AI Features in Oracle AI Database 26ai

With Oracle Database 26ai release, Oracle has natively integrated AI into Oracle Database.

This native integration brings the AI to Data, rather than Data to AI. This integration enables you to develop AI Powered Applications using Oracle AI Database.

In this course you will learn all the AI Features built into Oracle Database and kick-start your AI journey with your Oracle Database.

You will learn the following:

• AI Basics & Concepts

• Oracle AI Vector Search

• Retrieval Augmented Generation (RAG)

• Select AI

• Oracle AI Vector Search Features, Benefits, and Capabilities

• Oracle AI Vector Search Workflow

• ONNX Models

• Load AI Models into Oracle Database

• Access third-party Models from Database

• Generate Vector Embeddings

• Store Embeddings in Database

• Create Vector Indexes (HNSW, IVF)

• Create Hybrid Vector Indexes

• Use SQL Functions for Vector Operations

• Use Vector Search PL/SQL Packages

• Perform Vector Search & Hybrid Vector Search

• Work with LLM powered APIs & Retrieval Augmented Generation (RAG)

After completing this course, you'll equip yourself with future-proof skills in AI-powered data management, making you a valuable asset in the evolving tech landscape.

You will become an AI powered DBA 💪..!

Course Overview

• What you will Learn?

• Audience

• Benefits

• Requirements

Preparing the Practice Environment

• Download and Import Pre-built VM

• Perform Sanity Checks

• Become Familiar with Practice Environment

General Concepts of Vector Search

• What is Vector Search?

• Use cases for Vector Search

• Examples of Vector Search

• How does Vector Search Work?

• What are Vector Embeddings?

• How to Generate Vector Embeddings?

• Embedding Models

• Vector Databases

Overview of Oracle Vector Search

• Overview of Oracle AI Vector Search

• Why use Oracle AI Vector Search

• Oracle AI Vector Search Workflow

Generate Vector Embeddings

• About Vector Generation

• Import Pretrained Models in ONNX format

• Access Third-party Models using REST APIs

Store Vector Embeddings

• Create Tables using VECTOR Data Type

• Insert Vectors into tables using INSERT statement

• Load Vector Data using SQL*Loader

• Unload and Load Vectors using Oracle Data Pump

Create Vector Indexes and Hybrid Vector Indexes

• What are Vector Indexes?

• Why Vector Index?

• In-Memory Neighbor Graph Vector Index

• Neighbor Partition Vector Index

• Sizing the VECTOR POOL

• Guidelines for using Vector Indexes

• Hybrid Vector Indexes

• When to use Hybrid Vector Index?

Use SQL Functions for Vector Operations

• Vector Distance Functions

  • VECTOR_DISTANCE

  • L1_DISTANCE

  • L2_DISTANCE

  • COSINE_DISTANCE

  • INNER_PRODUCT

  • HAMMING_DISTANCE

  • JACCARD_DISTANCE

  • TO_VECTOR

  • VECTOR_CHUNKS

  • VECTOR_EMBEDDING

• Vector Distance Metrics

  • Euclidean and Euclidean Squared Distances

  • Cosine Similarity

  • Dot Product Similarity

  • Manhattan Distance

  • Hamming Distance

  • Jaccard Similarity

Query Data with Similarity and Hybrid Searches

• Perform Exact Similarity Search

• Perform Approximate Similarity Search using Vector Indexes

• Perform Multi-Vector Similarity Search

• Perform Hybrid Search

Work with LLM-Powered APIs and Retrieval Augmented Generation (RAG)

• Use LLM-Powered APIs to generate Summary and Text

• Use RAG to complement LLMs

SELECT AI

• What is Select AI?

• Select AI Concepts

• Select AI Use Cases

• Implementing Select AI