mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-02-20 01:30:01 +08:00
6c218331b1
- Simplify skill-creator installation flow - Refine skill selection prompt for better matching - Add parameter alias and env variable hints for tools - Skip linkai-agent when unconfigured - Create skills/ dir in workspace on init
484 lines
18 KiB
Python
484 lines
18 KiB
Python
"""
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System Prompt Builder - 系统提示词构建器
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实现模块化的系统提示词构建，支持工具、技能、记忆等多个子系统
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"""
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from __future__ import annotations
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import os
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from typing import List, Dict, Optional, Any
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from dataclasses import dataclass
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from common.log import logger
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@dataclass
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class ContextFile:
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"""上下文文件"""
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path: str
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content: str
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class PromptBuilder:
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"""提示词构建器"""
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def __init__(self, workspace_dir: str, language: str = "zh"):
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"""
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初始化提示词构建器
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Args:
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workspace_dir: 工作空间目录
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language: 语言 ("zh" 或 "en")
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"""
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self.workspace_dir = workspace_dir
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self.language = language
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def build(
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self,
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base_persona: Optional[str] = None,
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user_identity: Optional[Dict[str, str]] = None,
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tools: Optional[List[Any]] = None,
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context_files: Optional[List[ContextFile]] = None,
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skill_manager: Any = None,
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memory_manager: Any = None,
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runtime_info: Optional[Dict[str, Any]] = None,
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is_first_conversation: bool = False,
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**kwargs
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) -> str:
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"""
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构建完整的系统提示词
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Args:
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base_persona: 基础人格描述（会被context_files中的AGENT.md覆盖）
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user_identity: 用户身份信息
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tools: 工具列表
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context_files: 上下文文件列表（AGENT.md, USER.md, RULE.md等）
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skill_manager: 技能管理器
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memory_manager: 记忆管理器
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runtime_info: 运行时信息
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is_first_conversation: 是否为首次对话
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**kwargs: 其他参数
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Returns:
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完整的系统提示词
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"""
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return build_agent_system_prompt(
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workspace_dir=self.workspace_dir,
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language=self.language,
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base_persona=base_persona,
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user_identity=user_identity,
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tools=tools,
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context_files=context_files,
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skill_manager=skill_manager,
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memory_manager=memory_manager,
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runtime_info=runtime_info,
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is_first_conversation=is_first_conversation,
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**kwargs
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)
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def build_agent_system_prompt(
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workspace_dir: str,
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language: str = "zh",
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base_persona: Optional[str] = None,
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user_identity: Optional[Dict[str, str]] = None,
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tools: Optional[List[Any]] = None,
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context_files: Optional[List[ContextFile]] = None,
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skill_manager: Any = None,
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memory_manager: Any = None,
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runtime_info: Optional[Dict[str, Any]] = None,
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is_first_conversation: bool = False,
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**kwargs
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) -> str:
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"""
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构建Agent系统提示词
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顺序说明（按重要性和逻辑关系排列）:
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1. 工具系统 - 核心能力，最先介绍
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2. 技能系统 - 紧跟工具，因为技能需要用 read 工具读取
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3. 记忆系统 - 独立的记忆能力
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4. 工作空间 - 工作环境说明
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5. 用户身份 - 用户信息（可选）
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6. 项目上下文 - AGENT.md, USER.md, RULE.md（定义人格、身份、规则）
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7. 运行时信息 - 元信息（时间、模型等）
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Args:
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workspace_dir: 工作空间目录
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language: 语言 ("zh" 或 "en")
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base_persona: 基础人格描述（已废弃，由AGENT.md定义）
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user_identity: 用户身份信息
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tools: 工具列表
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context_files: 上下文文件列表
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skill_manager: 技能管理器
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memory_manager: 记忆管理器
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runtime_info: 运行时信息
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is_first_conversation: 是否为首次对话
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**kwargs: 其他参数
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Returns:
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完整的系统提示词
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"""
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sections = []
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# 1. 工具系统（最重要，放在最前面）
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if tools:
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sections.extend(_build_tooling_section(tools, language))
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# 2. 技能系统（紧跟工具，因为需要用 read 工具）
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if skill_manager:
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sections.extend(_build_skills_section(skill_manager, tools, language))
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# 3. 记忆系统（独立的记忆能力）
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if memory_manager:
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sections.extend(_build_memory_section(memory_manager, tools, language))
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# 4. 工作空间（工作环境说明）
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sections.extend(_build_workspace_section(workspace_dir, language, is_first_conversation))
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# 5. 用户身份（如果有）
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if user_identity:
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sections.extend(_build_user_identity_section(user_identity, language))
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# 6. 项目上下文文件（AGENT.md, USER.md, RULE.md - 定义人格）
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if context_files:
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sections.extend(_build_context_files_section(context_files, language))
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# 7. 运行时信息（元信息，放在最后）
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if runtime_info:
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sections.extend(_build_runtime_section(runtime_info, language))
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return "\n".join(sections)
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def _build_identity_section(base_persona: Optional[str], language: str) -> List[str]:
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"""构建基础身份section - 不再需要，身份由AGENT.md定义"""
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# 不再生成基础身份section，完全由AGENT.md定义
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return []
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def _build_tooling_section(tools: List[Any], language: str) -> List[str]:
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"""Build tooling section with concise tool list and call style guide."""
