JOJO
a2cf547400
- 新增 modules/i18n.py：tr() + 进程级语言缓存 + modules/i18n_messages/ 域文案包自动聚合 - 新增 29 个域文案包，共 1153 条双语 key；90+ 源文件 1146 处用户可见消息 tr 化 - ui_locale 存入 personalization.json（用户级共享），前后端双向同步 - 前端匹配点双语兼容（history/shared/ChatArea/taskPolling/upload 等正则） - 修复语言判等陷阱：审批等待加稳定 code 字段；conversation.py 不存在判等改双语 helper - 边界：日志/prompt 注入/子智能体工具回填/容器内嵌脚本不迁移
584 lines
26 KiB
Python
584 lines
26 KiB
Python
import asyncio
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import json
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Set
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try:
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from config import (
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OUTPUT_FORMATS, DATA_DIR, PROMPTS_DIR, NEED_CONFIRMATION,
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MAX_TERMINALS, TERMINAL_BUFFER_SIZE, TERMINAL_DISPLAY_SIZE,
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MAX_READ_FILE_CHARS, READ_TOOL_DEFAULT_MAX_CHARS,
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READ_TOOL_DEFAULT_CONTEXT_BEFORE, READ_TOOL_DEFAULT_CONTEXT_AFTER,
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READ_TOOL_MAX_CONTEXT_BEFORE, READ_TOOL_MAX_CONTEXT_AFTER,
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READ_TOOL_DEFAULT_MAX_MATCHES, READ_TOOL_MAX_MATCHES,
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READ_TOOL_MAX_FILE_SIZE,
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TERMINAL_SANDBOX_MOUNT_PATH,
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TERMINAL_SANDBOX_MODE,
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TERMINAL_SANDBOX_CPUS,
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TERMINAL_SANDBOX_MEMORY,
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PROJECT_MAX_STORAGE_MB,
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CUSTOM_TOOLS_ENABLED,
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WORKSPACE_SKILLS_DIRNAME,
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WORKSPACE_MEMORY_DIRNAME,
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PROJECT_AGENTS_SKILLS_DIRNAME,
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)
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except ImportError:
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import sys
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project_root = Path(__file__).resolve().parents[2]
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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 (
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OUTPUT_FORMATS, DATA_DIR, PROMPTS_DIR, NEED_CONFIRMATION,
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MAX_TERMINALS, TERMINAL_BUFFER_SIZE, TERMINAL_DISPLAY_SIZE,
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MAX_READ_FILE_CHARS, READ_TOOL_DEFAULT_MAX_CHARS,
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READ_TOOL_DEFAULT_CONTEXT_BEFORE, READ_TOOL_DEFAULT_CONTEXT_AFTER,
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READ_TOOL_MAX_CONTEXT_BEFORE, READ_TOOL_MAX_CONTEXT_AFTER,
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READ_TOOL_DEFAULT_MAX_MATCHES, READ_TOOL_MAX_MATCHES,
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READ_TOOL_MAX_FILE_SIZE,
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TERMINAL_SANDBOX_MOUNT_PATH,
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TERMINAL_SANDBOX_MODE,
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TERMINAL_SANDBOX_CPUS,
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TERMINAL_SANDBOX_MEMORY,
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PROJECT_MAX_STORAGE_MB,
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CUSTOM_TOOLS_ENABLED,
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WORKSPACE_SKILLS_DIRNAME,
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WORKSPACE_MEMORY_DIRNAME,
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PROJECT_AGENTS_SKILLS_DIRNAME,
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)
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from modules.file_manager import FileManager
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from modules.search_engine import SearchEngine
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from modules.terminal_ops import TerminalOperator
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from modules.memory_manager import MemoryManager
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from modules.terminal_manager import TerminalManager
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from modules.todo_manager import TodoManager
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from modules.sub_agent import SubAgentManager
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from modules.webpage_extractor import extract_webpage_content, tavily_extract
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from modules.ocr_client import OCRClient
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from modules.easter_egg_manager import EasterEggManager
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from modules.personalization_manager import (
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load_personalization_config,
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build_personalization_prompt,
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)
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from modules.skills_manager import (
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get_skills_catalog,
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build_skills_list,
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merge_enabled_skills,
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build_skills_prompt,
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infer_private_skills_dir,
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)
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from modules.custom_tool_registry import CustomToolRegistry, build_default_tool_category
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from modules.custom_tool_executor import CustomToolExecutor
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from modules.container_monitor import collect_stats, inspect_state
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from core.tool_config import TOOL_CATEGORIES
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from utils.api_client import APIClient
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from utils.context_manager import ContextManager
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from utils.tool_result_formatter import format_tool_result_for_context
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from utils.logger import setup_logger
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from config.model_profiles import (
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get_model_profile,
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get_model_prompt_replacements,
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get_model_context_window,
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)
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from modules.i18n import tr
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logger = setup_logger(__name__)
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DISABLE_LENGTH_CHECK = True
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class MainTerminalToolsReadMixin:
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@staticmethod
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def _normalize_skill_name(skill_name: Any) -> str:
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raw = str(skill_name or "").strip()
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if not raw:
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return ""
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normalized = raw.replace("\\", "/").strip("/")
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for prefix in (f"{WORKSPACE_SKILLS_DIRNAME}/", f"{PROJECT_AGENTS_SKILLS_DIRNAME}/"):
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if normalized.lower().startswith(prefix):
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normalized = normalized[len(prefix):]
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break
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if normalized.lower().endswith("/skill.md"):
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normalized = normalized[: -len("/SKILL.md")]
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return normalized.strip()
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def _resolve_skill_id(self, skill_name: Any) -> Dict[str, Any]:
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normalized_input = self._normalize_skill_name(skill_name)
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if not normalized_input:
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return {"success": False, "error": tr("tools_read.skill_name_empty")}
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try:
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personalization = load_personalization_config(self.data_dir)
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except Exception:
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personalization = {}
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scan_project_agents = (
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bool(personalization.get("agents_skills_scan_enabled", True))
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if isinstance(personalization, dict)
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else True
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)
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catalog = get_skills_catalog(
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private_dir=infer_private_skills_dir(self.data_dir),
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project_path=self.project_path,
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scan_project_agents=scan_project_agents,
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)
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enabled_skills = merge_enabled_skills(
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personalization.get("enabled_skills") if isinstance(personalization, dict) else None,
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catalog,
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personalization.get("skills_catalog_snapshot") if isinstance(personalization, dict) else None,
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)
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enabled_set = set(enabled_skills or [])
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filtered_catalog = [item for item in catalog if item.get("id") in enabled_set] if enabled_set else list(catalog)
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normalized_lower = normalized_input.lower()
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def _resolve_item(item: Dict[str, str]) -> Dict[str, Any]:
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sid = item.get("id")
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primary_dir = str(item.get("display_dir") or WORKSPACE_SKILLS_DIRNAME).strip("/")
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conflict_dir = str(item.get("conflict_dir") or "").strip("/")
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if conflict_dir:
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# 同名冲突：报错并引导改用 read_file 按具体路径查看
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return {
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"success": False,
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"error": tr(
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"tools_read.skill_name_conflict",
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sid=sid,
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primary_dir=primary_dir,
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conflict_dir=conflict_dir,
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),
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}
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return {"success": True, "skill_id": sid, "display_dir": primary_dir}
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# 1) 优先按 skill id 精确匹配（忽略大小写）
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id_map = {str(item.get("id", "")).lower(): item for item in filtered_catalog if item.get("id")}
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if normalized_lower in id_map:
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return _resolve_item(id_map[normalized_lower])
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# 2) 再按 label 匹配（忽略大小写）
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label_matches: List[str] = []
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for item in filtered_catalog:
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label = str(item.get("label") or "").strip().lower()
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if label and label == normalized_lower and item.get("id"):
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label_matches.append(item["id"])
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if len(label_matches) == 1:
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return _resolve_item(id_map[label_matches[0].lower()])
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if len(label_matches) > 1:
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return {
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"success": False,
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"error": tr("tools_read.skill_name_ambiguous", matches=', '.join(sorted(label_matches)))
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}
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return {"success": False, "error": tr("tools_read.skill_not_found", name=normalized_input)}
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def _handle_read_skill_tool(self, arguments: Dict) -> Dict:
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skill_name = arguments.get("skill_name")
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resolved = self._resolve_skill_id(skill_name)
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if not resolved.get("success"):
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return resolved
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skill_id = resolved["skill_id"]
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display_dir = str(resolved.get("display_dir") or WORKSPACE_SKILLS_DIRNAME).strip("/")
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read_args = {
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"path": f"{display_dir}/{skill_id}/SKILL.md",
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"type": "read",
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}
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result = self._handle_read_tool(read_args)
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if not result.get("success"):
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return result
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result["skill_id"] = skill_id
