chore: regenerate discoveryengine client (googleapis#28702)

Generated in GitHub action: https://togithub.com/googleapis/googleapis/google-api-java-client-services/actions/workflows/codegen.yaml

Author: yoshi-code-bot

clients/google-api-services-discoveryengine/v1/2.0.0/README.md

@@ -22,7 +22,7 @@ Add the following lines to your `pom.xml` file:
     <dependency>
       <groupId>com.google.apis</groupId>
       <artifactId>google-api-services-discoveryengine</artifactId>
-      <version>v1-rev20250810-2.0.0</version>
+      <version>v1-rev20250816-2.0.0</version>
     </dependency>
   </dependencies>
 </project>
@@ -35,7 +35,7 @@ repositories {
   mavenCentral()
 }
 dependencies {
-  implementation 'com.google.apis:google-api-services-discoveryengine:v1-rev20250810-2.0.0'
+  implementation 'com.google.apis:google-api-services-discoveryengine:v1-rev20250816-2.0.0'
 }
 ```
 

clients/google-api-services-discoveryengine/v1/2.0.0/com/google/api/services/discoveryengine/v1/model/GoogleCloudDiscoveryengineV1SearchRequest.java

@@ -202,6 +202,61 @@ public final class GoogleCloudDiscoveryengineV1SearchRequest extends com.google.
   @com.google.api.client.util.Key
   private GoogleCloudDiscoveryengineV1SearchRequestQueryExpansionSpec queryExpansionSpec;
 
+  /**
+   * Optional. The ranking expression controls the customized ranking on retrieval documents. This
+   * overrides ServingConfig.ranking_expression. The syntax and supported features depend on the
+   * `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it
+   * defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to
+   * `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by
+   * "+". * ranking_expression = function, { " + ", function }; Supported functions: * double *
+   * relevance_score * double * dotProduct(embedding_field_path) Function variables: *
+   * `relevance_score`: pre-defined keywords, used for measure relevance between query and document.
+   * * `embedding_field_path`: the document embedding field used with query embedding vector. *
+   * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector.
+   * Example ranking expression: If document has an embedding field doc_embedding, the ranking
+   * expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If
+   * ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and
+   * combinations of those chained using + or * operators) are supported: * `double` * `signal` *
+   * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with
+   * second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN,
+   * 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 |
+   * double, else returns signal1. Here are a few examples of ranking formulas that use the
+   * supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 *
+   * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score`
+   * with slight `semantic_smilarity_score` adjustment. - `0.2 *
+   * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank
+   * by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add
+   * constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 *
+   * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by
+   * the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of
+   * `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`:
+   * semantic similarity adjustment that is calculated using the embeddings generated by a
+   * proprietary Google model. This score determines how semantically similar a search query is to a
+   * document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25)
+   * ranking function. This score is calculated using a probabilistic model to estimate the
+   * probability that a document is relevant to a given query. * `relevance_score`: semantic
+   * relevance adjustment that uses a proprietary Google model to determine the meaning and intent
+   * behind a user's query in context with the content in the documents. * `pctr_rank`: predicted
+   * conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the
+   * relevance and attractiveness of a search result from a user's perspective. A higher pCTR
+   * suggests that the result is more likely to satisfy the user's query and intent, making it a
+   * valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank *
+   * `document_age`: The time in hours elapsed since the document was last updated, a floating-point
+   * number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank.
+   * Uses proprietary Google model to determine the keyword-based overlap between the query and the
+   * document. * `base_rank`: the default rank of the result
+   * The value may be {@code null}.
+   */
+  @com.google.api.client.util.Key
+  private java.lang.String rankingExpression;
+
+  /**
+   * Optional. The backend to use for the ranking expression evaluation.
+   * The value may be {@code null}.
+   */
+  @com.google.api.client.util.Key
+  private java.lang.String rankingExpressionBackend;
+
   /**
    * Optional. The specification for returning the relevance score.
    * The value may be {@code null}.
@@ -701,6 +756,122 @@ public GoogleCloudDiscoveryengineV1SearchRequest setQueryExpansionSpec(GoogleClo
     return this;
   }
 
+  /**
+   * Optional. The ranking expression controls the customized ranking on retrieval documents. This
+   * overrides ServingConfig.ranking_expression. The syntax and supported features depend on the
+   * `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it
+   * defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to
+   * `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by
+   * "+". * ranking_expression = function, { " + ", function }; Supported functions: * double *
+   * relevance_score * double * dotProduct(embedding_field_path) Function variables: *
+   * `relevance_score`: pre-defined keywords, used for measure relevance between query and document.
+   * * `embedding_field_path`: the document embedding field used with query embedding vector. *
+   * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector.
