bb16d3a5c9
访谈与阶段 - 新增 app/agents/stage_constants.py：集中 CHAT_STAGES、章节分类/顺序、阶段到默认 memoir 类别等，与 MemoirState 默认槽位顺序对齐；减少散落在 prompts 内的重复常量。 - 新增 app/agents/chat/prompt_context.py：以 ChatPromptContext 汇总 guided 系统提示所需字段（阶段、槽位、轮次、人设、记忆证据、回复长度模式、背景声线、职业等），统一走 get_guided_conversation_prompt。 - 大幅收敛 app/agents/chat/prompts_conversation.py；调整 prompts.py、stage_prompts.py、stage_detection.py；同步 interview_agent、profile_agent、helpers 与 state_schema，使对话侧构造提示的方式一致、可测。 回忆录流水线 - memoir/prompts.py 删除已迁至 stage_constants / 独立模板的大段常量与图片占位相关逻辑；classification / extraction / fidelity / narrative agents 与 orchest（全量历史仍可用于计数，注入模型时按轮次与字符上限截断）、image_prompt_fallback_disabled。 - dependencies 增加 get_llm_provider_fast（LRU 缓存，可与默认共用密钥与 base_url）。 任务与编排 - memoir_tasks：prepare_batches 注入 llm_fast；开启独立快档模型时打结构化日志。 - chapter_cover_tasks、story_image_tasks：与图片 prompt / JSON 工具路径或策略变更对齐（import 与行为一致）。 - story_pipeline_sync 等小处同步。 其它核心 - langchain_llm、text_normalize 随上述调用链微调。 开发者体验 - .cursor/settings.json：启用 redis-development、postman 插件。 测试 - 新增 test_image_prompt_policy：覆盖「禁止回退」等图片 prompt 策略。 - 更新 test_interview_prompts、test_interview_reply_length、test_experience_regressions、test_json_and_memory_utils，匹配新常量位置、json_utils 与对话/长度行为。
339 lines
14 KiB
Python
339 lines
14 KiB
Python
"""
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InterviewAgent：正式访谈 Specialist
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负责状态感知回复、开场白，不负责 Redis 持久化（由 Orchestrator 统一处理）
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"""
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import time
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from typing import Any, List, Optional
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from langchain_core.messages import HumanMessage, SystemMessage
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from app.agents.chat.agent_turn import AgentChatTurn
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from app.agents.chat.helpers import format_history_string, get_history_with_window
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from app.agents.chat.personas import normalize_interview_persona
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from app.agents.chat.prompt_context import ChatPromptContext
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from app.agents.chat.stage_detection import keyword_fallback_primary_stage
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from app.agents.chat.interview_reply_length import compute_reply_plan
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from app.agents.chat.prompts_conversation import (
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SLOT_NAME_MAP,
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get_opening_prompt,
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)
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from app.agents.state_schema import MemoirStateSchema
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from app.agents.chat.reply_limits import (
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nonempty_segments_or_fallback,
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segments_from_llm_response,
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truncate_chat_segments,
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)
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from app.core.agent_logging import (
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agent_span,
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log_agent_payload,
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log_agent_summary,
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)
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from app.core.config import settings
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from app.core.dependencies import get_llm_provider
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from app.core.logging import get_logger
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from app.features.conversation.input_normalize import normalize_chat_input_for_agent
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logger = get_logger(__name__)
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# 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）
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_FALLBACK_REPLY = "刚才网络不太稳，没接上。你可以再说一遍，或稍后再试。"
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def _get_langchain_llm():
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try:
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provider = get_llm_provider()
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return getattr(provider, "langchain_llm", None)
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except Exception:
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return None
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def _message_contents_char_count(messages: List[Any]) -> int:
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n = 0
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for m in messages:
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c = getattr(m, "content", None)
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if isinstance(c, str):
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n += len(c)
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return n
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class InterviewAgent:
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"""正式访谈 Specialist Agent"""
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def __init__(self):
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self.llm = _get_langchain_llm()
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def _detect_user_stage(self, user_message: str) -> str:
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"""关键词回退：与 stage_detection 一致（多阶段打分）。"""
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return keyword_fallback_primary_stage(user_message)
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def _estimate_same_topic_turns(
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self, history_messages: List[Any], current_filled_slots: dict
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) -> int:
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"""估算同一话题的连续轮数（保守：宁可多陪聊几轮再换）。"""
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n_pairs = len(history_messages) // 2
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if n_pairs <= 1:
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return n_pairs
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recent_window = min(n_pairs, 5)
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recent = history_messages[-(recent_window * 2) :]
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nonempty_user_turns = 0
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for i in range(0, len(recent), 2):
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msg = recent[i]
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text = msg.content if hasattr(msg, "content") else str(msg)
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if len(text.strip()) > 5:
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nonempty_user_turns += 1
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return nonempty_user_turns
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def _resolve_text_for_model(
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self,
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user_message: str,
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normalized_user_message: Optional[str],
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) -> str:
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"""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）。"""
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if normalized_user_message is not None:
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return (normalized_user_message or "").strip()
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llm_n = None
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if settings.chat_input_normalize_enabled and (
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(settings.chat_input_normalize_mode or "").strip().lower() == "llm"
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):
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llm_n = self.llm
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return normalize_chat_input_for_agent(user_message or "", llm=llm_n)
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async def generate_response_with_state(
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self,
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conversation_id: str,
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user_message: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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detected_user_stage: Optional[str] = None,
