File Uploads

The MicroDC platform supports uploading files and creating download tokens for multimodal AI workflows.

Upload a File

from microdc import Client

client = Client(api_key="mDC_...")

upload_result = client.upload_file("contract.pdf", description="Legal contract")
print(f"File ID: {upload_result['id']}")
print(f"File size: {upload_result['file_size']} bytes")

The upload response contains:

Field	Description
id	File ID (use as token for DocumentCall)
original_name	Original filename
file_path	Server-side path
file_size	Size in bytes

Create a Download Token

Generate a one-time download URL for an uploaded file:

token_data = client.create_download_token(
    file_id=upload_result['id'],
    expires_in_minutes=60,    # 1-10080 minutes
    max_uses=1                # 1-100 uses
)

print(f"Download URL: {token_data['download_url']}")
print(f"Expires at: {token_data['expires_at']}")

Upload and Tokenize (One Step)

Combine upload and token creation:

file_token = client.upload_and_tokenize(
    "contract.pdf",
    description="Legal contract for analysis",
    expires_in_minutes=60,
    max_uses=1
)

print(f"File ID: {file_token['file_id']}")
print(f"Download URL: {file_token['download_url']}")

Use with Document Processing

Files must be uploaded before creating DocumentCall jobs:

from microdc import Client, DocumentCall

client = Client(api_key="mDC_...")

# Upload file
upload_result = client.upload_file("document.pdf")
file_token = upload_result['id']

# Process with docling
job = DocumentCall(model="docling")
job.add_file(file_token)

job_id = client.send_job(job)
client.wait_for_all()

result = client.get_job_details(job_id)
print(result.result)

Use with LLM (Vision/Multimodal)

Use download URLs to pass files to LLM models:

from microdc import Client, LLMChat

client = Client(api_key="mDC_...")

# Upload and get download URL
file_token = client.upload_and_tokenize(
    "product_photo.jpg",
    expires_in_minutes=30
)

# Vision model analysis
chat = LLMChat(model="gpt-4-vision")
chat.input_modalities = ["text", "image"]
chat.output_modalities = ["text"]

chat.set_system("You are a product analyst.")
chat.add_user_message("What product is this?")

job_id = client.send_job(chat)
details = client.wait_for_job(job_id)
print(details.result)

Batch File Upload

from microdc import Client, DocumentCall

client = Client(api_key="mDC_...")

files = ["report_q1.pdf", "report_q2.pdf", "report_q3.pdf"]

# Upload all files
tokens = []
for filepath in files:
    result = client.upload_file(filepath)
    tokens.append(result['id'])
    print(f"Uploaded {filepath}")

# Submit processing jobs
job_ids = []
for i, token in enumerate(tokens):
    job = DocumentCall(model="docling")
    job.add_file(token)
    job.metadata = {"filename": files[i]}
    job_ids.append(client.send_job(job))

client.wait_for_all(timeout=600)