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.

A creative writing multi-agent solution to help users write articles.

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.

Workshop for Azure OpenAI Service

Vendor Contracts GenAI Solution

A creative writing multi-agent solution to help users write articles using Aspire and Semantic Kernel

AI Orchestration Samples

This repository offers a solution for deploying a Generative AI (GenAI) system on Azure using Retrieval-Augmented Generation (RAG). It features a RAG Chat API integrated with CosmosDB and HTML files, enabling customer support teams to resolve issues effectively.

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.

This sample has the full End2End process of creating RAG application with Prompty and Azure AI Foundry. It includes GPT 3.5 Turbo LLM application code, evaluations, deployment automation with AZD CLI, GitHub actions for evaluation and deployment and intent mapping for multiple LLM task mapping.

Azure AI Search integration for Xperience by Kentico

A lightweight Python library for metadata-rich document chunking in Retrieval-Augmented Generation (RAG) workflows. It leverages Azure AI Document Intelligence to enhance chunking by retaining hierarchical structure, page numbers, and bounding boxes for seamless integration with PDF viewers.

Build a generative AI application using LangChain.js, from local to Azure

A simple sample UI for your Azure AI Search index. Built with React, TypeScript and Azure Static Web Apps

(early development) A solution accelerator showcasing how to build agentic RAG apps using Azure AI Content Understanding & Azure AI Agent Service

Code Samples for getting started with Vector Search, Embeddings, Vector Databases and Retrieval Augmented Generation

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.

Deploying a Retrieval-Augmented Generation (RAG) solution on Azure, featuring a RAG Chat API that uses customer data and technical HTML files to enhance customer support troubleshooting. It leverages Azure AI Search for vector storage and Azure OpenAI for model inference, ensuring security, scalability, and adherence to best practices.

ASP.NET Core with Azure AI Search

IP and use case assets for CSU