feat & refactor: 重构agents目录结构；AI回复模块agent结构封装

api/app/agents/chat/profile_agent.py

@@ -0,0 +1,132 @@
+"""
+ProfileAgent：用户资料收集 Specialist
+负责提取资料、资料追问、资料收集开场白，不负责 Redis 持久化（由 Orchestrator 统一处理）
+"""
+import json
+from typing import Any, Dict, List, Optional
+
+from langchain_core.messages import AIMessage, HumanMessage
+
+from app.core.dependencies import get_llm_provider
+from app.core.logging import get_logger
+
+from app.agents.chat.helpers import format_history_string, get_history_messages
+from app.agents.prompts.profile_prompts import (
+    get_profile_extraction_prompt,
+    get_profile_followup_prompt,
+    get_profile_greeting_prompt,
+)
+
+logger = get_logger(__name__)
+
+
+def _get_langchain_llm():
+    try:
+        provider = get_llm_provider()
+        return getattr(provider, "langchain_llm", None)
+    except Exception:
+        return None
+
+
+class ProfileAgent:
+    """用户资料收集 Specialist Agent"""
+
+    def __init__(self):
+        self.llm = _get_langchain_llm()
+
+    async def extract_profile_from_message(
+        self,
+        user_message: str,
+        missing_fields: List[str],
+        conversation_id: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """从用户消息中提取资料字段，不持久化"""
+        if not self.llm or not missing_fields:
+            return {}
+        recent_dialogue = ""
+        if conversation_id:
+            history_messages = await get_history_messages(conversation_id)
+            recent = history_messages[-4:] if len(history_messages) > 4 else history_messages
+            parts = []
+            for msg in recent:
+                if isinstance(msg, HumanMessage):
+                    parts.append(f"用户: {msg.content}")
+                elif isinstance(msg, AIMessage):
+                    parts.append(f"助手: {msg.content}")
+            recent_dialogue = "\n".join(parts) if parts else ""
+        try:
+            prompt = get_profile_extraction_prompt(
+                user_message, missing_fields, recent_dialogue=recent_dialogue or None
+            )
+            response = await self.llm.ainvoke(prompt)
+            content = response.content.strip()
+            parsed = json.loads(content)
+            result = {}
+            if "birth_year" in parsed and parsed["birth_year"] is not None:
+                raw = parsed["birth_year"]
+                if isinstance(raw, int) and 1900 <= raw <= 2100:
+                    result["birth_year"] = raw
+                elif isinstance(raw, str) and raw.isdigit():
+                    y = int(raw)
+                    if y < 100:
+                        y = 1900 + y if y >= 50 else 2000 + y
+                    if 1900 <= y <= 2100:
+                        result["birth_year"] = y
+            if "birth_place" in parsed and parsed["birth_place"]:
+                result["birth_place"] = str(parsed["birth_place"])
+            if "grew_up_place" in parsed and parsed["grew_up_place"]:
+                result["grew_up_place"] = str(parsed["grew_up_place"])
+            if "occupation" in parsed and parsed["occupation"]:
+                result["occupation"] = str(parsed["occupation"])
+            return result
+        except (json.JSONDecodeError, Exception) as e:
+            logger.error("提取资料信息失败: %s", e)
+            return {}
+
+    async def generate_profile_followup(
+        self,
+        conversation_id: str,
+        user_message: str,
+        missing_fields: List[str],
+        filled_fields: Dict[str, str],
+        nickname: str = "",
+    ) -> List[str]:
+        """生成资料追问回复，不持久化（由 Orchestrator 负责）"""
+        if not self.llm:
+            return ["谢谢！还能告诉我更多吗？"]
+        try:
+            prompt = get_profile_followup_prompt(
+                missing_fields, filled_fields, user_message, nickname
+            )
+            history_messages = await get_history_messages(conversation_id)
+            history_string = format_history_string(history_messages)
+            full_prompt = f"{prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
+            response = await self.llm.ainvoke(full_prompt)
+            response_text = response.content if hasattr(response, "content") else str(response)
+            messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
+            return messages[:3] if messages else [response_text]
+        except Exception as e:
+            logger.error("生成资料跟进回复失败: %s", e)
+            return ["谢谢分享！能再告诉我一些吗？"]
+
+    async def generate_profile_greeting(
+        self,
+        conversation_id: str,
+        missing_fields: List[str],
+        nickname: str = "",
+    ) -> List[str]:
+        """生成资料收集开场白，不持久化（由 Orchestrator 负责）"""
+        if not self.llm:
+            return ["你好！在开始之前，能告诉我你是哪一年出生的吗？"]
+        try:
+            prompt = get_profile_greeting_prompt(missing_fields, nickname)
+            history_messages = await get_history_messages(conversation_id)
+            history_string = format_history_string(history_messages)
+            full_prompt = f"{prompt}\n\n{history_string}" if history_string else prompt
+            response = await self.llm.ainvoke(full_prompt)
+            response_text = response.content if hasattr(response, "content") else str(response)
+            messages = [msg.strip() for msg in response_text.split("[SPLIT]") if msg.strip()]
+            return messages[:2] if messages else [response_text]
+        except Exception as e:
+            logger.error("生成资料收集开场白失败: %s", e)
+            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？"]