langchain.tools.powerbi.tool.QueryPowerBITool

class langchain.tools.powerbi.tool.QueryPowerBITool(*, name: str = 'query_powerbi', description: str = '\n    Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.\n\n    Example Input: "How many rows are in table1?"\n    ', args_schema: Optional[Type[BaseModel]] = None, return_direct: bool = False, verbose: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False, llm_chain: LLMChain, powerbi: PowerBIDataset, examples: Optional[str] = '\nQuestion: How many rows are in the table <table>?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(<table>))\n----\nQuestion: How many rows are in the table <table> where <column> is not empty?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(<table>, <table>[<column>] <> "")))\n----\nQuestion: What was the average of <column> in <table>?\nDAX: EVALUATE ROW("Average", AVERAGE(<table>[<column>]))\n----\n', session_cache: Dict[str, Any] = None, max_iterations: int = 5, output_token_limit: int = 4000, tiktoken_model_name: Optional[str] = None)

Bases: BaseTool

Tool for querying a Power BI Dataset.

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 description: str = '\n    Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.\n\n    Example Input: "How many rows are in table1?"\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 examples: Optional[str] = '\nQuestion: How many rows are in the table <table>?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(<table>))\n----\nQuestion: How many rows are in the table <table> where <column> is not empty?\nDAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(<table>, <table>[<column>] <> "")))\n----\nQuestion: What was the average of <column> in <table>?\nDAX: EVALUATE ROW("Average", AVERAGE(<table>[<column>]))\n----\n'

param handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = False

Handle the content of the ToolException thrown.

param llm_chain: langchain.chains.llm.LLMChain [Required]

param max_iterations: int = 5

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. You can use these to eg identify a specific instance of a tool with its use case.

param name: str = 'query_powerbi'

The unique name of the tool that clearly communicates its purpose.

param output_token_limit: int = 4000

param powerbi: langchain.utilities.powerbi.PowerBIDataset [Required]

param return_direct: bool = False

Whether to return the tool’s output directly. Setting this to True means

that after the tool is called, the AgentExecutor will stop looping.

param session_cache: Dict[str, Any] [Optional]

param tags: Optional[List[str]] = None

Optional list of tags associated with the tool. Defaults to None These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

param tiktoken_model_name: Optional[str] = None

param verbose: bool = False

Whether to log the tool’s progress.

__call__(tool_input: str, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → str

Make tool callable.

async ainvoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any

async arun(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

Run the tool asynchronously.

invoke(input: Union[str, Dict], config: Optional[RunnableConfig] = None, **kwargs: Any) → Any

validator raise_deprecation  »  all fields

Raise deprecation warning if callback_manager is used.

run(tool_input: Union[str, Dict], verbose: Optional[bool] = None, start_color: Optional[str] = 'green', color: Optional[str] = 'green', callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

Run the tool.

validator validate_llm_chain_input_variables  »  llm_chain

Make sure the LLM chain has the correct input variables.

property args: dict

property is_single_input: bool

Whether the tool only accepts a single input.

model Config

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True