11:30 PM - 12:30 AM (UTC)

In this series, we'll dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Join us for the kick-off session, where we'll do a live demo of the RAG solution and explain how it all works. We'll step through the RAG flow from Azure AI Search to Azure OpenAI, deploy the app to Azure, and discuss the Azure architecture.

11:30 PM - 12:30 AM (UTC)

In this series, we'll dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat In our second session, we'll show you how to customize the RAG solution for your own domain - adding your own data, modifying the prompts, and personalizing the UI. Plus, we'll give you tips for local development for faster feature iteration.

11:30 PM - 12:30 AM (UTC)

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Our RAG solution uses Azure AI Search to find matching documents, using state-of-the-art retrieval mechanisms. We'll dive into the mechanics of vector embeddings, hybrid search with RRF, and semantic ranking. We'll also discuss the data ingestion process, highlighting the differences between manual ingestion and integrated vectorization

11:30 PM - 12:30 AM (UTC)

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Do your documents contain images or charts? Our RAG solution has two different approaches to handling multimedia documents, and we'll dive into both approaches in this session. The first approach is purely during ingestion time, where it replaces media in the documents with LLM-generated descriptions. The second approach stores images of the media alongside vector embeddings of the images, and sends both text and images to a multimodal LLM for question answering. Learn about both approaches in this session so that you can decide what to use for your app. 📌 Get more RAG resources!

11:30 PM - 12:30 AM (UTC)

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat In our RAG flow, the app first searches a knowledge base for relevant matches to a user's query, then sends the results to the LLM along with the original question. What if you have documents that should only be accessed by a subset of your users, like a group or a single user? Then you need data access controls to ensure that document visibility is respected during the RAG flow. In this session, we'll show an approach using Azure AI Search with data access controls to only search the documents that can be seen by the logged in user. We'll also demonstrate a feature for user-uploaded documents that uses data access controls along with Azure Data Lake Storage Gen2. 📌 Get more RAG resources!

11:30 PM - 12:30 AM (UTC)

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Learn how we store chat history using either IndexedDB for client-side storage or Azure Cosmos DB for persistent storage. We'll discuss the API architecture and data schema choices, doing both a live demo of the app and a walkthrough of the code. 📌 Get more RAG resources!

11:30 PM - 12:30 AM (UTC)

In this series, we dive deep into our most popular, fully-featured, and open-source RAG solution: https://aka.ms/ragchat Our RAG solution includes optional features for speech input and output, powered either by the free browser SDKs or by the powerful Azure Speech API. We also offer a tight integration with the VoiceRAG solution, for those of you who want a real-time voice interface. Learn about all the ways you can add speech to your RAG chat in this session! 📌 Get more RAG resources!