DESKTOP-72TV0V4\caoxiaozhu
0e0f988264
- 新增 intent_router.py 意图路由模块 - 优化 context.py 上下文管理 - 增强 loop.py Agent 运行循环 - 更新 memory.py 记忆模块 - 修复 builtin.py 工具函数 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
128 lines
3.5 KiB
Python
128 lines
3.5 KiB
Python
"""Context builder for assembling agent prompts."""
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import platform
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from pathlib import Path
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from typing import Any
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class ContextBuilder:
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"""Builds the context (system prompt + messages) for the agent."""
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def __init__(self, workspace: Path):
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"""Initialize the context builder.
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Args:
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workspace: Workspace directory
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"""
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self.workspace = workspace
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def build_system_prompt(self) -> str:
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"""Build the system prompt with identity and runtime info."""
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workspace_path = str(self.workspace.expanduser().resolve())
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system = platform.system()
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runtime = f"{system} {platform.machine()}"
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return f"""# X-Agents Assistant
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You are an AI assistant built on the X-Agents platform.
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## Runtime
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{runtime}
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## Workspace
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Your workspace is at: {workspace_path}
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## Guidelines
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- Be helpful and concise
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- Think step by step when needed
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- Ask for clarification when the request is ambiguous
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## Tool Usage Guidelines
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**IMPORTANT**: Only use tools when explicitly requested by the user:
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**Use tools for**:
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- Searching the web for current information
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- Executing code or commands
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- Reading or writing files
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- Performing calculations
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**DO NOT use tools for**:
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- Simple questions and greetings (e.g., "介绍一下武汉", "你好", "什么是AI")
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- General knowledge that you already know
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- Conversational responses
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For simple informational questions, respond directly from your knowledge without calling any tools.
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"""
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def build_messages(
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self,
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history: list[dict[str, Any]],
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current_message: str,
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) -> list[dict[str, Any]]:
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"""Build the complete message list for an LLM call.
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Args:
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history: Conversation history
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current_message: Current user message
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Returns:
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List of messages for LLM
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"""
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return [
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{"role": "system", "content": self.build_system_prompt()},
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*history,
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{"role": "user", "content": current_message},
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]
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def add_assistant_message(
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self,
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messages: list[dict[str, Any]],
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content: str | None,
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tool_calls: list[dict[str, Any]] | None = None,
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reasoning_content: str | None = None,
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) -> list[dict[str, Any]]:
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"""Add an assistant message to the message list.
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Args:
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messages: Current message list
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content: Assistant message content
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tool_calls: Optional tool calls
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reasoning_content: Optional reasoning from model
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Returns:
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Updated message list
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"""
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msg = {"role": "assistant", "content": content or ""}
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if tool_calls:
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msg["tool_calls"] = tool_calls
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if reasoning_content:
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msg["reasoning_content"] = reasoning_content
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messages.append(msg)
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return messages
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def add_tool_result(
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self,
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messages: list[dict[str, Any]],
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tool_call_id: str,
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tool_name: str,
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result: str,
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) -> list[dict[str, Any]]:
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"""Add a tool result to the message list.
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Args:
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messages: Current message list
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tool_call_id: ID of the tool call
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tool_name: Name of the tool
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result: Tool execution result
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Returns:
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Updated message list
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"""
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"name": tool_name,
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"content": result,
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})
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return messages
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