Thinking Dataset: Leveraging Real-World Data for Strategic Business Insights

Table of Contents

• Overview
• Features
• Installation
• Usage
• Quick Start
• Project Structure
• Contributing
• Resources
• License
• Citations
• Acknowledgements
• Contact

Overview

Thinking Dataset leverages real-world data and efficient pipelines for business insights. We integrate robust data workflows (PDF to SQL ingestion), tactical TDD (details), and a streamlined SQLite design (details). Multi-stage AI generates STaR case studies for actionable strategies.

Features

• 🔄 End-to-End Pipeline: Download, process, transform, and store large datasets.
• 💾 SQLite Backend: Lightweight, fast, and easy to manage, with optional Parquet.
• ✅ Comprehensive Testing: Thorough TDD coverage and validation checks.
• 🖥️ Flexible CLI: Modular Click commands for quick execution of tasks.
• 🔀 Data Transformation: Granular pipes for cleaning, merging, and deriving data.
• 📚 STaR Case Studies: Generate synthetic scenarios alongside real data for deeper insights.
• ⚡ Parallel Execution: Efficiently process big data with optional concurrency.
• 🔒 Secure Config: Manage environment variables discretely with .env files.

Quick Start

Prerequisites

• Python 3.12 or later
• Git
• A cloud-based account (e.g., Huggingface) or a GPU (RTX 3090 or greater) for processing, or both

Setup

1. Clone the repository:

   git clone https://github.com/MultiTonic/thinking-dataset.git
   cd thinking-dataset
   

2. Install uv package manager:

   First add the package into the global environment:

   pip install uv
   

   Then add uv tools directory to PATH*:

   uv tool update-shell
   

3. Set up the project:

   uv run setup
   

   *You may need to restart your terminal session for the changes to update.

This will create a virtual environment, install the project dependencies, and activate the virtual environment.

4. Set up environment variables:

   Copy the .env.sample file to .env and change the values as needed:

   cp .env.sample .env
   

   Update the .env file with your credentials:

   # Required settings
   HF_ORG="my_huggingface_organization"
   HF_USER="my_huggingface_username"
   HF_READ_TOKEN="my_huggingface_read_access_token"
   HF_WRITE_TOKEN="my_huggingface_write_access_token"
   
   # Required configuration
   CONFIG_PATH="config/config.yaml"
   
   # One or more providers
   OLLAMA_SERVER_URL="http://localhost:11434"
   OPENAI_API_TOKEN="your_openai_api_token"
   RUNPOD_API_TOKEN="your_runpod_api_token"
   

Usage

For complete usage instructions and examples, see the Usage Guide.

Running the Download Command

To download all parquet files from the Cablegate dataset using Hugging Face CLI:

thinking-dataset download

Running All CLI Commands

To execute all CLI commands for the project:

python assets/scripts/run_cli_commands.py

Project Structure

The following directory structure provides an overview of how the project is organized:

thinking-dataset/
├── config/                 # Configuration files
├── assets/                 # Assets directory for external resources
│   ├── prompts/            # Prompt templates
│   ├── scripts/            # Utility scripts
│   ├── resources/          # External project data
│   ├── templates/          # JSON prompt templates
├── data/                   # Data directory
├── docs/                   # Project documentation
├── reports/                # Generated reports
├── tests/                  # Test files
├── thinking_dataset/       # Core project code
│   ├── commands/           # CLI command implementations
│   ├── connectors/         # Data connectors
│   ├── config/             # Configuration loaders and management
│   ├── datasets/           # Dataset definitions and processing
│   │   ├── operations/     # Data operations and transformations
│   ├── db/                 # Database support
│   │   ├── operations/     # Database operations and transactions
│   ├── dto/                # Data Transfer Objects (DTO)
│   ├── io/                 # File I/O operations
│   ├── pipeworks/          # Pipelines and pipes for data processing
│   │   ├── pipelines/      # Pipeline management and control
│   │   ├── pipes/          # Pipes used for data frame processing
│   ├── providers/          # AI data providers
│   ├── tonics/             # Data utility functions and helpers
│   ├── utils/              # General-purpose utility helpers
│   ├── main.py             # Main execution file
└── setup.py                # Project setup
└── .env                    # Private environment variables file

Contributing

Contributions are welcome! Fork the repository, make your changes, and create a pull request. Ensure your code follows the project's standards and includes tests. See Contributing for guidelines.

Resources

• GitHub Repository
• Python
• Ollama
• Discord: 🌟Tonic's Better Prompts

License

This dataset is licensed under the MIT License.

Citations

Please use the following BibTeX entry to cite this dataset:

@software{thinking-dataset,
  author = {Kara Rawson, Joseph Pollack, and et al.},
  title = {Thinking-Dataset: Leveraging Real-World Data for Strategic Business Insights and STaR Case Study Generation},
  year = {2025},
  howpublished = {\url{https://github.com/MultiTonic/thinking-dataset}},
  note = {Accessed: 2025-01-25}
}

Acknowledgements

Special thanks to our contributors:

• Kara Rawson - Lead Engineer
• Joseph Pollack - Creator & Business Leader
• MultiTonic Team - Support and Collaboration
• Hugging Face - Robust tools and infrastructure for dataset management

Contact

For questions or support, please contact us at:

• Email: info@multitonics.ai
• Discord: Join our Discord