---
title: What are vector databases?
description: Understand vector databases. Learn how n8n provides vector databases, along with the key components to work with them, including embeddings, retrievers, and document loaders.
contentType: explanation
---

# What are vector databases?

Vector databases store information as numbers:

> A vector database is a type of database that stores data as high-dimensional vectors, which are mathematical representations of features or attributes. ([source](https://learn.microsoft.com/en-us/semantic-kernel/memories/vector-db))

This enables fast and accurate similarity searches. With a vector database, instead of using conventional database queries, you can search for relevant data based on semantic and contextual meaning.

## A simplified example

A vector database could store the sentence "n8n is a source-available automation tool that you can self-host", but instead of storing it as text, the vector database stores an array of dimensions (numbers between 0 and 1) that represent its features. This doesn't mean turning each letter in the sentence into a number. Instead, the vectors in the vector database describe the sentence. 

Suppose that in a vector store `0.1` represents `automation tool`, `0.2` represents `source available`, and `0.3` represents `can be self-hosted`. You could end up with the following vectors:

| Sentence | Vector (array of dimensions) |
| -------- | ------ |
| n8n is a source-available automation tool that you can self-host | [0.1, 0.2, 0.3] |
| Zapier is an automation tool | [0.1] |
| Make is an automation tool | [0.1] |
| Confluence is a wiki tool that you can self-host | [0.3] |

/// note | This example is very simplified
In practice, vectors are far more complex. A vector can range in size from tens to thousands of dimensions. The dimensions don't have a one-to-one relationship to a single feature, so you can't translate individual dimensions directly into single concepts. This example gives an approximate mental model, not a true technical understanding.
///


## Demonstrating the power of similarity search

Qdrant provides [vector search demos](https://qdrant.tech/demo/) to help users understand the power of vector databases. The [food discovery demo](https://food-discovery.qdrant.tech/) shows how a vector store can help match pictures based on visual similarities.

> This demo uses data from Delivery Service. Users may like or dislike the photo of a dish, and the app will recommend more similar meals based on how they look. It's also possible to choose to view results from the restaurants within the delivery radius. ([source](https://qdrant.tech/demo/))

For full technical details, refer to the [Qdrant demo-food-discovery GitHub repository](https://github.com/qdrant/demo-food-discovery).

## Embeddings, retrievers, text splitters, and document loaders

Vector databases require other tools to function:

- Document loaders and text splitters: document loaders pull in documents and data, and prepare them for [embedding](/glossary.md#ai-embedding). Document loaders can use text splitters to break documents into chunks.
- Embeddings: these are the tools that turn the data (text, images, and so on) into vectors, and back into raw data. Note that n8n only supports text embeddings.
- Retrievers: retrievers fetch documents from vector databases. You need to pair them with an embedding to translate the vectors back into data.





