PDF Inputs

import OpenAI from 'openai';
const openai = new OpenAI({
  baseURL: 'https://api.tramlabs.io/v1',
  apiKey: '<TRAM_AI_API_KEY>',
});
const result = await openai.chat.completions.create({
  model: 'claude-sonnet-4.6',
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'What are the main points in this document?',
        },
        {
          type: 'file',
          file: {
            filename: 'document.pdf',
            fileData: 'https://bitcoin.org/bitcoin.pdf',
          },
        },
      ],
    },
  ],
  // Optional: Configure PDF processing engine
  plugins: [
    {
      id: 'file-parser',
      pdf: {
        engine: 'mistral-ocr',
      },
    },
  ],
  stream: false,
});
console.log(result);

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://api.tramlabs.io/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY_REF}",
    "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
                }
            },
        ]
    }
]
# Optional: Configure PDF processing engine
# PDF parsing will still work even if the plugin is not explicitly set
plugins = [
    {
        "id": "file-parser",
        "pdf": {
            "engine": "cloudflare-ai"  # defaults to "mistral-ocr". See Pricing above
        }
    }
]
payload = {
    "model": "Gemma 3 27B IT",
    "messages": messages,
    "plugins": plugins
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())

import requests
import json
import base64
from pathlib import Path
# First, encode and send the PDF
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://api.tramlabs.io/v1/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY_REF}",
    "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}"
# Initial request with the 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": "Gemma 3 27B IT",
    "messages": messages
}
response = requests.post(url, headers=headers, json=payload)
response_data = response.json()
# Store the annotations from the response
file_annotations = None
if response_data.get("choices") and len(response_data["choices"]) > 0:
    if "annotations" in response_data["choices"][0]["message"]:
        file_annotations = response_data["choices"][0]["message"]["annotations"]
# Follow-up request using the annotations (without sending the PDF again)
if file_annotations:
    follow_up_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
                    }
                }
            ]
        },
        {
            "role": "assistant",
            "content": "The document contains information about...",
            "annotations": file_annotations
        },
        {
            "role": "user",
            "content": "Can you elaborate on the second point?"
        }
    ]
    follow_up_payload = {
        "model": "Gemma 3 27B IT",
        "messages": follow_up_messages
    }
    follow_up_response = requests.post(url, headers=headers, json=follow_up_payload)
    print(follow_up_response.json())