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The Ultimate Guide to Embeddings and Vector Databases
Artificial Intelligence   Data Science   Latest   Machine Learning

The Ultimate Guide to Embeddings and Vector Databases

Last Updated on September 27, 2024 by Editorial Team

Author(s): Richard Warepam

Originally published on Towards AI.

These 2 Tools Are Behind Any Advanced AI Application

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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?

Β· What Are Embeddings?Β· Vector Databases: Where Embeddings LiveΒ· Getting Started with OpenAI’s Embeddings ∘ Exploring Different Types of Embeddings ∘ Storing Embeddings: Enter Vector Databases ∘ Searching Vector DatabasesΒ· 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.

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