59d4b19d7d
- Phase1/2：移除 MemoirOrchestrator.run 与 process_memoir_segments 别名；文档改为 process_memoir_phase1。 - 槽位校验集中到 stage_constants（filter_stage_slots），批处理与顺序路径及 state_service 写库一致。 - StoryRoute：no_llm/parse_error/invalid_target 保守 new_story；短篇护栏不覆盖这些 fallback。 - Phase2 低置信单路径可选延迟（StoryPipelineResult.deferred）：不写 Chapter/Story，Segment 记录 defer 元数据，冷却内不重复消费；上限后停自动重试，Phase1 同类目新段唤醒池内段。 - Alembic 0017：segments 表 narrative_defer_* 列。 - ProfileAgent：经 LlmGateway/注入 Provider 统一聊天与 JSON，新增测试。 - ImagePromptOrchestrator：LLM 初始化失败可依配置降级或硬失败；补充策略测试。 - 配套单测与 README/本地开发文档表述更新。 Co-authored-by: Cursor <cursoragent@cursor.com>
362 lines
13 KiB
Python
362 lines
13 KiB
Python
"""
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ProfileAgent：用户资料收集 Specialist
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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）
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"""
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import time
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from typing import Any, Dict, List, Optional
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from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
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from app.agents.chat.helpers import format_history_string, get_history_with_window
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from app.agents.chat.prompts_profile import (
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get_profile_extraction_prompt,
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get_profile_followup_prompt,
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get_profile_greeting_prompt,
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)
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from app.agents.chat.reply_limits import (
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nonempty_segments_or_fallback,
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segments_from_llm_response,
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truncate_chat_segments,
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)
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from app.agents.chat.schemas import ProfileExtractionOutput
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from app.core.agent_logging import agent_span, log_agent_payload, log_agent_summary
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from app.core.config import settings
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from app.core.llm_call import allm_json_call
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from app.core.llm_gateway import LlmGateway, LlmUseCase
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from app.core.logging import get_logger
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from app.ports.llm import LLMProvider
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logger = get_logger(__name__)
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class _ProviderBackedProfileGateway:
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def __init__(self, provider: LLMProvider) -> None:
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self._provider = provider
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async def chat_text(
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self,
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messages: list[dict],
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*,
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use_case: LlmUseCase | None = None,
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temperature: float | None = None,
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model: str | None = None,
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max_tokens: int | None = None,
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) -> str:
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resolved_temperature = temperature
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if resolved_temperature is None:
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resolved_temperature = (
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use_case.temperature
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if use_case and use_case.temperature is not None
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else 0.7
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)
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return await self._provider.complete(
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messages,
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temperature=resolved_temperature,
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model=model if model is not None else (use_case.model if use_case else None),
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max_tokens=(
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max_tokens
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if max_tokens is not None
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else (use_case.max_tokens if use_case else None)
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),
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)
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async def json_object(
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self,
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prompt: str,
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schema: type[ProfileExtractionOutput],
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*,
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use_case: LlmUseCase,
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fallback_factory: Any = None,
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) -> ProfileExtractionOutput:
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return await allm_json_call(
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getattr(self._provider, "langchain_llm", None),
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prompt,
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schema,
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max_tokens=use_case.max_tokens or 1024,
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agent=use_case.name,
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fallback_factory=fallback_factory,
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)
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def _langchain_messages_to_port(messages: List[Any]) -> list[dict]:
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"""LangChain message 列表 → ``LLMProvider.complete`` 的 ``role/content`` 结构。"""
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out: list[dict] = []
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for m in messages:
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if isinstance(m, SystemMessage):
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out.append({"role": "system", "content": str(m.content)})
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elif isinstance(m, HumanMessage):
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out.append({"role": "user", "content": str(m.content)})
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elif isinstance(m, AIMessage):
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out.append({"role": "assistant", "content": str(m.content)})
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else:
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c = getattr(m, "content", None)
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out.append({"role": "user", "content": str(c) if c is not None else ""})
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return out
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def _message_contents_char_count(messages: List[Any]) -> int:
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n = 0
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for m in messages:
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c = getattr(m, "content", None)
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if isinstance(c, str):
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n += len(c)
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return n
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class ProfileAgent:
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"""用户资料收集 Specialist Agent"""
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def __init__(
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self,
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llm_provider: LLMProvider | None = None,
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llm_gateway: Any | None = None,
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) -> None:
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if llm_gateway is not None:
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self._llm_gateway = llm_gateway
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elif llm_provider is not None:
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self._llm_gateway = _ProviderBackedProfileGateway(llm_provider)
