What’s new in 8.14edit
Coming in 8.14.
Here are the highlights of what’s new and improved in Elasticsearch 8.14!
Query phase KNN now supports query_vector_builderedit
It is now possible to pass model_text and model_id within a knn query
in the query DSL to convert a text query into a dense vector and run the
nearest neighbor query on it, instead of requiring the dense vector to be
directly passed (within the query_vector parameter). Similar to the
top-level knn query (executed in the DFS phase), it is possible to supply
a query_vector_builder object containing a text_embedding object with
model_text (the text query to be converted into a dense vector) and
model_id (the identifier of a deployed model responsible for transforming
the text query into a dense vector). Note that an embedding model with the
referenced model_id needs to be deployed on a ML node.
in the cluster.
A SIMD (Neon) optimised vector distance function for merging int8 Scalar Quantized vectors has been addededit
An optimised int8 vector distance implementation for aarch64 has been added. This implementation is currently only used during merging. The vector distance implementation outperforms Lucene’s Pamana Vector implementation for binary comparisons by approx 5x (depending on the number of dimensions). It does so by means of SIMD (Neon) intrinsics compiled into a separate native library and link by Panama’s FFI. Comparisons are performed on off-heap mmap’ed vector data. Macro benchmarks, SO_Dense_Vector with scalar quantization enabled, shows significant improvements in merge times, approximately 3 times faster.