langchain.tools.spark_sql.tool.QueryCheckerTool¶
class langchain.tools.spark_sql.tool.QueryCheckerTool[source]¶
Bases: 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/
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param args_schema: Optional[Type[BaseModel]] = None¶
Pydantic model class to validate and parse the tool’s input arguments.
param callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated. Please use callbacks instead.
param callbacks: Callbacks = None¶
Callbacks to be called during tool execution.
param db: SparkSQL [Required]¶
param description: str = '\n    Use this tool to double check if your query is correct before executing it.\n    Always use this tool before executing a query with query_sql_db!\n    '¶
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False¶
Handle the content of the ToolException thrown.
param llm: langchain.schema.language_model.BaseLanguageModel [Required]¶
param llm_chain: langchain.chains.llm.LLMChain [Required]¶
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the tool. Defaults to None
This metadata will be associated with each call to this tool,
and passed as arguments to the handlers defined in callbacks.