Build an AI-Powered Space Invaders Game
Build an AI-Powered Space Invaders Game: Integrating LLMs into HTML5 Games with Microsoft Foundry Local Introduction What if your game could talk back to you? Imagine playing Space Invaders while an AI commander taunts you during battle, delivers personalized mission briefings, and provides real-time feedback based on your performance. This isn't science fiction it's something you can build today using HTML, JavaScript, and a locally-running AI model. In this tutorial, we'll explore how to create an HTML5 game with integrated Large Language Model (LLM) features using Microsoft Foundry Local. You'll learn how to combine classic game development with modern AI capabilities, all running entirely on your own machine—no cloud services, no API costs, no internet connection required during gameplay. We'll be working with the Space Invaders - AI Commander Edition project, which demonstrates exactly how to architect games that leverage local AI. Whether you're a student learning game development, exploring AI integration patterns, or building your portfolio, this guide provides practical, hands-on experience with technologies that are reshaping how we build interactive applications. What You'll Learn By the end of this tutorial, you'll understand how to combine traditional web development with local AI inference. These skills transfer directly to building chatbots, interactive tutorials, AI-enhanced productivity tools, and any application where you want intelligent, context-aware responses. Set up Microsoft Foundry Local for running AI models on your machine Understand the architecture of games that integrate LLM features Use GitHub Copilot CLI to accelerate your development workflow Implement AI-powered game features like dynamic commentary and adaptive feedback Extend the project with your own creative AI features Why Local AI for Games? Before diving into the code, let's understand why running AI locally matters for game development. Traditional cloud-based AI services have limitations that make them impractical for real-time gaming experiences. Latency is the first challenge. Cloud API calls typically take 500ms to several seconds, an eternity in a game running at 60 frames per second. Local inference can respond in tens of milliseconds, enabling AI responses that feel instantaneous and natural. When an enemy ship appears, your AI commander can taunt you immediately, not three seconds later. Cost is another consideration. Cloud AI services charge per token, which adds up quickly when generating dynamic content during gameplay. Local models have zero per-use cost, once installed, they run entirely on your hardware. This frees you to experiment without worrying about API bills. Privacy and offline capability complete the picture. Local AI keeps all data on your machine, perfect for games that might handle player information. And since nothing requires internet connectivity, your game works anywhere, on planes, in areas with poor connectivity, or simply when you want to play without network access. Understanding Microsoft Foundry Local Microsoft Foundry Local is a runtime that enables you to run small language models (SLMs) directly on your computer. It's designed for developers who want to integrate AI capabilities into applications without requiring cloud infrastructure. Think of it as having a miniature AI assistant living on your laptop. Foundry Local handles the complex work of loading AI models, managing memory, and processing inference requests through a simple API. You send text prompts, and it returns AI-generated responses, all happening locally on your CPU or GPU. The models are optimized to run efficiently on consumer hardware, so you don't need a supercomputer. For our Space Invaders game, Foundry Local powers the "AI Commander" feature. During gameplay, the game sends context about what's happening, your score, accuracy, current level, enemies remaining and receives back contextual commentary, taunts, and encouragement. The result feels like playing alongside an AI companion who actually understands the game. Setting Up Your Development Environment Let's get your machine ready for AI-powered game development. We'll install Foundry Local, clone the project, and verify everything works. The entire setup takes about 10-15 minutes. Step 1: Install Microsoft Foundry Local Foundry Local installation varies by operating system. Open your terminal and run the appropriate command: # Windows (using winget) winget install Microsoft.FoundryLocal # macOS (using Homebrew) brew install microsoft/foundrylocal/foundrylocal These commands download and install the Foundry Local runtime along with a default small language model. The installation includes everything needed to run AI inference locally. Verify the installation by running: foundry --version If you see a version number, Foundry Local is ready. If you encounter errors, ensure you have administrator/sudo privileges and that your package manager is up to date. Step 2: Install Node.js (If Not Already Installed) Our game's AI features require a small Node.js server to communicate between the browser and Foundry Local. Check if Node.js is installed: node --version If you see a version number (v16 or higher recommended), you're set. Otherwise, install Node.js: # Windows winget install OpenJS.NodeJS.LTS # macOS brew install node # Linux sudo apt install nodejs npm Node.js provides the JavaScript runtime that powers our proxy server, bridging browser code with the local AI model. Step 3: Clone the Project Get the Space Invaders project onto your machine: git clone https://github.com/leestott/Spaceinvaders-FoundryLocal.git cd Spaceinvaders-FoundryLocal This downloads all game files, including the HTML interface, game logic, AI integration module, and server code. Step 4: Install Dependencies and Start the Server Install the Node.js packages and launch the AI-enabled server: npm install npm start The first command downloads required packages (primarily for the proxy server). The second starts the server, which listens for AI requests from the game. You should see output indicating the server is running on port 3001. Step 5: Play the Game Open your browser and navigate to: http://localhost:3001 You should see Space Invaders with "AI: ONLINE" displayed in the game HUD, indicating that AI features are active. Use arrow keys or A/D to move, SPACE to fire, and P to pause. The AI Commander will start providing commentary as you play! Understanding the Project Architecture Now that the game is running, let's explore how the different pieces fit together. Understanding this architecture will help you modify the game and apply these patterns to your own projects. The project follows a clean separation of concerns, with each file handling a specific responsibility: Spaceinvaders-FoundryLocal/ ├── index.html # Main game page and UI structure ├── styles.css # Retro arcade visual styling ├── game.js # Core game logic and rendering ├── llm.js # AI integration module ├── sound.js # Web Audio API sound effects ├── server.js # Node.js proxy for Foundry Local └── package.json # Project configuration index.html: Defines the game canvas and UI elements. It's the entry point that loads all other modules. game.js: Contains the game loop, physics, collision detection, scoring, and rendering logic. This is the heart of the game. llm.js: Handles all communication with the AI backend. It formats game state into prompts and processes AI responses. server.js: A lightweight Express server that proxies requests between the browser and Foundry Local. sound.js: Synthesizes retro sound effects using the Web Audio API—no audio files needed! How the AI Integration Works The magic of the AI Commander happens through a simple but powerful pattern. Let's trace the flow from gameplay event to AI response. When something interesting happens in the game, you clear a wave, achieve a combo, or lose a life, the game logic in game.js triggers an AI request. This request includes context about the current game state: your score, accuracy percentage, current level, lives remaining, and what just happened. The llm.js module formats this context into a prompt. For example, when you clear a wave with 85% accuracy, it might construct: You are an AI Commander in a Space Invaders game. The player just cleared wave 3 with 85% accuracy. Score: 12,500. Lives: 3. Provide a brief, enthusiastic comment (1-2 sentences). This prompt travels to server.js , which forwards it to Foundry Local. The AI model processes the prompt and generates a response like: "Impressive accuracy, pilot! Wave 3 didn't stand a chance. Keep that trigger finger sharp!" The response flows back through the server to the browser, where llm.js passes it to the game. The game displays the message in the HUD, creating the illusion of playing alongside an AI companion. This entire round trip typically completes in 50-200 milliseconds, fast enough to feel responsive without interrupting gameplay. Using GitHub Copilot CLI to Explore and Modify the Code GitHub Copilot CLI accelerates your development workflow by letting you ask questions and generate code directly in your terminal. Let's use it to understand and extend the Space Invaders project. Installing Copilot CLI If you haven't installed Copilot CLI yet, here's the quick setup: # Install GitHub CLI winget install GitHub.cli # Windows brew install gh # macOS # Authenticate with GitHub gh auth login # Add Copilot extension gh extension install github/gh-copilot # Verify installation gh copilot --help With Copilot CLI ready, you can interact with AI directly from your terminal while working on the project. Exploring Code with Copilot CLI Use Copilot to understand unfamiliar code. Navigate to the project directory and try: gh copilot explain "How does llm.js communicate with the server?" Copilot analyzes the code and explains the communication pattern, helping you understand the architecture without reading every line manually. You can also ask about specific functions: gh copilot explain "What does the generateEnemyTaunt function do?" This accelerates onboarding to unfamiliar codebases, a valuable skill when working with open source projects or joining teams. Generating New Features Want to add a new AI feature? Ask Copilot to help generate the code: gh copilot suggest "Create a function that asks the AI to generate a mission briefing at the start of each level, including the level number and a random mission objective" Copilot generates starter code that you can customize and integrate. This combination of AI-powered development tools and AI-integrated gameplay demonstrates how LLMs are transforming both how we build games and how games behave. Customizing the AI Commander The default AI Commander provides generic gaming commentary, but you can customize its personality and responses. Open llm.js to find the prompt templates that control AI behavior. Changing the AI's Personality The system prompt defines who the AI "is." Find the base prompt and modify it: // Original const systemPrompt = "You are an AI Commander in a Space Invaders game."; // Customized - Drill Sergeant personality const systemPrompt = `You are Sergeant Blaster, a gruff but encouraging drill sergeant commanding space cadets. Use military terminology, call the player "cadet," and be tough but fair.`; // Customized - Supportive Coach personality const systemPrompt = `You are Coach Nova, a supportive and enthusiastic gaming coach. Use encouraging language, celebrate small victories, and provide gentle guidance when players struggle.