""" 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 time from typing import Any, Dict, List, Optional from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from app.agents.chat.helpers import format_history_string, get_history_with_window from app.agents.chat.prompts_profile import ( get_profile_extraction_prompt, get_profile_followup_prompt, get_profile_greeting_prompt, ) from app.agents.chat.reply_limits import ( nonempty_segments_or_fallback, segments_from_llm_response, truncate_chat_segments, ) from app.agents.chat.schemas import ProfileExtractionOutput from app.core.agent_logging import agent_span, log_agent_payload, log_agent_summary from app.core.config import settings from app.core.llm_call import allm_json_call from app.core.llm_gateway import LlmGateway, LlmUseCase from app.core.logging import get_logger from app.ports.llm import LLMProvider from app.core.runtime_constants import agent_log_defaults from app.features.conversation.constants import chat from app.features.story.constants import story logger = get_logger(__name__) _FOLLOWUP_FALLBACK_ZH = "谢谢分享！能再告诉我一些吗？" _FOLLOWUP_FALLBACK_EN = "Thanks for sharing — could you tell me a bit more?" _GREETING_FALLBACK_ZH = "你好！在开始之前，能告诉我你是哪一年出生的吗？" _GREETING_FALLBACK_EN = ( "Hi! Before we get started, could you tell me what year you were born?" ) _GREETING_FALLBACK_FULL_ZH = ( "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？" ) _GREETING_FALLBACK_FULL_EN = ( "Hi! Before we dive into life stories, could you introduce yourself a little — for example, what year were you born?" ) def _profile_followup_fallback(language: str) -> str: return _FOLLOWUP_FALLBACK_EN if language == "en" else _FOLLOWUP_FALLBACK_ZH def _profile_greeting_fallback(language: str) -> str: return _GREETING_FALLBACK_EN if language == "en" else _GREETING_FALLBACK_ZH def _profile_greeting_fallback_full(language: str) -> str: return _GREETING_FALLBACK_FULL_EN if language == "en" else _GREETING_FALLBACK_FULL_ZH class _ProviderBackedProfileGateway: def __init__(self, provider: LLMProvider) -> None: self._provider = provider async def chat_text( self, messages: list[dict], *, use_case: LlmUseCase | None = None, temperature: float | None = None, model: str | None = None, max_tokens: int | None = None, ) -> str: resolved_temperature = temperature if resolved_temperature is None: resolved_temperature = ( use_case.temperature if use_case and use_case.temperature is not None else 0.7 ) return await self._provider.complete( messages, temperature=resolved_temperature, model=model if model is not None else (use_case.model if use_case else None), max_tokens=( max_tokens if max_tokens is not None else (use_case.max_tokens if use_case else None) ), ) async def json_object( self, prompt: str, schema: type[ProfileExtractionOutput], *, use_case: LlmUseCase, fallback_factory: Any = None, ) -> ProfileExtractionOutput: return await allm_json_call( getattr(self._provider, "langchain_llm", None), prompt, schema, max_tokens=use_case.max_tokens or 1024, agent=use_case.name, fallback_factory=fallback_factory, ) def _langchain_messages_to_port(messages: List[Any]) -> list[dict]: """LangChain message 列表 → ``LLMProvider.complete`` 的 ``role/content`` 结构。""" out: list[dict] = [] for m in messages: if isinstance(m, SystemMessage): out.append({"role": "system", "content": str(m.content)}) elif isinstance(m, HumanMessage): out.append({"role": "user", "content": str(m.content)}) elif isinstance(m, AIMessage): out.append({"role": "assistant", "content": str(m.content)}) else: c = getattr(m, "content", None) out.append({"role": "user", "content": str(c) if c is not None else ""}) return out def _message_contents_char_count(messages: List[Any]) -> int: n = 0 for m in messages: c = getattr(m, "content", None) if isinstance(c, str): n += len(c) return n class ProfileAgent: """用户资料收集 Specialist Agent""" def __init__( self, llm_provider: LLMProvider | None = None, llm_gateway: Any | None = None, ) -> None: if llm_gateway is not None: self._llm_gateway = llm_gateway elif llm_provider is not None: self._llm_gateway = _ProviderBackedProfileGateway(llm_provider) else: self._llm_gateway = LlmGateway() async def _invoke_chat( self, messages: List[Any], *, max_tokens: int, conversation_id: Optional[str], agent_name: str, ) -> str: port_messages = _langchain_messages_to_port(messages) llm_t0 = time.perf_counter() with agent_span( logger, f"{agent_name}.llm", conversation_id=conversation_id or "" ): response_text = await self._llm_gateway.chat_text( port_messages, use_case=LlmUseCase("chat.profile", max_tokens=max_tokens), max_tokens=max_tokens, ) logger.info( "event=chat_llm_done agent={} response_latency_ms={:.2f}", agent_name, (time.perf_counter() - llm_t0) * 1000, ) return response_text or "" async def _segments_from_response( self, response_text: str, *, max_segments: int, max_chars_per_segment: int, fallback: str, ) -> List[str]: log_agent_payload( logger, "ProfileAgent._segments_from_response.raw_response", response_text, ) raw_list = segments_from_llm_response(response_text, max_segments=max_segments) if not raw_list: raw_list = [response_text.strip()] out = truncate_chat_segments( raw_list, max_segments=max_segments, max_chars_per_segment=max_chars_per_segment, ) segments = out if out else [response_text.strip()[:max_chars_per_segment]] return nonempty_segments_or_fallback(segments, fallback=fallback) async def extract_profile_from_message( self, user_message: str, missing_fields: List[str], conversation_id: Optional[str] = None, language: str = "zh", ) -> Dict[str, Any]: """从用户消息中提取资料字段，不持久化""" if not missing_fields: return {} recent_dialogue = "" if conversation_id: hw = await get_history_with_window( conversation_id, max_pairs=chat.history_max_pairs, max_chars=chat.history_max_chars, ) recent = hw.window[-4:] if len(hw.window) > 4 else hw.window parts = [] user_label = "User" if language == "en" else "用户" asst_label = "Assistant" if language == "en" else "助手" for msg in recent: if isinstance(msg, HumanMessage): parts.append(f"{user_label}: {msg.content}") elif isinstance(msg, AIMessage): parts.append(f"{asst_label}: {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, language=language, ) parsed = await self._llm_gateway.json_object( prompt, ProfileExtractionOutput, use_case=LlmUseCase( "ProfileAgent.extract_profile_from_message", max_tokens=chat.profile_extract_max_tokens, ), fallback_factory=lambda: ProfileExtractionOutput(), ) result = {} if 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 parsed.birth_place: result["birth_place"] = str(parsed.birth_place) if parsed.grew_up_place: result["grew_up_place"] = str(parsed.grew_up_place) if parsed.occupation: result["occupation"] = str(parsed.occupation) bp = result.get("birth_place") gp = result.get("grew_up_place") if bp and not gp: result["grew_up_place"] = bp elif gp and not bp: result["birth_place"] = gp return result except Exception as e: logger.error("提取资料信息失败: {}", 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 = "", interview_stage_hint: str = "", language: str = "zh", ) -> List[str]: """生成资料追问回复，不持久化（由 Orchestrator 负责）""" try: prompt = get_profile_followup_prompt( missing_fields, filled_fields, nickname, interview_stage_hint=interview_stage_hint, language=language, ) hw = await get_history_with_window( conversation_id, max_pairs=chat.history_max_pairs, max_chars=chat.history_max_chars, ) messages: List[Any] = [SystemMessage(content=prompt)] messages.extend(hw.window) messages.append(HumanMessage(content=user_message)) log_agent_payload( logger, "ProfileAgent.followup.prompt", format_history_string( messages, omit_system_body=agent_log_defaults.omit_system_message_body, ), ) prompt_chars = _message_contents_char_count(messages) logger.info( "event=chat_prompt_built agent=ProfileAgent.generate_profile_followup " "prompt_chars={} history_pairs_total={} history_pairs_windowed={}", prompt_chars, hw.turn_total, len(hw.window) // 2, ) response_text = await self._invoke_chat( messages, max_tokens=chat.profile_followup_max_tokens, conversation_id=conversation_id, agent_name="ProfileAgent.generate_profile_followup", ) segments = await self._segments_from_response( response_text, max_segments=3, max_chars_per_segment=chat.interview_max_chars_per_segment, fallback=_profile_followup_fallback(language), ) log_agent_summary( logger, "ProfileAgent.followup segments={} conversation_id={}", len(segments), conversation_id, ) return segments except Exception as e: logger.error("生成资料跟进回复失败: {}", e) return [_profile_followup_fallback(language)] async def generate_profile_greeting( self, conversation_id: str, missing_fields: List[str], nickname: str = "", language: str = "zh", ) -> List[str]: """生成资料收集开场白，不持久化（由 Orchestrator 负责）""" try: prompt = get_profile_greeting_prompt( missing_fields, nickname, language=language ) hw = await get_history_with_window( conversation_id, max_pairs=chat.history_max_pairs, max_chars=chat.history_max_chars, ) messages: List[Any] = [SystemMessage(content=prompt)] messages.extend(hw.window) if language == "en": kickoff = ( "(Continue from the context above and warmly carry on the profile-gathering opener.)" if hw.window else "(Please deliver your profile-gathering opener.)" ) else: kickoff = ( "（请根据上文自然接话，继续资料收集开场。）" if hw.window else "（请说出资料收集开场白。）" ) messages.append(HumanMessage(content=kickoff)) log_agent_payload( logger, "ProfileAgent.greeting.prompt", format_history_string( messages, omit_system_body=agent_log_defaults.omit_system_message_body, ), ) prompt_chars = _message_contents_char_count(messages) logger.info( "event=chat_prompt_built agent=ProfileAgent.generate_profile_greeting " "prompt_chars={} history_pairs_total={} history_pairs_windowed={}", prompt_chars, hw.turn_total, len(hw.window) // 2, ) response_text = await self._invoke_chat( messages, max_tokens=chat.profile_followup_max_tokens, conversation_id=conversation_id, agent_name="ProfileAgent.generate_profile_greeting", ) segments = await self._segments_from_response( response_text, max_segments=2, max_chars_per_segment=chat.interview_max_chars_per_segment, fallback=_profile_greeting_fallback(language), ) log_agent_summary( logger, "ProfileAgent.greeting segments={} conversation_id={}", len(segments), conversation_id, ) return segments except Exception as e: logger.error("生成资料收集开场白失败: {}", e) return [_profile_greeting_fallback_full(language)]