Power Up Your Open WebUI with Azure AI Speech: Quick STT & TTS Integration
Introduction Ever found yourself wishing your web interface could really talk and listen back to you? With a few clicks (and a bit of code), you can turn your plain Open WebUI into a full-on voice assistant. In this post, you’ll see how to spin up an Azure Speech resource, hook it into your frontend, and watch as user speech transforms into text and your app’s responses leap off the screen in a human-like voice. By the end of this guide, you’ll have a voice-enabled web UI that actually converses with users, opening the door to hands-free controls, better accessibility, and a genuinely richer user experience. Ready to make your web app speak? Let’s dive in. Why Azure AI Speech? We use Azure AI Speech service in Open Web UI to enable voice interactions directly within web applications. This allows users to: Speak commands or input instead of typing, making the interface more accessible and user-friendly. Hear responses or information read aloud, which improves usability for people with visual impairments or those who prefer audio. Provide a more natural and hands-free experience especially on devices like smartphones or tablets. In short, integrating Azure AI Speech service into Open Web UI helps make web apps smarter, more interactive, and easier to use by adding speech recognition and voice output features. If you haven’t hosted Open WebUI already, follow my other step-by-step guide to host Ollama WebUI on Azure. Proceed to the next step if you have Open WebUI deployed already. Learn More about OpenWeb UI here. Deploy Azure AI Speech service in Azure. Navigate to the Azure Portal and search for Azure AI Speech on the Azure portal search bar. Create a new Speech Service by filling up the fields in the resource creation page. Click on “Create” to finalize the setup. After the resource has been deployed, click on “View resource” button and you should be redirected to the Azure AI Speech service page. The page should display the API Keys and Endpoints for Azure AI Speech services, which you can use in Open Web UI. Settings things up in Open Web UI Speech to Text settings (STT) Head to the Open Web UI Admin page > Settings > Audio. Paste the API Key obtained from the Azure AI Speech service page into the API key field below. Unless you use different Azure Region, or want to change the default configurations for the STT settings, leave all settings to blank. Text to Speech settings (TTS) Now, let's proceed with configuring the TTS Settings on OpenWeb UI by toggling the TTS Engine to Azure AI Speech option. Again, paste the API Key obtained from Azure AI Speech service page and leave all settings to blank. You can change the TTS Voice from the dropdown selection in the TTS settings as depicted in the image below: Click Save to reflect the change. Expected Result Now, let’s test if everything works well. Open a new chat / temporary chat on Open Web UI and click on the Call / Record button. The STT Engine (Azure AI Speech) should identify your voice and provide a response based on the voice input. To test the TTS feature, click on the Read Aloud (Speaker Icon) under any response from Open Web UI. The TTS Engine should reflect Azure AI Speech service! Conclusion And that’s a wrap! You’ve just given your Open WebUI the gift of capturing user speech, turning it into text, and then talking right back with Azure’s neural voices. Along the way you saw how easy it is to spin up a Speech resource in the Azure portal, wire up real-time transcription in the browser, and pipe responses through the TTS engine. From here, it’s all about experimentation. Try swapping in different neural voices or dialing in new languages. Tweak how you start and stop listening, play with silence detection, or add custom pronunciation tweaks for those tricky product names. Before you know it, your interface will feel less like a web page and more like a conversation partner.September Calendar IS HERE!
🚀✨ Another month, another exciting calendar from the Microsoft Hero ✨🚀 From 🌍 different time zones, and 🌟 diverse topics, we’re bringing incredible sessions designed for everyone, whether you’re just starting your journey or already an expert in Microsoft and the cloud. This month, we’ve packed the calendar with amazing speakers from across the globe 🌐 who will be sharing their invaluable knowledge and real-world experiences. 🙌 💡 Join our live sessions, learn from inspiring experts, and take a step closer to transforming your career, boosting your skills, and making an impact in your organization. ⏰ Just like last month, we’re covering multiple time zones, from Australia 🇦🇺, to Europe 🇪🇺, to the Americas 🌎, so no matter where you are, there’s a session waiting for you! 👉 Don’t miss out, register today, get ready, and let’s grow together from Zero to Hero! 💪🚀 Santhoshkumar Anandakrishnan https://streamyard.com/watch/3CCPGbvGeEfZ?wt.mc_id=MVP_350258 September 4, 2025 11:00 AM CET September 4, 2025 07:00 PM AEST Arafat Tehsin https://streamyard.com/watch/Nyq7gkQEhXkm?wt.mc_id=MVP_350258 September 9, 2025 11:00 AM CET September 9, 2025 07:00 PM AEST Kim Berg https://streamyard.com/watch/6AyAT6PhD9xv?wt.mc_id=MVP_350258 September 13, 2025 06:00 PM CET Andrew O'Young https://streamyard.com/watch/qTvq25R7dfmu?wt.mc_id=MVP_350258 September 16, 2025 11:00 AM CET September 16, 2025 07:00 PM AEST Pam DeGraffenreid https://streamyard.com/watch/UmwbDn9Gimn8?wt.mc_id=MVP_350258 September 20, 2025 06:00 PM CET Anthony Porter https://streamyard.com/watch/8SFHqmDB3gxH?wt.mc_id=MVP_350258 September 29, 2025 09:00 AM CET September 29, 2025 05:00 PM AESTCreate Stunning AI Videos with Sora on Azure AI Foundry!
