Level Up Your Python Game with Generative AI Free Livestream Series This October!
If you've been itching to go beyond basic Python scripts and dive into the world of AI-powered applications, this is your moment. Join Pamela Fox and Gwyneth Peña-Siguenza Gwthrilled to announce a brand-new free livestream series running throughout October, focused on Python + Generative AI and this time, we’re going even deeper with Agents and the Model Context Protocol (MCP). Whether you're just starting out with LLMs or you're refining your multi-agent workflows, this series is designed to meet you where you are and push your skills to the next level. 🧠 What You’ll Learn Each session is packed with live coding, hands-on demos, and real-world examples you can run in GitHub Codespaces. Here's a taste of what we’ll cover: 🎥 Why Join? Live coding: No slides-only sessions — we build together, step by step. All code shared: Clone and run in GitHub Codespaces or your local setup. Community support: Join weekly office hours and our AI Discord for Q&A and deeper dives. Modular learning: Each session stands alone, so you can jump in anytime. 🔗 Register for the full series 🌍 ¿Hablas español? We’ve got you covered! Gwyneth Peña-Siguenza will be leading a parallel series in Spanish, covering the same topics with localized examples and demos. 🔗 Regístrese para la serie en español Whether you're building your first AI app or architecting multi-agent systems, this series is your launchpad. Come for the code, stay for the community — and leave with a toolkit that scales. Let’s build something brilliant together. 💡 Join the discussions and share your exprience at the Azure AI Discord CommunityFueling the Agentic Web Revolution with NLWeb and PostgreSQL
We’re excited to announce that NLWeb (Natural Language Web), Microsoft’s open project for natural language interfaces on websites now supports PostgreSQL. With this enhancement, developers can leverage PostgreSQL and NLWeb to transform any website into an AI-powered application or Model Context Protocol (MCP) server. This integration allows organizations to utilize a familiar, robust database as the foundation for conversational AI experiences, streamlining deployment and maximizing data security and scalability. Soon, autonomous agents, not just human users, will consume and interpret website content, transforming how information is accessed and utilized online. During Microsoft //Build 2025, Microsoft introduced the era of the open agentic web, in which the internet is an open agentic web a new paradigm in which autonomous agents seamlessly interact across individual, organizational, team and end-to-end business contexts. To realize the future of an open agentic web, Microsoft announced the NLWeb project. NLWeb transforms any website to an AI-powered application with just a few lines of code and by connecting to an AI model and a knowledge base. In this post, we’ll cover: What NLWeb is and how it works with vector databases How pgvector enables vector similarity search in PostgreSQL for NLWeb Get started using NLWeb with Postgres Let’s dive in and see how Postgres + NLWeb can redefine conversational web interfaces while keeping your data in a familiar, powerful database. What is NLWeb? A Quick Overview of Conversational Web Interfaces NLWeb is an open project developed by Microsoft to simplify adding conversational AI interfaces to websites. How NLWeb works under the hood: Processes existing data/website content that exists in semi-structured formats like Schema.org, RSS, and other data that websites already publish Embeds and indexes all the content in a vector store (i.e PostgreSQL with pgvector) Routes user queries through several processes which handle natural langague understanding, reranking and retrieval. Answers queries with an LLM The result is a high-quality natural language interface on top of web data, giving developers the ability to let users “talk to” web data. By default, every NLWeb instance is also a Model Context Protocol (MCP) server, allowing websites to make their content discoverable and accessible to agents and other participants in the MCP ecosystem if they choose. Importantly, NLWeb is platform-agnostic and supports many major operating systems, AI models, and vector stores and the NLWeb project is modular by design, so developers can bring their own retrieval system, model APIs, and define their own extensions. NLWeb with PostgreSQL PostgreSQL is now embedded into the NLWeb reference stack as a native retriever, creating a scalable and flexible path for deploying NLWeb instances using open-source infrastructure. Retrieval Powered by pgvector NLWeb leverages pgvector, a PostgreSQL extension for efficient vector similarity search, to handle natural language retrieval at scale. By integrating pgvector into the NLWeb stack, teams can eliminate the need for external vector databases. Web data stored in PostgreSQL becomes immediately searchable and usable for NLWeb experiences, streamlining