renjianbo
beff3fac8d
- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions, schedules, executions, team_members) and unbind goals/tasks before delete - Remove hardcoded personality templates in Android, replace with dynamic system prompt generation from name + description - Set promptSectionsEnabled=false to bypass PromptComposer for personality - Add Tencent Cloud Linux deployment guide (Docker Compose) - Accumulated backend service updates, frontend UI fixes, Android app changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
373 lines
13 KiB
Python
373 lines
13 KiB
Python
"""
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工具结果流式美化引擎 — Streamlined Output
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参考 Claude Code:
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- src/utils/streamlinedTransform.ts — 累积计数 + 文本断点
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- src/utils/collapseReadSearch.ts — 搜索/读取折叠分组
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- src/utils/groupToolUses.ts — 同类型工具归并
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将工具调用过程转译为自然语言描述，让用户看到简洁摘要而非原始 JSON。
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"""
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from __future__ import annotations
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import logging
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from enum import Enum
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from typing import Any, Callable, Dict, List, Optional, Set, Tuple
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logger = logging.getLogger(__name__)
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# ──────────────────────────── 工具分类 ────────────────────────────
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class ToolCategory(str, Enum):
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SEARCH = "search" # 搜索类 (grep, glob, web_search)
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READ = "read" # 读取类 (file_read, list_files)
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WRITE = "write" # 写入类 (file_write, file_edit, deploy)
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COMMAND = "command" # 执行类 (bash, code_execute, database)
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OTHER = "other" # 其他
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# 参考 Claude Code streamlinedTransform.ts L38-50
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SEARCH_TOOLS: Set[str] = {
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"grep", "search_content", "search_files", "search_code",
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"web_search", "web_fetch", "find_files", "rg", "rg_search",
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}
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READ_TOOLS: Set[str] = {
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"file_read", "list_files", "read_file", "read_dir",
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"system_info", "entity_search", "knowledge_graph_search",
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"browser_use", "image_ocr", "image_vision",
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"url_parse", "http_request",
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}
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WRITE_TOOLS: Set[str] = {
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"file_write", "file_edit", "write_file", "edit_file",
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"deploy_push", "docker_manage", "notebook_edit",
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"pdf_generate", "excel_process",
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}
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COMMAND_TOOLS: Set[str] = {
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"code_execute", "database_query", "git_operation",
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"agent_create", "agent_call", "create_task", "task_plan",
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"adb_log", "crypto_util", "schedule_create", "schedule_delete",
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"project_scaffold", "tool_register",
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"feishu_create_doc", "feishu_create_sheet", "feishu_send_approval",
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"feishu_upload_file", "send_email",
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"bash", "shell", "cmd",
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}
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def categorize_tool(tool_name: str) -> ToolCategory:
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"""根据工具名称分类。"""
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if not tool_name:
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return ToolCategory.OTHER
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name = tool_name.lower().strip()
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if name in SEARCH_TOOLS:
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return ToolCategory.SEARCH
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if name in READ_TOOLS:
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return ToolCategory.READ
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if name in WRITE_TOOLS:
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return ToolCategory.WRITE
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if name in COMMAND_TOOLS:
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return ToolCategory.COMMAND
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return ToolCategory.OTHER
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# ──────────────────────────── 计数器 ────────────────────────────
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class ToolCounts:
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"""累积工具调用计数。"""
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__slots__ = ("searches", "reads", "writes", "commands", "other")
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def __init__(self):
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self.searches: int = 0
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self.reads: int = 0
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self.writes: int = 0
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self.commands: int = 0
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self.other: int = 0
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def add(self, category: ToolCategory) -> None:
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if category == ToolCategory.SEARCH:
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self.searches += 1
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elif category == ToolCategory.READ:
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self.reads += 1
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elif category == ToolCategory.WRITE:
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self.writes += 1
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elif category == ToolCategory.COMMAND:
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self.commands += 1
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else:
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self.other += 1
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def has_any(self) -> bool:
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return any((self.searches, self.reads, self.writes, self.commands, self.other))
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def reset(self) -> None:
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self.searches = 0
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self.reads = 0
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self.writes = 0
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self.commands = 0
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self.other = 0
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# ──────────────────────────── 摘要生成 ────────────────────────────
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def _plural_en(n: int, singular: str, plural: str = "") -> str:
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"""英文复数（用于工具名）。"""
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if n == 1:
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return singular
