Starting with Oracle Database 23ai release, Oracle started embedding AI capabilities directly into Oracle Database.

One of the most important capability is Oracle AI Vector Search.

AI Vector Search is a new breakthrough technology to help find similar unstructured data based on their semantic content.

Traditionally, databases are good at querying structured data that is stored as strings, numbers and dates.

However, enterprises are facing an ever-increasing volumes of unstructured data such as documents, images, videos which databases traditionally have not been so good at querying.

And the reason for that is because these new types of data need to be searched based on their semantic content or its meaning, rather than the key words or pixels that make them up.

This is where Oracle AI Vector Search comes-in...

Oracle AI Vector Search can help extract value from all of our data assets - structured as well as unstructured data enabling ultra-sophisticated AI Search Applications.

In this course, you will leverage the key capability of Oracle Database 23ai to design and manage Artificial Intelligence (AI) workloads using the new Oracle AI Vector Data type and Vector Search feature.

You will learn how to create tables with vector data type, load data, and the query them based on semantics, rather than keywords. You will learn to perform semantic search on unstructured data by combining it with your relational data in one single system.

With hands-on practices, you'll be be able to reinforce the learning of the new AI Vector Search feature and its capabilities.

After completion this course, you should be able to:

• Describe a Vector Database and the use cases for Vector databases like Oracle Database 23ai

• Understand how Vector Databases work

• Distinguish Vector database from the Traditional databases

• Key Advantages of Vector Databases

• Describe AI Vector Search features, benefits, and capabilities

• Understand Oracle AI Vector Search Workflow

• Run basic queries on vectors

• Perform DML and DDL operations on vectors

• Upload Vector Embedding Models into Oracle Database

• Generate Vector Embeddings using AI Models

• Store Embeddings in Oracle Tables

• Create Vector Indexes

• Use SQL Functions for Vector Operations

• Perform Vector Search

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

Course Overview

• What you will Learn?

• Audience

• Benefits

• Requirements

Overview of Vector Databases

• What is Vector Database?

• What are Vector Embeddings?

• What is Vector Search?

• Advantages of Vector Search

• How does Vector Database work?

• Difference between Vector Database and Traditional Database

• Role of Vector Database in AI & ML application development

• Examples of Vector Databases

Preparing the Practice Environment

• Download and Import Pre-built VM

• Perform Sanity Checks

• Become Familiar with Practice Environment

Overview of Oracle Vector Search

• Overview of Oracle AI Vector Search

• Why use Oracle AI Vector Search

• Oracle AI Vector Search Workflow

Becoming Familiar with Vector Data and Vector Operations

• Creating Table with Vector Data Type Column

• Inserting Vector Data

• Selecting Vector Data

• Perform DDL, DML Operations on Vector Data

• Prohibited Operations

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

  • VECTOR

  • TO_VECTOR

  • VECTOR_NORM

  • VECTOR_DIMENSION_COUNT

  • VECTOR_DIMS

  • VECTOR_DIMENSION_FORMAT

• 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