Source code for langchain_community.tools.spark_sql.tool

# flake8: noqa
"""Tools for interacting with Spark SQL."""
from typing import Any, Dict, Optional

from langchain_core.pydantic_v1 import BaseModel, Field, root_validator

from langchain_core.language_models import BaseLanguageModel
from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain_core.prompts import PromptTemplate
from langchain_community.utilities.spark_sql import SparkSQL
from langchain_core.tools import BaseTool
from langchain_community.tools.spark_sql.prompt import QUERY_CHECKER


[docs]class BaseSparkSQLTool(BaseModel): """Base tool for interacting with Spark SQL.""" db: SparkSQL = Field(exclude=True) class Config(BaseTool.Config): pass
[docs]class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool): """Tool for querying a Spark SQL.""" name: str = "query_sql_db" description: str = """ Input to this tool is a detailed and correct SQL query, output is a result from the Spark SQL. If the query is not correct, an error message will be returned. If an error is returned, rewrite the query, check the query, and try again. """ def _run( self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: """Execute the query, return the results or an error message.""" return self.db.run_no_throw(query)
[docs]class InfoSparkSQLTool(BaseSparkSQLTool, BaseTool): """Tool for getting metadata about a Spark SQL.""" name: str = "schema_sql_db" description: str = """ Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables. Be sure that the tables actually exist by calling list_tables_sql_db first! Example Input: "table1, table2, table3" """ def _run( self, table_names: str, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: """Get the schema for tables in a comma-separated list.""" return self.db.get_table_info_no_throw(table_names.split(", "))
[docs]class ListSparkSQLTool(BaseSparkSQLTool, BaseTool): """Tool for getting tables names.""" name: str = "list_tables_sql_db" description: str = "Input is an empty string, output is a comma separated list of tables in the Spark SQL." def _run( self, tool_input: str = "", run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: """Get the schema for a specific table.""" return ", ".join(self.db.get_usable_table_names())
[docs]class QueryCheckerTool(BaseSparkSQLTool, BaseTool): """Use an LLM to check if a query is correct. Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/""" template: str = QUERY_CHECKER llm: BaseLanguageModel llm_chain: Any = Field(init=False) name: str = "query_checker_sql_db" description: str = """ Use this tool to double check if your query is correct before executing it. Always use this tool before executing a query with query_sql_db! """ @root_validator(pre=True) def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: if "llm_chain" not in values: from langchain.chains.llm import LLMChain values["llm_chain"] = LLMChain( llm=values.get("llm"), # type: ignore[arg-type] prompt=PromptTemplate( template=QUERY_CHECKER, input_variables=["query"] ), ) if values["llm_chain"].prompt.input_variables != ["query"]: raise ValueError( "LLM chain for QueryCheckerTool need to use ['query'] as input_variables " "for the embedded prompt" ) return values def _run( self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: """Use the LLM to check the query.""" return self.llm_chain.predict( query=query, callbacks=run_manager.get_child() if run_manager else None ) async def _arun( self, query: str, run_manager: Optional[AsyncCallbackManagerForToolRun] = None, ) -> str: return await self.llm_chain.apredict( query=query, callbacks=run_manager.get_child() if run_manager else None )