Skip to content

Commit

Permalink
Minor ann docs update (elastic#94783)
Browse files Browse the repository at this point in the history
Replace the link to the deprecated knn search API and
added a link to the nightly benchmarks in Rally.
  • Loading branch information
jimczi authored Mar 31, 2023
1 parent f4b19ad commit 57cbbb3
Show file tree
Hide file tree
Showing 2 changed files with 11 additions and 7 deletions.
3 changes: 3 additions & 0 deletions docs/reference/how-to/knn-search.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,9 @@ For example your index might also include text fields and numerics, which also
benefit from using filesystem cache. It's recommended to run benchmarks with
your specific dataset to ensure there's a sufficient amount of memory to give
good search performance.
You can find https://elasticsearch-benchmarks.elastic.co/#tracks/so_vector[here]
and https://elasticsearch-benchmarks.elastic.co/#tracks/dense_vector[here] some examples
of datasets and configurations that we use for our nightly benchmarks.

[discrete]
include::search-speed.asciidoc[tag=warm-fs-cache]
Expand Down
15 changes: 8 additions & 7 deletions docs/reference/mapping/types/dense-vector.asciidoc
Original file line number Diff line number Diff line change
Expand Up @@ -60,9 +60,14 @@ similarity.

In many cases, a brute-force kNN search is not efficient enough. For this
reason, the `dense_vector` type supports indexing vectors into a specialized
data structure to support fast kNN retrieval through the
<<knn-search-api, kNN search API>>. You can enable indexing by setting the
`index` parameter:
data structure to support fast kNN retrieval through the <<search-api-knn, `knn` option>> in the search API

NOTE: Indexing vectors for approximate kNN search is an expensive process. It
can take substantial time to ingest documents that contain vector fields with
`index` enabled. See <<tune-knn-search,k-nearest neighbor (kNN) search>> to
learn more about the memory requirements.

You can enable indexing by setting the `index` parameter:

[source,console]
--------------------------------------------------
Expand All @@ -86,10 +91,6 @@ PUT my-index-2
efficient kNN search. Like most kNN algorithms, HNSW is an approximate method
that sacrifices result accuracy for improved speed.

NOTE: Indexing vectors for approximate kNN search is an expensive process. It
can take substantial time to ingest documents that contain vector fields with
`index` enabled.

Dense vector fields cannot be indexed if they are within
<<nested, `nested`>> mappings.

Expand Down

0 comments on commit 57cbbb3

Please sign in to comment.