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Some more resources: - A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge, https://arxiv.org/abs/2310.11703 https://arxiv.or
by ignoramous 3y ago
Some more resources:
- A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge, https://arxiv.org/abs/2310.11703 https://arxiv.org/abs/2310.11703
- Survey of Vector Database Management Systems, https://arxiv.org/abs/2310.14021 https://arxiv.org/abs/2310.14021
- What are Embeddings, https://raw.githubusercontent.com/veekaybee/what_are_embeddings/main/embeddings.pdf https://raw.githubusercontent.com/veekaybee/what_are_embeddi...
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h/t: https://twitter.com/eatonphil/status/1745524630624862314 https://twitter.com/eatonphil/status/1745524630624862314 and https://twitter.com/ChristophMolnar/status/1745731602682982675 https://twitter.com/ChristophMolnar/status/17457316026829826...
- fzliu 3y agoThrowing a few more on here (mix of beginner and advanced): - Wikipedia article: https://en.wikipedia.org/wiki/Vector_database https://en.wikipedia.org/wiki/Vector_database - Vector Database 101: https://zilliz.com/learn/introduction-to-unstructured-data https://zilliz.com/learn/introduction-to-unstructured-data - ANN & Similarity search: https://vinija.ai/concepts/ann-similarity-search/ https://vinija.ai/concepts/ann-similarity-search/ - Distributed database: https://15445.courses.cs.cmu.edu/fall2021/notes/21-distributed.pdf https://15445.courses.cs.cmu.edu/fall2021/notes/21-distribut...
- rammy1234 3y agoThrowing one more - https://www.pinecone.io/learn/vector-database/ https://www.pinecone.io/learn/vector-database/
- simonw 3y agoHere's my attempt at this: Embeddings: What they are and why they matter https://simonwillison.net/2023/Oct/23/embeddings/ https://simonwillison.net/2023/Oct/23/embeddings/ Available as an annotated presentation + an optional 38 minute video.
- Scorpiion 3y agoThanks for writing this one Simon, I read it some time ago and I just wanted to say thanks and recommend it to folks browsing the comments, it's really good!
- AndyNemmity 3y agoVery helpful to make it clear, in concrete terms
- sva_ 3y agoNicely written and very approachable. Might add a paragraph on why to use cosine similarity, as it gives a chance to illustrate how the n-dimensional vector embedding is used.
- soVeryTired 3y agoI still don't understand why cosine similarity is used (other than maybe being cheap to compute). I've read a bunch of hand-wavey articles but none of them add up for me. If all your vectors have the same 2-norm, the cosine similarity of two vectors is a monotonic function of their euclidean distance (i.e. the minimum in one measure will be the minimum in the other). If all your vectors don't have the same norm, where's the sense in projecting them onto the unit sphere and measuring the angle between them? You're throwing away important information about the embedding. Could anyone explain?
- simonw 3y agoHonestly, I use cosine similarity because that's what everyone else does and I haven't spent even a moment considering any of the alternatives.
- pjot 3y agoCosine similarity is not affected by the size of the vector but only by the angle between them. This means that vectors with large or small values will have the same cosine similarity as long as they point in the same direction. In semantic search the magnitude of vectors isn’t relevant - if you needed a measure that took magnitude into account than Euclidean would make sense. E.g. image embeddings based on pixel intensity.