# Text

This quickstart walks you through making your first text generation request with Infron.

### Using the Infron API directly

{% tabs %}
{% tab title="Curl" %}

```sh
curl https://llm.onerouter.pro/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
  "model": "deepseek/deepseek-v3.2",
  "messages": [
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
}'
```

{% endtab %}

{% tab title="Python" %}

```python
import requests
import json

response = requests.post(
  url="https://llm.onerouter.pro/v1/chat/completions",
  headers={
    "Authorization": "Bearer <API_KEY>",
    "Content-Type": "application/json"
  },
  data=json.dumps({
    "model": "deepseek/deepseek-v3.2", 
    "messages": [
      {
        "role": "user",
        "content": "What is the meaning of life?"
      }
    ]
  })
)
print(response.json()["choices"][0]["message"]["content"])
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: 'Bearer <API_KEY>',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'deepseek/deepseek-v3.2',
    messages: [
      {
        role: 'user',
        content: 'What is the meaning of life?',
      },
    ],
  }),
});
```

{% endtab %}
{% endtabs %}

The API also supports [streaming](broken://pages/zaGkdltLztTFWVpP9DNN).

### **Using the OpenAI SDK**

Get started with just a few lines of code using your preferred SDK or framework.

{% tabs %}
{% tab title="Python" %}

```python
from openai import OpenAI

client = OpenAI(
  base_url="https://llm.onerouter.pro/v1",
  api_key="<API_KEY>",
)

completion = client.chat.completions.create(
  model="deepseek/deepseek-v3.2",
  messages=[
    {
      "role": "user",
      "content": "What is the meaning of life?"
    }
  ]
)

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

{% endtab %}

{% tab title="TypeScript" %}

```typescript
import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: 'https://llm.onerouter.pro/v1',
  apiKey: '<API_KEY>',
});

async function main() {
  const completion = await openai.chat.completions.create({
    model: 'deepseek/deepseek-v3.2',
    messages: [
      {
        role: 'user',
        content: 'What is the meaning of life?',
      },
    ],
  });

  console.log(completion.choices[0].message);
}

main();
```

{% endtab %}
{% endtabs %}

### Using third-party SDKs

For information about using third-party SDKs and frameworks with Infron, please see our [frameworks documentation](/docs/frameworks-and-integrations/overview.md).

### Next steps

* Learn about [provider and model routing with fallbacks](/docs/routing-and-gateway/inference-provider-routing.md)
* Try other APIs: [OpenAI-compatible](/docs/llm-apis/openai-compatible-api/overview.md), [Anthropic-compatible](/docs/llm-apis/anthropic-compatible-api/overview.md), or [OpenResponses](/docs/llm-apis/openresponses-api/overview.md)


---

# Agent Instructions: 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://infronai.gitbook.io/docs/overview/quickstart/text.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.