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memory_evidence_text: str = "",
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background_voice: str = "default",
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normalized_user_message: Optional[str] = None,
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occupation: str = "",
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) -> AgentChatTurn:
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"""生成状态感知的访谈回复，不持久化（由 Orchestrator 负责）"""
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if not self.llm:
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logger.warning("InterviewAgent: LLM 未配置，返回兜底文案")
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return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)
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try:
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text_for_model = self._resolve_text_for_model(
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user_message, normalized_user_message
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)
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empty_slots = memoir_state.empty_slots_for_current_stage()
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filled_slots = {
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key: value.snippet
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for key, value in memoir_state.slots.get(
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memoir_state.current_stage, {}
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).items()
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if value.snippet
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}
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if detected_user_stage is not None:
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du = detected_user_stage
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else:
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du = self._detect_user_stage(text_for_model)
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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conversation_turn_total = hw.turn_total
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same_topic_turns = self._estimate_same_topic_turns(hw.window, filled_slots)
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all_stages_coverage = memoir_state.all_stages_coverage()
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persona = normalize_interview_persona(settings.chat_interview_persona)
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reply_plan = compute_reply_plan(
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text_for_model,
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background_voice=background_voice,
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settings=settings,
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)
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ctx = ChatPromptContext(
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current_stage=memoir_state.current_stage,
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empty_slots=empty_slots,
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filled_slots=filled_slots,
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user_message=text_for_model,
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conversation_turn_total=conversation_turn_total,
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same_topic_turns=same_topic_turns,
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all_stages_coverage=all_stages_coverage,
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detected_user_stage=du,
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user_profile_context=user_profile_context,
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persona=persona,
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memory_evidence_text=memory_evidence_text,
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reply_length_mode=reply_plan.mode.value,
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background_voice=background_voice,
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occupation=occupation,
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)
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system_prompt = ctx.guided_system_prompt()
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messages: List[Any] = [SystemMessage(content=system_prompt)]
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messages.extend(hw.window)
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messages.append(HumanMessage(content=text_for_model))
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history_pairs_windowed = len(hw.window) // 2
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window_chars = sum(len(getattr(m, "content", "") or "") for m in hw.window)
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logger.info(
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"event=history_window_applied total={} windowed={} chars={}",
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conversation_turn_total,
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history_pairs_windowed,
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window_chars,
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)
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log_agent_payload(
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logger,
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"InterviewAgent.generate_response.prompt",
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format_history_string(messages),
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)
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chat_llm = self.llm.bind(max_tokens=reply_plan.max_tokens)
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prompt_chars = _message_contents_char_count(messages)
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llm_t0 = time.perf_counter()
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with agent_span(
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logger,
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"InterviewAgent.generate_response.llm",
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conversation_id=conversation_id,
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stage=memoir_state.current_stage,
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):
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logger.info(
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"event=chat_prompt_built agent=InterviewAgent.generate_response_with_state "
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"prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
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prompt_chars,
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conversation_turn_total,
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history_pairs_windowed,
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)
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response = await chat_llm.ainvoke(messages)
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response_ms = (time.perf_counter() - llm_t0) * 1000
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logger.info(
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"event=chat_llm_done agent=InterviewAgent.generate_response_with_state "
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"response_latency_ms={:.2f}",
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response_ms,
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)
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response_text = (
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response.content if hasattr(response, "content") else str(response)
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)
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log_agent_payload(
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logger, "InterviewAgent.generate_response.raw_response", response_text
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)
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raw_list = segments_from_llm_response(
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response_text,
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max_segments=reply_plan.max_segments,
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)
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if not raw_list:
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raw_list = [response_text.strip()]
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out = truncate_chat_segments(
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raw_list,
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max_segments=reply_plan.max_segments,
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max_chars_per_segment=reply_plan.max_chars_per_segment,
