java 框架可通过以下三种方式集成 ai 技术：通过 api 访问、使用 java 客户端库以及采用开放标准。api 访问可轻松使用 ai 提供商提供的各种 ai 服务。java 客户端库允许直接与 ai 服务交互，简化了集成过程。开放标准如 protocol buffers 或 grpc 可实现与提供商无关的 ai 集成。
Java 框架与人工智能 (AI) 的集成方法
随着 AI 在企业中的普及，将 AI 技术集成到 Java 应用程序变得越来越重要。以下是常见的方法：
1. 通过 API 访问
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import com.google.cloud.aiplatform.v1.EndpointServiceClient;import com.google.cloud.aiplatform.v1.EndpointServiceSettings;import com.google.cloud.aiplatform.v1.PredictRequest;import com.google.cloud.aiplatform.v1.PredictResponse;import java.io.IOException;public class AiApiExample { public static void main(String[] args) throws IOException { // Set the endpoint URI String endpoint = "YOUR_ENDPOINT_URI"; // Initialize the client EndpointServiceSettings settings = EndpointServiceSettings.newBuilder().build(); EndpointServiceClient client = EndpointServiceClient.create(settings); // Prepare the prediction request PredictRequest.Builder requestBuilder = PredictRequest.newBuilder(); requestBuilder.setEndpoint(endpoint); // Add the input data here PredictRequest request = requestBuilder.build(); // Perform the prediction PredictResponse response = client.predict(request); // Process the prediction response // ... }}
2. 使用 Java 客户端库
一些 AI 提供商提供 Java 客户端库，允许直接与 AI 服务交互，从而简化了集成。
import com.google.cloud.automl.v1beta1.ImageClassificationPredictResponse;import com.google.cloud.automl.v1beta1.PredictRequest;import com.google.cloud.automl.v1beta1.PredictRequest.ParamsEntry;import com.google.cloud.automl.v1beta1.PredictResponse;import com.google.cloud.automl.v1beta1.PredictionServiceClient;import com.google.cloud.automl.v1beta1.PredictionServiceSettings;import java.io.IOException;import java.nio.file.Paths;public class AiClientLibExample { public static void main(String[] args) throws IOException { // Set the endpoint URI String endpoint = "YOUR_ENDPOINT_URI"; // Set the prediction input String filePath = "YOUR_IMAGE_FILE_PATH"; // Initialize the client PredictionServiceSettings settings = PredictionServiceSettings.newBuilder().build(); PredictionServiceClient client = PredictionServiceClient.create(settings); // Prepare the prediction request PredictRequest.Builder requestBuilder = PredictRequest.newBuilder(); requestBuilder.setEndpoint(endpoint); requestBuilder.putParams( "score_threshold", ParamsEntry.newBuilder().setDoubleValue(0.5).build()); requestBuilder.addImage(Paths.get(filePath)); PredictRequest request = requestBuilder.build(); // Perform the prediction PredictResponse response = client.predict(request); // Process the prediction response for (ImageClassificationPredictResponse prediction : response.getPayloadList().expandList().getImageClassification()) { // Process the prediction result // ... } }}
3. 使用开放标准
如 Protocol Buffers 或 gRPC，可用于与 AI 服务通信。通过这种方法，可以实现与提供商无关的 AI 集成。
import com.google.protobuf.ByteString;import io.grpc.ManagedChannel;import io.grpc.ManagedChannelBuilder;import io.grpc.StatusRuntimeException;import org.tensorflow.framework.TensorShapeProto;import org.tensorflow.framework.TensorProto;import org.tensorflow.serving.apis.Model;import org.tensorflow.serving.apis.PredictRequest;import org.tensorflow.serving.apis.PredictResponse;import org.tensorflow.serving.apis.PredictionServiceGrpc;public class AiOpenStandardExample { public static void main(String[] args) throws Exception { // Set the server address String serverAddress = "YOUR_SERVER_ADDRESS"; // Connect to the server ManagedChannel channel = ManagedChannelBuilder.forTarget(serverAddress).usePlaintext().build(); PredictionServiceGrpc.PredictionServiceBlockingStub stub = PredictionServiceGrpc.newBlockingStub(channel); // Prepare the prediction request TensorProto input = TensorProto.newBuilder() .addDtype(TensorProto.DataType.DT_FLOAT) .addShape(TensorShapeProto.getDefaultInstance()) .addFloatVal(1.0f) .addFloatVal(2.0f) .build(); PredictRequest request = PredictRequest.newBuilder() .setModel(Model.newBuilder().setName("YOUR_MODEL_NAME").build()) .putInputs("input1", input) .build(); // Perform the prediction try { PredictResponse response = stub.predict(request); // Process the prediction response TensorProto output = response.getOutputsMap().get("output1"); float prediction = output.getFloatVal(0); // ... } catch (StatusRuntimeException e) { // Handle error e.printStackTrace(); } }}
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