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Version: 2.30

Azure Document Intelligence

haystack_integrations.components.converters.azure_doc_intelligence.converter​

AzureDocumentIntelligenceConverter​

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

This component uses the azure-ai-documentintelligence package (v1.0.0+) and outputs GitHub Flavored Markdown for better integration with LLM/RAG applications.

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

Key features:

  • Markdown output with preserved structure (headings, tables, lists)
  • Inline table integration (tables rendered as markdown tables)
  • Improved layout analysis and reading order
  • Support for section headings

To use this component, you need an active Azure account and a Document Intelligence or Cognitive Services resource. For setup instructions, see Azure documentation.

Usage example​

python
import os
from haystack_integrations.components.converters.azure_doc_intelligence import (
    AzureDocumentIntelligenceConverter,
)
from haystack.utils import Secret

converter = AzureDocumentIntelligenceConverter(
    endpoint=os.environ["AZURE_DI_ENDPOINT"],
    api_key=Secret.from_env_var("AZURE_DI_API_KEY"),
)

results = converter.run(sources=["invoice.pdf", "contract.docx"])
documents = results["documents"]

# Documents contain markdown with inline tables
print(documents[0].content)

init​

python
__init__(
    endpoint: str,
    *,
    api_key: Secret = Secret.from_env_var("AZURE_DI_API_KEY"),
    model_id: str = "prebuilt-layout",
    store_full_path: bool = False
) -> None

Creates an AzureDocumentIntelligenceConverter component.

Parameters:

  • endpoint (str) – The endpoint URL of your Azure Document Intelligence resource. Example: "https://YOUR_RESOURCE.cognitiveservices.azure.com/"
  • api_key (Secret) – API key for Azure authentication. Can use Secret.from_env_var() to load from AZURE_DI_API_KEY environment variable.
  • model_id (str) – Azure model to use for analysis. Options:
  • "prebuilt-layout": Layout analysis with table and structure detection (default)
  • "prebuilt-read": Fast OCR for text extraction
  • Custom model IDs from your Azure resource
  • store_full_path (bool) – If True, stores complete file path in metadata. If False, stores only the filename (default).

warm_up​

python
warm_up() -> None

Initializes the Azure Document Intelligence client.

run​

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

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, list[Document] | list[dict]] – 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​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

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

from_dict​

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

Deserializes the component from a dictionary.

Parameters:

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

Returns:

  • AzureDocumentIntelligenceConverter – The deserialized component.