`; These personality changes dramatically alter the game's feel without changing any gameplay code. It's a powerful example of how AI can add variety to games with minimal development effort. Adding New Commentary Triggers Currently the AI responds to wave completions and game events. You can add new triggers in game.js : // Add AI commentary when player achieves a kill streak if (killStreak >= 5 && !streakCommentPending) { requestAIComment('killStreak', { count: killStreak }); streakCommentPending = true; } // Add AI reaction when player narrowly avoids death if (nearMissOccurred) { requestAIComment('nearMiss', { livesRemaining: lives }); } Each new trigger point adds another opportunity for the AI to engage with the player, making the experience more dynamic and personalized. Understanding the Game Features Beyond AI integration, the Space Invaders project demonstrates solid game development patterns worth studying. Let's explore the key features. Power-Up System The game includes eight different power-ups, each with unique effects: SPREAD (Orange): Fires three projectiles in a spread pattern LASER (Red): Powerful beam with high damage RAPID (Yellow): Dramatically increased fire rate MISSILE (Purple): Homing projectiles that track enemies SHIELD (Blue): Grants an extra life EXTRA LIFE (Green): Grants two extra lives BOMB (Red): Destroys all enemies on screen BONUS (Gold): Random score bonus between 250-750 points Power-ups demonstrate state management, tracking which power-up is active, applying its effects to player actions, and handling timeouts. Study the power-up code in game.js to understand how temporary state modifications work. Leaderboard System The game persists high scores using the browser's localStorage API: // Saving scores localStorage.setItem('spaceInvadersScores', JSON.stringify(scores)); // Loading scores const savedScores = localStorage.getItem('spaceInvadersScores'); const scores = savedScores ? JSON.parse(savedScores) : []; This pattern works for any data you want to persist between sessions—game progress, user preferences, or accumulated statistics. It's a simple but powerful technique for web games. Sound Synthesis Rather than loading audio files, the game synthesizes retro sound effects using the Web Audio API in sound.js . This approach has several benefits: no external assets to load, smaller project size, and complete control over sound parameters. Examine how oscillators and gain nodes combine to create laser sounds, explosions, and victory fanfares. This knowledge transfers directly to any web project requiring audio feedback. Extending the Project: Ideas for Students Ready to make the project your own? Here are ideas ranging from beginner-friendly to challenging, each teaching valuable skills. Beginner: Customize Visual Theme Modify styles.css to create a new visual theme. Try changing the color scheme from green to blue, or create a "sunset" theme with orange and purple gradients. This builds CSS skills while making the game feel fresh. Intermediate: Add New Enemy Types Create a new enemy class in game.js with different movement patterns. Perhaps enemies that move in sine waves, or boss enemies that take multiple hits. This teaches object-oriented programming and game physics. Intermediate: Expand AI Interactions Add new AI features like: Pre-game mission briefings that set up the story Dynamic difficulty hints when players struggle Post-game performance analysis and improvement suggestions AI-generated names for enemy waves Advanced: Multiplayer Commentary Modify the game for two-player support and have the AI provide play-by-play commentary comparing both players' performance. This combines game networking concepts with advanced AI prompting. Advanced: Voice Integration Use the Web Speech API to speak the AI Commander's responses aloud. This creates a more immersive experience and demonstrates browser speech synthesis capabilities. Troubleshooting Common Issues If something isn't working, here are solutions to common problems. "AI: OFFLINE" Displayed in Game This means the game can't connect to the AI server. Check that: The server is running ( npm start shows no errors) You're accessing the game via http://localhost:3001 , not directly opening the HTML file Foundry Local is installed correctly ( foundry --version works) Server Won't Start If npm start fails: Ensure you ran npm install first Check that port 3001 isn't already in use by another application Verify Node.js is installed ( node --version ) AI Responses Are Slow Local AI performance depends on your hardware. If responses feel sluggish: Close other resource-intensive applications Ensure your laptop is plugged in (battery mode may throttle CPU) Consider that first requests may be slower as the model loads Key Takeaways Local AI enables real-time game features: Microsoft Foundry Local provides fast, free, private AI inference perfect for gaming applications Clean architecture matters: Separating game logic, AI integration, and server code makes projects maintainable and extensible AI personality is prompt-driven: Changing a few lines of prompt text completely transforms how the AI interacts with players Copilot CLI accelerates learning: Use it to explore unfamiliar code and generate new features quickly The patterns transfer everywhere: Skills from this project apply to chatbots, assistants, educational tools, and any AI-integrated application Conclusion and Next Steps You've now seen how to integrate AI capabilities into a browser-based game using Microsoft Foundry Local. The Space Invaders project demonstrates that modern AI features don't require cloud services or complex infrastructure, they can run entirely on your laptop, responding in milliseconds. More importantly, you've learned patterns that extend far beyond gaming. The architecture of sending context to an AI, receiving generated responses, and integrating them into user experiences applies to countless applications: customer support bots, educational tutors, creative writing tools, and accessibility features. Your next step is experimentation. Clone the repository, modify the AI's personality, add new commentary triggers, or build an entirely new game using these patterns. The combination of GitHub Copilot CLI for development assistance and Foundry Local for runtime AI gives you powerful tools to bring intelligent applications to life. Start playing, start coding, and discover what you can create when your games can think. Resources Space Invaders - AI Commander Edition Repository - Full source code and documentation Play Space Invaders Online - Try the basic version without AI features Microsoft Foundry Local Documentation - Official installation and API guide GitHub Copilot CLI Documentation - Installation and usage guide GitHub Education - Free developer tools for students Web Audio API Documentation - Learn about browser sound synthesis Canvas API Documentation - Master HTML5 game renderingUnlocking AI-Driven Data Access: Azure Database for MySQL Support via the Azure MCP Server
Step into a new era of data-driven intelligence with the fusion of Azure MCP Server and Azure Database for MySQL, where your MySQL data is no longer just stored, but instantly conversational, intelligent and action-ready. By harnessing the open-standard Model Context Protocol (MCP), your AI agents can now query, analyze and automate in natural language, accessing tables, surfacing insights and acting on your MySQL-driven business logic as easily as chatting with a colleague. It’s like giving your data a voice and your applications a brain, all within Azure’s trusted cloud platform. We are excited to announce that we have added support for Azure Database for MySQL in Azure MCP Server. The Azure MCP Server leverages the Model Context Protocol (MCP) to allow AI agents to seamlessly interact with various Azure services to perform context-aware operations such as querying databases and managing cloud resources. Building on this foundation, the Azure MCP Server now offers a set of tools that AI agents and apps can invoke to interact with Azure Database for MySQL - enabling them to list and query databases, retrieve schema details of tables, and access server configurations and parameters. These capabilities are delivered through the same standardized interface used for other Azure services, making it easier to the adopt the MCP standard for leveraging AI to work with your business data and operations across the Azure ecosystem. Before we delve into these new tools and explore how to get started with them, let’s take a moment to refresh our understanding of MCP and the Azure MCP Server - what they are, how they work, and why they matter. MCP architecture and key components The Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services in a scalable, standardized, and secure manner. MCP dictates a client-server architecture with four key components: MCP Host, MCP Client, MCP Server and external data sources, services and APIs that provide the data context required to enhance AI models. To explain briefly, an MCP Host (AI apps and agents) includes an MCP client component that connects to one or more MCP Servers. These servers are lightweight programs that securely interface with external data sources, services and APIs and exposes them to MCP clients in the form of standardized capabilities called tools, resources and prompts. Learn more: MCP Documentation What is Azure MCP Server? Azure offers a multitude of cloud services that help developers build robust applications and AI solutions to address business needs. The Azure MCP Server aims to expose these powerful services for agentic usage, allowing AI systems to perform operations that are context-aware of your Azure resources and your business data within them, while ensuring adherence to the Model Context Protocol. It supports a wide range of Azure services and tools including Azure AI Search, Azure Cosmos DB, Azure Storage, Azure Monitor, Azure CLI and Developer CLI extensions. This means that you can empower AI agents, apps and tools to: Explore your Azure resources, such as listing and retrieving details on your Azure subscriptions, resource groups, services, databases, and tables. Search, query and analyze your data and logs. Execute CLI and Azure Developer CLI commands directly, and more! Learn more: Azure MCP Server GitHub Repository Introducing new Azure MCP Server tools to interact with Azure Database for MySQL The Azure MCP Server now includes the following tools that allow AI agents to interact with Azure Database for MySQL and your valuable business data residing in these servers, in accordance with the MCP standard: Tool Description Example Prompts azmcp_mysql_server_list List all MySQL servers in a subscription & resource group "List MySQL servers in resource group 'prod-rg'." "Show MySQL servers in region 'eastus'." azmcp_mysql_server_config_get Retrieve the configuration of a MySQL server "What is the backup retention period for server 'my-mysql-server'?" "Show storage allocation for server 'my-mysql-server'." azmcp_mysql_server_param_get Retrieve a specific parameter of a MySQL server "Is slow_query_log enabled on server my-mysql-server?" "Get innodb_buffer_pool_size for server my-mysql-server." azmcp_mysql_server_param_set Set a specific parameter of a MySQL server to a specific value "Set max_connections to 500 on server my-mysql-server." "Set wait_timeout to 300 on server my-mysql-server." azmcp_mysql_table_list List