PDF Inputs

Send PDF documents to any model for analysis and summarization.

Supported Formats

• URL: Send publicly accessible PDFs directly without encoding (hint: Bucky may be useful)
• Base64: Required for local files or private documents

When a model supports file input natively, the PDF is passed directly to the model. Otherwise, the PDF will get parsed and the parsed results will be passed to the model.

PDF Processing Engines

Configure PDF processing using the plugins parameter:

Engine	Description
pdf-text	Best for well-structured PDFs
mistral-ocr	Best for scanned documents or PDFs with images
native	Uses model's built-in file processing (highly recommended)

If you don't specify an engine, Hack Club AI defaults to the model's native capability first (which usually will be the best option), then falls back to mistral-ocr.

Example: PDF via URL

curl https://ai.hackclub.com/proxy/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-32b",
    "messages": [
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "What are the main points in this document?"},
          {
            "type": "file",
            "file": {
              "filename": "bitcoin.pdf",
              "file_data": "https://bitcoin.org/bitcoin.pdf"
            }
          }
        ]
      }
    ],
    "plugins": [
      {
        "id": "file-parser",
        "pdf": {"engine": "pdf-text"}
      }
    ]
  }'

Example: Base64-encoded PDF

import base64
import requests

def encode_pdf(path):
    with open(path, "rb") as f:
        return f"data:application/pdf;base64,{base64.b64encode(f.read()).decode()}"

response = requests.post(
    "https://ai.hackclub.com/proxy/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen/qwen3-32b",
        "messages": [{
            "role": "user",
            "content": [
                {"type": "text", "text": "Summarize this document"},
                {
                    "type": "file",
                    "file": {
                        "filename": "document.pdf",
                        "file_data": encode_pdf("path/to/document.pdf")
                    }
                }
            ]
        }],
        "plugins": [{"id": "file-parser", "pdf": {"engine": "mistral-ocr"}}]
    }
)

Reusing File Annotations

When you send a PDF, the response may include annotations in the assistant message. Include these in subsequent requests to skip re-parsing and save costs:

response = requests.post(url, headers=headers, json={...})
result = response.json()

annotations = result["choices"][0]["message"].get("annotations")

follow_up = requests.post(url, headers=headers, json={
    "model": "qwen/qwen3-32b",
    "messages": [
        {"role": "user", "content": [...]},  # Original message with PDF
        {
            "role": "assistant",
            "content": result["choices"][0]["message"]["content"],
            "annotations": annotations  # Include these to skip re-parsing!
        },
        {"role": "user", "content": "Can you elaborate on point 2?"}
    ]
})