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{:target=_blank .external-link})
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{:target=_blank .external-link} to help users understand the power of vector databases. The food discovery demo{:target=_blank .external-link} 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{:target=_blank .external-link})
For full technical details, refer to the Qdrant demo-food-discovery GitHub repository{:target=_blank .external-link}.
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. 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.