添加AI代理模块

api/agents/conversation_agent.py

@@ -0,0 +1,155 @@
+"""
+对话 Agent：基于访谈问题清单，动态选择问题，实时生成回应
+"""
+import os
+from typing import List, Optional
+
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain.prompts import PromptTemplate
+from langchain_openai import ChatOpenAI
+
+from .prompts import ConversationStage, get_conversation_prompt
+
+
+class ConversationAgent:
+    """对话 Agent"""
+    
+    def __init__(self):
+        # 初始化 LLM（使用环境变量配置）
+        # 优先使用 LLM_API_KEY 和 LLM_BASE_URL，如果没有则使用 OPENAI_API_KEY
+        api_key = os.getenv("LLM_API_KEY") or os.getenv("OPENAI_API_KEY", "")
+        base_url = os.getenv("LLM_BASE_URL", "")
+        model_name = os.getenv("OPENAI_MODEL", "gpt-4o")
+        
+        if not api_key:
+            self.llm = None
+            self.memories: dict[str, ConversationBufferMemory] = {}
+            return
+        
+        # 如果提供了 base_url，需要处理路径（langchain 会自动添加 /v1/chat/completions）
+        llm_kwargs = {
+            "temperature": 0.7,
+            "model": model_name,
+            "openai_api_key": api_key,
+        }
+        
+        if base_url:
+            # 移除可能的 /v1/chat/completions 路径，langchain 会自动添加
+            if base_url.endswith("/v1/chat/completions"):
+                base_url = base_url[:-20]  # 移除 "/v1/chat/completions"
+            elif base_url.endswith("/v1"):
+                base_url = base_url[:-3]  # 移除 "/v1"
+            # 确保 base_url 以 / 结尾（如果没有）
+            if base_url and not base_url.endswith("/"):
+                base_url += "/"
+            llm_kwargs["openai_api_base"] = base_url
+        
+        try:
+            self.llm = ChatOpenAI(**llm_kwargs)
+        except Exception:
+            self.llm = None
+        
+        # 对话记忆
+        self.memories: dict[str, ConversationBufferMemory] = {}
+        
+        # 对话记忆
+        self.memories: dict[str, ConversationBufferMemory] = {}
+    
+    def _get_memory(self, conversation_id: str) -> ConversationBufferMemory:
+        """获取或创建对话记忆"""
+        if conversation_id not in self.memories:
+            self.memories[conversation_id] = ConversationBufferMemory(
+                return_messages=True,
+                memory_key="history"
+            )
+        return self.memories[conversation_id]
+    
+    def generate_response(
+        self,
+        conversation_id: str,
+        user_message: str,
+        current_stage: Optional[ConversationStage] = None,
+        covered_topics: Optional[List[str]] = None
+    ) -> str:
+        """
+        生成 Agent 回应
+        
+        Args:
+            conversation_id: 对话 ID
+            user_message: 用户消息
+            current_stage: 当前对话阶段
+            covered_topics: 已聊过的话题列表
+        
+        Returns:
+            Agent 回应文本
+        """
+        if current_stage is None:
+            current_stage = ConversationStage.CHILDHOOD
+        
+        if covered_topics is None:
+            covered_topics = []
+        
+        # 如果没有配置 LLM，返回默认回应
+        if not self.llm:
+            return "抱歉，LLM 服务未配置。请设置 LLM_API_KEY 或 OPENAI_API_KEY 环境变量。"
+        
+        # 获取系统提示词
+        system_prompt = get_conversation_prompt(current_stage, covered_topics, user_message)
+        
+        # 获取对话记忆
+        memory = self._get_memory(conversation_id)
+        
+        # 创建对话链
+        prompt_template = PromptTemplate(
+            input_variables=["history", "input"],
+            template=f"{system_prompt}\n\n{{history}}\n\nHuman: {{input}}\n\nAssistant:"
+        )
+        
+        chain = ConversationChain(
+            llm=self.llm,
+            prompt=prompt_template,
+            memory=memory,
+            verbose=False
+        )
+        
+        # 生成回应
+        response = chain.predict(input=user_message)
+        
+        return response
+    
+    def detect_stage(self, conversation_id: str, user_message: str) -> ConversationStage:
+        """
+        检测对话阶段
+        
+        Args:
+            conversation_id: 对话 ID
+            user_message: 用户消息
+        
+        Returns:
+            检测到的对话阶段
+        """
+        # 简单的关键词检测（实际应该使用更智能的方法）
+        message_lower = user_message.lower()
+        
+        if any(word in message_lower for word in ["童年", "小时候", "出生", "家庭背景"]):
+            return ConversationStage.CHILDHOOD
+        elif any(word in message_lower for word in ["上学", "学校", "老师", "同学", "教育"]):
+            return ConversationStage.EDUCATION
+        elif any(word in message_lower for word in ["工作", "职业", "事业", "公司", "同事"]):
+            return ConversationStage.CAREER
+        elif any(word in message_lower for word in ["伴侣", "孩子", "家庭", "家人", "结婚"]):
+            return ConversationStage.FAMILY
+        elif any(word in message_lower for word in ["信念", "价值观", "座右铭", "坚持", "原则"]):
+            return ConversationStage.BELIEFS
+        elif any(word in message_lower for word in ["总结", "回顾", "感激", "希望", "未来"]):
+            return ConversationStage.SUMMARY
+        else:
+            # 默认返回当前阶段或童年阶段
+            return ConversationStage.CHILDHOOD
+    
+    def clear_memory(self, conversation_id: str):
+        """清除对话记忆"""
+        if conversation_id in self.memories:
+            del self.memories[conversation_id]
+