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#

# Source code for langchain.agents.agent_toolkits.powerbi.base

    """Power BI agent."""
    from typing import Any, Dict, List, Optional

    from langchain.agents import AgentExecutor
    from langchain.agents.agent_toolkits.powerbi.prompt import (
        POWERBI_PREFIX,
        POWERBI_SUFFIX,
    )
    from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
    from langchain.agents.mrkl.base import ZeroShotAgent
    from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
    from langchain.callbacks.base import BaseCallbackManager
    from langchain.chains.llm import LLMChain
    from langchain.llms.base import BaseLLM
    from langchain.utilities.powerbi import PowerBIDataset

    def create_pbi_agent(
        llm: BaseLLM,
        toolkit: Optional[PowerBIToolkit],
        powerbi: Optional[PowerBIDataset] = None,
        callback_manager: Optional[BaseCallbackManager] = None,
        prefix: str = POWERBI_PREFIX,
        suffix: str = POWERBI_SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        examples: Optional[str] = None,
        input_variables: Optional[List[str]] = None,
        top_k: int = 10,
        verbose: bool = False,
        agent_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Dict[str, Any],
    ) -> AgentExecutor:
        """Construct a pbi agent from an LLM and tools."""
        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()

        agent = ZeroShotAgent(
            llm_chain=LLMChain(
                llm=llm,
                prompt=ZeroShotAgent.create_prompt(
                    tools,
                    prefix=prefix.format(top_k=top_k),
                    suffix=suffix,
                    format_instructions=format_instructions,
                    input_variables=input_variables,
                ),
                callback_manager=callback_manager,  # type: ignore
                verbose=verbose,
            ),
            allowed_tools=[tool.name for tool in tools],
            **(agent_kwargs or {}),
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,
            callback_manager=callback_manager,
            verbose=verbose,
            **kwargs,
        )