> For the complete documentation index, see [llms.txt](https://infronai.gitbook.io/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infronai.gitbook.io/docs/features/multimodal-input/pdf-inputs.md).

# PDF Inputs

Infron AI supports PDF processing through the `/v1/chat/completions` API. PDFs can be sent as **direct URLs** or **base64-encoded data URLs** in the messages array, via the file content type. This feature works on **any** model on Infron AI.

* **URL support**: Send publicly accessible PDFs directly without downloading or encoding
* **Base64 support**: Required for local files or private documents that aren't publicly accessible

PDFs also work in the chat room for interactive testing.

You can send both PDFs and other file types in the same request.

### Using PDF URLs

For publicly accessible PDFs, you can send the URL directly without needing to download and encode the file:

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

```python
import requests
import json

url = "https://llm.onerouter.pro/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What are the main points in this document?"
            },
            {
                "type": "file",
                "file": {
                    "filename": "document.pdf",
                    "file_data": "https://domain.org/file.pdf"
                }
            },
        ]
    }
]

payload = {
    "model": "{{MODEL}}",
    "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
const response = await fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: '{{MODEL}}',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'What are the main points in this document?',
          },
          {
            type: 'file',
            file: {
              filename: 'document.pdf',
              file_data: 'https://bitcoin.org/bitcoin.pdf',
            },
          },
        ],
      },
    ],

  }),
});

const data = await response.json();
console.log(data);
```

{% endtab %}
{% endtabs %}

### Using Base64 Encoded PDFs

For local PDF files or when you need to send PDF content directly, you can base64 encode the file:

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

```python
import requests
import json
import base64
from pathlib import Path

def encode_pdf_to_base64(pdf_path):
    with open(pdf_path, "rb") as pdf_file:
        return base64.b64encode(pdf_file.read()).decode('utf-8')

url = "https://llm.onerouter.pro/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

# Read and encode the PDF
pdf_path = "path/to/your/document.pdf"
base64_pdf = encode_pdf_to_base64(pdf_path)
data_url = f"data:application/pdf;base64,{base64_pdf}"

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What are the main points in this document?"
            },
            {
                "type": "file",
                "file": {
                    "filename": "document.pdf",
                    "file_data": data_url
                }
            },
        ]
    }
]


payload = {
    "model": "{{MODEL}}",
    "messages": messages
}

response = requests.post(url, headers=headers, json=payload)
print(response.json())
```

{% endtab %}

{% tab title="TypeScript" %}

```typescript
async function encodePDFToBase64(pdfPath: string): Promise<string> {
  const pdfBuffer = await fs.promises.readFile(pdfPath);
  const base64PDF = pdfBuffer.toString('base64');
  return `data:application/pdf;base64,${base64PDF}`;
}

// Read and encode the PDF
const pdfPath = 'path/to/your/document.pdf';
const base64PDF = await encodePDFToBase64(pdfPath);

const response = await fetch('https://llm.onerouter.pro/v1/chat/completions', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: '{{MODEL}}',
    messages: [
      {
        role: 'user',
        content: [
          {
            type: 'text',
            text: 'What are the main points in this document?',
          },
          {
            type: 'file',
            file: {
              filename: 'document.pdf',
              file_data: base64PDF,
            },
          },
        ],
      },
    ],
 
  }),
});

const data = await response.json();
console.log(data);
```

{% endtab %}
{% endtabs %}


---

# 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, and the optional `goal` query parameter:

```
GET https://infronai.gitbook.io/docs/features/multimodal-input/pdf-inputs.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.