The Ultimate Guide to Embeddings and Vector Databases

Author(s): Richard Warepam

Originally published on Towards AI.

These 2 Tools Are Behind Any Advanced AI Application

This member-only story is on us. Upgrade to access all of Medium.

If you are not a member, click here to read the full article. But as an appreciation, please don’t forget to 👏 clap if you had a good read.

Artificial intelligence is on the rise and two concepts are becoming increasingly crucial for anyone building AI products: embeddings and vector databases.

These powerful tools are the secret sauce behind many advanced AI applications, from chatbots with long-term memory to semantic search engines that can understand the meaning behind your queries.

But what exactly are they, and how can you harness their potential?

· What Are Embeddings?· Vector Databases: Where Embeddings Live· Getting Started with OpenAI’s Embeddings ∘ Exploring Different Types of Embeddings ∘ Storing Embeddings: Enter Vector Databases ∘ Searching Vector Databases· Wrapping Up

They’re not just random numbers; embeddings are numerical representations that models learn through extensive training.

These vectors capture the relationships within the data by analyzing how often certain patterns co-occur.

Suppose you’re trying to organize a massive library. Instead of arranging books alphabetically or by genre, what if you could somehow map out how similar or different each book is to every other book in the collection?

That’s essentially… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI