e4bf0710c7
数据库 - 新增迁移 0003：timeline_events.memory_source_id 外键 → memory_sources，便于按 ingest 源做时间线幂等 后端 - 记忆 - 新增 ingest 后 LLM 富化（摘要/事实/时间线），可配置开关与最大字符数 - 新增证据包组装：合并 chunk、摘要、事实、时间线、故事等检索结果；支持空 query 时是否仍带 rolling 等开关 - repo/retriever/service/router/schemas/summarizer/timeline/extractor 等扩展；文档 memory-retrieval.md 更新 后端 - 对话 WS - 增加 PING/PONG；分段 ASR 日志与空音频处理；转写失败与「无助手回复」错误提示更明确 - 助手多段回复持久化使用统一分隔符，与分段逻辑一致 后端 - Agent - reply_limits：按 [SPLIT] 与段落拆段，并保证非空 fallback，供 WS 与 TTS 多段下发 后端 - 回忆录任务 - transcript ingest 记录 source_id；任务成功结?
233 lines
9.2 KiB
Python
233 lines
9.2 KiB
Python
"""
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InterviewAgent：正式访谈 Specialist
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负责状态感知回复、开场白，不负责 Redis 持久化（由 Orchestrator 统一处理）
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"""
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from typing import Any, List, Optional
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from app.agents.chat.agent_turn import AgentChatTurn
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from app.agents.chat.stage_detection import keyword_fallback_primary_stage
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from app.core.dependencies import get_llm_provider
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from app.core.logging import get_logger
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from app.agents.chat.helpers import format_history_string, get_history_messages
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from app.agents.chat.prompts_conversation import (
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SLOT_NAME_MAP,
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get_guided_conversation_prompt,
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get_opening_prompt,
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)
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from app.agents.state_schema import MemoirStateSchema
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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.core.agent_logging import (
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agent_span,
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log_agent_payload,
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log_agent_summary,
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)
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from app.core.config import settings
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logger = get_logger(__name__)
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# LLM 不可用或调用失败时对用户展示（不暴露异常细节、不触发 TTS）
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_FALLBACK_REPLY = "刚才网络不太稳，没接上。你可以再说一遍，或稍后再试。"
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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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class InterviewAgent:
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"""正式访谈 Specialist Agent"""
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def __init__(self):
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self.llm = _get_langchain_llm()
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def _detect_user_stage(self, user_message: str) -> str:
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"""关键词回退：与 stage_detection 一致（多阶段打分）。"""
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return keyword_fallback_primary_stage(user_message)
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def _estimate_same_topic_turns(
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self, history_messages: List[Any], current_filled_slots: dict
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) -> int:
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"""估算同一话题的连续轮数"""
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if len(history_messages) < 4:
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return len(history_messages) // 2
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recent_messages = history_messages[-6:]
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keywords_per_turn = []
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for i in range(0, len(recent_messages), 2):
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if i + 1 < len(recent_messages):
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human_msg = (
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recent_messages[i].content
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if hasattr(recent_messages[i], "content")
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else str(recent_messages[i])
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)
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ai_msg = (
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recent_messages[i + 1].content
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if hasattr(recent_messages[i + 1], "content")
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else str(recent_messages[i + 1])
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)
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keywords_per_turn.append((human_msg + ai_msg)[:100])
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if len(keywords_per_turn) >= 3:
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return 3
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return len(keywords_per_turn)
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async def generate_response_with_state(
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self,
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conversation_id: str,
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user_message: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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detected_user_stage: Optional[str] = None,
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) -> AgentChatTurn:
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"""生成状态感知的访谈回复，不持久化（由 Orchestrator 负责）"""
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if not self.llm:
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logger.warning("InterviewAgent: LLM 未配置，返回兜底文案")
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return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)
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try:
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empty_slots = memoir_state.empty_slots_for_current_stage()
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filled_slots = {
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key: value.snippet
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for key, value in memoir_state.slots.get(
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memoir_state.current_stage, {}
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).items()
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if value.snippet
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}
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if detected_user_stage is not None:
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du = detected_user_stage
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else:
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du = self._detect_user_stage(user_message)
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history_messages = await get_history_messages(conversation_id)
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conversation_turn = len(history_messages) // 2
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same_topic_turns = self._estimate_same_topic_turns(
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history_messages, filled_slots
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)
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all_stages_coverage = memoir_state.all_stages_coverage()
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system_prompt = get_guided_conversation_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots=empty_slots,
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filled_slots=filled_slots,
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user_message=user_message,
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conversation_turn=conversation_turn,
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same_topic_turns=same_topic_turns,
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all_stages_coverage=all_stages_coverage,
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detected_user_stage=du,
