Hi, i have written an update query to update a specific attribute(orderstatus) with value ({ “Packing”:{“status”:”completed}, “Shipping”:{“status”:”Completed”}) if its null

Example:

[“purchaseorders”:

  {

    “orders”:[{

              “ordernum”: “A1”,

              “orderstatus”: null

 },

{

              “ordernum”: “A2”,

              “orderstatus”: { “Packing”:{“status”:”completed”}, “Shipping”:{“status”:”Completed”}

} }  ]   }]

Written below query to process. But after update, it inserts backslash and double quotes that encloses the whole string

{ “ordernum”: “A1”, “orderstatus”: “{ “Packing”:{“status”:”completed”}, “Shipping”:{“status”:”Completed”}”}

Query used:

update

 purchaseorder

set 

 details = JSON_MODIFY(details, CONCAT(‘$.purchaseorders.orders[‘,@i,’].orderstatus), statusText);

any help to get this updated properly for the orderstatus is null. thanks in advance

​Hi, i have written an update query to update a specific attribute(orderstatus) with value ({ “Packing”:{“status”:”completed}, “Shipping”:{“status”:”Completed”}) if its null Example:[“purchaseorders”:  {    “orders”:[{              “ordernum”: “A1”,              “orderstatus”: null },{              “ordernum”: “A2”,              “orderstatus”: { “Packing”:{“status”:”completed”}, “Shipping”:{“status”:”Completed”}} }  ]   }] Written below query to process. But after update, it inserts backslash and double quotes that encloses the whole string{ “ordernum”: “A1”, “orderstatus”: “{ “Packing”:{“status”:”completed”}, “Shipping”:{“status”:”Completed”}”} Query used:update purchaseorderset  details = JSON_MODIFY(details, CONCAT(‘$.purchaseorders.orders[‘,@i,’].orderstatus), statusText); any help to get this updated properly for the orderstatus is null. thanks in advance  Read More

​

I am trying to find out how to open a file type with an app from the Windows App store.

If I select Open With from the right click menu I am given a list of apps, but the app I want to select from the app store is not listed.

If I select choose an app on your PC then I have to navigate to the exe, but the exe for Windows App store apps is hidden away in a secure folder that even if I had the full path to the exe I don’t have permissions to navigate to that path if I’m trying to open with as my standard user account, so I can’t select it from the choose an app on your PC file selector.

If I choose Browse apps in the Microsoft Store, then it opens the store looking for that file extension, and the app I’m looking for actually shows up in the search, but my only option is to open or install the app from the Windows Store. Opening it from here does not actually set that app as the default for that file type, it just opens the app and it doesn’t even open the app with the context of the file I was trying to run “Open With” on.

Is the only way to set the “open with” default for a file extension to open with an app in the Windows store by modifying the registry directly? This is the only way I have found that works, but it seems very dangerous and not a great option for everyone.

​I am trying to find out how to open a file type with an app from the Windows App store. If I select Open With from the right click menu I am given a list of apps, but the app I want to select from the app store is not listed. If I select choose an app on your PC then I have to navigate to the exe, but the exe for Windows App store apps is hidden away in a secure folder that even if I had the full path to the exe I don’t have permissions to navigate to that path if I’m trying to open with as my standard user account, so I can’t select it from the choose an app on your PC file selector. If I choose Browse apps in the Microsoft Store, then it opens the store looking for that file extension, and the app I’m looking for actually shows up in the search, but my only option is to open or install the app from the Windows Store. Opening it from here does not actually set that app as the default for that file type, it just opens the app and it doesn’t even open the app with the context of the file I was trying to run “Open With” on. Is the only way to set the “open with” default for a file extension to open with an app in the Windows store by modifying the registry directly? This is the only way I have found that works, but it seems very dangerous and not a great option for everyone.  Read More

​

Application Insights Code Optimizations for .NET is now generally available

Application Insights Code Optimizations helps you identify and resolve performance bottlenecks at the code level in your .NET applications running on Azure. Utilizing an advanced AI-based model, it analyzes Application Insights profiler traces and displays actionable next steps in the Azure portal at no additional cost. This saves time and increases your productivity from detection in the Azure portal to code-level resolution with GitHub Copilot.

The AI model is developed in-house based on Microsoft’s experience with large-scale cloud services like Azure, Teams, Microsoft 365, and Xbox. It is not trained or fine-tuned using your code or your performance traces. Once your App Insights profiler is collecting and uploading traces for your .NET applications, the Code Optimizations analysis happens automatically.

Identify – Application Insights Code Optimizations

Based on your feedback, you can see all Code Optimizations recommendations across Azure subscriptions and Application Insights resources in the Azure portal. This helps you keep track of code-level performance recommendations for all your environments in one place (including dev, test, pre-production and production).

