PrivyDoc: Building a Zero-Data-Leak AI with Foundry Local & Microsoft's Agent Framework
Tired of choosing between powerful AI insights and sacrificing your data's privacy? PrivyDoc offers a groundbreaking solution. In this article, Microsoft MVP in AI, Shivam Goyal, introduces his innovative project that brings robust AI document analysis directly to your local machine, ensuring zero data ever leaves your device. Discover how PrivyDoc leverages two cutting-edge Microsoft technologies: Foundry Local: The secret sauce for 100% on-device AI processing, allowing advanced models to run securely without cloud dependency. Microsoft Agent Framework: The intelligent orchestrator that builds a sophisticated multi-agent pipeline, handling everything from text extraction and entity recognition to summarization and sentiment analysis. Learn about PrivyDoc's intuitive web UI, its multi-format support, and crucial features that make it perfect for sensitive industries like legal, healthcare, and finance. Say goodbye to privacy concerns and hello to AI-powered document intelligence without compromise.Level up your Python + AI skills with our complete series
We've just wrapped up our live series on Python + AI, a comprehensive nine-part journey diving deep into how to use generative AI models from Python. The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To help you apply what you've learned, all of our code examples work with GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. If you're a Spanish speaker, check out the Spanish version of the series. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across many types of content. In this session, we explore different vector embedding models, such as the OpenAI text-embedding-3 series, through both visualizations and Python code. We compare distance metrics, use quantization to reduce vector size, and experiment with multimodal embedding models. Slides for this session Code repository with examples: vector-embedding-demos Python + AI: Retrieval Augmented Generation 📺 Watch recording In our third session, we explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that provides context to the LLM, enabling it to deliver well-grounded answers for a particular domain. The RAG approach works with many types of data sources, including CSVs, webpages, documents, and databases. In this session, we walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vision models 📺 Watch recording Our fourth session is all about vision models! Vision models are LLMs that can accept both text and images, such as GPT-4o and GPT-4o mini. You can use these models for image captioning, data extraction, question answering, classification, and more! We use Python to send images to vision models, build a basic chat-with-images app, and create a multimodal search engine. Slides for this session Code repository with examples: openai-chat-vision-quickstart Python + AI: Structured outputs 📺 Watch recording In our fifth session, we discover how to get LLMs to output structured responses that adhere to a schema. In Python, all you need to do is define a Pydantic BaseModel to get validated output that perfectly meets your needs. We focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples demonstrate the many ways you can use structured responses, such as entity extraction, classification, and agentic workflows. Slides for this session Code repository with examples: python-openai-demos Python + AI: Quality and safety 📺 Watch recording This session covers a crucial topic: how to use AI safely and how to evaluate the quality of AI outputs. There are multiple mitigation layers when working with LLMs: the model itself, a safety system on top, the prompting and context, and the application user experience. We focus on Azure tools that make it easier to deploy safe AI systems into production. We demonstrate how to configure the Azure AI Content Safety system when working with Azure AI models and how to handle errors in Python code. Then we use the Azure AI Evaluation SDK to evaluate the safety and quality of output from your LLM. Slides for this session Code repository with examples: ai-quality-safety-demos Python + AI: Tool calling 📺 Watch recording In the final part of the series, we focus on the technologies needed to build AI agents, starting with the foundation: tool calling (also known as function calling). We define tool call specifications using both JSON schema and Python function definitions, then send these definitions to the LLM. We demonstrate how to properly handle tool call responses from LLMs, enable parallel tool calling, and iterate over multiple tool calls. Understanding tool calling is absolutely essential before diving into agents, so don't skip over this foundational session. Slides for this session Code repository with examples: python-openai-demos Python + AI: Agents 📺 Watch recording In the penultimate session, we build AI agents! We use Python AI agent frameworks such as the new agent-framework from Microsoft and the popular LangGraph framework. Our agents start simple and then increase in complexity, demonstrating different architectures such as multiple tools, supervisor patterns, graphs, and human-in-the-loop workflows. Slides for this session Code repository with examples: python-ai-agent-frameworks-demos Python + AI: Model Context Protocol 📺 Watch recording In the final session, we dive into the hottest technology of 2025: MCP (Model Context Protocol). This open protocol makes it easy to extend AI agents and chatbots with custom functionality, making them more powerful and flexible. We demonstrate how to use the Python FastMCP SDK to build an MCP server running locally and consume that server from chatbots like GitHub Copilot. Then we build our own MCP client to consume the server. Finally, we discover how easy it is to connect AI agent frameworks like LangGraph and Microsoft agent-framework to MCP servers. With great power comes great responsibility, so we briefly discuss the security risks that come with MCP, both as a user and as a developer. Slides for this session Code repository with examples: python-mcp-demoHow to use any Python AI agent framework with free GitHub Models
