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50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
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# Matryoshka (Adaptive-Length) Embeddings
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Matryoshka embeddings are a new class of embedding models introduced in the
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TODO-YYY paper [_TODO title_](https://arxiv.org/abs/2205.13147). They allow one
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to truncate excess dimensions in large vector, without lossing much quality.
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Let's say your embedding model generate 1024-dimensional vectors. If you have 1
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million of these 1024-dimensional vectors, they would take up `4.096 GB` of
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space! You're not able to reduce the dimensions without lossing a lot of
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quality - if you were to remove half of the dimensions 512-dimensional vectors,
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you could expect to also lose 50% or more of the quality of results. There are
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other dimensional-reduction techniques, like [PCA](#TODO), but this requires a
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complicated and expensive training process.
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Matryoshka embeddings, on the other hand, _can_ be truncated, without losing
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quality. Using [`mixedbread.ai`](#TODO) `mxbai-embed-large-v1` model, they claim
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that
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They are called "Matryoshka" embeddings because ... TODO
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## Matryoshka Embeddings with `sqlite-vec`
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You can use a combination of [`vec_slice()`](/api-reference#vec_slice) and
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[`vec_normalize()`](/api-reference#vec_slice) on Matryoshka embeddings to
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truncate.
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```sql
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select
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vec_normalize(vec_slice(title_embeddings, 0, 256)) as title_embeddings_256d
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from vec_articles;
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```
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## Benchmarks
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## Suppported Models
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https://supabase.com/blog/matryoshka-embeddings#which-granularities-were-openais-text-embedding-3-models-trained-on
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`text-embedding-3-small`: 1536, 512 `text-embedding-3-large`: 3072, 1024, 256
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https://x.com/ZainHasan6/status/1757519325202686255
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`text-embeddings-3-large:` 3072, 1536, 1024, 512
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https://www.mixedbread.ai/blog/binary-mrl
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`mxbai-embed-large-v1`: 1024, 512, 256, 128, 64
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`nomic-embed-text-v1.5`: 768, 512, 256, 128, 64
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