Simplify app dev with cloud-native PostgreSQL in Azure HorizonDB | DEM364

Microsoft Developer · Beginner ·🔧 Backend Engineering ·1mo ago

Key Takeaways

Streamline app development with cloud-native PostgreSQL in Azure HorizonDB

Original Description

Enterprise developers creating AI‑driven applications face growing complexity as vector search, models, and retrieval pipelines sprawl across services. In this session, see how Azure HorizonDB streamlines the stack by embedding AI and search directly in the database. Learn to run hybrid vector queries, apply BM25 relevance, call managed AI models from SQL, and prototype agentic workflows—shipping faster with less architectural overhead. To learn more, please check out these resources: * https://aka.ms/build26/DEM364 𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Maxim Lukiyanov 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Build 2026 event. View even more sessions on-demand and learn about Microsoft Build at https://build.microsoft.com DEM364 | English (US) | Cloud platform & data Demo | (300) Advanced #MSBuild Chapters: 0:00 - Introduction to the demo: Simplify app development with Cloud Native PostgreSQL in Azure Horizon DB 00:01:31 - AI integration: Built-in models, pipelines, and functions for AI agents 00:05:00 - Provisioning Horizon DB with AI model bundles and vector search extensions 00:06:41 - Introduction to using embeddings for AI reasoning on furniture styles 00:07:20 - Overview of available models: chat, text embedding, and semantic ranker 00:13:43 - Simplified vector generation pipeline supporting incremental updates 00:19:05 - Visualization of query plan to understand internal execution and joins 00:20:08 - Applying semantic re-ranking for top 10 relevant results 00:20:57 - Transition to knowledge graphs as the next step
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Chapters (9)

Introduction to the demo: Simplify app development with Cloud Native PostgreSQ
1:31 AI integration: Built-in models, pipelines, and functions for AI agents
5:00 Provisioning Horizon DB with AI model bundles and vector search extensions
6:41 Introduction to using embeddings for AI reasoning on furniture styles
7:20 Overview of available models: chat, text embedding, and semantic ranker
13:43 Simplified vector generation pipeline supporting incremental updates
19:05 Visualization of query plan to understand internal execution and joins
20:08 Applying semantic re-ranking for top 10 relevant results
20:57 Transition to knowledge graphs as the next step
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