JOJO
16900b0a20
- 通知：inject_runtime_user_message 不再把子智能体任务 id 透传为事件顶层 task_id（sender setdefault 不覆盖，前端任务归属守卫丢弃事件；socket 通道 在轮询模式下不渲染，双通道断头导致只能刷新后从历史看到）。后台指令因透传 的是 command_id 而不受影响 - 通知：修复 _persist_and_echo_preceding_notice 回显写在 except 分支内， 批量通知前 N-1 条从不实时回显 - 压缩：移除「压缩中切换/新建对话需确认并取消压缩」拦截（多对话独立运行后 无存在意义） - 压缩：新增 compressionConversationId，输入/发送/停止锁按对话隔离，/new 新建页不再被其他对话的压缩状态锁定 - 压缩：metadata 记录 compression_pid，status/compression_status 读取时按 pid 懒清理进程重启后的残留 in_progress 标记；run_deep_compression 异常时 自动清理标记
694 lines
27 KiB
Python
694 lines
27 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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import os
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from datetime import datetime
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from functools import wraps
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Tuple
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def _load_summary_prompt(web_terminal) -> str:
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"""从 prompts/deep_compression_summary.txt 加载压缩总结提示词。"""
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try:
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return web_terminal.load_prompt("deep_compression_summary").strip()
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except Exception:
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return (
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"由于当前对话过长，系统正在自动压缩。请你基于已有上下文输出一份可继续执行的工作总结。"
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)
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def _build_summary_prompt(template: str, *, compression_index: int, latest_user_input: str) -> str:
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"""把压缩轮次、最新用户输入、增量条目占位符替换进总结提示词模板。
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- {compression_index}: 当前压缩轮次（总是替换）
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- {latest_user_input}: 最新一条真实用户输入原文
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- {incremental_item}: 第2次压缩起插入增量总结条目，首次压缩替换为空
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"""
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prompt = template or ""
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latest = (latest_user_input or "").strip() or "（无）"
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if compression_index > 1:
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incremental_item = (
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"9) 自上次压缩之后到现在新完成的工作：单独列出这段时间内新完成的工作与关键结果，"
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"此前已被历次压缩总结覆盖的内容不必重复。\n"
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)
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else:
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incremental_item = ""
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prompt = prompt.replace("{compression_index}", str(compression_index))
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prompt = prompt.replace("{latest_user_input}", latest)
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prompt = prompt.replace("{incremental_item}", incremental_item)
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return prompt
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def _emit(sender, event_type: str, payload: Dict[str, Any]):
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if not callable(sender):
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return
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try:
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sender(event_type, payload)
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except Exception:
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pass
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def _clear_compression_state_on_error(func):
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"""压缩过程异常退出时清理持久化的 compression_in_progress 标记。
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压缩中途异常（或调用侧取消）若不清标记，对话 metadata 会永久残留
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in_progress=True，前端据此锁输入栏/拦切换对话（只能删对话解决）。
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"""
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@wraps(func)
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async def wrapper(*args, **kwargs):
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try:
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return await func(*args, **kwargs)
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except Exception:
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try:
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web_terminal = kwargs.get("web_terminal")
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conversation_id = kwargs.get("conversation_id")
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cm = getattr(web_terminal, "context_manager", None)
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if cm is not None and conversation_id:
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if getattr(cm, "current_conversation_id", None) == conversation_id:
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cm.set_compression_state(in_progress=False)
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else:
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target_manager = (
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cm._get_conversation_manager_for_id(conversation_id)
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if hasattr(cm, "_get_conversation_manager_for_id")
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else cm.conversation_manager
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)
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target_manager.update_conversation_metadata(conversation_id, {
