# Source: https://docs.aimlapi.com/api-references/embedding-models/alibaba-cloud/qwen-text-embedding-v4.md

# qwen-text-embedding-v4

{% columns %}
{% column width="66.66666666666666%" %}
{% hint style="info" %}
This documentation is valid for the following list of our models:

* `alibaba/qwen-text-embedding-v4`
  {% endhint %}
  {% endcolumn %}

{% column width="33.33333333333334%" %} <a href="https://aimlapi.com/app/alibaba/qwen-text-embedding-v4" class="button primary">Try in Playground</a>
{% endcolumn %}
{% endcolumns %}

## Model Overview

A compact language model supporting over 100 languages. It features a 4B parameter architecture, a context length of up to 32K tokens, and outputs embeddings with up to 2560 dimensions. Embeddings exhibit tighter intra-cluster cohesion and sharper inter-cluster separation compared to [qwen-text-embedding-v3](https://docs.aimlapi.com/api-references/embedding-models/alibaba-cloud/qwen-text-embedding-v3).

## Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our [Quickstart guide](https://docs.aimlapi.com/quickstart/setting-up).

## API Schema

## POST /v1/embeddings

>

```json
{"openapi":"3.0.0","info":{"title":"AI/ML Gateway","version":"1.0"},"servers":[{"url":"https://api.aimlapi.com"}],"security":[{"access-token":[]}],"components":{"securitySchemes":{"access-token":{"scheme":"bearer","bearerFormat":"<YOUR_AIMLAPI_KEY>","type":"http","description":"Bearer key"}},"schemas":{"Embedding.v1.CreateEmbeddingsResponseDTO":{"type":"object","properties":{"object":{"type":"string","enum":["object"]},"data":{"type":"array","items":{"type":"object","properties":{"object":{"type":"string","enum":["embedding"]},"index":{"type":"number"},"embedding":{"type":"array","items":{"type":"number"}}},"required":["object","index","embedding"]}},"model":{"type":"string"},"usage":{"type":"object","properties":{"total_tokens":{"type":"number","nullable":true}}}},"required":["object","data","model","usage"]}}},"paths":{"/v1/embeddings":{"post":{"operationId":"EmbeddingsController_createEmbeddings_v1","parameters":[],"requestBody":{"required":true,"content":{"application/json":{"schema":{"type":"object","properties":{"model":{"enum":["alibaba/qwen-text-embedding-v4"]},"input":{"anyOf":[{"type":"string","minLength":1},{"type":"array","items":{"type":"string"},"minItems":1}],"description":"Input text to embed, encoded as a string or array of tokens."},"dimensions":{"type":"integer","minimum":64,"maximum":2048,"default":1024,"description":"The number of dimensions for the embedding. Default is 1024."}},"required":["model","input"]}}}},"responses":{"200":{"description":"","content":{"application/json":{"schema":{"$ref":"#/components/schemas/Embedding.v1.CreateEmbeddingsResponseDTO"}}}}},"tags":["Embeddings"]}}}}
```

## Code Example

{% tabs %}
{% tab title="Python" %}

<pre class="language-python"><code class="lang-python">import openai
<strong>
</strong># Initialize the API client
client = openai.OpenAI(
    # Insert your AIML API Key instead of &#x3C;YOUR_AIMLAPI_KEY>
    api_key="&#x3C;YOUR_AIMLAPI_KEY>",
    base_url="https://api.aimlapi.com/v1",
)

# Define the text for which to generate an embedding
text = "Laura is a DJ."

# Request the embedding
response = client.embeddings.create(
    input=text,
    model="alibaba/qwen-text-embedding-v4"
)

# Print the embedding
print(response)
</code></pre>

{% endtab %}

{% tab title="JS" %}

```javascript
import OpenAI from "openai";
import util from "util";

// Initialize the API client
const client = new OpenAI({
  // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
  apiKey: "<YOUR_AIMLAPI_KEY>",
  baseURL: "https://api.aimlapi.com/v1",
});

// Define the text for which to generate an embedding
const text = "Laura is a DJ.";

const response = await client.embeddings.create({
  input: text,
  model: "alibaba/qwen-text-embedding-v4",
});

// Convert embedding to a regular array (not TypedArray)
const pythonLikeResponse = {
  ...response,
  data: response.data.map(item => ({
    ...item,
    embedding: Array.from(item.embedding),
  })),
};

// Python-like print
console.log(
  util.inspect(pythonLikeResponse, {
    depth: null,
    maxArrayLength: null,
    compact: true,
  })
);
```

{% endtab %}
{% endtabs %}

This example shows how to set up an API client, send text to the embedding API, and print the response with the embedding vector. See how large a vector response the model generates from just a single short input phrase.

<details>

<summary>Response</summary>

{% code overflow="wrap" %}

```json
CreateEmbeddingResponse(data=[Embedding(embedding=[-0.1248791441321373, 0.022519821301102638, -0.06440284103155136, -0.0016516941832378507, -0.07079307734966278, 0.06540372222661972, -0.012751608155667782, 0.06559620052576065, -0.02534923516213894, -0.006650084163993597, 0.060668785125017166, 0.03968878090381622, -0.04049718379974365, -0.021557435393333435, 0.03533879667520523, -0.014830361120402813, 0.041382577270269394, 0.017428802326321602, -0.01000880915671587, -0.0008950185729190707, -0.01976739801466465, 0.0008144187740981579, -0.05716570094227791, -0.005095831584185362, 0.00028510671108961105, 0.033548761159181595, -0.06294001638889313, -0.03601246699690819, -0.0015169602120295167, -0.02221185900270939, -0.013598507270216942, -0.001029151026159525, -0.03345252200961113, -0.052122801542282104, -0.009003116749227047, -0.02221185900270939, -0.026638831943273544, -0.006346932612359524, -0.06702052801847458, -0.048042286187410355, 0.024271363392472267, 0.055895350873470306, 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model='text-embedding-v3', object='list', usage=Usage(prompt_tokens=4, total_tokens=4), id='8ef7c577-75e9-9056-ba09-dc4331f81f6d', meta={'usage': {'credits_used': 1}})
```

{% endcode %}

</details>

You can find a more advanced example of using embedding vectors in our article [Find Relevant Answers: Semantic Search with Text Embeddings](https://docs.aimlapi.com/use-cases/find-relevant-answers-semantic-search-with-text-embeddings) in the Use Cases section.