Special credit to Rory Preddy for creating the GitHub resource that enable us to learn more about Azure Sora. Reach him out on LinkedIn to say thanks. Introduction Artificial Intelligence (AI) is revolutionizing content creation, and video generation is at the forefront of this transformation. OpenAI's Sora, a groundbreaking text-to-video model, allows creators to generate high-quality videos from simple text prompts. When paired with the powerful infrastructure of Azure AI Foundry, you can harness Sora's capabilities with scalability and efficiency, whether on a local machine or a remote setup. In this blog post, I’ll walk you through the process of generating AI videos using Sora on Azure AI Foundry. We’ll cover the setup for both local and remote environments. Requirements: Azure AI Foundry with sora model access A Linux Machine/VM. Make sure that the machine already has the package below: Java JRE 17 (Recommended) OR later Maven Step Zero – Deploying the Azure Sora model on AI Foundry Navigate to the Azure AI Foundry portal and head to the “Models + Endpoints” section (found on the left side of the Azure AI Foundry portal) > Click on the “Deploy Model” button > “Deploy base model” > Search for Sora > Click on “Confirm”. Give a deployment name and specify the Deployment type > Click “Deploy” to finalize the configuration. You should receive an API endpoint and Key after successful deploying Sora on Azure AI Foundry. Store these in a safe place because we will be using them in the next steps. Step one – Setting up the Sora Video Generator in the local/remote machine. Clone the roryp/sora repository on your machine by running the command below: git clone https://github.com/roryp/sora.git cd sora Then, edit the application.properties file in the src/main/resources/ folder to include your Azure OpenAI Credentials. Change the configuration below: azure.openai.endpoint=https://your-openai-resource.cognitiveservices.azure.com azure.openai.api-key=your_api_key_here If port 8080 is used for another application, and you want to change the port for which the web app will run, change the “server.port” configuration to include the desired port. Allow appropriate permissions to run the “mvnw” script file. chmod +x mvnw Run the application ./mvnw spring-boot:run Open your browser and type in your localhost/remote host IP (format: [host-ip:port]) in the browser search bar. If you are running a remote host, please do not forget to update your firewall/NSG to allow inbound connection to the configured port. You should see the web app to generate video with Sora AI using the API provided on Azure AI Foundry. Now, let’s generate a video with Sora Video Generator. Enter a prompt in the first text field, choose the video pixel resolution, and set the video duration. (Due to technical limitation, Sora can only generate video of a maximum of 20 seconds). Click on the “Generate video” button to proceed. The cost to generate the video should be displayed below the “Generate Video” button, for transparency purposes. You can click on the “View Breakdown” button to learn more about the cost breakdown. The video should be ready to download after a maximum of 5 minutes. You can check the status of the video by clicking on the “Check Status” button on the web app. The web app will inform you once the download is ready and the page should refresh every 10 seconds to fetch real-time update from Sora. Once it is ready, click on the “Download Video” button to download the video. Conclusion Generating AI videos with Sora on Azure AI Foundry is a game-changer for content creators, marketers, and developers. By following the steps outlined in this guide, you can set up your environment, integrate Sora, and start creating stunning AI-generated videos. Experiment with different prompts, optimize your workflow, and let your imagination run wild! Have you tried generating AI videos with Sora or Azure AI Foundry? Share your experiences or questions in the comments below. Don’t forget to subscribe for more AI and cloud computing tutorials!Configure Embedding Models on Azure AI Foundry with Open Web UI
Introduction Let’s take a closer look at an exciting development in the AI space. Embedding models are the key to transforming complex data into usable insights, driving innovations like smarter chatbots and tailored recommendations. With Azure AI Foundry, Microsoft’s powerful platform, you’ve got the tools to build and scale these models effortlessly. Add in Open Web UI, a intuitive interface for engaging with AI systems, and you’ve got a winning combo that’s hard to beat. In this article, we’ll explore how embedding models on Azure AI Foundry, paired with Open Web UI, are paving the way for accessible and impactful AI solutions for developers and businesses. Let’s dive in! To proceed with configuring the embedding model from Azure AI Foundry on Open Web UI, please firstly configure the requirements below. Requirements: Setup Azure AI Foundry Hub/Projects Deploy Open Web UI – refer to my previous article on how you can deploy Open Web UI on Azure VM. Optional: Deploy LiteLLM with Azure AI Foundry models to work on Open Web UI - refer to my previous article on how you can do this as well. Deploying Embedding Models on Azure AI Foundry Navigate to the Azure AI Foundry site and deploy an embedding model from the “Model + Endpoint” section. For the purpose of this demonstration, we will deploy the “text-embedding-3-large” model