all tables in a MySQL database "List tables starting with 'tmp_' in database 'appdb'." "How many tables are in database 'analytics'?" azmcp_mysql_table_schema_get Get the schema of a specific table in a MySQL database "Show indexes for table 'transactions' in database 'billing'." "What is the primary key for table 'users' in database 'auth'?" azmcp_mysql_database_query Executes a SELECT query on a MySQL Database. The query must start with SELECT and cannot contain any destructive SQL operations for security reasons. “How many orders were placed in the last 30 days in the salesdb.orders table?” “Show the number of new users signed up in the last week in appdb.users grouped by day.” These interactions are secured using Microsoft Entra authentication, which enables seamless, identity-based access to Azure Database for MySQL - eliminating the need for password storage and enhancing overall security. How are these new tools in the Azure MCP Server different from the standalone MCP Server for Azure Database for MySQL? We have integrated the key capabilities of the Azure Database for MySQL MCP server into the Azure MCP Server, making it easier to connect your agentic apps not only to Azure Database for MySQL but also to other Azure services through one unified and secure interface! How to get started Installing and running the Azure MCP Server is quick and easy! Use GitHub Copilot in Visual Studio Code to gain meaningful insights from your business data in Azure Database for MySQL. Pre-requisites Install Visual Studio Code. Install GitHub Copilot and GitHub Copilot Chat extensions. An Azure Database for MySQL with Microsoft Entra authentication enabled. Ensure that the MCP Server is installed on a system with network connectivity and credentials to connect to Azure Database for MySQL. Installation and Testing Please use this guide for installation: Azure MCP Server Installation Guide Try the following prompts with your Azure Database for MySQL: Azure Database for MySQL tools for Azure MCP Server Try it out and share your feedback! Start using Azure MCP Server with the MySQL tools today and let our cloud services become your AI agent’s most powerful ally. We’re counting on your feedback - every comment, suggestion, or bug-report helps us build better tools together. Stay tuned: more features and capabilities are on the horizon! Feel free to comment below or write to us with your feedback and queries at AskAzureDBforMySQL@service.microsoft.com.Build Smarter with Azure HorizonDB
By: Maxim Lukiyanov, PhD, Principal PM Manager; Abe Omorogbe, Senior Product Manager; Shreya R. Aithal, Product Manager II; Swarathmika Kakivaya, Product Manager II Today, at Microsoft Ignite, we are announcing a new PostgreSQL database service - Azure HorizonDB. You can read the announcement here, and in this blog you can learn more about HorizonDB’s AI features and development tools. Azure HorizonDB is designed for the full spectrum of modern database needs - from quickly building new AI applications, to scaling enterprise workloads to unprecedented levels of performance and availability, to managing your databases efficiently and securely. To help with building new AI applications we are introducing 3 features: DiskANN Advanced Filtering, built-in AI model management, and integration with Microsoft Foundry. To help with database management we are introducing a set of new capabilities in PostgreSQL extension for Visual Studio Code, as well as announcing General Availability of the extension. Let’s dive into AI features first. DiskANN Advanced Filtering We are excited to announce a new enhancement in the Microsoft’s state of the art vector indexing algorithm DiskANN – DiskANN Advanced Filtering. Advanced Filtering addresses a common problem in vector search – combining vector search with filtering. In real-world applications where queries often include constraints like price ranges, ratings, or categories, traditional vector search approaches, such as pgvector’s HNSW, rely on multiple step retrieval and post-filtering, which can make search extremely slow. DiskANN Advanced Filtering solves this by combining filter and search into one operation - while the graph of vectors is traversed during the vector search, each vector is also checked for filter predicate match, ensuring that only the correct vectors are retrieved. Under the hood, it works in a 3-step process: first creating a bitmap of relevant rows using indexes on attributes such as price or rating, then performing a filter-aware graph traversal against the bitmap, and finally, validating and ordering the results for accuracy. This integrated approach delivers dramatically faster and more efficient filtered vector searches. Initial benchmarks show that enabling Advanced Filtering on DiskANN reduces query latency by up to 3x, depending on filter selectivity. AI Model Management Another exciting feature of HorizonDB is AI Model Management. This feature automates Microsoft Foundry model provisioning during database deployment and instantly activates database semantic operators. This eliminates tens of setup and configuration steps and simplifies the development of new AI apps and agents. AI Model Management elevates the experience of using semantic operators within PostgreSQL. When activated, it provisions key models for embedding, semantic ranking and generation via Foundry, installs and configures the azure_ai extension to enable the operators, establishes secure connections, integrates model management, monitoring and cost management within HorizonDB. What would otherwise require significant manual effort and context-switching between Foundry and PostgreSQL for configuration, management, and monitoring is now possible with just a few clicks, all without leaving the PostgreSQL environment. You can also continue to bring your own Foundry models, with a simplified and enhanced process for registering your custom model endpoints in the azure_ai extension. Microsoft Foundry Integration Microsoft Foundry offers a comprehensive technology stack for building AI apps and agents. But building modern agents capable of reasoning, acting, and collaborating is impossible without connection to data. To facilitate that connection, we are excited to announce a new PostgreSQL connector in Microsoft Foundry. The connector is designed using a new standard in data connectivity – Model Context Protocol (MCP). It enables Foundry agents to interact with HorizonDB securely and intelligently, using natural language instead of SQL, and leveraging Microsoft Entra ID to ensure secure connection. In addition to HorizonDB this connector also supports Azure Database for PostgreSQL (ADP). This integration allows Foundry agents to perform tasks like: Exploring database schemas Retrieving records and insights Performing analytical queries Executing vector similarity searches for semantic search use cases All through natural language, without compromising enterprise security or compliance. To get started with Foundry Integration, follow these setup steps to deploy your own HorizonDB (requires participation in Private Preview) or ADP and connect it to Foundry in just a few steps. PostgreSQL extension for VS Code is Generally Available We’re excited to announce that the PostgreSQL extension for Visual Studio Code is now Generally Available. This extension garnered significant popularity within the PostgreSQL community since it’s preview in May’25 reaching more than 200K installs. It is the easiest way to connect to a PostgreSQL database from your favorite editor, manage your databases, and take advantage of built-in AI capabilities without ever leaving VS Code. The extension works with any PostgreSQL whether it's on-premises or in the cloud, and also supports unique features of Azure HorizonDB and Azure Database for PostgreSQL (ADP). One of the key new capabilities is Metrics Intelligence, which uses Copilot and real-time telemetry of HorizonDB or ADP to help you diagnose and fix performance issues in seconds. Instead of digging through logs and query plans, you can open the Performance Dashboard, see a CPU spike, and ask Copilot to investigate. The extension sends a rich prompt that tells Copilot to analyze live metrics, identify the root cause, and propose an actionable fix. For example, Copilot might find a full table scan on a large table, recommend a composite index on the filter columns, create that index, and confirm the query plan now uses it. The result is dramatic: you can investigate and resolve the CPU spike in seconds, with no manual scripting or guesswork, and with no prior PostgreSQL expertise required. The extension also makes it easier to work with graph data. HorizonDB and ADP support open-source graph extension Apache AGE. This turns these services into fully managed graph databases. You can run graph queries against HorizonDB and immediately visualize the results as an interactive graph inside VS Code. This helps you understand relationships in your data faster, whether you’re exploring customer journeys, network topologies, or knowledge graphs - all without switching tools. In Conclusion Azure HorizonDB brings together everything teams need to build, run, and manage modern, AI-powered applications on PostgreSQL. With DiskANN Advanced Filtering, you can deliver low-latency, filtered vector search at scale. With built-in AI Model Management and Microsoft Foundry integration, you can provision models, wire up semantic operators, and connect agents to your data with far fewer steps and far less complexity. And with the PostgreSQL extension for Visual Studio Code, you get an intuitive, AI-assisted experience for performance tuning and graph visualization, right inside the tools you already use. HorizonDB is now available in private preview. If you’re interested in building AI apps and agents on a fully managed, PostgreSQL-compatible service with built-in AI and rich developer tooling, sign-up for Private Preview: https://aka.ms/PreviewHorizonDB.September 2025 Recap: Azure Database for PostgreSQL
