JOJO
f25523e441
- 对话条目：修复「…」按钮因 action-wrap 宽度未随 32px 列同步而溢出条目圆角外（并移除无效的分组列宽覆盖） - 工作区入口按钮：移除右侧当前工作区名称 - 切换工作区：重排期间禁用子对话 FLIP 位移与展开折叠过渡，全部瞬间移动（不影响新建/复制/删除动画） - 删除动画：before-leave 用 offsetTop 锁定离场元素原位，修复 flex 容器 absolute 静态位置跳到顶部导致非首条删除无左滑 - 个人空间：修复「按项目分组对话」勾选框 SVG path 多重复一段导致描边错位 - 搜索：改为后端 /api/conversations/search（标题+首条用户消息首句匹配，带实例级缓存），分组模式 all_workspaces=1 跨工作区搜索并按工作区分类展示；移除前端 N+1 预览请求与 project_path 噪音匹配
468 lines
19 KiB
Python
468 lines
19 KiB
Python
# utils/conversation_manager.py - 对话持久化管理器（集成Token统计）
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import json
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import os
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import re
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import time
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import tempfile
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import threading
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from datetime import datetime
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from pathlib import Path
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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import time
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try:
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from config import DATA_DIR, HOST_WORKSPACES_FILE
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except ImportError:
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import sys
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from pathlib import Path
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project_root = Path(__file__).resolve().parents[1]
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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from config import DATA_DIR, HOST_WORKSPACES_FILE
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try:
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from utils.perf_log import perf_log
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except Exception:
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perf_log = None
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@dataclass
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class ConversationMetadata:
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"""对话元数据"""
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id: str
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title: str
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created_at: str
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updated_at: str
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project_path: Optional[str]
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project_relative_path: Optional[str]
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thinking_mode: bool
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total_messages: int
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total_tools: int
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run_mode: str = "fast"
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model_key: Optional[str] = None
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has_images: bool = False
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has_videos: bool = False
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status: str = "active" # active, archived, error
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class ListSearchMixin:
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"""ConversationManager list search mixin 能力 mixin。"""
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def get_conversation_list(self, limit: int = 50, offset: int = 0, non_empty: bool = False, multi_agent_mode: Optional[bool] = None) -> Dict:
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"""
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获取对话列表
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Args:
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limit: 限制数量
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offset: 偏移量
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non_empty: 仅返回有内容（total_messages > 0）的对话，分页基于过滤后的结果
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multi_agent_mode: 若为 True 仅返回多智能体模式对话；若为 False 仅返回常规对话；None 不过滤
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Returns:
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Dict: 包含对话列表和统计信息
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"""
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t0 = time.perf_counter()
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if perf_log:
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perf_log("get_conversation_list enter", extra={"limit": limit, "offset": offset, "non_empty": non_empty, "multi_agent_mode": multi_agent_mode})
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try:
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def _filter_by_multi_agent(items):
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"""按 multi_agent_mode 元数据过滤。"""
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if multi_agent_mode is None:
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return items
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result = []
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for conv_id, meta in items:
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conv_ma = bool(meta.get("multi_agent_mode", False))
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if conv_ma == multi_agent_mode:
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result.append((conv_id, meta))
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return result
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if non_empty:
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# 过滤模式：全量加载后剔除空对话，再在过滤结果上分页，
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# 保证 total / has_more 与"有内容对话"的真实数量一致。
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index = self._ensure_index_covering(limit=10000, offset=0)
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sorted_conversations = sorted(
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index.items(),
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key=lambda x: x[1].get("updated_at") or "",
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reverse=True
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)
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sorted_conversations = [
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item for item in sorted_conversations
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if (item[1].get("total_messages", 0) or 0) > 0
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]
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if multi_agent_mode is not None:
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sorted_conversations = _filter_by_multi_agent(sorted_conversations)
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total = len(sorted_conversations)
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conversations = sorted_conversations[offset:offset + limit]
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else:
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# 总对话数按文件数统计，防止初始索引截断导致"没有更多"按钮消失
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total_files = len(self._iter_conversation_files(sort_by_mtime=False))
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index = self._ensure_index_covering(limit=limit, offset=offset)
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# 按更新时间倒序排列（处理 None 值）
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sorted_conversations = sorted(
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index.items(),
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key=lambda x: x[1].get("updated_at") or "",
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reverse=True
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)
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if multi_agent_mode is not None:
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sorted_conversations = _filter_by_multi_agent(sorted_conversations)
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# 分页