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else:
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self._llm_gateway = LlmGateway()
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async def _invoke_chat(
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self,
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messages: List[Any],
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*,
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max_tokens: int,
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conversation_id: Optional[str],
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agent_name: str,
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) -> str:
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port_messages = _langchain_messages_to_port(messages)
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llm_t0 = time.perf_counter()
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with agent_span(
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logger, f"{agent_name}.llm", conversation_id=conversation_id or ""
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):
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response_text = await self._llm_gateway.chat_text(
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port_messages,
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use_case=LlmUseCase("chat.profile", max_tokens=max_tokens),
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max_tokens=max_tokens,
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)
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logger.info(
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"event=chat_llm_done agent={} response_latency_ms={:.2f}",
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agent_name,
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(time.perf_counter() - llm_t0) * 1000,
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)
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return response_text or ""
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async def _segments_from_response(
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self,
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response_text: str,
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*,
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max_segments: int,
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max_chars_per_segment: int,
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fallback: str,
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) -> List[str]:
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log_agent_payload(
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logger,
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"ProfileAgent._segments_from_response.raw_response",
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response_text,
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)
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raw_list = segments_from_llm_response(response_text, max_segments=max_segments)
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if not raw_list:
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raw_list = [response_text.strip()]
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out = truncate_chat_segments(
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raw_list,
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max_segments=max_segments,
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max_chars_per_segment=max_chars_per_segment,
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)
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segments = out if out else [response_text.strip()[:max_chars_per_segment]]
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return nonempty_segments_or_fallback(segments, fallback=fallback)
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async def extract_profile_from_message(
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self,
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user_message: str,
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missing_fields: List[str],
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conversation_id: Optional[str] = None,
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) -> Dict[str, Any]:
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"""从用户消息中提取资料字段，不持久化"""
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if not missing_fields:
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return {}
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recent_dialogue = ""
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if conversation_id:
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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recent = hw.window[-4:] if len(hw.window) > 4 else hw.window
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parts = []
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for msg in recent:
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if isinstance(msg, HumanMessage):
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parts.append(f"用户: {msg.content}")
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elif isinstance(msg, AIMessage):
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parts.append(f"助手: {msg.content}")
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recent_dialogue = "\n".join(parts) if parts else ""
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try:
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prompt = get_profile_extraction_prompt(
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user_message, missing_fields, recent_dialogue=recent_dialogue or None
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)
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parsed = await self._llm_gateway.json_object(
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prompt,
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ProfileExtractionOutput,
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use_case=LlmUseCase(
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"ProfileAgent.extract_profile_from_message",
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max_tokens=settings.chat_profile_extract_max_tokens,
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),
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fallback_factory=lambda: ProfileExtractionOutput(),
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)
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result = {}
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if parsed.birth_year is not None:
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raw = parsed.birth_year
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if isinstance(raw, int) and 1900 <= raw <= 2100:
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result["birth_year"] = raw
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elif isinstance(raw, str) and raw.isdigit():
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y = int(raw)
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if y < 100:
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y = 1900 + y if y >= 50 else 2000 + y
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if 1900 <= y <= 2100:
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result["birth_year"] = y
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if parsed.birth_place:
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result["birth_place"] = str(parsed.birth_place)
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if parsed.grew_up_place:
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result["grew_up_place"] = str(parsed.grew_up_place)
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if parsed.occupation:
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result["occupation"] = str(parsed.occupation)
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bp = result.get("birth_place")
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gp = result.get("grew_up_place")
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if bp and not gp:
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result["grew_up_place"] = bp
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elif gp and not bp:
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result["birth_place"] = gp
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return result
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except Exception as e:
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logger.error("提取资料信息失败: {}", e)
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return {}
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async def generate_profile_followup(
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self,
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conversation_id: str,
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user_message: str,
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missing_fields: List[str],
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filled_fields: Dict[str, str],