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)
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if not out:
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out = [response_text.strip()[: reply_plan.max_chars_per_segment]]
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out = nonempty_segments_or_fallback(out, fallback=_FALLBACK_REPLY)
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log_agent_summary(
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logger,
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"InterviewAgent.generate_response segments={} conversation_id={} "
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"reply_length_mode={} max_tokens={}",
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len(out),
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conversation_id,
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reply_plan.mode.value,
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reply_plan.max_tokens,
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)
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return AgentChatTurn(messages=out, skip_tts=False)
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except Exception as e:
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logger.error("生成回应失败: {}", e, exc_info=True)
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return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)
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async def generate_opening_message(
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self,
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conversation_id: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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background_voice: str = "default",
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occupation: str = "",
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) -> List[str]:
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"""生成空对话开场白，不持久化（由 Orchestrator 负责）"""
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if not self.llm:
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return ["你好呀～ 又见面了，今天有没有哪段回忆或近况想聊聊？"]
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try:
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empty_slots = memoir_state.empty_slots_for_current_stage()
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empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
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persona = normalize_interview_persona(settings.chat_interview_persona)
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prompt = get_opening_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots_readable=empty_slots_readable,
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user_profile_context=user_profile_context,
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persona=persona,
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background_voice=background_voice,
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occupation=occupation,
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)
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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messages: List[Any] = [SystemMessage(content=prompt)]
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messages.extend(hw.window)
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if not hw.window:
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messages.append(
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HumanMessage(content="（对话刚开始，请自然地说出你的开场白。）")
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)
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else:
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messages.append(
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HumanMessage(content="（请根据上文，自然接续并说出你的开场白。）")
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)
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log_agent_payload(
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logger,
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"InterviewAgent.opening.prompt",
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format_history_string(messages),
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)
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opening_llm = self.llm.bind(max_tokens=settings.chat_opening_max_tokens)
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prompt_chars = _message_contents_char_count(messages)
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llm_t0 = time.perf_counter()
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with agent_span(
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logger,
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"InterviewAgent.opening.llm",
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conversation_id=conversation_id,
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):
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logger.info(
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"event=chat_prompt_built agent=InterviewAgent.generate_opening_message "
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"prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
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prompt_chars,
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hw.turn_total,
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len(hw.window) // 2,
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)
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response = await opening_llm.ainvoke(messages)
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logger.info(
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"event=chat_llm_done agent=InterviewAgent.generate_opening_message "
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"response_latency_ms={:.2f}",
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(time.perf_counter() - llm_t0) * 1000,
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)
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response_text = (
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response.content if hasattr(response, "content") else str(response)
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)
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log_agent_payload(
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logger, "InterviewAgent.opening.raw_response", response_text
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)
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raw_list = segments_from_llm_response(response_text, max_segments=2)
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if not raw_list:
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raw_list = [response_text.strip()]
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open_plan = compute_reply_plan(
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"x" * 50,
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background_voice=background_voice,
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settings=settings,
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)
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out = truncate_chat_segments(
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raw_list,
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max_segments=2,
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max_chars_per_segment=open_plan.max_chars_per_segment,
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)
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log_agent_summary(
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logger,
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"InterviewAgent.opening segments={} conversation_id={}",
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len(out),
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conversation_id,
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)
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segments = (
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out
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if out
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else [response_text.strip()[: open_plan.max_chars_per_segment]]
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)
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return nonempty_segments_or_fallback(
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segments,
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fallback="你好呀～ 又见面了，最近有没有什么事想跟我说说？",
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)
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except Exception as e:
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logger.error("生成开场白失败: {}", e, exc_info=True)
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return ["你好呀～ 又见面了，最近有没有什么事想跟我说说？"]
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