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# One-line summaries for known tools (details are in the tool schema)
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core_summaries = {
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"read": "读取文件内容",
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"write": "创建或覆盖文件",
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"edit": "精确编辑文件",
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"ls": "列出目录内容",
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"grep": "搜索文件内容",
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"find": "按模式查找文件",
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"bash": "执行shell命令",
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"terminal": "管理后台进程",
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"web_search": "网络搜索",
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"web_fetch": "获取URL内容",
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"browser": "控制浏览器",
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"memory_search": "搜索记忆",
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"memory_get": "读取记忆内容",
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"env_config": "管理API密钥和技能配置",
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"scheduler": "管理定时任务和提醒",
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"send": "发送文件给用户",
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}
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# Preferred display order
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tool_order = [
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"read", "write", "edit", "ls", "grep", "find",
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"bash", "terminal",
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"web_search", "web_fetch", "browser",
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"memory_search", "memory_get",
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"env_config", "scheduler", "send",
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]
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# Build name -> summary mapping for available tools
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available = {}
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for tool in tools:
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name = tool.name if hasattr(tool, 'name') else str(tool)
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available[name] = core_summaries.get(name, "")
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# Generate tool lines: ordered tools first, then extras
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tool_lines = []
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for name in tool_order:
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if name in available:
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summary = available.pop(name)
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tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
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for name in sorted(available):
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summary = available[name]
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tool_lines.append(f"- {name}: {summary}" if summary else f"- {name}")
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lines = [
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"## 工具系统",
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"",
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"可用工具（名称大小写敏感，严格按列表调用）:",
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"\n".join(tool_lines),
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"",
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"工具调用风格：",
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"",
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"- 在多步骤任务、敏感操作或用户要求时简要解释决策过程",
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"- 持续推进直到任务完成，完成后向用户报告结果。",
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"- 回复中涉及密钥、令牌等敏感信息必须脱敏。",
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"",
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]
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return lines
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def _build_skills_section(skill_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
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"""构建技能系统section"""
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if not skill_manager:
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return []
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# 获取read工具名称
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read_tool_name = "read"
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if tools:
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for tool in tools:
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tool_name = tool.name if hasattr(tool, 'name') else str(tool)
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if tool_name.lower() == "read":
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read_tool_name = tool_name
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break
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lines = [
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"## 技能系统（mandatory）",
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"",
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"在回复之前：扫描下方 <available_skills> 中的 <description> 条目。",
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"",
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f"- 如果恰好有一个技能(Skill)明确适用：使用 `{read_tool_name}` 读取其 <location> 处的 SKILL.md，然后严格遵循它",
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"- 如果多个技能都适用则选择最匹配的一个，如果没有明确适用的则不要读取任何 SKILL.md",
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"- 读取 SKILL.md 后直接按其指令执行，无需多余的预检查",
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"",
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"**注意**: 永远不要一次性读取多个技能，只在选择后再读取。技能和工具不同，必须先读取其SKILL.md并按照文件内容运行。",
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"",
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"以下是可用技能："
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]
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# 添加技能列表（通过skill_manager获取）
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try:
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skills_prompt = skill_manager.build_skills_prompt()
