Uploaded July 2026 | Updated September 2026, 2 weeks ago

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Learn how AI Vector Search in Oracle AI Database helps you search unstructured data by meaning, not just by keywords.

This session starts with a practical Oracle APEX demo that shows similarity search in action using image and text search examples. From there, you’ll learn the core concepts behind vector search: what vectors and embeddings are, how they represent semantic meaning, how to generate and store vectors in Oracle AI Database, and how to perform similarity searches directly with SQL.

The session also covers vector indexes and closes with a Retrieval-Augmented Generation (RAG) demo, showing how AI Vector Search can retrieve relevant enterprise context to ground generative AI responses.

Chapters:
00:00 Introduction and session overview
02:00 Similarity search demo
03:00 National Parks image search in Oracle APEX
08:00 Inspecting search results and SQL output
10:00 What vectors and embeddings are
16:00 Measuring distance between vectors
18:00 LiveLab schema and sample data
23:00 Generating vector embeddings
26:00 Choosing embedding models
27:00 Storing vectors in Oracle AI Database
31:00 Storing image vectors in database tables
34:00 Vector Search SQL and distance functions
35:00 Writing similarity search queries
38:00 Vector indexes in Oracle AI Database
43:00 RAG demo: support incident assistant
53:00 How RAG uses AI Vector Search
57:00 Wrap-up

#OracleAIDatabase #VectorSearch #RAG #OracleAPEX #SQL #GenerativeAI