I ❤️ when companies offer free tiers for developer services, since it gives everyone a way to learn new technologies without breaking the bank. Free tiers are especially important for students and people between jobs, when the desire to learn is high but the available cash is low. That's why I'm such a fan of GitHub Models: free, high-quality generative AI models available to anyone with a GitHub account. The available models include the latest OpenAI LLMs (like o3-mini), LLMs from the research community (like Phi and Llama), LLMs from other popular providers (like Mistral and Jamba), multimodal models (like gpt-4o and llama-vision-instruct) and even a few embedding models (from OpenAI and Cohere). With access to such a range of models, you can prototype complex multi-model workflows to improve your productivity or heck, just make something fun for yourself. 🤗 To use GitHub Models, you can start off in no-code mode: open the playground for a model, send a few requests, tweak the parameters, and check out the answers. When you're ready to write code, select "Use this model". A screen will pop up where you can select a programming language (Python/JavaScript/C#/Java/REST) and select an SDK (which varies depending on model). Then you'll get instructions and code for that model, language, and SDK. But here's what's really cool about GitHub Models: you can use them with all the popular Python AI frameworks, even if the framework has no specific integration with GitHub Models. How is that possible? The vast majority of Python AI frameworks support the OpenAI Chat Completions API, since that API became a defacto standard supported by many LLM API providers besides OpenAI itself. GitHub Models also provide OpenAI-compatible endpoints for chat completion models. Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 To prove it, I've made a new repository with examples from eight different Python AI agent packages, all working with GitHub Models: python-ai-agent-frameworks-demos. There are examples for AutoGen, LangGraph, Llamaindex, OpenAI Agents SDK, OpenAI standard SDK, PydanticAI, Semantic Kernel, and SmolAgents. You can open that repository in GitHub Codespaces, install the packages, and get the examples running immediately. Now let's walk through the API connection code for GitHub Models for each framework. Even if I missed your favorite framework, I hope my tips here will help you connect any framework to GitHub Models. OpenAI I'll start with openai , the package that started it all! import openai client = openai.OpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") The code above demonstrates the two key parameters we'll need to configure for all frameworks: api_key : When using OpenAI.com, you pass your OpenAI API key here. When using GitHub Models, you pass in a Personal Access Token (PAT). If you open the repository (or any repository) in GitHub Codespaces, a PAT is already stored in the GITHUB_TOKEN environment variable. However, if you're working locally with GitHub Models, you'll need to generate a PAT yourself and store it. PATs expire after a while, so you need to generate new PATs every so often. base_url : This parameter tells the OpenAI client to send all requests to "https://models.inference.ai.azure.com" instead of the OpenAI.com API servers. That's the domain that hosts the OpenAI-compatible endpoint for GitHub Models, so you'll always pass that domain as the base URL. If we're working with the new openai-agents SDK, we use very similar code, but we must use the AsyncOpenAI client from openai instead. Lately, Python AI packages are defaulting to async, because it's so much better for performance. import agents import openai client = openai.AsyncOpenAI( base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"]) model = agents.OpenAIChatCompletionsModel( model="gpt-4o", openai_client=client) spanish_agent = agents.Agent( name="Spanish agent", instructions="You only speak Spanish.", model=model) PydanticAI Now let's look at all of the packages that make it really easy for us, by allowing us to directly bring in an instance of either OpenAI or AsyncOpenAI . For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: import openai import pydantic_ai import pydantic_ai.models.openai client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") model = pydantic_ai.models.openai.OpenAIModel( "gpt-4o", provider=OpenAIProvider(openai_client=client)) spanish_agent = pydantic_ai.Agent( model, system_prompt="You only speak Spanish.") Semantic Kernel For Semantic Kernel, the code is very similar. We configure an AsyncOpenAI client, then construct an OpenAIChatCompletion object from Semantic Kernel, and add that object to the kernel. import openai import semantic_kernel.connectors.ai.open_ai import semantic_kernel.agents chat_client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") chat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion( ai_model_id="gpt-4o", async_client=chat_client) kernel.add_service(chat) spanish_agent = semantic_kernel.agents.ChatCompletionAgent( kernel=kernel, name="Spanish agent" instructions="You only speak Spanish") AutoGen Next, we'll check out a few frameworks that have their own wrapper of the OpenAI clients, so we won't be using any classes from openai directly. For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: import autogen_ext.models.openai import autogen_agentchat.agents client = autogen_ext.models.openai.OpenAIChatCompletionClient( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") spanish_agent = autogen_agentchat.agents.AssistantAgent( "spanish_agent", model_client=client, system_message="You only speak Spanish") LangGraph For LangGraph, we configure a very similar object, which even has the same parameter names: import langchain_openai import langgraph.graph model = langchain_openai.ChatOpenAI( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com", ) def call_model(state): messages = state["messages"] response = model.invoke(messages) return {"messages": [response]} workflow = langgraph.graph.StateGraph(MessagesState) workflow.add_node("agent", call_model) SmolAgents Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: import smolagents model = smolagents.OpenAIServerModel( model_id="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = smolagents.CodeAgent(model=model) Llamaindex I saved Llamaindex for last, as it is the most different. The llama-index package has a different constructor for OpenAI.com versus OpenAI-like servers, so I opted to use that OpenAILike constructor instead. However, I also needed an embeddings model for my example, and the package doesn't have an OpenAIEmbeddingsLike constructor, so I used the standard OpenAIEmbedding constructor. import llama_index.embeddings.openai import llama_index.llms.openai_like import llama_index.core.agent.workflow Settings.llm = llama_index.llms.openai_like.OpenAILike( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com", is_chat_model=True) Settings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding( model="text-embedding-3-small", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = llama_index.core.agent.workflow.ReActAgent( tools=query_engine_tools, llm=Settings.llm) Choose your models wisely! In all of the examples above, I specified the gpt-4o model. The gpt-4o model is a great choice for agents because it supports function calling, and many agent frameworks only work (or work best) with models that natively support function calling. Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: gpt-4o gpt-4o-mini o3-mini AI21-Jamba-1.5-Large AI21-Jamba-1.5-Mini Codestral-2501 Cohere-command-r Ministral-3B Mistral-Large-2411 Mistral-Nemo Mistral-small You might find that some models work better than others, especially if you're using agents with multiple tools. With GitHub Models, it's very easy to experiment and see for yourself, by simply changing the model name and re-running the code. Join the AI Agents Hackathon We are currently running a free virtual hackathon from April 8th - 30th, to challenge developers to create agentic applications using Microsoft technologies. You could build an agent entirely using GitHub Models and submit it to the hackathon for a chance to win amazing prizes! You can also join our 30+ streams about building AI agents, including a stream all about prototyping with GitHub Models. Learn more and register at https://aka.ms/agentshackCombating Digitally Altered Images: Deepfake Detection
