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# Source code for langchain_community.agent_toolkits.powerbi.base

    """Power BI agent."""
    from __future__ import annotations

    from typing import TYPE_CHECKING, Any, Dict, List, Optional

    from langchain_core.callbacks import BaseCallbackManager
    from langchain_core.language_models import BaseLanguageModel

    from langchain_community.agent_toolkits.powerbi.prompt import (
        POWERBI_PREFIX,
        POWERBI_SUFFIX,
    )
    from langchain_community.agent_toolkits.powerbi.toolkit import PowerBIToolkit
    from langchain_community.utilities.powerbi import PowerBIDataset

    if TYPE_CHECKING:
        from langchain.agents import AgentExecutor

    def create_pbi_agent(
        llm: BaseLanguageModel,
        toolkit: Optional[PowerBIToolkit] = None,
        powerbi: Optional[PowerBIDataset] = None,
        callback_manager: Optional[BaseCallbackManager] = None,
        prefix: str = POWERBI_PREFIX,
        suffix: str = POWERBI_SUFFIX,
        format_instructions: Optional[str] = None,
        examples: Optional[str] = None,
        input_variables: Optional[List[str]] = None,
        top_k: int = 10,
        verbose: bool = False,
        agent_executor_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> AgentExecutor:
        """Construct a Power BI agent from an LLM and tools."""
        from langchain.agents import AgentExecutor
        from langchain.agents.mrkl.base import ZeroShotAgent
        from langchain.chains.llm import LLMChain

        if toolkit is None:
            if powerbi is None:
                raise ValueError("Must provide either a toolkit or powerbi dataset")
            toolkit = PowerBIToolkit(powerbi=powerbi, llm=llm, examples=examples)
        tools = toolkit.get_tools()
        tables = powerbi.table_names if powerbi else toolkit.powerbi.table_names
        prompt_params = (
            {"format_instructions": format_instructions}
            if format_instructions is not None
            else {}
        )
        agent = ZeroShotAgent(
            llm_chain=LLMChain(
                llm=llm,
                prompt=ZeroShotAgent.create_prompt(
                    tools,
                    prefix=prefix.format(top_k=top_k).format(tables=tables),
                    suffix=suffix,
                    input_variables=input_variables,
                    **prompt_params,
                ),
                callback_manager=callback_manager,
                verbose=verbose,
            ),
            allowed_tools=[tool.name for tool in tools],
            **kwargs,
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,
            callback_manager=callback_manager,
            verbose=verbose,
            **(agent_executor_kwargs or {}),
        )