> For the complete documentation index, see [llms.txt](https://docs.cloudeka.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cloudeka.ai/deka-llm/detail-deka-llm/playground/integrasi-api-preview.md).

# Integrasi API Preview

API Integration Preview is a feature provided by the Service Portal Cloudeka that displays an auto-generated code snippet for each prompt you submit to the model via the Interactive Chat panel. So, every time you test a prompt in the Playground Tab, Deka LLM automatically generates a ready-to-use API call example.

<figure><img src="/files/93evmwnCDLpqZRJHRx43" alt=""><figcaption></figcaption></figure>

## cURL

**cURL (Client URL)** is a command-line tool used to send requests to servers using various protocols such as HTTP, HTTPS, and FTP. Below is an example of a `cURL` command generated when you input a prompt.

<figure><img src="/files/FuZbYkc4LvWCKT3bIXvS" alt=""><figcaption></figcaption></figure>

{% code lineNumbers="true" %}

```url
curl https://dekallm.cloudeka.ai/v1/chat/completions \
  -H "Authorization": "Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta/llama-4-maverick-instruct",
    "messages": [{"role": "user", "content": ""}],
    "temperature": 0.6,
    "top_p": 0.7
  }'
```

{% endcode %}

Explanation of the cURL Command.

```
curl https://dekallm.cloudeka.ai/v1/chat/completions \
```

This line `curl https://dekallm.cloudeka.ai/v1/chat/completions \`defines the API endpoint URL used by Deka LLM to request a chat completion.

```
  -H "Authorization": "Bearer YOUR_API_KEY" \
```

{% hint style="success" %}
This line corresponds to the API Key you entered in the API Key field.
{% endhint %}

This line `-H "Authorization": "Bearer YOUR_API_KEY" \` is used to authenticate the API request by providing the API key in a Bearer token format.

```
  -H "Content-Type: application/json" \
```

This line `-H "Content-Type: application/json" \` sets the request payload format to JSON.

```
  -d '{
    "model": "meta/llama-4-maverick-instruct",
    "messages": [{"role": "user", "content": ""}],
    "temperature": 0.6,
    "top_p": 0.7
  }'

```

This JSON object is sent via an HTTP POST request and contains:

* **`"model": "meta/llama-4-maverick-instruct",`**

  Specifies the LLM model to use in Deka LLM.
* **`"messages": [{"role": "user", "content": ""}],`**

  An array of messages, where `"role": "user"` represents the you, and `"content": ""` is the prompt.
* **`temperature": 0.6`**

  Controls the creativity or randomness of the response (higher value = more creative).
* **`"top_p": 0.7`**

  Configures nucleus sampling to control the cumulative probability for token selection, affecting the randomness.

## Python

**Python** is a programming language known for its simple syntax, readability, and support for imperative, functional, and object-oriented paradigms. Below is the example code auto-generated when you input a prompt.

<figure><img src="/files/2HbiGg7l2eXDnbdVosXa" alt=""><figcaption></figcaption></figure>

{% code lineNumbers="true" %}

```
from openai import OpenAI

client = OpenAI(
    base_url="https://dekallm.cloudeka.ai/v1",
    api_key="YOUR_API_KEY",
)

completion = client.chat.completions.create(
    model="baai/bge-multilingual-gemma2",
    messages=[{"role": "user", "content": ""}],
    temperature=0.6,
    top_p=0.7,
)
print(completion.choices[0].message.content)
```

{% endcode %}

Explanation of the Python Code:

```
from openai import OpenAI
```

Imports the `OpenAI` class from the official OpenAI Python SDK, which provides an interface to interact with LLM API.

```
client = OpenAI(
    base_url="https://dekallm.cloudeka.ai/v1",
    api_key="YOUR_API_KEY",
)
```

This line is used to create a client instance to communicate with the Deka LLM API endpoint. There are two important parameters used

* `base_url` displays the URL of the Deka LLM endpoint,
* `api_key` is sed to authenticate requests and is taken from the API Key column.

```
completion = client.chat.completions.create(
    model="meta/llama-4-maverick-instruct",
    messages=[{"role": "user", "content": ""}],
    temperature=0.6,
    top_p=0.7,
)
```

This line `client.chat.completions.create()`is used to send a request for chat completion. There are four important parameters used, namely:

* `model="meta/llama-4-maverick-instruct",`

  Specifies the LLM model to use in Deka LLM.
* `messages=[{"role": "user", "content": ""}],`

  An array of messages, where `"role": "user"` represents the you, and `"content": ""` is the prompt.
* `temperatur=0.6`

  Controls the creativity or randomness of the response (higher value = more creative).
* and `top_p=0.7`

  Configures nucleus sampling to control the cumulative probability for token selection, affecting the randomness.

```
print(completion.choices[0].message.content)
```

This line `print(completion.choices[0].message.content)` is used to represent the model's response to the message you send.

## Node.js

**Node.js** is a runtime environment for executing JavaScript code outside the browser. Below is an example Node.js code generated when you input a prompt.

<figure><img src="/files/ZH6oOWQTboFGtVCu7U59" alt=""><figcaption></figcaption></figure>

```
import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://dekallm.cloudeka.ai/v1",
});

const chatCompletion = await openai.chat.completions.create({
  model: "baai/bge-multilingual-gemma2",
  messages: [{ role: "user", content: "" }],
  temperature: 0.6,
  top_p: 0.7,
});
```

Explanation of the Node.js Code:

```
import OpenAI from "openai";
```

This line `import OpenAI from "openai";` imports the OpenAI class from the official Node.js SDK, which allows you to interact with the Deka LLM API.

```
const openai = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://dekallm.cloudeka.ai/v1",
});
```

This line `const openai = new OpenAI({...});` is used to create your instance with`apiKey` and`baseURL` configurations.

```
const chatCompletion = await openai.chat.completions.create({
  model: "baai/bge-multilingual-gemma2",
  messages: [{ role: "user", content: "" }],
  temperature: 0.6,
  top_p: 0.7,
});
```

This line `const chatCompletion = await openai.chat.completions.create({...});` is used to send a chat completion request to the model you are using in Deka LLM with parameters. There are four important parameters used, namely:

* `model="meta/llama-4-maverick-instruct",`

  Specifies the LLM model to use in Deka LLM.
* `messages=[{"role": "user", "content": ""}],`

  An array of messages, where `"role": "user"` represents the you, and `"content": ""` is the prompt.
* `temperatur=0.6`

  Controls the creativity or randomness of the response (higher value = more creative).
* and `top_p=0.7`

  Configures nucleus sampling to control the cumulative probability for token selection, affecting the randomness.