infrastructure and enhancing security. PostgreSQL's robust governance features and wide adoption align with NLWeb’s mission to enable conversational AI for any website or content platform. With pgvector retrieval built in, developers can confidently launch NLWeb instances on their own databases no additional infrastructure required. Implementation example We are going to use NLWeb and Postgres, to create a conversational AI app and MCP server that will let us chat with content from the Talking Postgres with Claire Giordano Podcast! Prerequisites An active Azure account. Enable and configure the pg_vector extensions. Create an Azure AI Foundry project. Deploy models gpt-4.1, gpt-4.1-mini and text-embedding-3-small. Install Visual Studio Code. Install the Python extension. Install Python 3.11.x. Install the Azure CLI (latest version). Getting started All the code and sample datasets are available in this GitHub repository. Step 1: Setup NLWeb Server 1. Clone or download the code from the repo. git clone https://github.com/microsoft/NLWeb cd NLWeb 2. Open a terminal to create a virtual python environment and activate it. python -m venv myenv source myenv/bin/activate # Or on Windows: myenv\Scripts\activate 3. Go to the 'code/python' folder in NLWeb to install the dependencies. cd code/python pip install -r requirements.txt 4. Go to the project root folder in NLWeb and copy the .env.template file to a new .env file cd ../../ cp .env.template .env 5. In the .env file, update the API key you will use for your LLM endpoint of choice and update the Postgres connection string. For example: AZURE_OPENAI_ENDPOINT="https://TODO.openai.azure.com/" AZURE_OPENAI_API_KEY="<TODO>" # If using Postgres connection string POSTGRES_CONNECTION_STRING="postgresql://<HOST>:<PORT>/<DATABASE>?user=<USERNAME>&sslmode=require" POSTGRES_PASSWORD="<PASSWORD>" 6. Update your config files (located in the config folder) to make sure your preferred providers match your .env file. There are three files that may need changes. config_llm.yaml: Update the first line to the LLM provider you set in the .env file. By default it is Azure OpenAI. You can also adjust the models you call here by updating the models noted. By default, we are assuming 4.1 and 4.1-mini. config_embedding.yaml: Update the first line to your preferred embedding provider. By default it is Azure OpenAI, using text-embedding-3-small. config_retrieval.yaml: Update the first line to postgres. You should update write_endpoint to postgres and You should update postgres retrieval endpoint is enabled to 'true' in the following list of possible endpoints. Step 2: Initialize Postgres Server Go to the 'code/python/misc folder in NLWeb to run Postgres initializer. NOTE: If you are using Azure Postgres Flexible server make sure you have `vector` extension allow-listed and make sure the database has the vector extension enabled, cd code/python/misc python postgres_load.py Step 3: Ingest Data from Talk Postgres Podcast Now we will load some data in our local vector database to test with. We've listed a few RSS feeds you can choose from below. Go to the 'code/python folder in NLWeb and run the command. The format of the command is as follows (make sure you are still in the 'python' folder when you run this): python -m data_loading.db_load <RSS URL> <site-name> Talking Postgres with Claire Giordano Podcast: python -m data_loading.db_load https://feeds.transistor.fm/talkingpostgres Talking-Postgres (Optional) You can check the documents table in your Postgres database and verify the table looks like the one below. To verify all the data from the website was uploaded. Test NLWeb Server Start your NLWeb server (again from the 'python' folder): python app-file.py Go to http://localhost:8000/ Start ask questions about the Talking Postgres with Claire Giordano Podcast, you may try different modes. Trying List Mode: Sample Prompt: “I want to listen to something that talks about the advances in vector search such as DiskANN” Trying Generate Mode Sample Prompt: “What did Shireesh Thota say about the future of Postgres?” Running NLWeb with MCP 1. If you do not already have it, install MCP in your venv: pip install mcp 2. Next, configure your Claude MCP server. If you don’t have the config file already, you can create the file at the following locations: macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json The default MCP JSON file needs to be modified as shown below: macOS Example Configuration { “mcpServers”: { “ask_nlw”: { “command”: “/Users/yourname/NLWeb/myenv/bin/python”, “args”: [ “/Users/yourname/NLWeb/code/chatbot_interface.py”, “—server”, “http://localhost:8000”, “—endpoint”, “/mcp” ], “cwd”: “/Users/yourname/NLWeb/code” } } } Windows Example Configuration { “mcpServers”: { “ask_nlw”: { “command”: “C:\\Users\\yourusername\\NLWeb\\myenv\\Scripts\\python”, “args”: [ “C:\\Users\\yourusername\\NLWeb\\code\\chatbot_interface.py”, “—server”, “http://localhost:8000”, “—endpoint”, “/mcp” ], “cwd”: “C:\\Users\\yourusername\\NLWeb\\code” } } } Note: For Windows paths, you need to use double backslashes (\\) to escape the backslash character in JSON. 3. Go to the 'code/python’ folder in NLWeb and run the command. Enter your virtual environment and start your NLWeb local server. Make sure it is configured to access the data you would like to ask about from Claude. # On macOS source ../myenv/bin/activate python app-file.py # On Windows ..