In today's digital age, the rise of deepfake technology poses significant threats to credibility, privacy, and security. This article delves into our Deepfake Detection Project, a robust solution designed to combat the misuse of AI-generated content.AI Agents: Building Trustworthy Agents- Part 6
This blog post, Part 6 in a series on AI agents, focuses on building trustworthy AI agents. It emphasizes the importance of safety and security in agent design and deployment. The post details a system message framework for creating robust and scalable prompts, outlining a four-step process from meta prompt to iterative refinement. It then explores various threats to AI agents, including task manipulation, unauthorized access, resource overloading, knowledge base poisoning, and cascading errors, providing mitigation strategies for each. The post also highlights the human-in-the-loop approach for enhanced trust and control, providing a code example using AutoGen. Finally, it links to further resources on responsible AI, model evaluation, and risk assessment, along with the previous posts in the series.Fix Broken Migrations with AI Powered Debugging in VS Code Using GitHub Copilot
Data is at the heart of every application. But evolving your schema is risky business. One broken migration, and your dev or prod environment can go down. We've all experienced it: mismatched columns, orphaned constraints, missing fields, or that dreaded "table already exists" error. But what if debugging migrations didn’t have to be painful? What if you could simply describe the error or broken state, and AI could fix your migration in seconds? In this blog, you’ll learn how to: Use GitHub Copilot to describe and fix broken migrations with natural language Catch schema issues like incorrect foreign keys before they block your workflow Validate and deploy your database changes using GibsonAI CLI Broken migrations are nothing new. Whether you're working on a side project or part of a large team, it’s all too easy to introduce schema issues that can block deployments or corrupt local environments. Traditionally, fixing them means scanning SQL files, reading error logs, and manually tracking down what went wrong. But what if you could skip all that? What if you could simply describe the issue in plain English and AI would fix it for you? That’s exactly what GitHub Copilot let you do, right from within VS Code. What You Need: Visual Studio Code Installed Account in GitHub Sign up with GitHub Copilot GibsonAI CLI installed and logged in Let’s Break (and Fix) a Migration: Here’s a common mistake. Say you create two tables: users and posts. CREATE TABLE users ( id UUID PRIMARY KEY, name TEXT, email TEXT UNIQUE ); CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES user(id) ); The problem? The posts table refers to a table called user, but you named it users. This one-word mistake breaks the migration. If you've worked with relational databases, you’ve probably run into this exact thing. Just Ask a GitHub Copilot: Instead of troubleshooting manually, open Copilot Chat and ask: “My migration fails because posts.user_id references a missing user table. Can you fix the foreign key?” Copilot understands what you're asking. It reads the context and suggests the fix: CREATE TABLE posts ( id UUID PRIMARY KEY, title TEXT, user_id UUID REFERENCES users(id) ); It even explains what changed, so you learn along the way. Wait — how does Copilot know what I mean? GitHub Copilot is smart enough to understand your code, your errors, and even what you’re asking in plain English. It doesn’t directly connect to GibsonAI. You’ll use the GibsonAI CLI for that, but Copilot helps you figure things out and fix your code faster. Validating with GibsonAI Once Copilot gives you the fixed migration, it’s time to test it. Run: gibson validate This checks your migration and schema consistency. When you're ready to apply it, just run: gibson deploy GibsonAI handles the rest so no broken chains, no surprises. Why This Works Manual debugging of migrations is frustrating and error prone. GibsonAI with GitHub Copilot: Eliminates guesswork in debugging You don’t need to Google every error Reduces time to fix production schema issues You stay in one tool: VS Code You learn while debugging Whether you're a student learning SQL or a developer on a fast moving team, this setup helps you recover faster and ship safer. Fixing migrations used to be all trial and error, digging through files and hoping nothing broke. It was time-consuming and stressful. Now with GitHub Copilot and GibsonAI, fixing issues is fast and simple. Copilot helps you write and correct migrations. GibsonAI lets you validate and deploy with confidence. So next time your migration fails, don’t panic. Just describe the issue to GitHub Copilot, run a quick check with GibsonAI, and get back to building. Ready to try it yourself? Sign up atgibsonai.com Want to Go Further? If you’re ready to explore more powerful workflows with GibsonAI, here are two great next steps: GibsonAI MCP Server – Enable Copilot Agent Mode to integrate schema intelligence directly into your dev environment. Automatic PR Creation for Schema Changes – The in-depth guide on how to automate pull requests for database updates using GibsonAI. Want to Know More About GitHub Copilot? Explore these resources to get the most out of Copilot: Get Started with GitHub Copilot Introduction to prompt engineering with GitHub Copilot GitHub Copilot Agent Mode GitHub Copilot Customization Use GitHub Copilot Agent Mode to create a Copilot Chat application in 5 minutes Deploy Your First App Using GitHub Copilot for Azure: A Beginner’s Guide That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft 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 and explore more benefits on the Microsoft Learn Student Hub.
CampusSphere: Building the Future of Campus AI with Microsoft's Agentic Framework
Project Overview We are a team of Imperial College Students committed to improving campus life through innovative multi-agent solutions. CampusSphere leverages Microsoft Azure AI capabilities to automate core university campus services. We created an end-to-end solution that allows both students and staff to access a multi-agent framework for room/gym booking, attendance tracking, calendar management, IoT monitoring and more. 🔭 Our Initial Vision: Reimagining Campus Technology When our team at Imperial College London embarked on the CampusSphere project as part of Microsoft's Agentic Campus initiative, we had one clear ambition: to create an intelligent campus ecosystem that would fundamentally change how students, faculty, and staff interact with university services. The inspiration came from a simple observation—despite living in an age of advanced AI, campus technology remained frustratingly fragmented. Students juggled multiple portals for course registration, room booking, dining services, and academic support. Faculty members navigated separate systems for teaching, research, and administrative tasks. The result? Countless hours wasted on mundane navigation tasks that could be better spent on learning, teaching, and innovation. Our vision was ambitious: create a single, intelligent interface that could understand natural language, anticipate user needs, and seamlessly integrate with existing campus infrastructure. We didn't just want to build another campus app—we wanted to demonstrate how Microsoft's agentic AI technologies could create a truly intelligent campus companion. 🧠 Enter CampusSphere CampusSphere is an intelligent campus assistant made up of multiple AI agents, each with a specific domain of expertise — all communicating seamlessly through a centralized architecture. Think of it as a digital concierge for campus life, where your calendar, attendance, IoT data, and facility bookings are coordinated by specialized GPT-powered agents. Here’s what we built: TriageAgent – the brain of the system, using Retrieval-Augmented Generation (RAG) to understand user intent CalendarAgent – handles scheduling, bookings, and reminders AttendanceAgent – tracks check-ins automatically IoTAgent – monitors real-time sensor data from classrooms and labs GymAgent – manages access and reservations for sports facilities 30+ MCP Tools – perform SQL queries, scrape web data, and connect with external APIs All of this is built on Microsoft Azure AI, Semantic Kernel, and Model Context Protocol (MCP) — making it scalable, secure, and lightning fast. 🖥️ The Tech Stack Our Azure-powered architecture showcases a modular and scalable approach to real-time data processing and intelligent agent coordination. The frontend is built using React with a Vite development server, providing a fast and responsive user interface. When users submit a prompt, it travels to a Flask backend server acting as the Triage agent, which intelligently delegates tasks to a FastAPI agent service. This FastAPI service asynchronously communicates with individual agents and handles responses efficiently. Complex queries are routed to MCP Tools, which interact with the CosmosDB-powered Campus Database. Simultaneously, real-time synthetic IoT data is pushed into the database via Azure Function Apps and Azure IoT Hub. Authentication is securely managed: users log in through the frontend, receive a token from the database API server, and use it for authorized access to MCP services, with permissions enforced based on user roles using our custom MCP server implementation. This robust architecture enables seamless integration, real-time data flow, and secure multi-agent collaboration across Azure services. Our system leverages a multi-agent architecture designed to intelligently coordinate task execution across specialized services. At the core is the TriageAgent, which uses Retrieval-Augmented Generation (RAG) to interpret user prompts, enrich them with relevant context, and determine the optimal response path. Based on the nature of the request, it may handle the response directly, seek clarification, or delegate tasks to specific agents via FastAPI. Each specialized agent has a clearly defined role: AttendanceAgent: Interfaces with CosmosDB-backed FastAPI endpoints to check student attendance, using filters like event name, student ID, or date. IoTAgent: Monitors room conditions (e.g., temperature, CO₂ levels) and flags anomalies using real-time data from Azure IoT Hub, processed via FastAPI. CalendarAgent: Handles scheduling, availability checks, and event creation by querying or updating CosmosDB through FastAPI. Future integration with Microsoft Graph API is planned for direct calendar syncing. Gym Slot Agent: Checks available times for gym sessions using dedicated MCP tools. The triage agent serves as the orchestrator, breaking down complex requests (like "Book a gym session") into subtasks. It consults relevant agents (e.g., calendar and gym slot agents), merges results, and then confirms the final action with the user. This distributed and asynchronous workflow reduces backend load and enhances both responsiveness and reliability of the system. 🔮 What’s Next? Integrating CampusSphere with live systems via Microsoft OAuth is crucial for enhancing its capabilities. This integration will grant the agent authenticated access to a wider range of student data, moving beyond synthetic datasets. This expanded access to real-world information will enable deeply personalized advice, such as tailored course selection, scholarship recommendations, event suggestions, and deadline reminders, transforming CampusSphere into a sophisticated, proactive personal assistant. 