138 lines
5.9 KiB
Python
138 lines
5.9 KiB
Python
"""
|
||
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？"]
|