\myenv\Scripts\activate python app-file.py 4. Open Claude Desktop. It should ask you to trust the 'ask_nlw' external connection if it is configured correctly. After clicking yes and the welcome page appears, you should see 'ask_nlw' in the bottom right '+' options. Select it to start a query. 5. To query NLWeb, just type 'ask_nlw' in your prompt to Claude. You'll notice that you also get the full JSON script for your results. Remember, you must have your local NLWeb server started to use this option. Learn More Vector Store in Azure Postgres Flexible Server Generative AI in Azure Postgres Flexible Server NLWeb GitHub repo includes: A reference server for handling natural language queries PGvector integrationJS AI Build-a-thon: Project Showcase
In the JS AI Build-a-thon, quest 9 was all about shipping faster, smarter, and more confidently. This quest challenged developers to skip the boilerplate and focus on what really matters, solving real-world problems with production-ready AI templates and cloud-native tools. And wow, did our builders deliver, with a massive 28 projects. From personalized chatbots to full-stack RAG applications, participants used the power of the Azure Developer CLI (azd) and robust templates to turn ideas into fully deployed AI solutions on Azure, in record time. What This Quest Was About Quest 9 focused on empowering developers to: Build AI apps faster with production-ready templates Use Azure Developer CLI (azd) to deploy with just a few commands Leverage Infrastructure-as-Code (IaC) to provision everything from databases to APIs Follow best practices out of the box — scalable, secure, and maintainable Participants explored a curated gallery of templates and learned how to adapt them to their unique use cases. No Azure experience? No problem. azd made setup, deployment, and teardown as simple as azd up. 🥁And the winner is..... With the massive votes from the community, the winning project as AI Academic advisor by Aryanjstar AI Career Navigator - Your Personal AI Career Coach by Aryanjstar [Project Submission] AI Career Navigator - Your Personal AI Career Coach · Issue #47 · Azure-Samples/JS-AI-Build-a-thon Aryan, a Troop Leader of the Geo Cyber Study Jam, created the AI Career Navigator to address common challenges faced by developers in the tech job market, such as unclear skill paths, resume uncertainty, and interview anxiety. Built using the Azure Search OpenAI Demo template, the tool offers features like resume-job description matching, skill gap analysis, and dynamic interview preparation. The project leverages a robust RAG setup and Azure integration to deliver a scalable, AI-powered solution for career planning. 🥉 Other featured projects: Deepmine-sentinel by Josephat-Onkoba [Project Submission] Deepmine-Sentinel · Issue #29 · Azure-Samples/JS-AI-Build-a-thon DeepMine Sentinel AI is an intelligent safety assistant designed to tackle the urgent risks facing workers in the mining industry, one of the most hazardous sectors globally. Built by customizing the “Get Started with Chat” Azure template, this solution offers real-time safety guidance and monitoring to prevent life-threatening incidents like cave-ins, toxic gas exposure, and equipment accidents. In regions where access to safety expertise is limited, DeepMine Sentinel bridges the gap by delivering instant, AI-powered support underground, ensuring workers can access critical information and protocols when they need it most. With a focus on accessibility, real-world impact, and life-saving potential, this project demonstrates how AI can be a powerful force for good in high-risk environments. PetPal - Your AI Pet Care Assistant by kelcho-spense [Project Submission] PetPal - Your AI Pet Care Assistant · Issue #70 · Azure-Samples/JS-AI-Build-a-thon PetPal is an AI-powered pet care assistant designed to support pet owners, especially first-timers, by offering instant, reliable answers to common pet-related concerns. From health and nutrition advice to emergency support and behavioral training, PetPal uses a serverless architecture powered by LangChain.js, Azure OpenAI, and Retrieval-Augmented Generation (RAG) to deliver accurate, context-aware responses. The app features a warm, pet-themed interface built with Lit and TypeScript, and includes thoughtful customizations like pet profile management, personalized chat history, and species-specific guidance. With backend services hosted on Azure Functions and data stored in Azure Cosmos DB, PetPal is production-ready, scalable, and focused on reducing anxiety while promoting responsible