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nickname: str = "",
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interview_stage_hint: str = "",
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) -> List[str]:
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"""生成资料追问回复，不持久化（由 Orchestrator 负责）"""
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try:
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prompt = get_profile_followup_prompt(
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missing_fields,
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filled_fields,
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nickname,
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interview_stage_hint=interview_stage_hint,
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)
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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messages: List[Any] = [SystemMessage(content=prompt)]
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messages.extend(hw.window)
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messages.append(HumanMessage(content=user_message))
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log_agent_payload(
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logger,
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"ProfileAgent.followup.prompt",
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format_history_string(
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messages,
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omit_system_body=settings.agent_log_omit_system_message_body,
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),
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)
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prompt_chars = _message_contents_char_count(messages)
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logger.info(
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"event=chat_prompt_built agent=ProfileAgent.generate_profile_followup "
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"prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
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prompt_chars,
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hw.turn_total,
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len(hw.window) // 2,
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)
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response_text = await self._invoke_chat(
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messages,
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max_tokens=settings.chat_profile_followup_max_tokens,
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conversation_id=conversation_id,
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agent_name="ProfileAgent.generate_profile_followup",
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)
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segments = await self._segments_from_response(
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response_text,
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max_segments=3,
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max_chars_per_segment=settings.chat_interview_max_chars_per_segment,
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fallback="谢谢分享！能再告诉我一些吗？",
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)
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log_agent_summary(
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logger,
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"ProfileAgent.followup segments={} conversation_id={}",
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len(segments),
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conversation_id,
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)
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return segments
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except Exception as e:
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logger.error("生成资料跟进回复失败: {}", e)
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return ["谢谢分享！能再告诉我一些吗？"]
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async def generate_profile_greeting(
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self,
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conversation_id: str,
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missing_fields: List[str],
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nickname: str = "",
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) -> List[str]:
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"""生成资料收集开场白，不持久化（由 Orchestrator 负责）"""
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try:
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prompt = get_profile_greeting_prompt(missing_fields, nickname)
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hw = await get_history_with_window(
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conversation_id,
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max_pairs=settings.chat_history_max_pairs,
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max_chars=settings.chat_history_max_chars,
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)
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messages: List[Any] = [SystemMessage(content=prompt)]
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messages.extend(hw.window)
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if hw.window:
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messages.append(
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HumanMessage(content="（请根据上文自然接话，继续资料收集开场。）")
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)
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else:
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messages.append(HumanMessage(content="（请说出资料收集开场白。）"))
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log_agent_payload(
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logger,
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"ProfileAgent.greeting.prompt",
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format_history_string(
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messages,
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omit_system_body=settings.agent_log_omit_system_message_body,
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),
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)
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prompt_chars = _message_contents_char_count(messages)
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logger.info(
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"event=chat_prompt_built agent=ProfileAgent.generate_profile_greeting "
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"prompt_chars={} history_pairs_total={} history_pairs_windowed={}",
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prompt_chars,
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hw.turn_total,
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len(hw.window) // 2,
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)
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response_text = await self._invoke_chat(
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messages,
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max_tokens=settings.chat_profile_followup_max_tokens,
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conversation_id=conversation_id,
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agent_name="ProfileAgent.generate_profile_greeting",
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)
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segments = await self._segments_from_response(
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response_text,
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max_segments=2,
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max_chars_per_segment=settings.chat_interview_max_chars_per_segment,
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fallback="你好！在开始之前，能告诉我你是哪一年出生的吗？",
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)
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log_agent_summary(
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logger,
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"ProfileAgent.greeting segments={} conversation_id={}",
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len(segments),
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conversation_id,
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)
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return segments
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except Exception as e:
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logger.error("生成资料收集开场白失败: {}", e)
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return [
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"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？"
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]
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