Synchronous API

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.
    # China (Beijing) region URL. Replace {WorkspaceId} with your actual workspace ID. URLs differ by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

completion = client.embeddings.create(
    model="qwen3.7-text-embedding",
    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 qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4).
    encoding_format="float"
)

print(completion.model_dump_json())

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"))
                // China (Beijing) region URL. Replace {WorkspaceId} with your actual workspace ID. URLs differ by region.
                .baseUrl("https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1")
                .build();

        // Create parameters for the vectorization request.
        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
                .model("qwen3.7-text-embedding")
                .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 qwen3.7-text-embedding, 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://help.aliyun.com/en/model-studio/error-code");
            System.err.println("Error details: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "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"
}'

# Call via the dashscope command line
export DASHSCOPE_API_KEY="your-api-key"
# Replace {WorkspaceId} with your Workspace ID, and cn-beijing with the corresponding region (Singapore: ap-southeast-1, US East: us-east-1)
export DASHSCOPE_HTTP_BASE_URL="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"
dashscope embeddings create -m text-embedding-v3 -i "Hello world"

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.
    # China (Beijing) region URL. Replace {WorkspaceId} with your actual workspace ID. URLs differ by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

completion = client.embeddings.create(
    model="qwen3.7-text-embedding",
    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 qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4).
    encoding_format="float"
)

print(completion.model_dump_json())

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"))
                // China (Beijing) region URL. Replace {WorkspaceId} with your actual workspace ID. URLs differ by region.
                .baseUrl("https://{WorkspaceId}.cn-beijing.maas.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("qwen3.7-text-embedding")
                        .input(EmbeddingCreateParams.Input.ofString(text))
                        // Sets the embedding dimensions (only for qwen3.7-text-embedding, 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 --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "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"
}'

import os
import dashscope
# This configuration is for the China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. The region value varies by region.
client = dashscope.Client(
    # If you have not set an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx",
    # API keys are region-specific. To get an API key, see https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    workspace="{WorkspaceId}",
    region="cn-beijing",
)

resp = client.embeddings.create(
    model="qwen3.7-text-embedding",
    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.',
    dimension=1024,  # Specifies the output embedding dimension. This parameter is available only for qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4 models.
    output_type="dense&sparse"
)

print(resp)

import java.util.Arrays;
import com.alibaba.dashscope.embeddings.TextEmbedding;
import com.alibaba.dashscope.embeddings.TextEmbeddingParam;
import com.alibaba.dashscope.embeddings.TextEmbeddingResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.Constants;

/**
 * Qwen text embedding model call example.
 */
public final class Main {
    public static void main(String[] args) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. Configurations differ by region.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        try {
            // Build the request parameters.
            TextEmbeddingParam param = TextEmbeddingParam
                    .builder()
                    .model("qwen3.7-text-embedding")  // Use the text-embedding-v4 model.
                    .texts(Arrays.asList("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."))  // Input text.
                    .parameter("dimension", 1024)  // Specifies the output embedding dimension. This parameter is available only for qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4 models.
                    .build();

            // Create a model instance and call the model.
            TextEmbedding textEmbedding = new TextEmbedding();
            TextEmbeddingResult result = textEmbedding.call(param);

            // Print the result.
            System.out.println(result);

        } catch (ApiException | NoApiKeyException e) {
            System.out.println("Call failed: " + e.getMessage());
        }
    }
}

curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "input": {
        "texts": [
        "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."
        ]
    },
    "parameters": {
        "dimension": 1024,
        "output_type": "dense"
    }
}'

import os
import dashscope
from dashscope.v2 import DashScopeError
# This configuration is for the China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. The region value varies by region.
client = dashscope.Client(
    # If you have not set an environment variable, replace this line with your Model Studio API key: api_key="sk-xxx",
    # API keys are region-specific. To get an API key, see https://help.aliyun.com/en/model-studio/get-api-key
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    workspace="{WorkspaceId}",
    region="cn-beijing",
)