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total = max(len(sorted_conversations), total_files if multi_agent_mode is None else len(sorted_conversations))
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conversations = sorted_conversations[offset:offset+limit]
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# 格式化结果
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result = []
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for conv_id, metadata in conversations:
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result.append({
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"id": conv_id,
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"title": metadata.get("title", "未命名对话"),
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"created_at": metadata.get("created_at"),
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"updated_at": metadata.get("updated_at"),
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"project_path": metadata.get("project_path"),
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"project_relative_path": metadata.get("project_relative_path"),
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"thinking_mode": metadata.get("thinking_mode", False),
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"total_messages": metadata.get("total_messages", 0),
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"total_tools": metadata.get("total_tools", 0),
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"status": metadata.get("status", "active"),
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"multi_agent_mode": bool(metadata.get("multi_agent_mode", False))
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})
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elapsed_ms = (time.perf_counter() - t0) * 1000
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if perf_log:
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perf_log("get_conversation_list done", elapsed_ms=elapsed_ms, extra={"result_count": len(result), "total": total})
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return {
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"conversations": result,
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"total": total,
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"limit": limit,
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"offset": offset,
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"has_more": offset + limit < total
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}
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except Exception as e:
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elapsed_ms = (time.perf_counter() - t0) * 1000
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if perf_log:
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perf_log("get_conversation_list error", elapsed_ms=elapsed_ms, extra={"error": str(e)})
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print(f"⌘ 获取对话列表失败: {e}")
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return {
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"conversations": [],
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"total": 0,
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"limit": limit,
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"offset": offset,
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"has_more": False
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}
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def get_recent_conversation_summaries(
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self,
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limit: int = 10,
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*,
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exclude_conversation_id: Optional[str] = None,
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first_message_max_chars: int = 100,
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) -> List[Dict]:
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"""返回当前工作区最近对话摘要：标题 + 首条用户消息。"""
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try:
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target_limit = max(1, int(limit))
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listing = self.get_conversation_list(limit=10000, offset=0)
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summaries: List[Dict] = []
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for item in listing.get("conversations") or []:
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conv_id = item.get("id")
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if not conv_id or conv_id == exclude_conversation_id:
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continue
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data = self.load_conversation(conv_id) or {}
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first_user_message = self._extract_first_user_message(
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data.get("messages") or [],
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max_chars=max(1, int(first_message_max_chars or 100)),
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)
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if not first_user_message:
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continue
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summaries.append({
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"id": conv_id,
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"title": item.get("title") or data.get("title") or "未命名对话",
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"created_at": item.get("created_at") or data.get("created_at"),
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"updated_at": item.get("updated_at") or data.get("updated_at"),
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"total_messages": item.get("total_messages") or (data.get("metadata") or {}).get("total_messages", 0),
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"total_tools": item.get("total_tools") or (data.get("metadata") or {}).get("total_tools", 0),
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"first_user_message": first_user_message,
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})
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if len(summaries) >= target_limit:
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break
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return summaries
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except Exception as e:
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print(f"⌘ 获取最近对话摘要失败: {e}")
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return []
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def search_conversation_summaries(
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self,
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query: str = "",
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*,
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keywords: Optional[List[str]] = None,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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limit: int = 10,
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first_message_max_chars: int = 100,
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exclude_conversation_id: Optional[str] = None,
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) -> List[Dict]:
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"""在当前工作区内搜索标题 + 首条用户消息，可按创建日期过滤。"""
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try:
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raw_keywords = keywords if isinstance(keywords, list) else []
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normalized_keywords = [str(item).strip().lower() for item in raw_keywords if str(item or "").strip()]
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fallback_query = (query or "").strip().lower()
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if fallback_query:
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normalized_keywords.append(fallback_query)
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normalized_keywords = normalized_keywords[:3]
