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# Source code for langchain_community.agent_toolkits.spark_sql.base

    """Spark SQL agent."""
    from __future__ import annotations

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

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

    from langchain_community.agent_toolkits.spark_sql.prompt import SQL_PREFIX, SQL_SUFFIX
    from langchain_community.agent_toolkits.spark_sql.toolkit import SparkSQLToolkit

    if TYPE_CHECKING:
        from langchain.agents.agent import AgentExecutor

    def create_spark_sql_agent(
        llm: BaseLanguageModel,
        toolkit: SparkSQLToolkit,
        callback_manager: Optional[BaseCallbackManager] = None,
        callbacks: Callbacks = None,
        prefix: str = SQL_PREFIX,
        suffix: str = SQL_SUFFIX,
        format_instructions: Optional[str] = None,
        input_variables: Optional[List[str]] = None,
        top_k: int = 10,
        max_iterations: Optional[int] = 15,
        max_execution_time: Optional[float] = None,
        early_stopping_method: str = "force",
        verbose: bool = False,
        agent_executor_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> AgentExecutor:
        """Construct a Spark SQL agent from an LLM and tools."""
        from langchain.agents.agent import AgentExecutor
        from langchain.agents.mrkl.base import ZeroShotAgent
        from langchain.chains.llm import LLMChain

        tools = toolkit.get_tools()
        prefix = prefix.format(top_k=top_k)
        prompt_params = (
            {"format_instructions": format_instructions}
            if format_instructions is not None
            else {}
        )
        prompt = ZeroShotAgent.create_prompt(
            tools,
            prefix=prefix,
            suffix=suffix,
            input_variables=input_variables,
            **prompt_params,
        )
        llm_chain = LLMChain(
            llm=llm,
            prompt=prompt,
            callback_manager=callback_manager,
            callbacks=callbacks,
        )
        tool_names = [tool.name for tool in tools]
        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
        return AgentExecutor.from_agent_and_tools(
            agent=agent,
            tools=tools,
            callback_manager=callback_manager,
            callbacks=callbacks,
            verbose=verbose,
            max_iterations=max_iterations,
            max_execution_time=max_execution_time,
            early_stopping_method=early_stopping_method,
            **(agent_executor_kwargs or {}),
        )