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

# voyage-law-2

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

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

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

## Model Overview

This model leads the MTEB leaderboard for legal retrieval by a significant margin, outperforming OpenAI v3 large by an average of 6% across eight legal retrieval datasets and by over 10% on three key benchmarks (LeCaRDv2, LegalQuAD, and GerDaLIR). With a 16K context length and training on extensive long-context legal documents, voyage-law-2 excels in retrieving information across lengthy texts. Notably, it also matches or surpasses performance on general-purpose corpora across domains.

## 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-1e2f1017f077054fa361a442474bde8bccafcd62%2Fvoyage-law-2.json?alt=media&token=ac5bd267-e25a-4c01-9380-073d2544341a>" path="/v1/embeddings" method="post" %}
[voyage-law-2.json](https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-1e2f1017f077054fa361a442474bde8bccafcd62%2Fvoyage-law-2.json?alt=media\&token=ac5bd267-e25a-4c01-9380-073d2544341a)
{% 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-law-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-law-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=[0.001145921996794641, -0.014177074655890465, 0.03125114366412163, -0.004943643696606159, 0.040808435529470444, -0.04112643748521805, -0.002148799132555723, 0.022367684170603752, -0.005523522850126028, 0.03991401568055153, 0.0011472610058262944, -0.04464377462863922, -0.008764686062932014, 0.007735587190836668, -0.04927933216094971, -0.07220178842544556, 0.014300036244094372, -0.01632966846227646, -0.006595244165509939, -0.010660311207175255, -0.029766302555799484, -0.006220588460564613, -0.010902494192123413, -0.0033697024919092655, 0.03782792389392853, -0.025107743218541145, -0.019067829474806786, -0.024837607517838478, -0.03289186581969261, -0.019047968089580536, 0.029241429641842842, 0.03866409882903099, -0.004571191035211086, 0.0077200778760015965, -0.02732025273144245, -0.011416708119213581, 0.0006872184458188713, 0.004462233744561672, 0.004217428155243397, -0.0007986027630977333, -0.05442861467599869, 0.022397488355636597, 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object='list', usage=Usage(prompt_tokens=None, total_tokens=6), 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.