Hello Azure Community, We are back with another round of updates for Azure Database for PostgreSQL! September is packed with powerful enhancements, from the public preview of PostgreSQL 18 to the general availability of Azure Confidential Computing, plus several new capabilities designed to boost performance, security, and developer experience. Stay tuned as we dive deeper into each of these feature updates. Before we dive into the feature highlights, let’s take a look at PGConf NYC 2025 highlights. PGConf NYC 2025 Highlights Our Postgres team was glad to be part of PGConf NYC 2025! As a Platinum sponsor, Microsoft joined the global PostgreSQL community for three days of sessions covering performance, extensibility, cloud, and AI, highlighted by Claire Giordano’s keynote, “What Microsoft is Building for Postgres—2025 in Review,” along with deep dives from core contributors and engineers. If you missed it, you can catch up here: Keynote slides: What Microsoft is Building for Postgres—2025 in Review by Claire Giordano at PGConf NYC 2025 Day 3 wrap-up: Key takeaways, highlights, and insights from the Azure Database for PostgreSQL team. Feature Highlights Near Zero Downtime scaling for High Availability (HA) enabled servers - Generally Available Azure Confidential Computing for Azure Database for PostgreSQL - Generally Available PostgreSQL 18 on Azure Database for PostgreSQL - Public Preview PostgreSQL Discovery & Assessment in Azure Migrate - Public Preview LlamaIndex Integration with Azure Postgres Latest Minor Versions GitHub Samples: Entra ID Token Refresh for PostgreSQL VS Code Extension for PostgreSQL enhancements Near Zero Downtime scaling for High Availability (HA) enabled servers – Generally Available Scaling compute for high availability (HA) enabled Azure Database for PostgreSQL servers just got faster. With Near Zero Downtime (NZD) scaling, compute changes such as vCore or tier modifications are now complete with minimal interruption, typically under 30 seconds using HA failover which maintains the connection string. The service provisions a new primary and standby instance with the updated configuration, synchronizes them with the existing setup, and performs a quick failover. This significantly reduces downtime compared to traditional scaling (which could take 2–10 minutes), improving overall availability. Visit our documentation for full details on how Near Zero Downtime scaling works. Azure Confidential Computing for Azure Database for PostgreSQL - Generally Available Azure Confidential Computing (ACC) Confidential Virtual Machines (CVMs) are now generally available for Azure Database for PostgreSQL. This capability brings hardware-based protection for data in use, ensuring your most sensitive information remains secure, even while being processed. With CVMs, your PostgreSQL flexible server instance runs inside a Trusted Execution Environment (TEE), a secure, hardware-backed enclave that encrypts memory and isolates it from the host OS, hypervisor, and even Azure operators. This means your data enjoys end-to-end protection: at rest, in transit, and in use. Key Benefits: End-to-End Security: Data protected at rest, in transit, and in use Enhanced Privacy: Blocks unauthorized access during processing Compliance Ready: Meets strict security standards for regulated workloads Confidence in Cloud: Hardware-backed isolation for critical data Discover how Azure Confidential Computing enhances PostgreSQL check out the blog announcement. PostgreSQL 18 on Azure Database for PostgreSQL – Public Preview PostgreSQL 18 is now available in public preview on Azure Database for PostgreSQL, launched the same day as the PostgreSQL community release. PostgreSQL 18 introduces new performance, scalability, and developer productivity improvements. With this preview, you get early access to the latest community release on a fully managed Azure service. By running PostgreSQL 18 on flexible server, you can test application compatibility, explore new SQL and performance features, and prepare for upgrades well before general availability. This preview release gives you the opportunity to validate your workloads, extensions, and development pipelines in a dedicated preview environment while taking advantage of the security, high availability, and management capabilities in Azure. With PostgreSQL 18 in preview, you are among the first to experience the next generation of PostgreSQL on Azure, ensuring your applications are ready to adopt it when it reaches full general availability. To learn more about preview, read https://aka.ms/pg18 PostgreSQL Discovery & Assessment in Azure Migrate – Public Preview The PostgreSQL Discovery & Assessment feature is now available in public preview on Azure Migrate, making it easier to plan your migration journey to Azure. Migrating PostgreSQL workloads can be challenging without clear visibility into your existing environment. This feature solves that problem by delivering deep insights into on-premises PostgreSQL deployments, making migration planning easier and more informed. With this feature, you can discover PostgreSQL instances across your infrastructure, assess migration readiness and identify potential blockers, receive configuration-based SKU recommendations for Azure Database for PostgreSQL, and estimate costs for running your workloads in Azure all in one unified experience. Key Benefits: Comprehensive Visibility: Understand your on-prem PostgreSQL landscape Risk Reduction: Identify blockers before migration Optimized Planning: Get tailored SKU and cost insights Faster Migration: Streamlined assessment for a smooth transition Learn more in our blog: PostgreSQL Discovery and Assessment in Azure Migrate LlamaIndex Integration with Azure Postgres The support for native LlamaIndex integration is now available for Azure Database for PostgreSQL! This enhancement brings seamless connectivity between Azure Database for PostgreSQL and LlamaIndex, allowing developers to leverage Azure PostgreSQL as a secure and high-performance vector store for their AI agents and applications. Specifically, this package adds support for: Microsoft Entra ID (formerly Azure AD) authentication when connecting to your Azure Database for PostgreSQL instances, and, DiskANN indexing algorithm when indexing your (semantic) vectors. This package makes it easy to connect LlamaIndex to your Azure PostgreSQL instances whether you're building intelligent agents, semantic search, or retrieval-augmented generation (RAG) systems. Explore the full guide here: https://aka.ms/azpg-llamaindex Latest Postgres minor versions: 17.6, 16.9, 15.13, 14.18 and 13.21 PostgreSQL minor versions 17.6, 16.9, 15.13, 14.18 and 13.21 are now supported by Azure Database for PostgreSQL. These minor version upgrades are automatically performed as part of the monthly planned maintenance in Azure Database for PostgreSQL. The upgrade automation ensures that your databases are always running the latest optimized versions without requiring manual intervention. This release fixes 3 security vulnerabilities and more than 55 bugs reported over the last several months. PostgreSQL minor versions are backward-compatible, so updates won’t affect your applications. For details about the release, see PostgreSQL community announcement. GitHub Samples: Entra ID Token Refresh for PostgreSQL We have introduced code samples for Entra ID token refresh, built specifically for Azure Database for PostgreSQL. These samples simplify implementing automatic token acquisition and refresh, helping you maintain secure, uninterrupted connectivity without manual intervention. By using these examples, you can keep sessions secure, prevent connection drops from expired tokens, and streamline integration with Azure Identity libraries for PostgreSQL workloads. What’s Included: Ready-to-use code snippets for token acquisition and refresh for Python and .NET Guidance for integrating with Azure Identity libraries Explore the samples repository on https://aka.ms/pg-access-token-refresh and start implementing it today. VS Code Extension for PostgreSQL enhancements A new version for VS Code Extension for PostgreSQL is out! This update introduces a Server Dashboard that provides high-level metadata and real-time performance metrics, along with historical insights for Azure Database for PostgreSQL Flexible Server. You can even use GitHub Copilot Chat to ask performance questions in natural language and receive diagnostic SQL queries in response. Additional enhancements include: A new keybinding for “Run Current Statement” in the Query Editor Support for dragging Object Explorer entities into the editor with properly quoted identifiers Ability to connect to databases via socket file paths Key fixes: Preserves the state of the Explain Analyze toolbar toggle Removes inadvertent logging of sensitive information from extension logs Stabilizes memory usage during long-running dashboard sessions Don’t forget to update to the latest version in the marketplace to take advantage of these enhancements and visit our GitHub repository to learn more about this month’s release. We’d love your feedback! Help us improve the Server Dashboard and other features by sharing your thoughts on GitHub . Azure Postgres Learning Bytes 🎓 Setting up logical replication between two servers This section will walk through setting up logical replication between two Azure Database for PostgreSQL flexible server instances. Logical replication replicates data changes from a source (publisher) server to a target (subscriber) server. Prerequisites PostgreSQL versions supported by logical replication (publisher/subscriber compatible). Network connectivity: subscriber must be able to connect to the publisher (VNet/NSG/firewall rules). A replication role on the publisher (or a role with REPLICATION privilege). Step 1: Configure Server Parameters on both publisher and subscriber: On Publisher: wal_level=logical max_worker_processes=16 max_replication_slots=10 max_wal_senders=10 track_commit_timestamp=on On Subscriber: wal_level=logical max_worker_processes=16 max_replication_slots=10 max_wal_senders=10 track_commit_timestamp=on max_worker_processes = 16 max_sync_workers_per_subscription = 6 autovacuum = OFF (during initial copy) max_wal_size = 64GB checkpoint_timeout = 3600 Step 2: Create Publication (Publisher) and alter role with replication privilege ALTER ROLE <replication_user> WITH REPLICATION; CREATE PUBLICATION pub FOR ALL TABLES; Step 3: Create Subscription (Subscriber) CREATE SUBSCRIPTION <subscription-name> CONNECTION 'host=<publisher_host> dbname=<db> user=<user> password=<pwd>' PUBLICATION <publication-name>;</publication-name></pwd></user></db></publisher_host></subscription-name> Step 4: Monitor Publisher: This shows active processes on the publisher, including replication workers. SELECT application_name, wait_event_type, wait_event, query, backend_type FROM pg_stat_activity WHERE state = 'active'; Subscriber: The ‘pg_stat_progress_copy’ table tracks the progress of the initial data copy for each table. SELECT * FROM pg_stat_progress_copy; To explore more details on how to get started with logical replication, visit our blog on Tuning logical replication for Azure Database for PostgreSQL. Conclusion That’s all for the September 2025 feature highlights! We remain committed to making Azure Database for PostgreSQL more powerful and secure with every release. Stay up to date on the latest enhancements by visiting our Azure Database for PostgreSQL blog updates link. Your feedback matters and helps us shape the future of PostgreSQL on Azure. If you have suggestions, ideas, or questions, we’d love to hear from you: https://aka.ms/pgfeedback. We look forward to sharing even more exciting capabilities in the coming months. Stay tuned!Model Mondays S2:E4 Understanding AI Developer Experiences with Leo Yao