+   * Example ranking expression: If document has an embedding field doc_embedding, the ranking
+   * expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If
+   * ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and
+   * combinations of those chained using + or * operators) are supported: * `double` * `signal` *
+   * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with
+   * second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN,
+   * 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 |
+   * double, else returns signal1. Here are a few examples of ranking formulas that use the
+   * supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 *
+   * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score`
+   * with slight `semantic_smilarity_score` adjustment. - `0.2 *
+   * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank
+   * by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add
+   * constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 *
+   * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by
+   * the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of
+   * `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`:
+   * semantic similarity adjustment that is calculated using the embeddings generated by a
+   * proprietary Google model. This score determines how semantically similar a search query is to a
+   * document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25)
+   * ranking function. This score is calculated using a probabilistic model to estimate the
+   * probability that a document is relevant to a given query. * `relevance_score`: semantic
+   * relevance adjustment that uses a proprietary Google model to determine the meaning and intent
+   * behind a user's query in context with the content in the documents. * `pctr_rank`: predicted
+   * conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the
+   * relevance and attractiveness of a search result from a user's perspective. A higher pCTR
+   * suggests that the result is more likely to satisfy the user's query and intent, making it a
+   * valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank *
+   * `document_age`: The time in hours elapsed since the document was last updated, a floating-point
+   * number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank.
+   * Uses proprietary Google model to determine the keyword-based overlap between the query and the
+   * document. * `base_rank`: the default rank of the result
+   * @return value or {@code null} for none
+   */
+  public java.lang.String getRankingExpression() {
+    return rankingExpression;
+  }
+
+  /**
+   * Optional. The ranking expression controls the customized ranking on retrieval documents. This
+   * overrides ServingConfig.ranking_expression. The syntax and supported features depend on the
+   * `ranking_expression_backend` value. If `ranking_expression_backend` is not provided, it
+   * defaults to `RANK_BY_EMBEDDING`. If ranking_expression_backend is not provided or set to
+   * `RANK_BY_EMBEDDING`, it should be a single function or multiple functions that are joined by
+   * "+". * ranking_expression = function, { " + ", function }; Supported functions: * double *
+   * relevance_score * double * dotProduct(embedding_field_path) Function variables: *
+   * `relevance_score`: pre-defined keywords, used for measure relevance between query and document.
+   * * `embedding_field_path`: the document embedding field used with query embedding vector. *
+   * `dotProduct`: embedding function between `embedding_field_path` and query embedding vector.
+   * Example ranking expression: If document has an embedding field doc_embedding, the ranking
+   * expression could be `0.5 * relevance_score + 0.3 * dotProduct(doc_embedding)`. If
+   * ranking_expression_backend is set to `RANK_BY_FORMULA`, the following expression types (and
+   * combinations of those chained using + or * operators) are supported: * `double` * `signal` *
+   * `log(signal)` * `exp(signal)` * `rr(signal, double > 0)` -- reciprocal rank transformation with
+   * second argument being a denominator constant. * `is_nan(signal)` -- returns 0 if signal is NaN,
+   * 1 otherwise. * `fill_nan(signal1, signal2 | double)` -- if signal1 is NaN, returns signal2 |
+   * double, else returns signal1. Here are a few examples of ranking formulas that use the
+   * supported ranking expression types: - `0.2 * semantic_similarity_score + 0.8 *
+   * log(keyword_similarity_score)` -- mostly rank by the logarithm of `keyword_similarity_score`
+   * with slight `semantic_smilarity_score` adjustment. - `0.2 *
+   * exp(fill_nan(semantic_similarity_score, 0)) + 0.3 * is_nan(keyword_similarity_score)` -- rank
+   * by the exponent of `semantic_similarity_score` filling the value with 0 if it's NaN, also add
+   * constant 0.3 adjustment to the final score if `semantic_similarity_score` is NaN. - `0.2 *
+   * rr(semantic_similarity_score, 16) + 0.8 * rr(keyword_similarity_score, 16)` -- mostly rank by
+   * the reciprocal rank of `keyword_similarity_score` with slight adjustment of reciprocal rank of
+   * `semantic_smilarity_score`. The following signals are supported: * `semantic_similarity_score`:
+   * semantic similarity adjustment that is calculated using the embeddings generated by a
+   * proprietary Google model. This score determines how semantically similar a search query is to a
+   * document. * `keyword_similarity_score`: keyword match adjustment uses the Best Match 25 (BM25)
+   * ranking function. This score is calculated using a probabilistic model to estimate the
+   * probability that a document is relevant to a given query. * `relevance_score`: semantic
+   * relevance adjustment that uses a proprietary Google model to determine the meaning and intent
+   * behind a user's query in context with the content in the documents. * `pctr_rank`: predicted
+   * conversion rate adjustment as a rank use predicted Click-through rate (pCTR) to gauge the
+   * relevance and attractiveness of a search result from a user's perspective. A higher pCTR
+   * suggests that the result is more likely to satisfy the user's query and intent, making it a
+   * valuable signal for ranking. * `freshness_rank`: freshness adjustment as a rank *
+   * `document_age`: The time in hours elapsed since the document was last updated, a floating-point
+   * number (e.g., 0.25 means 15 minutes). * `topicality_rank`: topicality adjustment as a rank.