DASHSCOPE_MAX_BATCH_SIZE = 10

inputs = ['A swift wind, a high sky, and the gibbons cry mournfully.', 'A clear islet, white sand, and the birds fly back.', 'Boundless rustling woods shed their leaves.', 'The endless Yangtze River comes rolling in.']

result = None
batch_counter = 0
try:
    for i in range(0, len(inputs), DASHSCOPE_MAX_BATCH_SIZE):
        batch = inputs[i:i + DASHSCOPE_MAX_BATCH_SIZE]
        resp = client.embeddings.create(
            model="qwen3.7-text-embedding",
            input=batch,
            dimension=1024  # Specifies the output embedding dimension. This parameter is available only for qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4 models.
        )
        if result is None:
            result = resp
        else:
            for emb in resp.embeddings:
                emb.index += batch_counter
                result.embeddings.append(emb)
            result.usage.total_tokens += resp.usage.total_tokens
        batch_counter += len(batch)
except DashScopeError as e:
    print(f"HTTP status code: {e.status_code}")
    print(f"Error code: {e.code}")
    print(f"Error message: {e.message}")
    print("For more information, see https://help.aliyun.com/en/model-studio/developer-reference/error-code")

print(result)

import java.util.Arrays;
import java.util.List;
import com.alibaba.dashscope.embeddings.TextEmbedding;
import com.alibaba.dashscope.embeddings.TextEmbeddingParam;
import com.alibaba.dashscope.embeddings.TextEmbeddingResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.embeddings.TextEmbeddingResultItem;
import com.alibaba.dashscope.utils.Constants;

public final class Main {
    private static final int DASHSCOPE_MAX_BATCH_SIZE = 10;

    public static void main(String[] args) {
        // China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. Configurations differ by region.
        Constants.baseHttpApiUrl = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1";
        List<String> inputs = Arrays.asList(
                "A swift wind, a high sky, and the gibbons cry mournfully.",
                "A clear islet, white sand, and the birds fly back.",
                "Boundless rustling woods shed their leaves.",
                "The endless Yangtze River comes rolling in."
        );

        TextEmbeddingResult result = null;
        int batchCounter = 0;

        for (int i = 0; i < inputs.size(); i += DASHSCOPE_MAX_BATCH_SIZE) {
            List<String> batch = inputs.subList(i, Math.min(i + DASHSCOPE_MAX_BATCH_SIZE, inputs.size()));
            TextEmbeddingParam param = TextEmbeddingParam.builder()
                    .model("qwen3.7-text-embedding")
                    .texts(batch)
                    .parameter("dimension", 1024)  // Specifies the output embedding dimension. This parameter is available only for qwen3.7-text-embedding, text-embedding-v3, and text-embedding-v4 models.
                    .build();

            TextEmbedding textEmbedding = new TextEmbedding();
            try {
                TextEmbeddingResult resp = textEmbedding.call(param);
                if (resp != null) {
                    if (result == null) {
                        result = resp;
                    } else {
                        for (var emb : resp.getOutput().getEmbeddings()) {
                            emb.setTextIndex(emb.getTextIndex() + batchCounter);
                            result.getOutput().getEmbeddings().add(emb);
                        }
                        result.getUsage().setTotalTokens(result.getUsage().getTotalTokens() + resp.getUsage().getTotalTokens());
                    }
                } else {
                    System.out.println(resp);
                }
            } catch (ApiException | NoApiKeyException e) {
                e.printStackTrace();
            }
            batchCounter += batch.size();
        }

        System.out.println(result);
    }
}

curl --location 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "input": {
        "texts": [
          "A swift wind, a high sky, and the gibbons cry mournfully.",
          "A clear islet, white sand, and the birds fly back.",
          "Boundless rustling woods shed their leaves.",
          "The endless Yangtze River comes rolling in."
        ]
    },
    "parameters": {
          "dimension": 1024,
          "output_type": "dense"
    }
}'