Merge branch 'main' into create_compute_reservation_from_vm

Author: TetyanaYahodska

.kokoro/tests/build_cloud_functions.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2022 Google LLC.
+# Copyright 2022 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

.kokoro/tests/build_cloud_run.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2019 Google LLC.
+# Copyright 2019 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

.kokoro/tests/teardown_cloud_functions.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# Copyright 2022 Google LLC.
+# Copyright 2022 Google LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

CONTRIBUTING.md

@@ -8,8 +8,6 @@ In this repository, we are looking for patches that:
 * Fix bugs
 * Improve clarity and understandability
 
-If you want to contribute a full sample / tutorial, please consider contributing to our [community pages](https://cloud.google.com/community) [[How To](https://cloud.google.com/community/tutorials/write)] ([code](https://github.com/GoogleCloudPlatform/community)).
-
 ## Contributor License Agreement
 
 Contributions to this project must be accompanied by a Contributor License

SAMPLE_FORMAT.md

@@ -348,33 +348,39 @@ public static void exampleSnippet(String projectId, String filePath) {
     // Snippet content ...
 }
 ```
+
 ### Method Structure
+
 Method arguments should be limited to what is absolutely required for testing (ideally having at
 most 4 arguments). In most cases, this is project specific information or the path to an external
 file. For example, project specific information (such as `projectId`) or a `filePath` for an
 external file is acceptable, while an argument for the type of a file or a specific action is not.
- 
+
 Any declared function arguments should include a no-arg, main method with examples for how
 the user can initialize the method arguments and call the entrypoint for the snippet. If the
 values for these variables need to be replaced by the user, be explicit that
 they are example values only.
 
-Snippet methods should specify a return type of `void` and avoid returning any value wherever
-possible. Instead, show the user how to interact with a returned object programmatically by printing
-some example attributes to the console. 
+Snippet methods should return data that can be used in the calling method to show the user how to
+interact with a returned object programmatically.
+
 ```java
 public static void main(String[] args) {
     // TODO(developer): Replace these variables before running the sample.
     String projectId = "my-project-id";
     String filePath = "path/to/image.png";
-    inspectImageFile(projectId, filePath);
+    List<String> results = inspectImageFile(projectId, filePath);
+    for (String result : results) {
+      // process result ...
+    }
 }
 
