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Introduce blog post for disk-based k-NN #3616

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Description

Adds a blog for disk-based vector search

Issues Resolved

#3615

Check List

  • Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the BSD-3-Clause License.

Adds a blog post for disk-based k-NN. Included is a set of results and
images.

Signed-off-by: John Mazanec <[email protected]>

| Metric/Configuration | in-memory | on_disk_8x | in_memory_8x | on_disk_16x | in_memory_16x | on_disk_32x | in_memory_32x |
|-----------------------------------|-----------|------------|--------------|-------------|---------------|-------------|---------------|
| recall@100 (ratio) | 0.95 | 0.98 | 0.98 | 0.97 | 0.96 | 0.94 | 0.95 |
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This shows that 32x compression just works and we should not add on_disk here

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Added both just for sake of transparency. For some data sets, the re-scoring does not significantly help.

@kolchfa-aws kolchfa-aws self-assigned this Feb 4, 2025
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Signed-off-by: Fanit Kolchina <[email protected]>
Co-authored-by: John Mazanec <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>
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@kolchfa-aws @jmazanec15 Editorial review complete. Please see my comments and changes and let me know if you have any questions. Thanks!

Cc: @pajuric

Interestingly, for this dataset, the on-disk approach with rescoring produces similar recall to the in-memory approach without rescoring, but the in-memory approach is substantially faster. This is most likely because the Cohere v3 model has been optimized to work very well with binary quantized data (see [this blog post](https://cohere.com/blog/int8-binary-embeddings)).

## Learnings

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Line 260: We haven't actually referenced ANN prior to this. Instead of "ANN approach", do we mean "nearest neighbor approach"?

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Approximate nearest neighbor search

Co-authored-by: Nathan Bower <[email protected]>
Signed-off-by: kolchfa-aws <[email protected]>
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@pajuric Could you please edit the meta for this blog, and it will be ready to publish. Thanks!

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4 participants