langchain/libs/experimental/langchain_experimental/agents/agent_toolkits/spark/base.py

"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional

from langchain.agents.agent import AgentExecutor
from langchain.agents.mrkl.base import ZeroShotAgent
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains.llm import LLMChain
from langchain.llms.base import BaseLLM

from langchain_experimental.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain_experimental.tools.python.tool import PythonAstREPLTool


def _validate_spark_df(df: Any) -> bool:
    try:
        from pyspark.sql import DataFrame as SparkLocalDataFrame

        return isinstance(df, SparkLocalDataFrame)
    except ImportError:
        return False


def _validate_spark_connect_df(df: Any) -> bool:
    try:
        from pyspark.sql.connect.dataframe import DataFrame as SparkConnectDataFrame

        return isinstance(df, SparkConnectDataFrame)
    except ImportError:
        return False


def create_spark_dataframe_agent(
    llm: BaseLLM,
    df: Any,
    callback_manager: Optional[BaseCallbackManager] = None,
    prefix: str = PREFIX,
    suffix: str = SUFFIX,
    input_variables: Optional[List[str]] = None,
    verbose: bool = False,
    return_intermediate_steps: bool = False,
    max_iterations: Optional[int] = 15,
    max_execution_time: Optional[float] = None,
    early_stopping_method: str = "force",
    agent_executor_kwargs: Optional[Dict[str, Any]] = None,
    **kwargs: Any,
) -> AgentExecutor:
    """Construct a Spark agent from an LLM and dataframe."""

    if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
        raise ImportError("Spark is not installed. run `pip install pyspark`.")

    if input_variables is None:
        input_variables = ["df", "input", "agent_scratchpad"]
    tools = [PythonAstREPLTool(locals={"df": df})]
    prompt = ZeroShotAgent.create_prompt(
        tools, prefix=prefix, suffix=suffix, input_variables=input_variables
    )
    partial_prompt = prompt.partial(df=str(df.first()))
    llm_chain = LLMChain(
        llm=llm,
        prompt=partial_prompt,
        callback_manager=callback_manager,
    )
    tool_names = [tool.name for tool in tools]
    agent = ZeroShotAgent(
        llm_chain=llm_chain,
        allowed_tools=tool_names,
        callback_manager=callback_manager,
        **kwargs,
    )
    return AgentExecutor.from_agent_and_tools(
        agent=agent,
        tools=tools,
        callback_manager=callback_manager,
        verbose=verbose,
        return_intermediate_steps=return_intermediate_steps,
        max_iterations=max_iterations,
        max_execution_time=max_execution_time,
        early_stopping_method=early_stopping_method,
        **(agent_executor_kwargs or {}),
    )