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Documentation Index

Fetch the complete documentation index at: https://docs.sinkove.com/llms.txt

Use this file to discover all available pages before exploring further.

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Prerequisites

Python 3.12+

Required for SDK

API Key

From your dashboard

Organization ID

Your org UUID
API keys and Organization IDs are covered in the Get Started section.

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1. Install the SDK

pip install sinkove-sdk

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2. Set Your API Key

export SINKOVE_API_KEY="your-api-key-here"

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3. Create Your First Dataset

Create first_dataset.py: 
import uuid
from sinkove import Client

# Your IDs (replace with actual values)
ORGANIZATION_ID = uuid.UUID("your-organization-id")
MODEL_ID = uuid.UUID("your-model-id")

# Initialize client
client = Client(ORGANIZATION_ID)

# Create dataset
print("Creating dataset...")
dataset = client.datasets.create(
    model_id=MODEL_ID,
    num_samples=10,
    args={"prompt": "chest x-ray showing pneumonia"}
)

print(f"Dataset created! ID: {dataset.id}")

# Wait and download
print("Waiting for completion...")
dataset.wait()

print("Downloading...")
dataset.download("my_first_dataset.zip", strategy="replace")
print("✓ Complete!")

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4. Run the Script

python first_dataset.py
Expected output: 
Creating dataset...
Dataset created! ID: 123e4567-e89b-12d3-a456-426614174000
Waiting for completion...
Downloading...
✓ Complete!

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Understanding the Code

  • Client Initialization: Client(ORGANIZATION_ID) connects using your API key
  • Dataset Creation: Specifies model, sample count, and generation parameters
  • Waiting: dataset.wait() blocks until generation completes
  • Downloading: Saves the generated dataset locally

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Common Patterns

# Check dataset status
print(f"State: {dataset.state}, Ready: {dataset.ready}")

# Handle existing datasets
dataset = client.datasets.get(uuid.UUID("existing-dataset-id"))
if dataset.ready:
    dataset.download("dataset.zip")

# List all datasets
datasets = client.datasets.list()
for ds in datasets:
    print(f"{ds.id}: {ds.state}")

​
Error Handling

try:
    client = Client(ORGANIZATION_ID)
    dataset = client.datasets.create(MODEL_ID, 10, {"prompt": "test"})
    dataset.wait(timeout=300)
    dataset.download("output.zip")
except ValueError as e:
    print(f"Configuration error: {e}")
except TimeoutError:
    print("Dataset generation took too long")
except Exception as e:
    print(f"Unexpected error: {e}")

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Next Steps

Python SDK Guide

Complete SDK documentation and advanced features

Code Examples

Advanced patterns and real-world use cases

API Reference

Complete method and class documentation

Model Catalog

Browse available AI models

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Quick Reference

# Essential methods
dataset = client.datasets.create(model_id, num_samples, args)
dataset = client.datasets.get(dataset_id)
datasets = client.datasets.list()
dataset.download("output.zip", strategy="replace", wait=True)
dataset.wait(timeout=600)

# Check status
dataset.ready  # bool
dataset.state  # "PENDING", "STARTED", "READY", "FAILED"