This week in Model Mondays, we put the spotlight on the AI Toolkit for Visual Studio Code - and explore the tools and workflows that make building generative AI apps and agents easier for developers. Read on for my recap. This post was generated with AI help and human revision & review. To learn more about our motivation and workflows, please refer to this document in our website. About Model Mondays Model Mondays is a weekly series designed to help you grow your Azure AI Foundry Model IQ step by step. Each week includes: 5-Minute Highlights – Quick news and updates about Azure AI models and tools on Monday 15-Minute Spotlight – Deep dive into a key model, protocol, or feature on Monday 30-Minute AMA on Friday – Live Q&A with subject matter experts from the Monday livestream If you're looking to grow your skills with the latest in AI model development, this series is a great place to begin. Useful links: Register for upcoming livestreams Watch past episodes Join the AMA on AI Developer Experiences Visit the Model Mondays forum Spotlight On: AI Developer Experiences 1. What is this topic and why is it important? AI Developer Experiences focus on making the process of building, testing, and deploying AI models as efficient as possible. With the right tools—such as the AI Toolkit and Azure AI Foundry extensions for Visual Studio Code—developers can eliminate unnecessary friction and focus on innovation. This is essential for accelerating the real-world impact of generative AI. 2. What is one key takeaway from the episode? The integration of Azure AI Foundry with Visual Studio Code allows developers to manage models, run experiments, and deploy applications directly from their preferred development environment. This unified workflow enhances productivity and simplifies the AI development lifecycle. 3. How can I get started? Here are a few resources to explore: Install the AI Toolkit for VS Code Explore Azure AI Foundry Documentation Join the Microsoft Tech Community to follow and contribute to discussions 4. What’s New in Azure AI Foundry? Azure AI Foundry continues to evolve to meet developer needs with more power, flexibility, and productivity. Here are some of the latest updates highlighted in this week’s episode: AI Toolkit for Visual Studio Code Now with deeper integration, allowing developers to manage models, run experiments, and deploy applications directly within their editor—streamlining the entire workflow. Prompt Shields Enhanced security capabilities designed to protect generative AI applications from prompt injection and unsafe content, improving reliability in production environments. Model Router A new intelligent routing system that dynamically directs model requests to the most suitable model available—enhancing performance and efficiency at scale. Expanded Model Catalog The catalog now includes more open-source and proprietary models, featuring the latest from Hugging Face, OpenAI, and other leading providers. Improved Documentation and Sample Projects Newly added guides and ready-to-use examples to help developers get started faster, understand workflows, and build confidently. My A-Ha Moment Before watching this episode, setting up an AI development environment always felt like a challenge. There were so many moving parts—configurations, integrations, and dependencies—that it was hard to know where to begin. Seeing the AI Toolkit in action inside Visual Studio Code changed everything for me. It was a realization moment: “That’s it? I can explore models, test prompts, and deploy apps—without ever leaving my editor?” This episode made it clear that building with AI doesn’t have to be complex or intimidating. With the right tools, experimentation becomes faster and far more enjoyable. Now, I’m genuinely excited to build, test, and explore new generative AI solutions because the process finally feels accessible. Coming Up Next Week In the next episode, we’ll be exploring Fine-Tuning and Distillation with Dave Voutila. This session will focus on how to adapt Azure OpenAI models to your unique use cases and apply best practices for efficient knowledge transfer. Register here to reserve your spot and be part of the conversation. Join the Community Building in AI is better when we do it together. That’s why the Azure AI Developer Community exists—to support your journey and provide resources every step of the way. Join the Discord for real-time discussions, events, and peer learning Explore the Forum to catch up on AMAs, ask questions, and connect with other developers About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador passionate about cloud technologies and artificial intelligence. I enjoy learning, building, and helping others grow in tech. Connect with me: LinkedIn GitHub Dev.to Microsoft Tech CommunityJune 2025 Recap: Azure Database for PostgreSQL
Hello Azure Community, We have introduced a range of exciting new features and updates to Azure Database for PostgreSQL in June. From general availability of PG 17 to public preview of the SSD v2 storage tier for High Availability, there have been some significant feature announcements across multiple areas in the last month. Stay tuned as we dive deeper into each of these feature updates. Before that, let’s look at POSETTE 2025 highlights. POSETTE 2025 Highlights We hosted POSETTE: An Event for Postgres 2025 in June! This year marked our 4th annual event featuring 45 speakers and a total of 42 talks. PostgreSQL developers, contributors, and community members came together to share insights on topics covering everything from AI-powered applications to deep dives into PostgreSQL internals. If you missed it, you can catch up by watching the POSETTE livestream sessions. If this conference sounds interesting to you and want to be part of it next year, don’t forget to subscribe to POSETTE news. Feature Highlights General Availability of PostgreSQL 17 with 'In-Place' upgrade support General Availability of Online Migration Migration service support for PostgreSQL 17 Public Preview of SSD v2 High Availability New Region: Indonesia Central VS Code Extension for PostgreSQL enhancements Enhanced role management Ansible collection released for latest REST API version General Availability of PostgreSQL 17 with 'In-Place' upgrade support PostgreSQL 17 is now generally available on Azure Database for PostgreSQL flexible server, bringing key community innovations to your workloads. You’ll see faster vacuum operations, richer JSON processing, smarter query planning (including better join ordering and parallel execution), dynamic logical replication controls, and enhanced security & audit-logging features—backed by Azure’s five-year support policy. You can easily upgrade to PostgreSQL 17 using the in-place major version upgrade feature available through the Azure portal and CLI, without changing server endpoints or reconfiguring applications. The process includes built-in validations and rollback safety to help ensure a smooth and reliable upgrade experience. For more details, read the PostgreSQL 17 release announcement blog. General Availability of Online Migration We're excited to announce that Online Migration is now generally available for the Migration service for Azure Database for PostgreSQL! Online migration minimizes downtime by keeping your source database operational during the migration process, with continuous data synchronization until cut over. This is particularly beneficial for mission-critical applications that require minimal downtime during migration. This milestone brings production-ready online migration capabilities supporting various source environments including on-premises PostgreSQL, Azure VMs, Amazon RDS, Amazon Aurora, and Google Cloud SQL. For detailed information about the capabilities and how to get started, visit our Migration service documentation. Migration service support for PostgreSQL 17 Building on our PostgreSQL 17 general availability announcement, the Migration service for Azure Database for PostgreSQL now fully supports PostgreSQL 17. This means you can seamlessly migrate your existing PostgreSQL instances from various source platforms to Azure Database for PostgreSQL flexible server running PostgreSQL 17. With this support, organizations can take advantage of the latest PostgreSQL 17 features and performance improvements while leveraging our online migration capabilities for minimal downtime transitions. The migration service maintains full compatibility with PostgreSQL 17's enhanced security features, improved query planning, and other community innovations. Public Preview of SSD v2 High Availability We’re excited to announce the public preview High availability (HA) support for the Premium SSD v2 storage tier in Azure Database for PostgreSQL flexible server. This support allows you to enable Zone-Redundant HA using Premium SSD v2 during server deployments. In addition to high availability on SSDv2 you now get improved resiliency and 10 second failover times when using Premium SSD v2 with zone-redundant HA, helping customers build resilient, high-performance PostgreSQL applications with minimal overhead. This feature is particularly well-suited for mission-critical workloads, including those in financial services, real-time analytics, retail, and multi-tenant SaaS platforms. Key Benefits of Premium SSD v2: Flexible disk sizing: Scale from 32 GiB to 64 TiB in 1-GiB increments Fast failovers: Planned or unplanned failovers typically around 10 seconds Independent performance configuration: Achieve up to 80,000 IOPS and 1,200 Mbps throughput without resizing your disk. Baseline performance: Free throughput of 125 MB/s and 3,000 IOPS for disks up to 399 GiB, and 500 MB/s and 12,000 IOPS for disks 400 GiB and above at no additional cost. For more details, please refer to the Premium SSD v2 HA blog. New Region: Indonesia Central New region rollout! Azure Database for PostgreSQL flexible server is now available in Indonesia Central, giving customers in and around the region lower latency and data residency options. This continues our mission to bring Azure PostgreSQL closer to where you build and run your apps. For the full list of regions visit: Azure Database for PostgreSQL Regions. VS Code Extension for PostgreSQL enhancements The brand-new VS code extension for PostgreSQL launched in mid-May and has already garnered over 122K installs from the Visual Studio Marketplace! And the kickoff blog about this new IDE for PostgreSQL in VS Code has had over 150K views. This extension makes it easier for developers to seamlessly interact with PostgreSQL databases. We have been committed to make this experience better and have introduced several enhancements to improve reliability and compatibility updates. You can now have better control over service restarts and process terminations on supported operating systems. Additionally, we have added support for parsing additional connection-string formats in the “Create Connection” flow, making it more flexible and user-friendly. We also resolved Entra token-fetching failures for newly created accounts, ensuring a smoother onboarding experience. On the feature front, you can now leverage Entra Security Groups and guest accounts across multiple tenants when establishing new connections, streamlining permission management in complex Entra environments. Don’t forget to update to the latest version in the marketplace to take advantage of these enhancements and visit our GitHub repository to learn more about this month’s release. If you learn best by video, these 2 videos are a great way to learn more about this new VS Code extension: POSETTE 2025: Introducing Microsoft’s VS Code Extension for PostgreSQL Demo of using VS code extension for PostgreSQL Enhanced role management With the introduction of PostgreSQL 16, a strict role hierarchy structure has been implemented. As a result, GRANT statements that were functional in PostgreSQL 11-15 may no longer work in PostgreSQL 16. We have improved the administrative flexibility and addressed this limitation in Azure Database for PostgreSQL flexible server across all PostgreSQL versions. Members of ‘azure_pg_admin’ can now manage, and access objects owned by any role that is non-restricted, giving control and permission over user-defined roles. To learn more about this improvement, please refer to our documentation on roles. Ansible collection released for latest REST API version A new version of Ansible collection for Azure Database for PostgreSQL flexible server is now released. Version 3.6.0 now includes the latest GA REST API features. This update introduces several enhancements, such as support for virtual endpoints, on-demand backups, system-assigned identity, storage auto-grow, and seamless switchover of read replicas to a new site (Read Replicas - Switchover), among many other improvements. To get started with using please visit flexible server Ansible collection link. Azure Postgres Learning Bytes 🎓 Using PostgreSQL VS code extension with agent mode The VS Code extension for PostgreSQL has been trending amongst the developer community. In this month's Learning Bytes section, we want to share how to enable the extension and use GitHub Copilot to create a database in Agent Mode, add dummy data, and visualize it using the Agent Mode and VS Code extension. Step 1: Download the VS code Extension for PostgreSQL Step 2: Check GitHub Copilot and Agent mode is enabled Go to File -> Preferences -> Settings (Ctrl + ,). Search and enable "chat.agent.enabled" and "pgsql copilot.enable". Reload VS Code to apply changes. Step 3: Connect to Azure Database for PostgreSQL Use the extension to enter instance details and establish a connection. Create and view schemas under Databases -> Schemas. Step 4: Visualize and Populate Data Right-click the database to visualize schemas. Ask the agent to insert dummy data or run queries. Conclusion That's all for the June 2025 feature updates! We are dedicated to continuously improve Azure Database for PostgreSQL with every release. Stay updated with the latest updates to our features by following this link. Your feedback is important and helps us continue to improve. If you have any suggestions, ideas, or questions, we’d love to hear from you. Share your thoughts here: aka.ms/pgfeedback We look forward to bringing you even more exciting updates throughout the year, stay tuned!New Generative AI Features in Azure Database for PostgreSQL
