# Background 

This toolkit is designed for storing and managing Tibetan Buddhist texts and essential annotations. It ensures efficient data retrieval to support both educational use and AI model training. By preserving and organizing these sacred texts, our goal is to promote deeper learning and research, thereby fostering a broader understanding of Tibetan Buddhist teachings.

### STAM 
STAM stands for Stand-off Text Annotation Model. It is a data model in which all information related to a text is represented as separate annotations. STAM allows annotations to be stored in a structured and flexible format, making them easy to manage, access, and update. We have chosen STAM as the annotation model for this toolkit because it aligns with our goals and meets our requirements.

### Pecha
Traditionally, Pecha refers to a Tibetan book format consisting of loose-leaf pages stacked between two wooden boards and wrapped in cloth. In this toolkit, however, we define Pecha as a data model used to store and manage Tibetan Buddhist texts and their annotations. In our context, a Pecha represents a collection of texts along with their corresponding annotations.

### Annotations 
Annotations provide additional information about the text. They are stored separately from the main text to allow for easier access, management, and modification. Various types of annotations are supported, including: Segmentation, Alignment, Pagination, Footnotes and more.These annotations enrich the text and enable advanced use cases like linguistic analysis, translation, and research.


### Alignment 
Alignment refers to mapping relationships between two or more texts. This process is crucial for creating parallel texts, which are widely used in translation, commentary analysis, and language learning. Alignments help link corresponding sections across different versions or types of texts—whether it's between a root text and its translation, a commentary, or other related materials.