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"compression_in_progress": False,
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"compression_mode": None,
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"compression_stage": None,
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"compression_job_id": None,
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"compression_resume_payload": None,
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"compression_pid": None,
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})
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except Exception:
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pass
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raise
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return wrapper
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def heal_stale_compression_flag(
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target_manager,
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conversation_id: str,
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metadata: Dict[str, Any],
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context_manager=None,
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) -> bool:
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"""读取压缩进行态并懒清理残留标记，返回有效的 in_progress。
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compression_in_progress 持久化在对话 metadata 中。进程在压缩中途被杀时标记
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残留为 True（压缩随进程死亡，不可能仍在进行），会导致前端误以为仍在压缩。
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判定依据：标记写入时记录了发起进程的 pid（compression_pid，见
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compression_mixin.set_compression_state）；与当前进程 pid 不一致（或缺失，
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即旧版本写入的标记）即判定为残留并清除（磁盘 + 内存双清）。
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"""
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if not bool((metadata or {}).get("compression_in_progress", False)):
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return False
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flag_pid = (metadata or {}).get("compression_pid")
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if flag_pid is not None and flag_pid == os.getpid():
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return True
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clear_updates = {
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"compression_in_progress": False,
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"compression_mode": None,
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"compression_stage": None,
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"compression_job_id": None,
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"compression_resume_payload": None,
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"compression_pid": None,
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}
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try:
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target_manager.update_conversation_metadata(conversation_id, clear_updates)
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except Exception:
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pass
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# 同步内存中的 metadata：该对话若正被加载，工具循环的
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# is_compression_in_progress() 读的是内存副本，不清理会一直误判压缩中。
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try:
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if (
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context_manager is not None
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and getattr(context_manager, "current_conversation_id", None) == conversation_id
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and isinstance(getattr(context_manager, "conversation_metadata", None), dict)
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):
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context_manager.conversation_metadata.update(clear_updates)
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except Exception:
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pass
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return False
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def _normalize_deep_compression_records(metadata: Dict[str, Any]) -> List[Dict[str, Any]]:
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records = metadata.get("deep_compression_records")
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if not isinstance(records, list):
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return []
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normalized: List[Dict[str, Any]] = []
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for item in records:
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if not isinstance(item, dict):
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continue
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try:
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count = int(item.get("count", 0) or 0)
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except Exception:
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count = 0
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path = str(item.get("compact_file") or "").strip()
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if count <= 0 or not path:
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continue
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try:
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user_inputs_before = int(item.get("user_inputs_before", 0) or 0)
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except Exception:
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user_inputs_before = 0
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normalized.append({
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"count": count,
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"compact_file": path,
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"created_at": item.get("created_at"),
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"source_conversation_id": item.get("source_conversation_id"),
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"compressed_conversation_id": item.get("compressed_conversation_id"),
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"user_inputs_before": user_inputs_before,
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"summary": str(item.get("summary") or ""),
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})
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normalized.sort(key=lambda x: (int(x.get("count") or 0), str(x.get("created_at") or "")))
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deduped: List[Dict[str, Any]] = []
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seen = set()
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for rec in normalized:
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key = (int(rec.get("count") or 0), str(rec.get("compact_file") or ""))
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if key in seen:
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continue
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seen.add(key)
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deduped.append(rec)
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return deduped
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def _collect_runtime_state_lines(web_terminal, conversation_id: str) -> List[str]:
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"""收集压缩后模型续接所需的运行时状态。
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- 子智能体编号：仅传统模式列出（该模式下编号由模型指定且重复会创建失败）；
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多智能体模式不列编号（模型可通过 list_active_sub_agents 工具查询活跃实例）。
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- 持久终端：只列数量与名称。
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"""
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lines: List[str] = []
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is_multi_agent = bool(getattr(web_terminal, "multi_agent_mode", False))
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if not is_multi_agent:
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used: List[int] = []
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manager = getattr(web_terminal, "sub_agent_manager", None)
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if manager is not None:
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try:
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agents_map = getattr(manager, "conversation_agents", None) or {}
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raw_used = agents_map.get(conversation_id) or []
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used = sorted(int(x) for x in raw_used)
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except Exception:
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used = []
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if used:
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next_id = max(used) + 1
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used_text = "、".join(str(x) for x in used)
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lines.append(
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f"- 子智能体编号：本对话已使用编号 {used_text}，下一个可用编号为 {next_id}。"
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"新建子智能体时请使用该编号，重复使用已用编号会创建失败。"
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)
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else:
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lines.append("- 子智能体编号：本对话尚未创建过子智能体，下一个可用编号为 1。")
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terminal_names: List[str] = []
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terminal_manager = getattr(web_terminal, "terminal_manager", None)
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if terminal_manager is not None:
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try:
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terminals = getattr(terminal_manager, "terminals", None) or {}
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terminal_names = [str(name) for name in terminals.keys()]
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except Exception:
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terminal_names = []
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if terminal_names:
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names_text = "、".join(terminal_names)
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lines.append(f"- 持久终端：当前共有 {len(terminal_names)} 个终端会话：{names_text}。")
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else:
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lines.append("- 持久终端：当前没有终端会话。")
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return lines
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def _build_guide_message(*, compression_index: int, compact_file: str) -> str:
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"""生成文件模式的引导语：仅提示压缩文件位置，由模型自行阅读。"""
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return f"当前对话已经被第{compression_index}次压缩。请阅读 {compact_file} 并继续工作。"
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def _read_compact_file_content(project_path: Path, relative_path: str) -> str:
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"""读取 compact 文件全文，读取失败时返回空串。"""
