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Mastering MongoDB 7.0 - Fourth Edition

You're reading from  Mastering MongoDB 7.0 - Fourth Edition

Product type Book
Published in Jan 2024
Publisher Packt
ISBN-13 9781835460474
Pages 434 pages
Edition 4th Edition
Languages
Concepts
Authors (7):
Marko Aleksendrić Marko Aleksendrić
Profile icon Marko Aleksendrić
Arek Borucki Arek Borucki
Profile icon Arek Borucki
Leandro Domingues Leandro Domingues
Profile icon Leandro Domingues
Malak Abu Hammad Malak Abu Hammad
Profile icon Malak Abu Hammad
Elie Hannouch Elie Hannouch
Profile icon Elie Hannouch
Rajesh Nair Rajesh Nair
Profile icon Rajesh Nair
Rachelle Palmer Rachelle Palmer
Profile icon Rachelle Palmer
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Table of Contents (20) Chapters

Preface 1. Chapter 1: Introduction to MongoDB 2. Chapter 2: The MongoDB Architecture 3. Chapter 3: Developer Tools 4. Chapter 4: Connecting to MongoDB 5. Chapter 5: CRUD Operations and Basic Queries 6. Chapter 6: Schema Design and Data Modeling 7. Chapter 7: Advanced Querying in MongoDB 8. Chapter 8: Aggregation 9. Chapter 9: Multi-Document ACID Transactions 10. Chapter 10: Index Optimization 11. Chapter 11: MongoDB Atlas: Powering the Future of Developer Data Platforms 12. Chapter 12: Monitoring and Backup in MongoDB 13. Chapter 13: Introduction to Atlas Search 14. Chapter 14: Integrating Applications with MongoDB 15. Chapter 15: Security 16. Chapter 16: Auditing 17. Chapter 17: Encryption 18. Index 19. Other Books You May Enjoy

Atlas Vector Search and its role in AI applications

In 2023, MongoDB introduced a public preview of its new product, Atlas Vector Search. Built on the MongoDB Atlas developer data platform, this innovative feature is designed to power intelligent applications with semantic search and generative AI capabilities over any type of data.

What does vector search entail?

Vector search is a technique that enables semantic search, which involves querying data based on its inherent meaning. This method utilizes machine learning models, often referred to as encoders, to convert various forms of data—such as text, audio, and images—into high-dimensional vectors. These vectors encapsulate the semantic essence of the data, which can then be sifted through to identify similar content based on the proximity of vectors in a high-dimensional space. Vectorized search can therefore effectively supplement traditional keyword-based search methods. It's also gaining significant attention...

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