Vector DB & AI Embeddings

The term "embeddings" often surfaces, when learning about how data is represented and processed by AI models. But what exactly are AI embeddings, and why should you, as an end user, care about them? Let's explore this concept in simple terms.

What are AI Embeddings?

Embeddings are a fancy way to represent something in a high-dimensional space. Whether it is a sentence, a document, or an image, the AI models try to learn how to represent such inputs in a compact representation. These representations are called embeddings. Embeddings hold the essence of the object which the model has learnt from the data.

Let's understand this with an example: Suppose that your model represents objects like King, Queen, Man, and Woman into a higher dimension. This representation is expected to hold the meaning of these words. Like a King is a man with royalty (Man + Royalty ≈ King), Queen is a woman with royalty (Woman + Royalty ≈ Queen), and so on.

This allows the model to understand a relation between these terms. So, in other words, we should be able to show that a King - Man ≈ Royalty. And Royalty + Woman ≈ Queen! Isn't that amazing!

All famous models are based on this concept of embeddings and with the excessive increase in LLMs, it is expected to grow even more! Storing and retreiving these embeddings is important. That's where Vector database comes in!

Why Should You Care?

Here are some benefits of using AI Embeddings through a vector database.

Enhanced Efficiency

By transforming raw data into compact embeddings, AI algorithms can operate more efficiently. Tasks like search, recommendation systems, and classification can be performed faster and with greater accuracy.

Improved Performance

Embeddings facilitate better understanding of data relationships, leading to improved model performance. For instance, in recommendation systems, embeddings can capture nuanced user preferences, resulting in more personalized recommendations.

Versatility

AI embeddings are versatile and applicable across various domains. Whether you're analyzing text, images, audio, or even sensor data, embeddings provide a unified framework for representation and analysis.

Future-Proofing

Investing in AI embeddings future-proofs your data infrastructure. As AI technologies continue to evolve, embeddings serve as a robust foundation for implementing advanced algorithms and applications.