🤝Meet the Team Behind CampusSphere Our success stemmed from a diverse team of innovators who brought together expertise from multiple domains: Benny Liu - https://www.linkedin.com/in/zong-benny-liu-393a4621b/ Lucas Ng - https://www.linkedin.com/in/lucas-ng-11b317203/ Lu Ju - https://www.linkedin.com/in/lu-ju/ Bruno Duaso - https://www.linkedin.com/in/bruno-duaso-jimeno-744464262/ Martim Coutinho - https://www.linkedin.com/in/martim-pereira-coutinho-116308233/ Krischad Pourpongpan - https://www.linkedin.com/in/krischadpua/ Yixu Pan - https://www.linkedin.com/in/yixu-pan/ Our collaborative approach enabled us to create a sophisticated agentic AI system that demonstrates the powerful potential of Microsoft's AI technologies in educational environments. 🧑💻 Project Repository: GitHub - Imperial-Microsoft-Agentic-Campus/CampusSphere Contribute to Imperial-Microsoft-Agentic-Campus/CampusSphere development by creating an account on GitHub. github.com Have questions about implementing similar solutions at your institution? Connect with our team members on LinkedIn—we're always excited to share knowledge and collaborate on innovative campus technology projects. 📚Get Started with Microsoft's AI Tools Ready to explore the technologies that made CampusSphere possible? Here are essential resources: Microsoft Semantic Kernel: The core framework for building AI agent orchestration systems. Learn how to create, coordinate, and manage multiple AI agents working together seamlessly. AI Agents for Beginners: A comprehensive guide to understanding and building AI agents from the ground up. Perfect for getting started with agentic AI development. Model Context Protocol (MCP): Learn about the protocol that enables secure connections between AI models and external tools and services—essential for building integrated AI systems. Windows AI Toolkit: Microsoft's toolkit for developing AI applications on Windows, providing local AI model development capabilities and deployment tools. Azure Container Apps: Understand how to deploy and scale containerized AI applications in the cloud, perfect for hosting multi-agent systems. Azure Cosmos DB Security: Essential security practices for managing data in AI applications, covering encryption, access control, and compliance.Learn How to Build Smarter AI Agents with Microsoft’s MCP Resources Hub
If you've been curious about how to build your own AI agents that can talk to APIs, connect with tools like databases, or even follow documentation you're in the right place. Microsoft has created something called MCP, which stands for Model‑Context‑Protocol. And to help you learn it step by step, they’ve made an amazing MCP Resources Hub on GitHub. In this blog, I’ll Walk you through what MCP is, why it matters, and how to use this hub to get started, even if you're new to AI development. What is MCP (Model‑Context‑Protocol)? Think of MCP like a communication bridge between your AI model and the outside world. Normally, when we chat with AI (like ChatGPT), it only knows what’s in its training data. But with MCP, you can give your AI real-time context from: APIs Documents Databases Websites This makes your AI agent smarter and more useful just like a real developer who looks up things online, checks documentation, and queries databases. What’s Inside the MCP Resources Hub? The MCP Resources Hub is a collection of everything you need to learn MCP: Videos Blogs Code examples Here are some beginner-friendly videos that explain MCP: Title What You'll Learn VS Code Agent Mode Just Changed Everything See how VS Code and MCP build an app with AI connecting to a database and following docs. The Future of AI in VS Code Learn how MCP makes GitHub Copilot smarter with real-time tools. Build MCP Servers using Azure Functions Host your own MCP servers using Azure in C#, .NET, or TypeScript. Use APIs as Tools with MCP See how to use APIs as tools inside your AI agent. Blazor Chat App with MCP + Aspire Create a chat app powered by MCP in .NET Aspire Tip: Start with the VS Code videos if you’re just beginning. Blogs Deep Dives and How-To Guides Microsoft has also written blogs that explain MCP concepts in detail. Some of the best ones include: Build AI agent tools using remote MCP with Azure Functions: Learn how to deploy MCP servers remotely using Azure. Create an MCP Server with Azure AI Agent Service : Enables Developers to create an agent with Azure AI Agent Service and uses the model context protocol (MCP) for consumption of the agents in compatible clients (VS Code, Cursor, Claude Desktop). Vibe coding with GitHub Copilot: Agent mode and MCP support: MCP allows you to equip agent mode with the context and capabilities it needs to help you, like a USB port for intelligence. When you enter a chat prompt in agent mode within VS Code, the model can use different tools to handle tasks like understanding database schema or querying the web. Enhancing AI Integrations with MCP and Azure API Management Enhance AI integrations using MCP and Azure API Management Understanding and Mitigating Security Risks in MCP Implementations Overview of security risks and mitigation strategies for MCP implementations Protecting Against Indirect Injection Attacks in MCP Strategies to prevent indirect injection attacks in MCP implementations Microsoft Copilot Studio MCP Announcement of the Microsoft Copilot Studio MCP lab Getting started with MCP for Beginners 9 part course on MCP Client and Servers Code Repositories Try it Yourself Want to build something with MCP? Microsoft has shared open-source sample code in Python, .NET, and TypeScript: Repo Name Language Description Azure-Samples/remote-mcp-apim-functions-python Python