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start_key = (start_date or "").strip()
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end_key = (end_date or "").strip()
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listing = self.get_conversation_list(limit=10000, offset=0)
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results: List[Dict] = []
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for item in listing.get("conversations") or []:
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conv_id = item.get("id")
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if not conv_id or conv_id == exclude_conversation_id:
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continue
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created_at = str(item.get("created_at") or "")
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date_key = created_at[:10]
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if start_key and date_key and date_key < start_key:
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continue
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if end_key and date_key and date_key > end_key:
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continue
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data = self.load_conversation(conv_id) or {}
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first_message = self._extract_first_user_message(
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data.get("messages") or [],
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max_chars=max(1, int(first_message_max_chars or 100)),
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)
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# 空对话（没有任何用户文本）不应出现在搜索/列表结果中，
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# 否则无关键词 list 最近对话时会出现大量“新对话”。
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if not first_message:
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continue
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title = item.get("title") or data.get("title") or "未命名对话"
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haystack = f"{title} {first_message}".lower()
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if normalized_keywords and not any(keyword in haystack for keyword in normalized_keywords):
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continue
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results.append({
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"id": conv_id,
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"title": title,
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"first_user_message": first_message,
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"created_at": item.get("created_at") or data.get("created_at"),
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"updated_at": item.get("updated_at") or data.get("updated_at"),
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"total_messages": item.get("total_messages") or (data.get("metadata") or {}).get("total_messages", 0),
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"total_tools": item.get("total_tools") or (data.get("metadata") or {}).get("total_tools", 0),
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})
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if len(results) >= max(1, int(limit or 10)):
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break
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return results
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except Exception as e:
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print(f"⌘ 搜索对话摘要失败: {e}")
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return []
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def delete_conversation(self, conversation_id: str) -> bool:
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"""
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删除对话
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Args:
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conversation_id: 对话ID
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Returns:
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bool: 删除是否成功
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"""
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try:
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# 删除对话文件
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file_path = self._get_conversation_file_path(conversation_id)
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if file_path.exists():
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file_path.unlink()
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# 从索引中删除
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index = self._load_index()
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if conversation_id in index:
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del index[conversation_id]
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self._save_index(index)
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# 如果删除的是当前对话，清除当前对话ID
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if self.current_conversation_id == conversation_id:
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self.current_conversation_id = None
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print(f"🗑️ 已删除对话: {conversation_id}")
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return True
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except Exception as e:
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print(f"⌘ 删除对话失败 {conversation_id}: {e}")
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return False
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def archive_conversation(self, conversation_id: str) -> bool:
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"""
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归档对话（标记为已归档，不删除）
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Args:
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conversation_id: 对话ID
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Returns:
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bool: 归档是否成功
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"""
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try:
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# 更新对话状态
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conversation_data = self.load_conversation(conversation_id)
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if not conversation_data:
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return False
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conversation_data["metadata"]["status"] = "archived"
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conversation_data["updated_at"] = datetime.now().isoformat()
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# 保存更新
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self._save_conversation_file(conversation_id, conversation_data)
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self._update_index(conversation_id, conversation_data)
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print(f"📦 已归档对话: {conversation_id}")
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return True
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except Exception as e:
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print(f"⌘ 归档对话失败 {conversation_id}: {e}")
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return False
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def search_conversations(self, query: str, limit: int = 20, multi_agent_mode: Optional[bool] = None) -> List[Dict]:
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"""
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搜索对话（标题 / 首条用户消息首句，与原前端客户端搜索口径一致）
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Args:
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query: 搜索关键词
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limit: 限制数量
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multi_agent_mode: True 仅多智能体对话；False 仅常规对话；None 不过滤
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Returns:
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List[Dict]: 匹配的对话列表
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"""
