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Merged
l-trotta merged 1 commit into from
Dec 16, 2025
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[8.19] Regenerate client #1114

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203 changes: 0 additions & 203 deletions java-client/src/main/java/co/elastic/clients/elasticsearch/ElasticsearchAsyncClient.java
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Original file line number Diff line number Diff line change
Expand Up @@ -67,8 +67,6 @@
import co.elastic.clients.elasticsearch.core.IndexResponse;
import co.elastic.clients.elasticsearch.core.InfoRequest;
import co.elastic.clients.elasticsearch.core.InfoResponse;
import co.elastic.clients.elasticsearch.core.KnnSearchRequest;
import co.elastic.clients.elasticsearch.core.KnnSearchResponse;
import co.elastic.clients.elasticsearch.core.MgetRequest;
import co.elastic.clients.elasticsearch.core.MgetResponse;
import co.elastic.clients.elasticsearch.core.MsearchRequest;
Expand Down Expand Up @@ -3282,207 +3280,6 @@ public CompletableFuture<InfoResponse> info() {
return this.transport.performRequestAsync(InfoRequest._INSTANCE, InfoRequest._ENDPOINT, this.transportOptions);
}

// ----- Endpoint: knn_search

/**
* Run a knn search.
* <p>
* NOTE: The kNN search API has been replaced by the <code>knn</code> option in
* the search API.
* <p>
* 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.
* <p>
* 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.
* <p>
* 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.
* <p>
* A kNN search response has the exact same structure as a search API response.
* However, certain sections have a meaning specific to kNN search:
* <ul>
* <li>The document <code>_score</code> is determined by the similarity between
* the query and document vector.</li>
* <li>The <code>hits.total</code> object contains the total number of nearest
* neighbor candidates considered, which is
* <code>num_candidates * num_shards</code>. The
* <code>hits.total.relation</code> will always be <code>eq</code>, indicating
* an exact value.</li>
* </ul>
*
* @see <a href=
* "https://www.elastic.co/guide/en/elasticsearch/reference/8.19/knn-search-api.html">Documentation
* on elastic.co</a>
*/

public <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(KnnSearchRequest request,
Class<TDocument> tDocumentClass) {
@SuppressWarnings("unchecked")
JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
endpoint = new EndpointWithResponseMapperAttr<>(endpoint,
"co.elastic.clients:Deserializer:_global.knn_search.Response.TDocument",
getDeserializer(tDocumentClass));

return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
}

/**
* Run a knn search.
* <p>
* NOTE: The kNN search API has been replaced by the <code>knn</code> option in
* the search API.
* <p>
* 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.
* <p>
* 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.
* <p>
* 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.
* <p>
* A kNN search response has the exact same structure as a search API response.
* However, certain sections have a meaning specific to kNN search:
* <ul>
* <li>The document <code>_score</code> is determined by the similarity between
* the query and document vector.</li>
* <li>The <code>hits.total</code> object contains the total number of nearest
* neighbor candidates considered, which is
* <code>num_candidates * num_shards</code>. The
* <code>hits.total.relation</code> will always be <code>eq</code>, indicating
* an exact value.</li>
* </ul>
*
* @param fn
* a function that initializes a builder to create the
* {@link KnnSearchRequest}
* @see <a href=
* "https://www.elastic.co/guide/en/elasticsearch/reference/8.19/knn-search-api.html">Documentation
* on elastic.co</a>
*/

public final <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(
Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn, Class<TDocument> tDocumentClass) {
return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), tDocumentClass);
}

/**
* Overload of {@link #knnSearch(KnnSearchRequest, Class)}, where Class is
* defined as Void, meaning the documents will not be deserialized.
*/

public CompletableFuture<KnnSearchResponse<Void>> knnSearch(KnnSearchRequest request) {
@SuppressWarnings("unchecked")
JsonEndpoint<KnnSearchRequest, KnnSearchResponse<Void>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<Void>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
}

/**
* Overload of {@link #knnSearch(Function, Class)}, where Class is defined as
* Void, meaning the documents will not be deserialized.
*/

public final CompletableFuture<KnnSearchResponse<Void>> knnSearch(
Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn) {
return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), Void.class);
}

/**
* Run a knn search.
* <p>
* NOTE: The kNN search API has been replaced by the <code>knn</code> option in
* the search API.
* <p>
* 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.
* <p>
* 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.
* <p>
* 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.
* <p>
* A kNN search response has the exact same structure as a search API response.
* However, certain sections have a meaning specific to kNN search:
* <ul>
* <li>The document <code>_score</code> is determined by the similarity between
* the query and document vector.</li>
* <li>The <code>hits.total</code> object contains the total number of nearest
* neighbor candidates considered, which is
* <code>num_candidates * num_shards</code>. The
* <code>hits.total.relation</code> will always be <code>eq</code>, indicating
* an exact value.</li>
* </ul>
*
* @see <a href=
* "https://www.elastic.co/guide/en/elasticsearch/reference/8.19/knn-search-api.html">Documentation
* on elastic.co</a>
*/

public <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(KnnSearchRequest request,
Type tDocumentType) {
@SuppressWarnings("unchecked")
JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse> endpoint = (JsonEndpoint<KnnSearchRequest, KnnSearchResponse<TDocument>, ErrorResponse>) KnnSearchRequest._ENDPOINT;
endpoint = new EndpointWithResponseMapperAttr<>(endpoint,
"co.elastic.clients:Deserializer:_global.knn_search.Response.TDocument",
getDeserializer(tDocumentType));

return this.transport.performRequestAsync(request, endpoint, this.transportOptions);
}

/**
* Run a knn search.
* <p>
* NOTE: The kNN search API has been replaced by the <code>knn</code> option in
* the search API.
* <p>
* 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.
* <p>
* 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.
* <p>
* 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.
* <p>
* A kNN search response has the exact same structure as a search API response.
* However, certain sections have a meaning specific to kNN search:
* <ul>
* <li>The document <code>_score</code> is determined by the similarity between
* the query and document vector.</li>
* <li>The <code>hits.total</code> object contains the total number of nearest
* neighbor candidates considered, which is
* <code>num_candidates * num_shards</code>. The
* <code>hits.total.relation</code> will always be <code>eq</code>, indicating
* an exact value.</li>
* </ul>
*
* @param fn
* a function that initializes a builder to create the
* {@link KnnSearchRequest}
* @see <a href=
* "https://www.elastic.co/guide/en/elasticsearch/reference/8.19/knn-search-api.html">Documentation
* on elastic.co</a>
*/

public final <TDocument> CompletableFuture<KnnSearchResponse<TDocument>> knnSearch(
Function<KnnSearchRequest.Builder, ObjectBuilder<KnnSearchRequest>> fn, Type tDocumentType) {
return knnSearch(fn.apply(new KnnSearchRequest.Builder()).build(), tDocumentType);
}

// ----- Endpoint: mget

/**
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