by OpenAI. You should be receiving a URL endpoint and API Key to the embedding model deployed just now. Take note of that credential because we will be using it in Open Web UI. Configuring the embedding models on Open Web UI Now head to the Open Web UI Admin Setting Page > Documents and Select Azure Open AI as the Embedding Model Engine. Copy and Paste the Base URL, API Key, the Embedding Model deployed on Azure AI Foundry and the API version (not the model version) into the fields below: Click “Save” to reflect the changes. Expected Output Now let us look into the scenario for when the embedding model configured on Open Web UI and when it is not. Without Embedding Models configured. With Azure Open AI Embedding models configured. Conclusion And there you have it! Embedding models on Azure AI Foundry, combined with the seamless interaction offered by Open Web UI, are truly revolutionizing how we approach AI solutions. This powerful duo not only simplifies the process of building and deploying intelligent systems but also makes cutting-edge technology more accessible to developers and businesses of all sizes. As we move forward, it’s clear that such integrations will continue to drive innovation, breaking down barriers and unlocking new possibilities in the AI landscape. So, whether you’re a seasoned developer or just stepping into this exciting field, now’s the time to explore what Azure AI Foundry and Open Web UI can do for you. Let’s keep pushing the boundaries of what’s possible!Unleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration
Artificial Intelligence is evolving rapidly, and one of the most pressing challenges is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) solves this problem by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, accessibility, and AI reasoning capabilities. What is Model Context Protocol (MCP)? MCP is a protocol designed to enable AI models, such as Azure OpenAI models, to interact seamlessly with external tools and services. Think of MCP as a universal USB-C connector for AI, allowing language models to fetch information, interact with APIs, and execute tasks beyond their built-in knowledge. Key Features of MCP Standardized Communication – MCP provides a structured way for AI models to interact with various tools. Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights. Secure & Scalable – Enables safe and scalable integration with enterprise applications. Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods. MCP Architecture & How It Works MCP follows a client-server architecture that allows AI models to interact with external tools efficiently. Here’s how it works: Components of MCP MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.). Data Sources – Various backend systems, including local storage, cloud databases, and external APIs. Data Flow in MCP The AI model sends a request (e.g., "fetch user profile data"). The MCP client forwards the request to the appropriate MCP server. The MCP server retrieves the required data from a database or API. The response is sent back to the AI model via the MCP client. Integrating MCP with Azure OpenAI Services Microsoft has integrated MCP with Azure OpenAI Services, allowing GPT models to interact with external services and fetch live data. This means AI models are no longer limited to static knowledge but can access real-time information. Benefits of Azure OpenAI Services + MCP Integration ✔ Real-time Data Fetching – AI assistants can retrieve fresh information from APIs, databases, and internal systems. ✔ Contextual AI Responses – Enhances AI responses by providing accurate, up-to-date information. ✔ Enterprise-Ready – Secure and scalable for business applications, including finance, healthcare, and retail. Hands-On Tools for MCP Implementation To implement MCP effectively, Microsoft provides two powerful tools: Semantic Workbench and AI Gateway. Microsoft Semantic Workbench A development environment for prototyping AI-powered assistants and integrating MCP-based functionalities. Features: Build and test multi-agent AI assistants. Configure settings and interactions between AI models and external tools. Supports GitHub Codespaces for cloud-based development. Explore Semantic Workbench Workbench interface examples Microsoft AI Gateway A plug-and-play interface that allows developers to experiment with MCP using Azure API Management. Features: Credential Manager – Securely handle API credentials. Live Experimentation – Test AI model interactions with external tools. Pre-built Labs – Hands-on learning for developers. Explore AI Gateway Setting Up MCP with Azure OpenAI Services Step 1: Create a Virtual Environment First, create a virtual environment using Python: python -m venv .venv Activate the environment: # Windows venv\Scripts\activate # MacOS/Linux source .venv/bin/activate Step 2: Install Required Libraries Create a requirements.txt file and add the following dependencies: langchain-mcp-adapters langgraph langchain-openai Then, install the required libraries: pip install -r requirements.txt Step 3: Set Up OpenAI API Key Ensure you have your OpenAI API key set up: # Windows setx OPENAI_API_KEY "<your_api_key> # MacOS/Linux export OPENAI_API_KEY=<your_api_key> Building an MCP Server This server performs basic mathematical operations like addition and multiplication. Create the Server File First, create a new Python file: touch math_server.py Then, implement the server: from mcp.server.fastmcp import FastMCP # Initialize the server mcp = FastMCP("Math") MCP.tool() def add(a: int, b: int) -> int: return a + b MCP.tool() def multiply(a: int, b: int) -> int: return a * b if __name__ == "__main__": mcp.run(transport="stdio") Your MCP server is now ready to run. Building an MCP Client This client connects to the MCP server and interacts with it. Create the Client File First, create a new file: touch client.py Then, implement the client: import asyncio from mcp import ClientSession, StdioServerParameters from langchain_openai import ChatOpenAI from mcp.client.stdio import stdio_client # Define server parameters server_params = StdioServerParameters( command="python", args=["math_server.py"], ) # Define the model model = ChatOpenAI(model="gpt-4o") async def run_agent(): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() tools = await load_mcp_tools(session) agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (4 + 6) x 14?"}) return agent_response["messages"][3].content if __name__ == "__main__": result = asyncio.run(run_agent()) print(result) Your client is now set up and ready to interact with the MCP server. Running the MCP Server and Client Step 1: Start the MCP Server Open a terminal and run: python math_server.py This starts the MCP server, making it available for client connections. Step 2: Run the MCP Client In another terminal, run: python client.py Expected Output 140 This means the AI agent correctly computed (4 + 6) x 14 using both the MCP server and GPT-4o. Conclusion Integrating MCP with Azure OpenAI Services enables AI applications to securely interact with external tools, enhancing functionality beyond text-based responses. With standardized communication and improved AI capabilities, developers can build smarter and more interactive AI-powered solutions. By following this guide, you can set up an MCP server and client, unlocking the full potential of AI with structured external interactions. Next Steps: Explore more MCP tools and integrations. Extend your MCP setup to work with additional APIs. Deploy your solution in a cloud environment for broader accessibility. For further details, visit the GitHub repository for MCP integration examples and best practices. MCP GitHub Repository MCP Documentation Semantic Workbench AI Gateway MCP Video Walkthrough MCP Blog MCP Github End to End DemoBuilding an AI-Powered ESG Consultant Using Azure AI Services: A Case Study
In today's corporate landscape, Environmental, Social, and Governance (ESG) compliance has become increasingly important for stakeholders. To address the challenges of analyzing vast amounts of ESG data efficiently, a comprehensive AI-powered solution called ESGai has been developed. This blog explores how Azure AI services were leveraged to create a sophisticated ESG consultant for publicly listed companies. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAh The Challenge: Making Sense of Complex ESG Data Organizations face significant challenges when analyzing ESG compliance data. Manual analysis is time-consuming, prone to errors, and difficult to scale. ESGai was designed to address these pain points by creating an AI-powered virtual consultant that provides detailed insights based on publicly available ESG data. Solution Architecture: The Three-Agent System ESGai implements a sophisticated three-agent architecture, all powered by Azure's AI capabilities: Manager Agent: Breaks down complex user queries into manageable sub-questions containing specific keywords that facilitate vector search retrieval. The system prompt includes generalized document headers from the vector database for context. Worker Agent: Processes the sub-questions generated by the Manager, connects to the vector database to retrieve relevant text chunks, and provides answers to the sub-questions. Results are stored in Cosmos DB for later use. Director Agent: Consolidates the answers from the Worker agent into a comprehensive final response tailored specifically to the user's original query. It's important to note that while conceptually there are three agents, the Worker is actually a single agent that gets called multiple times - once for each sub-question generated by the Manager. Current Implementation State The current MVP implementation has several limitations that are planned for expansion: Limited Company Coverage: The vector database currently stores data for only 2 companies, with 3 documents per company (Sustainability Report, XBRL, and BRSR). Single Model Deployment: Only one GPT-4o model is currently deployed to handle all agent functions. Basic Storage Structure: The Blob container has a simple structure with a single directory. While Azure Blob storage doesn't natively support hierarchical folders, the team plans to implement virtual folders in the future. Free Tier Limitations: Due to funding constraints, the AI Search service is using the free tier, which limits vector data storage to 50MB. Simplified Vector Database: The current index stores all 6 files (3 documents × 2 companies) in a single vector database without filtering capabilities or schema definition. Azure Services Powering ESGai The implementation of ESGai leverages multiple Azure services for a robust and scalable architecture: Azure AI Services: Provides pre-built APIs, SDKs, and services that incorporate AI capabilities without requiring extensive machine learning expertise. This includes access to 62 pre-trained