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Alibaba Cloud Model Studio:Synchronous API reference

Last Updated:Jul 03, 2025

Text Embedding is a multilingual unified text embedding model developed by Tongyi Lab based on large language models (LLMs). It supports multiple mainstream languages and provides efficient embedding services for text data. It is suitable for natural language processing tasks such as RAG, text classification, and sentiment analysis.

Model overview

Name

Data type

Vector dimension

Maximum number of lines

Maximum tokens per line (Note)

Supported languages

text-embedding-v3

float(32)

1,024 (default), 768, 512

8,192

10

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

Name

Unit price

(Million input tokens)

Free quota (Note)

Rate limit (Triggered when either limit is exceeded)

Queries per minute (QPM)

Tokens per minute (TPM)

text-embedding-v3

$0.07

500,000 tokens

Valid for 180 days after activation

6,000

24,000,000

Prerequisites

You must first obtain an API key and set the API key as an environment variable. If you need to use SDK, you must install the SDK.

OpenAI

base_url for SDK: https://dashscope-intl.aliyuncs.com/compatible-mode/v1

Endpoint for HTTP: POST https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings

Request body

Input string

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured environment variables, replace this with your API Key
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url of Alibaba Cloud Model Studio
)

completion = client.embeddings.create(
    model="text-embedding-v3",
    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) {
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null) {
            System.out.println("DASHSCOPE_API_KEY not found in environment variables");
            return;
        }
        String baseUrl = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings";
        HttpClient client = HttpClient.newHttpClient();

        Map<String, Object> requestBody = new HashMap<>();
        requestBody.put("model", "text-embedding-v3");
        requestBody.put("input", "The wind is strong and the sky is high, the apes wail sadly. The islet is clear, the sand is white, and birds fly back. Endless falling leaves descend rustling, the mighty Yangtze River flows on endlessly");
        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());
        }
    }
}

curl

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": "The wind howls fiercely in the high sky as monkeys cry in sorrow, the clear sandbank shines white as birds fly back, endless falling leaves descend rustling everywhere, the mighty Yangtze River flows on endlessly",  
    "dimension": "1024",  
    "encoding_format": "float"
}'

Input string list

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you haven't configured environment variables, replace this with your API Key
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url for Alibaba Cloud Model Studio service
)

completion = client.embeddings.create(
    model="text-embedding-v3",
    input=['The wind is strong and the sky is high, the apes wail sadly', 'The sandbar is clear and white, birds fly back', 'Endless falling leaves descend rustling', 'The endless Yangtze River rolls on'],
    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 API Key from environment variables, if not configured, please replace it with your API Key*/
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null) {
            System.out.println("DASHSCOPE_API_KEY not found in environment variables");
            return;
        }
        String baseUrl = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings";
        HttpClient client = HttpClient.newHttpClient();
        Map<String, Object> requestBody = new HashMap<>();
        requestBody.put("model", "text-embedding-v3");
        List<String> inputList = Arrays.asList("The wind is strong and the sky is high, the monkey's cry is sad", "The sandbar is clear and the sand is white, birds fly back", "Endless falling leaves come down rustling", "The endless Yangtze River flows on and on");
        requestBody.put("input", inputList);
        requestBody.put("encoding_format", "float");

        try {
            /** Convert the request body to JSON string*/
            String requestBodyString = JsonUtils.toJson(requestBody);

            /**Build 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 request and receive 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 exception*/
            System.err.println("Error: " + e.getMessage());
        }
    }
}

curl

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": [
        "The wind is strong, the sky is high, and the apes wail sadly",
        "The sandbar is clear, the sand is white, and birds fly back", 
        "Endless falling leaves descend rustling", 
        "The endless Yangtze River flows rolling on"
        ],
    "dimension": 1024,
    "encoding_format": "float"
}'

Input file

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # If you have not configured environment variables, replace this with your API Key
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url of Alibaba Cloud Model Studio
)
# Make sure to replace 'texts_to_embedding.txt' with your own file name or path
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    completion = client.embeddings.create(
        model="text-embedding-v3",
        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 API Key from environment variables. If not configured, replace it directly with your API Key*/
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null) {
            System.out.println("DASHSCOPE_API_KEY not found in environment variables");
            return;
        }
        String baseUrl = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings";
        HttpClient client = HttpClient.newHttpClient();

        /** Read input file*/
        StringBuilder inputText = new StringBuilder();
        try (BufferedReader reader = new BufferedReader(new FileReader("<full path to the 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-v3");
        requestBody.put("input", inputText.toString().trim());
        requestBody.put("dimensions", 1024);
        requestBody.put("encoding_format", "float");

        try {
            String requestBodyString = JsonUtils.toJson(requestBody);

            /**Build 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());
        }
    }
}

curl

Make sure to replace 'texts_to_embedding.txt' with your own file name or path
FILE_CONTENT=$(cat texts_to_embedding.txt | jq -Rs .)
curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": ['"$FILE_CONTENT"'],
    "dimension": 1024,
    "encoding_format": "float"
}'

model string Required

The model to use. Valid value: text-embedding-v3.

input array<string> or string or file Required

The input text, can be a string, a list of strings, or a file. Each line of the file contains one piece of content to be embedded.