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return plural or singular + "s"
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def get_tool_summary_text(counts: ToolCounts) -> Optional[str]:
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"""生成工具调用累计摘要文本（中文）。
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参考 Claude Code streamlinedTransform.ts L73-104
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"""
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parts: List[str] = []
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if counts.searches > 0:
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parts.append(f"搜索了 {counts.searches} 个模式")
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if counts.reads > 0:
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parts.append(f"读取了 {counts.reads} 个文件")
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if counts.writes > 0:
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parts.append(f"写入了 {counts.writes} 个文件")
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if counts.commands > 0:
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parts.append(f"执行了 {counts.commands} 条命令")
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if counts.other > 0:
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parts.append(f"调用了 {counts.other} 个工具")
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if not parts:
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return None
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return "、".join(parts)
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def get_search_read_summary(
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search_count: int,
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read_count: int,
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is_active: bool = False,
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list_count: int = 0,
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) -> str:
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"""生成搜索/读取操作的摘要文本（用于折叠组）。
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参考 Claude Code collapseReadSearch.ts L961-1066
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"""
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parts: List[str] = []
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if search_count > 0:
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if is_active:
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verb = "正在搜索" if len(parts) == 0 else "搜索"
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else:
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verb = "已搜索" if len(parts) == 0 else "搜索了"
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parts.append(f"{verb} {search_count} 个模式")
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if read_count > 0:
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if is_active:
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verb = "正在读取" if len(parts) == 0 else "读取"
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else:
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verb = "已读取" if len(parts) == 0 else "读取了"
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parts.append(f"{verb} {read_count} 个文件")
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if list_count > 0:
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if is_active:
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verb = "正在列出" if len(parts) == 0 else "列出"
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else:
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verb = "已列出" if len(parts) == 0 else "列出了"
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parts.append(f"{verb} {list_count} 个目录")
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text = "，".join(parts)
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if is_active:
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text += "…"
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return text
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# ──────────────────────────── 流式转换器 ────────────────────────────
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class StreamlinedTransformer:
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"""有状态的流式转换器：在文本消息之间累积工具计数。
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参考 Claude Code streamlinedTransform.ts L130-193
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用法:
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transformer = StreamlinedTransformer()
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for event in stream:
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transformed = transformer.transform(event)
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if transformed:
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yield transformed
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"""
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def __init__(self, enabled: bool = True):
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self.enabled = enabled
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self._counts = ToolCounts()
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self._pending_tool_results: List[Dict[str, Any]] = []
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def transform(self, event: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""转换单个 SSE 事件。返回 None 表示该事件应被过滤。"""
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if not self.enabled:
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return event
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event_type = event.get("type", "")
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# ── 文本消息：直接输出，重置计数 ──
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if event_type in ("message", "final"):
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self._counts.reset()
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self._pending_tool_results.clear()
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return event
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# ── 思考/推理：保留（可配置是否过滤） ──
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if event_type == "think":
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# 保留 think 事件供前端展示推理过程
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return event
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# ── 工具调用：累积计数 ──
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if event_type == "tool_call":
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tool_name = event.get("name", "") or event.get("tool_name", "")
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category = categorize_tool(tool_name)
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self._counts.add(category)
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# 转发工具调用事件（带有分类信息）
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return {
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**event,
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"tool_category": category.value,
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}
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# ── 工具结果：暂存，等下次文本消息时清除 ──
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if event_type == "tool_result":
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self._pending_tool_results.append(event)
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# 发出累计摘要而非原始结果
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summary = get_tool_summary_text(self._counts)
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if summary:
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return {
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"type": "streamlined_summary",
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"summary": summary,
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"counts": {
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"searches": self._counts.searches,
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"reads": self._counts.reads,
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"writes": self._counts.writes,
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"commands": self._counts.commands,
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"other": self._counts.other,
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},
