WIN-JHFT4D3SIVT\caoxiaozhu
36c93a764f
Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-openagent) Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
from __future__ import annotations
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from typing import Any
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from app.agents.schemas.learning import SessionRetrospective
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def _classify_task_type(query_text: str) -> str:
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normalized = (query_text or "").lower()
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if any(token in normalized for token in ("总结", "分析", "对比", "report", "analyze")):
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return "analysis"
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if any(token in normalized for token in ("安排", "提醒", "日程", "todo", "task")):
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return "planning_or_execution"
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if any(token in normalized for token in ("文档", "资料", "年报", "search", "查")):
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return "retrieval"
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return "general"
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def build_session_retrospective(
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*,
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request_id: str,
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session_id: str,
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user_query: str,
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state: dict[str, Any] | None,
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runtime_context: dict[str, Any] | None = None,
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) -> SessionRetrospective:
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state = state or {}
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if hasattr(runtime_context, "model_dump"):
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runtime_context = runtime_context.model_dump(mode="json")
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runtime_context = runtime_context or {}
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skill_shortlist = state.get("skill_shortlist") or []
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used_skill_names = [
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item.get("skill_name")
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for item in skill_shortlist
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if isinstance(item, dict) and item.get("skill_name")
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]
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task_refs = []
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for task in (state.get("completed_tasks") or [])[:4]:
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if isinstance(task, dict):
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task_refs.append(
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{
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"task_id": task.get("task_id"),
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"title": task.get("title"),
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"status": task.get("status"),
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}
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)
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event_refs = []
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for event in (state.get("event_trace") or [])[:8]:
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if isinstance(event, dict):
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event_refs.append(
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{
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"event_type": event.get("event_type"),
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"task_id": event.get("task_id"),
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"agent_id": event.get("agent_id"),
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}
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)
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verification_evidence = []
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for evidence in (state.get("verification_evidence") or [])[:6]:
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if isinstance(evidence, dict):
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verification_evidence.append(evidence)
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verification_status = state.get("verification_status")
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execution_mode = state.get("execution_mode")
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primary_agent = state.get("current_agent") or "master"
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retrospective_shortlist = state.get("retrospective_shortlist") or []
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summary_parts = [
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f"本轮请求按 {execution_mode or 'unknown'} 模式处理",
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f"主要负责 agent 为 {primary_agent}",
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]
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if verification_status:
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summary_parts.append(f"验证结果为 {verification_status}")
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if used_skill_names:
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summary_parts.append(f"命中技能候选 {', '.join(used_skill_names[:3])}")
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if retrospective_shortlist:
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summary_parts.append(f"参考了 {len(retrospective_shortlist)} 条历史复盘")
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final_response = state.get("final_response")
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outcome = "completed" if final_response else "failed"
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if not final_response and verification_status == "passed":
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outcome = "completed"
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if final_response and verification_status == "skipped":
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outcome = "partial"
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return SessionRetrospective(
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retrospective_id=request_id,
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user_id=str(runtime_context.get("user_id") or ""),
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conversation_id=session_id,
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response_message_id=request_id,
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query_text=user_query,
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final_response=final_response,
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summary="；".join(summary_parts) + "。",
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task_type=_classify_task_type(user_query),
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execution_mode=execution_mode,
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primary_agent=primary_agent,
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verification_status=verification_status,
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verification_summary=state.get("verification_summary"),
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used_skill_names=used_skill_names,
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evidence_refs=verification_evidence,
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task_refs=task_refs,
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event_refs=event_refs,
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context_snapshot={
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"runtime_request_context": runtime_context,
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"recommended_runtime_mode": runtime_context.get("recommended_runtime_mode"),
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"parallel_worthiness": state.get("parallel_worthiness"),
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"retrospective_shortlist_count": len(retrospective_shortlist),
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"scheduled_subtask_count": len(state.get("scheduled_subtasks") or []),
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"merge_report": dict(state.get("merge_report") or {}),
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"verification_report": dict(state.get("verification_report") or {}),
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},
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outcome=outcome,
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)
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