Oracle AI Vector Search, introduced in Oracle Database 23ai, allows you to search data based on the meaning (semantics) and context of words or phrases rather than keywords.

With Oracle AI Vector Search, Oracle brings AI into database engine, making it as an AI native Database.

AI is now built into the database !

Vector Search is designed for Artificial Intelligence (AI) workloads.

A major advantage of Vector Search is the ability to search different media types – text, document, image, audio, video (structured & unstructured data). This allows organizations to build applications using objects - text docs, JSON, images, video, audio, and others - no matter how complex they are.

Using Vector Search organizations can unlock the full potential of their data (both structured and unstructured) , providing users with highly relevant and contextually accurate results.

In this course, you will leverage the key capability of Oracle Database 26ai to design and manage Artificial Intelligence (AI) workloads using the new Oracle AI Vector Data type and Vector Search feature.

You will learn the following:

• Describe Oracle AI Vector Search Features, Benefits, and Capabilities

• Understand Oracle AI Vector Search Workflow

• 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

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