AI Vector Search & Vector Embeddings in Oracle AI Database 26ai Explained for Oracle DBA

Description

Can your Oracle database understand unstructured data? Now it can. In this video, we explore Oracle AI Vector Search and walk through the end-to-end workflow in Oracle AI Database 26ai—using pure SQL and native database features. Oracle AI Vector Search is purpose-built for AI and ML workloads, enabling you to search data based on meaning and context, not just keywords. This makes it ideal for unstructured data such as documents, text, and logs—directly inside the Oracle AI Database 26ai. What You’ll Learn in This Video ✔ How vector embeddings are generated from unstructured data ✔ How embeddings are stored natively using the VECTOR data type ✔ When and why vector indexes are used to accelerate similarity search (optional) ✔ How to combine vector similarity search with relational and keyword filters ✔ How this workflow enables modern AI use cases using familiar SQL 🤖 Beyond Search: RAG Made Simple As an optional advanced step, similarity search results can be: Converted into a prompt Sent to a Large Language Model (LLM) Used to build a complete RAG (Retrieval-Augmented Generation) pipeline All of this—without moving data outside the database. Perfect for Oracle DBAs, architects, and developers looking to bring AI directly into the database layer using Oracle AI Database 26ai. 👉 Like, subscribe, and stay tuned for upcoming deep-dive videos on vector indexes, AI Smart Scans, Active Data Guard AI Inferences and RAG inference demos. Presenters: Sneha Nitin Pednekar and Rob Watson.