---
title: "AzureOCRDocumentConverter"
id: azureocrdocumentconverter
slug: "/azureocrdocumentconverter"
description: "`AzureOCRDocumentConverter` converts files to documents using Azure's Document Intelligence service. It supports the following file formats: PDF (both searchable and image-only), JPEG, PNG, BMP, TIFF, DOCX, XLSX, PPTX, and HTML."
---

# AzureOCRDocumentConverter

`AzureOCRDocumentConverter` converts files to documents using Azure's Document Intelligence service. 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](../preprocessors.mdx) , or right at the beginning of an indexing pipeline |
| **Mandatory init variables** | "endpoint": The endpoint of your Azure resource  <br /> <br />"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  <br /> <br />"raw_azure_response": A list of raw responses from Azure |
| **API reference** | [Converters](/reference/converters-api) |
| **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`](../../concepts/data-classes.mdx#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](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/quickstarts/get-started-sdks-rest-api) 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](https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/choose-model-feature) 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`:

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

### On its own

```python
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

```python
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}})
```
