Implementing Vector Search in Your Application with SQL Server 2025
Microsoft Developer presents an MVP Edition episode featuring Joseph D’Antoni, demonstrating how to implement vector search using SQL Server 2025 and Azure SQL for AI-powered applications.
Implementing Vector Search in Your Application with SQL Server 2025
Presented by Microsoft Developer, this Data Exposed: MVP Edition episode, featuring Joseph D’Antoni, dives into:
Key Topics
- Semantic Search: Moves beyond basic concepts, detailing how semantic search enables applications to understand context and meaning in data queries.
- Vector Search in SQL Server 2025: Explains vector storage and retrieval and shows implementation steps using Transact-SQL functions and SQL Server’s new vector features.
- Data Ingestion at Scale: Techniques to ingest and manage large datasets for AI workloads, focusing on performance optimization within SQL Server and Azure SQL environments.
- Application Integration: Integrating vector search into real-world app scenarios to support modern AI needs.
Demo
- Step-by-step demo showing how to set up vector search on SQL Server 2025
- Implementing supporting features using T-SQL
Resources
- T-SQL Vector Search Functions Documentation
- SQL Server Vector AI Overview
- Azure SQL Vector Search Sample Code
About the Presenter
Joseph D’Antoni is a Principal Consultant and Microsoft Data Platform MVP, with over 20 years of experience in database platforms, performance tuning, infrastructure, and disaster recovery.
Community and Further Learning
- Connect on Twitter: Anna Hoffman, AzureSQL
- Check out other MVP Edition episodes and the broader Data Exposed series
- Subscribe for more SQL Server and Azure SQL tips: Microsoft Azure SQL, Microsoft SQL Server, Microsoft Developer
This episode equips developers and data professionals with practical knowledge to implement and optimize vector search and semantic search solutions for AI-powered apps using Microsoft data technologies.