 // This is an example snippet for showing best practices.
-public static void exampleSnippet(String projectId, String filePath) {
+public static List<String> exampleSnippet(String projectId, String filePath) {
     // Snippet content ...
 }
 ```
+
 ### Exception Handling
 Samples should include examples and details of how to catch and handle common `Exceptions` that are
 the result of improper interactions with the client or service. Lower level exceptions that are 

aiplatform/resources/dog_newspaper.png

@@ -0,0 +1,110 @@
+/*
+ * Copyright 2024 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package aiplatform;
+
+// [START generativeaionvertexai_batch_code_predict]
+
+import com.google.cloud.aiplatform.v1.BatchPredictionJob;
+import com.google.cloud.aiplatform.v1.GcsDestination;
+import com.google.cloud.aiplatform.v1.GcsSource;
+import com.google.cloud.aiplatform.v1.JobServiceClient;
+import com.google.cloud.aiplatform.v1.JobServiceSettings;
+import com.google.cloud.aiplatform.v1.LocationName;
+import com.google.gson.Gson;
+import com.google.protobuf.InvalidProtocolBufferException;
+import com.google.protobuf.Value;
+import com.google.protobuf.util.JsonFormat;
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.Map;
+
+public class BatchCodePredictionSample {
+
+  public static void main(String[] args) throws IOException, InterruptedException {
+    // TODO(developer): Replace these variables before running the sample.
+    String project = "YOUR_PROJECT_ID";
+    String location = "us-central1";
+    // inputUri: URI of the input dataset.
+    // Could be a BigQuery table or a Google Cloud Storage file.
+    // E.g. "gs://[BUCKET]/[DATASET].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
+    String inputUri = "gs://cloud-samples-data/batch/prompt_for_batch_code_predict.jsonl";
+    // outputUri: URI where the output will be stored.
+    // Could be a BigQuery table or a Google Cloud Storage file.
+    // E.g. "gs://[BUCKET]/[OUTPUT].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
+    String outputUri = "gs://YOUR_BUCKET/batch_code_predict_output";
+    String codeModel = "code-bison";
+
+    batchCodePredictionSample(project, location, inputUri, outputUri, codeModel);
+  }
+
+  // Perform batch code prediction using a pre-trained code generation model.
+  // Example of using Google Cloud Storage bucket as the input and output data source
+  public static BatchPredictionJob batchCodePredictionSample(
+      String project, String location, String inputUri, String outputUri, String codeModel)
+      throws IOException {
+    BatchPredictionJob response;
+    JobServiceSettings jobServiceSettings =  JobServiceSettings.newBuilder()
+        .setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
+    LocationName parent = LocationName.of(project, location);
+    String modelName = String.format(
+        "projects/%s/locations/%s/publishers/google/models/%s", project, location, codeModel);
+    // Construct your modelParameters
+    Map<String, String> modelParameters = new HashMap<>();
+    modelParameters.put("maxOutputTokens", "200");
+    modelParameters.put("temperature", "0.2");
+    modelParameters.put("topP", "0.95");
+    modelParameters.put("topK", "40");
+    Value parameterValue = mapToValue(modelParameters);
+
+    // Initialize client that will be used to send requests. This client only needs to be created
+    // once, and can be reused for multiple requests.
+    try (JobServiceClient client = JobServiceClient.create(jobServiceSettings)) {
+      BatchPredictionJob batchPredictionJob =
+          BatchPredictionJob.newBuilder()
+              .setDisplayName("my batch code prediction job " + System.currentTimeMillis())
+              .setModel(modelName)
+              .setInputConfig(
+                  BatchPredictionJob.InputConfig.newBuilder()
+                      .setGcsSource(GcsSource.newBuilder().addUris(inputUri).build())
+                      .setInstancesFormat("jsonl")
+                      .build())
+              .setOutputConfig(
+                  BatchPredictionJob.OutputConfig.newBuilder()
+                      .setGcsDestination(GcsDestination.newBuilder()
+                          .setOutputUriPrefix(outputUri).build())
+                      .setPredictionsFormat("jsonl")
+                      .build())
+              .setModelParameters(parameterValue)
+              .build();
+
+      response = client.createBatchPredictionJob(parent, batchPredictionJob);
+
+      System.out.format("response: %s\n", response);
+      System.out.format("\tName: %s\n", response.getName());
+    }
+    return response;
+  }
+
+  private static Value mapToValue(Map<String, String> map) throws InvalidProtocolBufferException {
+    Gson gson = new Gson();
+    String json = gson.toJson(map);
+    Value.Builder builder = Value.newBuilder();
+    JsonFormat.parser().merge(json, builder);
+    return builder.build();
+  }
+}
+// [END generativeaionvertexai_batch_code_predict]

aiplatform/src/main/java/aiplatform/BatchCodePredictionSample.java

@@ -5,7 +5,7 @@
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
- *     http://www.apache.org/licenses/LICENSE-2.0
+ * http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
@@ -16,98 +16,100 @@
 
 package aiplatform;
 
-// [START aiplatform_batch_text_predict]
+// [START generativeaionvertexai_batch_text_predict]
 
 import com.google.cloud.aiplatform.v1.BatchPredictionJob;
-import com.google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig;
-import com.google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig;
 import com.google.cloud.aiplatform.v1.GcsDestination;
 import com.google.cloud.aiplatform.v1.GcsSource;
 import com.google.cloud.aiplatform.v1.JobServiceClient;
 import com.google.cloud.aiplatform.v1.JobServiceSettings;
-import com.google.cloud.aiplatform.v1.LocationName;
+import com.google.gson.Gson;
 import com.google.protobuf.InvalidProtocolBufferException;
 import com.google.protobuf.Value;
 import com.google.protobuf.util.JsonFormat;
 import java.io.IOException;
-
+import java.util.HashMap;
+import java.util.Map;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.TimeoutException;
 
