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Home Page Alibaba Cloud Model Studio API Reference (Models) Text Embedding General-purpose text embedding Synchronous API

Synchronous API

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The general-purpose text embedding model converts text data into numerical vectors for downstream tasks like semantic search, recommendation, clustering, and classification.

Model overview

ModelEmbedding dimensionsMax rows

Maxtokensper line

Price (per 1K input tokens)Supported languages

Free quota(Note)

qwen3.7-text-embedding

2,560, 2,048, 1,536, 1,024 (default), 768, 512, 256

20

128,000

CNY 0.0005

Batch call: CNY 0.00025

Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 200 other major languages and dialects

1 million tokens each

Validity: 90 days after you activate Model Studio

qwen3.7-text-embedding-flash

1,024 (default), 768, 512, 256

20

128,000

CNY 0.000125

Batch call: CNY 0.000063

Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 200 other major languages and dialects

text-embedding-v4

Part of the Qwen3-Embedding series

2,048, 1,536, 1,024 (default), 768, 512, 256, 128, 64

10

8,192

CNY 0.0005

Batch call: CNY 0.00025

Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 100 other major languages, plus multiple programming languages

text-embedding-v3

1,024 (default), 768, 512, 256, 128, or 64

Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 50 other major languages

text-embedding-v2

1,536

25

2,048

CNY 0.0007

Batch call: CNY 0.00035

Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian

500,000 tokens each

Validity: 90 days after you activate Model Studio

text-embedding-v1

Chinese, English, Spanish, French, Portuguese, Indonesian

For model rate limits, see Rate limiting.

Prerequisites

Users familiar with the OpenAI ecosystem can use the OpenAI-compatible API for a quick migration. The DashScope API provides more unique features.

Obtain an API key and export the API key as an environment variable. If you use an SDK to make calls, install the DashScope SDK.

OpenAI compatibility

Public cloud

Thebase_urlto configure for SDK calls:https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1

Theendpointto configure for HTTP calls:POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/embeddings

Replace {WorkspaceId} with your actual workspace ID.

Request body

modelstringrequired

The name of the model to call. See the Model overview table for model names.

inputarray<string> or string or filerequired

The input text to process. The input can be a string, an array of strings, or a file. Limits on text length and batch size vary by model version:

  • qwen3.7-text-embedding model:
    • String input: Up to 128,000 tokens.
    • String list or file input: Up to 20 items (lines), with each item (line) up to 128,000 tokens.
  • text-embedding-v3 / v4 models:
    • String input: Maximum length of 8,192 tokens.
    • String list or file input: A maximum of 10 items (lines), where each item (line) can be up to 8,192 tokens.
  • text-embedding-v1 / v2 models:
    • String input: Maximum length of 2,048 tokens.
    • String list or file input: A maximum of 25 items (lines), where each item (line) can be up to 2,048 tokens.

dimensions integer optional

The dimension of the output embedding vectors. Must be one of the following values: 2560 (for qwen3.7-text-embedding only),2048 (for text-embedding-v4 only), 1536 (for text-embedding-v4 only), 1024, 768, 512, 256, 128, or 64. The default value is 1024.

encoding_format string optional

The returned embedding format. Currently, only float is supported.

Input string

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"

ImportantThe SDK Expert interactive assistant can accomplish the same development and troubleshooting in natural language. See

DashScope SDK Expert.

Input string list

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"
}'

Input file

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.
    # 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"
)
# 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="qwen3.7-text-embedding",
        input=f,
        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())

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"))
                // 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();

        // 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("qwen3.7-text-embedding")
                    .input(EmbeddingCreateParams.Input.ofString(fileContent.toString()))
                    // 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);
            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://help.aliyun.com/en/model-studio/error-code");
            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://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "input": ['"$FILE_CONTENT"'],
    "dimensions": 1024,
    "encoding_format": "float"
}'

Response object

dataarray

A list of the resulting embedding objects.

Property

embeddinglist

The embedding vector, returned as an array of floating-point numbers.

indexinteger

The index of the corresponding input text in the input array.

objectstring

The object type. The value is always embedding.

modelstring

The name of the model used for this call.

objectstring

The object type. The value is always list.

usageobject

Token usage. qwen3.7-text-embedding-flash returns prompt_tokens and total_tokens; other existing text embedding models return only total_tokens.

