feat & refactor: 重构agents目录结构；AI回复模块agent结构封装
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yangshilin
143
api/app/agents/chat/interview_agent.py
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143
api/app/agents/chat/interview_agent.py
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"""
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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.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.prompts import get_guided_conversation_prompt, get_opening_prompt
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from app.agents.prompts.conversation_prompts import SLOT_NAME_MAP
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from app.agents.state_schema import MemoirStateSchema
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logger = get_logger(__name__)
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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": ["童年", "小时候", "出生", "家乡", "小镇", "爸妈", "父亲", "母亲", "爷爷", "奶奶", "外公", "外婆", "幼儿园"],
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"education": ["上学", "学校", "老师", "同学", "教育", "大学", "高中", "初中", "小学", "考试", "毕业", "读书", "高考", "课堂"],
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"career": ["工作", "职业", "事业", "公司", "同事", "创业", "升职", "跳槽", "老板", "行业", "项目", "加班", "薪水", "面试"],
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"family": ["伴侣", "孩子", "家庭", "家人", "结婚", "爱人", "老婆", "老公", "丈夫", "妻子", "儿子", "女儿", "婚礼", "恋爱"],
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"belief": ["信念", "价值观", "座右铭", "坚持", "原则", "信仰", "意义", "感悟", "遗憾", "骄傲"],
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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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) -> List[str]:
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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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return ["抱歉，LLM 服务未配置。请设置 DEEPSEEK_API_KEY 或 LLM_API_KEY 环境变量。"]
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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(memoir_state.current_stage, {}).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 = response.content if hasattr(response, "content") else str(response)
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messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
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return messages[:3] if messages else [response_text]
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except Exception as e:
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logger.error("生成回应失败: %s", e)
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return [f"抱歉，生成回应时出现错误: {str(e)}"]
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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～ 有空聊聊你的人生故事吗？你小时候是在哪儿长大的？"]
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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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if not empty_slots_readable:
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empty_slots_readable = ["成长的地方", "难忘的事", "重要的人"]
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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 = response.content if hasattr(response, "content") else str(response)
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messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
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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～ 有空聊聊你的人生故事吗？你童年里印象最深的一件事是什么？"]
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