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.
This sample has the full End2End process of creating RAG application with Prompty and Azure AI Foundry. It includes GPT-4 LLM application code, evaluations, deployment automation with AZD CLI, GitHub actions for evaluation and deployment and intent mapping for multiple LLM task mapping.
A simple example implementation of the VoiceRAG pattern to power interactive voice generative AI experiences using RAG with Azure AI Search and Azure OpenAI's gpt-4o-realtime-preview model.
A creative writing multi-agent solution to help users write articles.
A TypeScript sample app for the Retrieval Augmented Generation pattern running on Azure, using Azure AI Search for retrieval and Azure OpenAI and LangChain large language models (LLMs) to power ChatGPT-style and Q&A experiences.
Build a generative AI application using LangChain.js, from local to Azure
A creative writing multi-agent solution to help users write articles using Aspire and Semantic Kernel
Learn How To Observe, Manage, and Scale, Agentic AI Apps Using Azure AI Foundry - with this hands-on workshop
Resources for the AI Tour Talk on "Advanced Retrieval for your AI Apps and Agents" on Azure - slides, talk recording, demo recording, demo setup instructions.
This repository offers a Python framework for a retrieval-augmented generation (RAG) pipeline using text and images from MHTML documents, leveraging Azure AI and OpenAI services. It includes ingestion and enrichment flows, a RAG with Vision pipeline, and evaluation tools.
Azure AI Search Simulator provides a lightweight environment to emulate Azure AI Search in pull or push modes. It offers a compatible API surface and works seamlessly with the official SDK, enabling local development, testing, and debugging without requiring a live search service.
AZD template for deploying Azure Copilot Studio with Azure AI search
🧠 Stop building AI that forgets. Master MCP (Model Context Protocol) with production-ready semantic memory, hybrid RAG, and the WARNERCO Schematica teaching app. FastMCP + LangGraph + Vector/Graph stores. Your AI assistant's long-term memory starts here.
Workshop for building intelligent AI solutions using Azure AI Foundry, featuring Vector Search, RAG, Agentic AI, and multi-agent orchestration with LangChain and Azure AI Search.
A simple sample UI for your Azure AI Search index. Built with React, TypeScript and Azure Static Web Apps
Enterprise RAG application on Azure with Azure AI hybrid search (vector + semantic + BM25), NDJSON streaming, MSAL SSO, and Fluent UI 9. Features document ingestion pipeline, project isolation, citation attribution, and multi-service architecture (FastAPI + Azure Functions + React).
File-first memory infrastructure for AI agents, built with .NET 8 and Azure backends
Reusable accelerator for Foundry IQ live grounding with Fabric Ontology and MCP Server Knowledge Sources.
ASP.NET Core with Azure AI Search
Labs for agentic AI — covering Microsoft Foundry, Foundry Agent Service, Foundry Models, Workflow Designer, Foundry IQ, Foundry Tools, Foundry Control Plane and Azure AI Search
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