Property

prompt_tokensinteger

Returned only by qwen3.7-text-embedding-flash. The total number of tokens in the request input.

total_tokens integer

The total number of tokens in the request, used for billing and audit. This count is determined by how the model's tokenizer parses the input string.

idstring

A unique request identifier, used for tracing and troubleshooting.

{
  "data": [
    {
      "embedding": [
        -0.0695386752486229, 0.030681096017360687, ...
      ],
      "index": 0,
      "object": "embedding"
    },
    ...
    {
      "embedding": [
        -0.06348952651023865, 0.060446035116910934, ...
      ],
      "index": 5,
      "object": "embedding"
    }
  ],
  "model": "qwen3.7-text-embedding",
  "object": "list",
  "usage": {
    "total_tokens": 184
  },
  "id": "73591b79-d194-9bca-8bb5-xxxxxxxxxxxx"
}

{
  "data": [
    {
      "embedding": [-0.0695386752486229, 0.030681096017360687, ...],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "qwen3.7-text-embedding-flash",
  "object": "list",
  "usage": {
    "prompt_tokens": 184,
    "total_tokens": 184
  },
  "id": "73591b79-d194-9bca-8bb5-xxxxxxxxxxxx"
}

{
    "error": {
        "message": "Incorrect API key provided. ",
        "type": "invalid_request_error",
        "param": null,
        "code": "invalid_api_key"
    }
}

DashScope

Public cloud

base_urlfor SDK calls: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1

Endpoint for HTTP calls: POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding

Replace {WorkspaceId} with your actual workspace ID.

Request body

modelstringrequired

The model to use. For a list of available models, see the Model overview table.

inputstringorarray<string>required

The text to process. The input can be a string, an array of strings, or a file. The supported text length and batch size vary by model version.

  • qwen3.7-text-embedding and qwen3.7-text-embedding-flash models:
    • String input: Up to 128,000 tokens.
    • String list or file input: Up to 20 items (lines), with each item (line) up to 128,000 tokens.
  • text-embedding-v3 / v4 models:
    • String input: Up to 8,192 tokens.
    • String list or file input: Up to 10 items (lines), with each item up to 8,192 tokens.
  • text-embedding-v1 / v2 models:
    • String input: Up to 2,048 tokens.
    • String list or file input: Up to 25 items (lines), with each item up to 2,048 tokens.

text_type stringoptional

When making an HTTP call, place text_typein the parameters object.

Text converted to embeddings can be applied to downstream tasks such as retrieval, clustering, and classification. For asymmetric tasks such as retrieval, it is recommended to differentiate between query text (query) and document text (document) to achieve better retrieval performance. For symmetric tasks such as indexing, clustering, and classification, you can simply use the system default value of document.

dimension integer optional

When making an HTTP call, place dimensionin the parameters object.

Specifies the embedding dimension for the output vector. Valid values are 2560 (for qwen3.7-text-embedding only),2048 (for text-embedding-v4 only), 1536 (for text-embedding-v4 only), 1024, 768, 512, 256, 128, or 64. Defaults to 1024.

output_type string optional

When making an HTTP call, place output_typein the parameters object.

Specifies the output vector type. This parameter applies only to the qwen3.7-text-embedding,``text-embedding-v3, and text-embedding-v4 models. Valid values are dense, sparse, and dense&sparse. Defaults to dense, which returns only the dense vector representation. Sparse vector outputs (sparse and dense&sparse) are supported only through the DashScope API.

instruct string optional

Provides custom instructions to guide the model in understanding the query intent. English instructions are recommended, as they typically improve performance by 1% to 5%.

Input string

import dashscope
from http import HTTPStatus
# China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. Configurations differ by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

resp = dashscope.TextEmbedding.call(
    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) if resp.status_code == HTTPStatus.OK else 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"
    }
}'

Input string list

import dashscope
from http import HTTPStatus
# China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. Configurations differ by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

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
for i in range(0, len(inputs), DASHSCOPE_MAX_BATCH_SIZE):
    batch = inputs[i:i + DASHSCOPE_MAX_BATCH_SIZE]
    resp = dashscope.TextEmbedding.call(
        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 resp.status_code == HTTPStatus.OK:
        if result is None:
            result = resp
        else:
            for emb in resp.output['embeddings']:
                emb['text_index'] += batch_counter
                result.output['embeddings'].append(emb)
            result.usage['total_tokens'] += resp.usage['total_tokens']
    else:
        print(resp)
    batch_counter += len(batch)

