
Most common position in a pipeline	Before PreProcessors , or right at the beginning of an indexing pipeline
Mandatory init variables	"endpoint": The endpoint of your Azure resource "api_key": The API key of your Azure resource. Can be set with `AZURE_AI_API_KEY` environment variable.
Mandatory run variables	"sources": A list of file paths
Output variables	"documents": A list of documents "raw_azure_response": A list of raw responses from Azure
API reference	Converters
GitHub link	https://github.com/deepset-ai/haystack/blob/main/haystack/components/converters/azure.py

Overview

AzureOCRDocumentConverter takes a list of file paths or ByteStream objects as input and uses Azure services 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_AI_API_KEY environment variable by default. Otherwise, you can pass an api_key at initialization – see code examples below.

When you initialize the component, you can optionally set the model_id, which refers to the model you want to use. Please refer to Azure documentation for a list of available models. The default model is "prebuilt-read".

The AzureOCRDocumentConverter doesn’t extract the tables from a file as plain text but generates separate Document objects of type table that maintain the two-dimensional structure of the tables.

Usage

You need to install azure-ai-formrecognizer package to use the AzureOCRDocumentConverter:

pip install "azure-ai-formrecognizer>=3.2.0b2"

On its own

from pathlib import Path

from haystack.components.converters import AzureOCRDocumentConverter
from haystack.utils import Secret

converter = AzureOCRDocumentConverter(
    endpoint="azure_resource_url",
    api_key=Secret.from_token("<your-api-key>")
)

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

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.converters import AzureOCRDocumentConverter
from haystack.components.preprocessors import DocumentCleaner
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.writers import DocumentWriter
from haystack.utils import Secret

document_store = InMemoryDocumentStore()

pipeline = Pipeline()
pipeline.add_component("converter", AzureOCRDocumentConverter(endpoint="azure_resource_url", api_key=Secret.from_token("<your-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}})