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rel = str(relative_path or "").strip()
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if not rel:
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return ""
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try:
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target = (Path(project_path) / rel).resolve()
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return target.read_text(encoding="utf-8").strip()
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except Exception:
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return ""
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def _read_summary_from_record(record: Dict[str, Any]) -> str:
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"""从 deep_compression_records 条目中读取保存的总结内容。"""
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summary = record.get("summary")
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return str(summary).strip() if isinstance(summary, str) else ""
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def _build_user_inputs_section(
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user_inputs: List[str],
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previous_records: List[Dict[str, Any]],
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current_count: int,
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) -> str:
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"""构建'用户的所有输入'区块，按历史压缩轮次插入分段标记。"""
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if not user_inputs:
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return "（无）"
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breakpoints: Dict[int, int] = {}
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for rec in previous_records:
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count = int(rec.get("count") or 0)
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before = int(rec.get("user_inputs_before") or 0)
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if count > 0 and before > 0:
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breakpoints[before] = count
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sorted_breaks = sorted(breakpoints.items(), key=lambda x: x[0])
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break_iter = iter(sorted_breaks)
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next_break = next(break_iter, None)
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lines: List[str] = []
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last_break_index = 0
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for idx, text in enumerate(user_inputs, start=1):
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lines.append(f"{idx}. {text}")
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if next_break and idx == next_break[0]:
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lines.append(f"<第{next_break[1]}次压缩>")
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last_break_index = idx
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next_break = next(break_iter, None)
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# 当前压缩之后还有新增输入时，追加当前压缩标记
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if len(user_inputs) > last_break_index:
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lines.append(f"<当前触发的第{current_count}次压缩>")
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return "\n".join(lines)
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def _build_summaries_section(
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previous_records: List[Dict[str, Any]],
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current_summary: str,
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current_count: int,
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) -> List[str]:
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"""构建'历次压缩总结'区块，按顺序列出每次压缩的总结。"""
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lines: List[str] = []
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for rec in previous_records:
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count = int(rec.get("count") or 0)
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if count <= 0:
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continue
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summary = _read_summary_from_record(rec)
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lines.append(f"### 第{count}次的总结")
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lines.append(summary or "（读取失败）")
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lines.append("")
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lines.append(f"### 第{current_count}次的总结")
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lines.append(current_summary or "（生成失败）")
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return lines
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def _build_inject_guide_message(
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*,
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compression_index: int,
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current_record: Dict[str, Any],
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previous_records: List[Dict[str, Any]],
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user_inputs: List[str],
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latest_user_input: str,
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runtime_state_lines: Optional[List[str]] = None,
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) -> str:
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"""生成直接注入模式的引导语：把历次压缩总结、用户输入按顺序拼入正文。"""
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lines: List[str] = [