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result["skill_name"] = skill_name
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return result
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def _handle_recall_project_memory(self, name: str) -> Dict:
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"""处理 recall_project_memory：读取 .astrion/memory/{name}.md"""
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safe_name = str(name).strip()
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if not safe_name or "/" in safe_name or "\\" in safe_name:
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return {"success": False, "error": tr("tools_read.memory_name_invalid", name=name)}
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file_path = f"{WORKSPACE_MEMORY_DIRNAME}/{safe_name}.md"
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read_args = {
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"path": file_path,
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"type": "read",
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}
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result = self._handle_read_tool(read_args)
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if result.get("success"):
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result["memory_name"] = safe_name
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return result
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def _handle_search_project_memory(self, keywords: List[str], max_results: int = 5) -> Dict:
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"""处理 search_project_memory：在 .astrion/memory/*.md 中做关键词全文检索。
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评分：名称命中 +10，描述命中 +5，正文每行命中 +1（单关键词最多计 5 行）。
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返回 top-N 结果，附匹配行片段（行号基于完整文件，可直接配合 read_file extract 使用）。
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"""
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clean_keywords: List[str] = []
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for kw in keywords or []:
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kw_text = str(kw or "").strip()
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if kw_text and kw_text not in clean_keywords:
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clean_keywords.append(kw_text)
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clean_keywords = clean_keywords[:5]
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if not clean_keywords:
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return {"success": False, "error": tr("tools_read.search_memory_needs_keywords")}
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max_results = self._clamp_int(max_results, 5, 1, 10)
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memory_dir = Path(self.project_path) / WORKSPACE_MEMORY_DIRNAME
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if not memory_dir.exists() or not memory_dir.is_dir():
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empty_text = tr("tools_read.memory_dir_not_exists")
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return {
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"success": True,
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"count": 0,
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"keywords": clean_keywords,
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"results": [],
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"content": empty_text,
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"summary": empty_text,
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}
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lowered = [(kw, kw.lower()) for kw in clean_keywords]
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scored: List[Dict[str, Any]] = []
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for md_file in sorted(memory_dir.glob("*.md")):
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try:
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text = md_file.read_text(encoding="utf-8")
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except Exception:
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continue
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lines = text.split("\n")
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name = md_file.stem
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description = ""
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body_start_idx = 0 # 0-based，正文起始行（跳过 frontmatter）
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if lines and lines[0].strip() == "---":
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for i in range(1, len(lines)):
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if lines[i].strip() == "---":
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for fm_line in lines[1:i]:
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fm_stripped = fm_line.strip()
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if fm_stripped.startswith("name:"):
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name = fm_stripped.split(":", 1)[1].strip() or name
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elif fm_stripped.startswith("description:"):
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description = fm_stripped.split(":", 1)[1].strip()
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body_start_idx = i + 1
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break
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name_lower = name.lower()
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desc_lower = description.lower()
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body_lines = lines[body_start_idx:]
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score = 0
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matched_keywords: List[str] = []
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for kw, kw_lower in lowered:
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kw_score = 0
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if kw_lower in name_lower:
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kw_score += 10
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if kw_lower in desc_lower:
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kw_score += 5
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body_hits = sum(1 for line in body_lines if kw_lower in line.lower())
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kw_score += min(body_hits, 5)
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if kw_score > 0:
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score += kw_score
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matched_keywords.append(kw)
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if score <= 0:
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continue
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snippets: List[Dict[str, Any]] = []
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for idx in range(body_start_idx, len(lines)):
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line_stripped = lines[idx].strip()
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if not line_stripped:
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continue
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line_lower = line_stripped.lower()
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if any(kw_lower in line_lower for _, kw_lower in lowered):
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snippet_text = line_stripped if len(line_stripped) <= 120 else line_stripped[:117] + "..."
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snippets.append({"line": idx + 1, "text": snippet_text})
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if len(snippets) >= 3:
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break
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scored.append({
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"file": md_file.name,
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"name": name,
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"description": description,
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"score": score,
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"matched_keywords": matched_keywords,
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"snippets": snippets,
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})
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scored.sort(key=lambda item: (-item["score"], -len(item["matched_keywords"]), item["name"]))
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top = scored[:max_results]
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if not top:
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empty_text = tr(
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"tools_read.search_no_match_content",
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keywords="、".join(clean_keywords),
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)
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return {
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"success": True,
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"count": 0,
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"keywords": clean_keywords,
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"results": [],
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"content": empty_text,
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"summary": tr("tools_read.search_no_match_summary"),
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}
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content_lines = [
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tr(
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"tools_read.search_found_header",
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count=len(top),
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keywords="、".join(clean_keywords),
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),
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"",
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]
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for rank, item in enumerate(top, start=1):
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content_lines.append(tr("tools_read.search_result_item", rank=rank, name=item['name'], file=item['file']))
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if item["description"]:
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content_lines.append(tr("tools_read.search_result_desc", description=item['description']))
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if item["snippets"]:
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content_lines.append(tr("tools_read.search_result_snippets_label"))
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for snippet in item["snippets"]:
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content_lines.append(f" L{snippet['line']}: {snippet['text']}")
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content_lines.append("")
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content_lines.append(tr("tools_read.search_read_full_hint"))
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content_text = "\n".join(content_lines).strip()
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return {
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"success": True,
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"count": len(top),
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"keywords": clean_keywords,
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"results": top,
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"content": content_text,
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"summary": tr("tools_read.search_found_summary", count=len(top)),
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}