Recommendations are updated every hour. By default, the view shows a rolling 24-hour window of recently identified issues. When your application is under higher load, you might see more recommendations, while during quieter periods, you might not see any. There is a 30-day history for you to review and analyze past events.

Resolve – GitHub Copilot with Code Optimizations extension (preview)

Developers are very excited about how effectively they can consult with GitHub Copilot for code fixes specific to issues detected by Code Optimizations. In either Visual Studio or Visual Studio Code, you can install the Application Insights Code Optimizations extension (preview) and have GitHub Copilot provide a code fix grounded in the Code Optimizations insights, helping to better address your specific performance issues. Please note that GitHub Copilot is required to use this capability.

How does it work? Simply open the code base corresponding to your running app on Azure that is set up for Code Optimizations. Install the extension and ask the copilot to review the Code Optimizations insights by using the @code_optimizations skill.

Application Insights profiler for .NET

The Application Insights profiler for .NET, collects performance traces of your application, then Code Optimizations automatically analyzes these traces and provides insights and actionable next steps. The Profiler runs alongside your application and is designed to run in production environments.

In response to your feedback, we have made improvements to reduce the Profiler’s impact on your servers. The AI model for Code Optimizations has enabled us to lower the trace duration by 75% – to 30 seconds, down from the previous 120 seconds. Additionally, we made several internal profiler optimizations to further reduce the overhead, including reducing the trace size by up to 55%. These changes are now being rolled out globally.

By default, Profiler actively collects traces approximately every hour for 30 seconds or during periods of high CPU or memory usage for 30 seconds. During these times, it typically adds up to 15% of CPU and memory overhead to your server. The hourly traces (called sampling) are great for proactive tuning, while the high CPU and memory traces (called triggers) are useful for reactive troubleshooting. You can further customize the Application Insights profiler settings based on your needs.

What’s next for Code Optimizations?

We are continuing to evolve the features and capabilities based on your feedback – tell us, please, how you use Code Optimizations and how we could prioritize the next investments – https://aka.ms/CodeOptimizations/Feedback.

Next steps

Review your Code Optimizations https://aka.ms/CodeOptimizations
Read more on Azure Learn https://aka.ms/CodeOptimizations/Docs

Frequently asked questions

How was the Code Optimizations AI model trained? The model was trained on Microsoft’s source code for large-scale cloud services such as Azure, Teams, Microsoft 365, and Xbox. It is not trained or fine-tuned using your code or your performance traces.
How much does Code Optimizations cost? Code Optimizations is a feature of Application Insights (documentation, pricing), it analyzes Application Insights profiler traces at no additional cost to you.
How can I provide feedback? We advise you to use the thumb-up/down functionality in the Azure portal or fill out a quick feedback.

​Microsoft Tech Community – Latest Blogs –Read More 

I have two different excel files both are large tables that have headings for each column. Each column has a filter that has a range extended to the bottom of the table. In one file I am able to insert rows throughout the table and they can be filtered once created. In the other file I am unable to insert rows, when I right click a row the option to insert is greyed out. Can anyone help me out? I need to insert rows in this file.

​I have two different excel files both are large tables that have headings for each column. Each column has a filter that has a range extended to the bottom of the table. In one file I am able to insert rows throughout the table and they can be filtered once created. In the other file I am unable to insert rows, when I right click a row the option to insert is greyed out. Can anyone help me out? I need to insert rows in this file.  Read More

​

Dears,

could you please assist regarding the below sample of data to extract y4-02-cb-232 for the first row by using regex match and xlookup. 

حافز ****** الوحدة y4-02-cb-232  – **** عم*****ى يوسفحافز ** الوحدة Y4-05-CD-122 – على * ** ** ** *حافز * الوحدة Y5-10-CO-222 -احمد **** *** * **حافز بيع *** Y4-06-CD-121 – محمد ****** ***

Thanks in advance

​Dears,could you please assist regarding the below sample of data to extract y4-02-cb-232 for the first row by using regex match and xlookup. حافز ****** الوحدة y4-02-cb-232  – **** عم*****ى يوسفحافز ** الوحدة Y4-05-CD-122 – على * ** ** ** *حافز * الوحدة Y5-10-CO-222 -احمد **** *** * **حافز بيع *** Y4-06-CD-121 – محمد ****** *** Thanks in advance  Read More

​

  Welcome to the Candidly Copilot podcast episode 3. In this edition of Candidly Copilot, we discuss those first steps with Microsoft 365 Copilot, or the initial Crawl. Discussed and demonstrated are: Microsoft Copilot, Microsoft Copilot with Enterprise Data Protection, and Microsoft 365 Copilot. Additionally, hosts Chad Stout and Michael Gannotti shared the prompt of the week (a winner for Forms).

Resources:

Check out ALL upcoming, and previously recorded, Candidly Copilot podcasts here
Prompt of the Week:  “I’d like to create a survey for a group of Smartter Health Pediatric Cancer Institute Clinicians to better understand their day-to-day experience as Clinicians, communicating with colleagues, finding the appropriate information for their work with patients, and completing their professional training and education. I’d also like to understand how they are feeling about the demands on their time in regards to work hours in the hospital, how is this affecting time outside of the workplace, and how they think their time could be better allocated to improve productivity in the workplace, improve educational and training opportunities, remove stress in the workplace and at home, and by extension improve patient outcomes.”
Microsoft 365 Copilot – Microsoft Adoption

Thanks for visiting!

 Chad Stout                              Michael Gannotti

​Microsoft Tech Community – Latest Blogs –Read More 

I don’t want to create a meeting just a little reminder if I just post in the date I want it lets me type in what I assume is a reminder.  I click on it and I think its just a reminder – can I also send various people this same reminder? because it is not a meeting.  Just wondering if I am overthinking this?

​I don’t want to create a meeting just a little reminder if I just post in the date I want it lets me type in what I assume is a reminder.  I click on it and I think its just a reminder – can I also send various people this same reminder? because it is not a meeting.  Just wondering if I am overthinking this?  Read More

​

Who Should Read This 

This document will be beneficial to Federal Executives including Chief Information Officer, Chief Artificial Intelligence Officer, Chief Technology Officer, Chief Information Security Officer, Chief Data Officer, AI Lead, AI Scientist, and Data Scientist, among others. 

Introduction 

This white paper explores the philosophy and implementation strategy of the Federal Responsible and Secure Artificial Intelligence (AI) Sandbox, an initiative aimed at promoting responsible and secure AI practices within federal government agencies. A subsequent article will follow, providing an in-depth examination of the technical aspects of the Federal Responsible and Secure AI Sandbox. AI is transforming federal government operations by enhancing efficiency, fostering innovation, allowing the workforce to do more with less, and empowering employees to focus on creative tasks while reducing repetitive work. As AI technologies become essential in federal agencies, establishing a responsible, secure, and ethical framework for their development and deployment is crucial. This need aligns with the Office of Management and Budget (OMB) M-24-10, which mandates the appointment of Chief AI Officers, the formation of agency AI governance boards, the development of compliance plans, and the creation of AI use case inventory. Emphasizing best practices from both the private sector and academia within this framework can accelerate risk management, addressing a traditionally measured governmental response to emerging risks. An exemplary tool in this regard is the Massachusetts Institute of Technology (MIT) AI Risk Repository, a dynamic and extensive database that catalogs and classifies a wide range of AI-related risks, thereby supporting informed decision-making for policymakers, researchers, and industry professionals.

Dispersed Data 

We must acknowledge the considerable value of data, especially in how it is structured across various operating divisions, staffing divisions, and bureaus, where it is currently fragmented. These divisions frequently encounter challenges in accessing each other’s data, highlighting the need for an effective data brokerage system. Such a system would allow divisions to access and utilize dispersed data, significantly enhancing its value. 

Introducing new tools and creating an environment that encourages the exploration of dispersed data can lead to new discoveries in previously uncharted areas. This strategy not only capitalizes on the inherent value of data but also opens possibilities for previously unattainable innovative uses and applications. 

AI Executive Orders, Laws, and Regulations 

In recent years, several laws and executive orders have been enacted to govern the use of AI across various sectors in the United States, ensuring that its deployment is ethical, secure, and compliant with existing regulations. Notably, the US needs to maintain its competitive advantage by leveraging AI. To leverage this capability responsibly, the Executive Order on Maintaining American Leadership in Artificial Intelligence (2019) and the Executive Order on Promoting the Use of Trustworthy AI in the Federal Government (2020) set the groundwork for AI governance. The National AI Initiative Act of 2020 further bolsters this foundation by promoting AI research and policy development. In the realm of standards and frameworks, the National Institute of Standards and Technology (NIST) has developed the AI Risk Management Framework (AI RMF) to guide risk assessments in AI systems. 

Specific sectors have additional regulatory requirements. For instance, the Health Insurance Portability and Accountability Act (HIPAA) ensures the protection of personal health information in AI applications within healthcare. The Federal Information Security Management Act (FISMA) requires federal agencies to safeguard their data and systems, including those using AI technologies. While the General Data Protection Regulation (GDPR) is an EU regulation, its implications extend to U.S. entities handling data of EU citizens,impacting international AI practices. 