and informed pet ownership. MLSA LearnBot by Shunlexxi [Project Submission] MLSA LearnBot · Issue #67 · Azure-Samples/JS-AI-Build-a-thon Navigating the Microsoft Learn Student Ambassadors program can be overwhelming. To solve this, a student built an Intelligent Chatbot Q&A App using a Microsoft azd template, transforming a generic AI chatbot into a tailored assistant for Student Ambassadors and aspiring members. By integrating essential documentation, FAQs, and natural language support with Azure services like App Service, AI Search, and OpenAI, this tool empowers students to get instant, reliable answers and navigate their roles with ease. The front end was customized to reflect a student-friendly brand, and deployment was simplified using azd for a seamless, production-ready experience. You okay? Meet Vish AI, your mental health companion by ToshikSoni [Project Submission] You okay? Meet Vish AI, your mental health companion · Issue #38 · Azure-Samples/JS-AI-Build-a-thon Vish.AI is an empathetic GenAI companion designed to support emotional well-being, especially for individuals facing depression, loneliness, and mental burnout. Built using Azure’s AI Chat RAG template and enhanced with LangChain for conversational memory, the assistant offers a deeply personalized experience that remembers past interactions and responds with both emotional intelligence and informed support. By integrating a curated collection of resources on mental health into its RAG system, Vish.AI provides meaningful guidance and a comforting presence, available anytime, anywhere. Created to bridge the gap for those who may not feel comfortable opening up to friends or family, this project combines AI with a human touch to offer always-accessible care, demonstrating how thoughtful technology can help make life a little lighter for those quietly struggling. Want to Catch Up? If you missed the Build-a-thon or want to explore other quests (there are 9!), check them out here: 👉 GitHub - Azure-Samples/JS-AI-Build-a-thon If you want to catch up with how the challenge went and how you can get started, check out 👉JS AI Build‑a‑thon: Wrapping Up an Epic June 2025! | Microsoft Community Hub Join the Community The conversation isn’t over. The Quests are now self-paced. We’re keeping the momentum going over on Discord in the #js-ai-build-a-thon channel. Drop your questions, showcase your builds, or just come hang out with other builders. 👉 Join the community on Join the Azure AI Foundry Discord Server! Additional Resources 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScriptBuild AI-Ready Apps and Agents with PostgreSQL on Azure
As developers, we’re constantly looking for ways to build smarter, faster, and more scalable applications. The Microsoft Reactor series, Build AI apps with Azure Database for PostgreSQL, is a four-part livestream experience designed to help you do just that—by combining the power of PostgreSQL with Azure’s AI capabilities. Dive into the world of AI apps and agents with Azure Database for PostgreSQL in this engaging video series—your ideal starting point for building intelligent solutions and improving your workflow. Get ready to explore the fundamentals of AI and discover how vector support in databases can elevate your applications. Uncover how innovative tools like the Visual Studio Code extension for PostgreSQL and GitHub Copilot can make your database work faster and more efficient. You'll also see how to create intelligent apps and AI agents using frameworks such as LangChain and Semantic Kernel. Why This Series Matters PostgreSQL is already a favorite among developers for its flexibility and open-source strength. But when paired with Azure’s AI services, it becomes a launchpad for intelligent applications. This series walks you through how to: Orchestrate AI agents using PostgreSQL as a foundation. Enhance semantic search with vector support and indexes like DiskANN. Integrate Azure AI services to enrich your data and user experiences. Boost productivity with tools like the Visual Studio Code PostgreSQL extension and GitHub Copilot. What You'll Learn Each session is packed with practical insights: Episode 1: Laying the foundation: AI-powered apps and agents with Azure Database for PostgreSQL We introduce key AI concepts, setting the stage for a deeper understanding of Large Language Models (LLMs) and its applications, we will explore the capabilities of Azure Database for PostgreSQL, focusing on how its vector support enables advanced semantic search through technologies like DiskANN indexes. We'll also discuss the Azure AI extension, which brings powerful AI features to your data projects, helping you enrich your applications with enhanced search relevance and intelligent insights, and provide a solid foundation for leveraging these tools in your own solutions. Register here Episode 2: Accelerate your data and AI tasks with the VS Code extension for PostgreSQL and GitHub Copilot This talk will delve into how the Visual Studio Code extension for PostgreSQL can streamline your database management, while GitHub Copilot's AI-powered assistance can boost your productivity. Learn how to seamlessly integrate these tools to enhance your workflow, automate repetitive tasks, and write efficient code faster. Whether you're a developer, data scientist, or database administrator, this session will provide you with practical insights and techniques to elevate your data and AI projects. Join us to learn how to effectively use these advanced tools and take your data skills to the next level. Register here Episode 3: Build your own AI copilot for financial apps with PostgreSQL Join us to discover how to transform traditional financial applications into intelligent, AI-powered solutions with Azure Database for PostgreSQL. In this hands-on session, you'll learn to integrate generative AI for high-quality responses to financial queries using PDF-based Statements of Work and invoices, perform AI-driven data validation, apply the Azure AI extension, implement vector search with DiskANN indexes, enhance results with semantic re-ranking, use the LangChain framework, and leverage GraphRAG on Azure Database for PostgreSQL. By the end, you’ll have gained practical skills to build end-to-end AI-driven applications using your own data and projects. Register here Episode 4: Build advanced AI Agents with PostgreSQL Using a sample dataset of legal cases, we’ll show how AI technologies empower intelligent agents to provide high-quality answers to legal queries. In this session, you’ll learn to build an advanced AI agent with Azure Database for PostgreSQL, integrating generative AI for enhanced data validation, retrieval-augmented generation (RAG), semantic re-ranking, Semantic Kernel, and GraphRAG via the Apache AGE Graph extension. This practical demonstration offers insights into developing robust, intelligent solutions using your own data. Register here Join us for an inspiring and hands-on experience—don’t miss out! Get the full series details and register now: https://aka.ms/postgres-ai-reactor-seriesMicrosoft Build 2024: Essential Guide for AI Developers at Startups and Cloud-First Companies
Generative AI is advancing fast, with OpenAI’s GPT-4o leading the way. GPT-4o boasts improved multilingual understanding, faster responses, lower costs, and real-time processing of text, audio, and images. This boosts new Generative AI (GenAI) use cases. Explore cutting-edge solutions like models, frameworks, vector databases, and LLM observability platforms. Born-in-the-cloud companies are at the forefront of this AI revolution. Be part of the future at Microsoft Build 2024!Make your own private ChatGPT
Introduction Creating your own private ChatGPT allows you to leverage AI capabilities while ensuring data privacy and security. This guide walks you through building a secure, customized chatbot using tools like Azure OpenAI, Cosmos DB and Azure App service. Why Build a Private ChatGPT? With the rise of AI-driven applications, organizations, people often face challenges related to data privacy, customization, and integration. Building a private ChatGPT addresses these concerns by: Maintaining Data Privacy: Keep sensitive information within your infrastructure. Customizing Responses: Tailor the chatbot’s behavior and language to suit your requirements. Ensuring Security: Leverage enterprise-grade security protocols. Avoiding Data Sharing: Prevent your data from being used to train external models. If organizations do not take these measures their data may go into future model training and can leak your sensitive data to public. Eg: Chatgpt collects personal data mentioned in their privacy policy Prerequisites Before you begin, ensure you have: Access to Azure OpenAI Service. A development environment set up with Python. Basic knowledge of FastAPI and MongoDB. An Azure account with necessary permissions. If you do not have Azure subscription, try Azure for students for FREE. Step 1: Set Up Azure OpenAI Log in to the Azure Portal and create an Azure OpenAI resource. Deploy a model, such as GPT-4o (multimodal), and note down the endpoint and API key. Note there is also an option of keyless authentication. Configure permissions to control access. Step 2: Use Chatgpt like app sample You can select any repository to be as base template for your app, in this I will be using the third option AOAIchat. It is developed by me. GitHub - mckaywrigley/chatbot-ui: AI chat for any model. Azure-Samples/azure-search-openai-demo: A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. sourabhkv/AOAIchat: Azure OpenAI chat This architecture diagram represents a typical flow for a private ChatGPT application with the following components: App UX (User Interface): This is the front-end application (mobile, web, or desktop) where users interact with the chatbot. It sends the user's input (prompt) and displays the AI's responses. App Service: Acts as the backend application, handling user requests and coordinating