_bulk request at all.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-bulk">Documentation
* on elastic.co
*/
@@ -714,7 +712,7 @@ public CompletableFuturekeyword family.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-field-caps">Documentation
* on elastic.co
*/
@@ -2113,7 +2111,7 @@ public CompletableFuturekeyword family.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-field-caps">Documentation
* on elastic.co
*/
@@ -2219,7 +2217,7 @@ public CompletableFuture
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.application/x-ndjson.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-msearch">Documentation
* on elastic.co
*/
@@ -3734,7 +3531,7 @@ public application/x-ndjson.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-msearch">Documentation
* on elastic.co
*/
@@ -3829,7 +3626,7 @@ public _index.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-mtermvectors">Documentation
* on elastic.co
*/
@@ -4044,7 +3841,7 @@ public CompletableFuture_index.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-mtermvectors">Documentation
* on elastic.co
*/
@@ -4139,7 +3936,7 @@ public CompletableFuturerouting only to hit a particular shard.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-termvectors">Documentation
* on elastic.co
*/
@@ -6596,7 +6393,7 @@ public _now (the current timestamp).
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-update">Documentation
* on elastic.co
*/
@@ -6684,7 +6481,7 @@ public _bulk request at all.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-bulk">Documentation
* on elastic.co
*/
@@ -714,7 +712,7 @@ public BulkResponse bulk(BulkRequest request) throws IOException, ElasticsearchE
* a function that initializes a builder to create the
* {@link BulkRequest}
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-bulk">Documentation
* on elastic.co
*/
@@ -886,7 +884,7 @@ public final BulkResponse bulk(Functionkeyword family.
*
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-field-caps">Documentation
* on elastic.co
*/
@@ -2126,7 +2124,7 @@ public FieldCapsResponse fieldCaps(FieldCapsRequest request) throws IOException,
* a function that initializes a builder to create the
* {@link FieldCapsRequest}
* @see Documentation
+ * "https://www.elastic.co/docs/api/doc/elasticsearch/v9/operation/operation-field-caps">Documentation
* on elastic.co
*/
@@ -2146,7 +2144,7 @@ public final FieldCapsResponse fieldCaps(Function
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.
- * NOTE: The kNN search API has been replaced by the knn option in
- * the search API.
- *
- * Perform a k-nearest neighbor (kNN) search on a dense_vector field and return - * the matching documents. Given a query vector, the API finds the k closest - * vectors and returns those documents as search hits. - *
- * Elasticsearch uses the HNSW algorithm to support efficient kNN search. Like - * most kNN algorithms, HNSW is an approximate method that sacrifices result - * accuracy for improved search speed. This means the results returned are not - * always the true k closest neighbors. - *
- * The kNN search API supports restricting the search using a filter. The search - * will return the top k documents that also match the filter query. - *
- * A kNN search response has the exact same structure as a search API response. - * However, certain sections have a meaning specific to kNN search: - *
_score is determined by the similarity between
- * the query and document vector.hits.total object contains the total number of nearest
- * neighbor candidates considered, which is
- * num_candidates * num_shards. The
- * hits.total.relation will always be eq, indicating
- * an exact value.