Data Simulator

data-simulator is a lightweight Python library for generating synthetic datasets from your own corpus — perfect for testing, evaluating, or fine-tuning LLM Applications.

Motivation

Real documents contain a mix of useful and irrelevant content. When generating synthetic data, this leads to:

• Queries that real users would never ask
• Test sets that don't reflect actual usage
• Wasted effort optimizing for the wrong things

Data Simulator filters out low-quality content first, then generates realistic queries and answers that match how your system will actually be used.

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Getting Started

Install from PyPI:

pip install llm-data-simulator

Or install it locally:

git clone https://github.com/langwatch/data-simulator.git
cd data-simulator
pip install -e .

Run the built-in test script:

python test.py

Example test.py

from data_simulator import DataSimulator
from dotenv import load_dotenv
import os
from data_simulator.utils import display_results

load_dotenv()

generator = DataSimulator(api_key=os.getenv("OPENAI_API_KEY"))

results = generator.generate_from_docs(
    file_paths=["test_data/nike_10k.pdf"],
    context="You're a financial support assistant for Nike, helping a financial analyst decide whether to invest in the stock.",
    example_queries="how much revenue did nike make last year\nwhat risks does nike face\nwhat are nike's top 3 priorities"
)

display_results(results)

Output Format

{
  "id": "chunk_42",
  "document": "Nike reported annual revenue of $44.5 billion for fiscal year 2022, an increase of 5% compared to the previous year.",
  "query": "What was Nike's revenue growth in 2022?",
  "answer": "Nike's revenue grew by 5% in fiscal year 2022, reaching $44.5 billion."
}

Project Structure

The project follows a modular, object-oriented design:

• simulator.py: Contains the main DataSimulator class that orchestrates the data generation process
• llm.py: Houses the LLMProcessor class that handles all LLM-related operations
• document_processor.py: Provides the DocumentProcessor class for loading and chunking documents
• prompts.py: Stores all prompt templates used for LLM interactions
• utils.py: Contains utility functions like display_results for formatting output

License

MIT License