JOJO
21f3acdf16
GET /api/conversations/<id>/bootstrap 一次性返回进入对话所需的 meta/messages/running/task_replay（needs_rebuild/replay_from 判定移植自 前端 compression.ts），只读语义不写对话文件，规避 safe_nav 双轨与旧内存 回写面。设计文档：docs/conversation_load_unification_plan.md
238 lines
9.8 KiB
Python
238 lines
9.8 KiB
Python
"""对话进入聚合接口（方案 B：统一加载协议）。
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GET /api/conversations/<id>/bootstrap 一次性返回进入对话所需的全部数据：
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元数据、文件态消息、运行状态聚合与任务重放决策（needs_rebuild/replay_from）。
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只读语义：不写对话文件、不调用 terminal.load_conversation、不切换工作区级 terminal
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的当前上下文，天然规避 safe_nav 双轨问题与旧内存回写面。为聚合后台运行状态，
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会按需获取/创建对话级 terminal（与既有 running-status 端点行为一致，属内存态操作）。
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设计文档：docs/conversation_load_unification_plan.md
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from flask import Blueprint, jsonify, request, session
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from server.auth_helpers import api_login_required, get_current_username
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from server.context import get_user_resources
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from server.tasks import task_manager
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from server.tasks.helpers import _task_public_payload
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from server.utils_common import debug_log
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conversation_bootstrap_bp = Blueprint("conversation_bootstrap", __name__)
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_ACTIVE_TASK_STATUSES = {"pending", "running", "cancel_requested"}
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# 与前端 compression.ts needsRebuild 判据中的事件类型集合对齐
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_ASSISTANT_RESPONSE_EVENTS = {
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"ai_message_start", "thinking_start", "thinking_chunk", "thinking_end",
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"text_start", "text_chunk", "text_end",
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"tool_preparing", "tool_start", "tool_update", "tool_complete",
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}
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_ASSISTANT_CONTENT_EVENTS = {
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"thinking_chunk", "text_chunk",
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"tool_preparing", "tool_start", "tool_update", "tool_complete",
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}
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def _normalize_conv_id(conversation_id: str) -> str:
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conv = (conversation_id or "").strip()
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if not conv:
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return conv
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return conv if conv.startswith("conv_") else f"conv_{conv}"
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def _find_active_main_task(username: str, normalized_id: str):
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"""取该对话最新的活动主任务（对齐 running-status 语义，rec 侧做前缀归一）。"""
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main_rec = None
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for rec in task_manager.list_tasks(username):
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rec_cid = _normalize_conv_id(getattr(rec, "conversation_id", "") or "")
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if rec_cid != normalized_id or rec.status not in _ACTIVE_TASK_STATUSES:
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continue
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if main_rec is None or rec.created_at > main_rec.created_at:
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main_rec = rec
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return main_rec
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def _analyze_task_events(events: List[Dict[str, Any]]) -> Dict[str, bool]:
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"""分析任务事件流的流式状态（移植自前端 compression.ts needsRebuild 判据）。"""
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in_thinking = False
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in_text = False
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has_text_chunk_event = False
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has_assistant_response_event = False
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has_assistant_content_event = False
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for event in events:
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event_type = str((event or {}).get("type") or "")
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if event_type in _ASSISTANT_RESPONSE_EVENTS:
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has_assistant_response_event = True
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if event_type in _ASSISTANT_CONTENT_EVENTS:
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has_assistant_content_event = True
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if event_type == "thinking_start":
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in_thinking = True
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elif event_type == "thinking_end":
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in_thinking = False
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if event_type == "text_start":
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in_text = True
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elif event_type == "text_end":
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in_text = False
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if event_type == "text_chunk":
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has_text_chunk_event = True
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return {
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"in_thinking": in_thinking,
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"in_text": in_text,
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"has_text_chunk_event": has_text_chunk_event,
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"has_assistant_response_event": has_assistant_response_event,
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"has_assistant_content_event": has_assistant_content_event,
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"force_rebuild_for_streaming_text": in_text or has_text_chunk_event or in_thinking,
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}
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def _is_empty_assistant_message(message: Optional[Dict[str, Any]]) -> bool:
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"""文件态的「空 assistant」：无正文、无思考内容、无工具调用。
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对应前端判据「末条 assistant 无 actions」——actions 是渲染层结构，
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文件态用内容字段等价判断。
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"""
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if not isinstance(message, dict):
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return True
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if str(message.get("content") or "").strip():
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return False
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if str(message.get("reasoning_content") or "").strip():
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return False
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if message.get("tool_calls"):
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return False
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return True
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def _decide_task_replay(messages: List[Dict[str, Any]], events: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""移植前端 needsRebuild 判据：任一成立即 needs_rebuild=True。
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1. 文件态末条不是 assistant（任务产生的 assistant 内容未落盘）；
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2. 文件态末条是空 assistant；
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3. 事件流处于流式中段（inText / hasTextChunkEvent / inThinking）；
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4. 文件态末条 assistant 带 tool_calls（工具结果消息尚未落盘，视为进行中）。
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"""
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last_message = messages[-1] if messages else None
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is_assistant = isinstance(last_message, dict) and last_message.get("role") == "assistant"
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event_state = _analyze_task_events(events)
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empty_assistant = _is_empty_assistant_message(last_message) if is_assistant else False
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has_pending_tool_calls = bool(
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is_assistant and isinstance(last_message, dict) and last_message.get("tool_calls")
