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Synchronous API for text embedding ​

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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 ​

Singapore

China (Beijing)

China (Hong Kong)

ModelEmbedding dimensionsMax rowsMax tokens per line( Note)Price (per 1M input tokens)Supported languagesFree quota (Note)
text-embedding-v4 > Part of the Qwen3-Embedding series2,048, 1,536, 1,024 (default), 768, 512, 256, 128, 64108,192$0.07Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 100 other major languages1 million tokens Validity: 90 days after you activate Model Studio
text-embedding-v31,024 (default), 768, 512Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 50 other major languages500,000 tokens Validity: 90 days after you activate Model Studio
ModelEmbedding dimensionsMax rowsMax tokens per linePrice (per 1M input tokens)Supported languages
text-embedding-v4 > Part of the Qwen3-Embedding series2,048, 1,536, 1,024 (default), 768, 512, 256, 128, 64108,192$0.072Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 100 other major languages, plus multiple programming languages
ModelEmbedding dimensionsMax rowsMax tokens per linePrice (per 1M input tokens)Supported languages
text-embedding-v4 > Part of the Qwen3-Embedding series2,048, 1,536, 1,024 (default), 768, 512, 256, 128, 64108,192$0.07Chinese, English, Spanish, French, Portuguese, Indonesian, Japanese, Korean, German, Russian, and over 100 other major languages, plus multiple programming languages

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 ​

Thebase_url to configure for SDK calls:

  • Singapore: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1. Replace WorkspaceId with your actual workspace ID.

  • China (Beijing): https://dashscope.aliyuncs.com/compatible-mode/v1

  • China (Hong Kong): https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1. Replace WorkspaceId with your actual workspace ID.

Theendpoint to configure for HTTP calls:

  • Singapore: POSThttps://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/embeddings. Replace WorkspaceId with your actual workspace ID.

  • China (Beijing): POSThttps://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

  • China (Hong Kong): POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/compatible-mode/v1/embeddings. Replace WorkspaceId with your actual workspace ID.

Important

The legacy Singapore domain https://dashscope-intl.aliyuncs.comand China (Hong Kong) domain https://cn-hongkong.dashscope.aliyuncs.comwill be discontinued. Migrate to https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com (Singapore) and https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com (China (Hong Kong)) as soon as possible.