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logger.debug(f"[PromptBuilder] Skills prompt length: {len(skills_prompt) if skills_prompt else 0}")
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if skills_prompt:
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lines.append(skills_prompt.strip())
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lines.append("")
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else:
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logger.warning("[PromptBuilder] No skills prompt generated - skills_prompt is empty")
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except Exception as e:
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logger.warning(f"Failed to build skills prompt: {e}")
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import traceback
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logger.debug(f"Skills prompt error traceback: {traceback.format_exc()}")
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return lines
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def _build_memory_section(memory_manager: Any, tools: Optional[List[Any]], language: str) -> List[str]:
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"""构建记忆系统section"""
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if not memory_manager:
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return []
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# 检查是否有memory工具
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has_memory_tools = False
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if tools:
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tool_names = [tool.name if hasattr(tool, 'name') else str(tool) for tool in tools]
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has_memory_tools = any(name in ['memory_search', 'memory_get'] for name in tool_names)
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if not has_memory_tools:
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return []
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lines = [
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"## 记忆系统",
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"",
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"在回答关于以前的工作、决定、日期、人物、偏好或待办事项的任何问题之前：",
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"",
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"1. 不确定记忆文件位置 → 先用 `memory_search` 通过关键词和语义检索相关内容",
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"2. 已知文件位置 → 直接用 `memory_get` 读取相应的行 (例如：MEMORY.md, memory/YYYY-MM-DD.md)",
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"3. search 无结果 → 尝试用 `memory_get` 读取MEMORY.md及最近两天记忆文件",
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"",
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"**记忆文件结构**:",
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"- `MEMORY.md`: 长期记忆（核心信息、偏好、决策等）",
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"- `memory/YYYY-MM-DD.md`: 每日记忆，记录当天的事件和对话信息",
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"",
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"**写入记忆**:",
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"- 追加内容 → `edit` 工具，oldText 留空",
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"- 修改内容 → `edit` 工具，oldText 填写要替换的文本",
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"- 新建文件 → `write` 工具",
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"- **禁止写入敏感信息**：API密钥、令牌等敏感信息严禁写入记忆文件",
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"",
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"**使用原则**: 自然使用记忆，就像你本来就知道；不用刻意提起，除非用户问起。",
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"",
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]
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return lines
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def _build_user_identity_section(user_identity: Dict[str, str], language: str) -> List[str]:
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"""构建用户身份section"""
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if not user_identity:
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return []
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lines = [
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"## 用户身份",
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"",
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]
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if user_identity.get("name"):
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lines.append(f"**用户姓名**: {user_identity['name']}")
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if user_identity.get("nickname"):
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lines.append(f"**称呼**: {user_identity['nickname']}")
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if user_identity.get("timezone"):
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lines.append(f"**时区**: {user_identity['timezone']}")
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if user_identity.get("notes"):
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lines.append(f"**备注**: {user_identity['notes']}")
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lines.append("")
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return lines
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def _build_docs_section(workspace_dir: str, language: str) -> List[str]:
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"""构建文档路径section - 已移除，不再需要"""
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# 不再生成文档section
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return []
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def _build_workspace_section(workspace_dir: str, language: str, is_first_conversation: bool = False) -> List[str]:
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"""构建工作空间section"""
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lines = [
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"## 工作空间",
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"",
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f"你的工作目录是: `{workspace_dir}`",
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"",
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"**路径使用规则** (非常重要):",
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"",
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f"1. **相对路径的基准目录**: 所有相对路径都是相对于 `{workspace_dir}` 而言的",
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f" - ✅ 正确: 访问工作空间内的文件用相对路径，如 `AGENT.md`",
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f" - ❌ 错误: 用相对路径访问其他目录的文件 (如果它不在 `{workspace_dir}` 内)",
