What are Vectors?

How Does AI Answer Everything in Seconds?

Just like an experienced employee can instantly recall information gathered over years and answer you, AI also uses information previously processed into its vector database to generate responses in seconds. This speed comes from organizing information in a special way as vectors.

What is a Vector?

Think of a vector as the "address" of a word or sentence in space. Just like every building in a city has latitude-longitude coordinates, every word exists at a specific point in multidimensional space.

For example:

The word "Cat" might be at point (5, 3, 8) in space

The word "Dog" at point (5, 4, 7)

The word "Car" at point (2, 9, 1)

Notice that "cat" and "dog" are at nearby coordinates because they're both pets. "Car" is at a very different point because it's a completely different concept.

The Relationship Between Vectors and Semantic Search

Thanks to this spatial arrangement, when you tell AI "give me information about pets," it scans all points in that region (cat, dog, bird, hamster). Just like when you say "I'm looking for a restaurant in Brooklyn" you look at the Brooklyn area of the map, the system also looks at the space region where semantically similar words are located.

Let's Test It

Father - Mother + Uncle = ?

Ask this equation to ChatGPT or any AI you prefer. If it has a good "embedding" model and "reasoning" model, it will give you the answer "aunt."

Note: *Fast-running or low-thinking-capacity models like 4.1-mini will string together random sentences to not leave you without an answer.

Key Points of a Good Vector Database

1. Choosing the Right "Translator" (Embedding Model)

A model that understands Turkish well should be selected, otherwise the system can't distinguish between "salary increase" and "salary cut."

2. Splitting Your Data to the Right Size (Chunk Size)

Texts should be divided into chunks that are neither too long (context is lost) nor too short (meaning is fragmented), just like bite sizes in a meal.

3. Clean Data Preparation

Tables, images, and headers in PDFs should be properly parsed so the system doesn't give confusing information.

4. Smart Labeling (Metadata)

Labels like "date, department, prepared by" should be added to each document so you can easily filter later.

What About Your Data?

Think about it: How many years of accumulated knowledge does your company have? How many reports, how many emails, how many documents?

Probably when one of your employees asks "Can we find the proposal we prepared for customer X last year?", everyone starts searching for hours. Or a new hire is trying to solve from scratch a problem that was solved 3 years ago.

This is exactly where vector databases come into play.

If you're curious about how it works, we can show you the examples we've created.

hello@betaspacestudio.com