Agents

您可以将 Runnable 传递给代理。确保您已安装 langchainhub： pip install langchainhub

from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_community.chat_models import ChatAnthropic

model = ChatAnthropic(model="claude-2")

@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])

从可运行对象构建代理通常涉及以下几件事：

1. 中间步骤的数据处理。这些需要以语言模型可以识别的方式表示。这应该与提示中的说明紧密结合

2. 提示本身

3. 该模型，如果需要的话还可以包含停止标记

4. 输出解析器 - 应与提示指定要格式化的内容的方式同步。

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, verbose=True)

agent_executor.invoke({"input": "whats the weather in New york?"})

> Entering new AgentExecutor chain...
 <tool>search</tool><tool_input>weather in New York32 degrees <tool>search</tool>
<tool_input>weather in New York32 degrees <final_answer>The weather in New York is 32 degrees

> Finished chain.

{'input': 'whats the weather in New york?',
 'output': 'The weather in New York is 32 degrees'}