deepset-ai--haystack/docs-website/reference/integrations-api/azure_form_recognizer.md

title, id, description, slug

title	id	description	slug
Azure Form Recognizer	integrations-azure_form_recognizer	Azure Form Recognizer integration for Haystack	/integrations-azure_form_recognizer

haystack_integrations.components.converters.azure_form_recognizer.converter

AzureOCRDocumentConverter

Converts files to documents using Azure's Document Intelligence service.

Supported file formats are: PDF, JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML.

To use this component, you need an active Azure account and a Document Intelligence or Cognitive Services resource. For help with setting up your resource, see Azure documentation.

Usage example

import os
from datetime import datetime
from haystack_integrations.components.converters.azure_form_recognizer import AzureOCRDocumentConverter
from haystack.utils import Secret

converter = AzureOCRDocumentConverter(
    endpoint=os.environ["CORE_AZURE_CS_ENDPOINT"],
    api_key=Secret.from_env_var("CORE_AZURE_CS_API_KEY"),
)
results = converter.run(
    sources=["test/test_files/pdf/react_paper.pdf"],
    meta={"date_added": datetime.now().isoformat()},
)
documents = results["documents"]
print(documents[0].content)
# 'This is a text from the PDF file.'

init

__init__(
    endpoint: str,
    api_key: Secret = Secret.from_env_var("AZURE_AI_API_KEY"),
    model_id: str = "prebuilt-read",
    preceding_context_len: int = 3,
    following_context_len: int = 3,
    merge_multiple_column_headers: bool = True,
    page_layout: Literal["natural", "single_column"] = "natural",
    threshold_y: float | None = 0.05,
    store_full_path: bool = False,
) -> None

Creates an AzureOCRDocumentConverter component.

Parameters:

• endpoint (str) – The endpoint of your Azure resource.
• api_key (Secret) – The API key of your Azure resource.
• model_id (str) – The ID of the model you want to use. For a list of available models, see [Azure documentation] (https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature).
• preceding_context_len (int) – Number of lines before a table to include as preceding context (this will be added to the metadata).
• following_context_len (int) – Number of lines after a table to include as subsequent context ( this will be added to the metadata).
• merge_multiple_column_headers (bool) – If True, merges multiple column header rows into a single row.
• page_layout (Literal['natural', 'single_column']) – The type reading order to follow. Possible options:
• natural: Uses the natural reading order determined by Azure.
• single_column: Groups all lines with the same height on the page based on a threshold determined by threshold_y.
• threshold_y (float | None) – Only relevant if single_column is set to page_layout. The threshold, in inches, to determine if two recognized PDF elements are grouped into a single line. This is crucial for section headers or numbers which may be spatially separated from the remaining text on the horizontal axis.
• store_full_path (bool) – If True, the full path of the file is stored in the metadata of the document. If False, only the file name is stored.

run

run(
    sources: list[str | Path | ByteStream],
    meta: dict[str, Any] | list[dict[str, Any]] | None = None,
) -> dict[str, Any]

Convert a list of files to Documents using Azure's Document Intelligence service.

Parameters:

• sources (list[str | Path | ByteStream]) – List of file paths or ByteStream objects.
• meta (dict[str, Any] | list[dict[str, Any]] | None) – Optional metadata to attach to the Documents. This value can be either a list of dictionaries or a single dictionary. If it's a single dictionary, its content is added to the metadata of all produced Documents. If it's a list, the length of the list must match the number of sources, because the two lists will be zipped. If sources contains ByteStream objects, their meta will be added to the output Documents.

Returns:

• dict[str, Any] – A dictionary with the following keys:
• documents: List of created Documents
• raw_azure_response: List of raw Azure responses used to create the Documents

to_dict

to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

• dict[str, Any] – Dictionary with serialized data.

from_dict

from_dict(data: dict[str, Any]) -> AzureOCRDocumentConverter

Deserializes the component from a dictionary.

Parameters:

• data (dict[str, Any]) – The dictionary to deserialize from.

Returns:

• AzureOCRDocumentConverter – The deserialized component.