### Request bodyInput string Input string list Input file Python Java curl python import os from openai import OpenAI client = OpenAI( # If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key api_key=os.getenv("DASHSCOPE_API_KEY"), # If the environment variable is not set, replace the placeholder with your API key. # This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" ) completion = client.embeddings.create( model="text-embedding-v4", input='The clothes are of good quality and look good, definitely worth the wait. I love them.', dimensions=1024, encoding_format="float" ) print(completion.model_dump_json()) java import java.net.URI; import java.net.http.HttpClient; import java.net.http.HttpRequest; import java.net.http.HttpResponse; import java.util.HashMap; import java.util.Map; import com.alibaba.dashscope.utils.JsonUtils; public final class Main { public static void main(String[] args) { // If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key String apiKey = System.getenv("DASHSCOPE_API_KEY"); if (apiKey == null) { System.out.println("DASHSCOPE_API_KEY not found in environment variables"); return; } // This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. String baseUrl = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/embeddings"; HttpClient client = HttpClient.newHttpClient(); Map<String, Object> requestBody = new HashMap<>(); requestBody.put("model", "text-embedding-v4"); requestBody.put("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."); requestBody.put("dimensions", 1024); requestBody.put("encoding_format", "float"); try { String requestBodyString = JsonUtils.toJson(requestBody); HttpRequest request = HttpRequest.newBuilder() .uri(URI.create(baseUrl)) .header("Content-Type", "application/json") .header("Authorization", "Bearer " + apiKey) .POST(HttpRequest.BodyPublishers.ofString(requestBodyString)) .build(); HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString()); if (response.statusCode() == 200) { System.out.println("Response: " + response.body()); } else { System.out.printf("Failed to retrieve response, status code: %d, response: %s%n", response.statusCode(), response.body()); } } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } } } > If you use a model in the China (Beijing) region, you must use an API key from that region and replace the URL with https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings. This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. curl curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 Java curl python import os from openai import OpenAI client = OpenAI( # If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key api_key=os.getenv("DASHSCOPE_API_KEY"), # If the environment variable is not set, replace the placeholder with your API key. # This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" ) 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, encoding_format="float" ) print(completion.model_dump_json()) java import java.net.URI; import java.net.http.HttpClient; import java.net.http.HttpRequest; import java.net.http.HttpResponse; import java.util.HashMap; import java.util.Map; import java.util.List; import java.util.Arrays; import com.alibaba.dashscope.utils.JsonUtils; public final class Main { public static void main(String[] args) { // Get the API key from an environment variable. If not configured, replace it with your API key. // If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key String apiKey = System.getenv("DASHSCOPE_API_KEY"); if (apiKey == null) { System.out.println("DASHSCOPE_API_KEY not found in environment variables"); return; } // This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. String baseUrl = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/embeddings"; HttpClient client = HttpClient.newHttpClient(); Map<String, Object> requestBody = new HashMap<>(); requestBody.put("model", "text-embedding-v4"); 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."); requestBody.put("input", inputList); requestBody.put("encoding_format", "float"); try { // Convert the request body to a JSON string. String requestBodyString = JsonUtils.toJson(requestBody); // Build the HTTP request. HttpRequest request = HttpRequest.newBuilder() .uri(URI.create(baseUrl)) .header("Content-Type", "application/json") .header("Authorization", "Bearer " + apiKey) .POST(HttpRequest.BodyPublishers.ofString(requestBodyString)) .build(); // Send the request and receive the response. HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString()); if (response.statusCode() == 200) { System.out.println("Response: " + response.body()); } else { System.out.printf("Failed to retrieve response, status code: %d, response: %s%n", response.statusCode(), response.body()); } } catch (Exception e) { // Catch and print the exception. System.err.println("Error: " + e.getMessage()); } } } > If you use a model in the China (Beijing) region, you must use an API key from that region and replace the URL with https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings. This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. curl curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.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 Java curl python import os from openai import OpenAI client = OpenAI( # If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key api_key=os.getenv("DASHSCOPE_API_KEY"), # If the environment variable is not set, replace the placeholder with your API key. # This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. base_url="https://{WorkspaceId}.ap-southeast-1.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="text-embedding-v4", input=f, encoding_format="float" ) print(completion.model_dump_json()) java import java.net.URI; import java.net.http.HttpClient; import java.net.http.HttpRequest; import java.net.http.HttpResponse; import java.util.HashMap; import java.util.Map; import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import com.alibaba.dashscope.utils.JsonUtils; public class Main { public static void main(String[] args) { // Get the API key from an environment variable. If not configured, replace it with your API key. // If you use a model in the China (Beijing) region, you must use an API key from that region. Get one at: https://bailian.console.alibabacloud.com/?tab=model#/api-key String apiKey = System.getenv("DASHSCOPE_API_KEY"); if (apiKey == null) { System.out.println("DASHSCOPE_API_KEY not found in environment variables"); return; } // This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. String baseUrl = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/embeddings"; HttpClient client = HttpClient.newHttpClient(); // Read the input file. StringBuilder inputText = new StringBuilder(); try (BufferedReader reader = new BufferedReader(new FileReader("<path_to_your_content_root>"))) { String line; while ((line = reader.readLine()) != null) { inputText.append(line).append("\n"); } } catch (IOException e) { System.err.println("Error reading input file: " + e.getMessage()); return; } Map<String, Object> requestBody = new HashMap<>(); requestBody.put("model", "text-embedding-v4"); requestBody.put("input", inputText.toString().trim()); requestBody.put("dimensions", 1024); requestBody.put("encoding_format", "float"); try { String requestBodyString = JsonUtils.toJson(requestBody); // Build the HTTP request. HttpRequest request = HttpRequest.newBuilder() .uri(URI.create(baseUrl)) .header("Content-Type", "application/json") .header("Authorization", "Bearer " + apiKey) .POST(HttpRequest.BodyPublishers.ofString(requestBodyString)) .build(); HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString()); if (response.statusCode() == 200) { System.out.println("Response: " + response.body()); } else { System.out.printf("Failed to retrieve response, status code: %d, response: %s%n", response.statusCode(), response.body()); } } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } } } > If you use a model in the China (Beijing) region, you must use an API key from that region and replace the URL with https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings. This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. > Replace 'texts_to_embedding.txt' with your file name or path. ```curl FILE_CONTENT=$(cat texts_to_embedding.txt
modelstring required The name of the model to call. See the Model overview table for model names.
inputarray<string> or string or file required The input text to process. The input can be a string, an array of strings, or a file. If the input is a string, the maximum length is 8,192 tokens. If the input is a list of strings or a file, the maximum batch size is 10 items (lines), and each item (line) can contain up to 8,192 tokens.
dimensionsinteger optional The dimension of the output embedding vectors. Must be one of the following values: 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_formatstring optional The returned embedding format. Currently, only float is supported.
### Response objectSuccessful response Error response json { "data": [ { "embedding": [ -0.0695386752486229, 0.030681096017360687, ... ], "index": 0, "object": "embedding" }, ... { "embedding": [ -0.06348952651023865, 0.060446035116910934, ... ], "index": 5, "object": "embedding" } ], "model": "text-embedding-v4", "object": "list", "usage": { "prompt_tokens": 184, "total_tokens": 184 }, "id": "73591b79-d194-9bca-8bb5-xxxxxxxxxxxx" } json { "error": { "message": "Incorrect API key provided. ", "type": "invalid_request_error", "param": null, "code": "invalid_api_key" } }
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. object string The object type. The value is always embedding.
modelstring The name of the model used for this call.
object string The object type. The value is always list.
usageobject Property prompt_tokens integer The number of tokens in the input text. total_tokens integer The total number of tokens in the input. This count is determined by how the model's tokenizer parses the input string.
id string A unique request identifier, used for tracing and troubleshooting.

