# Integrasi API Preview

API Integration Preview is a feature provided by the Service Portal AI 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&#x20;

**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",`**&#x20;

  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:

* &#x20;`model="meta/llama-4-maverick-instruct",`

  Specifies the LLM model to use in Deka LLM.
* `messages=[{"role": "user", "content": ""}],`&#x20;

  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`&#x20;

  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.&#x20;

```
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:

* &#x20;`model="meta/llama-4-maverick-instruct",`

  Specifies the LLM model to use in Deka LLM.
* `messages=[{"role": "user", "content": ""}],`&#x20;

  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`&#x20;

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


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.cloudeka.ai/guidance-for-individual/deka-llm/detail-deka-llm/playground/integrasi-api-preview.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.