Armchair Architects: LLMs & Vector Databases (Part 1)

From the creator

Vector databases are designed to store, manage, and index massive quantities of high-dimensional vector data efficiently that can help different types of queries, such as nearest neighbor. In this episode of the #AzureEnblementShow, Uli, Eric (@mougue) and David discuss how vector databases convert data to integers, cover some of the use cases of vector databases, and the benefits of embedding. This is part one of a two-part series. Resources • Vector search in Azure AI Search https://learn.microsoft.com/en-us/azure/search/vector-search-overview • Geospatial data processing and analytics https://learn.microsoft.com/en-us/azure/architecture/example-scenario/data/geospatial-data-processing-analytics-azure • Microsoft Azure AI Fundamentals: Natural Language Processing https://learn.microsoft.com/en-us/training/paths/explore-natural-language-processing/ • Azure Database for PostgreSQL https://learn.microsoft.com/en-us/training/paths/introduction-to-azure-postgres/ • Vector DB Lookup tool for flows in Azure AI Studio https://learn.microsoft.com/en-us/azure/ai-studio/how-to/prompt-flow-tools/vector-db-lookup-tool Related episodes • Armchair Architects: LLMs & Vector Databases (Part 2) https://aka.ms/azenable/142 • Watch more episodes in the Armchair Architects Series https://aka.ms/azenable/ArmchairArchitects • Watch more episodes in the Well-Architected Series https://aka.ms/azenable/yt/wa-playlist Chapters 0:00 Introduction 0:38 Data stored as integers 1:30 Text conv

Published 2023-12-12 · Watch on YouTube ↗
Availability and playback restrictions are controlled by the original channel.