Am 15.01.23 um 16:36 schrieb Michael Sokolov:
I would suggest building Lucene from source and adding your own
similarity function to VectorSimilarity. That is the proper extension
point for similarity functions. If you find there is some substantial
benefit, it wouldn't be a big lift to add so
I would suggest building Lucene from source and adding your own
similarity function to VectorSimilarity. That is the proper extension
point for similarity functions. If you find there is some substantial
benefit, it wouldn't be a big lift to add something like that. However
I'm dubious about the li
Hi Adrien
Thanks for your feedback! Whereas I am not sure I fully understand what
you mean
At the moment I am using something like:
float[] vector = ...;
FieldType vectorFieldType = KnnVectorField.createFieldType(vector.length,
VectorSimilarityFunction.COSINE);
KnnVectorField vectorField =ne
Hi Michael,
You could create a custom KNN vectors format that ignores the vector
similarity configured on the field and uses its own.
Le sam. 14 janv. 2023, 21:33, Michael Wechner a
écrit :
> Hi
>
> IIUC Lucene currently supports
>
> VectorSimilarityFunction.COSINE
> VectorSimilarityFunction.DO
Hi
IIUC Lucene currently supports
VectorSimilarityFunction.COSINE
VectorSimilarityFunction.DOT_PRODUCT
VectorSimilarityFunction.EUCLIDEAN
whereas some embedding models have been trained with other metrics.
Also see
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdi