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Building Production-Grade Web Applications with Supabase

Building Production-Grade Web Applications with Supabase

By : David Lorenz
4.5 (11)
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Building Production-Grade Web Applications with Supabase

Building Production-Grade Web Applications with Supabase

4.5 (11)
By: David Lorenz

Overview of this book

Discover the powerful capabilities of Supabase, the cutting-edge, open-source platform flipping the script on backend architecture. Guided by David Lorenz, a battle-tested software architect with over two decades of development experience, this book will transform the way you approach your projects and make you a Supabase expert. In this comprehensive guide, you'll build a secure, production-grade multi-tenant ticket system, seamlessly integrated with Next.js. You’ll build essential skills for effective data manipulation, authentication, and file storage, as well as master Supabase's advanced capabilities including automating tasks with cron scheduling, performing similarity searches with artificial intelligence, testing your database, and leveraging real-time updates. By the end of the book, you'll have a deeper understanding of the platform and be able to confidently utilize Supabase in your own web applications, all thanks to David's excellent expertise.
Table of Contents (20 chapters)
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1
Part 1:Creating the Foundations of the Ticket System App
5
Part 2: Adding Multi-Tenancy and Learning RLS
10
Part 3: Managing Tickets and Interactions
15
Part 4: Diving Deeper into Security and Advanced Features

Adding an AI-based semantic ticket search

In this section, we’ll add a semantic AI search to our tickets, such that there’s no need for the search to match exact words anymore but simply to match with the meanings of words. To implement an AI-based content search, we store a so-called embedding, a mathematical vector representation of the content alongside the row.

Even though embeddings are far more complex than this, I’d like to give you an analogy to understand embeddings at their core. Think of two topics, dogs and cars. Let’s say, for a given set of words, we define the amount of dog topics and car topics between 0 and 1, where 1 means “highest possible semantic meaning of topic” and 0 means “no semantic meaning of the topic.”

Now let’s take the sample phrase, “My dog likes going for a walk.” How much “dog” is in this phrase semantically? There’s no clear answer to this as this...

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Building Production-Grade Web Applications with Supabase
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