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81 lines
3.7 KiB
Text
81 lines
3.7 KiB
Text
[[release-highlights]]
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== What's new in {minor-version}
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coming::[{minor-version}]
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Here are the highlights of what's new and improved in {es} {minor-version}!
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ifeval::["{release-state}"!="unreleased"]
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For detailed information about this release, see the <<es-release-notes>> and
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<<breaking-changes>>.
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// Add previous release to the list
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Other versions:
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{ref-bare}/8.14/release-highlights.html[8.14]
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| {ref-bare}/8.13/release-highlights.html[8.13]
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| {ref-bare}/8.12/release-highlights.html[8.12]
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| {ref-bare}/8.11/release-highlights.html[8.11]
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| {ref-bare}/8.10/release-highlights.html[8.10]
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| {ref-bare}/8.9/release-highlights.html[8.9]
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| {ref-bare}/8.8/release-highlights.html[8.8]
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| {ref-bare}/8.7/release-highlights.html[8.7]
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| {ref-bare}/8.6/release-highlights.html[8.6]
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| {ref-bare}/8.5/release-highlights.html[8.5]
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| {ref-bare}/8.4/release-highlights.html[8.4]
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| {ref-bare}/8.3/release-highlights.html[8.3]
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| {ref-bare}/8.2/release-highlights.html[8.2]
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| {ref-bare}/8.1/release-highlights.html[8.1]
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| {ref-bare}/8.0/release-highlights.html[8.0]
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endif::[]
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// tag::notable-highlights[]
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[discrete]
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[[stored_fields_are_compressed_with_zstandard_instead_of_lz4_deflate]]
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=== Stored fields are now compressed with ZStandard instead of LZ4/DEFLATE
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Stored fields are now compressed by splitting documents into blocks, which
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are then compressed independently with ZStandard. `index.codec: default`
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(default) uses blocks of at most 14kB or 128 documents compressed with level
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0, while `index.codec: best_compression` uses blocks of at most 240kB or
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2048 documents compressed at level 3. On most datasets that we tested
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against, this yielded storage improvements in the order of 10%, slightly
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faster indexing and similar retrieval latencies.
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{es-pull}103374[#103374]
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[discrete]
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[[query_phase_knn_supports_query_vector_builder]]
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=== Query phase KNN now supports query_vector_builder
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It is now possible to pass `model_text` and `model_id` within a `knn` query
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in the [query DSL](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-knn-query.html) to convert a text query into a dense vector and run the
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nearest neighbor query on it, instead of requiring the dense vector to be
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directly passed (within the `query_vector` parameter). Similar to the
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[top-level knn query](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html) (executed in the DFS phase), it is possible to supply
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a `query_vector_builder` object containing a `text_embedding` object with
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`model_text` (the text query to be converted into a dense vector) and
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`model_id` (the identifier of a deployed model responsible for transforming
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the text query into a dense vector). Note that an embedding model with the
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referenced `model_id` needs to be [deployed on a ML node](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-deploy-models.html).
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in the cluster.
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{es-pull}106068[#106068]
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[discrete]
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[[simd_neon_optimised_vector_distance_function_for_merging_int8_scalar_quantized_vectors_has_been_added]]
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=== A SIMD (Neon) optimised vector distance function for merging int8 Scalar Quantized vectors has been added
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An optimised int8 vector distance implementation for aarch64 has been added.
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This implementation is currently only used during merging.
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The vector distance implementation outperforms Lucene's Pamana Vector
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implementation for binary comparisons by approx 5x (depending on the number
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of dimensions). It does so by means of SIMD (Neon) intrinsics compiled into a
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separate native library and link by Panama's FFI. Comparisons are performed on
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off-heap mmap'ed vector data.
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Macro benchmarks, SO_Dense_Vector with scalar quantization enabled, shows
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significant improvements in merge times, approximately 3 times faster.
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{es-pull}106133[#106133]
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// end::notable-highlights[]
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