# Source: https://docs.aimlapi.com/api-references/embedding-models/anthropic/voyage-multilingual-2.md

# voyage-multilingual-2

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

* `voyage-multilingual-2`
  {% endhint %}
  {% endcolumn %}

{% column width="33.33333333333334%" %} <a href="https://aimlapi.com/app/voyage-multilingual-2" class="button primary">Try in Playground</a>
{% endcolumn %}
{% endcolumns %}

## Model Overview

Optimized for multilingual retrieval and retrieval-augmented generation (RAG), this model surpasses alternatives like OpenAI v3 large and Cohere multilingual v3 across most languages, including French, German, Japanese, Spanish, and Korean. On average, it outperforms the second-best model by 5.6%, while maintaining strong performance in English. Additionally, it supports a large 32K context length.

## 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

{% openapi src="<https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-9b27d31f5fb3f152e8456f5b1a40b26cbacc0653%2Fvoyage-multilingual-2.json?alt=media&token=d3fe44f4-58c7-4e52-8593-c8fb6e05117d>" path="/v1/embeddings" method="post" %}
[voyage-multilingual-2.json](https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-9b27d31f5fb3f152e8456f5b1a40b26cbacc0653%2Fvoyage-multilingual-2.json?alt=media\&token=d3fe44f4-58c7-4e52-8593-c8fb6e05117d)
{% endopenapi %}

## 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="voyage-multilingual-2"
)

# 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: "voyage-multilingual-2",
});

// 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=[0.057845164090394974, -0.0009027316118590534, 0.0018743404652923346, -0.013910274021327496, -0.01372489146888256, -0.022771991789340973, -0.046326544135808945, 0.03561760112643242, 0.012907890602946281, 0.0007679080590605736, 0.0006164147634990513, -0.029242057353258133, 0.010541332885622978, -0.045086756348609924, 0.04110909253358841, 0.04766305536031723, -0.02904568240046501, -0.032282181084156036, 0.03853169083595276, 0.05892375484108925, -0.01231437362730503, -0.04767551273107529, 0.011599955148994923, 0.02466391772031784, -0.009037940762937069, 0.013361179269850254, 0.0178985595703125, 0.033255256712436676, -0.018267124891281128, 0.009587126784026623, 0.05244050174951553, -0.012508914805948734, -0.018404515460133553, -0.057481732219457626, 0.029757903888821602, -0.027167314663529396, -0.05003126338124275, 0.0206646416336298, 0.04715307056903839, -0.0335824228823185, 0.019010121002793312, -0.0026166026946157217, 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model='voyage-multilingual-2', object='list', usage=Usage(prompt_tokens=None, total_tokens=7), meta={'usage': {'credits_used': 2}})
```

{% 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.