自定义多动作代理

本笔记本介绍如何创建自己的自定义代理。

代理由两部分组成：

• 工具：代理可用的工具。
• 代理类本身：决定采取哪个动作。

在本笔记本中，我们将演示如何创建一个可以预测/一次采取多个步骤的自定义代理。

from langchain.agents import Tool, AgentExecutor, BaseMultiActionAgent
from langchain import OpenAI, SerpAPIWrapper

def random_word(query: str) -> str:
    print("\nNow I'm doing this!")
    return "foo"

search = SerpAPIWrapper()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events",
    ),
    Tool(
        name="RandomWord",
        func=random_word,
        description="call this to get a random word.",
    ),
]

from typing import List, Tuple, Any, Union
from langchain.schema import AgentAction, AgentFinish


class FakeAgent(BaseMultiActionAgent):
    """Fake Custom Agent."""

    @property
    def input_keys(self):
        return ["input"]

    def plan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.

        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.

        Returns:
            Action specifying what tool to use.
        """
        if len(intermediate_steps) == 0:
            return [
                AgentAction(tool="Search", tool_input=kwargs["input"], log=""),
                AgentAction(tool="RandomWord", tool_input=kwargs["input"], log=""),
            ]
        else:
            return AgentFinish(return_values={"output": "bar"}, log="")

    async def aplan(
        self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
    ) -> Union[List[AgentAction], AgentFinish]:
        """Given input, decided what to do.

        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observations
            **kwargs: User inputs.

        Returns:
            Action specifying what tool to use.
        """
        if len(intermediate_steps) == 0:
            return [
                AgentAction(tool="Search", tool_input=kwargs["input"], log=""),
                AgentAction(tool="RandomWord", tool_input=kwargs["input"], log=""),
            ]
        else:
            return AgentFinish(return_values={"output": "bar"}, log="")


agent = FakeAgent()

agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent, tools=tools, verbose=True
)

agent_executor.run("How many people live in canada as of 2023?")

> Entering new AgentExecutor chain...
The current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.
Now I'm doing this!
foo
> Finished chain.