tnh-scholar 0.2.2

TNH Scholar is an AI-driven project designed to explore, query, and translate the teachings of Thich Nhat Hanh and Plum Village community.

TNH Scholar README

TNH Scholar is an AI-driven project designed to explore, query, process and translate the teachings of Thich Nhat Hanh and the Plum Village community. The project provides tools for practitioners and scholars to engage with mindfulness and spiritual wisdom through natural language processing and machine learning models.

Vision & Goals

TNH Scholar aims to make the teachings of Thich Nhat Hanh and the Plum Village tradition more accessible and discoverable through modern AI techniques. By combining natural language processing, machine learning, semantic search, and careful curation, we create pathways for practitioners and scholars to translate, search, organize, process and otherwise find meaningful connections among the body of teachings.

Features

TNH Scholar is currently in active prototyping. Key capabilities:

• Audio and transcript processing: audio-transcribe with diarization and YouTube support
• Text formatting and translation: tnh-gen CLI (in development; currently tnh-fab, deprecated) for punctuation, translation, sectioning, and prompt-driven processing. See ADR-TG01 and ADR-TG02 for architecture details.
• Acquisition utilities: ytt-fetch for transcripts; token-count and nfmt for prep and planning
• Setup and configuration: tnh-setup plus guided config in Getting Started
• Prompt system: See ADRs under docs/architecture/prompt-system/index.md for decisions and roadmap

⚠️ CLI Tool Migration Notice: The tnh-fab command-line tool is deprecated and will be replaced by tnh-gen in an upcoming release. The tool remains functional with a deprecation warning. See the TNH-Gen Architecture documentation for migration details.

Quick Start

Installation (PyPI)

pip install tnh-scholar
tnh-setup

Prerequisites: Python 3.12.4+, OpenAI API key (CLI tools), Google Vision (optional OCR), pip or Poetry.

Development setup (from source)

Follow DEV_SETUP.md for the full workflow. Short version:

pyenv install 3.12.4
poetry config virtualenvs.in-project true
make setup        # runtime deps
make setup-dev    # runtime + dev deps (recommended)

Set OpenAI credentials

export OPENAI_API_KEY="your-api-key"

Example usage

Transcribe Audio from YouTube:

audio-transcribe --yt_url "https://youtube.com/watch?v=example" --split --transcribe

Download Video Transcripts:

ytt-fetch "https://youtube.com/watch?v=example" -l en -o transcript.txt

Process Text (currently using tnh-fab; migrating to tnh-gen):

# Note: tnh-fab is deprecated; tnh-gen is in development
tnh-fab translate -l vi input.txt
tnh-fab section input.txt

Getting Started

• Practitioners: Install, configure credentials, and follow the Quick Start Guide; workflows live in the User Guide.
• Developers: Set up via DEV_SETUP.md and Contributing; review System Design and the CLI docs; run make docs to view locally.
  • Project Philosophy & Vision: Developers and researchers should review the conceptual foundations in docs/project/vision.md, docs/project/philosophy.md, docs/project/principles.md, and docs/project/conceptual-architecture.md to understand the system’s long-term direction and design intent.
• Researchers: Explore Research for experiments and direction; see Architecture for pipelines/ADRs (e.g., ADR-K01).

Documentation Overview

Comprehensive documentation is available in multiple formats:

• Online Documentation: aaronksolomon.github.io/tnh-scholar/
• GitHub Repository: github.com/aaronksolomon/tnh-scholar

Documentation Structure

• Getting Started – Installation, setup, and first steps
• CLI Docs – Command-line tool documentation
• User Guide – Detailed usage guides, prompts, and workflows
• API Reference – Python API documentation for programmatic use
• Architecture – Design decisions, ADRs, and system overview
• Development – Contributing guidelines and development setup
• Research – Research notes, experiments, and background
• Documentation Operations – Documentation roadmap and maintenance

Architecture Overview

• Documentation strategy: ADR-DD01 and ADR-DD02
• GenAI, transcription, and prompt system ADRs live under Architecture (see ADR-A*, ADR-TR*, ADR-PT*).
• System design references: Object–Service Design and System Design.

Development

• Common commands: make test, make lint, make format, make docs, poetry run mypy src/
• Optional dependency groups (development only): tnh-scholar[ocr], tnh-scholar[gui], tnh-scholar[query], tnh-scholar[dev]
• Troubleshooting and workflows: DEV_SETUP.md

Contributing

See CONTRIBUTING.md for coding standards, testing expectations, and PR workflow. We welcome contributions from practitioners, developers, and scholars.

Project Status

TNH Scholar is currently in alpha stage (v0.1.3). Expect ongoing API and workflow changes during active development.

Support & Community

• Bug reports & feature requests: GitHub Issues
• Questions & discussions: GitHub Discussions

Documentation Map

For an auto-generated list of every document (titles and metadata), see the Documentation Index.

License

This project is licensed under the GPL-3.0 License.

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For more information, visit the full documentation or explore the source code.