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f"当前对话已被第{compression_index}次压缩。以下为按时间顺序汇总的用户输入、历次压缩总结以及最近一次输入，请据此继续工作。",
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"",
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"用户的所有输入",
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]
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lines.append(_build_user_inputs_section(user_inputs, previous_records, compression_index))
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lines.append("")
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lines.append("历次压缩总结")
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lines.append("")
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summary_lines = _build_summaries_section(
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previous_records,
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current_record.get("summary", ""),
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compression_index,
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)
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lines.extend(summary_lines)
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lines.append("")
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lines.append("用户的最近一次输入：")
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if (latest_user_input or "").strip():
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lines.append(latest_user_input.strip())
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else:
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lines.append("（无）")
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if runtime_state_lines:
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lines.append("")
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lines.append("当前运行时状态")
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lines.extend(runtime_state_lines)
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return "\n".join(lines).strip()
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def _extract_text_only(content: Any) -> str:
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts: List[str] = []
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for item in content:
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if not isinstance(item, dict):
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continue
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if item.get("type") == "text":
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txt = item.get("text")
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if isinstance(txt, str) and txt.strip():
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parts.append(txt)
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return "\n".join(parts).strip()
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return str(content)
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def _collect_user_texts(messages: List[Dict[str, Any]]) -> List[str]:
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result: List[str] = []
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for msg in messages:
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if msg.get("role") != "user":
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continue
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metadata = msg.get("metadata") if isinstance(msg.get("metadata"), dict) else {}
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if str(metadata.get("message_source") or "").strip().lower() != "user":
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continue
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text = _extract_text_only(msg.get("content"))
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if text.strip():
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result.append(text.strip())
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return result
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async def _generate_summary(web_terminal, prompt: str, retries: int = 5) -> Tuple[str, Optional[str]]:
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last_reason: Optional[str] = None
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context = web_terminal.build_context()
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messages = web_terminal.build_messages(context, prompt)
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# build_messages 不会自动附加 user_input，压缩总结必须显式注入
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if prompt and isinstance(prompt, str):
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messages = list(messages) + [{"role": "user", "content": prompt}]
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# 关键：总结请求必须与"用户正常发一条消息"完全无差别，才能 100% 命中前缀缓存。
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# 因此 tools 必须照常传入（缺失 tools 字段会改变请求前缀、破坏缓存）。
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# 模型即便返回 tool_calls 也会被忽略——我们只取 content；prompt 已要求其直接输出总结。
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tools = web_terminal.define_tools()
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for _ in range(max(1, retries)):
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try:
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response_text = ""
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async for chunk in web_terminal.api_client.chat(messages, tools=tools, stream=False):
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if not isinstance(chunk, dict):
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continue
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choices = chunk.get("choices") or []
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if choices:
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msg = choices[0].get("message") or {}