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"",
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"2. **访问其他目录**: 如果要访问工作空间之外的目录（如项目代码、系统文件），**必须使用绝对路径**",
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f" - ✅ 正确: 例如 `~/chatgpt-on-wechat`、`/usr/local/`",
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f" - ❌ 错误: 假设相对路径会指向其他目录",
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"",
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"3. **路径解析示例**:",
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f" - 相对路径 `memory/` → 实际路径 `{workspace_dir}/memory/`",
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f" - 绝对路径 `~/chatgpt-on-wechat/docs/` → 实际路径 `~/chatgpt-on-wechat/docs/`",
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"",
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"4. **不确定时**: 先用 `bash pwd` 确认当前目录，或用 `ls .` 查看当前位置",
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"",
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"**重要说明 - 文件已自动加载**:",
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"",
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"以下文件在会话启动时**已经自动加载**到系统提示词的「项目上下文」section 中，你**无需再用 read 工具读取它们**：",
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"",
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"- ✅ `AGENT.md`: 已加载 - 你的人格和灵魂设定",
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"- ✅ `USER.md`: 已加载 - 用户的身份信息",
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"- ✅ `RULE.md`: 已加载 - 工作空间使用指南和规则",
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"",
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"**交流规范**:",
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"",
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"- 在对话中，不要直接输出工作空间中的技术细节，特别是不要输出 AGENT.md、USER.md、MEMORY.md 等文件名称",
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"- 例如用自然表达例如「我已记住」而不是「已更新 MEMORY.md」",
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"",
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]
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# 只在首次对话时添加引导内容
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if is_first_conversation:
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lines.extend([
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"**🎉 首次对话引导**:",
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"",
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"这是你的第一次对话！进行以下流程：",
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"",
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"1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待",
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"2. **简短介绍能力**：一行说明你能帮助解答问题、管理计算机、创造技能，且拥有长期记忆能不断成长",
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"3. **询问核心问题**：",
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" - 你希望给我起个什么名字？",
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" - 我该怎么称呼你？",
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" - 你希望我们是什么样的交流风格？（一行列举选项：如专业严谨、轻松幽默、温暖友好、简洁高效等）",
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"4. **风格要求**：温暖自然、简洁清晰，整体控制在 100 字以内",
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"5. 收到回复后，用 `write` 工具保存到 USER.md 和 AGENT.md",
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"",
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"**重要提醒**:",
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"- AGENT.md、USER.md、RULE.md 已经在系统提示词中加载，无需再次读取。不要将这些文件名直接发送给用户",
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"- 能力介绍和交流风格选项都只要一行，保持精简",
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"- 不要问太多其他信息（职业、时区等可以后续自然了解）",
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"",
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])
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return lines
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def _build_context_files_section(context_files: List[ContextFile], language: str) -> List[str]:
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"""构建项目上下文文件section"""
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if not context_files:
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return []
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|
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# 检查是否有AGENT.md
|
||
has_agent = any(
|
||
f.path.lower().endswith('agent.md') or 'agent.md' in f.path.lower()
|
||
for f in context_files
|
||
)
|
||
|
||
lines = [
|
||
"# 项目上下文",
|
||
"",
|
||
"以下项目上下文文件已被加载：",
|
||
"",
|
||
]
|
||
|
||
if has_agent:
|
||
lines.append("如果存在 `AGENT.md`，请体现其中定义的人格和语气。避免僵硬、模板化的回复；遵循其指导，除非有更高优先级的指令覆盖它。")
|
||
lines.append("")
|
||
|
||
# 添加每个文件的内容
|
||
for file in context_files:
|
||
lines.append(f"## {file.path}")
|
||
lines.append("")
|
||
lines.append(file.content)
|
||
lines.append("")
|
||
|
||
return lines
|
||
|
||
|
||
def _build_runtime_section(runtime_info: Dict[str, Any], language: str) -> List[str]:
|
||
"""构建运行时信息section - 支持动态时间"""
|
||
if not runtime_info:
|
||
return []
|
||
|
||
lines = [
|
||
"## 运行时信息",
|
||
"",
|
||
]
|
||
|
||
# Add current time if available
|
||
# Support dynamic time via callable function
|
||
if callable(runtime_info.get("_get_current_time")):
|
||
try:
|
||
time_info = runtime_info["_get_current_time"]()
|
||
time_line = f"当前时间: {time_info['time']} {time_info['weekday']} ({time_info['timezone']})"
|
||
lines.append(time_line)
|
||
lines.append("")
|
||
except Exception as e:
|
||
logger.warning(f"[PromptBuilder] Failed to get dynamic time: {e}")
|
||
elif runtime_info.get("current_time"):
|
||
# Fallback to static time for backward compatibility
|
||
time_str = runtime_info["current_time"]
|
||
weekday = runtime_info.get("weekday", "")
|
||
timezone = runtime_info.get("timezone", "")
|
||
|
||
time_line = f"当前时间: {time_str}"
|
||
if weekday:
|
||
time_line += f" {weekday}"
|
||
if timezone:
|
||
time_line += f" ({timezone})"
|
||
|
||
lines.append(time_line)
|
||
lines.append("")
|
||
|
||
# Add other runtime info
|
||
runtime_parts = []
|
||
if runtime_info.get("model"):
|
||
runtime_parts.append(f"模型={runtime_info['model']}")
|
||
if runtime_info.get("workspace"):
|
||
runtime_parts.append(f"工作空间={runtime_info['workspace']}")
|
||
# Only add channel if it's not the default "web"
|
||
if runtime_info.get("channel") and runtime_info.get("channel") != "web":
|
||
runtime_parts.append(f"渠道={runtime_info['channel']}")
|
||
|
||
if runtime_parts:
|
||
lines.append("运行时: " + " | ".join(runtime_parts))
|
||
lines.append("")
|
||
|
||
return lines
|