Go Pro!Bootcamp

Bootcamp

Study group

Collaborate with peers in your dedicated #study-group channel.

Code reviews

Submit projects for review using the /review command in your #code-reviews channel

Learn Embeddings and Vector Databases

Enroll for freeGet started!

Join 147 other students

Log in to get

Access to all our free courses
Interactive hands-on content
100s of code challenges
Join a friendly community
Enroll for free
Subscribe to access!Subscribe to access!

Subscribe to access to this course and ALL other courses. You get a 30-day money-back guarantee, no questions asked.

Subscription includes

All courses and career paths
100s of coding challenges
Certificates of completion
Exclusive Pro members chat
The course creator Guil Hernandez

with Guil Hernandez

Course level: Intermediate

Learn how to improve the accuracy and reliability of LLM-based apps by implementing Retrieval-augmented Generation (RAG) using embeddings and a vector database.

You'll learn

What is an embedding?

Setting up a vector database

Supabase & pgvector

Semantic search

Similarity search

Chunking text documents

RAG

You'll build

screenshot
Movie Recommendation Engine

Find the perfect movie for movie night every time using Embeddings.

man

Prerequisites

Before taking this course, you should have an intermediate level understanding of Vanilla JS including working with APIs and async JavaScript. Below are our suggested resources to get you up to speed.

Meet your teacher

The course creator

Guil Hernandez

Lifelong learner, enthusiastic about changing lives through tech. Enjoys water sports and exploring the South Florida waters. 🏄🏻‍♂️ ☀️

Why this course rocks

This course will teach you how to craft and use embeddings in vector databases.

Start off by getting the hang of embeddings and why they're key in AI's thinking process.

Then you'll get hands-on practice, as you'll be chunking text documents, generating embeddings, and plugging them into vector databases using tools like Supabase.

As you build out your app, you will use similarity searches to find the relevant embeddings in your vector database.

Finally, you'll combine these results with the ChatCompletions API from OpenAI to create human-like chat responses.

This course is a mix of theory and interactive challenges. By the end, you won't just get the tech stuff; you'll actually have built a proof-of-concept AI Movie Recommendation engine that you can add to your portfolio.

F to the A oracle to the Q
What is AI?

AI (artificial intelligence) is like having a super-smart computer buddy who can do things that humans normally do, like recognize pictures of cats or tell you what the weather is like outside. They're kind of like a cross between R2-D2 and Hermione Granger - nerdy, helpful, and always up for a challenge! Just don't expect them to have feelings or opinions on whether pineapple belongs on pizza.

What is the OpenAI API?

The OpenAI API gives us access to AI models in our apps. By interacting with the API, we can leverage the power of these AI models to perform a wide range of tasks, such as natural language understanding, text generation, image generation, and more.

What is an embedding?

To “embed” means to place one object into a different space. In AI, we often create embeddings of text snippets, transforming the characters into an array of numbers. This enables the AI to better understand the underlying meaning of the text snippet.