models for chat completions through the AI Foundry portal. Azure OpenAI: Hosts the GPT-4o model for generating responses and the Ada embedding model for vectorization. The service combines OpenAI's advanced language models with Azure's security and enterprise features. Azure AI Foundry: Serves as an integrated platform for developing, deploying, and governing generative AI applications. It offers a centralized management centre that consolidates subscription information, connected resources, access privileges, and usage quotas. Azure AI Search (formerly Cognitive Search): Provides both full-text and vector search capabilities using the OpenAI ada-002 embedding model for vectorization. It's configured with hybrid search algorithms (BM25 RRF) for optimal chunk ranking. Azure Storage Services: Utilizes Blob Storage for storing PDFs, Business Responsibility Sustainability Reports (BRSRs), and other essential documents. It integrates seamlessly with AI Search using indexers to track database changes. Cosmos DB: Employs MongoDB APIs within Cosmos DB as a NoSQL database for storing chat history between agents and users. Azure App Services: Hosts the web application using a B3-tier plan optimized for cost efficiency, with GitHub Actions integrated for continuous deployment. Project Evolution: From Concept to Deployment The development of ESGai followed a structured approach through several phases: Phase 1: Data Cleaning Extracted specific KPIs from XML/XBRL datasets and BRSR reports containing ESG data for 1,000 listed companies Cleaned and standardized data to ensure consistency and accuracy Phase 2: RAG Framework Development Implemented Retrieval-Augmented Generation (RAG) to enhance responses by dynamically fetching relevant information Created a workflow that includes query processing, data retrieval, and response generation Phase 3: Initial Deployment Deployed models locally using Docker and n8n automation tools for testing Identified the need for more scalable web services Phase 4: Transition to Azure Services Migrated automation workflows from n8n to Azure AI Foundry services Leveraged Azure's comprehensive suite of AI services, storage solutions, and app hosting capabilities Technical Implementation Details Model Configurations: The GPT model is configured with: Model version: 2024-11-20 Temperature: 0.7 Max Response Token: 800 Past Messages: 10 Top-p: 0.95 Frequency/Presence Penalties: 0 The embedding model uses OpenAI-text-embedding-Ada-002 with 1536 dimensions and hybrid semantic search (BM25 RRF) algorithms. Cost Analysis and Efficiency A detailed cost breakdown per user query reveals: App Server: $390-400 AI Search: $5 per query RAG Query Processing: $4.76 per query Agent-specific costs: Manager: $0.05 (30 input tokens, 210 output tokens) Worker: $3.71 (1500 input tokens, 1500 output tokens) Director: $1.00 (600 input tokens, 600 output tokens) Challenges and Solutions The team faced several challenges during implementation: Quota Limitations: Initial deployments encountered token quota restrictions, which were resolved through Azure support requests (typically granted within 24 hours). Cost Optimization: High costs associated with vectorization required careful monitoring. The team addressed this by shutting down unused services and deploying on services with free tiers. Integration Issues: GitHub Actions raised errors during deployment, which were resolved using GitHub's App Service Build Service. Azure UI Complexity: The team noted that Azure AI service naming conventions were sometimes confusing, as the same name is used for both parent and child resources. Free Tier Constraints: The AI Search service's free tier limitation of 50MB for vector data storage restricts the amount of company information that can be included in the current implementation. Future Roadmap The current implementation is an MVP with several areas for expansion: Expand the database to include more publicly available sustainability reports beyond the current two companies Optimize token usage by refining query handling processes Research alternative embedding models to reduce costs while maintaining accuracy Implement a more structured storage system with virtual folders in Blob storage Upgrade from the free tier of AI Search to support larger data volumes Develop a proper schema for the vector database to enable filtering and more targeted searches Scale to multiple GPT model deployments for improved performance and redundancy Conclusion ESGai demonstrates how advanced AI techniques like Retrieval-Augmented Generation can transform data-intensive domains such as ESG consulting. By leveraging Azure's comprehensive suite of AI services alongside a robust agent-based architecture, this solution provides users with actionable insights while maintaining scalability and cost efficiency. https://youtu.be/5-oBdge6Q78?si=Vb9aHx79xk3VGYAhStep-by-Step Tutorial: Building an AI Agent Using Azure AI Foundry