with other services. Functions: Receives user inputs and prepares them for processing by the Azure OpenAI service. Streams AI responses back to the App UX. Reads from and writes to Cosmos DB to manage chat history. Azure OpenAI Service: This is the core AI service, processing the user input and generating responses using models like GPT-4o. The App Service sends the user input (along with context) to this service and receives the AI-generated responses. Cosmos DB: A NoSQL database used to store and manage chat history. Operations: Writes user messages and AI-generated responses for future reference or analysis. Reads chat history to provide context for AI responses, enabling more intelligent and contextual conversations. Data Flow: User inputs are sent from the App UX to the App Service. The App Service forwards the input (with additional context, if needed) to Azure OpenAI. Azure OpenAI generates a response, which is streamed back to the App UX via the App Service. The App Service writes user inputs and AI responses to Cosmos DB for persistence. This architecture ensures scalability, secure data handling, and the ability to provide contextual responses by integrating database and AI services. What can you do with my template? AOAIchat supports personal, enterprise chat enabled by RAG People can enable RAG mode if they want to search within their database, else it behaves like normal ChatGPT. It supports multimodality, (supports image, text input) also depends on model deployed in Azure AI foundry. Step 3: Deploy to Azure Deploy a Cosmos DB account in nearest region Deploy Azure OpenAI model (gpt-4o, gpt-4o-mini recommended) Deploy Azure App service, try using container I would recommend B1plan to your nearest region, select docker registry sourabhkv/aoaichatdb:0.1 startup command uvicorn app:app --host 0.0.0.0 --port 80 After app service starts, put all environment variables The application requires the following environment variables to be set for proper configuration: Environment Variable Description AZURE_OPENAI_ENDPOINT The endpoint for Azure OpenAI API. AZURE_OPENAI_API_KEY API key for accessing Azure OpenAI. DEPLOYMENT_NAME Azure OpenAI deployment name. API_VERSION API version for Azure OpenAI. MAX_TOKENS Maximum tokens for API responses. MONGO_DETAILS MongoDB connection string. AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint> AZURE_OPENAI_API_KEY=<your_azure_openai_api_key> DEPLOYMENT_NAME=<your_deployment_name> API_VERSION=<your_api_version> MAX_TOKENS=<max_tokens> MONGO_DETAILS=<your_mongo_connection_string> Optional feature: implement authentication to secure access. Within app service select Authentication and select service providers. I went with Entra based authentication with single tenant. There is option of multi-tenant, personal accounts as well. Restart App service and within 2 minutes your private ChatGPT is ready. Pricing Pricing may depend on the plan you have deployed resources and region. Check Azure calculator for price estimation. My estimate for pricing I deployed all my resources in Sweden central Cosmos DB config - Cosmos DB for MongoDB (RU) serverless config with single write master, 2 GB transactional storage, 2 backup plan (FREE) ~ 0.75$ Azure OpenAI service - plan S0, model gpt-4o-mini global deployment, Input 20000 tokens, Output 10000 tokens ~ 9.00$ App service plan - OS Linux, Tier B1, instance count 1 ~13.14$ Total monthly cost = 22.89$ This price may vary in future, in region I calculated my configuration in Azure calculator Governance Azure OpenAI provides content filters to block any kind of input that violates responsible AI practices. Categories include Hate and Fairness Sexual Violence Self-harm User Prompt Attacks (direct and indirect) The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Azure OpenAI Service includes default safety settings applied to all models set as medium. Content filters can be modified to different level depending on use case. It supports RAG, I have provided detailed solution for it in my GitHub. Practical implementation GE Aerospace, in partnership with Microsoft and Accenture, has launched a company-wide generative AI platform, leveraging Microsoft Azure and Azure OpenAI Service. This solution aims to transform asset tracking and compliance in aviation, enabling quick access to maintenance records and reducing manual processing time from days to minutes. It supports informed decision-making by providing insights into aircraft leasing, compliance gaps, and asset health. For enterprises implementing private ChatGPT solutions, this illustrates the potential of generative AI for streamlining document-intensive processes while ensuring data security and compliance through cloud-based infrastructure like Azure. GE Aerospace Launches Company-wide Generative AI Platform for Employees | GE Aerospace News Build your own private ChatGPT style app with enterprise-ready architecture - By Microsoft Mechanics How to make private ChatGPT for FREE? It can be FREE if all of the setup is running locally on your hardware. Cosmos DB <-> MongoDB. Azure OpenAI <-> Ollama / LM studio Refer this NOTE : I have used gpt-4o, gpt-4o-mini these values are hardcoded in webpage, if you are using other models, you might have to change them in index.html. App Service <-> Local machine Register for Github models to access API for FREE. Note: GitHub models have rate limit for different models. Useful links sourabhkv/AOAIchat: Azure OpenAI chat What is RAG? Get started with Azure OpenAI API Chat with Azure OpenAI models using your own data🤖 Agent Loop Demos 🤖