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content = msg.get("content")
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if isinstance(content, str):
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response_text = content
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if response_text.strip():
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return response_text.strip(), None
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last_reason = "模型返回空内容"
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except Exception as exc:
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last_reason = str(exc)
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await asyncio.sleep(0.2)
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return f"生成总结失败（{last_reason or '未知原因'}）", last_reason
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def _write_compact_file(
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project_path: Path,
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*,
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compression_index: int,
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summary_text: str,
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user_inputs: List[str],
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latest_user_input: str,
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previous_records: List[Dict[str, Any]],
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runtime_state_lines: Optional[List[str]] = None,
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) -> str:
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compact_dir = project_path / ".astrion" / "compact_result"
|
||
compact_dir.mkdir(parents=True, exist_ok=True)
|
||
filename = f"compact_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{compression_index:03d}.md"
|
||
file_path = compact_dir / filename
|
||
|
||
lines: List[str] = [
|
||
f"# 对话已被第{compression_index}次压缩",
|
||
"",
|
||
"## 用户的所有输入",
|
||
]
|
||
lines.append(_build_user_inputs_section(user_inputs, previous_records, compression_index))
|
||
lines.append("")
|
||
lines.append("## 历次压缩总结")
|
||
lines.append("")
|
||
summary_lines = _build_summaries_section(
|
||
previous_records,
|
||
summary_text,
|
||
compression_index,
|
||
)
|
||
lines.extend(summary_lines)
|
||
lines.append("")
|
||
lines.append("## 用户最新的一次输入")
|
||
if (latest_user_input or "").strip():
|
||
lines.append(latest_user_input.strip())
|
||
else:
|
||
lines.append("（无）")
|
||
if runtime_state_lines:
|
||
lines.append("")
|
||
lines.append("## 当前运行时状态")
|
||
lines.extend(runtime_state_lines)
|
||
file_path.write_text("\n".join(lines).strip() + "\n", encoding="utf-8")
|
||
return str(file_path.relative_to(project_path))
|
||
|
||
|
||
def _mark_history_compacted(history: List[Dict[str, Any]], *, round_index: int, now: str) -> int:
|
||
"""把当前对话历史中所有尚未标记的消息打上 deep_compacted 标记（in-place）。
|
||
|
||
深压缩按"整段前缀"处理：本次压缩时，历史里所有还没被标记的消息整体视为已压缩前缀。
|
||
这天然保证 assistant.tool_calls 与其 tool 响应被一并标记/排除，不会出现配对悬空。
|
||
原文保留在 metadata 中，持久化与重新加载时照常显示，仅在 build_messages 构建请求时被跳过。
|
||
返回本次新标记的消息条数。
|
||
"""
|
||
if not isinstance(history, list):
|
||
return 0
|
||
marked = 0
|
||
for msg in history:
|
||
if not isinstance(msg, dict):
|
||
continue
|
||
metadata = msg.get("metadata")
|
||
if not isinstance(metadata, dict):
|
||
metadata = {}
|
||
if metadata.get("deep_compacted"):
|
||
continue
|
||
metadata["deep_compacted"] = True
|
||
metadata["deep_compacted_round"] = round_index
|
||
metadata["deep_compacted_at"] = now
|
||
msg["metadata"] = metadata
|
||
marked += 1
|
||
return marked
|
||
|
||
|
||
@_clear_compression_state_on_error
|
||
async def run_deep_compression(
|
||
*,
|
||
web_terminal,
|
||
workspace,
|
||
conversation_id: str,
|
||
mode: str,
|
||
sender=None,
|
||
) -> Dict[str, Any]:
|
||
cm = web_terminal.context_manager
|
||
target_manager = (
|
||
cm._get_conversation_manager_for_id(conversation_id)
|
||
if hasattr(cm, "_get_conversation_manager_for_id")
|
||
else cm.conversation_manager
|
||
)
|
||
conv_data = target_manager.load_conversation(conversation_id)
|
||
if not conv_data:
|
||
return {"success": False, "error": f"对话不存在: {conversation_id}"}
|
||
|
||
metadata = conv_data.get("metadata", {}) or {}
|
||
if metadata.get("compression_in_progress"):
|
||
return {"success": False, "error": "对话正在压缩中", "in_progress": True}
|
||
|
||
# 读取个性化压缩设置：
|
||
# - compress_form: file（生成文件，引导语提示位置） / inject（把历次压缩内容注入引导语）
|
||
try:
|
||
from modules.personalization_manager import load_personalization_config
|
||
pconfig = load_personalization_config(workspace.data_dir) or {}
|
||
except Exception:
|
||
pconfig = {}
|
||
compress_form = str(pconfig.get("deep_compress_form") or "file").strip().lower()
|
||
if compress_form not in ("file", "inject"):
|
||
compress_form = "file"
|
||
# compress_behavior 固定规则（无个性化开关）：
|
||
# - 手动压缩：只生成压缩消息（引导语），不自动续接，等待用户继续发送消息才工作。
|
||
# - 自动深压缩：当前任务尚未完成才触发，压缩后必须继续工作。
|
||
compress_behavior = "wait" if mode == "manual" else "continue"
|
||
|
||
# in-place 压缩全程操作"当前对话"的内存历史：总结、标记、状态写入都依赖
|
||
# cm.conversation_history / cm.conversation_metadata。若当前对话不是目标对话，
|
||
# 必须先切换过去，否则总结会基于错误历史、压缩状态也会写错对话。
|
||
if getattr(cm, "current_conversation_id", None) != conversation_id:
|
||
try:
|
||
web_terminal.load_conversation(conversation_id)
|
||
except Exception as exc:
|
||
return {"success": False, "error": f"加载目标对话失败: {exc}"}
|
||
|
||
compression_count = int(metadata.get("compression_count", 0) or 0)
|
||
previous_records = _normalize_deep_compression_records(metadata)
|
||
previous_max_count = max([int(item.get("count") or 0) for item in previous_records], default=0)
|
||
target_count = max(compression_count, previous_max_count) + 1
|
||
job_id = f"cmp_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}"
|
||
