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