356 lines
16 KiB
Python
356 lines
16 KiB
Python
"""
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对话 Agent：基于访谈问题清单，动态选择问题，实时生成回应
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支持异步调用和 Redis 会话存储，支持用户基础资料收集和时代背景融入
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"""
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import json
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from app.core.logging import get_logger
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from langchain_core.messages import AIMessage, HumanMessage
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from app.core.redis import redis_service
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from app.core.dependencies import get_llm_provider
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from app.agents.prompts import (
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ConversationStage,
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get_conversation_prompt,
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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.prompts.profile_prompts import (
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get_profile_greeting_prompt,
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get_profile_extraction_prompt,
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get_profile_followup_prompt,
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format_user_profile_context,
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get_missing_profile_fields,
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)
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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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"""从 port 获取 LangChain LLM 实例（供 Agent 使用）"""
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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 ConversationAgent:
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"""对话 Agent（支持异步和 Redis 存储）"""
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def __init__(self):
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self.llm = _get_langchain_llm()
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async def _get_history_messages(self, conversation_id: str) -> List[Any]:
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history = await redis_service.get_conversation_history(conversation_id)
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messages = []
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for msg in history:
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if msg["role"] == "human":
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messages.append(HumanMessage(content=msg["content"]))
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elif msg["role"] == "ai":
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messages.append(AIMessage(content=msg["content"]))
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return messages
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async def _save_message(
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self,
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conversation_id: str,
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role: str,
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content: str,
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message_type: str = "text",
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voice_session_id: str | None = None,
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timestamp: datetime | str | int | None = None,
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):
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await redis_service.add_message(
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conversation_id,
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role,
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content,
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message_type=message_type,
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voice_session_id=voice_session_id,
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timestamp=timestamp.isoformat() if isinstance(timestamp, datetime) else timestamp,
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)
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def _format_history_string(self, messages: List[Any]) -> str:
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history_parts = []
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for msg in messages:
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if isinstance(msg, HumanMessage):
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history_parts.append(f"Human: {msg.content}")
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elif isinstance(msg, AIMessage):
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history_parts.append(f"Assistant: {msg.content}")
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return "\n\n".join(history_parts)
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async def generate_response(
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self,
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conversation_id: str,
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user_message: str,
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current_stage: Optional[ConversationStage] = None,
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covered_topics: Optional[List[str]] = None,
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) -> str:
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if current_stage is None:
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current_stage = ConversationStage.CHILDHOOD
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if covered_topics is None:
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covered_topics = []
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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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system_prompt = get_conversation_prompt(current_stage, covered_topics, user_message)
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history_messages = await self._get_history_messages(conversation_id)
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history_string = self._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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await self._save_message(conversation_id, "human", user_message)
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await self._save_message(conversation_id, "ai", response_text)
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return 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_profile_greeting(
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self,
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conversation_id: str,
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missing_fields: List[str],
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nickname: str = "",
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) -> List[str]:
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if not self.llm:
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return ["你好！在开始之前，能告诉我你是哪一年出生的吗？"]
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try:
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prompt = get_profile_greeting_prompt(missing_fields, nickname)
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history_messages = await self._get_history_messages(conversation_id)
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history_string = self._format_history_string(history_messages)
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full_prompt = f"{prompt}\n\n{history_string}" if history_string else prompt
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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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await self._save_message(conversation_id, "ai", response_text)
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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("生成资料收集开场白失败: %s", e)
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return ["你好！在我们开始聊人生故事之前，能先简单介绍一下你自己吗？比如你是哪一年出生的？"]
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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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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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await self._save_message(conversation_id, "ai", response_text)
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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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async def extract_profile_from_message(
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self,
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user_message: str,
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missing_fields: List[str],
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conversation_id: Optional[str] = None,
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) -> Dict[str, Any]:
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if not self.llm or not missing_fields:
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return {}
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recent_dialogue = ""
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if conversation_id:
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history_messages = await self._get_history_messages(conversation_id)
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recent = history_messages[-4:] if len(history_messages) > 4 else history_messages
