add google vertex ai

backend/pom.xml

@@ -36,6 +36,11 @@
 			<groupId>com.google.cloud</groupId>
 			<artifactId>google-cloud-speech</artifactId>
 		</dependency>
+		<dependency>
+			<groupId>com.google.cloud</groupId>
+			<artifactId>google-cloud-aiplatform</artifactId>
+			<version>3.42.0</version>
+		</dependency>
 		<dependency>
 			<groupId>org.projectlombok</groupId>
 			<artifactId>lombok</artifactId>

backend/src/main/java/com/example/BriefMe/BriefMeApplication.java

@@ -1,13 +1,20 @@
 package com.example.BriefMe;
 
+import com.example.BriefMe.service.impl.PredictTextSummarizationSample;
+import java.io.IOException;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.boot.SpringApplication;
 import org.springframework.boot.autoconfigure.SpringBootApplication;
 
+
 @SpringBootApplication
 @Slf4j
 public class BriefMeApplication {
-	public static void main(String[] args) {
+	public static void main(String[] args) throws IOException {
+
 		SpringApplication.run(BriefMeApplication.class, args);
+		PredictTextSummarizationSample predictTextSummarization = new PredictTextSummarizationSample();
+		predictTextSummarization.predict();
+
 	}
 }

backend/src/main/java/com/example/BriefMe/request/VertexAIParameters.java

@@ -0,0 +1,13 @@
+package com.example.BriefMe.request;
+
+import lombok.AllArgsConstructor;
+import lombok.Data;
+
+@Data
+@AllArgsConstructor
+public class VertexAIParameters {
+    Double temperature;
+    Integer maxOutputTokens;
+    Double topP;
+    Integer topK;
+}

backend/src/main/java/com/example/BriefMe/request/VertexAIPrompt.java

@@ -0,0 +1,10 @@
+package com.example.BriefMe.request;
+
+import lombok.AllArgsConstructor;
+import lombok.Data;
+
+@Data
+@AllArgsConstructor
+public class VertexAIPrompt {
+    String prompt;
+}

backend/src/main/java/com/example/BriefMe/service/impl/PredictTextSummarizationSample.java

@@ -0,0 +1,82 @@
+package com.example.BriefMe.service.impl;
+
+import com.example.BriefMe.request.VertexAIParameters;
+import com.example.BriefMe.request.VertexAIPrompt;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import java.io.IOException;
+import org.springframework.stereotype.Service;
+import com.google.cloud.aiplatform.v1beta1.EndpointName;
+import com.google.cloud.aiplatform.v1beta1.PredictResponse;
+import com.google.cloud.aiplatform.v1beta1.PredictionServiceClient;
+import com.google.cloud.aiplatform.v1beta1.PredictionServiceSettings;
+import com.google.protobuf.Value;
+import com.google.protobuf.util.JsonFormat;
+import java.util.ArrayList;
+import java.util.List;
+
+
+@Service
+public class PredictTextSummarizationSample {
+    public void predict() throws IOException {
+        String text ="";
+        VertexAIPrompt vertexAIPrompt = new VertexAIPrompt("Provide a short summary in five numeric bullet points:" + text);
+        ObjectMapper objectMapper = new ObjectMapper();
+        String vertexAIRequestString = objectMapper.writeValueAsString(vertexAIPrompt);
+
+        VertexAIParameters vertexAIParameters = new VertexAIParameters(0.2, 256, 0.95, 40);
+        String vertexAIParamsString = objectMapper.writeValueAsString(vertexAIParameters);
+
+
+        String project = "artful-lane-419217";
+        String location = "us-central1";
+        String publisher = "google";
+        String model = "text-bison@001";
+
+        predictTextSummarization(vertexAIRequestString, vertexAIParamsString, project, location, publisher, model);
+    }
+
+    // Get summarization from a supported text model
+    public void predictTextSummarization(
+            String instance,
+            String parameters,
+            String project,
+            String location,
+            String publisher,
+            String model)
+            throws IOException {
+        String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
+
+        PredictionServiceSettings predictionServiceSettings =
+                PredictionServiceSettings.newBuilder()
+                        .setEndpoint(endpoint)
+                        .build();
+
+        // 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 (PredictionServiceClient predictionServiceClient =
+                PredictionServiceClient.create(predictionServiceSettings)) {
+            final EndpointName endpointName =
+                    EndpointName.ofProjectLocationPublisherModelName(project, location, publisher, model);
+
+            // Use Value.Builder to convert instance to a dynamically typed value that can be
+            // processed by the service.
+            Value.Builder instanceValue = Value.newBuilder();
+            JsonFormat.parser().merge(instance, instanceValue);
+            List<Value> instances = new ArrayList<>();
+            instances.add(instanceValue.build());
+
+            // Use Value.Builder to convert parameter to a dynamically typed value that can be
+            // processed by the service.
+            Value.Builder parameterValueBuilder = Value.newBuilder();
+            JsonFormat.parser().merge(parameters, parameterValueBuilder);
+            Value parameterValue = parameterValueBuilder.build();
+
+            PredictResponse predictResponse =
+                    predictionServiceClient.predict(endpointName, instances, parameterValue);
+
+            System.out.println("Predict Response");
+            System.out.println(predictResponse);
+        }
+    }
+
+}