Vector index type now supports sparse option

site/content/3.12/develop/http-api/indexes/vector.md

@@ -57,14 +57,21 @@ paths:
  A list with exactly one attribute path to specify
  where the vector embedding is stored in each document. The vector data needs
  to be populated before creating the index.
- 
+
  If you want to index another vector embedding attribute, you need to create a
  separate vector index.
  type: array
  minItems: 1
  maxItems: 1
  items:
  type: string
+ sparse:
+ description: |
+ Whether to create a sparse index that excludes documents with
+ the attribute for indexing missing or set to `null`. This
+ attribute is defined by `fields`.
+ type: boolean
+ default: false
  parallelism:
  description: |
  The number of threads to use for indexing.

site/content/3.12/index-and-search/indexing/working-with-indexes/vector-indexes.md

@@ -65,15 +65,18 @@ centroids and the quality of vector search thus degrades.
 - **fields** (array of strings): A list with a single attribute path to specify
  where the vector embedding is stored in each document. The vector data needs
  to be populated before creating the index.
- 
+
  If you want to index another vector embedding attribute, you need to create a
  separate vector index.
+- **sparse** (boolean): Whether to create a sparse index that excludes documents
+ with the attribute for indexing missing or set to `null`. This attribute is
+ defined by `fields`. Default: `false`.
 - **parallelism** (number):
- The number of threads to use for indexing. The default is `2`.
+ The number of threads to use for indexing. Default: `2`.
 - **inBackground** (boolean):
  Set this option to `true` to keep the collection/shards available for
  write operations by not using an exclusive write lock for the duration
- of the index creation. The default is `false`.
+ of the index creation. Default: `false`.
 - **params**: The parameters as used by the Faiss library.
  - **metric** (string): Whether to use `cosine` or `l2` (Euclidean) distance calculation.
  - **dimension** (number): The vector dimension. The attribute to index needs to
@@ -89,11 +92,11 @@ centroids and the quality of vector search thus degrades.
  number of documents.
  - **defaultNProbe** (number, _optional_): How many neighboring centroids to
  consider for the search results by default. The larger the number, the slower
- the search but the better the search results. The default is `1`. You should
+ the search but the better the search results. Default: `1`. You should
  generally use a higher value here or per query via the `nProbe` option of
  the vector similarity functions.
  - **trainingIterations** (number, _optional_): The number of iterations in the
- training process. The default is `25`. Smaller values lead to a faster index
+ training process. Default: `25`. Smaller values lead to a faster index
  creation but may yield worse search results. 
  - **factory** (string, _optional_): You can specify an index factory string that is
  forwarded to the underlying Faiss library, allowing you to combine different

site/content/3.12/release-notes/version-3.12/whats-new-in-3-12.md

@@ -1443,6 +1443,13 @@ utilizing vector indexes in queries.
 Furthermore, a new error code `ERROR_QUERY_VECTOR_SEARCH_NOT_APPLIED` (1554)
 has been added.
 
+---
+
+<small>Introduced in: v3.12.6</small>
+
+Vector indexes can now be sparse to exclude documents with the embedding attribute
+for indexing missing or set to `null`.
+
 ## Server options
 
 ### Effective and available startup options

site/content/3.13/develop/http-api/indexes/vector.md

@@ -57,14 +57,21 @@ paths:
  A list with exactly one attribute path to specify
  where the vector embedding is stored in each document. The vector data needs
  to be populated before creating the index.
- 
+
  If you want to index another vector embedding attribute, you need to create a
  separate vector index.
  type: array
  minItems: 1
  maxItems: 1
  items:
  type: string
+ sparse:
+ description: |
+ Whether to create a sparse index that excludes documents with
+ the attribute for indexing missing or set to `null`. This
+ attribute is defined by `fields`.
+ type: boolean
+ default: false
  parallelism:
  description: |
  The number of threads to use for indexing.

site/content/3.13/index-and-search/indexing/working-with-indexes/vector-indexes.md

@@ -65,15 +65,18 @@ centroids and the quality of vector search thus degrades.
 - **fields** (array of strings): A list with a single attribute path to specify
  where the vector embedding is stored in each document. The vector data needs
  to be populated before creating the index.
- 
+
  If you want to index another vector embedding attribute, you need to create a
  separate vector index.
+- **sparse** (boolean): Whether to create a sparse index that excludes documents
+ with the attribute for indexing missing or set to `null`. This attribute is
+ defined by `fields`. Default: `false`.
 - **parallelism** (number):
- The number of threads to use for indexing. The default is `2`.
+ The number of threads to use for indexing. Default: `2`.
 - **inBackground** (boolean):
  Set this option to `true` to keep the collection/shards available for
  write operations by not using an exclusive write lock for the duration
- of the index creation. The default is `false`.
+ of the index creation. Default: `false`.
 - **params**: The parameters as used by the Faiss library.
  - **metric** (string): Whether to use `cosine` or `l2` (Euclidean) distance calculation.
  - **dimension** (number): The vector dimension. The attribute to index needs to
@@ -89,11 +92,11 @@ centroids and the quality of vector search thus degrades.
  number of documents.
  - **defaultNProbe** (number, _optional_): How many neighboring centroids to
  consider for the search results by default. The larger the number, the slower
- the search but the better the search results. The default is `1`. You should
+ the search but the better the search results. Default: `1`. You should
  generally use a higher value here or per query via the `nProbe` option of
  the vector similarity functions.
  - **trainingIterations** (number, _optional_): The number of iterations in the
- training process. The default is `25`. Smaller values lead to a faster index
+ training process. Default: `25`. Smaller values lead to a faster index
  creation but may yield worse search results. 
  - **factory** (string, _optional_): You can specify an index factory string that is
  forwarded to the underlying Faiss library, allowing you to combine different

site/content/3.13/release-notes/version-3.12/whats-new-in-3-12.md

@@ -1443,6 +1443,13 @@ utilizing vector indexes in queries.
 Furthermore, a new error code `ERROR_QUERY_VECTOR_SEARCH_NOT_APPLIED` (1554)
 has been added.
 
+---
+
+<small>Introduced in: v3.12.6</small>
+
+Vector indexes can now be sparse to exclude documents with the embedding attribute
+for indexing missing or set to `null`.
+
 ## Server options
 
 ### Effective and available startup options