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# Source code for langchain_community.agent_toolkits.powerbi.chat_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.chat_models import BaseChatModel

    from langchain_community.agent_toolkits.powerbi.prompt import (
        POWERBI_CHAT_PREFIX,
        POWERBI_CHAT_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
        from langchain.agents.agent import AgentOutputParser
        from langchain.memory.chat_memory import BaseChatMemory

    def create_pbi_chat_agent(
        llm: BaseChatModel,
        toolkit: Optional[PowerBIToolkit] = None,
        powerbi: Optional[PowerBIDataset] = None,
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = None,
        prefix: str = POWERBI_CHAT_PREFIX,
        suffix: str = POWERBI_CHAT_SUFFIX,
        examples: Optional[str] = None,
        input_variables: Optional[List[str]] = None,
        memory: Optional[BaseChatMemory] = 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 a Chat LLM and tools.

        If you supply only a toolkit and no Power BI dataset, the same LLM is used for both.
        """
        from langchain.agents import AgentExecutor
        from langchain.agents.conversational_chat.base import ConversationalChatAgent
        from langchain.memory import ConversationBufferMemory

        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
        agent = ConversationalChatAgent.from_llm_and_tools(
            llm=llm,
            tools=tools,
            system_message=prefix.format(top_k=top_k).format(tables=tables),
            human_message=suffix,
            input_variables=input_variables,
            callback_manager=callback_manager,
            output_parser=output_parser,
            verbose=verbose,
            **kwargs,
        )
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,
            callback_manager=callback_manager,
            memory=memory
            or ConversationBufferMemory(memory_key="chat_history", return_messages=True),
            verbose=verbose,
            **(agent_executor_kwargs or {}),
        )