Generative AI using Azure AI Integrated Search Vector Embeddings

Name: Generative AI using Azure AI Integrated Search Vector Embeddings
Uploaded: 2023-11-19T02:32:59
Duration: 10 min 58 s
Description: This video walks through the process of setting up an Azure AI Search service with integrated vector embeddings to index documents for use in Retrieval Augmented Generation (RAG) within generative AI Q&A solutions. It then demonstrates creating a Streamlit Python application that interacts with an OpenAI large language model, utilizing the indexed data from Azure AI Search as the RAG data source.

Nov 19, 2023818 views10:58

Azure AI Foundry

This video walks through the process of setting up an Azure AI Search service with integrated vector embeddings to index documents for use in Retrieval Augmented Generation (RAG) within generative AI Q&A solutions. It then demonstrates creating a Streamlit Python application that interacts with an OpenAI large language model, utilizing the indexed data from Azure AI Search as the RAG data source.

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Description

This video demonstrates how Azure AI Search integrated vector embeddings are used to index documents for Retrieval Augmented Generation (RAG) when using a large language model for generative AI Q&A solutions. The video creates a new Azure AI Vector index, populates it with PDF data, and creates a Streamlit Python application to interact with an OpenAI LLM using Azure AI Search as RAG data source. This video has an associated blog post with additional discussion and information here: https://robkerr.ai/azure-ai-search-integrated-vector-embeddings/ Quick Links: 0:00 Introduction 0:41 Create Search Service 1:48 Create Index 4:42 Populate Index 5:47 Explore Vector Index 7:55 Streamlist App Demo 10:30 Summary