TON Copilot Dataset

Project Overview

TON Copilot Dataset is an initiative to create a comprehensive and continuously updated dataset for training Large Language Models (LLMs) specialized in the Telegram Open Network (TON) ecosystem. This dataset aims to serve as a foundation for developing AI-powered assistants and tools that can effectively support developers, users, and enthusiasts within the TON ecosystem.

Installation and Usage

Prerequisites

• Node.js (version 14 or later)
• npm (usually comes with Node.js)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/your-username/ton-copilot-dataset.git
   cd ton-copilot-dataset
   

2. Install dependencies:

   npm install
   

3. Build the project:

   npm run build
   

Setting up Environment Variables

This project uses environment variables for configuration. Follow these steps to set up your environment:

1. Create a .env file in the root directory of the project.

2. Add your GitHub personal access token to the .env file:

   GITHUB_TOKEN=your_github_personal_access_token_here
   

   Replace your_github_personal_access_token_here with your actual GitHub token.

Usage

Listing Repositories

To list repositories for GitHub accounts:

1. Create a file named github_account_list.txt in the project root directory. Add GitHub account names (users or organizations), one per line.

2. Run the following command:

   npm run list-repos
   

   This will generate a list of repositories in ./misc/github_repos_list.json.

Cloning Repositories

To clone the repositories:

1. Ensure you have a JSON file with the repository information (e.g., github_repos_list.json).

2. Run the following command:

   npm run clone-repos -- /path/to/your/github_repos_list.json -o ./cloned_repos
   

   Replace /path/to/your/github_repos_list.json with the actual path to your JSON file.

   This will clone the repositories into the ./cloned_repos directory, organized by account name.

Additional Commands

• Format code:

  npm run format
  

• Check code formatting:

  npm run format:check
  

For more detailed information about each command, refer to the source code or run the command with the --help flag.

Key Features

1. Multi-source Data Collection

   • Official documentation
   • GitHub repositories
   • Telegram group messages

2. Continuous Updates

   • Automated data ingestion from various sources
   • Regular refresh cycles to maintain relevance

3. Domain-specific Focus

   • Tailored for TON ecosystem expertise
   • Covers smart contracts, blockchain architecture, and TON-specific protocols

Methodology for Building a Domain-Specific LLM Dataset

1. Source Identification

   • Identify authoritative and relevant data sources
   • Ensure diverse representation of content types

2. Data Collection

   • Implement web scraping for documentation and GitHub repos
   • Utilize Telegram API for group message extraction
   • Establish data collection frequency and update mechanisms

3. Data Preprocessing

   • Clean and normalize text data
   • Remove duplicates and irrelevant content
   • Standardize formatting across different sources

4. Data Annotation

   • Develop a tagging system for content categorization
   • Implement named entity recognition for TON-specific terms
   • Create a glossary of domain-specific terminology

5. Quality Assurance

   • Establish manual review processes for data accuracy
   • Implement automated checks for data integrity
   • Regularly validate dataset against expert knowledge

6. Version Control

   • Maintain dataset versioning for traceability
   • Document changes and updates between versions

7. Ethical Considerations

   • Ensure compliance with data privacy regulations
   • Obtain necessary permissions for data usage
   • Anonymize personal information in Telegram messages

8. Scalability Planning

   • Design data pipeline for handling increasing data volumes
   • Implement efficient storage and retrieval mechanisms

9. Evaluation Metrics

   • Develop benchmarks for assessing dataset quality
   • Create test sets for evaluating LLM performance on TON-specific tasks

10. Community Engagement

    • Establish feedback loops with TON developers and users
    • Incorporate community contributions and corrections

By following this methodology, the TON Copilot Dataset project aims to create a high-quality, domain-specific dataset that will enable the development of advanced AI models tailored to the TON ecosystem.