by: Maxim Lukiyanov, PhD, Principal PM Manager This week at Microsoft Build conference, we're excited to unveil a suite of new Generative AI capabilities in Azure Database for PostgreSQL flexible server. These features unlock a new class of applications powered by an intelligent database layer, expanding the horizons of what application developers can achieve. In this post, we’ll give you a brief overview of these announcements. Data is the fuel of AI. Looking back, the intelligence of Large Language Models (LLMs) can be reframed as intelligence that emerged from the vast data they were trained on. The LLMs just happened to be this technological leap necessary to extract that knowledge, but the knowledge itself was hidden in the data all along. In modern AI applications, the Retrieval-Augmented Generation (RAG) pattern applies this same principle to real-time data. RAG extracts relevant facts from data on the fly to augment an LLM’s knowledge. At Microsoft, we believe this principle will continue to transform technology. Every bit of data will be squeezed dry of every bit of knowledge it holds. And there’s no better place to find the most critical and up-to-date data than in databases. Today, we're excited to announce the next steps on our journey to make databases smarter – so they can help you capture the full potential of your data. Fast and accurate vector search with DiskANN First, we’re announcing the General Availability of DiskANN vector indexing in Azure Database for PostgreSQL. Vector search is at the heart of the RAG pattern, and it continues to be a cornerstone technology for the new generation of AI Agents - giving it contextual awareness and access to fresh knowledge hidden in data. DiskANN brings years of state-of-the-art innovation in vector indexing from Microsoft Research directly to our customers. This release introduces supports for vectors up to 16,000 dimensions — far surpassing the 2,000-dimension limit of the standard pgvector extension in PostgreSQL. This enables the development of highly accurate applications using high-dimensional embeddings. We’ve also accelerated index creation with enhanced memory management, parallel index building, and other optimizations – delivering up to 3x faster index builds while reducing disk I/O. Additionally, we're excited to announce the Public Preview of Product Quantization – a cutting-edge vector compression technique that delivers exceptional compression while maintaining high accuracy. DiskANN Product Quantization enables efficient storage of large vector volumes, making it ideal for production workloads where both performance and cost matter. With Product Quantization enabled, DiskANN offers up to 10x faster performance and 4x cost savings compared to pgvector HNSW. You can learn more about DiskANN in a dedicated blog post. Semantic operators in the database Next, we’re announcing the Public Preview of Semantic Operators in Azure Database for PostgreSQL – bringing a new intelligence layer to relational algebra, integrated directly into the SQL query engine. While vector search is foundational to the Generative AI (GenAI) apps and agents, it only scratches the surface of what’s possible. Semantic relationships between elements of the enterprise data are not visible to the vector search. This knowledge exists within the data but is lost at the lowest level of the stack – vector search – and this loss propagates upward, limiting the agent’s ability to reason about the data. This is where new Semantic Operators come in. Semantic Operators leverage LLMs to add semantic understanding of operational data. Today, we’re introducing four operators: generate() – a versatile generation operator capable of ChatGPT-style responses. is_true() – a semantic filtering operator that evaluates filter conditions and joins in natural language. extract() – a knowledge extraction operator that extracts hidden semantic relationships and other knowledge from your data, bringing a new level of intelligence to your GenAI apps and agents. rank() - a highly accurate semantic ranking operator, offering two types of state-of-the-art re-ranking models: Cohere Rank-v3.5 or OpenAI gpt-4.1 models from Azure AI Foundry Model Catalog. You can learn more about Semantic Operators in a dedicated blog post. Graph database and GraphRAG knowledge graph support Finally, we’re announcing the General Availability of GraphRAG support and the General Availability of the Apache AGE extension in Azure Database for PostgreSQL. Apache AGE extension on Azure Database for PostgreSQL offers a cost-effective, managed graph database service powered by PostgreSQL engine – and serves as the foundation for building GraphRAG applications. The semantic relationships in the data once extracted can be stored in various ways within the database. While relational tables with referential integrity can represent some relationships, this approach is suboptimal for knowledge graphs. Semantic relationships are dynamic; many aren’t known ahead of time and can’t be effectively modeled by a fixed schema. Graph databases provide a much more flexible structure, enabling knowledge graphs to be expressed naturally. Apache AGE supports openCypher, the emerging standard for querying graph data. OpenCypher offers an expressive, intuitive language well-suited for knowledge graph queries. We believe that combining semantic operators with graph support in Azure Database for PostgreSQL creates a compelling data platform for the next generation of AI agents — capable of effectively extracting, storing, and retrieving semantic relationships in your data. You can learn more about graph support in a separate blog post. Resources to help you get started We’re also happy to announce availability of the new resources and tools for application developers: Model Context Protocol (MCP) is an emerging open protocol designed to integrate AI models with external data sources and services. We have integrated MCP server for Azure Database for PostgreSQL into the Azure MCP Server, making it easy to connect your agentic apps not only to Azure Database for PostgreSQL, but to other Azure services as well through one unified interface. To learn more, refer to this blog post. New Solution Accelerator which showcases all of the capabilities we have announced today working together in one solution solving real world problems of ecommerce retail reimagined for agentic era. New PostgreSQL extension for VSCode for application developers and database administrators alike, bringing new generation of query editing and Copilot experiences to the world of PostgreSQL. And read about New enterprise features making Azure Database for PostgreSQL faster and more secure in the accompanying post. Begin your journey Generative AI innovation continues its advancement, bringing new opportunities every month. We’re excited for what is to come and look forward to sharing this journey of discovery with our customers. With today’s announcements - DiskANN vector indexing, Semantic Operators, and GraphRAG - Azure Database for PostgreSQL is ready to help you explore new boundaries of what’s possible. We invite you to begin your Generative AI journey today by exploring our new Solution Accelerator.Part 1 - Develop a VS Code Extension for Your Capstone Project
API Guardian - My Capstone Project As software and APIs evolve, developers encounter significant difficulties in maintaining and updating API endpoints. Breaking changes can lead to system instability, while outdated or unclear documentation makes maintenance less efficient. These challenges are further compounded by the time-consuming nature of updating dependencies and the tendency to prioritize new features over maintenance tasks. The absence of effective tools and processes to tackle these issues reduces overall productivity and developer efficiency. To address this, API Guardian was created as a Visual Studio Code extension that identifies API endpoints in a project and checks their functionality before deployment. This solution was developed to help developers save time spent fixing issues caused by breaking or non-breaking changes and to alleviate the difficulties in performing maintenance due to unclear or outdated documentation. Features and Capabilities This extension has 3 main features: Feature 1. Developers can decide if the extension will scan or skip specified files in the project. Press “Enter” to scan/skip all files. Type the file name (e.g., main.py) and press “Enter” to scan/skip a single file. Type file names with a delimiter (e.g., main.py | pythonFile.py) and press “Enter” to scan/skip multiple files. Feature 2. Custom hover messages when developers mouse over identified APIs This hover message will vary based on the status of the APIs. If the API returns a success status, the hover message will only show the completed API and its status. However, if an error occurs, the hover message will include this additional information: (1) API Name, (2) Official API Link, (3) Error Message, (4) Title of Recommended Fix and (5) Link to the Recommended Fix. Feature 3. Excel Report with Details of Identified APIs After all the identified APIs have been tested, an excel report will exported with the following information to allow developers to easily identify the APIs in the project. What Technology and Products does it involved? Building a Visual Studio Code extension and publishing it to the Visual Studio Marketplace involves a mix of technologies and tools. The project was initiated using the NPM package, generator-code, to set up a JavaScript project for developing the extension. All the extension's logic will be developed and managed within the "extension.js" file generated during the setup process. Once ready for deployment, we will package the extension using "vsce" to generate a ".vsix" file, which will then be used for deployment to the Visual Studio Code Marketplace. The deployment process involves requiring the user to create a publishing account and using tools like vsce to upload and manage the extension's version, updates, and metadata. As part of this process, you would need to create a Personal Access Token (PAT) from Azure DevOps. This token is used to verify your identity and authenticate the publishing tool, allowing you to securely upload your extension to the Visual Studio Marketplace. The PAT provides the necessary permissions for tasks such as version management, publishing new releases, and updating the extension metadata. What did I learn? Throughout this journey, I learned not just about the technical stack but also about the value of detailed project setup and secure publishing processes. While the technical steps can be challenging, they’re incredibly rewarding, and I’m excited to dive deeper into it moving forward. I’m looking forward to exploring how the extension can be further improved and enhanced. If you're interested in learning more about how my API guidance was built, keep an eye out for my next post! API Guardian https://marketplace.visualstudio.com/items?itemName=APIGuardian-vsc.api About the Authors Main Author - Ms Joy Cheng Yee Shing, BSc (Hon) Computing Science Academic Supervisor - Dr Peter Yau, Microsoft MVPDeploy Your First App Using GitHub Copilot for Azure: A Beginner’s Guide