This blog post provides a comprehensive tutorial on building an AI agent using Azure AI Agent service and the Azure AI Foundry portal. AI agents represent a powerful new paradigm in application development, offering a more intuitive and dynamic way to interact with software. They can understand natural language, reason about user requests, and take actions to fulfill those requests. This tutorial will guide you through the process of creating and deploying an intelligent agent on Azure. We'll cover setting up an Azure AI Foundry hub, crafting effective instructions to define the agent's behavior, including recognizing user intent, processing requests, and generating helpful responses. We'll also discuss testing the agent's conversational abilities and provide additional resources for expanding your knowledge of AI agents and the Azure AI ecosystem. This hands-on guide is perfect for anyone looking to explore the practical application of Azure's conversational AI capabilities and build intelligent virtual assistants. Join us as we dive into the exciting world of AI agents.How to Build AI Agents in 10 Lessons
Microsoft has released an excellent learning resource for anyone looking to dive into the world of AI agents: "AI Agents for Beginners". This comprehensive course is available free on GitHub. It is designed to teach the fundamentals of building AI agents, even if you are just starting out. What You'll Learn The course is structured into 10 lessons, covering a wide range of essential topics including: Agentic Frameworks: Understand the core structures and components used to build AI agents. Design Patterns: Learn proven approaches for designing effective and efficient AI agents. Retrieval Augmented Generation (RAG): Enhance AI agents by incorporating external knowledge. Building Trustworthy AI Agents: Discover techniques for creating AI agents that are reliable and safe. AI Agents in Production: Get insights into deploying and managing AI agents in real-world applications. Hands-On Experience The course includes practical code examples that utilize: Azure AI Foundry GitHub Models These examples help you learn how to interact with Language Models and use AI Agent frameworks and services from Microsoft, such as: Azure AI Agent Service Semantic Kernel Agent Framework AutoGen - A framework for building AI agents and applications Getting Started To get started, make sure you have the proper set-up. Here are the 10 lessons Intro to AI Agents and Agent Use Cases Exploring AI Agent Frameworks Understanding AI Agentic Design Principles Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Multi-Agent Design Patterns Metacognition in AI Agents AI Agents in Production Multi-Language Support To make learning accessible to a global audience, the course offers multi-language support. Get Started Today! If you are eager to learn about AI agents, this course is an excellent starting point. You can find the complete course materials on GitHub at AI Agents for Beginners.Build AI Agents with Azure Database for PostgreSQL and Azure AI Agent Service
Introduction AI agents are revolutionizing how applications interact with data by combining large language models (LLMs) with external tools and databases. This blog will show you how to combine Azure Database for PostgreSQL with Azure AI Agent Service to create intelligent AI agents that can search and analyze your data. We'll use a legal research assistant as our example and walk through setup, implementation, and testing. With just a few hours of work, you can build an AI solution that would have taken weeks of traditional development. Why AI Agents Matter AI agents can improve productivity by handling repetitive, time-consuming tasks. AI agents can transform how businesses interact with their data by automating complex workflows, providing more accurate information retrieval, and enabling natural language interfaces to databases. What are AI agents? AI agents go beyond simple chatbots by combining large language models (LLMs) with external tools and databases. Unlike standalone LLMs or standard RAG systems, AI agents can: Plan: Break down complex tasks into smaller, sequential steps. Use Tools: Leverage APIs, code execution, search systems to gather information or perform actions. Perceive: Understand and process inputs from various data sources. Remember: Store and recall previous interactions for better decision-making. By connecting AI agents to databases like Azure Database for PostgreSQL, agents can deliver more accurate, context-aware responses based on your data. AI agents extend beyond basic human conversation to carry out tasks based on natural language. These tasks traditionally required coded logic; however, agents can plan the tasks needed to execute based on user-provided context. Agents can be implemented using various GenAI frameworks including LangChain, LangGraph, LlamaIndex and Semantic Kernel. All these frameworks support using Azure Database for PostgreSQL as a tool. This uses the Azure AI Agents Service for agent planning, tool usage, and perception, while using Azure Database for PostgreSQL as a tool for vector database and semantic search capabilities. Real-World Use Case: Legal Research Assistant In this tutorial, we'll build an AI agent that helps legal teams research relevant cases to support their clients in Washington state. Our agent will: Accept natural language queries about legal situations. Use vector search in Azure Database for PostgreSQL to find relevant case precedents. Analyze and summarize the findings in a format useful for legal professionals. Prerequisites Azure Resources An active Azure account. Azure Database for PostgreSQL Flexible Server instance running PG 14 or higher. With pg_vector and azure_ai extensions enabled Azure AI Foundry Project Deployed Azure GPT-4o-mini endpoint. Deployed Azure text-embedding-small endpoint. Local Setup Install Visual Studio Code. Install the Python extension. Install Python 3.11.x. Install Azure CLI.