IndexingParametersConfiguration Class

Return a dict that can be turned into json using json.dump.

as_dict(*, exclude_readonly: bool = False) -> dict[str, Any]

Keyword-Only Parameters

Returns

Remove all items from D.

clear() -> None

copy() -> Model

Get the value for key if key is in the dictionary, else default. :param str key: The key to look up. :param any default: The value to return if key is not in the dictionary. Defaults to None :returns: D[k] if k in D, else d. :rtype: any

get(key: str, default: Any = None) -> Any

Parameters

items() -> ItemsView[str, Any]

Returns

keys() -> KeysView[str]

Returns

Removes specified key and return the corresponding value. :param str key: The key to pop. :param any default: The value to return if key is not in the dictionary :returns: The value corresponding to the key. :rtype: any :raises KeyError: If key is not found and default is not given.

pop(key: str, default: ~typing.Any = <object object>) -> Any

Parameters

Removes and returns some (key, value) pair :returns: The (key, value) pair. :rtype: tuple :raises KeyError: if D is empty.

popitem() -> tuple[str, Any]

Same as calling D.get(k, d), and setting D[k]=d if k not found :param str key: The key to look up. :param any default: The value to set if key is not in the dictionary :returns: D[k] if k in D, else d. :rtype: any

setdefault(key: str, default: ~typing.Any = <object object>) -> Any

Parameters

Updates D from mapping/iterable E and F. :param any args: Either a mapping object or an iterable of key-value pairs.

update(*args: Any, **kwargs: Any) -> None

values() -> ValuesView[Any]

Returns

If true, will create a path //document//file_data that is an object representing the original file data downloaded from your blob data source. This allows you to pass the original file data to a custom skill for processing within the enrichment pipeline, or to the Document Extraction skill.

allow_skillset_to_read_file_data: bool | None

Specifies the data to extract from Azure blob storage and tells the indexer which data to extract from image content when "imageAction" is set to a value other than "none". This applies to embedded image content in a .PDF or other application, or image files such as .jpg and .png, in Azure blobs. Known values are: "storageMetadata", "allMetadata", and "contentAndMetadata".

data_to_extract: str | _models.BlobIndexerDataToExtract | None

For CSV blobs, specifies the end-of-line single-character delimiter for CSV files where each line starts a new document (for example, "|").

delimited_text_delimiter: str | None

For CSV blobs, specifies a comma-delimited list of column headers, useful for mapping source fields to destination fields in an index.

delimited_text_headers: str | None

For JSON arrays, given a structured or semi-structured document, you can specify a path to the array using this property.

document_root: str | None

Comma-delimited list of filename extensions to ignore when processing from Azure blob storage. For example, you could exclude ".png, .mp4" to skip over those files during indexing.

excluded_file_name_extensions: str | None

"standard" and "private".

execution_environment: str | _models.IndexerExecutionEnvironment | None

For Azure blobs, set to false if you want to continue indexing if a document fails indexing.

fail_on_unprocessable_document: bool | None

For Azure blobs, set to false if you want to continue indexing when an unsupported content type is encountered, and you don't know all the content types (file extensions) in advance.

fail_on_unsupported_content_type: bool | None

For CSV blobs, indicates that the first (non-blank) line of each blob contains headers.

first_line_contains_headers: bool | None

Determines how to process embedded images and image files in Azure blob storage. Setting the "imageAction" configuration to any value other than "none" requires that a skillset also be attached to that indexer. Known values are: "none", "generateNormalizedImages", and "generateNormalizedImagePerPage".

image_action: str | _models.BlobIndexerImageAction | None

For Azure blobs, set this property to true to still index storage metadata for blob content that is too large to process. Oversized blobs are treated as errors by default. For limits on blob size, see https://learn.microsoft.com/azure/search/search-limits-quotas-capacity.

index_storage_metadata_only_for_oversized_documents: bool | None

Comma-delimited list of filename extensions to select when processing from Azure blob storage. For example, you could focus indexing on specific application files ".docx, .pptx, .msg" to specifically include those file types.

indexed_file_name_extensions: str | None

Specifies the max header depth that will be considered while grouping markdown content. Default is h6. Known values are: "h1", "h2", "h3", "h4", "h5", and "h6".

markdown_header_depth: str | _models.MarkdownHeaderDepth | None

Specifies the submode that will determine whether a markdown file will be parsed into exactly one search document or multiple search documents. Default is oneToMany. Known values are: "oneToMany" and "oneToOne".

markdown_parsing_submode: str | _models.MarkdownParsingSubmode | None

Represents the parsing mode for indexing from an Azure blob data source. Known values are: "default", "text", "delimitedText", "json", "jsonArray", "jsonLines", and "markdown".

parsing_mode: str | _models.BlobIndexerParsingMode | None

Determines algorithm for text extraction from PDF files in Azure blob storage. Known values are: "none" and "detectAngles".

pdf_text_rotation_algorithm: str | _models.BlobIndexerPDFTextRotationAlgorithm | None

Increases the timeout beyond the 5-minute default for Azure SQL database data sources, specified in the format "hh:mm:ss".

query_timeout: str | None