PDF Inputs

How to send PDFs to Infron AI models

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:

Python

import requests
import json

url = "https://llm.onerouter.pro/api/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())

TypeScript

Using Base64 Encoded PDFs

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

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())

TypeScript