Thinking Dataset v0.0.1: A Foundation for Strategic AI-Driven Business Intelligence with STaR Case Study Generation

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Thinking Dataset:

Framework for Strategic Business Insights

Release v0.0.1 - Initial Release

Overview

The Thinking Dataset Project v0.0.1 introduces the foundational framework for generating strategic business insights and STaR (Situation, Task, Action, Result) case studies. This initial release sets up the basic infrastructure for analyzing complex strategic scenarios, ethical dilemmas, and decision-making processes.

Key Features

• Basic Pipeline Infrastructure: Initial implementation of data ingestion and preprocessing pipelines
• SQLite Database Setup: Basic data storage and management system
• CLI Tool: Essential command-line interface for basic dataset operations
• Initial Adapters: Basic support for Hugging Face and Ollama endpoints
• Core Documentation: Basic documentation covering installation and usage

Components

Core Features

• Basic data ingestion functionality
• Initial preprocessing pipeline
• Foundational case study format
• Basic model evaluation framework
• Initial adapter implementations

Technical Implementation

• Python 3.10+ support
• Basic SQLite integration
• Initial pipeline configuration
• Basic environment setup
• Standard logging system

Installation

Prerequisites

• Python 3.10 or later
• Git
• A cloud-based account (e.g., OpenAI) 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 the uv package management tool using pip:

   pip install uv

   Alternate: install via package manager use pip install -e . and skip to step 4.

3. Install the required packages using uv and thinking-dataset.toml:

   uv install -f thinking-dataset.toml

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 the following variables:

   HF_ORG="DataTonic"
   HF_USER="my_huggingface_username"
   
   HF_READ_TOKEN="my_huggingface_read_access_token"
   HF_WRITE_TOKEN="my_huggingface_write_access_token"
   
   OLLAMA_SERVER_URL="http://localhost:11434"
   OPENAI_API_TOKEN="your_openai_api_token"
   RUNPOD_API_TOKEN="your_runpod_api_token"
   
   CONFIG_PATH="config/config.yaml"
   

Breaking Changes

None (Initial Release)

Bug Fixes

• Initial release, no bug fixes to report

Dependencies

• Python 3.10+
• SQLite
• pandas
• scikit-learn
• rich
• python-dotenv
• Hugging Face Transformers
• Testcontainers
• Runpod
• Additional dependencies listed in thinking-dataset.toml

Security Updates

• Initial security configurations implemented
• Basic authentication and authorization flows established

Documentation Updates

• Added initial project documentation
• Included installation guide
• Basic usage instructions
• Architecture overview

Known Issues

1. Limited to text-based data processing in this release
2. GPU support requires RTX 3090 or greater
3. Some advanced features planned for future releases

Upgrade Instructions

Initial release - no upgrade needed.

Contributors

Special thanks to our initial contributors:

• Kara Rawson (Lead Engineer)
• Joseph Pollack (Creator & Business Leader)
• MultiTonic Team

Support

For support and questions:

• Create an issue on GitHub
• Join our Discord
• Email: info@tonic-ai.com

Version

• Release: v0.0.1
• Date: 2024-01-25
• Commit: 0716d8d (Initial commit)

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This discussion was created from the release Thinking Dataset v0.0.1: A Foundation for Strategic AI-Driven Business Intelligence with STaR Case Study Generation.