(latest version) Project Implementation All the code and sample datasets are available in this GitHub repository. Step 1: Set Up Vector Search in Azure Database for PostgreSQL First, we'll prepare our database to store and search legal case data using vector embeddings: Environment Setup: If using macOS / bash: python -m venv .pg-azure-ai source .pg-azure-ai/bin/activate pip install -r requirements.txt Windows / PowerShell python -m venv .pg-azure-ai .pg-azure-ai \Scripts\Activate.ps1 pip install -r requirements.txt Windows / cmd.exe: python -m venv .pg-azure-ai .pg-azure-ai \Scripts\activate.bat pip install -r requirements.txt Configure Environment Variables: Create a .env file with your credentials: AZURE_OPENAI_API_KEY="" AZURE_OPENAI_ENDPOINT="" EMBEDDING_MODEL_NAME="" AZURE_PG_CONNECTION="" Load documents and vectors The Python file load_data/main.py serves as the central entry point for loading data into Azure Database for PostgreSQL. This code processes sample cases data, including information about cases in Washington. High level details of main.py: Database setup and Table Creation: Creates necessary extensions, sets up OpenAI API settings, and manages database tables by dropping existing ones and creating new ones for storing case data. Data Ingestion: Reads data from a CSV file and inserts it into a temporary table, then processes and transfers this data into the main cases table. Embedding Generation: Adds a new column for embeddings in the cases table and generates embeddings for case opinions using OpenAI's API, storing them in the new column. The embedding process will take ~3-5 minutes To start the data loading process, run the following command from the load_data directory: python main.py Here's the output of main.py: Extensions created successfully OpenAI connection established successfully Cases table created successfully Temp cases table created successfully Data loaded into temp_cases_data table successfully Data loaded into cases table successfully Adding Embeddings, this will take a while around 3-5 mins... Embeddings added successfully All Data loaded successfully! Step 2: Create Postgres tool for the Agent In this step we will be configuring AI agent tools to retrieve data from Postgres and then using the Azure AI Agent Service SDK to connect your AI agent to the Postgres database. Define a function for your agent to call Start by defining a function for your agent to call by describing its structure and any required parameters in a docstring. Include all your function definitions in a single file, legal_agent_tools.py which you can then import into your main script. def vector_search_cases(vector_search_query: str, start_date: datetime ="1911-01-01", end_date: datetime ="2025-12-31", limit: int = 10) -> str: """ Fetches the cases information in Washington State for the specified query. :param query(str): The query to fetch cases for specifically in Washington. :type query: str :param start_date: The start date for the search, defaults to "1911-01-01" :type start_date: datetime, optional :param end_date: The end date for the search, defaults to "2025-12-31" :type end_date: datetime, optional :param limit: The maximum number of cases to fetch, defaults to 10 :type limit: int, optional :return: Cases information as a JSON string. :rtype: str """ db = create_engine(CONN_STR) query = """ SELECT id, name, opinion, opinions_vector <=> azure_openai.create_embeddings( 'text-embedding-3-small', %s)::vector as similarity FROM cases WHERE decision_date BETWEEN %s AND %s ORDER BY similarity LIMIT %s; """ # Fetch cases information from the database df = pd.read_sql(query, db, params=(vector_search_query,datetime.strptime(start_date, "%Y-%m-%d"), datetime.strptime(end_date, "%Y-%m-%d"),limit)) cases_json = json.dumps(df.to_json(orient="records")) return cases_json Step 3: Create and Configure the AI Agent with Postgres Now we'll set up the AI agent and integrate it with our PostgreSQL tool. The Python file src/simple_postgres_and_ai_agent.py serves as the central entry point for creating and using your agent. High level details of simple_postgres_and_ai_agent.py: Create an Agent: Initializes the agent in your Azure AI Project with a specific model. Add Postgres tool: During the agent initialization, the Postgres tool to do vector search on your Postgres DB is added. Create a Thread: Sets up a communication thread. This will be used to send messages to the agent to process Run the Agent and Call Postgres tool: Processes the user's query using the agent and tools. The agent can plan with tools to use to get the correct answer. In this use case the agent will call the Postgres tool based on the function signature and docstring to do vector search and retrieve the relevant data to answer the question. Display the Agent’s Response: Outputs the agent's response to the user's query. Find the Project Connection String in Azure AI Foundry: In your Azure AI Foundry project you will find you Project Connection String from the Overview page of the project we will use this string to connect the project to the AI agent SDK. We will be adding this string to the .env file. Connection Setup: Add these variables to your .env file in the root directory: PROJECT_CONNECTION_STRING=" " MODEL_DEPLOYMENT_NAME="gpt-4o-mini" AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED="true" Create the Agent with Tool Access We will create the agent in the AI Foundry