AzureDocumentIntelligenceConverter

AzureDocumentIntelligenceConverter converts files to Documents using Azure's Document Intelligence service with GitHub Flavored Markdown output for better LLM/RAG integration. It supports the following file formats: PDF (both searchable and image-only), JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML.

Most common position in a pipeline	Before PreProcessors, or right at the beginning of an indexing pipeline
Mandatory init variables	endpoint: The endpoint URL of your Azure Document Intelligence resource api_key: The API key for Azure authentication. Can be set with AZURE_DI_API_KEY environment variable.
Mandatory run variables	sources: A list of file paths or ByteStream objects
Output variables	documents: A list of documents raw_azure_response: A list of raw responses from Azure
API reference	Azure Document Intelligence
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/azure_doc_intelligence

Overview

AzureDocumentIntelligenceConverter takes a list of file paths or ByteStream objects as input and uses Azure's Document Intelligence service to convert the files to a list of documents. Optionally, metadata can be attached to the documents through the meta input parameter. You need an active Azure account and a Document Intelligence or Cognitive Services resource to use this integration. Follow the steps described in the Azure documentation to set up your resource.

The component uses an AZURE_DI_API_KEY environment variable by default. Otherwise, you can pass an api_key at initialization — see code examples below.

This component uses the azure-ai-documentintelligence package (v1.0.0+) and outputs GitHub Flavored Markdown, preserving document structure such as headings, tables, and lists. Tables are rendered as inline markdown tables rather than being extracted as separate documents.

When you initialize the component, you can optionally set the model_id, which refers to the model you want to use. Available options include:

• "prebuilt-document": General document analysis (default)
• "prebuilt-read": Fast OCR for text extraction
• "prebuilt-layout": Enhanced layout analysis with better table and structure detection
• Custom model IDs from your Azure resource

Refer to the Azure documentation for a full list of available models.

info

This component replaces the legacy AzureOCRDocumentConverter, which uses the older azure-ai-formrecognizer package. The AzureDocumentIntelligenceConverter uses the newer azure-ai-documentintelligence SDK and produces Markdown output instead of plain text, making it better suited for LLM and RAG applications.

Usage

You need to install the azure-doc-intelligence-haystack integration to use the AzureDocumentIntelligenceConverter:

shell

pip install azure-doc-intelligence-haystack

On its own

python

from pathlib import Path

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

converter = AzureDocumentIntelligenceConverter(
    endpoint="https://YOUR_RESOURCE.cognitiveservices.azure.com/",
    api_key=Secret.from_env_var("AZURE_DI_API_KEY"),
)

result = converter.run(sources=[Path("my_file.pdf")])
documents = result["documents"]

In a pipeline

python

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack.utils import Secret
from haystack_integrations.components.converters.azure_doc_intelligence import (
    AzureDocumentIntelligenceConverter,
)

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component(
    "converter",
    AzureDocumentIntelligenceConverter(
        endpoint="https://YOUR_RESOURCE.cognitiveservices.azure.com/",
        api_key=Secret.from_env_var("AZURE_DI_API_KEY"),
    ),
)
pipeline.add_component("cleaner", DocumentCleaner())
pipeline.add_component(
    "splitter",
    DocumentSplitter(split_by="sentence", split_length=5),
)
pipeline.add_component("writer", DocumentWriter(document_store=document_store))
pipeline.connect("converter", "cleaner")
pipeline.connect("cleaner", "splitter")
pipeline.connect("splitter", "writer")

file_names = ["my_file.pdf"]
pipeline.run({"converter": {"sources": file_names}})