2fded6fbd9
- Drop interview_reply_length and utterance_substance; always run stage LLM and memory retrieval when enabled; trim Settings fields and .env.example. - Replace guided/opening prompts with compact fact blocks plus unified behavior guidance; slim background_voice and persona to tone hints. - InterviewAgent uses fixed chat_interview max_tokens/chars/segments. Also includes stacked work: profile followup/extract path, evaluation rubric and judge schema updates, transcript SPLIT handling in execution service, user export markdown split tests, and golden case fixture.
296 lines
11 KiB
Python
296 lines
11 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.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.dependencies import get_llm_provider
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from app.core.llm_call import allm_json_call
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from app.core.logging import get_logger
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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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logger = get_logger(__name__)
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def _get_langchain_llm():
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try:
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provider = get_llm_provider()
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return getattr(provider, "langchain_llm", None)
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except Exception:
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return None
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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__(self):
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self.llm = _get_langchain_llm()
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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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chat_llm = self.llm.bind(max_tokens=max_tokens)
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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 = await chat_llm.ainvoke(messages)
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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 (
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response.content if hasattr(response, "content") else str(response)
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) 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 self.llm or 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 allm_json_call(
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self.llm,
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prompt,
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ProfileExtractionOutput,
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max_tokens=settings.chat_profile_extract_max_tokens,
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agent="ProfileAgent.extract_profile_from_message",
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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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if not self.llm:
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return ["谢谢！还能告诉我更多吗？"]
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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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if not self.llm:
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return ["你好！在开始之前，能告诉我你是哪一年出生的吗？"]
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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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