Synchronous API

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If the environment variable is not set, replace the placeholder with your API key.
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # The base_url for the Model Studio service.
)

completion = client.embeddings.create(
    model="text-embedding-v4",
    input='The quality of the clothes is excellent and they are beautiful. It was well worth the wait. I love them and will buy from here again.',
    dimensions=1024, # Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
    encoding_format="float"
)

print(completion.model_dump_json())

Java

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

public class Main {
    public static void main(String[] args) {
        // Create a client using the API key from the environment variable.
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // Create parameters for the vectorization request.
        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                .model("text-embedding-v4")
                .input(EmbeddingCreateParams.Input.ofString("The quality of the clothes is excellent and they are beautiful. It was well worth the wait. I love them and will buy from here again."))
                // Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
                .dimensions(1024)
                .build();

        try {
            // Send the request and get the response.
            CreateEmbeddingResponse response = client.embeddings().create(params);
            System.out.println(response);
        } catch (Exception e) {
            System.err.println("Request failed. See the following page for error codes:");
            System.err.println("https://www.alibabacloud.com/help/en/model-studio/faq-about-alibaba-cloud-model-studio#1c38f58abfcml");
            System.err.println("Error details: " + 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": "The wind is strong, the sky is high, and the apes cry mournfully. The islet is clear, the sand is white, and the birds fly back. The boundless forest sheds its leaves shower by shower. The endless river rolls on wave after wave.",  
    "dimensions": 1024,  
    "encoding_format": "float"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If the environment variable is not set, replace the placeholder with your API key.
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # The base_url for the Model Studio service.
)

completion = client.embeddings.create(
    model="text-embedding-v4",
    input=['The wind is strong, the sky is high, and the apes cry mournfully.', 'The islet is clear, the sand is white, and the birds fly back.', 'The boundless forest sheds its leaves shower by shower.', 'The endless river rolls on wave after wave.'],
    dimensions=1024, # Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
    encoding_format="float"
)

print(completion.model_dump_json())

Java

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

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

public class Main {
    public static void main(String[] args) {
        // Create a client using the API key from the environment variable.
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // Create a list of input strings.
        List<String> inputList = Arrays.asList(
            "The wind is strong, the sky is high, and the apes cry mournfully.",
            "The islet is clear, the sand is white, and the birds fly back.",
            "The boundless forest sheds its leaves shower by shower.",
            "The endless river rolls on wave after wave."
        );

        // List to store all responses.
        List<CreateEmbeddingResponse> responses = new ArrayList<>();

        // Process each string in a loop.
        for (String text : inputList) {
            try {
                // Create parameters for the vectorization request.
                EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                        .model("text-embedding-v4")
                        .input(EmbeddingCreateParams.Input.ofString(text))
                        // Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
                        .dimensions(1024)
                        .build();

                // Send the request and get the response.
                CreateEmbeddingResponse response = client.embeddings().create(params);
                responses.add(response);
                System.out.println("Processing text: " + text);
                System.out.println("Vectorization result: " + response);
                System.out.println("------------------------");
            } catch (Exception e) {
                System.err.println("Error processing text: " + text);
                System.err.println("Error details: " + e.getMessage());
                e.printStackTrace();
            }
        }

        // Print the summary information.
        System.out.println("\nProcessed a total of " + responses.size() + " texts.");
    }
} 

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": [
        "The wind is strong, the sky is high, and the apes cry mournfully.",
        "The islet is clear, the sand is white, and the birds fly back.", 
        "The boundless forest sheds its leaves shower by shower.", 
        "The endless river rolls on wave after wave."
        ],
    "dimensions": 1024,
    "encoding_format": "float"
}'

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If the environment variable is not set, replace the placeholder with your API key.
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"  # The base_url for the Model Studio service.
)
# Make sure to replace 'texts_to_embedding.txt' with your file name or path.
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    completion = client.embeddings.create(
        model="text-embedding-v4",
        input=f,
        dimensions=1024, # Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
        encoding_format="float"
    )
print(completion.model_dump_json())

Java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.embeddings.CreateEmbeddingResponse;
import com.openai.models.embeddings.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) {
        // Create a client using the API key from the environment variable.
        OpenAIClient client = OpenAIOkHttpClient.builder()
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .baseUrl("https://dashscope.aliyuncs.com/compatible-mode/v1")
                .build();

        // Make sure to replace 'texts_to_embedding.txt' with your file name or absolute path.
        String filePath = "/src/main/java/org/example/text_to_embedding.txt";

        try {
            // Read the file content.
            StringBuilder fileContent = new StringBuilder();
            try (BufferedReader reader = new BufferedReader(new FileReader(filePath))) {
                String line;
                while ((line = reader.readLine()) != null) {
                    fileContent.append(line).append("\n");
                }
            }

            // Create parameters for the vectorization request.
            EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                    .model("text-embedding-v4")
                    .input(EmbeddingCreateParams.Input.ofString(fileContent.toString()))
                    // Sets the embedding dimensions (only for text-embedding-v3 and text-embedding-v4).
                    .dimensions(1024)
                    .build();

            // Send the request and get the response.
            CreateEmbeddingResponse response = client.embeddings().create(params);
            System.out.println(response);

        } catch (IOException e) {
            System.err.println("Error reading the file: " + e.getMessage());
            e.printStackTrace();
        } catch (Exception e) {
            System.err.println("Request failed. See the following page for error codes:");
            System.err.println("https://www.alibabacloud.com/help/en/model-studio/faq-about-alibaba-cloud-model-studio#1c38f58abfcml");
            System.err.println("Error details: " + e.getMessage());
            e.printStackTrace();
        }
    }
} 

curl

Replace 'texts_to_embedding.txt' with your file name or path.

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"'],
    "dimensions": 1024,
    "encoding_format": "float"
}'