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}
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return None
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# ── 错误 ──
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if event_type == "error":
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return event
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# ── 其他事件类型：保留 ──
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return event
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def flush(self) -> Optional[Dict[str, Any]]:
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"""刷新最终的累计摘要。"""
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summary = get_tool_summary_text(self._counts)
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if summary:
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return {
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"type": "streamlined_summary",
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"summary": summary,
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"counts": {
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"searches": self._counts.searches,
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"reads": self._counts.reads,
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"writes": self._counts.writes,
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"commands": self._counts.commands,
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"other": self._counts.other,
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},
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}
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return None
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def reset(self) -> None:
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"""重置所有累积状态。"""
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self._counts.reset()
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self._pending_tool_results.clear()
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# ──────────────────────────── 批量折叠器 ────────────────────────────
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class ReadSearchCollapser:
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"""折叠连续的搜索/读取操作为单个摘要组。
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参考 Claude Code collapseReadSearch.ts — 按消息流折叠
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连续 search/read 类型的工具使用和结果。
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规则:
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- 连续的 search/read 工具调用会合并为一个组
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- writer/command 工具调用会打断组
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- 文本消息会打断组
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"""
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def __init__(self):
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self._group: List[Dict[str, Any]] = []
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self._search_count = 0
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self._read_count = 0
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self._list_count = 0
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self._is_active = False
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def feed_tool_call(self, tool_name: str, tool_input: Optional[Dict] = None) -> Optional[Dict[str, Any]]:
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"""处理一个工具调用，如果组被打断则返回 flushed 的组摘要。"""
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cat = categorize_tool(tool_name)
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if cat in (ToolCategory.SEARCH, ToolCategory.READ):
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# 可折叠：加入当前组
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if cat == ToolCategory.SEARCH:
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self._search_count += 1
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else:
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self._read_count += 1
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self._group.append({"name": tool_name, "input": tool_input})
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self._is_active = True
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# 返回进行中的摘要
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return {
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"type": "collapsed_progress",
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"summary": get_search_read_summary(
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self._search_count, self._read_count,
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is_active=True, list_count=self._list_count,
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),
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"search_count": self._search_count,
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"read_count": self._read_count,
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"list_count": self._list_count,
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}
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else:
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# 不可折叠：先 flush 组，再返回 None（调用方自行处理）
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flushed = self.flush()
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self._group = []
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self._is_active = False
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return flushed
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def flush(self) -> Optional[Dict[str, Any]]:
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"""输出当前折叠组的最终摘要。"""
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if self._search_count == 0 and self._read_count == 0 and self._list_count == 0:
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return None
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result = {
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"type": "collapsed_group",
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"summary": get_search_read_summary(
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self._search_count, self._read_count,
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is_active=False, list_count=self._list_count,
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),
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"search_count": self._search_count,
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"read_count": self._read_count,
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"list_count": self._list_count,
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"tool_count": len(self._group),
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}
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self._search_count = 0
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self._read_count = 0
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self._list_count = 0
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self._group = []
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self._is_active = False
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return result
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def reset(self) -> None:
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self._group = []
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self._search_count = 0
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self._read_count = 0
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self._list_count = 0
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self._is_active = False
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# ──────────────────────────── 工厂函数 ────────────────────────────
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def create_streamlined_transformer(enabled: bool = True) -> StreamlinedTransformer:
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"""创建 StreamlinedTransformer 实例。"""
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return StreamlinedTransformer(enabled=enabled)
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def create_read_search_collapser() -> ReadSearchCollapser:
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"""创建 ReadSearchCollapser 实例。"""
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return ReadSearchCollapser()
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