Moreover, ethical guidelines from entities such as the Defense Innovation Board provide principles for the Department of Defense’s AI use, emphasizing ethical considerations. Civil rights laws, including the Civil Rights Act and the Americans with Disabilities Act (ADA), ensure that AI technologies are non-discriminatory and accessible. Additionally, sector-specific regulations in the financial and transportation sectors, such as those from the Dodd-Frank Act, the Fair Credit Reporting Act (FCRA), the Federal Aviation Administration (FAA), and the National Highway Traffic Safety Administration (NHTSA), govern AI use in financial services, aviation, and autonomous vehicles. 

Problem Statement 

Despite the immense potential of AI, several concerns hinder its vast adoption in the federal space: 

Ethical and Bias Concerns: AI systems can perpetuate biases and ethical dilemmas, potentially leading to unfair outcomes and loss of public trust. 
Regulatory Compliance: Navigating the complex landscape of federal regulations and ensuring compliance with AI-related executive orders listed above.  
Risk Management: Identifying, assessing, and mitigating risks associated with AI deployment is critical but often lacks a structured approach. 
Inter-agency Collaboration: There is a need for cohesive collaboration both across different federal agencies and internally among the bureaus and operating divisions to share best practices and harmonize AI strategies.  
Cost-Benefit: Integrating AI into federal operations involves initial investment, ongoing maintenance, data management, training, integration with existing systems, scalability, and risk management costs. However, these costs can be outweighed by the benefits, including enhanced productivity, increased efficiency, and robust data analysis for informed decision-making. enhanced cybersecurity via threat detection and response. 

The AI Sandbox: A Solution for Responsible and Secure AI 

To meet their objectives, federal agencies are encouraged to develop a responsible and secure AI sandbox. This controlled environment will allow for the development, testing, evaluation, and sharing of AI technologies while ensuring adherence to ethical, reliable, secure, and responsible guidelines. Moreover, the AI Sandbox will allow sufficient testing before AI-powered applications securely head into production. Such a proactive approach not only fosters innovation and collaboration but also mitigates risks and enhances public trust through a commitment to transparency, accountability, and fairness in AI deployment, incorporating these principles within both the data and AI pipelines. 

Establishing this sandbox is crucial for fostering innovation and ensuring compliance with standards set by the National Institute of Standards and Technology (NIST) and OMB. The sandbox provides a safe space where developers of all skill levels can experiment with AI tools and applications without compromising the integrity of live environments. A well-designed AI sandbox significantly enhances business value by enabling the testing and development of AI systems under realistic conditions without risking actual data or operational systems. This setup promotes iterative testing of AI models, leading to more robust and dependable AI deployments. Such environments also encourage the reuse of existing tools and resources, minimizing duplication and waste. Safe and rapid iteration within a sandbox speeds up the refinement of AI applications, reducing time to deployment and improving return on investment. 

Integrating such a sandbox into federal IT operations proves advantageous for developing specialized domain models in secure environments (such as clouds) and for adopting pre-trained large language models (LLMs). A sandbox facilitates controlled testing of large language models (LLMs), AI red teaming, and jailbreaking, along with overall security assessments. This environment allows for better prediction of operational costs associated with API calls, compute resources, and data management when scaled.

Implementation 

Establishment of the Sandbox: 

Infrastructure: Developing the technical infrastructure required for the sandbox, including secure data environments and computational resources. 
Stakeholder Engagement: Engaging key stakeholders, including roles in IT, security, privacy, acquisition, civil rights, and governance boards to define sandbox objectives and priorities. 

Development of AI Projects: 

Pilot Programs: Launching pilot AI projects within the sandbox to test and refine AI solutions. 
Iterative Testing: Using an iterative approach to continuously test, evaluate, and improve AI systems. 

Compliance and Governance: 

Steering Committee: Establishing a steering committee comprising of key stakeholders from bureaus and operating divisions to oversee sandbox activities. 
Continuous Monitoring: Implementing continuous monitoring mechanisms to ensure AI systems adhere to ethical guidelines, security, and regulatory requirements. 

Training and Capacity Building: 

Workshops and Training: Conducting workshops and training sessions to build AI literacy and capacity within federal agencies. 
Resource Development: Developing resources, including guidelines and toolkits, to support responsible AI development and deployment. 

Distinguish between Data Pipeline and AI Pipeline 

In many scenarios, especially in complex and sensitive environments like federal operations, it can be advantageous to distinguish between a data pipeline and an AI pipeline. It is important to recognize that federal agencies operate interconnected systems where AI-generated content introduces new elements. Security and data governance systems are essential to safeguard this newly created data. 

