創建異步調用

參考以下示例代碼，通過content字段，傳入長文本內容，NLP自學習平臺會保存長文本，并進行異步模型預測。

package com.alibaba.nlp;

import com.aliyuncs.DefaultAcsClient;
import com.aliyuncs.IAcsClient;
import com.aliyuncs.exceptions.ClientException;
import com.aliyuncs.nlp_automl.model.v20191111.CreateAsyncPredictRequest;
import com.aliyuncs.nlp_automl.model.v20191111.CreateAsyncPredictResponse;
import com.aliyuncs.profile.DefaultProfile;
import com.google.gson.Gson;

public class NlpAutomlAsync {
    public static void main(String[] args) throws ClientException {
        DefaultProfile profile = DefaultProfile.getProfile(
            "cn-hangzhou",
            "<your-access-key-id>",
            "<your-access-key-secret>");
        IAcsClient client = new DefaultAcsClient(profile);

        CreateAsyncPredictRequest request = new CreateAsyncPredictRequest();
        request.setModelId(1818);
        request.setContent(longContent);
        CreateAsyncPredictResponse response = client.getAcsResponse(request);
        System.out.println("" + new Gson().toJson(response));
    }
}
            

另外，可以通過將本地文件內容進行base64編碼之后，作為字符串上傳NLP自學習平臺系統，NLP自學習平臺支持解析base64編碼的文件內容。并做文本抽取，進行模型預測調用。以下是參考示例代碼。

package com.alibaba.nlp;

import com.aliyuncs.DefaultAcsClient;
import com.aliyuncs.IAcsClient;
import com.aliyuncs.exceptions.ClientException;
import com.aliyuncs.nlp_automl.model.v20191111.CreateAsyncPredictRequest;
import com.aliyuncs.nlp_automl.model.v20191111.CreateAsyncPredictResponse;
import com.aliyuncs.profile.DefaultProfile;
import com.google.gson.Gson;

import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.util.Base64;

public class NlpAutomlAsyncDaily {
    public static void main(String[] args) throws ClientException, IOException {
        DefaultProfile profile = DefaultProfile.getProfile(
            "cn-hangzhou",
            "<your-access-key-id>",
            "<your-access-key-secret>");
        IAcsClient client = new DefaultAcsClient(profile);

        InputStream in = new FileInputStream("<local-file-dir>");
        byte[] data = new byte[in.available()];
        int readSize = in.read(data);
        System.out.println("read data size=" + readSize);
        in.close();
        String base64String = Base64.getEncoder().encodeToString(data);
        System.out.println("base64 string length=" + base64String.length());

        CreateAsyncPredictRequest request = new CreateAsyncPredictRequest();
        request.setModelId(2269);
        request.setFileType("<file-type>");
        request.setFileContent(base64String);
        CreateAsyncPredictResponse response = client.getAcsResponse(request);
        System.out.println("" + new Gson().toJson(response));
    }
}

調用成功之后，NLP自學習平臺返回一個asyncPredictId字段，用于查詢異步預測結果信息。

{
  "requestId": "65917545-CDBF-4246-8708-CD03ED4AFDED",
  "asyncPredictId": 1669
}

通過使用asyncPredictId參考下一章節獲取異步預測結果。

獲取異步調用結果

通過創建GetAsyncPredictRequest請求，查詢異步預測結果。

package com.alibaba.nlp;

import com.aliyuncs.DefaultAcsClient;
import com.aliyuncs.IAcsClient;
import com.aliyuncs.exceptions.ClientException;
import com.aliyuncs.nlp_automl.model.v20191111.GetAsyncPredictRequest;
import com.aliyuncs.nlp_automl.model.v20191111.GetAsyncPredictResponse;
import com.aliyuncs.profile.DefaultProfile;
import com.google.gson.Gson;

public class NlpAutomlAsync2 {
    public static void main(String[] args) throws ClientException {
        DefaultProfile profile = DefaultProfile.getProfile(
            "cn-hangzhou",
            "<your-access-key-id>",
            "<your-access-key-secret>");
        IAcsClient client = new DefaultAcsClient(profile);

        GetAsyncPredictRequest request = new GetAsyncPredictRequest();
        request.setAsyncPredictId(1669);
        GetAsyncPredictResponse response = client.getAcsResponse(request);
        System.out.println("" + new Gson().toJson(response));

    }
}