We announced the public preview of agent loop at Build 2025. Agent Loop is a new feature in Logic Apps to build AI Agents for use cases that span across industry domains and patterns. Here are some resources to learn more about them Agent loop concepts Agent loop how-to Agent loop public preview announcement In this article, share with you use cases implemented in Logic Apps using agent loop and other features. This video shows an autonomous Loan Approval Agent specifically that handles auto loans for a bank. The demo features an AI Agent that uses an Azure Open AI model, company's policies, and several tools to process loan application. For edge cases, huma in involved via Teams connector. This video shows an autonomous Product Return Agent for Fourth Coffee company. The returns are processed by agent based on company policy, and other criterions. In this case also, a human is involved when decisions are outside the agent's boundaries This video shows a commercial agent that grants credits for purchases of groceries and other products, for Northwind Stores. The Agent extracts financial information from an IBM Mainframe and an IBM i system to assess each requestor and updates the internal Northwind systems with the approved customers information. Multi-Agent scenario including both a codeful and declarative method of implementation. Note: This is pre-release functionality and is subject to change. If you are interested in further discussing Logic Apps codeful Agents, please fill out the following feedback form. Operations Agent (part 1): In this conversational agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow. Operations Agent (part 2): In this autonomous agent, we will perform Logic Apps operations such as repair and resubmit to ensure our integration platform is healthy and processing transactions. To ensure of compliance we will ensure all operational activities are logged in ServiceNow.🚀 Announcement: Azure Logic Apps Document Indexer in Azure Cosmos DB
We’re excited to announce the public preview of Azure Logic Apps as a document indexer for Azure Cosmos DB! With this release, you can now use Logic Apps connectors and templates to ingest documents directly into Cosmos DB’s vector store—powering AI workloads like Retrieval-Augmented Generation (RAG) with ease. This new capability orchestrates the full ingestion pipeline—from fetching documents to parsing, chunking, embedding, and indexing—allowing you to unlock insights from unstructured content across your enterprise systems. Check out the announcement from Azure Cosmos team about this capability! How It Works Here’s how Logic Apps powers the ingestion flow: Connect to Source Systems While Logic Apps has more than 1400+ prebuilt connectors to pull documents from various systems, this experience streamlines the entire process via out of box templates to pull data from sources like Azure Blob Storage. Parse and Chunk Documents AI-powered parsing actions extract raw text. Then, the Chunk Document action: Tokenizes content into language model-friendly units Splits it into semantically meaningful chunks This ensures optimal size and quality for embedding and retrieval. Generate Embeddings with Azure OpenAI The chunks are passed to Azure OpenAI via connector to generate embeddings (e.g., using text-embedding-3-small). These vectors capture the meaning of your content for precise semantic search. Write to Azure Cosmos DB Vector Store Embeddings and metadata (like title, tags, and timestamps) are indexed in Cosmos DB’s, using a schema optimized for filtering, semantic ranking, and retrieval. Logic Apps Templates: Fast Start, Full Flexibility We’ve created ready-to-use templates to help you get started fast: 📄 Blob Storage – Simple Text Parsing 🧾 Blob Storage – OCR with Azure Document Intelligence 📁 SharePoint – Simple Text Parsing 🧠 SharePoint – OCR with Azure Document Intelligence Each template is customizable—so you can adapt it to your business needs or expand it with additional steps. We’d Love Your Feedback We’re just getting started—and we’re building this with you. Tell us: What data sources should we support next? Are there specific formats or verticals you need (e.g., legal docs, invoices, contracts)? What enhancements would make ingestion even easier? 👉 Reply to this post or share feedback through this form. Your input shapes the future of AI-powered document indexing in Cosmos DB.🎙️ Announcement: Logic Apps connectors in Azure AI Search for Integrated Vectorization
