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 subprocessmodel = 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.stdouttool_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 know it 'Image' url", 'output': 'The Image URL for the docker pod named \'lobe-chat-wzk\' is "lobehub/lobe-chat"'}