# Source: https://docs.aimlapi.com/api-references/embedding-models/openai/text-embedding-ada-002.md

# text-embedding-ada-002

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

* `text-embedding-ada-002`
  {% endhint %}
  {% endcolumn %}

{% column width="33.33333333333334%" %} <a href="https://aimlapi.com/app/text-embedding-ada-002" class="button primary">Try in Playground</a>
{% endcolumn %}
{% endcolumns %}

## Model Overview

An efficient and reliable embedding model designed to convert text into numerical representations. It serves as a foundational tool for various natural language processing (NLP) applications, enabling machines to understand and process human language more effectively.

## 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-56e765841e8c66f5e026fb08241d595c6bf3d1ca%2Ftext-embedding-ada-002.json?alt=media&token=08c46a7d-738e-42be-a7d9-26f34966b447>" path="/v1/embeddings" method="post" %}
[text-embedding-ada-002.json](https://3927338786-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FROMd1X5PuqtikJ48n2N9%2Fuploads%2Fgit-blob-56e765841e8c66f5e026fb08241d595c6bf3d1ca%2Ftext-embedding-ada-002.json?alt=media\&token=08c46a7d-738e-42be-a7d9-26f34966b447)
{% 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="text-embedding-ada-002"
)

# 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: "text-embedding-ada-002",
});

// 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=[-0.009682911448180676, -0.012292581610381603, -0.02386169135570526, -0.02174294739961624, -0.0024966278579086065, 0.001669801538810134, -0.01511541847139597, 2.5636460122768767e-05, -0.006023558788001537, -0.023474115878343582, 0.004008169751614332, 0.006117222830653191, -0.007157215382903814, -0.02139413170516491, -0.006640448700636625, -0.010658307932317257, 0.01862942986190319, -0.00039847538573667407, 0.030049968510866165, -0.02293151058256626, -0.007635224144905806, 0.0016552675515413284, 0.011181534267961979, 0.00046024512266740203, -0.030592573806643486, -0.0018296762136742473, 0.007241189945489168, -0.005377600900828838, 0.01158202812075615, 0.018009310588240623, 0.010974827222526073, 0.008358697406947613, -0.018513157963752747, -0.004602451343089342, -0.009992971085011959, -0.04622475802898407, -0.0072928667068481445, -0.0032750072423368692, 0.007544789928942919, 0.017169564962387085, 0.0067115044221282005, 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usage=Usage(prompt_tokens=5, total_tokens=5), 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.