project and add the Postgres tools needed to query to Database. The code snippet below is an excerpt from the file simple_postgres_and_ai_agent.py. # Create an Azure AI Client project_client = AIProjectClient.from_connection_string( credential=DefaultAzureCredential(), conn_str=os.environ["PROJECT_CONNECTION_STRING"], ) # Initialize agent toolset with user functions functions = FunctionTool(user_functions) toolset = ToolSet() toolset.add(functions) agent = project_client.agents.create_agent( model= os.environ["MODEL_DEPLOYMENT_NAME"], name="legal-cases-agent", instructions= "You are a helpful legal assistant that can retrieve information about legal cases.", toolset=toolset ) Create Communication Thread: This code snippet, shows how to create a thread and message for the agent. The thread and message will be what the agent processes in a run. # Create thread for communication thread = project_client.agents.create_thread() # Create message to thread message = project_client.agents.create_message( thread_id=thread.id, role="user", content="Water leaking into the apartment from the floor above, What are the prominent legal precedents in Washington on this problem in the last 10 years?" ) Process the Request: This code snippet creates a run for the agent to process the message and use the appropriate tools to provide the best result. Using the tool, the agent will be able to call your Postgres and the vector search on the query “Water leaking into the apartment from the floor above”, to retrieve the data it will need to answer the question best. from pprint import pprint # Create and process agent run in thread with tools run = project_client.agents.create_and_process_run( thread_id=thread.id, agent_id=agent.id ) # Fetch and log all messages messages = project_client.agents.list_messages(thread_id=thread.id) pprint(messages['data'][0]['content'][0]['text']['value']) Run the Agent: To run the agent, run the following command from the src directory: python simple_postgres_and_ai_agent.py The agent will produce a similar result as below using the Azure Database for PostgreSQL tool to access case data saved in the Postgres Database. Snippet of output from agent: 1. Pham v. Corbett Citation: Pham v. Corbett, No. 4237124 Summary: This case involved tenants who counterclaimed against their landlord for relocation assistance and breach of the implied warranty of habitability due to severe maintenance issues, including water and sewage leaks. The trial court held that the landlord had breached the implied warranty and awarded damages to the tenants. 2. Hoover v. Warner Citation: Hoover v. Warner, No. 6779281 Summary: The Warners appealed a ruling finding them liable for negligence and nuisance after their road grading project caused water drainage issues affecting Hoover's property. The trial court found substantial evidence supporting the claim that the Warners' actions impeded the natural flow of water and damaged Hoover's property. Step 4: Testing and Debugging with Azure AI Foundry Playground After running your agent with Azure AI Agent SDK, the agent will be stored in your project, and you can experiment with the agent in the Agent playground. Using the Agent Playground: Navigate to the Agents section in Azure AI Foundry Find your agent in the list and click to open Use the playground interface to test various legal queries Test the query “Water leaking into the apartment from the floor above, What are the prominent legal precedents in Washington?”. The agent will pick the right tool to use and ask for the expected output for that query. Use sample_vector_search_cases_output.json as the sample output. Step 5: Debugging with Azure AI Foundry Tracing When developing the agent by using the Azure AI Foundry SDK, you can also debug the agent with Tracing. You will be able to debug the calls to tools like Postgres as well as seeing how to agent orchestrated each task. Debugging with Tracing: Click Tracing in the Azure AI Foundry menu Create a new Application Insights resource or connect an existing one View detailed traces of your agent's operations Learn more about how to set up tracing with the AI agent and Postgres in the advanced_postgres_and_ai_agent_with_tracing.py file on Github. Get Started Today By combining Azure Database for PostgreSQL with Azure AI Agent Service, developers can create intelligent agents that automate data retrieval, improve decision-making, and unlock powerful insights. Whether you're working on legal research, customer support, or data analytics, this setup provides a scalable and efficient solution to enhance your AI applications. Ready to build your own AI agent? Try building your own legal agent with Azure AI agent service and Postgres. 1. Setup Azure AI Foundry and Azure Database for PostgreSQL Flexible Server Setup up an AI Foundry Project and deploy models Deploy the “gpt-4o-mini” model and “text-embedding-small” models Setup Azure Database for PostgreSQL Flexible Server and pg_vector extension Allow the azure_ai extension 2. Run our end-to-end sample AI Agent using Azure Database for PostgreSQL tool 3. Customize for your use case: Replace legal data with your domain-specific information Adjust agent instructions for your specific needs Add additional tools as required Learn More Read more able Azure Database for PostgreSQL and the Azure AI Agent service. Learn more about Vector Search in Azure Database for PostgreSQL Learn more about Azure AI Agent Service
