Popular GitHub Repositories for Azure Ai Search

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 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.

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

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.

使用 Azure AI Foundry 打造智能 AI 解决方案的研讨会，涵盖向量搜索、检索式增强生成 (RAG)、自主式 AI 以及结合 LangChain 和 Azure AI 搜索的多智能体协同工作。

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.

This is the demo repo demonstrating how to use Azure serverless functions to create a document assistant with Azure OpenAI, Azure Content Safety. It will also includes a web app to interact with the Azure functions, built in React.

Azure AI Search および Azure Cosmos DB for MongoDB vCore を使ってベクター検索をするサンプルです。

Azure Cognitive Search: Utilizando AI Search para indexa??o e consulta de Dados

Deploys Azure AI Foundry using the Azure AI Baseline Reference Architecture, following best practices for networking, security, and model integration. It provisions infrastructure for chatting over data using Azure AI Search, event-driven processing for indexing, Bicep for IaC, and testing in the Playground interface.

Vendor Contracts GenAI Solution

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.