lightningrod-ai 0.1.15

Python SDK for Lightning Rod AI-powered forecasting dataset generation

Lightning Rod Python SDK

The Lightning Rod SDK provides a simple Python API for generating custom forecasting datasets to train your LLMs. Transform news articles, documents, and other real-world data into high-quality training samples automatically.

Based on our research: Future-as-Label: Scalable Supervision from Real-World Outcomes

👋 Quick Start

1. Install the SDK

pip install lightningrod-ai

2. Get your API key

Sign up at dashboard.lightningrod.ai to get your API key and $50 of free credits.

3. Generate your first dataset

Generate 1000+ forecasting questions in minutes - from raw sources to labeled dataset, automatically. ⚡

from lightningrod import LightningRod, BinaryAnswerType, QuestionPipeline, NewsSeedGenerator, ForwardLookingQuestionGenerator, WebSearchLabeler

lr = LightningRod(api_key="your-api-key")

binary_answer = BinaryAnswerType()

pipeline = QuestionPipeline(
    seed_generator=NewsSeedGenerator(
        start_date=datetime.now() - timedelta(days=90),
        end_date=datetime.now(),
        search_query=["Trump"],
    ),
    question_generator=ForwardLookingQuestionGenerator(
        instructions="Generate binary forecasting questions about Trump's actions and decisions.",
        examples=[
            "Will Trump impose 25% tariffs on all goods from Canada by February 1, 2025?",
            "Will Pete Hegseth be confirmed as Secretary of Defense by February 15, 2025?",
        ]
    ),
    labeler=WebSearchLabeler(answer_type=binary_answer),
)

dataset = lr.transforms.run(pipeline, max_questions=3000)
dataset.flattened(binary_answer)  # Ready-to-use data for your training pipelines

We use this to generate the Future-as-Label training dataset for our research paper.

✨ Examples

We have some example notebooks to help you get started! If you have trouble using the SDK, please submit an issue on Github.

Tutorials

Example Name	Path	Google Colab Link
Quick Start	notebooks/01_quick_start.ipynb
News Datasource	notebooks/02_news_datasource.ipynb
Custom Documents	notebooks/03_custom_documents_datasource.ipynb
Binary Answer Type	notebooks/04_binary_answer_type.ipynb
Continuous Answer Type	notebooks/05_continuous_answer_type.ipynb
Multiple Choice Answer Type	notebooks/06_multiple_choice_answer_type.ipynb
Free Response Answer Type	notebooks/07_free_response_answer_type.ipynb

End-to-end

Example Name	Path	Google Colab Link
Golf Forecasting	notebooks/e2e/golf_forecasting.ipynb
Trump Forecasting	notebooks/e2e/trump_forecasting.ipynb
Document Classification	notebooks/e2e/document_classification.ipynb
Polymarket Backtesting	notebooks/e2e/polymarket_backtesting.ipynb
Model Consensus	notebooks/e2e/model_consensus.ipynb

For complete API reference documentation, see API.md. This includes overview of the core system concepts, methods and types.