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"
    }
}'

Input file

Python

from http import HTTPStatus
from dashscope import TextEmbedding
# China (Beijing) region configuration. Replace {WorkspaceId} with your actual workspace ID. Configurations differ by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1"

with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    resp = TextEmbedding.call(
        model="qwen3.7-text-embedding",
        input=f,
        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 resp.status_code == HTTPStatus.OK:
        print(resp)
    else:
        print(resp)

Java

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
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;

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 (BufferedReader reader = new BufferedReader(new FileReader("<path_to_your_content_root>"))) {
            StringBuilder content = new StringBuilder();
            String line;
            while ((line = reader.readLine()) != null) {
                content.append(line).append("\n");
            }

            TextEmbeddingParam param = TextEmbeddingParam.builder()
                    .model("qwen3.7-text-embedding")
                    .text(content.toString())
                    .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();
            TextEmbeddingResult result = textEmbedding.call(param);

            if (result != null) {
                System.out.println(result);
            } else {
                System.out.println("Failed to get embedding: " + result);
            }
        } catch (IOException | ApiException | NoApiKeyException e) {
            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://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3.7-text-embedding",
    "input": {
        "texts": ['"$FILE_CONTENT"']
    },
    "parameters": {
        "dimension": 1024,
        "output_type": "dense"
    }
}'

Response object

status_code string

The HTTP status code. A value of 200 indicates success.

request_id string

A unique identifier for the request. Use this ID to trace and troubleshoot the request.

code string

The error code returned if the request fails. This field is empty for successful requests.

message string

A detailed error message if the request fails. This field is empty for successful requests.

outputobject

The result of the task.

Properties

embeddingsarray

The model's output for the request. This is an array of objects, with each object corresponding to an input text.

Properties

sparse_embedding array

The sparse vector representation of the corresponding string. This applies only to text-embedding-v3 and text-embedding-v4.

Properties

index integer

The index of the token in the vocabulary.

value float

Indicates the weight or importance score of the Token. The higher the value, the greater the importance or relevance of the Token in the current text context.

token string

The text of the token.

embedding array

The dense vector representation for the corresponding string.

text_index integer

The index of the corresponding text in the input array.

usageobject

Token usage. qwen3.7-text-embedding-flash returns prompt_tokens and total_tokens; other existing text embedding models return only total_tokens.

Properties

prompt_tokens integer

Returned only by qwen3.7-text-embedding-flash. The total number of tokens in the request input.

total_tokens integer

The total number of tokens in the request, used for billing and audit. This count is determined by how the model's tokenizer parses the input string.

{   "status_code": 200,
    "request_id": "1ba94ac8-e058-99bc-9cc1-7fdb37940a46",
    "code": "",
    "message": "",
    "output":{
        "embeddings": [
          {
             "sparse_embedding":[
               {"index":7149,"value":0.829,"token":"swift"},
               .....
               {"index":111290,"value":0.9004,"token":"mournfully"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 0
          },
          {
             "sparse_embedding":[
               {"index":246351,"value":1.0483,"token":"islet"},
               .....
               {"index":2490,"value":0.8579,"token":"back"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 1
          },
          {
             "sparse_embedding":[
               {"index":3759,"value":0.7065,"token":"Boundless"},
               .....
               {"index":1130,"value":0.815,"token":"leaves"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 2
          },
          {
             "sparse_embedding":[
               {"index":562,"value":0.6752,"token":"endless"},
               .....
               {"index":1589,"value":0.7097,"token":"in"}],
             "embedding": [-0.001945948973298072,-0.005336422007530928, ...],
             "text_index": 3
          }
        ]
    },
    "usage":{
        "total_tokens":27
    }
}

{
    "status_code": 200,
    "request_id": "1ba94ac8-e058-99bc-9cc1-7fdb37940a46",
    "code": "",
    "message": "",
    "output": {
        "embeddings": [
            {
                "embedding": [-0.006929283495992422, -0.005336422007530928, ...],
                "text_index": 0
            }
        ]
    },
    "usage": {
        "prompt_tokens": 27,
        "total_tokens": 27
    }
}

{
    "code":"InvalidApiKey",
    "message":"Invalid API-key provided.",
    "request_id":"xxxxxxxx"
}

Error codes

If a model call fails, see Error Messages.

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