Understand Vector Databases in 5 Minutes
What are vector databases and why do we need them? You can learn all that quickly in this short post.
Aug 20, 2025
AI
6 min
Vector databases are currently a hot topic in the field of artificial intelligence. But what are they? What do they do? How do they work? And finally, why do we need them? I didn’t know the answers to these questions, so I did a quick search to gain some new knowledge about the topic. Maybe you’re like me, so here is what I’ve learned. Let’s get started.
Why We Need Vector Databases
There are tons of data on and off the internet, and most of it is unstructured like social media posts, videos, images, or audio files. We cannot easily sort this data into classic databases like relational ones. Why is that?
Let’s say we have an image of a car. If we want to search for an image with a similar car, we would first need to create a relational database. Then we’d need to assign attributes to our car image, such as the type of car, its color, or the manufacturer. Only then could we perform the search, assuming we’ve done the same for all the other images in the database.
But assigning attributes manually is very time-consuming. So, we need an alternative. Vector databases come to the rescue.
What Is a Vector Database
It’s a database that indexes and stores vector embeddings for fast retrieval and similarity. It’s a special type of database that uses a different mechanism to store, sort, and retrieve data.
But hold on a minute. What are vector embeddings? We haven’t spoken about them yet! True, but they’re the essential part that makes the vector database work. Let’s quickly fill in the gap.
What Is a Vector Embedding
A dull name for something that mysterious, don’t you think? But their purpose is actually pretty easy to understand.
A vector embedding is created when unstructured data is transformed by an algorithm in a process called machine learning. It’s basically just a list of numbers that represent the underlying data in a different way. In a numerical way that computers can understand.
We can create (or rather, calculate) embeddings for a single word, a sentence, or an image—like the car image we imagined earlier.
Using numbers allows us to use vectors. Vectors can be easily handled and, more importantly, compared. This enables us to measure their distance or use nearest-neighbor search. That way we know which vectors are similar, meaning we know which images are similar. Pretty neat, right?
A worthy Mention of Indexing
Indexing vector embeddings is as important as the vectors themselves. Indexing creates a data structure that makes the search process faster. The indexing step maps the vector structure into a new data structure that enables quicker searching. Otherwise, simple vector length comparisons would again be too costly and time-consuming.
Use Cases
A few use cases to wrap this post up. We use vector databases in various ways, like:
long-term memory of LLMs
semantic search — search based on meaning and context rather than exact matches
similarity search — for images, videos, audio files, without needing words
ranking and recommendation engines — like those on e-commerce sites or elsewhere
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Vector databases are an essential part of AI tools. To know how they work is a key knowledge to have in order to navigate this field. Hopefully, you now have a basic understanding of them.
