340 lines
15 KiB
Python
340 lines
15 KiB
Python
"""子智能体工具定义、结果格式化与模型配置解析。"""
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from __future__ import annotations
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import json
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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from config.model_profiles import _parse_env_ref
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# 子智能体可用工具定义（与前端进度展示兼容）
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SUB_AGENT_TOOLS: List[Dict[str, Any]] = [
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{
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"type": "function",
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"function": {
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"name": "read_file",
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"description": "读取/搜索/抽取 UTF-8 文本文件。type=read/search/extract，仅单文件操作。",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string", "description": "文件路径：工作区内请用相对路径，工作区外可用绝对路径。"},
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"type": {"type": "string", "enum": ["read", "search", "extract"], "description": "读取模式。"},
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"max_chars": {"type": "integer", "description": "返回内容最大字符数，超出将截断。"},
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"start_line": {"type": "integer", "description": "[read] 起始行号（1-based）。"},
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"end_line": {"type": "integer", "description": "[read] 结束行号（>= start_line）。"},
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"query": {"type": "string", "description": "[search] 搜索关键词。"},
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"max_matches": {"type": "integer", "description": "[search] 最多返回多少条命中窗口。"},
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"context_before": {"type": "integer", "description": "[search] 命中行向上追加行数。"},
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"context_after": {"type": "integer", "description": "[search] 命中行向下追加行数。"},
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"case_sensitive": {"type": "boolean", "description": "[search] 是否区分大小写。"},
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"segments": {
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"type": "array",
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"description": "[extract] 需要抽取的行区间数组。",
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"items": {
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"type": "object",
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"properties": {
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"label": {"type": "string"},
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"start_line": {"type": "integer"},
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"end_line": {"type": "integer"},
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},
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"required": ["start_line", "end_line"],
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},
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"minItems": 1,
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},
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},
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"required": ["path", "type"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "write_file",
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"description": "创建或覆盖文件；append=true 时追加内容。",
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"parameters": {
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"type": "object",
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"properties": {
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"file_path": {"type": "string", "description": "文件路径：工作区内请用相对路径。"},
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"content": {"type": "string", "description": "要写入的内容。"},
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"append": {"type": "boolean", "default": False, "description": "是否追加到文件末尾。"},
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},
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"required": ["file_path", "content"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "edit_file",
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"description": "在文件中执行一处或多处精确字符串替换；建议先用 read_file 精确复制 old_string。",
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"parameters": {
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"type": "object",
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"properties": {
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"file_path": {"type": "string", "description": "文件路径：工作区内请用相对路径。"},
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"replacements": {
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"type": "array",
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"description": "替换组列表，每组包含 old_string/new_string/replace_all。",
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"items": {
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"type": "object",
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"properties": {
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"old_string": {"type": "string"},
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"new_string": {"type": "string"},
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"replace_all": {"type": "boolean", "default": False, "description": "是否替换所有匹配内容。"},
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},
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"required": ["old_string", "new_string"],
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},
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"minItems": 1,
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},
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},
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"required": ["file_path", "replacements"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "run_command",
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"description": "在当前终端环境执行一次性命令（非交互式）。",
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"parameters": {
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"type": "object",
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"properties": {
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"command": {"type": "string", "description": "要执行的命令。"},
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"timeout": {"type": "number", "description": "超时时间（秒），必填。"},
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"working_dir": {"type": "string", "description": "工作目录：工作区内请用相对路径。"},
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},
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"required": ["command", "timeout"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "web_search",
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"description": "网络搜索（Tavily）。用于外部资料或最新信息检索。",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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"max_results": {"type": "integer"},
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"topic": {"type": "string", "enum": ["general", "news", "finance"]},
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"time_range": {"type": "string", "enum": ["day", "week", "month", "year", "d", "w", "m", "y"]},
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"days": {"type": "integer"},
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"start_date": {"type": "string"},
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"end_date": {"type": "string"},
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"country": {"type": "string"},
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"include_domains": {"type": "array", "items": {"type": "string"}},
