232 lines
8.2 KiB
Python
232 lines
8.2 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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from typing import Any, List
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from app.agents.chat.agent_turn import AgentChatTurn
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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.agents.chat.helpers import format_history_string, get_history_messages
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from app.agents.chat.prompts_conversation import (
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SLOT_NAME_MAP,
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get_guided_conversation_prompt,
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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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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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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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"""根据关键词检测用户正在谈论的人生阶段"""
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message = user_message.lower()
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stage_keywords = {
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"childhood": [
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"童年",
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"小时候",
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"出生",
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"家乡",
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"小镇",
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"爸妈",
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"父亲",
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"母亲",
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"爷爷",
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"奶奶",
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"外公",
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"外婆",
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"幼儿园",
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],
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"education": [
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"上学",
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"学校",
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"老师",
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"同学",
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"教育",
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"大学",
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"高中",
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"初中",
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"小学",
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"考试",
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"毕业",
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"读书",
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"高考",
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"课堂",
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],
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"career": [
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"工作",
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"职业",
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"事业",
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"公司",
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"同事",
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"创业",
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"升职",
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"跳槽",
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"老板",
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"行业",
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"项目",
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"加班",
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"薪水",
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"面试",
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],
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"family": [
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"伴侣",
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"孩子",
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"家庭",
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"家人",
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"结婚",
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"爱人",
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"老婆",
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"老公",
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"丈夫",
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"妻子",
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"儿子",
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"女儿",
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"婚礼",
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"恋爱",
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],
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"belief": [
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"信念",
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"价值观",
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"座右铭",
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"坚持",
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"原则",
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"信仰",
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"意义",
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"感悟",
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"遗憾",
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"骄傲",
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],
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}
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for stage, keywords in stage_keywords.items():
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if any(word in message for word in keywords):
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return stage
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return ""
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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"""
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if len(history_messages) < 4:
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return len(history_messages) // 2
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recent_messages = history_messages[-6:]
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keywords_per_turn = []
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for i in range(0, len(recent_messages), 2):
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if i + 1 < len(recent_messages):
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human_msg = (
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recent_messages[i].content
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if hasattr(recent_messages[i], "content")
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else str(recent_messages[i])
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)
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ai_msg = (
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recent_messages[i + 1].content
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if hasattr(recent_messages[i + 1], "content")
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else str(recent_messages[i + 1])
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)
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keywords_per_turn.append((human_msg + ai_msg)[:100])
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if len(keywords_per_turn) >= 3:
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return 3
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return len(keywords_per_turn)
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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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) -> 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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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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detected_user_stage = self._detect_user_stage(user_message)
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history_messages = await get_history_messages(conversation_id)
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conversation_turn = len(history_messages) // 2
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same_topic_turns = self._estimate_same_topic_turns(
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history_messages, filled_slots
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)
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all_stages_coverage = memoir_state.all_stages_coverage()
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system_prompt = get_guided_conversation_prompt(
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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=user_message,
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conversation_turn=conversation_turn,
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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=detected_user_stage,
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user_profile_context=user_profile_context,
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)
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history_string = format_history_string(history_messages)
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full_prompt = f"{system_prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
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response = await self.llm.ainvoke(full_prompt)
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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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messages = [
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msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()
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]
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out = messages[:3] if messages else [response_text]
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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: %s", 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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) -> 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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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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)
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full_prompt = f"{prompt}\n\nAssistant:"
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response = await self.llm.ainvoke(full_prompt)
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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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messages = [
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msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()
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]
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return messages[:2] if messages else [response_text]
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except Exception as e:
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logger.error("生成开场白失败: %s", 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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