Specialization of Functions 

Data Pipeline: Focuses primarily on data collection, storage, processing, and management. It ensures that data is accurately captured, maintained, and made available in a structured format suitable for various uses. This pipeline handles tasks such as data cleansing, transformation, and aggregation, which are foundational before any advanced analysis or modeling. 

AI Pipeline: Concentrates on building, training, deploying, and monitoring AI models. This pipeline uses processed data to develop models that can generate insights, make predictions, or automate decisions. 

Security and Compliance 

Data Sensitivity and Privacy: Separate pipelines allow for more controlled and secure handling of sensitive data, with stringent access controls and compliance measures specific to data handling and storage. 

Regulatory Compliance: In environments subject to rigorous regulatory requirements, having distinct pipelines helps in implementing specific compliance measures more effectively, tailored to each stage of data handling and AI processing. 

Scalability and Maintenance 

Scalability: Separating the pipelines allows each to be scaled independently based on specific needs. For instance, data collection might need to be scaled differently compared to model training frequency. 

Ease of Maintenance: Issues can be isolated and addressed more efficiently when pipelines are separate. Updates or changes made to the AI models do not necessarily disrupt the data pipeline, and vice versa. 

Optimization of Resources 

Resource Allocation: Different resources (like computational power and storage) can be allocated more effectively according to the unique demands of each pipeline. For example, AI models might require more powerful GPUs for training, while data pipelines might need robust databases. 

Cost Efficiency: Managing resources based on the specific needs of data processing and AI model development can lead to better cost efficiency. 

Innovation and Flexibility 

Modularity: Having separate pipelines promotes modularity, allowing teams to experiment, update, and deploy changes in one area without affecting the other. This modularity is crucial for rapid testing and integration of new technologies or approaches. 

Adaptability: Separate pipelines enhance the ability to adapt to new data sources, emerging AI technologies, or changing business needs without comprehensive overhauls of the entire system. 

Risk Management 

Risk Isolation: By decoupling the data handling from AI model training and deployment, it’s easier to isolate and manage risks associated with each process. For instance, failure in the AI pipeline (e.g. a model producing incorrect predictions) will not compromise the integrity of the data pipeline. 

While there are advantages to maintaining distinct data and AI pipelines, the decision ultimately depends on the specific organizational needs, the nature of the data, the complexity of the AI tasks, and the regulatory environment. For federal applications, where security, compliance, and reliability are paramount, separating these pipelines can provide clearer governance, better risk management, and more focused compliance with legal and ethical standards. 

Responsibilities  

Setting up a responsible and secure AI sandbox varies based on the size of an agency and its progress in AI integration at a department-level, aligning with their AI strategy, a responsible and secure AI sandbox could sit in their headquarters, for larger agencies additional sandboxes could sit at operating divisions and bureaus level.  

Conclusion 

The Federal Responsible and Secure AI sandbox represents a proactive and structured approach to fostering responsible AI practices within federal agencies. By aligning with the NIST AI RMF, White House AI Executive Orders, OMB memos, and federal regulations and standards, the sandbox ensures a comprehensive framework for ethical AI development and deployment. Through collaboration, transparency, accountability, and continuous improvement, the sandbox will enable federal agencies to harness the transformative power of AI while safeguarding public trust and ensuring compliance with regulatory standards. This thought leadership initiative addresses current challenges and paves the way for a future where AI contributes positively, responsibly, securely to federal government operations, empowering every person and every organization to achieve more. 

​Microsoft Tech Community – Latest Blogs –Read More 

Intelligent apps with Azure OpenAI are easier than ever to configure and deploy, especially when using a PaaS platform like Azure App Service for hosting.

While this demo is a full tutorial for deploying an App Service Web App, Database, Managed Identity, and Azure OpenAI, most of the code and instructions for each section can be repurposed for individually unique use-cases. If you already know how to perform some of these actions (there’s ample documentation on most of this in Microsoft Learn Documentation), feel free to skip ahead and focus on the sections most useful to your needs. Each section builds on the previous, but the code and general principles can be applied to a wide range of applications.  

The application source code for this article is available on GitHub and was originally forked from the Azure Samples version of the Spring PetClinic application. This version of petclinic is largely the same, but with the addition of an Intelligent ChatBot powered by Azure OpenAI. All resources in this demo are securely connected using Azure Managed Identity for secure passwordless connections.

The Portal

Creating Azure Resources

Starting with the portal actions, while these can all be performed over CLI or Bicep, the portal is the easiest way to show the actions required to set up your application. 

App Service

We’ll start by creating our App Service Plan and Web App in the portal. In the Azure Portal, select App Service and choose to create a Web App.