Deploying an app for the first time can feel overwhelming. You may find yourself switching between tutorials, scanning documentation, and wondering if you missed a step. But what if you could do it all in one place? Now you can! With GitHub Copilot for Azure, you can receive real time deployment guidance without leaving the Visual Studio Code. While it won’t fully automate deployments, it serves as a step-by-step AI powered assistant, helping you navigate the process with clear, actionable instructions. No more endless tab switching or searching for the right tutorial—simply type, deploy, and learn, all within your IDE i.e. Visual Studio Code. If you are a student, you have access to exclusive opportunities! Whether you are exploring new technologies or experimenting with them, platforms like GitHub Education and the Microsoft Learn Student Hub provide free Azure credits, structured learning paths, and certification opportunities. These resources can help you gain hands-on experience with GitHub Copilot for Azure and streamline your journey toward deploying applications efficiently. Prerequisites: Before we begin, ensure you have the following: Account in GitHub. Sign up with GitHub Copilot. Account in Azure (Claim free credits using Azure for Students) Visual Studio Code installed. Step 1: Installation How to install GitHub Copilot for Azure? Open VS Code, in the leftmost panel, click on Extensions, type – ‘GitHub Copilot for Azure’, and install the first result which is by Microsoft. After this installation, you will be prompted to install – GitHub Copilot, Azure Tools, and other required installations. Click on allow and install all required extensions from the same method, as used above. Step 2: Enable How to enable GitHub Copilot in GitHub? Open GitHub click on top rightmost Profile pic, a left panel will open. Click on Your Copilot. Upon opening, enable it for IDE, as shown in the below Figure. Step 3: Walkthrough Open VSCode, and click on the GitHub Copilot icon from topmost right side. This will open the GitHub Copilot Chat. From here, you can customize the model type and Send commands. Type azure to work with Azure related tasks. Below figure will help to locate the things smoothly: Step 4: Generate Boilerplate Code with GitHub Copilot Let’s start by creating a simple HTML website that we will deploy to Azure Static Web Apps Service. Prompt for GitHub Copilot: Create a simple "Hello, World!" code with HTML. Copilot will generate a basic structure like this: Then, click on "Edit with Copilot." It will create an index.html file and add the code to it. Then, click on "Accept" and modify the content and style if needed before moving forward. Step 5: Deploy Your App Using Copilot Prompts Instead of searching for documentation, let’s use Copilot to generate deployment instructions directly within Visual Studio Code. Trigger Deployment Prompts Using azure To get deployment related suggestions, use azure in GitHub Copilot’s chat. In the chat text box at the bottom of the pane, type the following prompt after azure, then select Send (paper airplane icon) or press Enter on your keyboard: Prompt: azure How do I deploy a static website? Copilot will provide two options: deploying via Azure Blob Storage or Azure Static Web App Service. We will proceed with Azure Static Web Apps, so we will ask Copilot to guide us through deploying our app using this service. We will use the following prompt: azure I would like to deploy a site using Azure Static Web Apps. Please provide a step-by-step guide. Copilot will then return steps like: You will receive a set of instructions to deploy your website. To make it simpler, you can ask Copilot for a more detailed guide. To get a detailed guide, we will use the following prompt: azure Can you provide a more detailed guide and elaborate on GitHub Actions, including the steps to take for GitHub Actions? Copilot will then return steps like: See? That’s how you can experiment, ask questions, and get step-by-step guidance. Remember, the better the prompt, the better the results will be. Step 6: Learn as You Deploy One of the best features of Copilot is that you can ask follow-up questions if anything is unclear—all within Visual Studio Code, without switching tabs. Examples of Useful Prompts: What Azure services should I use with my app? What is GitHub Actions, and how does it work? What are common issues when deploying to Azure, and how can I fix them? Copilot provides contextual responses, guiding you through troubleshooting and best practices. You can learn more about this here. Conclusion: With GitHub Copilot for Azure, deploying applications is now more intuitive than ever. Instead of memorizing complex commands, you can use AI powered prompts to generate deployment steps in real time and even debug the errors within Visual Studio Code. 🚀 Next Steps: Experience with different prompts and explore how Copilot assists you. Try deploying more advanced applications, like Node.js or Python apps. GitHub Copilot isn’t just an AI assistant, it’s a learning tool. The more you engage with it, the more confident you’ll become in deploying and managing applications on Azure! Learn more about GitHub Copilot for Azure: Understand what GitHub Copilot for Azure Preview is and how it works. See example prompts for learning more about Azure and understanding your Azure account, subscription, and resources. See example prompts for designing and developing applications for Azure. See example prompts for deploying your application to Azure. See example prompts for optimizing your applications in Azure. See example prompts for troubleshooting your Azure resources. That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here.Prompt Engineering Simplified: AI Toolkit's Prompt Builder
In the age of generative AI, crafting effective prompts is no longer a nice-to-have, it's a must-have. Understanding how to communicate with these underlying models is the key to unlocking their true potential and getting the results we need. What are Prompts? Every time we want to communicate to the language model, we give set of instructions to these models, we refer to these inputs as Prompts. Prompts play a very crucial role while working with the GenAI models. The quality of a prompt directly impacts the output of GenAI models. Precise and well-crafted prompts are crucial for achieving desired results. What factors crafts an optimal Prompt? Crafting an optimal requires balancing clarity, specificity and context. Besides these, constraints are a critical factor in crafting effective prompts. Specificity Clearly define the expectations. The prompt should leave no room for misinterpretation. Precise language is the key. Avoid vague language. Vague prompts lead to vague or irrelevant responses. e.g., “Tell me about history” ➔ “Explain the economic causes of the French Revolution”. Clarity Use simple, unambiguous language. Avoid jargon unless your audience expects it, recommended to use action verbs like "write," "summarize," "explain," "translate". Context Provide background for e.g., “As a beginner in coding, how do I write a Python loop?”. Give the LLM enough context to understand the situation. Include relevant details, keywords, and background information Conciseness Trim unnecessary words (e.g., “Describe photosynthesis” vs. “Can you tell me about how plants use sunlight?”). Ensure the prompt remains relevant to the desired output Tone & Audience Alignment Match the tone to the goal (formal, casual, instructive). Example: For kids, “Explain how rainbows form in simple terms.” Explicit Instructions Directly state what is needed e.g., “Compare X and Y”, “List pros and cons,” “Write a poem about…”. Guiding Constraints Limit scope to avoid overly broad answers e.g., “Focus on environmental impacts, not economic ones”. Constraints reduce ambiguity, focus responses, and improve relevance. Few example constraints, Format: “Summarize in 3 bullet points.” Length: “Explain in 2 sentences.” Scope: “Focus on environmental impacts, not economic ones.” Style/Tone: “Write a casual email,” or “Use non-technical terms.” Technical limits: “Keep code examples under 50 lines.” Few advanced Considerations for AI/LLM Prompts Examples or Demonstrations Include examples to set expectations for e.g., “Write a limerick like this: There once was a cat from Peru…”. Step-by-Step Guidance Break complex tasks into steps for e.g., “First analyze the Python code, then suggest solutions”. Role Assignment Assign roles to guide the AI for e.g., “Act as a historian explaining World War 2”. Avoid Bias Neutral phrasing ensures fair responses for e.g., “Discuss pros and cons of renewable energy” vs. “Why is solar energy bad?” the former is a well-formed prompt. Prompt engineering is an iterative process. Experiment with different phrasings and structures to see what works best. Analyze the LLM's responses and refine prompts accordingly. Make adjustments to improve the accuracy and relevance of the output. Prompt Builder: From the above section, we know that crafting effective prompts is essential for robust AI engagement. Prompt Builder tool on AI Toolkit helps in this enhancement by streamlining the whole process of crafting prompts. Prompt builder helps the users by helping in the following areas, o Prompt Creation, Modification, and Evaluation: Customize prompts through an accessible and straightforward interface. o AI-Assisted Prompt Generation: Articulate the project concept using everyday language, and the AI-powered feature will produce prompts for your exploration. o Organized Output Capability: Craft the prompts to yield outputs in a consistent, standardized and predictable manner. o Automated Code Generation for Prompt Usage: Following model and prompt experimentation, transition to coding immediately by accessing automatically generated, executable Python code. This tool has three sections on the UI. Prompt configuration Response History Prompt Configuration Section: In the Prompt configuration section, there are 4 major sub sections, Model System Prompt User Prompt Add Prompt