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#

# Source code for langchain.agents.agent_toolkits.powerbi.chat_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_CHAT_PREFIX,
        POWERBI_CHAT_SUFFIX,
    )
    from langchain.agents.agent_toolkits.powerbi.toolkit import PowerBIToolkit
    from langchain.agents.conversational_chat.base import ConversationalChatAgent
    from langchain.callbacks.base import BaseCallbackManager
    from langchain.chat_models.base import BaseChatModel
    from langchain.memory import ConversationBufferMemory
    from langchain.memory.chat_memory import BaseChatMemory
    from langchain.utilities.powerbi import PowerBIDataset

    def create_pbi_chat_agent(
        llm: BaseChatModel,
        toolkit: Optional[PowerBIToolkit],
        powerbi: Optional[PowerBIDataset] = None,
        callback_manager: Optional[BaseCallbackManager] = 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_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Dict[str, Any],
    ) -> AgentExecutor:
        """Construct a pbi agent from an Chat LLM and tools.

        If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
        """
        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 = ConversationalChatAgent.from_llm_and_tools(
            llm=llm,
            tools=tools,
            system_message=prefix.format(top_k=top_k),
            user_message=suffix,
            input_variables=input_variables,
            callback_manager=callback_manager,
            verbose=verbose,
            **(agent_kwargs or {}),
        )
        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,
            **kwargs,
        )