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from langchain_core.tools.base import BaseTool
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
import pandas as pd
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.agents.agent_types import AgentType
class AnswerExcelTool(BaseTool):
name : str = "answer_excel_tool"
description: str = "Given the path to a file containing an excel file and a query, this tool tries to get an answer by querying the excel file."
def _run(query: str, file_path: str) -> str:
df = pd.read_excel(file_path)
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0),
agent_executor = create_pandas_dataframe_agent(
llm,
df,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
allow_dangerous_code=True # IMPORTANT: Understand the risks
)
return agent_executor(query) |