fallback,
    segments_from_llm_response,
    truncate_chat_segments,
)
from app.agents.chat.stage_detection import keyword_fallback_primary_stage
from app.agents.state_schema import MemoirStateSchema
from app.core.agent_logging import (
    agent_span,
    log_agent_payload,
    log_agent_summary,
)
from app.core.config import settings
from app.core.dependencies import get_llm_provider
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。"


def _get_langchain_llm():
    try:
        provider = get_llm_provider()
        return getattr(provider, "langchain_llm", None)
    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 = "",
        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,
    ) -> 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
            )
            empty_slots = memoir_state.prompt_empty_slots_for_current_stage()
            filled_slots = {
                key: value.snippet
                for key, value in memoir_state.slots.get(
                    memoir_state.current_stage, {}
                ).items()
                if value.snippet
            }
            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 = memoir_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_evidence_text,
                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 ""),
            )

            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
            )
            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 or memoir_state.recent_questions,
            )
            updated_recent_questions = update_recent_questions(
                recent_questions or memoir_state.recent_questions,
                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,
                },
            )
        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:
            empty_slots = memoir_state.prompt_empty_slots_for_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？"]