Oracle Database 23ai: Oracle AI Vector Search Fundamentals

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Training D1109439GC10

Oracle Database 23ai: Oracle AI Vector Search Fundamentals

IT Solutions Oracle Database Oracle

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Language: polish

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Czas trwania: 16h dydaktycznych

materiały szkoleniowe w języku angielskim

Training description

Leverage the key capability of Oracle Database 23ai to design and manage Artificial Intelligence (AI) workloads using the new Oracle AI Vector Search feature. 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.

Scope

Overview of Oracle AI Vector Search

Objectives

VECTOR Data Type

Vector Embeddings

Similarity Search

Vector Embedding Models

Import Embedding Models

Summary

Why use Oracle AI Vector Search?

Objectives

Benefits

Benefits of Oracle AI Vector Search

Examples

Summary

Oracle AI Vector Search Workflow

Objectives

Generate Vector Embeddings

Generate Embeddings Examples

Example: Load ONNX Model into the DB

Example: Generate the Embedding

Store Vector Embeddings

Vector Indexes

Query Data with Similarity Searches

The Complete Workflow

Summary

Running Basic Queries on Vectors

Objectives

Basic Queries

Basic Queries: Comparison Operations

Summary

Vector Indexes and Memory

Objectives

Vector Indexes

Vector Index Categories

Vector Pool in the SGA

Vector Pool

Vector Pool: Other Considerations

Memory Considerations

Vector Storage

Memory Considerations: In-Memory Neighbor Graph Indexes

Memory Considerations: Sample Calculation

Summary

DML Operations on Vectors

Objectives

Create a Table with a Vector Column

VECTOR Data Type

Declaration Formats and Explanation

Vector DML

Vector DML Using SQL*Loader

Summary

Vector DDL

Objectives

Tables with Different Vector Formats

Tables with Different Vector Formats: Example

Tables with Different Vector Formats: Insert Example

DDL Operations on Vectors

Prohibited Operations

VECTOR Data Type Restrictions

Summary

Creating and Finding the Nearest Vectors

Objectives

Vector Constructor

Vector Constructor Examples

Vector Distance

Vector Distance Operand

Vector Distance Metrics

Euclidean and Euclidean Squared Distances

Cosine Similarity

Dot Product Similarity

Manhattan Distance

Hamming Similarity

Vector Distance Examples

Shorthand Operators For Distances

Summary

Finding the Closest Vectors

Objectives

Exact Similarity Search

Euclidean

Euclidean Squared Distance

Approximate Similarity Search

Approximate Similarity Search or Exact Similarity Search?

Approximate Similarity Search

Approximate Similarity Search: HNSW

Approximate Similarity Search: IVF

Multi-Vector Similarity Search

Summary

Narrowing Search Results

Objectives

Attribute Filtering

Summary

Testing Other Distance Functions

Objectives

Other Distance Functions

Other Vector Distance Functions

L1_DISTANCE

L2_DISTANCE

COSINE_DISTANCE

INNER_PRODUCT

Summary

Testing Other Vector Functions

Objectives

Other Vector Functions

Vector Constructors

Vector Serializers

Vector Norm

Vector Dimension Count

Vector Dimension Format

Summary

Course Conclusion

Learning Summary

Thank You!

Training schedule

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… if you want to learn while sitting in the same room with the trainer and other participants.

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Oracle Database 23ai: Oracle AI Vector Search Fundamentals

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Oracle Database 23ai: Oracle AI Vector Search Fundamentals

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