可以先查看 上一节内容，会对本节有更好的理解。
安装依赖
pip install langchainhub
编写代码
核心代码
@tool def get_docker_info(docker_name: str) -> str: """Get information about a docker pod container info.""" result = subprocess.run(['docker', 'inspect', str(docker_name)], capture_output=True, text=True) return result.stdout
这里是通过执行 Shell的方式来获取状态的。
通过执行Docker指令之后，可以获取到一大段的文本内容，此时把这些内容交给大模型去处理，大模型对内容进行提取和推理，最终回答我们。
注意@tool注解，没有这个注解的话，无法使用
注意要写"""xxx""" 要写明该工具的介绍，大模型将根据介绍来选择是否调用
如果3.5的效果不好，可以尝试使用4
from langchain import hub from langchain.agents import AgentExecutor, tool from langchain.agents.output_parsers import XMLAgentOutputParser from langchain_openai import ChatOpenAI import subprocess model = ChatOpenAI( model="gpt-3.5-turbo", ) @tool def search(query: str) -> str: """Search things about current events.""" return "32 degrees" @tool def get_docker_info(docker_name: str) -> str: """Get information about a docker pod container info.""" result = subprocess.run(['docker', 'inspect', str(docker_name)], capture_output=True, text=True) return result.stdout tool_list = [search, get_docker_info] # Get the prompt to use - you can modify this! prompt = hub.pull("hwchase17/xml-agent-convo") # Logic for going from intermediate steps to a string to pass into model # This is pretty tied to the prompt def convert_intermediate_steps(intermediate_steps): log = "" for action, observation in intermediate_steps: log += ( f"<tool>{action.tool}</tool><tool_input>{action.tool_input}" f"</tool_input><observation>{observation}</observation>" ) return log # Logic for converting tools to string to go in prompt def convert_tools(tools): return "\n".join([f"{tool.name}: {tool.description}" for tool in tools]) agent = ( { "input": lambda x: x["input"], "agent_scratchpad": lambda x: convert_intermediate_steps( x["intermediate_steps"] ), } | prompt.partial(tools=convert_tools(tool_list)) | model.bind(stop=["</tool_input>", "</final_answer>"]) | XMLAgentOutputParser() ) agent_executor = AgentExecutor(agent=agent, tools=tool_list) message1 = agent_executor.invoke({"input": "whats the weather in New york?"}) print(f"message1: {message1}") message2 = agent_executor.invoke({"input": "what is docker pod which name 'lobe-chat-wzk' info? I want to know it 'Image' url"}) print(f"message2: {message2}")
执行代码
➜ python3 test10.py message1: {'input': 'whats the weather in New york?', 'output': 'The weather in New York is 32 degrees'} message2: {'input': "what is docker pod which name 'lobe-chat-wzk' info? I want to
