satif_ai.standardize

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async def astandardize(
        datasource: Datasource,
        output_path: SDIFPath,
        *,
        overwrite: bool = False,
        sdif_schema: Optional[Union[FilePath, Dict[str, Any]]] = None,
        tidy_adapter: Union[bool, TidyAdapter] = False,
        config: Optional[Dict[str, Any]] = None,
        standardizer: Optional[AsyncStandardizer] = None,
        mcp_server: Optional[Any] = None,
        mcp_session: Optional[Any] = None,
        llm_model: Optional[str] = None) -> StandardizationResult

Asynchronously standardizes a datasource into a single, canonical SDIF SQLite file.

This function serves as the primary entry point for the SATIF standardization layer. It orchestrates the conversion of various input file formats (e.g., CSV, Excel, PDF) from the provided datasource into a unified SDIF (Standard Data Interchange Format) SQLite file. The process may involve AI-driven parsing, schema adaptation, and data tidying, depending on the configuration and the capabilities of the underlying standardizer.

Arguments:

datasource - The source of the data to be standardized. This can be a single file path (str or Path), a list of file paths, or other datasource types supported by the chosen standardizer.

output_path - The path (str or Path) where the output SDIF SQLite database file will be created (e.g., "./output/my_data.sdif").

overwrite - If True, an existing SDIF file at output_path will be overwritten. Defaults to False.

sdif_schema - Optional. Path to an SDIF schema definition file (e.g., a JSON file) or a dictionary representing the schema. If provided, the standardization process (specifically if using the default AIStandardizer) may attempt to adapt the data to this target schema.

tidy_adapter - Optional. If True, a default TidyAdapter may be used. Alternatively, a specific TidyAdapter instance can be provided to perform data tidying processes (e.g., cleaning, normalization, restructuring tables). If False (default), no explicit tidying step is initiated by this top-level function, though underlying standardizers might perform their own internal tidying. The specifics depend on the standardizer's capabilities.

config - Optional. A dictionary for advanced or standardizer-specific configurations. This config is passed directly to the output_path0 method of the chosen standardizer.

output_path1 - Optional. An instance of an output_path2 subclass. If provided, this instance will be used for standardization. This allows for using pre-configured or custom standardizers. If None, a default AIStandardizer is instantiated using output_path4, output_path5, output_path6, sdif_schema, and tidy_adapter.

output_path4 - Optional. The MCP (Model Coordination Platform) server instance. Used if output_path1 is None for the default AIStandardizer.

output_path5 - Optional. The MCP session or transport object. Used if output_path1 is None for the default AIStandardizer.

output_path6 - Optional. The language model to be used by the default AIStandardizer if no output_path1 instance is provided (e.g., "gpt-4o"). Each standardizer may have its own default model.

Returns:

A overwrite8 object containing:

output_path: The absolute output_path0 to the created or updated SDIF database file.

output_path1: An optional dictionary detailing configurations used for each processed input file, if applicable and returned by the standardizer.

Raises:

output_path2 - If the datasource (or parts of it) does not exist.

output_path4 - If output_path exists and overwrite is False.

output_path7 - If input arguments are invalid (e.g., unsupported datasource type).

output_path8 - For general errors during the standardization process. Specific exceptions may also be raised by the underlying standardizer implementation.

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