Hi Stefan-
Can you clarify your example a little bit? It sounds like you want to facet
over three different match sets (one constrained by "Mark Twain" as the
author, one constrained by "American authors" and one constrained by the
"sci-fi" genre). Is that correct?
Cheers,
-Greg
On Fri, Feb 10,
Hi all,
Let’s say I have an index of books, similar to the example in the facet demo [1]
with a hierarchical facet field encapsulating `Genre / Author’s nationality /
Author’s name`.
I might like to find the latest publish date of a book written by Mark
Twain, the
sum of the prices of books writt
I think it would be good to provide something like a VectorRerankField
(sorry for the bad name, maybe FastVectorField would be amusing too),
that just stores vectors as docvalues (no HNSW) and has a
newRescorer() method that implements
org.apache.lucene.search.Rescorer. Then its easy to do as that
Hi
I use the vector search of Lucene, whereas the embeddings I get from
SentenceBERT for example.
According to
https://www.sbert.net/examples/applications/retrieve_rerank/README.html
a re-ranking with a cross-encoder after the vector search (bi-encoding)
can improve the ranking.
Would it
Hi,
the reason for this is that files in Lucene are always write-once. We
never ever change a file after it was written and committed in the
2-phase-commit. If you write some own index files, e.g. as part of an
Index Codec you must adhere this rule. See Docvalues or Livedocs
implementation fo