Recommended for Secure remote hosting Sample Python Azure Functions demonstrating remote MCP integration with Azure API Management Azure-Samples/remote-mcp-functions-python Python Sample Python Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-dotnet C# Sample .NET Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-typescript TypeScript Sample TypeScript Azure Functions demonstrating remote MCP integration Microsoft Copilot Studio MCP TypeScript Microsoft Copilot Studio MCP lab You can clone the repo, open it in VS Code, and follow the instructions to run your own MCP server. Using MCP with the AI Toolkit in Visual Studio Code To make your MCP journey even easier, Microsoft provides the AI Toolkit for Visual Studio Code. This toolkit includes: A built-in model catalog Tools to help you deploy and run models locally Seamless integration with MCP agent tools You can install the AI Toolkit extension from the Visual Studio Code Marketplace. Once installed, it helps you: Discover and select models quickly Connect those models to MCP agents Develop and test AI workflows locally before deploying to the cloud You can explore the full documentation here: Overview of the AI Toolkit for Visual Studio Code – Microsoft Learn This is perfect for developers who want to test things on their own system without needing a cloud setup right away. Why Should You Care About MCP? Because MCP: Makes your AI tools more powerful by giving them real-time knowledge Works with GitHub Copilot, Azure, and VS Code tools you may already use Is open-source and beginner-friendly with lots of tutorials and sample code It’s the future of AI development connecting models to the real world. Final Thoughts If you're learning AI or building software agents, don’t miss this valuable MCP Resources Hub. It’s like a starter kit for building smart, connected agents with Microsoft tools. Try one video or repo today. Experiment. Learn by doing and start your journey with the MCP for Beginners curricula.Build a Fully Offline RAG App with Foundry Local: No Cloud Required
A practical guide to building an on-device AI support agent using Retrieval-Augmented Generation, JavaScript, and Microsoft Foundry Local. The Problem: AI That Can't Go Offline Most AI-powered applications today are firmly tethered to the cloud. They assume stable internet, low-latency API calls, and the comfort of a managed endpoint. But what happens when your users are in an environment with zero connectivity a gas pipeline in a remote field, a factory floor, an underground facility? That's exactly the scenario that motivated this project: a fully offline RAG-powered support agent that runs entirely on a laptop. No cloud. No API keys. No outbound network calls. Just a local model, a local vector store, and domain-specific documents all accessible from a browser on any device. The Gas Field Support Agent - running entirely on-device What is RAG and Why Should You Care? Retrieval-Augmented Generation (RAG) is a pattern that makes language models genuinely useful for domain-specific tasks. Instead of hoping the model "knows" the answer from pre-training, you: Retrieve relevant chunks from your own documents Augment the model's prompt with those chunks as context Generate a response grounded in your actual data The result: fewer hallucinations, traceable answers, and an AI that works with your content. If you're building internal tools, customer support bots, field manuals, or knowledge bases, RAG is the pattern you want. Why fully offline? Data sovereignty, air-gapped environments, field operations, latency-sensitive workflows, and regulatory constraints all demand AI that doesn't phone home. Running everything locally gives you complete control over your data and eliminates any external dependency. The Tech Stack This project is deliberately simple — no frameworks, no build steps, no Docker: Layer Technology Why AI Model Foundry Local + Phi-3.5 Mini Runs locally, OpenAI-compatible API, no GPU needed Backend Node.js + Express Lightweight, fast, universally known Vector Store SQLite via better-sqlite3 Zero infrastructure, single file on disk Retrieval TF-IDF + cosine similarity No embedding model required, fully offline Frontend Single HTML file with inline CSS No build step, mobile-responsive, field-ready The total dependency footprint is just four npm packages: express , openai , foundry-local-sdk , and better-sqlite3 . Architecture Overview The system has five layers — all running on a single machine: Five-layer architecture: Client → Server → RAG Pipeline → Data → AI Model Client Layer — A single HTML file served by Express, with quick-action buttons and responsive chat Server Layer — Express.js handles API routes for chat (streaming + non-streaming), document upload, and health checks RAG Pipeline — The chat engine orchestrates retrieval and generation; the chunker handles TF-IDF vectorization Data Layer — SQLite stores document chunks and their TF-IDF vectors; source docs live as .md files AI Layer — Foundry Local runs Phi-3.5 Mini Instruct on CPU/NPU, exposing an OpenAI-compatible API Getting Started in 5 Minutes You need two prerequisites: Node.js 20+ — nodejs.org Foundry Local — Microsoft's on-device AI runtime: Terminal winget install Microsoft.FoundryLocal Then clone, install, ingest, and run: git clone https://github.com/leestott/local-rag.git cd local-rag npm install npm run ingest # Index the 20 gas engineering documents npm start # Start the server + Foundry Local Open http://127.0.0.1:3000 and start chatting. Foundry Local auto-downloads Phi-3.5 Mini (~2 GB) on first run. How the RAG Pipeline Works Let's trace what happens when a user asks: "How do I detect a gas leak?" RAG query flow: Browser → Server → Vector Store → Model → Streaming response Step 1: Document Ingestion Before any queries happen, npm run ingest reads every .md file from the docs/ folder, splits each into overlapping chunks (~200 tokens, 25-token overlap), computes a TF-IDF vector for each chunk, and stores everything in SQLite. Chunking example docs/01-gas-leak-detection.md → Chunk 1: "Gas Leak Detection – Safety Warnings: Ensure all ignition..." → Chunk 2: "...sources are eliminated. Step-by-step: 1. Perform visual..." → Chunk 3: "...inspection of all joints. 