DashScope ​

base_url for SDK calls:

  • Singapore: https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1. Replace WorkspaceId with your actual workspace ID.

  • China (Beijing): https://dashscope.aliyuncs.com/api/v1

  • China (Hong Kong): https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1. Replace WorkspaceId with your actual workspace ID.

Endpoint for HTTP calls:

  • Singapore: POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding. Replace WorkspaceId with your actual workspace ID.

  • China (Beijing): POST https://dashscope.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding

  • China (Hong Kong): POST https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding. Replace WorkspaceId with your actual workspace ID.

Important

The legacy China (Hong Kong) URL https://cn-hongkong.dashscope.aliyuncs.com/api/v1 will be deprecated soon. Please migrate to the new URL https://{WorkspaceId}.cn-hongkong.maas.aliyuncs.com/api/v1 as soon as possible.

Important

The legacy domain https://dashscope-intl.aliyuncs.com for the Singapore region will be deprecated soon. Please migrate to the new domain https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com as soon as possible.

### Request bodyInput string Input string list Input file Python Java curl python import dashscope from http import HTTPStatus # This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1' resp = dashscope.TextEmbedding.call( model=dashscope.TextEmbedding.Models.text_embedding_v4, input='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.', dimension=1024, output_type="dense&sparse" ) print(resp) if resp.status_code == HTTPStatus.OK else print(resp) ```java import java.util.Arrays; import java.util.concurrent.Semaphore; import com.alibaba.dashscope.common.ResultCallback; 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 { static { // This is the Singapore region URL. Replace WorkspaceId with your actual workspace ID. URLs differ by region. Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"; } public static void basicCall() throws ApiException, NoApiKeyException{ TextEmbeddingParam param = TextEmbeddingParam .builder() .model(TextEmbedding.Models.TEXT_EMBEDDING_V4) .texts(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.")).build(); TextEmbedding textEmbedding = new TextEmbedding(); TextEmbeddingResult result = textEmbedding.call(param); System.out.println(result); } public static void callWithCallback() throws ApiException, NoApiKeyException, InterruptedException{ TextEmbeddingParam param = TextEmbeddingParam .builder() .model(TextEmbedding.Models.TEXT_EMBEDDING_V3) .texts(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.")).build(); TextEmbedding textEmbedding = new TextEmbedding(); Semaphore sem = new Semaphore(0); textEmbedding.call(param, new ResultCallback<TextEmbeddingResult>() { @Override public void onEvent(TextEmbeddingResult message) { System.out.println(message); } @Override public void onComplete(){ sem.release(); } @Override public void onError(Exception err){ System.out.println(err.getMessage()); err.printStackTrace(); sem.release(); } }); sem.acquire(); } public static void main(String[] args){ try{ callWithCallback(); }catch(ApiException
modelstring required The model to use. For a list of available models, see the Model overview table.
inputstring orarray<string> required The text to process. The input can be a string, an array of strings, or a file. A single string can be up to 8,192 tokens. A list of strings or a file can contain up to 10 items (lines), with each item up to 8,192 tokens.
text_typestring optional > When making an HTTP call, place text_type in 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.
dimensioninteger optional > When making an HTTP call, place dimension in the parameters object. Specifies the embedding dimension for the output vector. Valid values are 2048 (for text-embedding-v4 only), 1536 (for text-embedding-v4 only), 1024, 768, 512, 256, 128, or 64. Defaults to 1024.
output_typestring optional > When making an HTTP call, place output_type in the parameters object. Specifies the output vector type. This parameter applies only to the 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.
instructstring 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%.
### Response objectSuccessful response Error response json { "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 } } json { "code":"InvalidApiKey", "message":"Invalid API-key provided.", "request_id":"xxxxxxxx" }
status_codestring The HTTP status code. A value of 200 indicates success.
request_idstring A unique identifier for the request. Use this ID to trace and troubleshoot the request.
codestring The error code returned if the request fails. This field is empty for successful requests.
messagestring 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_embeddingarray The sparse vector representation of the corresponding string. This applies only to text-embedding-v3 and text-embedding-v4. Properties indexinteger The index of the token in the vocabulary. valuefloat 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. tokenstring The text of the token. embeddingarray The dense vector representation for the corresponding string. text_indexinteger The index of the corresponding text in the input array.
usageobject Properties total_tokens integer The number of tokens in the input, as calculated by the model's tokenizer.

Error codes ​

If a model call fails, see Error Messages.

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