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parts = []
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for msg in recent:
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if isinstance(msg, HumanMessage):
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parts.append(f"用户: {msg.content}")
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elif isinstance(msg, AIMessage):
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parts.append(f"助手: {msg.content}")
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recent_dialogue = "\n".join(parts) if parts else ""
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try:
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prompt = get_profile_extraction_prompt(
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user_message, missing_fields, recent_dialogue=recent_dialogue or None
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)
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response = await self.llm.ainvoke(prompt)
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content = response.content.strip()
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parsed = json.loads(content)
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result = {}
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if "birth_year" in parsed and parsed["birth_year"] is not None:
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raw = parsed["birth_year"]
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if isinstance(raw, int) and 1900 <= raw <= 2100:
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result["birth_year"] = raw
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elif isinstance(raw, str) and raw.isdigit():
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y = int(raw)
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if y < 100:
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y = 1900 + y if y >= 50 else 2000 + y
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if 1900 <= y <= 2100:
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result["birth_year"] = y
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if "birth_place" in parsed and parsed["birth_place"]:
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result["birth_place"] = str(parsed["birth_place"])
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if "grew_up_place" in parsed and parsed["grew_up_place"]:
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result["grew_up_place"] = str(parsed["grew_up_place"])
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if "occupation" in parsed and parsed["occupation"]:
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result["occupation"] = str(parsed["occupation"])
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return result
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except (json.JSONDecodeError, Exception) as e:
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logger.error("提取资料信息失败: %s", e)
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return {}
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async def generate_profile_followup(
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self,
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conversation_id: str,
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user_message: str,
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missing_fields: List[str],
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filled_fields: Dict[str, str],
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nickname: str = "",
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is_from_voice: bool = False,
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voice_session_id: str | None = None,
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user_message_timestamp: datetime | None = None,
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) -> List[str]:
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if not self.llm:
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return ["谢谢！还能告诉我更多吗？"]
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try:
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prompt = get_profile_followup_prompt(missing_fields, filled_fields, user_message, nickname)
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history_messages = await self._get_history_messages(conversation_id)
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history_string = self._format_history_string(history_messages)
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full_prompt = f"{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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human_msg_type = "audio" if is_from_voice else "text"
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await self._save_message(
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conversation_id,
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"human",
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user_message,
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message_type=human_msg_type,
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voice_session_id=voice_session_id,
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timestamp=user_message_timestamp,
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)
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await self._save_message(conversation_id, "ai", response_text)
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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("生成资料跟进回复失败: %s", e)
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return ["谢谢分享！能再告诉我一些吗？"]
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def _detect_user_stage(self, user_message: str) -> str:
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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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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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is_from_voice: bool = False,
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voice_session_id: str | None = None,
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user_message_timestamp: datetime | None = None,
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) -> List[str]:
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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 self._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(history_messages, filled_slots)
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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 = self._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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human_msg_type = "audio" if is_from_voice else "text"
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await self._save_message(
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conversation_id,
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"human",
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user_message,
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message_type=human_msg_type,
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voice_session_id=voice_session_id,
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timestamp=user_message_timestamp,
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)
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await self._save_message(conversation_id, "ai", response_text)
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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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def _estimate_same_topic_turns(self, history_messages: List[Any], current_filled_slots: dict) -> int:
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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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def detect_stage(self, conversation_id: str, user_message: str) -> ConversationStage:
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message_lower = user_message.lower()
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if any(word in message_lower for word in ["童年", "小时候", "出生", "家庭背景"]):
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return ConversationStage.CHILDHOOD
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if any(word in message_lower for word in ["上学", "学校", "老师", "同学", "教育"]):
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return ConversationStage.EDUCATION
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if any(word in message_lower for word in ["工作", "职业", "事业", "公司", "同事"]):
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return ConversationStage.CAREER
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if any(word in message_lower for word in ["伴侣", "孩子", "家庭", "家人", "结婚"]):
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return ConversationStage.FAMILY
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if any(word in message_lower for word in ["信念", "价值观", "座右铭", "坚持", "原则"]):
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return ConversationStage.BELIEFS
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if any(word in message_lower for word in ["总结", "回顾", "感激", "希望", "未来"]):
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return ConversationStage.SUMMARY
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return ConversationStage.CHILDHOOD
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async def clear_memory(self, conversation_id: str):
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await redis_service.clear_conversation_history(conversation_id)
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