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# A Beginner’s Guide to Vector Embeddings

**[TigerData \(Creators of TimescaleDB\)](https://daily.dev/sources/tigerdata)** · 13 min read · 5 upvotes · 0 comments

## Summary

Vector embeddings are compact representations of data that capture structural or semantic relationships. They are used in various AI applications and can be created using neural networks. Different types of vector embeddings exist for different kinds of data. To create vector embeddings for your data, you can use embedding models or APIs.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.timescale.com/blog/a-beginners-guide-to-vector-embeddings/>

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Tags: [#embeddings](https://daily.dev/tags/embeddings), [#neural-networks](https://daily.dev/tags/neural-networks)

[View this post on daily.dev](https://daily.dev/posts/a-beginner-s-guide-to-vector-embeddings-gljthhdrn)

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