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

[652994331]

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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.

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[HelloWorldLTY]

Does this work?

[JennyTan5522]

Hi,

To create a react agent with the ability to embed csv_agent as a tool inside:
first, you may need convert your csv into DataFrame or store your CSV data into temporary database. This is especially useful for handling large datasets since LLMs might not good to directly evaluate large amounts of data and may produce incorrect results. Instead, you can use the LLM to generate SQL queries and rely on a database engine to execute those queries...

check the discussion from reddit (https://www.reddit.com/r/LocalLLaMA/comments/13nld5x/how_do_i_get_local_llm_to_analyze_an_whole_excel/)

Convert CSV Data into SQL

import pandas as pd
from sqlalchemy import create_engine, inspect
from langchain_community.tools.sql_database.tool import SQLDatabase

def data_ingestion(
    filename,
    table_name="data",
    db_url="sqlite:///data/data.db",
):
    # Load the CSV file into a DataFrame
    df = pd.read_csv(filename)
    # Replace spaces in column names with underscores for easier SQL querying
    df.columns = df.columns.str.replace(" ", "_")

    # Create a database engine and store the DataFrame into a SQL table
    engine = create_engine(db_url)
    df.to_sql(table_name, engine, if_exists="replace", index=False)

    # Optional: Inspect the table structure
    inspector = inspect(engine)
    columns_info = inspector.get_columns(table_name)

    # Return the SQL database for further integration
    return SQLDatabase.from_uri(db_url)

Custom DatabaseSQLToolkit
Refers to: https://python.langchain.com/docs/integrations/tools/sql_database/

import re
from typing import Any, Dict, List, Optional, Sequence, Union
from sqlalchemy.engine import Result
from langchain_community.agent_toolkits import SQLDatabaseToolkit
from langchain_community.tools.sql_database.tool import (
    InfoSQLDatabaseTool,
    ListSQLDatabaseTool,
    QuerySQLCheckerTool,
    QuerySQLDataBaseTool,
)
from langchain_core.callbacks import CallbackManagerForToolRun

class CustomSQLToolkit(SQLDatabaseToolkit):
    class _CustomQuerySQLDataBaseTool(QuerySQLDataBaseTool):
        description: str = (
            "Execute a SQL query against the database and get back the result. "
            "If the query is not correct, an error message will be returned. "
            "If an error is returned, rewrite the query, check the query, and try again."
        )
        
        def _run(
            self,
            query: str,
            run_manager: Optional[CallbackManagerForToolRun] = None,
        ) -> Union[str, Sequence[Dict[str, Any]], Result]:
            # Extract SQL query if wrapped in markdown
            if "```sql" in query:
                query = re.search(r"```sql(.*?)```", query, re.DOTALL).group(1)
            # Run the query and return results or errors
            result = self.db.run_no_throw(query)
            return "SQL Query returned no data" if not result else result

    def get_tools(self) -> List:
        """Get a list of tools in the toolkit."""
        list_sql_database_tool = ListSQLDatabaseTool(db=self.db)
        info_sql_database_tool = InfoSQLDatabaseTool(
            db=self.db, 
            description="Fetch schema and sample rows for tables."
        )
        query_sql_database_tool = self._CustomQuerySQLDataBaseTool(
            db=self.db, 
            description="Execute detailed SQL queries against the database."
        )
        query_sql_checker_tool = QuerySQLCheckerTool(
            db=self.db, 
            llm=self.llm, 
            description="Validate SQL queries before execution."
        )
        return [
            query_sql_database_tool,
            info_sql_database_tool,
            list_sql_database_tool,
            query_sql_checker_tool,
        ]

Integrating the tools

# Create the custom toolkit
db_toolkit = CustomSQLToolkit(db=db, llm=llm)

# Retrieve the tools and add them to your agent
tools = db_toolkit.get_tools()
agent_tools = tools + [ /* other tools you may want to include */ ]

Hit like if you found this helpful 👍😊

[JennyTan5522]

sure! jennytan5522@gmail.com