2. Check calibration date..." The overlap ensures no information falls between chunk boundaries — a critical detail in any RAG system. Step 2: Query → Retrieval When the user sends a question, the server converts it into a TF-IDF vector, compares it against every stored chunk using cosine similarity, and returns the top-K most relevant results. For 20 documents (~200 chunks), this executes in under 10ms. src/vectorStore.js /** Retrieve top-K most relevant chunks for a query. */ search(query, topK = 5) { const queryTf = termFrequency(query); const rows = this.db.prepare("SELECT * FROM chunks").all(); const scored = rows.map((row) => { const chunkTf = new Map(JSON.parse(row.tf_json)); const score = cosineSimilarity(queryTf, chunkTf); return { ...row, score }; }); scored.sort((a, b) => b.score - a.score); return scored.slice(0, topK).filter((r) => r.score > 0); } Step 3: Prompt Construction The retrieved chunks are injected into the prompt alongside system instructions: Prompt structure System: You are an offline gas field support agent. Safety-first... Context: [Chunk 1: Gas Leak Detection – Safety Warnings...] [Chunk 2: Gas Leak Detection – Step-by-step...] [Chunk 3: Purging Procedures – Related safety...] User: How do I detect a gas leak? Step 4: Generation + Streaming The prompt is sent to Foundry Local via the OpenAI-compatible API. The response streams back token-by-token through Server-Sent Events (SSE) to the browser: Safety-first response with structured guidance Expandable sources with relevance scores Foundry Local: Your Local AI Runtime Foundry Local is what makes the "offline" part possible. It's a runtime from Microsoft that runs small language models (SLMs) on CPU or NPU — no GPU required. It exposes an OpenAI-compatible API and manages model downloads, caching, and lifecycle automatically. The integration code is minimal if you've used the OpenAI SDK before, this will feel instantly familiar: src/chatEngine.js import { FoundryLocalManager } from "foundry-local-sdk"; import { OpenAI } from "openai"; // Start Foundry Local and load the model const manager = new FoundryLocalManager(); const modelInfo = await manager.init("phi-3.5-mini"); // Use the standard OpenAI client — pointed at the local endpoint const client = new OpenAI({ baseURL: manager.endpoint, apiKey: manager.apiKey, }); // Chat completions work exactly like the cloud API const stream = await client.chat.completions.create({ model: modelInfo.id, messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "How do I detect a gas leak?" } ], stream: true, }); Portability matters Because Foundry Local uses the OpenAI API format, any code you write here can be ported to Azure OpenAI or OpenAI's cloud API with a single config change. You're not locked in. Why TF-IDF Instead of Embeddings? Most RAG tutorials use embedding models for retrieval. We chose TF-IDF for this project because: Fully offline — no embedding model to download or run Zero latency — vectorization is instantaneous (just math on word frequencies) Good enough — for a curated collection of 20 domain-specific documents, TF-IDF retrieves the right chunks reliably Transparent — you can inspect the vocabulary and weights, unlike neural embeddings For larger collections (thousands of documents) or when semantic similarity matters more than keyword overlap, you'd swap in an embedding model. But for this use case, TF-IDF keeps the stack simple and dependency-free. Mobile-Responsive Field UI Field engineers use this app on phones and tablets often wearing gloves. The UI is designed for harsh conditions with a dark, high-contrast theme, large touch targets (minimum 48px), and horizontally scrollable quick-action buttons. Desktop view Mobile view The entire frontend is a single index.html file — no React, no build step, no bundler. This keeps the project accessible and easy to deploy anywhere. Runtime Document Upload Users can upload new documents without restarting the server. The upload endpoint receives markdown content, chunks it, computes TF-IDF vectors, and inserts the chunks into SQLite — all in memory, immediately available for retrieval. Drag-and-drop document upload with instant indexing Adapt This for Your Own Domain This project is a scenario sample designed to be forked and customized. Here's the three-step process: 1. Replace the Documents Delete the gas engineering docs in docs/ and add your own .md files with optional YAML front-matter: docs/my-procedure.md --- title: Troubleshooting Widget Errors category: Support id: KB-001 --- # Troubleshooting Widget Errors ...your content here... 2. Edit the System Prompt Open src/prompts.js and rewrite the instructions for your domain: src/prompts.js export const SYSTEM_PROMPT = `You are an offline support agent for [YOUR DOMAIN]. Rules: - Only answer using the retrieved context - If the answer isn't in the context, say so - Use structured responses: Summary → Details → Reference `; 3. Tune the Retrieval Adjust chunking and retrieval parameters in src/config.js : src/config.js export const config = { model: "phi-3.5-mini", chunkSize: 200, // smaller = more precise, less context per chunk chunkOverlap: 25, // prevents info from falling between chunks topK: 