Text limitations:

  • Quantity:

    • For string, up to 8,192 tokens.

    • For string list, up to 10 items, each up to 8,192 tokens.

    • For file, up to 10 lines, each up to 8,192 tokens.

dimension integer Optional

The dimension of the output vector.

Valid values: 1024, 768, 512.

Default value: 1024.

encoding_format string Optional

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

Response object

Successful response

{
  "data": [
    {
      "embedding": [
        -0.0695386752486229, 0.030681096017360687, ...
      ],
      "index": 0,
      "object": "embedding"
    },
    ...
    {
      "embedding": [
        -0.06348952651023865, 0.060446035116910934, ...
      ],
      "index": 5,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-v3",
  "object": "list",
  "usage": {
    "prompt_tokens": 184,
    "total_tokens": 184
  },
  "id": "73591b79-d194-9bca-8bb5-xxxxxxxxxxxx"
}

Error response

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

data array

The task output information.

Properties

embedding list

The value of the object returned by this call, which is an array of float data containing specific embeddings.

index integer

The index value of the segment of input text that the array element corresponds to.

object string

The type of object returned by this call. Default value: embedding.

model string

The name of the model that is called.

object string

The type of data returned by this call. Default value: list.

usage object

Properties

prompt_tokens integer

The number of tokens in the input text.

total_tokens integer

The number of tokens that the tokenizer of Text Embedding extracts from the input.

id string

The request ID, can be used for request tracing and troubleshooting.

DashScope

Public cloud

base_url for SDK: https://dashscope-intl.aliyuncs.com/compatible-mode/v1

Endpoint for HTTP: POST https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding

Request body

Input string

Python

import dashscope
from http import HTTPStatus
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

resp = dashscope.TextEmbedding.call(
    model=dashscope.TextEmbedding.Models.text_embedding_v3,
    input='The wind is strong and the sky is high, the apes cry sadly. The sandbar is clear, the sand is white, and birds fly back. Endless falling leaves come down rustling. The endless Yangtze River flows on and on',
    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 {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    public static void basicCall() throws ApiException, NoApiKeyException{
        TextEmbeddingParam param = TextEmbeddingParam
        .builder()
        .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
        .texts(Arrays.asList("The wind is strong, the sky is high, and the ape wails sadly", "The sandbar is clear, the sand is white, and birds fly back", "Endless falling leaves descend rustling", "The endless Yangtze River flows rolling on")).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("The wind is strong, the sky is high, and the ape wails sadly", "The sandbar is clear, the sand is white, and birds fly back", "Endless falling leaves descend rustling", "The endless Yangtze River flows rolling on")).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|NoApiKeyException|InterruptedException e){
      e.printStackTrace();
      System.out.println(e);

    }
      try {
        basicCall();
    } catch (ApiException | NoApiKeyException e) {
        System.out.println(e.getMessage());
    }
    System.exit(0);
  }
}

curl

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": [
        "The wind howls fiercely in the high sky as monkeys cry in sorrow, the clear sandbank gleams white as birds fly back, endless falling leaves descend rustling everywhere, while the mighty Yangtze River flows on endlessly"
        ]
    },
    "parameters": {
    		"dimension": 1024
    }
}'

Input string list

Python

import dashscope
from http import HTTPStatus
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
DASHSCOPE_MAX_BATCH_SIZE = 25

inputs = ['The wind is strong and the sky is high, the ape wails sadly', 'The sandbar is clear and white, birds fly back', 'Endless falling leaves descend rustling', 'The endless Yangtze River flows rolling on']

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=dashscope.TextEmbedding.Models.text_embedding_v3,
        input=batch,
        dimension=1024
    )
    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)

Java

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.utils.Constants;

public final class Main {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    private static final int DASHSCOPE_MAX_BATCH_SIZE = 25;

    public static void main(String[] args) {
        List<String> inputs = Arrays.asList(
                "The wind is strong and the sky is high, the apes wail sadly",
                "The islet is clear, the sand is white, birds fly back",
                "Endless falling leaves descend rustling",
                "The endless Yangtze River flows rolling on"
        );

