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--- |
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title: What are vector databases? |
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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. |
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contentType: explanation |
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--- |
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# What are vector databases? |
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Vector databases store information as numbers: |
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> 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){:target=_blank .external-link}) |
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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. |
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## A simplified example |
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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. |
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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: |
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| Sentence | Vector (array of dimensions) | |
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| -------- | ------ | |
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| n8n is a source-available automation tool that you can self-host | [0.1, 0.2, 0.3] | |
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| Zapier is an automation tool | [0.1] | |
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| Make is an automation tool | [0.1] | |
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| Confluence is a wiki tool that you can self-host | [0.3] | |
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/// note | This example is very simplified |
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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. |
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/// |
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## Demonstrating the power of similarity search |
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Qdrant provides [vector search demos](https://qdrant.tech/demo/){:target=_blank .external-link} to help users understand the power of vector databases. The [food discovery demo](https://food-discovery.qdrant.tech/){:target=_blank .external-link} shows how a vector store can help match pictures based on visual similarities. |
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> 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/){:target=_blank .external-link}) |
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For full technical details, refer to the [Qdrant demo-food-discovery GitHub repository](https://github.com/qdrant/demo-food-discovery){:target=_blank .external-link}. |
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## Embeddings, retrievers, text splitters, and document loaders |
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Vector databases require other tools to function: |
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- 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. |
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- 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. |
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- Retrievers: retrievers fetch documents from vector databases. You need to pair them with an embedding to translate the vectors back into data. |
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