life-echo/api/app/agents/chat/profile_agent.py

"""
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.chat.prompts_profile import (
    get_profile_extraction_prompt,
    get_profile_followup_prompt,
    get_profile_greeting_prompt,
)
from app.features.memoir.memoir_images.json_payload import extract_json_payload

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
            )
            json_llm = self.llm.bind(
                model_kwargs={"response_format": {"type": "json_object"}},
                max_tokens=512,
            )
            response = await json_llm.ainvoke(prompt)
            content = response.content.strip()
            parsed = json.loads(extract_json_payload(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？"]