同步接口API详情

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量，请在此处用您的API Key进行替换
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # 百炼服务的base_url
)

completion = client.embeddings.create(
    model="text-embedding-v4",
    input='衣服的质量杠杠的，很漂亮，不枉我等了这么久啊，喜欢，以后还来这里买',
    dimensions=1024, # 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
    encoding_format="float"
)

print(completion.model_dump_json())

Java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.CreateEmbeddingResponse;
import com.openai.models.EmbeddingCreateParams;

public class Main {
    public static void main(String[] args) {
        // 创建客户端，使用环境变量中的API密钥
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // 创建向量化请求参数
        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                .model("text-embedding-v4")
                .input("衣服的质量杠杠的，很漂亮，不枉我等了这么久啊，喜欢，以后还来这里买")
                // 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
                .dimensions(1024)
                .build();

        try {
            // 发送请求并获取响应
            CreateEmbeddingResponse response = client.embeddings().create(params);
            System.out.println(response);
        } catch (Exception e) {
            System.err.println("请求出错，请查看错误码对照网页：");
            System.err.println("https://help.aliyun.com/zh/model-studio/faq-about-alibaba-cloud-model-studio?spm=a2c4g.11186623.help-menu-2400256.d_0_17_0.18733a66lTrcHv#1c38f58abfcml");
            System.err.println("错误详情：" + e.getMessage());
            e.printStackTrace();
        }
    }
}

curl

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v4",
    "input": "风急天高猿啸哀，渚清沙白鸟飞回，无边落木萧萧下，不尽长江滚滚来",  
    "dimension": "1024",  
    "encoding_format": "float"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量，请在此处用您的API Key进行替换
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # 百炼服务的base_url
)

completion = client.embeddings.create(
    model="text-embedding-v4",
    input=['风急天高猿啸哀', '渚清沙白鸟飞回', '无边落木萧萧下', '不尽长江滚滚来'],
    dimensions=1024,# 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
    encoding_format="float"
)

print(completion.model_dump_json())

Java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.CreateEmbeddingResponse;
import com.openai.models.EmbeddingCreateParams;

import java.util.Arrays;
import java.util.List;
import java.util.ArrayList;

public class Main {
    public static void main(String[] args) {
        // 创建客户端，使用环境变量中的API密钥
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // 创建输入字符串列表
        List<String> inputList = Arrays.asList(
            "风急天高猿啸哀",
            "渚清沙白鸟飞回",
            "无边落木萧萧下",
            "不尽长江滚滚来"
        );

        // 存储所有响应的列表
        List<CreateEmbeddingResponse> responses = new ArrayList<>();

        // 循环处理每个字符串
        for (String text : inputList) {
            try {
                // 创建向量化请求参数
                EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                        .model("text-embedding-v4")
                        .input(text)
                        // 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
                        .dimensions(1024)
                        .build();

                // 发送请求并获取响应
                CreateEmbeddingResponse response = client.embeddings().create(params);
                responses.add(response);
                System.out.println("处理文本: " + text);
                System.out.println("向量化结果: " + response);
                System.out.println("------------------------");
            } catch (Exception e) {
                System.err.println("处理文本时出错: " + text);
                System.err.println("错误详情：" + e.getMessage());
                e.printStackTrace();
            }
        }

        // 打印总结信息
        System.out.println("\n总共处理了 " + responses.size() + " 个文本");
    }
} 

curl

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v4",
    "input": [
        "风急天高猿啸哀",
        "渚清沙白鸟飞回", 
        "无边落木萧萧下", 
        "不尽长江滚滚来"
        ],
    "dimension": 1024,
    "encoding_format": "float"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量，请在此处用您的API Key进行替换
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # 百炼服务的base_url
)
# 确保将 'texts_to_embedding.txt' 替换为您自己的文件名或路径
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    completion = client.embeddings.create(
        model="text-embedding-v4",
        input=f,
        dimensions=1024,# 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
        encoding_format="float"
    )
print(completion.model_dump_json())

Java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.CreateEmbeddingResponse;
import com.openai.models.EmbeddingCreateParams;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.nio.file.Paths;

public class Main {
    public static void main(String[] args) {
        // 创建客户端，使用环境变量中的API密钥
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // 确保将 'texts_to_embedding.txt' 替换为您自己的文件名或绝对路径
        String filePath = "/src/main/java/org/example/text_to_embedding.txt";

        try {
            // 读取文件内容
            StringBuilder fileContent = new StringBuilder();
            try (BufferedReader reader = new BufferedReader(new FileReader(filePath))) {
                String line;
                while ((line = reader.readLine()) != null) {
                    fileContent.append(line).append("\n");
                }
            }

            // 创建向量化请求参数
            EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                    .model("text-embedding-v4")
                    .input(fileContent.toString())
                    // 指定向量维度（仅 text-embedding-v3及 text-embedding-v4支持该参数）
                    .dimensions(1024)
                    .build();

            // 发送请求并获取响应
            CreateEmbeddingResponse response = client.embeddings().create(params);
            System.out.println(response);

        } catch (IOException e) {
            System.err.println("读取文件时出错：" + e.getMessage());
            e.printStackTrace();
        } catch (Exception e) {
            System.err.println("请求出错，请查看错误码对照网页：");
            System.err.println("https://help.aliyun.com/zh/model-studio/faq-about-alibaba-cloud-model-studio?spm=a2c4g.11186623.help-menu-2400256.d_0_17_0.18733a66lTrcHv#1c38f58abfcml");
            System.err.println("错误详情：" + e.getMessage());
            e.printStackTrace();
        }
    }
} 

curl

确保将 'texts_to_embedding.txt' 替换为您自己的文件名或路径

FILE_CONTENT=$(cat texts_to_embedding.txt | jq -Rs .)
curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v4",
    "input": ['"$FILE_CONTENT"'],
    "dimension": 1024,
    "encoding_format": "float"
}'