On the App Service Create page, we need to choose Java Version 11 and Java SE (Embedded Web Server). The choice of Java SE allows us to run any JVM based language (ex: Kotlin, Scala, etc.) with an embedded web server on the App Service Platform, in this case, a SpringBoot app written in standard Java. You’ll also need to name your Web App and choose an App Service Plan, I’ve chosen P0V3, but B1 should also work for this app.

Next, we’ll need to deploy a database.

MySQL Database

In the Azure Portal, select Azure Database for MySQL flexible servers, and choose the quick create option for the MySQL option (no WordPress needed. On the create page for the Flexible server, ensure that you have a standard (B1) compute SKU with at least 20GiB storage, Dev/Test workload is sufficient for a sample application like this one. After choosing a name, region, admin username and password, create your MySQL Server.  

Now we have a database, on to Azure OpenAI!

Azure OpenAI 

In the Azure Portal, choose Azure OpenAI service and click create. After choosing your options for naming, resource group, etc. (this demo uses an S0 sku), create your OpenAI service. In the Azure OpenAI Studio, be sure to select and deploy the gpt-35-turbo model, this is what we will use for chat completions in our SpringBoot app. 

Managed Identities

To connect all our resources together, we will use Managed Identity. This ensures our connections are handled by Microsoft Entra in a secure, passwordless setup. There are no secrets to manage here. With our Managed Identities properly configured, your Azure resources will automatically have the permissions to connect to each other securely without any need for secrets management.

Starting with our App Service Web App, let’s first enable a system assigned identity for this service under Settings > Identity.

After enabling our system assigned identity for our Web App, we’ll need to create some user assigned managed identity resources. One for our MySQL database and one for our Azure OpenAI resource. We’ll also use the Azure OpenAI identity for GitHub Actions later, but you can always create a Managed Identity resource per resource access if desired, or even use a single user managed identity for all resource access associated with this application. The passwordless end-result is the same either way.

Starting with our identity for Azure OpenAI, go to Managed Identities in the portal and click create. After naming your identity, go to Azure role assignments and click Add role assignment. Here, I’ve enabled this identity to be a few things, a Cognitive Services OpenAI user on my Azure OpenAI resource, a Cognitive Services Contributor on my resource group for this app, and a Website Contributor to the Staging slot of my App Service Web App. 

Next, let’s create our identity for the MySQL database. In this case, I left this one a bit open-ended. Following the same procedure as the last Managed Identity, but in this case making our MySQL related identity a Contributor to our resource-group as a whole. This allows us to effectively have one Managed Identity that can “contribute” to anything in the selected resource group. In this case, we’re only interested in the MySQL server. 

Securely Connecting Our Resources

Now that we’ve created all our managed identities, let’s add them to our Web App. After browsing to our App Service Web App in the portal, select Settings > Identity > User assigned. Here we’ll add the two identities we just created.

We’re almost done, but for this next connection, we need to use the Azure CLI and Service Connector. In your terminal, run the following command to securely connect your MySQL database to your Web App using Service Connector. 

az webapp connection create mysql-flexible –resource-group ${RESOURCE_GROUP} –name ${WEBAPP_NAME} –target-resource-group ${RESOURCE_GROUP} –server ${MYSQL_SERVER_NAME} –database petclinic –user-identity client-id=${MYSQL_MANAGED_IDENTITY_CLIENT_ID} subs-id=${SUBSCRIPTION_ID} mysql-identity-id=${MYSQL_MANAGED_IDENTITY_OBJECT_ID} –client-type java

Be sure to store the username produced here, this will be important later. Now all our resources are securely connected! On to the source code to put all these resources to good use.

The Source Code

Dependencies

Our first stop is the pom.xml file. Be sure to include the following dependencies in your pom.xml file. Remember, the full source code for this application is available on GitHub.

<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-openai</artifactId>
<version>1.0.0-beta.8</version>
</dependency>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-identity</artifactId>
<version>1.12.2</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>8.0.30</version>
</dependency>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-identity-extensions</artifactId>
<version>1.1.5</version>
</dependency>
</dependencies>

After adding the required dependencies, let’s look at the Chatbot source code itself.

Application Source Code for Intelligent Chatbot using SpringBoot & Azure OpenAI

Starting off with directory structure, the Chatbot in its entirety lives here: 

srcmainjavaorgspringframeworksamplespetclinicchat

This directory contains 3 files, ChatController.java, WebSocketConfig.java, and WebSocketEventListener.java. Let’s examine the ChatController individually and take a quick look at our WebSocket related files.

ChatController.java

This is some boilerplate code for a pretty standard Chatbot using Web Sockets running in a SpringBoot app. The goal of this basic chat controller is that it can be reused, modified, or otherwise repurposed for a wide range of uses. 