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|
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@staticmethod
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def _clamp_int(value, default, min_value=None, max_value=None):
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"""将输入转换为整数并限制范围。"""
|
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if value is None:
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return default
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try:
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num = int(value)
|
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except (TypeError, ValueError):
|
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return default
|
||
if min_value is not None:
|
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num = max(min_value, num)
|
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if max_value is not None:
|
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num = min(max_value, num)
|
||
return num
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|
||
@staticmethod
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def _parse_optional_line(value, field_name: str):
|
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"""解析可选的行号参数。"""
|
||
if value is None:
|
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return None, None
|
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try:
|
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number = int(value)
|
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except (TypeError, ValueError):
|
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return None, tr("tools_read.param_must_be_int", field_name=field_name)
|
||
if number < 1:
|
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return None, tr("tools_read.param_must_be_gte_1", field_name=field_name)
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||
return number, None
|
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|
||
@staticmethod
|
||
def _truncate_text_block(text: str, max_chars: int):
|
||
"""对单段文本应用字符限制。"""
|
||
if max_chars and len(text) > max_chars:
|
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return text[:max_chars], True, max_chars
|
||
return text, False, len(text)
|
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|
||
@staticmethod
|
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def _limit_text_chunks(chunks: List[Dict], text_key: str, max_chars: int):
|
||
"""对多个文本片段应用全局字符限制。"""
|
||
if max_chars is None or max_chars <= 0:
|
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return chunks, False, sum(len(chunk.get(text_key, "") or "") for chunk in chunks)
|
||
|
||
remaining = max_chars
|
||
limited_chunks: List[Dict] = []
|
||
truncated = False
|
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consumed = 0
|
||
|
||
for chunk in chunks:
|
||
snippet = chunk.get(text_key, "") or ""
|
||
snippet_len = len(snippet)
|
||
chunk_copy = dict(chunk)
|
||
|
||
if remaining <= 0:
|
||
truncated = True
|
||
break
|
||
|
||
if snippet_len > remaining:
|
||
chunk_copy[text_key] = snippet[:remaining]
|
||
chunk_copy["truncated"] = True
|
||
consumed += remaining
|
||
limited_chunks.append(chunk_copy)
|
||
truncated = True
|
||
remaining = 0
|
||
break
|
||
|
||
limited_chunks.append(chunk_copy)
|
||
consumed += snippet_len
|
||
remaining -= snippet_len
|
||
|
||
return limited_chunks, truncated, consumed
|
||
|
||
def _handle_read_tool(self, arguments: Dict) -> Dict:
|
||
"""集中处理 read_file 工具的三种模式。"""
|
||
file_path = arguments.get("path")
|
||
if not file_path:
|
||
return {"success": False, "error": tr("tools_read.missing_file_path")}
|
||
|
||
read_type = (arguments.get("type") or "read").lower()
|
||
if read_type not in {"read", "search", "extract"}:
|
||
return {"success": False, "error": tr("tools_read.unknown_read_type", read_type=read_type)}
|
||
|
||
max_chars = self._clamp_int(
|
||
arguments.get("max_chars"),
|
||
READ_TOOL_DEFAULT_MAX_CHARS,
|
||
1,
|
||
MAX_READ_FILE_CHARS
|
||
)
|
||
|
||
base_result = {
|
||
"success": True,
|
||
"type": read_type,
|
||
"path": None,
|
||
"encoding": "utf-8",
|
||
"max_chars": max_chars,
|
||
"truncated": False
|
||
}
|
||
|
||
if read_type == "read":
|
||
start_line, error = self._parse_optional_line(arguments.get("start_line"), "start_line")
