refactor: 删除重复的智能体
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Wenjie Zhang
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0b0dbe6a69
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8987cf5d8a
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from .graph import SampleMultiAgent
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__all__ = ["SampleMultiAgent"]
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from dataclasses import dataclass, field
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from typing import Annotated
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from src.agents.common.context import BaseContext
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from src.agents.common.mcp import MCP_SERVERS
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from src.agents.common.tools import gen_tool_info
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from .tools import get_tools
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@dataclass(kw_only=True)
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class Context(BaseContext):
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tools: Annotated[list[dict], {"__template_metadata__": {"kind": "tools"}}] = field(
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default_factory=list,
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metadata={
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"name": "工具",
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"options": gen_tool_info(get_tools()), # 这里的选择是所有的工具
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"description": "工具列表",
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},
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)
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mcps: list[str] = field(
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default_factory=list,
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metadata={"name": "MCP服务器", "options": list(MCP_SERVERS.keys()), "description": "MCP服务器列表"},
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)
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from langgraph.graph import END, START, StateGraph
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from langgraph.prebuilt import tools_condition
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from src.agents.common.toolagent import ToolAgent
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from .context import Context
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from .state import State
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from .tools import get_tools
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class SampleMultiAgent(ToolAgent):
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name = "MultiAgent智能体"
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description = "Supervisor智能体，具有调用其他子智能体的能力(在工具中添加)"
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# TODO[已完成]: 通过将其他agent封装为工具的方式添加了多智能体调度
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'''
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你是一个多智能体核心，通过多智能体调用的方式帮助用户完成一系列任务：
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1.当你需要知识库问答功能时，请调用对话聊天智能体实现
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2.当你需要加密计算的时候，请调用加密计算智能体实现
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'''
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.graph = None
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self.checkpointer = None
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self.context_schema = Context
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self.agent_tools = None
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def get_tools(self):
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return get_tools()
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async def get_graph(self, **kwargs):
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"""构建图"""
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if self.graph:
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return self.graph
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builder = StateGraph(State, context_schema=self.context_schema)
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builder.add_node("chatbot", self.llm_call)
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builder.add_node("tools", self.dynamic_tools_node)
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builder.add_edge(START, "chatbot")
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builder.add_conditional_edges(
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"chatbot",
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tools_condition,
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)
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builder.add_edge("tools", "chatbot")
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builder.add_edge("chatbot", END)
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self.checkpointer = await self._get_checkpointer()
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graph = builder.compile(checkpointer=self.checkpointer, name=self.name)
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self.graph = graph
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return graph
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def main():
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pass
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if __name__ == "__main__":
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main()
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# asyncio.run(main())
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"""Define the state structures for the agent."""
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from __future__ import annotations
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from collections.abc import Sequence
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from dataclasses import dataclass, field
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from typing import Annotated
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from langchain.messages import AnyMessage
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from langgraph.graph import add_messages
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from src.agents.common.state import BaseState
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@dataclass
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class State(BaseState):
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"""Defines the input state for the agent, representing a narrower interface to the outside world.
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This class is used to define the initial state and structure of incoming data.
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"""
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messages: Annotated[Sequence[AnyMessage], add_messages] = field(default_factory=list)
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import os
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from typing import Any
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from langchain.tools import tool
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from langchain_core.runnables import RunnableConfig
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from src.agents import agent_manager
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from src.agents.common.toolkits.mysql import get_mysql_tools
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from src.agents.common.tools import get_buildin_tools
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from src.utils import logger
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# TODO[修改建议]:能不能通过前端直接指定子智能体？
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# 调用子智能体后的日志是输出到tool_calls的
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@tool(name_or_callable="对话聊天智能体", description="调用指定智能体进行对话聊天的功能")
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async def call_chatbot(query: str, config: RunnableConfig) -> str:
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"""
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调用指定chatbot智能体进行对话聊天的功能
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Args:
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query: 根据需要构造的提问
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config: LangGraph运行时配置(自动注入)
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Returns:
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str: 最终的回答结果
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"""
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try:
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input = [{"role": "user", "content": query}]
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chatbot = agent_manager.get_agent("ChatbotAgent")
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configurable = config.get("configurable",{})
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input_context = {
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"thread_id":configurable.get("thread_id"),
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"user_id": configurable.get("user_id"),
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}
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message = await chatbot.invoke_messages(input,input_context=input_context)
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# 直接获取最后一个消息的内容
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final_answer = message.get('messages', [])[-1].content
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logger.info(f"ChatbotAgent: {final_answer}")
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return final_answer
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except Exception as e:
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logger.error(f"CallAgent error: {e}")
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raise
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@tool(name_or_callable="加密计算智能体", description="调用指定智能体进行加密计算的功能")
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async def call_react_agent(query: str, config: RunnableConfig) -> str:
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"""
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调用指定智能体进行加密计算的功能
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Args:
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query: 根据需要构造的提问
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config: LangGraph运行时配置(自动注入)
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Returns:
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str: 最终的回答结果
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"""
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try:
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input = [{"role": "user", "content": query}]
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chatbot = agent_manager.get_agent("ReActAgent")
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configurable = config.get("configurable",{})
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input_context = {
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"thread_id":configurable.get("thread_id"),
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"user_id": configurable.get("user_id"),
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}
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message = await chatbot.invoke_messages(input,input_context=input_context)
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# 直接获取最后一个消息的内容
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final_answer = message.get('messages', [])[-1].content
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logger.info(f"ReActAgent: {final_answer}")
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return final_answer
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except Exception as e:
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logger.error(f"CallAgent error: {e}")
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raise
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def get_tools() -> list[Any]:
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"""获取所有可运行的工具（给大模型使用）"""
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tools = get_buildin_tools()
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tools.append(call_chatbot)
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tools.append(call_react_agent)
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return tools
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