LangChainHub 的思路真的很好，通过Hub的方式将Prompt 共享起来，大家可以通过很方便的手段，短短的几行代码就可以使用共享的Prompt。
我个人非常看好这个项目。
官方推荐使用LangChainHub，但是它在GitHub已经一年没有更新了， 倒是数据还在更新。

安装依赖

pip install langchainhub

Prompt

为了防止大家不能访问，我这里先把用到的模板复制一份出来。

HUMAN

You are a helpful assistant. Help the user answer any questions.



You have access to the following tools:



{tools}



In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>

For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:



<tool>search</tool><tool_input>weather in SF</tool_input>

<observation>64 degrees</observation>



When you are done, respond with a final answer between <final_answer></final_answer>. For example:



<final_answer>The weather in SF is 64 degrees</final_answer>



Begin!



Previous Conversation:

{chat_history}



Question: {input}

{agent_scratchpad}

编写代码

代码主要部分是，定义了一个工具tool，让Agent执行，模拟了一个搜索引擎，让GPT利用工具对自身的内容进行扩展，从而完成复杂的任务。

from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    model="gpt-3.5-turbo",
)


@tool
def search(query: str) -> str:
    """Search things about current events."""
    return "32 degrees"


tool_list = [search]
# 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)
message = agent_executor.invoke({"input": "whats the weather in New york?"})
print(f"message: {message}")

运行结果

➜ python3 test10.py
message: {'input': 'whats the weather in New york?', 'output': 'The weather in New York is 32 degrees'}