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Requesty supports sending PDF documents to AI models for analysis, summarization, and question answering. This feature works with both the Chat Completions and Messages API endpoints.

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How It Works

PDF documents are sent as part of the message content using either base64 encoding or a URL. The AI model can then analyze the document and respond to questions about its contents.

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Chat Completions API

Send PDFs using the input_file content type. You can provide the PDF as either base64-encoded data or a URL.

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Using Base64-Encoded PDF

curl https://router.requesty.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "anthropic/claude-sonnet-4-20250514",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Summarize this PDF"
          },
          {
            "type": "input_file",
            "filename": "document.pdf",
            "file_data": "data:application/pdf;base64,JVBERi0="
          }
        ]
      }
    ]
  }'

​
Using PDF URL

curl https://router.requesty.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "anthropic/claude-sonnet-4-20250514",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Summarize this PDF"
          },
          {
            "type": "input_file",
            "filename": "document.pdf",
            "file_data": "https://example.com/document.pdf"
          }
        ]
      }
    ]
  }'

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Parameters

  • type: Must be "input_file"
  • filename: The name of the PDF file (e.g., "document.pdf")
  • file_data: Either base64-encoded PDF content or a URL to the PDF file
See the Chat Completions API documentation for more details.

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Messages API

Send PDFs using the document content type: 
curl https://router.requesty.ai/v1/messages \
  -H "x-api-key: YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "anthropic/claude-sonnet-4-20250514",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is in this PDF?"
          },
          {
            "type": "document",
            "source": {
              "type": "base64",
              "media_type": "application/pdf",
              "data": "JVBERi0=..."
            }
          }
        ]
      }
    ]
  }'

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Parameters

  • type: Must be "document"
  • source.type: Must be "base64"
  • source.media_type: Must be "application/pdf"
  • source.data: Base64-encoded PDF content
See the Messages API documentation for more details.

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Working with PDFs

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Python Example (Chat Completions)

import base64
from openai import OpenAI

requesty_api_key = "YOUR_REQUESTY_API_KEY"

client = OpenAI(
    api_key=requesty_api_key,
    base_url="https://router.requesty.ai/v1",
)

# Option 1: Using base64-encoded PDF from a file
with open("document.pdf", "rb") as pdf_file:
    pdf_data = base64.b64encode(pdf_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Summarize this PDF"
                },
                {
                    "type": "input_file",
                    "filename": "document.pdf",
                    "file_data": f"data:application/pdf;base64,{pdf_data}"
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

# Option 2: Using PDF URL
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "Summarize this PDF"
                },
                {
                    "type": "input_file",
                    "filename": "document.pdf",
                    "file_data": "https://example.com/document.pdf"
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

​
Python Example (Messages API)

import base64
from anthropic import Anthropic

requesty_api_key = "YOUR_REQUESTY_API_KEY"

client = Anthropic(
    api_key=requesty_api_key,
    base_url="https://router.requesty.ai/v1",
)

# Read and encode PDF
with open("document.pdf", "rb") as pdf_file:
    pdf_data = base64.b64encode(pdf_file.read()).decode('utf-8')

response = client.messages.create(
    model="anthropic/claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What is in this PDF?"
                },
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_data
                    }
                }
            ]
        }
    ]
)

print(response.content[0].text)

​
JavaScript/TypeScript Example (Chat Completions)

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: 'YOUR_REQUESTY_API_KEY',
  baseURL: 'https://router.requesty.ai/v1',
});

// Option 1: Using base64-encoded PDF from a file
const pdfBuffer = fs.readFileSync('document.pdf');
const pdfData = pdfBuffer.toString('base64');

const response = await client.chat.completions.create({
  model: 'anthropic/claude-sonnet-4-20250514',
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'Summarize this PDF'
        },
        {
          type: 'input_file',
          filename: 'document.pdf',
          file_data: f'data:application/pdf;base64,{pdfData}'
        }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

// Option 2: Using PDF URL
const urlResponse = await client.chat.completions.create({
  model: 'anthropic/claude-sonnet-4-20250514',
  messages: [
    {
      role: 'user',
      content: [
        {
          type: 'text',
          text: 'Summarize this PDF'
        },
        {
          type: 'input_file',
          filename: 'document.pdf',
          file_data: 'https://example.com/document.pdf'
        }
      ]
    }
  ]
});

console.log(urlResponse.choices[0].message.content);