|
||
if error:
|
||
return {"success": False, "error": error}
|
||
end_line_val = arguments.get("end_line")
|
||
end_line = None
|
||
if end_line_val is not None:
|
||
end_line, error = self._parse_optional_line(end_line_val, "end_line")
|
||
if error:
|
||
return {"success": False, "error": error}
|
||
if start_line and end_line < start_line:
|
||
return {"success": False, "error": tr("tools_read.end_line_ge_start_line")}
|
||
|
||
read_result = self.file_manager.read_text_segment(
|
||
file_path,
|
||
start_line=start_line,
|
||
end_line=end_line,
|
||
size_limit=READ_TOOL_MAX_FILE_SIZE
|
||
)
|
||
if not read_result.get("success"):
|
||
return read_result
|
||
|
||
content, truncated, char_count = self._truncate_text_block(read_result["content"], max_chars)
|
||
base_result.update({
|
||
"path": read_result["path"],
|
||
"content": content,
|
||
"line_start": read_result["line_start"],
|
||
"line_end": read_result["line_end"],
|
||
"total_lines": read_result["total_lines"],
|
||
"file_size": read_result["size"],
|
||
"char_count": char_count,
|
||
"message": tr(
|
||
"tools_read.read_success",
|
||
path=read_result["path"],
|
||
line_start=read_result["line_start"],
|
||
line_end=read_result["line_end"],
|
||
)
|
||
})
|
||
base_result["truncated"] = truncated
|
||
self.context_manager.load_file(read_result["path"])
|
||
return base_result
|
||
|
||
if read_type == "search":
|
||
query = arguments.get("query")
|
||
if not query:
|
||
return {"success": False, "error": tr("tools_read.search_requires_query")}
|
||
|
||
max_matches = self._clamp_int(
|
||
arguments.get("max_matches"),
|
||
READ_TOOL_DEFAULT_MAX_MATCHES,
|
||
1,
|
||
READ_TOOL_MAX_MATCHES
|
||
)
|
||
context_before = self._clamp_int(
|
||
arguments.get("context_before"),
|
||
READ_TOOL_DEFAULT_CONTEXT_BEFORE,
|
||
0,
|
||
READ_TOOL_MAX_CONTEXT_BEFORE
|
||
)
|
||
context_after = self._clamp_int(
|
||
arguments.get("context_after"),
|
||
READ_TOOL_DEFAULT_CONTEXT_AFTER,
|
||
0,
|
||
READ_TOOL_MAX_CONTEXT_AFTER
|
||
)
|
||
case_sensitive = bool(arguments.get("case_sensitive"))
|
||
|
||
search_result = self.file_manager.search_text(
|
||
file_path,
|
||
query=query,
|
||
max_matches=max_matches,
|
||
context_before=context_before,
|
||
context_after=context_after,
|
||
case_sensitive=case_sensitive,
|
||
size_limit=READ_TOOL_MAX_FILE_SIZE
|
||
)
|
||
if not search_result.get("success"):
|
||
return search_result
|
||
|
||
matches = search_result["matches"]
|
||
limited_matches, truncated, char_count = self._limit_text_chunks(matches, "snippet", max_chars)
|
||
|
||
base_result.update({
|
||
"path": search_result["path"],
|
||
"file_size": search_result["size"],
|
||
"query": query,
|
||
"max_matches": max_matches,
|
||
"actual_matches": len(matches),
|
||
"returned_matches": len(limited_matches),
|
||
"context_before": context_before,
|
||
"context_after": context_after,
|
||
"case_sensitive": case_sensitive,
|
||
"matches": limited_matches,
|
||
"char_count": char_count,
|
||
"message": tr(
|
||
"tools_read.search_success",
|
||
path=search_result["path"],
|
||
query=query,
|
||
count=len(limited_matches),
|
||
)
|
||
})
|
||
base_result["truncated"] = truncated
|
||
return base_result
|
||
|
||
# extract
|
||
segments = arguments.get("segments")
|
||
if not isinstance(segments, list) or not segments:
|
||
return {"success": False, "error": tr("tools_read.extract_requires_segments")}
|
||
|
||
extract_result = self.file_manager.extract_segments(
|
||
file_path,
|
||
segments=segments,
|
||
size_limit=READ_TOOL_MAX_FILE_SIZE
|
||
)
|
||
if not extract_result.get("success"):
|
||
return extract_result
|
||
|
||
limited_segments, truncated, char_count = self._limit_text_chunks(
|
||
extract_result["segments"],
|
||
"content",
|
||
max_chars
|
||
)
|
||
|
||
base_result.update({
|
||
"path": extract_result["path"],
|
||
"segments": limited_segments,
|
||
"file_size": extract_result["size"],
|
||
"total_lines": extract_result["total_lines"],
|
||
"segment_count": len(limited_segments),
|
||
"char_count": char_count,
|
||
"message": tr(
|
||
"tools_read.extract_success",
|
||
path=extract_result["path"],
|
||
count=len(limited_segments),
|
||
)
|
||
})
|
||
base_result["truncated"] = truncated
|
||
self.context_manager.load_file(extract_result["path"])
|
||
return base_result
|