While I won’t go line-by-line, I’ll highlight a few major parts of the code. The first being the dependencies associated with Azure OpenAI and Managed Identity, these are all essential.

import com.azure.ai.openai.OpenAIClient;
import com.azure.ai.openai.OpenAIClientBuilder;
import com.azure.ai.openai.models.ChatChoice;
import com.azure.ai.openai.models.ChatCompletions;
import com.azure.ai.openai.models.ChatCompletionsOptions;
import com.azure.ai.openai.models.ChatRequestMessage;
import com.azure.ai.openai.models.ChatRequestAssistantMessage;
import com.azure.ai.openai.models.ChatRequestSystemMessage;
import com.azure.ai.openai.models.ChatRequestUserMessage;
import com.azure.ai.openai.models.ChatResponseMessage;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.core.credential.TokenCredential;

Next let’s look at the ChatController class. The sendMessageAI method contains the majority of our Chatbot configuration. The following lines contain the Managed Identity connection to Azure OpenAI.

TokenCredential defaultCredential = new DefaultAzureCredentialBuilder().build();
OpenAIClient client = new OpenAIClientBuilder().credential(defaultCredential).endpoint(“https://java-demo.openai.azure.com/”).buildClient();

The remaining content of the method handles the AI portion of the chatbot, in this case, instructing our Azure OpenAI model on what role to play during our chat and handling the receiving and answering of user provided chat messages. All of this is done over the Web Socket spec defined in WebSocketConfig.java and WebSocketEventListener.java. 

The remaining methods in this file define the control flow for messages sent by the user (sendMessage), and users added to the chat (addUser). 

The Azure OpenAI connection and usage scenario may seem simple to set up (and it is), but the results are incredibly powerful and the possibilities for integrations with business apps are limitless.  Now that we’ve seen the OpenAI setup, let’s look at our database connection.

Database Connection with Managed Identity

Back when we made our managed identities and used Service Connector to connect our database and web app, you should have received a MySQL username that looks something like: aad_mysqlflexible_abcd. Now we’ll take this username and add it to our SpringBoot configuration file, in this case, the one for MySQL called application-mysql.properties. Edit the file to include your database URL and MYSQL username like the example below. 

# database init, supports mysql too
database=mysql
spring.datasource.url=${MYSQL_URL:jdbc:mysql://java-jdemo-mysql-db.mysql.database.azure.com/petclinic}
spring.datasource.username=${MYSQL_USER:aad_mysqlflexible_e8bir}
# SQL is written to be idempotent so this is safe
spring.sql.init.mode=always

Setting spring.sql.init.mode=always lets us execute the data.sql file in our resources/ directory automatically on startup. 

In just a few steps, we’ve connected a MySQL database to our App Service web app using managed identity and service connector for secure passwordless database connections. Next up, we’ll look at some of our deployment options.

Deployments

Deployments – CLI

Let’s look at deploying our app, starting with the Azure CLI and Maven. The first step with any Maven-based build and release for a Java application is to build our app. We can start by running a Maven build using the following command: 

mvn package -DskipTests

I’m skipping tests for the purpose of demonstration, but you may want to avoid this with a production app. 

Next up, deploy your application JAR file with the Azure CLI like this: 

az webapp deploy –resource-group ${RESOURCE_GROUP} –name ${WEBAPP_NAME} –src-path target/${APPNAME_AND_VERSION}.jar

Checking your deployed application, you should see something like this: 

Now browsing to the chat tab should give you the option to chat with an AI assistant powered by Azure OpenAI!

If that seems easy, it’s because it is. We can add our own data, modify the behavior of our chatbot, and even AI framework we’re using for the app, but we’ll save that for a later post. 

Deployments – GitHub Actions with Managed Identity

CLI deployments are a great way to get started, but CI/CD is typically the preferred deployment method for web apps. Using GitHub Actions with Azure App Service, CI/CD is straightforward and easy to configure. In this case, having our Managed Identities already configured will make it even easier. 

Starting off, we’ll need to browse to our web app in the Azure portal and select Deployment > Deployment Center. Here we can configure our app to deploy from a number of different sources, but we’ll select GitHub for now. Picking our Organization, Repository, and Branch, we’re then allowed to create a new workflow file or add a new one. Let’s just add a new one, which we can preview later. This workflow just performs a standard Maven build and deployment to our app on merges into main. 

We’ll address the notice at the top of the page in the next section, but for now let’s look at the authentication settings. Here we can use an existing user-assigned identity (Managed Identity), or we can create a new one. If you’re on a Free, Basic, or Standard SKU, select your preferred identity (or create a new one) and click save. Otherwise, click Discard and continue to the next section to set up a staging slot for your web app. 

Using Deployment Slots (Premium SKU Required)

To set up deployment slots for your app, you must be on a Premium SKU. Browse to your web app > Deployment > Deployment Slots and click Add. 