3, // chunks per query (more = richer context, slower) }; Extending to Multi-Agent Architectures Once you have a working RAG agent, the natural next step is multi-agent orchestration where specialized agents collaborate to handle complex workflows. With Foundry Local's OpenAI-compatible API, you can compose multiple agent roles on the same machine: Multi-agent concept // Each agent is just a different system prompt + RAG scope const agents = { safety: { prompt: safetyPrompt, docs: "safety/*.md" }, diagnosis: { prompt: diagnosisPrompt, docs: "faults/*.md" }, procedure: { prompt: procedurePrompt, docs: "procedures/*.md" }, }; // Router determines which agent handles the query function route(query) { if (query.match(/safety|warning|hazard/i)) return agents.safety; if (query.match(/fault|error|code/i)) return agents.diagnosis; return agents.procedure; } // Each agent uses the same Foundry Local model endpoint const response = await client.chat.completions.create({ model: modelInfo.id, messages: [ { role: "system", content: selectedAgent.prompt }, { role: "system", content: `Context:\n${retrievedChunks}` }, { role: "user", content: userQuery } ], stream: true, }); This pattern lets you build specialized agent pipelines a triage agent routes to the right specialist, each with its own document scope and system prompt, all running on the same local Foundry instance. For production multi-agent systems, explore Microsoft Foundry for cloud-scale orchestration when connectivity is available. Local-first, cloud-ready Start with Foundry Local for development and offline scenarios. When your agents need cloud scale, swap to Azure AI Foundry with the same OpenAI-compatible API your agent code stays the same. Key Takeaways 1 RAG = Retrieve + Augment + Generate Ground your AI in real documents — dramatically reducing hallucination and making answers traceable. 2 Foundry Local makes local AI accessible OpenAI-compatible API running on CPU/NPU. No GPU required. No cloud dependency. 3 TF-IDF + SQLite is viable For small-to-medium document collections, you don't need a dedicated vector database. 4 Same API, local or cloud Build locally with Foundry Local, deploy with Azure OpenAI — zero code changes. What's Next? Embedding-based retrieval — swap TF-IDF for a local embedding model for better semantic matching Conversation memory — persist chat history across sessions Multi-agent routing — specialized agents for safety, diagnostics, and procedures PWA packaging — make it installable as a standalone app on mobile devices Hybrid retrieval — combine keyword search with semantic embeddings for best results Get the code Clone the repo, swap in your own documents, and start building: git clone https://github.com/leestott/local-rag.git github.com/leestott/local-rag — MIT licensed, contributions welcome. Open source under the MIT License. Built with Foundry Local and Node.js.Edge AI for Student Developers: Learn to Run AI Locally
AI isn’t just for the cloud anymore. With the rise of Small Language Models (SLMs) and powerful local inference tools, developers can now run intelligent applications directly on laptops, phones, and edge devices—no internet required. If you're a student developer curious about building AI that works offline, privately, and fast, Microsoft’s Edge AI for Beginners course is your perfect starting point. What Is Edge AI? Edge AI refers to running AI models directly on local hardware—like your laptop, mobile device, or embedded system—without relying on cloud servers. This approach offers: ⚡ Real-time performance 🔒 Enhanced privacy (no data leaves your device) 🌐 Offline functionality 💸 Reduced cloud costs Whether you're building a chatbot that works without Wi-Fi or optimizing AI for low-power devices, Edge AI is the future of intelligent, responsive apps. About the Course Edge AI for Beginners is a free, open-source curriculum designed to help you: Understand the fundamentals of Edge AI and local inference Explore Small Language Models like Phi-2, Mistral-7B, and Gemma Deploy models using tools like Llama.cpp, Olive, MLX, and OpenVINO Build cross-platform apps that run AI locally on Windows, macOS, Linux, and mobile The course is hosted on GitHub and includes hands-on labs, quizzes, and real-world examples. You can fork it, remix it, and contribute to the community. What You’ll Learn Module Focus 01. Introduction What is Edge AI and why it matters 02. SLMs Overview of small language models 03. Deployment Running models locally with various tools 04. Optimization Speeding up inference and reducing memory 05. Applications Building real-world Edge AI apps Each module is beginner-friendly and includes practical exercises to help you build and deploy your own local AI solutions. Who Should Join? Student developers curious about AI beyond the cloud Hackathon participants looking to build offline-capable apps Makers and builders interested in privacy-first AI Anyone who wants to explore the future of on-device intelligence No prior AI experience required just a willingness to learn and experiment. Why It Matters Edge AI is a game-changer for developers. It enables smarter, faster, and more private applications that work anywhere. By learning how to deploy AI locally, you’ll gain skills that are increasingly in demand across industries—from healthcare to robotics to consumer tech. Plus, the course is: 💯 Free and open-source 🧠 Backed by Microsoft’s best practices 🧪 Hands-on and project-based 🌐 Continuously updated Ready to Start? Head to aka.ms/edgeai-for-beginners and dive into the modules. Whether you're coding in your dorm room or presenting at your next hackathon, this course will help you build smarter AI apps that run right where you need them on the edge.
