datacreator-sdk 0.1.6

Python SDK for the DataCreator AI dataset generation API

DataCreator AI – Software Development Kit (SDK)

A lightweight, proprietary Python client for accessing the DataCreator AI synthetic data generation API. This SDK is designed for desktop and server environments, enabling programmatic dataset generation with simple Python calls.

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Installation

Usage Overview

The DataCreatorClient provides a single primary method, generate(), that generates conversations suitable for fine-tuning AI models. The SDK handles communication, progress messages, response validation, and writing the final dataset to a .jsonl file.

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generate() Method

The generate() function triggers the synthetic data generation workflow and saves the final dataset locally.

Purpose

Initiates a dataset generation request and produces a .jsonl file containing structured conversational examples aligned with your specified theme.

Authentication

Please contact us at [e-mail] for a API Key. Once you've received your unique key, send it during instantiation as shown in the example code.

Parameters

The method accepts the following arguments:

• main_theme (str, required)
  The central topic around which the synthetic dataset should be generated.
  Example: "Daily tasks and personal planning for young working professionals" Any harmless and non-NSFW topic is allowed.

• num_of_turns (int, optional, default: 3)
  Number of conversational turns per datapoint. The maximum number of turns allowed is 5. A "turn" is typically one user → assistant exchange. The generation supports multiple message roles including user, assistant, system, and tool. Example: 2 turns produces:

  • user
  • assistant
  • user
  • assistant

• num_of_datapoints (int, optional, default: 100)
  Number of independent dialogues/conversations to generate. The maximum number of data points allowed per generation is 1000. Example: 10 produces 10 conversations.

• language (str, optional, default: "English")
  The language in which the dataset should be generated.

• system_prompt (str, optional)
  A custom instruction or persona that the assistant should follow throughout the conversations.

• max_tokens (int, optional, default: 2048)
  The maximum token limit for each conversation.

• use_rolling_temperatures (bool, optional, default: False)
  When set to True, the generator uses varying temperatures across data points to ensure higher lexical diversity.

• use_model_rotation (bool, optional, default: False)
  When set to True, the system rotates between different high-quality models to provide better structural diversity.

• output_file (str, optional, default: "dataset.jsonl")
  The path and filename where the final dataset will be written.
  Example: "data.jsonl"

The final dataset is a JSON Lines (.jsonl) file, where each line represents a conversation formatted as a list of messages. This format is fully compatible for fine-tuning with providers like OpenAI, Mistral, and Anthropic.

The SDK supports the following message roles:

• system: Sets the context or behavior of the assistant.
• user: The human/user prompt.
• assistant: The model's response.
• tool: Represents tool outputs or function calls (experimental).

JSONL Output Examples

The generated .jsonl file contains one JSON object per line. Here are three examples of how the data is structured:

Example 1: Standard Customer Support (Single Turn)

{"messages": [{"role": "system", "content": "You are a helpful assistant for a tech company."}, {"role": "user", "content": "My internet is slow, what should I do?"}, {"role": "assistant", "content": "I'm sorry to hear that. Have you tried restarting your router or checking for service outages in your area?"}]}

Example 2: Creative Writing (Multi-Turn)

{"messages": [{"role": "user", "content": "Can you help me start a sci-fi story about a robot on Mars?"}, {"role": "assistant", "content": "Sure! How about this: 'Unit 7 awoke to the sound of dust scratching against its metallic hull. The red horizon was empty...'. What happens next?"}, {"role": "user", "content": "The robot find a strange crystal."}, {"role": "assistant", "content": "The crystal pulsed with a soft blue light, unlike anything on the red planet. Unit 7 reached out its gripper arm..."}]}

Example 3: System Prompt Compliance

{"messages": [{"role": "system", "content": "You are a concise tutor who explains concepts in simple terms."}, {"role": "user", "content": "What is photosynthesis?"}, {"role": "assistant", "content": "Photosynthesis is the process where plants use sunlight, water, and carbon dioxide to create oxygen and energy in the form of sugar."}]}

Example Code

import os
from dotenv import load_dotenv
from datacreatoraisdk import DataCreatorClient

load_dotenv()

# Example usage
if __name__ == "__main__":
    client = DataCreatorClient(api_key=os.getenv("DATACREATOR_API_KEY"))
    client.generate(
        main_theme="Natural dialogues between a user and assistant asking about daily tasks, errands, and emotions.", 
        num_of_turns=2, 
        num_of_datapoints=100,
        use_rolling_temperatures=True,
        use_model_rotation=False
    )