A Quick Introduction to Document Processing Workflow with Azure Document Intelligence

In today’s digital age, document processing plays a crucial role in many business operations. However, manual extraction of information from documents can be time-consuming and prone to errors. This is where Azure Document Intelligence comes into play, providing an automated solution to streamline your document processing workflow.

What is Azure Document Intelligence?

Azure Document Intelligence is an AI service that enables you to extract insights and information from documents. It offers capabilities such as text recognition, entity recognition, and key phrase extraction, allowing you to process and analyze documents at scale.

With Azure Document Intelligence, you can automate document processing tasks, extract valuable data from unstructured documents, and gain valuable insights from your documents.

Benefits of Azure Document Intelligence for Document Processing

  • Simple text extraction: Azure Document Intelligence uses advanced AI capabilities to extract text and structure from documents, eliminating the need for manual labeling and saving time and resources.
  • Customized results: The service can provide tailored results for different document layouts, ensuring accurate data extraction for invoices, contracts, forms, and other document types.
  • Flexible deployment: Azure Document Intelligence also offers flexible deployment options, allowing users to ingest data from the cloud or at the edge. This flexibility enables businesses to choose the most suitable approach for their specific needs, whether it’s a centralized cloud-based solution or a distributed edge deployment.
  • Built-in security: Moreover, it’s important to note that Azure Document Intelligence prioritizes the security of data and trained models. Microsoft is renowned for its commitment to cybersecurity and invests billions annually in research and development. Users can trust that their sensitive information and trained models are protected at all times.

Setting Up Azure Document Intelligence

Before we dive into the code, you’ll need an Azure subscription and an Azure Document Intelligence resource. You can create a new resource through the Azure portal and retrieve the necessary credentials for authentication.

Installing the Azure Document Intelligence SDK for Python

To get started, you’ll need to install the Azure Document Intelligence SDK for Python. You can do this using pip:

pip install azure-ai-documentintelligence

Extracting Text from Documents

Let’s start by extracting text from a document using Azure Document Intelligence. In this example, we’ll extract text from a sample invoice document:




Explanation: This Python script leverages the Azure Document Intelligence API to analyze a PDF document. It starts by importing essential libraries and configuring the API client with the required endpoint and key. Next, it opens the PDF file for analysis. The analysis employs the prebuilt receipt model, and the results are stored in the `receipts` variable. The script proceeds to iterate over each document in `receipts`, displaying the type of each receipt, along with the values of the MerchantName and TransactionDate fields, along with their corresponding confidence scores.

You can explore more advanced features and capabilities of Azure Document Intelligence to further enhance your document processing workflows. Happy coding!

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Speed up Your Daily Development Using OpenAI from Microsoft Azure – Top 5 Tips

Are you looking for ways to speed up your daily development and increase coding productivity? Look no further than OpenAI from Microsoft Azure. With its powerful generative AI capabilities, OpenAI can revolutionize your coding workflow and help you build efficient solutions. In this article, we’ll explore the top 5 tips on how to leverage Azure OpenAI to its full potential, offering practical examples of ‘prompts’ that enable developers to pose high-quality questions for enhanced daily coding efficiency.

Tip 1: Craft Precise Questions – Mastering the ‘How Do I?’ Prompt

The ‘How do I?’ prompt serves as a fundamental yet powerful tool. By phrasing your questions precisely, you can pull targeted information from Azure OpenAI.

Consider the following prompt examples:

  • “How do I handle file uploads in Django?”
  • “How do I implement authentication in Flask applications?”
  • “How do I sort a list of dictionaries by a specific key in Python?” 

These prompt examples showcase the versatility of the ‘How do I?’ prompt framework, allowing you to target specific challenges you might encounter in your daily coding endeavors.

Tip 2: Seek Clarity in Comparisons

Effective decision-making often depends on understanding the nuances between technologies. The ‘What is the difference between…?‘ prompt enables developers to gain clarity in making informed choices. For instance:

  • “What is the difference between REST and GraphQL?”
  • “What is the difference between MongoDB and MySQL for database management?”
  • “What is the difference between AWS and Azure for cloud services?”

By incorporating such diverse ‘What is the difference between…?’ prompts into their interactions with Azure OpenAI, developers can tailor their inquiries to specific technologies or frameworks, facilitating a comprehensive understanding and informed decision-making process.

Tip 3: Real-world Problem Solving – Crafting Scenario-Based Prompts

Problem-solving with Azure OpenAI is a critical skill for developers looking to create innovative and efficient solutions. One tip to master this skill is crafting scenario-based prompts, which provide real-world situations that developers can tackle using Azure OpenAI tools. 

For example, imagine you are building a Python Flask app and you encounter CORS (Cross-Origin Resource Sharing) issues when making API requests. Here’s a probable prompt that you can use to deal with this scenario:

In a Python Flask app, how do I handle CORS-related issues when making API requests?

Tip 4: Supercharge Your Debugging

Debugging can be a time-consuming nightmare. Imagine instantly identifying the root of your code woes! OpenAI can help. By feeding your code into OpenAI, you can get valuable insights and suggestions that can greatly enhance your problem-solving abilities. You can use prompts like:

  • “Why is this code throwing an error? Explain the issue in detail.”
  • “I’m getting a ‘TypeError’ in line 17. What could be causing it?”
  • “Suggest alternative approaches to handle this exception.”

OpenAI can analyze your code and provide insightful explanations for errors, suggest alternative solutions, and even point you towards relevant documentation. This is where technology decision-making comes into play. 

Tip 5: Master the Art of Documentation

Clear and concise documentation is crucial for any codebase. OpenAI can help you write effective documentation with prompts like:

  • “Summarize the functionality of this class in plain English.”
  • “Generate code examples demonstrating how to use this function.”
  • “Create a table comparing the features of this library with its alternatives.”

OpenAI has the capability to analyze your code and generate clear explanations, code snippets, and comparisons, making your documentation comprehensive and easy to understand. 


By mastering the art of crafting effective prompts, you can transform OpenAI from a simple answer engine into a powerful decision-making companion. The key lies in framing your questions with clarity and purpose, unleashing the true potential of Azure OpenAI in your development journey. So, go ahead, experiment, and let your curiosity guide you as you leverage AI to navigate the ever-evolving landscape of technology.

Happy coding!

Why Azure is better than AWS when using VS Code from Microsoft

When it comes to cloud services, developers using the popular VS Code from Microsoft often find themselves choosing between Azure and AWS. While both platforms offer a range of features and capabilities, there are several key reasons why Azure stands out as the better choice for developers. In this article, I will explain why Azure’s seamless integration with VS Code, cost savings, performance advantages, and integration with existing Microsoft technologies make it my preferred option in the cloud.

Seamless Integration with VS Code

One key advantage of Azure is its compatibility with Microsoft products and services, such as Windows, Office, and the .Net programming language. For businesses already utilizing these technologies, Azure becomes a natural choice for their cloud platform. The seamless integration with VS Code eliminates the complexities of transferring data and programs to the cloud, making it a hassle-free experience for developers.

Azure streamlines the development process, allowing developers to focus on building applications without worrying about the details of the underlying infrastructure. This integration also enhances collaboration and productivity among development teams, as they can easily share code, track changes, and deploy applications directly from the familiar VS Code environment.

VS Code gives developers the confidence and convenience they need to leverage cloud services effectively. With Azure, developers can maximize their productivity and innovate faster, ultimately delivering high-quality solutions to their clients or end-users.

Cost Savings with Azure

Azure offers significant cost savings compared to AWS, especially for businesses running Windows Server and SQL Server workloads. With Azure’s pricing options, businesses can achieve their budget goals while maximizing their return on investment in the cloud.

When comparing Azure and AWS pricing, Azure emerges as the more cost-effective choice for Windows Server and SQL Server workloads, offering savings of up to 5 times in comparison. This cost advantage can have a substantial impact on a business’s overall cloud expenditure.

So, there you go, thats a quick round-up of some of the reasons why in my opinion, Azure in AWS is a better experience and outcome for developers using VS Code.

Happy coding !

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