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try:
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index = self._load_index()
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results = []
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query_lower = (query or "").lower()
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if not query_lower:
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return []
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for conv_id, metadata in index.items():
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if multi_agent_mode is not None:
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conv_ma = bool(metadata.get("multi_agent_mode", False))
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if conv_ma != multi_agent_mode:
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continue
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entry = {
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"id": conv_id,
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"title": metadata.get("title"),
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"created_at": metadata.get("created_at"),
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"updated_at": metadata.get("updated_at"),
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"project_path": metadata.get("project_path"),
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}
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# 搜索标题（权重最高）
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title = (metadata.get("title") or "").lower()
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if query_lower in title:
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results.append((100, entry["updated_at"] or "", {**entry, "match_type": "title"}))
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continue
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# 搜索首条用户消息首句
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first_sentence = self._get_first_user_sentence(conv_id, metadata)
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if first_sentence and query_lower in first_sentence:
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results.append((80, entry["updated_at"] or "", {**entry, "match_type": "content"}))
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# 分数优先，同分按更新时间新到旧
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results.sort(key=lambda x: (x[0], x[1]), reverse=True)
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# 返回前N个结果
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return [item[2] for item in results[:limit]]
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except Exception as e:
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print(f"⌘ 搜索对话失败: {e}")
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return []
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def _get_first_user_sentence(self, conversation_id: str, metadata: Optional[Dict] = None) -> str:
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"""读取对话首条用户消息并提取首句（小写）。
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实例级缓存（terminal 按工作区缓存，manager 实例跨请求存活），
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以 updated_at 作为失效依据，避免每次按键都重读对话文件。
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"""
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try:
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cache = getattr(self, "_search_sentence_cache", None)
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if cache is None:
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cache = {}
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self._search_sentence_cache = cache
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updated_at = (metadata or {}).get("updated_at") or ""
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cached = cache.get(conversation_id)
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if cached and cached[0] == updated_at:
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return cached[1]
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sentence = ""
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conversation_data = self.load_conversation(conversation_id)
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if conversation_data:
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for msg in conversation_data.get("messages") or []:
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if not isinstance(msg, dict) or msg.get("role") != "user":
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continue
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sentence = self._extract_first_sentence(self._extract_message_text(msg))
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break
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sentence_lower = sentence.lower()
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cache[conversation_id] = (updated_at, sentence_lower)
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# 防止缓存无限增长
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if len(cache) > 2000:
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cache.clear()
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return sentence_lower
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except Exception:
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return ""
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@staticmethod
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def _extract_message_text(msg: Dict) -> str:
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"""从消息中提取纯文本（与 build_review_lines 的 extract_text 口径一致）。"""
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content = msg.get("content")
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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 = []
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for item in content:
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if isinstance(item, dict) and item.get("type") == "text":
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parts.append(item.get("text") or "")
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elif isinstance(item, str):
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parts.append(item)
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return "".join(parts)
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if isinstance(content, dict):
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return content.get("text") or ""
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return str(msg.get("text") or "")
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@staticmethod
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def _extract_first_sentence(text: str) -> str:
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"""与旧前端 extractFirstSentence 口径一致：取到第一个句末标点为止。"""
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if not text:
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return ""
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normalized = re.sub(r"\s+", " ", str(text)).strip()
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m = re.match(r"(.+?[。！？.!?])", normalized)
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return m.group(1) if m else normalized
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def cleanup_old_conversations(self, days: int = 30) -> int:
|
||
"""
|
||
清理旧对话（可选功能）
|
||
|
||
Args:
|
||
days: 保留天数
|
||
|
||
Returns:
|
||
int: 清理的对话数量
|
||
"""
|
||
try:
|
||
from datetime import datetime, timedelta
|
||
cutoff_date = datetime.now() - timedelta(days=days)
|
||
cutoff_iso = cutoff_date.isoformat()
|
||
|
||
index = self._load_index()
|
||
to_delete = []
|
||
|
||
for conv_id, metadata in index.items():
|
||
updated_at = metadata.get("updated_at", "")
|
||
if updated_at < cutoff_iso and metadata.get("status") != "archived":
|
||
to_delete.append(conv_id)
|
||
|
||
deleted_count = 0
|
||
for conv_id in to_delete:
|
||
if self.delete_conversation(conv_id):
|
||
deleted_count += 1
|
||
|
||
if deleted_count > 0:
|
||
print(f"🧹 清理了 {deleted_count} 个旧对话")
|
||
|
||
return deleted_count
|
||
except Exception as e:
|
||
print(f"⌘ 清理旧对话失败: {e}")
|
||
return 0
|