cm.set_compression_state(
|
||
in_progress=True,
|
||
mode=mode,
|
||
stage="generating_summary",
|
||
job_id=job_id,
|
||
resume_payload={"conversation_id": conversation_id, "mode": mode},
|
||
)
|
||
_emit(sender, "compression_state", {
|
||
"conversation_id": conversation_id,
|
||
"in_progress": True,
|
||
"mode": mode,
|
||
"stage": "generating_summary",
|
||
"job_id": job_id,
|
||
})
|
||
|
||
# 提前收集用户输入与运行时状态：总结提示词需要嵌入最新输入与压缩轮次，
|
||
# compact 文件 / 注入引导语需要追加运行时状态区块。
|
||
messages = conv_data.get("messages") or []
|
||
user_inputs = _collect_user_texts(messages)
|
||
latest_user_input = user_inputs[-1] if user_inputs else ""
|
||
user_inputs_before = len(user_inputs)
|
||
runtime_state_lines = _collect_runtime_state_lines(web_terminal, conversation_id)
|
||
|
||
summary_prompt = _build_summary_prompt(
|
||
_load_summary_prompt(web_terminal),
|
||
compression_index=target_count,
|
||
latest_user_input=latest_user_input,
|
||
)
|
||
summary_text, summary_fail_reason = await _generate_summary(web_terminal, summary_prompt, retries=5)
|
||
if summary_fail_reason:
|
||
_emit(sender, "system_message", {"content": f"自动压缩总结失败，将使用失败占位文本：{summary_fail_reason}"})
|
||
|
||
cm.set_compression_state(
|
||
in_progress=True,
|
||
mode=mode,
|
||
stage="writing_compact",
|
||
job_id=job_id,
|
||
)
|
||
_emit(sender, "compression_state", {
|
||
"conversation_id": conversation_id,
|
||
"in_progress": True,
|
||
"mode": mode,
|
||
"stage": "writing_compact",
|
||
"job_id": job_id,
|
||
})
|
||
|
||
relative_compact_path = _write_compact_file(
|
||
Path(workspace.project_path),
|
||
compression_index=target_count,
|
||
summary_text=summary_text,
|
||
user_inputs=user_inputs,
|
||
latest_user_input=latest_user_input,
|
||
previous_records=previous_records,
|
||
runtime_state_lines=runtime_state_lines,
|
||
)
|
||
|
||
cm.set_compression_state(
|
||
in_progress=True,
|
||
mode=mode,
|
||
stage="marking_history",
|
||
job_id=job_id,
|
||
)
|
||
|
||
# === in-place 压缩：不创建/切换新对话，只把当前对话历史前缀打上 deep_compacted 标记 ===
|
||
now_iso = datetime.now().isoformat()
|
||
marked_count = _mark_history_compacted(
|
||
cm.conversation_history or [],
|
||
round_index=target_count,
|
||
now=now_iso,
|
||
)
|
||
# 标记后立即持久化历史（标记写在每条消息的 metadata 中）。
|
||
try:
|
||
cm.save_current_conversation()
|
||
except Exception as exc:
|
||
_emit(sender, "system_message", {"content": f"压缩标记保存失败：{exc}"})
|
||
|
||
# 关键：重置 current_context_tokens，避免自动压缩续接后阈值判断仍读到压缩前的大值而陷入死循环。
|
||
# 真实上下文长度会在下一次 API 响应后被重新写入。
|
||
try:
|
||
target_manager.update_token_statistics(
|
||
conversation_id,
|
||
input_tokens=0,
|
||
output_tokens=0,
|
||
total_tokens=0,
|
||
current_context_tokens=0,
|
||
)
|
||
except Exception as exc:
|
||
_emit(sender, "system_message", {"content": f"压缩后重置上下文统计失败：{exc}"})
|
||
|
||
current_record = {
|
||
"count": target_count,
|
||
"compact_file": relative_compact_path,
|
||
"created_at": now_iso,
|
||
"source_conversation_id": conversation_id,
|
||
"compressed_conversation_id": conversation_id,
|
||
"user_inputs_before": user_inputs_before,
|
||
"summary": summary_text,
|
||
}
|
||
all_records = previous_records + [current_record]
|
||
|
||
# 构建引导语（按压缩形式）。
|
||
if compress_form == "inject":
|
||
guide_message = _build_inject_guide_message(
|
||
compression_index=target_count,
|
||
current_record=current_record,
|
||
previous_records=previous_records,
|
||
user_inputs=user_inputs,
|
||
latest_user_input=latest_user_input,
|
||
runtime_state_lines=runtime_state_lines,
|
||
)
|
||
else:
|
||
guide_message = _build_guide_message(
|
||
compression_index=target_count,
|
||
compact_file=relative_compact_path,
|
||
)
|
||
|
||
# 更新对话 metadata：压缩记录 + 清理压缩状态标记（同一对话，无切换）。
|
||
# 同时清除 frozen prompt 缓存，使压缩后下一次请求自动重新加载动态内容。
|
||
REBUILD_FROZEN_KEYS = (
|
||
"frozen_main_system_prompt",
|
||
"frozen_permission_prompt",
|
||
"frozen_execution_prompt",
|
||
"frozen_recent_conversations_prompt",
|
||
"frozen_personalization_prompt",
|
||
"frozen_workspace_prompt",
|
||
"frozen_agents_md_prompt",
|
||
"frozen_skills_prompt",
|
||
"frozen_memory_prompt",
|
||
"frozen_custom_system_prompt",
|
||
"frozen_disabled_tools_prompt",
|
||
)
|
||
meta_updates = {
|
||
"compression_count": target_count,
|
||
"deep_compression_records": all_records,
|
||
"last_deep_compression_record": current_record,
|
||
"compression_in_progress": False,
|
||
"compression_mode": None,
|
||
"compression_stage": None,
|
||
"compression_job_id": None,
|
||
"compression_error": summary_fail_reason,
|
||
"compression_resume_payload": None,
|
||
"is_ultra_long_conversation": False,
|
||
}
|
||
for frozen_key in REBUILD_FROZEN_KEYS:
|
||
meta_updates[frozen_key] = None
|
||
target_manager.update_conversation_metadata(conversation_id, meta_updates)
|
||
# 同步内存中的 metadata，清除 frozen 缓存
|
||
try:
|
||
if getattr(cm, "current_conversation_id", None) == conversation_id and isinstance(cm.conversation_metadata, dict):
|
||
for frozen_key in REBUILD_FROZEN_KEYS:
|
||
cm.conversation_metadata.pop(frozen_key, None)
|
||
cm.conversation_metadata.update(meta_updates)
|
||
except Exception:
|
||
pass
|
||
|
||
_emit(sender, "compression_finished", {
|
||
"source_conversation_id": conversation_id,
|
||
"conversation_id": conversation_id,
|
||
"in_progress": False,
|
||
"compact_file": relative_compact_path,
|
||
"marked_count": marked_count,
|
||
"compress_form": compress_form,
|
||
"compress_behavior": compress_behavior,
|
||
"job_id": job_id,
|
||
})
|
||
return {
|
||
"success": True,
|
||
"in_place": True,
|
||
"compressed_conversation_id": conversation_id,
|
||
"compact_file": relative_compact_path,
|
||
"marked_count": marked_count,
|
||
"compress_form": compress_form,
|
||
"compress_behavior": compress_behavior,
|
||
"summary_failed": bool(summary_fail_reason),
|
||
"guide_message": guide_message,
|
||
}
|