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Remove the 'experimental' marking from vector fields. (#49120)

We wrapped up the API changes we wanted to make, and vector fields can now be
considered GA.
Julie Tibshirani 5 年之前
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dd6f0a35e4

+ 1 - 3
docs/reference/mapping/types/dense-vector.asciidoc

@@ -6,8 +6,6 @@
 <titleabbrev>Dense vector</titleabbrev>
 <titleabbrev>Dense vector</titleabbrev>
 ++++
 ++++
 
 
-experimental[]
-
 A `dense_vector` field stores dense vectors of float values.
 A `dense_vector` field stores dense vectors of float values.
 The maximum number of dimensions that can be in a vector should
 The maximum number of dimensions that can be in a vector should
 not exceed 1024. A `dense_vector` field is a single-valued field.
 not exceed 1024. A `dense_vector` field is a single-valued field.
@@ -53,4 +51,4 @@ PUT my_index/_doc/2
 
 
 Internally, each document's dense vector is encoded as a binary
 Internally, each document's dense vector is encoded as a binary
 doc value. Its size in bytes is equal to
 doc value. Its size in bytes is equal to
-`4 * dims + 4`, where `dims`—the number of the vector's dimensions.
+`4 * dims + 4`, where `dims`—the number of the vector's dimensions.

+ 0 - 4
docs/reference/vectors/vector-functions.asciidoc

@@ -3,15 +3,11 @@
 [[vector-functions]]
 [[vector-functions]]
 ===== Functions for vector fields
 ===== Functions for vector fields
 
 
-experimental[]
-
 NOTE: During vector functions' calculation, all matched documents are
 NOTE: During vector functions' calculation, all matched documents are
 linearly scanned. Thus, expect the query time grow linearly
 linearly scanned. Thus, expect the query time grow linearly
 with the number of matched documents. For this reason, we recommend
 with the number of matched documents. For this reason, we recommend
 to limit the number of matched documents with a `query` parameter.
 to limit the number of matched documents with a `query` parameter.
 
 
-====== `dense_vector` functions
-
 Let's create an index with a `dense_vector` mapping and index a couple
 Let's create an index with a `dense_vector` mapping and index a couple
 of documents into it.
 of documents into it.