Here you can name your staging slot and clone the settings of your production slot. After adding the slot, click on it to switch over to your new staging slot. Now on the staging slot, we’ll configure GitHub Actions deployments just as we did before. Under Deployment > Deployment Center we’ll fill out the form naming GitHub as our source, specifying Organization, Repository, Branch, selecting to add a new workflow and configuring our user-assigned identity for auth. After clicking save, a deployment will be kicked off and the app will be deployed to your staging slot. 

Slot Swap into Production

While the staging slot is great for ensuring our app is fully functional before we deploy to production, eventually, we’ll need to officially swap our slots and deploy our production app. To do this, go back to Deployment > Deployment slots and select Swap. Here you’ll need to set your source and the staging slot and target as the production slot. After clicking start swap and waiting for the changes to complete, your application from staging will be fully deployed and running in production. 

Now any time a change is made, the staging slot will be updated first and, after a manual review, can be swapped into production at any time. This also works in reverse, if a production app is having issues, swapping the previous version from the staging slot can effectively revert a change that had unintended consequences. 

Conclusion

Configuring and deploying secure OpenAI powered applications to Azure App Service has never been easier! With the flexibility of the PaaS platform, the passwordless connections, and the power of Azure OpenAI, the concepts demonstrated here can be applied to a wide range of applications. Reach out with any questions or comments, this article will contain links in the future to other versions of the same app using LangChain4j and SpringAI. 

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Linux has become the most popular operating system on Azure as over 60% of customer cores run Linux-based workloads. We are committed to continuously investing in the platform to enhance the Linux and open source experience in Azure, ensuring we meet the evolving needs of our customers.

AlmaLinux has been available in the Azure Marketplace for over three years and we are excited to announce it is now an endorsed Linux distribution in Azure. AlmaLinux is a free, community-owned, enterprise-grade Linux distribution that is binary compatible with Red Hat Enterprise Linux and offers long-term stability and security features. AlmaLinux is used by a wide range of customers in Azure and has become an increasing popular choice as CentOS Linux users look for alternatives following its end of life. The endorsement of AlmaLinux in Azure is a testament to its reliability, security, and performance.

“The AlmaLinux OS Foundation is thrilled that AlmaLinux has been recognized as an endorsed Linux distribution in Azure. This endorsement underscores our commitment to providing a stable, secure, and high-performance Linux distribution for enterprise environments. Being an endorsed distribution in Azure not only validates the reliability and quality of AlmaLinux but also ensures that our users can confidently deploy and manage their workloads in Azure.” – benny Vasquez, Chair of the Board, AlmaLinux OS Foundation

What does being an endorsed Linux distribution in Azure mean?

Customers have the flexibility to deploy any Linux distribution in Azure from several different image sources – Azure Marketplace, custom, or community gallery. Each source provides a different expectation for quality, utility, and support. A subset of the Linux distributions in Azure, which includes Ubuntu, Debian, Flatcar, Oracle Linux, Red Hat Enterprise Linux, SUSE Enterprise Linux, and now AlmaLinux, are known as endorsed distributions. While a Linux distribution being endorsed is not an official recommendation from Microsoft, it indicates that the distribution has met the following criteria:

Market and customer demand: We’ve received signals about the market and customer demand of the Linux distribution and its usage in Azure.
Contract: We have a contractual agreement ensuring update cadences and remediation targets with the provider of the distribution, which can be the actual provider of the Linux distribution (e.g. Red Hat, Canonical, SUSE, etc.) or another vendor who is responsible for image publishing.
Engineering relationship: There is a close engineering relationship between Microsoft and the Linux distribution provider with ongoing forums to discuss  issues, customer feedback, new Azure feature enablement and future updates.
Content mirror in Azure: The Linux distribution provider maintains content and infrastructure across multiple Azure regions ensuring consistency of the update user experience and providing speedy, in-network software installation.

As an endorsed Linux distribution, AlmaLinux will adhere to testing and update standards, while also receiving support as detailed in Microsoft’s Linux and open source support policy.

Get started with AlmaLinux on Azure

AlmaLinux is available through the Azure Marketplace for both x64 and Arm64 architectures, including Microsoft’s latest Cobalt 100-based virtual machines, as well as a customized image for HPC workloads. AlmaLinux images are also available through Community Gallery and Windows Subsystem for Linux (WSL).

Learn more

Read the AlmaLinux press release
View the session from Open Source Summit North America to get an inside look into the engineering efforts at Microsoft that ensure your favorite Linux distributions run well on Azure
Read the migration guide to learn how to convert your current OS to AlmaLinux using the AlmaLinux migration tool

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