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)
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needs_rebuild = (
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not is_assistant
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or empty_assistant
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or event_state["force_rebuild_for_streaming_text"]
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or has_pending_tool_calls
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)
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event_count = len(events)
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return {
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"event_count": event_count,
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"needs_rebuild": needs_rebuild,
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"replay_from": 0 if needs_rebuild else event_count,
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"decision_inputs": {
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"is_assistant_last": is_assistant,
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"empty_assistant_last": empty_assistant,
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"has_pending_tool_calls": has_pending_tool_calls,
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**event_state,
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},
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}
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@conversation_bootstrap_bp.route("/api/conversations/<conversation_id>/bootstrap", methods=["GET"])
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@api_login_required
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def bootstrap_conversation(conversation_id: str):
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"""进入对话聚合接口：meta + messages + running + task_replay（纯只读）。"""
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username = get_current_username()
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normalized_id = _normalize_conv_id(conversation_id)
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if not normalized_id:
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return jsonify({"success": False, "error": "缺少 conversation_id"}), 400
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workspace_id = (request.args.get("workspace_id") or "").strip() or None
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# 工作区级资源：只取 context_manager 读文件，绝不调用 load_conversation
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try:
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ws_terminal, _workspace = get_user_resources(username, workspace_id=workspace_id)
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except RuntimeError as exc:
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return jsonify({"success": False, "error": str(exc)}), 503
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ctx_manager = getattr(ws_terminal, "context_manager", None)
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if ctx_manager is None:
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return jsonify({"success": False, "error": "终端上下文不可用"}), 503
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cm = ctx_manager._get_conversation_manager_for_id(normalized_id)
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conversation_data = cm.load_conversation(normalized_id) if cm else None
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if not conversation_data:
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return jsonify({"success": False, "error": f"对话 {normalized_id} 不存在"}), 404
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raw_meta = conversation_data.get("metadata", {}) or {}
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messages = conversation_data.get("messages", []) or []
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run_mode = raw_meta.get("run_mode") or ("thinking" if raw_meta.get("thinking_mode") else "fast")
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# 运行状态聚合（对齐 tasks/api.py running-status：主 task + 三类后台）
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main_rec = _find_active_main_task(username, normalized_id)
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effective_workspace_id = (
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workspace_id
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or (getattr(main_rec, "workspace_id", None) if main_rec else None)
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or (session.get("workspace_id") or None)
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)
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bg_status = {
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"has_running_sub_agents": False,
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"has_running_background_commands": False,
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"has_running_multi_agent": False,
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}
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try:
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conv_terminal, _ = get_user_resources(
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username, workspace_id=effective_workspace_id, conversation_id=normalized_id
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)
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except Exception as exc:
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debug_log(f"[Bootstrap] 获取对话级终端失败: {exc}")
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conv_terminal = None
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if conv_terminal is not None:
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bg_status = task_manager.get_conversation_running_status(conv_terminal, normalized_id)
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is_main_running = main_rec is not None
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running = {
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"is_main_running": is_main_running,
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"main_task_id": getattr(main_rec, "task_id", None) if main_rec else None,
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"main_task_type": getattr(main_rec, "task_type", "chat") if main_rec else None,
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**bg_status,
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"is_truly_active": is_main_running or any(bg_status.values()),
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}
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data: Dict[str, Any] = {
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"conversation_id": normalized_id,
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"meta": {
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"title": conversation_data.get("title", "未知对话"),
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"run_mode": run_mode,
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"thinking_mode": bool(raw_meta.get("thinking_mode", run_mode != "fast")),
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"model_key": raw_meta.get("model_key"),
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"multi_agent_mode": bool(raw_meta.get("multi_agent_mode", False)),
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"permission_mode": raw_meta.get("permission_mode") or "",
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"execution_mode": raw_meta.get("execution_mode") or "",
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"network_permission": raw_meta.get("network_permission") or "",
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"messages_count": len(messages),
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},
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"messages": messages,
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"running": running,
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}
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if is_main_running:
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events = list(getattr(main_rec, "events", []) or [])
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replay = _decide_task_replay(messages, events)
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replay["task_id"] = main_rec.task_id
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# 任务摘要与全量事件：前端恢复时免去 GET /api/tasks 与 GET /api/tasks/{id} 两次请求
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replay["task"] = _task_public_payload(main_rec)
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replay["events"] = events
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data["task_replay"] = replay
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debug_log(
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f"[Bootstrap] conv={normalized_id} task={main_rec.task_id} "
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f"events={replay['event_count']} needs_rebuild={replay['needs_rebuild']} "
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f"inputs={replay['decision_inputs']}"
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)
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return jsonify({"success": True, "data": data})
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