        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(TextEmbedding.Models.TEXT_EMBEDDING_V3)
                    .texts(batch)
                    .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

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": [
          "The wind is strong, the sky is high, and the apes wail sadly",
          "The sandbar is clear, the sand is white, and birds fly back",
          "Endless falling leaves descend rustling",
          "The endless Yangtze River rolls on"
        ]
    },
    "parameters": {
    	  "dimension": 1024
    }
}'

Input file

Python

import dashscope
from http import HTTPStatus
from dashscope import TextEmbedding
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
# Make sure to replace 'texts_to_embedding.txt' with your own file name or path
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    resp = TextEmbedding.call(
        model=TextEmbedding.Models.text_embedding_v3,
        input=f,
        dimension=1024
    )

    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 {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    public static void main(String[] args) {
        // Make sure to replace 'tests_to_embedding.txt' with the full path of your file
        try (BufferedReader reader = new BufferedReader(new FileReader("tests_to_embedding.txt"))) {
            StringBuilder content = new StringBuilder();
            String line;
            while ((line = reader.readLine()) != null) {
                content.append(line).append("\n");
            }

            TextEmbeddingParam param = TextEmbeddingParam.builder()
                    .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
                    .text(content.toString())
                    .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

Make sure to replace 'texts_to_embedding.txt' with your own file name or path
FILE_CONTENT=$(cat texts_to_embedding.txt | jq -Rs .)
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": ['"$FILE_CONTENT"']
    },
    "parameters": {
        "dimension": 1024
    }
}'

model string Required

The model to use. Valid value: text-embedding-v3.

input string or array<string> Required

The input text, can be a string, a list of strings, or a file. Each line of the file contains one piece of content to be embedded.

Text limitations:

  • Quantity:

    • For string, up to 8,192 tokens.

    • For string list, up to 10 items, each up to 8,192 tokens.

    • For file, up to 10 lines, each up to 8,192 tokens.

text_type string Optional

After text is converted into vectors, it can be used in downstream tasks such as retrieval, clustering, and classification. For asymmetric tasks like retrieval, distinguish between query and document types for better performance. For symmetric tasks such as clustering and classification, just use the default value document.

dimension integer Optional

The dimension of the output vector.

Valid values: 1024, 768, 512.

Default value: 1024.

output_type string Optional

The output type. Valid values: dense, sparse, dense&sparse. Default value: dense, which specifies only dense vectors are returned.

Response object

Successful response

{   "status_code": 200, 
    "request_id": "1ba94ac8-e058-99bc-9cc1-7fdb37940a46", 
    "code": "", 
    "message": "",
    "output":{
        "embeddings": [
          {  
             "sparse_embedding":[
               {"index":7149,"value":0.829,"token":"wind"},
               .....
               {"index":111290,"value":0.9004,"token":"sorrow"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 0
          }, 
          {
             "sparse_embedding":[
               {"index":246351,"value":1.0483,"token":"islet"},
               .....
               {"index":2490,"value":0.8579,"token":"return"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 1
          },
          {
             "sparse_embedding":[
               {"index":3759,"value":0.7065,"token":"none"},
               .....
               {"index":1130,"value":0.815,"token":"down"}],
             "embedding": [-0.006929283495992422,-0.005336422007530928, ...],
             "text_index": 2
          },
          {
             "sparse_embedding":[
               {"index":562,"value":0.6752,"token":"not"},
               .....
               {"index":1589,"value":0.7097,"token":"come"}],
             "embedding": [-0.001945948973298072,-0.005336422007530928, ...],
             "text_index": 3
          }
        ]
    },
    "usage":{
        "total_tokens":27
    },
    "request_id":"xxxxxxxx"
}

Error response

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

status_code string

The status code that indicates the result of the request. For example, 200 indicates success.

request_id string

The request ID, can be used for tracing and troubleshooting.

code string

The error code. Empty if the request is successful.

message string

The error message. Empty if the request is successful.

output object

The output of the task.

Properties

embeddings array

The output of the request. Each element of the array corresponds to a piece of input text.

Properties

sparse_embedding array

The sparse embedding output.

Properties

index integer

The index of the word or character.

value float

The weight or importance score of the token. A higher value indicates that the token is more important or relevant in the current text context.

token string

The actual text unit or word.

embedding array

The dense embedding output.

text_index integer

The index value of the segment of input text that the array element corresponds to.

usage object

Properties

total_tokens integer

The number of tokens that the tokenizer of Text Embedding extracts from the input.

request_id string

The request ID, can be used for tracing and troubleshooting.

Error codes

If the call failed and an error message is returned, see Error messages for troubleshooting.