Table of Contents
Preface
Part 1: Fundamentals of Vector Search
1
Introduction to Vectors and Embeddings
Exploring the roles of supervised and unsupervised learning in vector search
What’s an embedding/vector?
What challenges are vectors solving?
The developer experience
Hugging Face
The market landscape and how it has accelerated the developer experience
Use cases and domains of application
AI-based search
Named Entity Recognition (NER)
Sentiment analysis
Text classification
Question-answering (QA)
Text summarization
How is Elastic playing a role in this space?
A primer on observability and cybersecurity
Summary
2
Getting Started with Vector Search in Elastic
Search experience in Elastic before vectors
Data type and its impact on relevancy ...
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