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},
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"required": ["query"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "extract_webpage",
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"description": "网页内容提取（Tavily）。mode=read 直接返回内容，mode=save 保存为文件。",
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"parameters": {
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"type": "object",
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"properties": {
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"mode": {"type": "string", "enum": ["read", "save"]},
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"url": {"type": "string"},
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"target_path": {"type": "string", "description": "[save] 保存路径，必须是 .md 文件。"},
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},
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"required": ["mode", "url"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "search_workspace",
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"description": "在本地目录内搜索文件名或文件内容（跨文件）。仅返回摘要。",
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"parameters": {
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"type": "object",
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"properties": {
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"mode": {"type": "string", "enum": ["file", "content"]},
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"query": {"type": "string"},
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"root": {"type": "string", "description": "搜索起点目录，默认 '.'。"},
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"use_regex": {"type": "boolean"},
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"case_sensitive": {"type": "boolean"},
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"max_results": {"type": "integer"},
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"max_matches_per_file": {"type": "integer"},
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"include_glob": {"type": "array", "items": {"type": "string"}},
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"exclude_glob": {"type": "array", "items": {"type": "string"}},
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"max_file_size": {"type": "integer"},
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},
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"required": ["mode", "query"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "read_mediafile",
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"description": "读取图片或视频文件，以 base64 形式返回给模型。非媒体文件将拒绝。",
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"parameters": {
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"type": "object",
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"properties": {
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"path": {"type": "string"},
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},
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"required": ["path"],
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},
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},
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},
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]
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FINISH_TOOL: Dict[str, Any] = {
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"type": "function",
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"function": {
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"name": "finish_task",
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"description": "完成当前任务并退出。调用此工具表示你已经完成了分配的任务，所有交付文件已准备好。",
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"parameters": {
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"type": "object",
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"properties": {
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"success": {"type": "boolean"},
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"summary": {"type": "string", "description": "任务完成摘要（50-200字）。"},
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},
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"required": ["success", "summary"],
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},
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},
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}
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def _format_tool_result(name: str, raw: Any) -> str:
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"""把主进程返回的工具结果格式化为文本，供 LLM 消费。"""
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if not isinstance(raw, dict):
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return str(raw) if raw is not None else ""
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if raw.get("success") is False:
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return raw.get("error") or raw.get("message") or "失败"
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if name == "read_file":
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if raw.get("type") == "read":
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return raw.get("content") or ""
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if raw.get("type") == "search":
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parts = []
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for match in raw.get("matches") or []:
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hits = ",".join(str(h) for h in (match.get("hits") or []))
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parts.append(f"[{match.get('id')}] L{match.get('line_start')}-{match.get('line_end')} hits:{hits}")
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parts.append(match.get("snippet") or "")
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parts.append("")
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return "\n".join(parts).strip()
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if raw.get("type") == "extract":
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parts = []
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for seg in raw.get("segments") or []:
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label = seg.get("label") or "segment"
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parts.append(f"[{label}] L{seg.get('line_start')}-{seg.get('line_end')}")
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parts.append(seg.get("content") or "")
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parts.append("")
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return "\n".join(parts).strip()
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return ""
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if name == "write_file":
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return raw.get("message") or "写入成功"
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if name == "edit_file":
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count = raw.get("replacements")
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msg = raw.get("message") or ""
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return f"已替换 {count} 处: {raw.get('path')}{'，' + msg if msg else ''}"
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if name == "run_command":
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return raw.get("output") or ""
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if name == "web_search":
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lines = [f"🔍 搜索查询: {raw.get('query')}", f"📅 搜索时间: {raw.get('searched_at') or datetime.now().isoformat()}", "", "📝 AI摘要:", raw.get("summary") or "", "", "---", "", "📊 搜索结果:"]
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for idx, item in enumerate(raw.get("results") or [], 1):
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lines.append(f"{idx}. {item.get('title') or ''}")
