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
[docs]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.
Args:
llm: The language model to use.
toolkit: The Spark SQL toolkit.
callback_manager: Optional. The callback manager. Default is None.
callbacks: Optional. The callbacks. Default is None.
prefix: Optional. The prefix for the prompt. Default is SQL_PREFIX.
suffix: Optional. The suffix for the prompt. Default is SQL_SUFFIX.
format_instructions: Optional. The format instructions for the prompt.
Default is None.
input_variables: Optional. The input variables for the prompt. Default is None.
top_k: Optional. The top k for the prompt. Default is 10.
max_iterations: Optional. The maximum iterations to run. Default is 15.
max_execution_time: Optional. The maximum execution time. Default is None.
early_stopping_method: Optional. The early stopping method. Default is "force".
verbose: Optional. Whether to print verbose output. Default is False.
agent_executor_kwargs: Optional. The agent executor kwargs. Default is None.
kwargs: Any. Additional keyword arguments.
Returns:
The agent executor.
"""
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 {}),
)