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

# voyage-large-2

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

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

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

## Model Overview

Voyage’s most powerful generalist embedding model, outperforming popular competing models.

## 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-3677b6dfb5c681eea77595a53a83546af9dec5bd%2Fvoyage-large-2.json?alt=media&token=9019b597-7720-47b1-a5e6-d8de941e6af5>" path="/v1/embeddings" method="post" %}
[voyage-large-2.json](https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-3677b6dfb5c681eea77595a53a83546af9dec5bd%2Fvoyage-large-2.json?alt=media\&token=9019b597-7720-47b1-a5e6-d8de941e6af5)
{% 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-large-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-large-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=[-0.01561100035905838, 0.0163817647844553, 0.01526249386370182, 0.028116634115576744, 0.026989677920937538, 0.0004941212828271091, -0.025271985679864883, -0.011951270513236523, -0.05632830783724785, 0.02140776626765728, -0.012444284744560719, 0.05962485820055008, -0.0359853059053421, -0.04648525267839432, -0.000635049189440906, -0.03689562901854515, 0.03731445223093033, -0.02737775258719921, -0.03597225993871689, 0.018330063670873642, 0.014466659165918827, 0.06400783360004425, -0.017127983272075653, -0.010960116051137447, 0.019682390615344048, -0.01969660073518753, -0.020545894280076027, -0.036266110837459564, 0.022564861923456192, 0.015046908520162106, 0.005086916033178568, -0.055712416768074036, -0.009372987784445286, -0.05443871021270752, 0.01082932110875845, 0.013921350240707397, 0.012792528606951237, 0.009856860153377056, 0.038214996457099915, -0.01748892292380333, 0.027313927188515663, 0.0010629575699567795, 0.009722599759697914, 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```

{% 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.