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user_profile_context=user_profile_context,
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)
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history_string = format_history_string(history_messages)
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full_prompt = f"{system_prompt}\n\n{history_string}\n\nHuman: {user_message}\n\nAssistant:"
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log_agent_payload(
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logger, "InterviewAgent.generate_response.prompt", full_prompt
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)
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chat_llm = self.llm.bind(max_tokens=settings.chat_interview_max_tokens)
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with agent_span(
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logger,
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"InterviewAgent.generate_response.llm",
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conversation_id=conversation_id,
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stage=memoir_state.current_stage,
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):
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response = await chat_llm.ainvoke(full_prompt)
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response_text = (
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response.content if hasattr(response, "content") else str(response)
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)
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log_agent_payload(
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logger, "InterviewAgent.generate_response.raw_response", response_text
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)
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raw_list = segments_from_llm_response(
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response_text,
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max_segments=settings.chat_interview_max_segments,
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)
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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=settings.chat_interview_max_segments,
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max_chars_per_segment=settings.chat_interview_max_chars_per_segment,
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)
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if not out:
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out = [
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response_text.strip()[
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: settings.chat_interview_max_chars_per_segment
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]
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]
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out = nonempty_segments_or_fallback(out, fallback=_FALLBACK_REPLY)
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log_agent_summary(
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logger,
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"InterviewAgent.generate_response segments={} conversation_id={}",
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len(out),
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conversation_id,
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)
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return AgentChatTurn(messages=out, skip_tts=False)
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except Exception as e:
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logger.error("生成回应失败: {}", e, exc_info=True)
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return AgentChatTurn(messages=[_FALLBACK_REPLY], skip_tts=True)
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async def generate_opening_message(
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self,
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conversation_id: str,
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memoir_state: MemoirStateSchema,
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user_profile_context: str = "",
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) -> List[str]:
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"""生成空对话开场白，不持久化（由 Orchestrator 负责）"""
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if not self.llm:
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return ["你好呀～ 又见面了，今天有没有哪段回忆或近况想聊聊？"]
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try:
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empty_slots = memoir_state.empty_slots_for_current_stage()
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empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
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prompt = get_opening_prompt(
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current_stage=memoir_state.current_stage,
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empty_slots_readable=empty_slots_readable,
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user_profile_context=user_profile_context,
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)
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full_prompt = f"{prompt}\n\nAssistant:"
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log_agent_payload(logger, "InterviewAgent.opening.prompt", full_prompt)
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opening_llm = self.llm.bind(max_tokens=settings.chat_opening_max_tokens)
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with agent_span(
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logger,
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"InterviewAgent.opening.llm",
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conversation_id=conversation_id,
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):
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response = await opening_llm.ainvoke(full_prompt)
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response_text = (
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response.content if hasattr(response, "content") else str(response)
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)
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log_agent_payload(
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logger, "InterviewAgent.opening.raw_response", response_text
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)
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raw_list = segments_from_llm_response(response_text, max_segments=2)
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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=2,
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max_chars_per_segment=settings.chat_interview_max_chars_per_segment,
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)
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log_agent_summary(
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logger,
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"InterviewAgent.opening segments={} conversation_id={}",
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len(out),
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conversation_id,
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)
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segments = (
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out
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if out
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else [
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response_text.strip()[
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: settings.chat_interview_max_chars_per_segment
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]
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]
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)
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return nonempty_segments_or_fallback(
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segments,
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fallback="你好呀～ 又见面了，最近有没有什么事想跟我说说？",
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)
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except Exception as e:
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logger.error("生成开场白失败: {}", e, exc_info=True)
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return ["你好呀～ 又见面了，最近有没有什么事想跟我说说？"]
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