how to create a react agent with csv_agent embedded inside , or with csv ability as tool?

[652994331]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

mrkl= initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)

Description

hi , I am new to langchain, it's awesome. just finished "toolkit (csv_agent)" and agent examples parts. from agent examples, i learnt a lot of methods how to build an react agents. I think the key component is: agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory). I found some agentType in the package but there is no csv , pandas and ..... . however, for toolkit part , we have some csv_agent, pandas_agent that are build with : agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True). create_csv_agent , create_panda_agent ... . my question is, what if I wanna build a zero shot react agent with the ability of csv_agent inside , like making csv_agent a tool or something. but I don't know how to make it. I found someone is using python ability to create agent. but #11429 shows that "PythonREPLTool" is a tool and build a react agent with python toolkinda make sense. but I can't find any csv , panda tool that can be use.

System Info

langchain/langchain_experimental == 0.1.5
ubuntu

[dosubot[bot]]

🤖

Hey @652994331, great to see you diving into LangChain again! Always a pleasure to help out a familiar face. 🚀

To create a zero-shot react agent in LangChain with the ability of a csv_agent embedded inside, you would need to create a csv_agent as a BaseTool and include it in the tools sequence when creating the react agent.

Here's an example of how you might do this:

from langchain import hub
from langchain_community.llms import OpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools.csv_agent import CSVAgent  # Assuming CSVAgent is a BaseTool

prompt = hub.pull("hwchase17/react")
model = OpenAI()
csv_agent = CSVAgent()  # Initialize your CSVAgent
tools = [csv_agent]  # Include CSVAgent in the tools sequence

agent = create_react_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

agent_executor.invoke({"input": "hi"})

In this example, CSVAgent is assumed to be a BaseTool that you have implemented. The CSVAgent should be able to handle CSV-related tasks. When you create the react agent, you include the CSVAgent in the tools sequence. This allows the react agent to use the CSVAgent when it needs to perform CSV-related tasks.

Please note that the actual implementation of CSVAgent is not provided in the context, so you would need to implement it yourself or find an existing implementation.

The BaseTool class in the LangChain codebase is an abstract class that provides a blueprint for creating tools in the LangChain framework. It inherits from the RunnableSerializable class and implements several methods and properties. The BaseTool class has two abstract methods _run and _arun that must be implemented by any class that inherits from BaseTool. These methods define the synchronous and asynchronous execution of the tool respectively.

Here is a simplified version of the BaseTool class:

class BaseTool(RunnableSerializable[Union[str, Dict], Any]):
    """Interface LangChain tools must implement."""

    name: str
    description: str
    args_schema: Optional[Type[BaseModel]] = None
    return_direct: bool = False
    verbose: bool = False
    callbacks: Callbacks = Field(default=None, exclude=True)
    callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
    tags: Optional[List[str]] = None
    metadata: Optional[Dict[str, Any]] = None
    handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False
    handle_validation_error: Optional[Union[bool, str, Callable[[ValidationError], str]]] = False

    @abstractmethod
    def _run(self, *args: Any, **kwargs: Any) -> Any:
        """Use the tool."""

    async def _arun(self, *args: Any, **kwargs: Any) -> Any:
        """Use the tool asynchronously."""

This class is used as a base for creating specific tools in the LangChain framework. Each tool must implement the _run and _arun methods to define its functionality.

You can find more information about the BaseTool class in the LangChain codebase.

Sources

• libs/langchain/langchain/agents/react/agent.py
• libs/core/langchain_core/tools.py

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[HelloWorldLTY]

Does this work?