lightningrod

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

Documentation: docs.lightningrod.ai

👋 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_seeds=3000)
dataset.flattened()  # Ready-to-use data for your training pipelines

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

🆕 New: Foresight-v3 Forecasting Model

We've released foresight-v3, our latest forecasting model. Use it via the OpenAI-compatible API for probability estimates on forecasting questions:

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://api.lightningrod.ai/api/public/v1"
)

response = client.chat.completions.create(
    model="LightningRodLabs/foresight-v3",
    messages=[{"role": "user", "content": "Will the Fed cut rates by 25bp in March 2025?"}]
)
print(response.choices[0].message.content)

See the API docs for full details, or try the Foresight-v3 notebook.

✨ Examples

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

Quick Start

Example Name	Path	Google Colab Link
Quick Start	notebooks/00_quickstart.ipynb	Open in Colab

Getting Started