Model: The Model section is the first subsection of the Prompt Configuration. Here, we select the model to use. The AI Toolkit offers a wide range of models, including remote models served from GitHub and those from providers such as OpenAI, Google, Anthropic, and Nvidia. For this tutorial we will be using OpenAI GPT-4o mini via GitHub System Prompt: In System prompt section, we provide instructions with relevant context to guide the system response. We can think of a system prompt as the "role" we give an AI before we ask it anything, like telling an actor what character to play. Generate Prompt: Upon choosing cloud-based / GitHub / Remote models, a new tool called as “Generate Prompt” is enabled, this is an AI Powered tool especially useful for crafting AI Powered well defined prompts which can be used in the “System Prompt” Section. Upon clicking on the “Generate Prompt” we can see a small window that pops up and asks for the input prompt. This can generate a prompt template by sharing basic details about the task. In this tutorial, let’s ask the LLM to generate prompt about “Professor in university teaching math”. Once the message is updated click on “Generate” button, and in a few seconds, we will have a well-structured prompt in the “System Prompt” section. The prompt that we generated is as follows Provide a detailed syllabus for a university-level mathematics course, including course objectives, weekly topics, assessment methods, and required materials. The syllabus should cover all essential components such as the course title, description, prerequisites, learning outcomes, weekly schedules, and any relevant policies regarding attendance, grading, and participation. # Steps 1. **Course Title and Description**: Clearly state the title of the course and provide a brief description of what the course will cover. 2. **Prerequisites**: List any required courses or knowledge necessary for students to enroll. 3. **Learning Outcomes**: Define what students are expected to learn by the end of the course. 4. **Weekly Schedule**: Outline topics for each week, along with any associated readings or assignments. 5. **Assessment Methods**: Describe how students will be evaluated (e.g., exams, quizzes, projects). 6. **Required Materials**: Include information on textbooks and other resources needed for the course. 7. **Course Policies**: State attendance, grading, and participation rules. # Output Format The output should be formatted as a structured syllabus, presented in clear sections with headings for each part. The document should be detailed yet concise, ideally around 3-5 pages in length. # Examples **Example 1** **Input:** Create a syllabus for a Calculus I course. **Output:** - **Course Title**: Calculus I - **Description**: An introduction to limits, derivatives, and integrals. - **Prerequisites**: Pre-Calculus or equivalent. - **Learning Outcomes**: Students will be able to calculate limits, differentiate basic functions, and understand the Fundamental Theorem of Calculus. - **Weekly Schedule**: - Week 1: Introduction to Limits - Week 2: Continuity - Week 3: Derivatives - ... - **Assessment Methods**: Midterm exam (30%), Final exam (40%), Weekly quizzes (20%), Participation (10%). - **Required Materials**: "Calculus: Early Transcendentals" by James Stewart. - **Course Policies**: Attendance required, late assignments will incur a penalty. **Example 2** **Input:** Design a syllabus for a Linear Algebra course. **Output:** - **Course Title**: Linear Algebra - **Description**: Study vector spaces, matrices, and linear transformations. - **Prerequisites**: None. - **Learning Outcomes**: Mastery of matrix operations and ability to solve systems of linear equations. - **Weekly Schedule**: - Week 1: Introduction to Vector Spaces - Week 2: Matrix Operations - Week 3: Determinants - ... - **Assessment Methods**: Two midterms (50%), Homework assignments (30%), Attendance (20%). - **Required Materials**: "Linear Algebra Done Right" by Sheldon Axler. - **Course Policies**: Participation in class discussions is mandatory. # Notes Ensure that the syllabus is comprehensive and tailored to the specific course topic. Consider including any unique teaching methods or technologies that will be employed during the course. User Prompt: User prompt is the specific question, instruction, or request that a person provides to the AI to elicit a response. It's the direct input from the user that initiates the AI's processing and generation of text. In AI Toolkit for a few models that support the multimodal feature, we can also upload images in this section. For this tutorial let’s input “Explain to me the Fourier equation in simple terms” Add Prompt: If any additional prompt needs to be added, we can configure more User or assistant prompt. So, in a conversation, we have: User Prompt: What the human says. Assistant Prompt: What the AI says. The major configuration part is now completed through this window, its now time to test the responses based on the LLM’s knowledge, in this case how well does GPT 4o mini behave in the role as university-level mathematics professor. In order to test it, we navigate to the next window, the Response section. Response Section: The Response section is where we finally get to see the responses. This section has the “Run” and “View Code” buttons. We can also choose the type of response we need. It can be a simple text or json schema. Upon choosing Json Schema, user will be prompted to “Prepare Schema”. Users can define their own schema or select from example. There are a few examples for the user to choose from. For this tutorial we will be using the simple text format. As we have our setup ready, we can directly click on the “Run” button, In a few seconds we have our well formatted and accurate answer on the screen, AI Toolkit‘s markdown capability can neatly format all the mathematical signs and equations. We can also add this to the “Assistant Prompt” by using the button provided. It provides better example for the LLM in the code later. The result from the LLM now seems very satisfactory with our well-crafted prompt. We can now proceed with the Code generation feature of the Prompt Builder tool of AI Toolkit. Upon clicking the “View Code” button, user is prompted to choose the SDK of their choice. This SDK lets us communicate with the API from the code. For this tutorial, we will use Azure AI Inference SDK. For more details on this SDK refer here. The code requires azure-ai-inference. Install the library by pip install azure-ai-inference """Run this model in Python > pip install azure-ai-inference """ import os from azure.ai.inference import ChatCompletionsClient from azure.ai.inference.models import AssistantMessage, SystemMessage, UserMessage from azure.ai.inference.models import ImageContentItem, ImageUrl, TextContentItem from azure.core.credentials import AzureKeyCredential # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings. # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens client = ChatCompletionsClient( endpoint = "https://models.inference.ai.azure.com", credential = AzureKeyCredential(os.environ["GITHUB_TOKEN"]), api_version = "2024-08-01-preview", ) response = client.complete( messages = [ SystemMessage(content = "Provide a detailed syllabus for a university-level mathematics course, including course objectives, weekly topics, assessment methods, and required materials.\n \nThe syllabus should cover all essential components such as the course title, description, prerequisites, learning outcomes, weekly schedules, and any relevant policies regarding attendance, grading, and participation.\n \n# Steps\n \n1. **Course Title and Description**: Clearly state the title of the course and provide a brief description of what the course will cover.\n2. **Prerequisites**: List any required courses or knowledge necessary for students to enroll.\n3. **Learning Outcomes**: Define what students are expected to learn by the end of the course.\n4. **Weekly Schedule**: Outline topics for each week, along with any associated readings or assignments.\n5. **Assessment Methods**: Describe how students will be evaluated (e.g., exams, quizzes, projects).\n6. **Required Materials**: Include information on textbooks and other resources needed for the course.\n7. **Course Policies**: State attendance, grading, and participation rules.\n \n# Output Format\n \nThe output should be formatted as a structured syllabus, presented in clear sections with headings for each part. The document should be detailed yet concise, ideally around 3-5 pages in length.\n \n# Examples\n \n**Example 1** \n**Input:** \nCreate a syllabus for a Calculus I course. \n**Output:** \n- **Course Title**: Calculus I \n- **Description**: An introduction to limits, derivatives, and integrals. \n- **Prerequisites**: Pre-Calculus or equivalent. \n- **Learning Outcomes**: Students will be able to calculate limits, differentiate basic functions, and understand the Fundamental Theorem of Calculus. \n- **Weekly Schedule**: \n - Week 1: Introduction to Limits \n - Week 2: Continuity \n - Week 3: Derivatives \n - ... \n- **Assessment Methods**: Midterm exam (30%), Final exam (40%), Weekly quizzes (20%), Participation (10%). \n- **Required Materials**: \"Calculus: Early Transcendentals\" by James Stewart. \n- **Course Policies**: Attendance required, late assignments will incur a penalty.\n \n**Example 2** \n**Input:** \nDesign a syllabus for a Linear Algebra course. \n**Output:** \n- **Course Title**: Linear Algebra \n- **Description**: Study vector spaces, matrices, and linear transformations. \n- **Prerequisites**: None. \n- **Learning Outcomes**: Mastery of matrix operations and ability to solve systems of linear equations. \n- **Weekly Schedule**: \n - Week 1: Introduction to Vector Spaces \n - Week 2: Matrix Operations \n - Week 3: Determinants \n - ... \n- **Assessment Methods**: Two midterms (50%), Homework assignments (30%), Attendance (20%). \n- **Required Materials**: \"Linear Algebra Done Right\" by Sheldon Axler. \n- **Course Policies**: Participation in class discussions is mandatory. \n \n# Notes\n \nEnsure that the syllabus is comprehensive and tailored to the specific course topic. Consider including any unique teaching methods or technologies that will be employed during the course."), UserMessage(content = [ TextContentItem(text = "Explain to me the Fourier equation in simple terms"), ]), ], model = "gpt-4o-mini", response_format = "text", max_tokens = 4096, temperature = 1, top_p = 1, ) print(response.choices[0].message.content) This Python code is ready to be modified and used in any Generative AI application. It can be modified with any Orchestration framework like Semantic Kernel to add more features or even make an agentic application. History Section: We also have the “History” and “New Prompt”. History shows all the previous sessions; we can revisit and resume working or perhaps check the output or regenerate the code. History” and “New Prompt” In essence, the Prompt Builder tool significantly streamlines the process of crafting effective prompts, saving developers valuable time. Beyond prompt creation, it also facilitates output evaluation, model behavior analysis, and generates quality code to accelerate application development. Stay tuned for upcoming blog posts, where we'll delve into even more advanced techniques for building powerful generative AI applications. You can also join our AI Sparks series to learn more about the capabilities of the AI Toolkit for Visual Studio Code.