We’re excited to announce that Azure Logic Apps connectors are now supported within AI Search as data sources for ingestion into Azure AI Search vector stores. This unlocks the ability to ingest unstructured documents from a variety of systems—including SharePoint, Amazon S3, Dropbox and many more —into your vector index using a low-code experience. This new capability is powered by Logic Apps templates, which orchestrate the entire ingestion pipeline—from extracting documents to embedding generation and indexing—so you can build Retrieval-Augmented Generation (RAG) applications with ease. Grounding AI with RAG: Why Document Ingestion Matters Retrieval-Augmented Generation (RAG) has become a cornerstone technique for building grounded and trustworthy AI systems. Instead of generating answers from the model’s pretraining alone, RAG applications fetch relevant information from external knowledge bases—giving LLMs access to accurate and up-to-date enterprise data. To power RAG, enterprises need a scalable way to ingest and index documents into a vector store. Whether you're working with policy documents, legal contracts, support tickets, or financial reports, getting this content into a searchable, semantic format is step one. Simplified Ingestion with Integrated Vectorization Azure AI Search’s Integrated Vectorization capability automates the process of turning raw content into semantically indexed vectors: Chunking: Documents are split into meaningful text segments Embedding: Each chunk is transformed into a vector using an embedding model like text-embedding-3-small or a custom model Indexing: Vectors and associated metadata are written into a searchable vector store Projection: Metadata is preserved to enable filtering, ranking, and hybrid queries This eliminates the need to build or maintain custom pipelines, making it significantly easier to adopt RAG in production environments. Ingest from Anywhere: Logic Apps + AI Search With today’s release, we’re extending ingestion to a variety of new data sources by integrating Logic Apps connectors directly with AI Search. This allows you to retrieve unstructured content from enterprise systems and seamlessly ingest it into the vector store. Here’s how the ingestion process works with Logic Apps: Connect to Source Systems Using prebuilt connectors, Logic Apps can fetch content from various data sources including Sharepoint document libraries, messages from Service Bur or Azure Queues, files from OneDrive or SFTP Server and more. You can trigger ingestion on demand or at schedule. Parse and Chunk Documents Next, Logic Apps uses built-in AI-powered document parsing actions to extract raw text. This is followed by the “Chunk Document” action, which: Tokenizes the document based on language model-friendly units Splits the content into semantically coherent chunks This ensures optimal chunk size for downstream embedding and retrieval. Note – Currently we default to a chunk size of 5000 in the workflows created for document ingestion. We’ll be updating the default chunk size to a smaller number in our next release. Meanwhile, you can update it in the workflow if you need a smaller chunk size. Generate Embeddings with Azure OpenAI The chunked text is then passed to the Azure OpenAI connector, where the text-embedding-3-small or another configured embedding model is used to generate high-dimensional vector representations. These vectors capture the semantic meaning of the content and are key to enabling accurate retrieval in RAG applications. Write to Azure AI Search Finally, the embeddings, along with any relevant metadata (e.g., document title, tags, timestamps), are written into the AI Search index. The index schema is created for you ——and can include fields for filtering, sorting, and semantic ranking. Logic Apps Templates: Fast Start, Flexible Design To help you get started, we’ve created Logic Apps templates specifically for RAG ingestion. These templates: Include all the steps mentioned above Are customizable if you want to update the default configuration Whether you’re ingesting thousands of PDFs from SharePoint or syncing files from Amazon S3 bucket, these templates provide a production-grade foundation for building your pipeline. Getting Started Here is step by step detailed documentation to get started using Integrated Vectorization with Logic Apps data sources 👉 Get started with Logic Apps data sources for AI Search ingestion 👉 Learn more about Integrated Vectorization in Azure AI Search We'd Love Your Feedback We're just getting started. Tell us: What other data sources would you like to ingest? What enhancements would make ingestion easier for your use case? Are there specific industry templates or formats we should support? 👉 Reply to this post or share your ideas through our feedback form We’re building this with you—so your feedback helps shape the future of AI-powered automation and RAG.
