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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 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 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.
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.
This project combines Azure AI Search, Azure OpenAI Service, LangChain, React.JS, and Python FastAPI to create an intelligent system for managing Jira issues. It features advanced AI search for seamless document retrieval, a user-friendly React.JS front-end, and a robust Python FastAPI back-end.
This project demonstrates a small-scale proof-of-concept deployment of an enterprise chatbot leveraging the power of Azure OpenAI and Azure AI Search, built and deployed using Streamlit. Utilizing the Retrieval-Augmented Generation (RAG) pattern.
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.