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

# voyage-code-2

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

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

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

## Model Overview

This embedding model is designed for semantic retrieval of code and related text from both natural language and code-based queries. In a comprehensive evaluation across 11 code retrieval tasks—sourced from popular datasets like HumanEval and MBPP—it achieved a significant 14.52% improvement in recall over competitors, including OpenAI and Cohere. Additionally, it demonstrated consistent gains, averaging 3.03%, across various general-purpose text datasets.

## 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-5808a74e4d0eab3f7f8308ab36d84298a9d5a046%2Fvoyage-code-2.json?alt=media&token=2836a546-a33b-4ba6-bb6e-ebc17415aaf5>" path="/v1/embeddings" method="post" %}
[voyage-code-2.json](https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-5808a74e4d0eab3f7f8308ab36d84298a9d5a046%2Fvoyage-code-2.json?alt=media\&token=2836a546-a33b-4ba6-bb6e-ebc17415aaf5)
{% 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-code-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-code-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 Python 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=[-0.015611, 0.016381765, 0.015262494, 0.028116634, 0.026989678, 0.000494121, -0.025271986, -0.011951271, -0.056328308, 0.021407766, -0.012444285, 0.059624858, -0.035985306, -0.046485253, -0.000635049, -0.036895629, 0.037314452, -0.027377753, -0.03597226, 0.018330064, 0.014466659, 0.064007834, -0.017127983, -0.010960116, 0.019682391, -0.019696601, -0.020545894, -0.036266111, 0.022564862, 0.015046909, 0.005086916, -0.055712417, -0.009372988, -0.05443871, 0.010829321, 0.01392135, 0.012792529, 0.00985686, 0.038214996, -0.017488923, 0.027313927, 0.001062958, 0.0097226, -0.03439014, 0.047425389, -0.037770782, 0.028628167, 0.009835372, 0.027161121, -0.004167856, -0.00672366, 0.000454347, -0.017227914, 0.027887423, 0.007638352, -0.010299153, -0.02586738, 0.021838237, 0.046358533, 0.026485134, 0.02549421, -0.042892404, 0.035525952, -0.037090834, 0.022934681, -0.027275261, -0.031381506, 0.002224463, -0.036881655, 0.018545765, 0.016824408, 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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.