thanks for your feedback and pointers!
To play with binary vectors the following project might be useful
https://github.com/cohere-ai/BinaryVectorDB
Re Lucene, I will try to better understand what you suggest below.
Thanks
Michael
Am 29.03.24 um 07:35 schrieb Shubham Chaudhary:
btw, what about native binary embedding quantization support by Lucene?
This sounds like a good idea to have in Lucene.
Would this require another VetctorField /VectorsFormat?
Based on current implementation, one way would be to use another KNN format
or alternatively maybe a better approach would be to make
Lucene99ScalarQuantizedVectorsFormat
<https://lucene.apache.org/core/9_9_1/core/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsFormat.html>
configurable
to accept the type of quantization like this new work in progress PR for
int4 quantization <https://github.com/apache/lucene/pull/13197> support
which takes the number of bits to use for quantizing as input. Since this
change allows passing 1 for bits to be used for quantization, it looks to
me like an enabler for binary quantization.
- Shubham
On Sun, Mar 24, 2024 at 4:34 AM Michael Wechner <michael.wech...@wyona.com>
wrote:
btw, what about native binary embedding quantization support by Lucene?
https://www.linkedin.com/posts/tomaarsen_binary-and-scalar-embedding-quantization-activity-7176966403332132864-lJzH?utm_source=share&utm_medium=member_desktop
Would this require another VetctorField /VectorsFormat?
Thanks
Michael
Am 19.03.24 um 21:57 schrieb Shubham Chaudhary:
Hi Michael,
Lucene already had int8 vector support since 9.5 (#1054
<https://github.com/apache/lucene/pull/1054>) but it was left to the
user
to get those quantized vectors and index using KnnByteVectorField
<
https://lucene.apache.org/core/9_5_0/core/org/apache/lucene/document/KnnByteVectorField.html
,
but with Lucene 9.9 out now there is a native support for int8 scalar
quantization (#12582 <https://github.com/apache/lucene/pull/12582>)
using
Lucene99ScalarQuantizedVectorsFormat
<
https://lucene.apache.org/core/9_9_1/core/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsFormat.html
that
expects a confidence interval from 90-100. Here is a nice blog(s) that
talks about how it works in Lucene.
-
https://www.elastic.co/search-labs/blog/articles/scalar-quantization-in-lucene
-
https://www.elastic.co/search-labs/blog/articles/scalar-quantization-101
Some other references :
-
https://lucene.apache.org/core/9_9_1/core/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsFormat.html
-
https://lucene.apache.org/core/9_9_1/core/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsReader.html
-
https://lucene.apache.org/core/9_9_1/core/org/apache/lucene/codecs/lucene99/Lucene99ScalarQuantizedVectorsWriter.html
On Wed, Mar 20, 2024 at 1:54 AM Michael Wechner <
michael.wech...@wyona.com>
wrote:
Hi
Cohere recently announced there "compressed" embeddings
https://twitter.com/Nils_Reimers/status/1769809006762037368
https://www.linkedin.com/posts/bhavsarpratik_rag-genai-search-activity-7175850704928989187-Ki1N/?utm_source=share&utm_medium=member_desktop
Does Lucene Vector Search support this already, or is somebody working
on this?
Thanks
Michael
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