 public class BatchTextPredictionSample {
 
-  public static void main(String[] args) throws IOException {
-    // TODO (Developer): Replace the input_uri and output_uri with your own GCS paths
+  public static void main(String[] args)
+      throws IOException, InterruptedException, ExecutionException, TimeoutException {
+    // TODO(developer): Replace these variables before running the sample.
     String project = "YOUR_PROJECT_ID";
     String location = "us-central1";
-    // inputUri (str, optional): URI of the input dataset.
+    // inputUri: URI of the input dataset.
     // Could be a BigQuery table or a Google Cloud Storage file.
     // E.g. "gs://[BUCKET]/[DATASET].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
     String inputUri = "gs://cloud-samples-data/batch/prompt_for_batch_text_predict.jsonl";
-    // outputUri (str, optional): URI where the output will be stored.
+    // outputUri: URI where the output will be stored.
     // Could be a BigQuery table or a Google Cloud Storage file.
     // E.g. "gs://[BUCKET]/[OUTPUT].jsonl" OR "bq://[PROJECT].[DATASET].[TABLE]"
-    String outputUri = "gs://batch-bucket-testing/batch_text_predict_output";
-    String codeModel = "text-bison";
+    String outputUri = "gs://YOUR_BUCKET/batch_text_predict_output";
+    String textModel = "text-bison";
 
-    batchTextPrediction(project, location, inputUri, outputUri, codeModel);
+    batchTextPrediction(project, inputUri, outputUri, textModel, location);
   }
 
   // Perform batch text prediction using a pre-trained text generation model.
   // Example of using Google Cloud Storage bucket as the input and output data source
-  public static void batchTextPrediction(
-      String project, String location, String inputUri,
-      String outputUri, String codeModel) throws IOException {
-    String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
-    JobServiceSettings jobServiceSettings =
-        JobServiceSettings.newBuilder().setEndpoint(endpoint).build();
-    // Construct your modelParameters
-    String parameters =
-        "{\n" + "  \"temperature\": 0.2,\n" + "  \"maxOutputTokens\": 200\n" + "}";
-    Value parameterValue = stringToValue(parameters);
+  static BatchPredictionJob batchTextPrediction(
+      String projectId, String inputUri, String outputUri, String textModel, String location)
+      throws IOException {
+    BatchPredictionJob response;
+    JobServiceSettings jobServiceSettings =  JobServiceSettings.newBuilder()
+        .setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
+    String parent = String.format("projects/%s/locations/%s", projectId, location);
     String modelName = String.format(
-        "projects/%s/locations/%s/publishers/google/models/%s", project, location, codeModel);
+        "projects/%s/locations/%s/publishers/google/models/%s", projectId, location, textModel);
+    // Construct model parameters
+    Map<String, String> modelParameters = new HashMap<>();
+    modelParameters.put("maxOutputTokens", "200");
+    modelParameters.put("temperature", "0.2");
+    modelParameters.put("topP", "0.95");
+    modelParameters.put("topK", "40");
+    Value parameterValue = mapToValue(modelParameters);
 
     // Initialize client that will be used to send requests. This client only needs to be created
     // once, and can be reused for multiple requests.
     try (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
 
-      GcsSource.Builder gcsSource = GcsSource.newBuilder();
-      gcsSource.addUris(inputUri);
-      InputConfig inputConfig =
-          InputConfig.newBuilder()
-              .setGcsSource(gcsSource)
-              .setInstancesFormat("jsonl")
-              .build();
-
-      GcsDestination.Builder gcsDestination = GcsDestination.newBuilder();
-      gcsDestination.setOutputUriPrefix(outputUri);
-      OutputConfig outputConfig =
-          OutputConfig.newBuilder()
-              .setGcsDestination(gcsDestination)
-              .setPredictionsFormat("jsonl")
-              .build();
-
-      BatchPredictionJob.Builder batchPredictionJob =
+      BatchPredictionJob batchPredictionJob =
           BatchPredictionJob.newBuilder()
               .setDisplayName("my batch text prediction job " + System.currentTimeMillis())
               .setModel(modelName)
-              .setInputConfig(inputConfig)
-              .setOutputConfig(outputConfig)
-              .setModelParameters(parameterValue);
+              .setInputConfig(
+                  BatchPredictionJob.InputConfig.newBuilder()
+                      .setGcsSource(GcsSource.newBuilder().addUris(inputUri).build())
+                      .setInstancesFormat("jsonl")
+                      .build())
+              .setOutputConfig(
+                  BatchPredictionJob.OutputConfig.newBuilder()
+                      .setGcsDestination(GcsDestination.newBuilder()
+                          .setOutputUriPrefix(outputUri).build())
+                      .setPredictionsFormat("jsonl")
+                      .build())
+              .setModelParameters(parameterValue)
+              .build();
 
-      LocationName parent = LocationName.of(project, location);
-      BatchPredictionJob response =
-          jobServiceClient.createBatchPredictionJob(parent, batchPredictionJob.build());
+      // Create the batch prediction job
+      response =
+          jobServiceClient.createBatchPredictionJob(parent, batchPredictionJob);
 
       System.out.format("response: %s\n", response);
       System.out.format("\tName: %s\n", response.getName());
     }
+    return response;
   }
 
-  // Convert a Json string to a protobuf.Value
-  static Value stringToValue(String value) throws InvalidProtocolBufferException {
+  private static Value mapToValue(Map<String, String> map) throws InvalidProtocolBufferException {
+    Gson gson = new Gson();
+    String json = gson.toJson(map);
     Value.Builder builder = Value.newBuilder();
-    JsonFormat.parser().merge(value, builder);
+    JsonFormat.parser().merge(json, builder);
     return builder.build();
   }
 }
-// [END aiplatform_batch_text_predict]
+// [END generativeaionvertexai_batch_text_predict]