+   * Uses proprietary Google model to determine the keyword-based overlap between the query and the
+   * document. * `base_rank`: the default rank of the result
+   * @param rankingExpression rankingExpression or {@code null} for none
+   */
+  public GoogleCloudDiscoveryengineV1SearchRequest setRankingExpression(java.lang.String rankingExpression) {
+    this.rankingExpression = rankingExpression;
+    return this;
+  }
+
+  /**
+   * Optional. The backend to use for the ranking expression evaluation.
+   * @return value or {@code null} for none
+   */
+  public java.lang.String getRankingExpressionBackend() {
+    return rankingExpressionBackend;
+  }
+
+  /**
+   * Optional. The backend to use for the ranking expression evaluation.
+   * @param rankingExpressionBackend rankingExpressionBackend or {@code null} for none
+   */
+  public GoogleCloudDiscoveryengineV1SearchRequest setRankingExpressionBackend(java.lang.String rankingExpressionBackend) {
+    this.rankingExpressionBackend = rankingExpressionBackend;
+    return this;
+  }
+
   /**
    * Optional. The specification for returning the relevance score.
    * @return value or {@code null} for none

clients/google-api-services-discoveryengine/v1/2.0.0/com/google/api/services/discoveryengine/v1/model/GoogleCloudDiscoveryengineV1SearchRequestDataStoreSpec.java

@@ -49,7 +49,8 @@ public final class GoogleCloudDiscoveryengineV1SearchRequestDataStoreSpec extend
 
   /**
    * Required. Full resource name of DataStore, such as `projects/{project}/locations/{location}/col
-   * lections/{collection_id}/dataStores/{data_store_id}`.
+   * lections/{collection_id}/dataStores/{data_store_id}`. The path must include the project number,
+   * project id is not supported for this field.
    * The value may be {@code null}.
    */
   @com.google.api.client.util.Key
@@ -106,7 +107,8 @@ public GoogleCloudDiscoveryengineV1SearchRequestDataStoreSpec setCustomSearchOpe
 
   /**
    * Required. Full resource name of DataStore, such as `projects/{project}/locations/{location}/col
-   * lections/{collection_id}/dataStores/{data_store_id}`.
+   * lections/{collection_id}/dataStores/{data_store_id}`. The path must include the project number,
+   * project id is not supported for this field.
    * @return value or {@code null} for none
    */
   public java.lang.String getDataStore() {
@@ -115,7 +117,8 @@ public java.lang.String getDataStore() {
 
   /**
    * Required. Full resource name of DataStore, such as `projects/{project}/locations/{location}/col
-   * lections/{collection_id}/dataStores/{data_store_id}`.
+   * lections/{collection_id}/dataStores/{data_store_id}`. The path must include the project number,
+   * project id is not supported for this field.
    * @param dataStore dataStore or {@code null} for none
    */
   public GoogleCloudDiscoveryengineV1SearchRequestDataStoreSpec setDataStore(java.lang.String dataStore) {

clients/google-api-services-discoveryengine/v1/2.0.0/com/google/api/services/discoveryengine/v1/model/GoogleCloudDiscoveryengineV1SearchResponseSearchResult.java

@@ -65,6 +65,13 @@ public final class GoogleCloudDiscoveryengineV1SearchResponseSearchResult extend
     com.google.api.client.util.Data.nullOf(GoogleCloudDiscoveryengineV1DoubleList.class);
   }
 
+  /**
+   * Optional. A set of ranking signals associated with the result.
+   * The value may be {@code null}.
+   */
+  @com.google.api.client.util.Key
+  private GoogleCloudDiscoveryengineV1SearchResponseSearchResultRankSignals rankSignals;
+
   /**
    * The chunk data in the search response if the SearchRequest.ContentSearchSpec.search_result_mode
    * is set to CHUNKS.
@@ -137,6 +144,23 @@ public GoogleCloudDiscoveryengineV1SearchResponseSearchResult setModelScores(jav
     return this;
   }
 
+  /**
+   * Optional. A set of ranking signals associated with the result.
+   * @return value or {@code null} for none
+   */
+  public GoogleCloudDiscoveryengineV1SearchResponseSearchResultRankSignals getRankSignals() {
+    return rankSignals;
+  }
+
+  /**
+   * Optional. A set of ranking signals associated with the result.
+   * @param rankSignals rankSignals or {@code null} for none
+   */
+  public GoogleCloudDiscoveryengineV1SearchResponseSearchResult setRankSignals(GoogleCloudDiscoveryengineV1SearchResponseSearchResultRankSignals rankSignals) {
+    this.rankSignals = rankSignals;
+    return this;
+  }
+
   @Override
   public GoogleCloudDiscoveryengineV1SearchResponseSearchResult set(String fieldName, Object value) {
     return (GoogleCloudDiscoveryengineV1SearchResponseSearchResult) super.set(fieldName, value);