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lines.append(f" 🔗 {item.get('url') or ''}")
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lines.append(f" 📄 {item.get('content') or ''}")
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return "\n".join(lines).strip()
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if name == "extract_webpage":
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if raw.get("mode") == "save":
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return f"已保存: {raw.get('path')}"
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return f"URL: {raw.get('url')}\n{raw.get('content') or ''}"
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if name == "search_workspace":
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if raw.get("mode") == "file":
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lines = [f"命中文件({len(raw.get('matches') or [])}):"]
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for idx, p in enumerate(raw.get("matches") or [], 1):
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lines.append(f"{idx}) {p}")
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return "\n".join(lines)
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if raw.get("mode") == "content":
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parts = []
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for item in raw.get("results") or []:
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parts.append(item.get("file") or "")
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for m in item.get("matches") or []:
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parts.append(f"- L{m.get('line')}: {m.get('snippet')}")
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parts.append("")
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return "\n".join(parts).strip()
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return ""
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if name == "read_mediafile":
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return raw.get("message") or "媒体已读取"
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return json.dumps(raw, ensure_ascii=False)
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def _build_sub_agent_profile(model_raw: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""把 sub_agent_models.json 中的模型条目转成 DeepSeekClient.apply_profile 所需格式。"""
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name = str(model_raw.get("name") or model_raw.get("model_name") or model_raw.get("model") or "").strip()
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url = str(_parse_env_ref(model_raw.get("url") or model_raw.get("base_url") or "") or "").strip()
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api_key = str(_parse_env_ref(model_raw.get("apikey") or model_raw.get("api_key") or "") or "").strip()
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if not name or not url or not api_key:
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return None
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modes_text = str(model_raw.get("modes") or model_raw.get("mode") or model_raw.get("supported_modes") or "").lower()
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supports_thinking = "thinking" in modes_text
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fast_only = modes_text == "fast"
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multimodal_text = str(model_raw.get("multimodal") or model_raw.get("multi_modal") or model_raw.get("multi") or "none").lower()
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multimodal = "none"
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if "video" in multimodal_text:
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multimodal = "image+video"
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elif "image" in multimodal_text:
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multimodal = "image"
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max_output = _to_int(model_raw.get("max_output") or model_raw.get("max_tokens") or model_raw.get("max_output_tokens"))
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max_context = _to_int(model_raw.get("max_context") or model_raw.get("context_window") or model_raw.get("max_context_tokens"))
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model_id = str(model_raw.get("model_id") or name).strip()
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extra = _pick_dict(model_raw, ["extra_parameter", "extra_params", "extra"])
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fast_extra = _pick_dict(model_raw, ["fast_extra_parameter", "fast_extra_params", "fast_extra"])
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thinking_extra = _pick_dict(model_raw, ["thinking_extra_parameter", "thinking_extra_params", "thinking_extra"])
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profile: Dict[str, Any] = {
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"name": name,
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"multimodal": multimodal,
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"context_window": max_context,
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"supports_thinking": supports_thinking,
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"fast_only": fast_only,
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"fast": {
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"base_url": url.rstrip("/"),
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"api_key": api_key,
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"model_id": model_id,
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"max_tokens": max_output,
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"context_window": max_context,
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"extra_params": {**extra, **fast_extra},
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},
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}
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if supports_thinking:
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profile["thinking"] = {
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"base_url": url.rstrip("/"),
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"api_key": api_key,
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"model_id": model_id,
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"max_tokens": max_output,
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"context_window": max_context,
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"extra_params": {**extra, **thinking_extra},
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}
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else:
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profile["thinking"] = None
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return profile
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def _to_int(value: Any) -> Optional[int]:
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try:
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num = int(value)
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return num if num > 0 else None
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except Exception:
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return None
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def _pick_dict(source: Dict[str, Any], keys: List[str]) -> Dict[str, Any]:
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for key in keys:
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value = source.get(key)
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if isinstance(value, dict):
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return value
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return {}
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