AIVSE: Vector Search Essentials

Preparing SQL Server DBAs and Developers for AI-Powered Semantic Search

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Overview

This online course provides a comprehensive introduction to vector search capabilities in SQL Server 2025. Designed for SQL Server DBAs and developers, you’ll learn why traditional search methods fall short for semantic queries and how vector search solves these challenges. Through detailed explanations and demonstrations using a property search scenario, you’ll gain the foundational knowledge needed to evaluate and implement vector search in your own environments.

The course uses SemanticShoresDB, a sample database with 100,000 property listings and pre-generated 1536-dimensional embeddings, available on GitHub.

Instructor: Joe Sack

Level: 200 (intermediate)

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Curriculum

Module 1: Course Introduction

Sets the stage for the course, introducing the instructor and outlining what you’ll learn across all modules.

  • Course objectives and target audience
  • Instructor background and experience
  • Module overview and learning path
  • Sample database introduction (SemanticShoresDB)

Module 2: Understanding the AI Data Problem

Explores why traditional SQL search methods fail for semantic queries and introduces the four-part vector search solution.

  • The semantic search problem
  • SQL Server pre-2025 search toolbox (LIKE, full-text search)
  • SQL Server 2025 vector search features
  • Real-world property search demonstrations
  • DBA bridge: equality vs. similarity
  • The four-part vector search solution

Module 3: Vectors and Embeddings

Explains the building blocks of semantic search: how vectors represent meaning as numbers and how embeddings are generated.

  • What is a vector (arrays of numbers, dimensions)
  • Embeddings: converting text to numbers
  • Quantization and storage considerations
  • Working with embedding models
  • Tokens and document chunking strategies

Module 4: VECTOR_DISTANCE and Distance Metrics

Covers how similarity is measured between vectors using distance metrics, with focus on the VECTOR_DISTANCE function.

  • Distance metrics overview (cosine distance)
  • Why cosine distance for semantic search
  • VECTOR_DISTANCE function syntax and usage
  • Demonstrations: similar text produces similar vectors
  • End-to-end property search example
  • Full-text vs. vector search comparison

Module 5: Vector Search Fundamentals

Deep dive into approximate nearest neighbor search, the DiskANN algorithm, and vector index implementation in SQL Server 2025.

  • The performance problem (why naive search fails at scale)
  • k-NN search concepts
  • Approximate Nearest Neighbor (ANN) algorithms
  • DiskANN algorithm deep dive
  • Vector index implementation in SQL Server 2025
  • VECTOR_SEARCH function and syntax
  • Performance, accuracy, and monitoring

Module 6: Hybrid Search

Shows how to combine traditional SQL filtering with semantic similarity for real-world query patterns.

  • Why hybrid search matters
  • Pure vector search limitations
  • Hybrid query patterns with filters
  • Hybrid search with JOINs
  • Combining vector search with full-text search
  • Post-filtering behavior and considerations

Module 7: Production Readiness and Performance

Covers everything needed to deploy vector search in production environments, from capacity planning to security.

  • Embedding generation strategies (ETL, batch, in-database)
  • Embedding API performance and reliability
  • Capacity planning (storage and memory)
  • Monitoring with existing DMVs
  • Security considerations
  • Maintenance and schema changes
  • Testing and cost management
  • DBA roadmap for implementation

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Questions?

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