TNH Scholar System Design¶

High-level system design describing the cyclical AI processing architecture powering TNH Scholar.

Terminology Note: This document uses historical "Pattern" terminology. In current TNH Scholar documentation, "Pattern" has been replaced with "Prompt" to align with industry standards. See ADR-DD03 for details.

When reading this document: "Pattern-based processing" → "Prompt-based processing"

1. Overview and Vision¶

TNH Scholar is an AI-enhanced content processing and transformation system designed to work with dharma materials. The system is distinguished by its cyclical learning architecture, where processing outputs can be used to generate training data for model improvement, creating a self-enhancing feedback loop.

1.1 Core Design Philosophy¶

The system embraces several key philosophical principles:

Evolutionary improvement through self-generated training data
Modular design enabling flexible pipeline construction
Balance of rapid prototyping with extensible architecture
Focus on AI-enhanced content processing and transformation

1.2 System Objectives¶

Primary objectives of the system include:

Transform multilingual content across various formats and mediums
Generate high-quality training data from processing operations
Enable continuous improvement of AI processing capabilities
Support flexible pipeline construction for varied content workflows

2. System Architecture¶

2.1 Core Components¶

The system is built around four primary subsystems that form a cyclical processing chain:

1. Content Acquisition System¶

Source content ingestion from various platforms
Metadata extraction and standardization
Initial content preparation and validation

2. Processing System¶

AI-enhanced content transformation
Pattern-based processing workflows
Metadata enrichment and validation

3. Training Data Generation¶

Automated extraction of training pairs
Quality assessment of generated data
Training data storage and management

4. Model Enhancement¶

Training data integration
Model fine-tuning and validation
Processing capability improvement

2.2 Processing Flow¶

The system (can) operate in a cyclical pattern. This diagram shows the flow of content through the system:

Content → AI Processing → Processed Content → Training Data → Model Training
    ↑                                                                  ↓
    └──────────────────── Enhanced Processing ◄────────────────────────┘

flowchart TD
    AP((AI Processing)) --> PC[Processed Content]
    PC --> TD[Training Data]
    TD --> MT[Model Training]
    MT --> AP
    RC[Raw Content] --> AP

    style AP fill:#bbf,stroke:#333,stroke-width:4px
    style PC fill:#bfb,stroke:#333,stroke-width:2px
    style TD fill:#fbf,stroke:#333,stroke-width:2px
    style MT fill:#fbb,stroke:#333,stroke-width:2px
    style RC fill:#f9f,stroke:#333,stroke-width:2px

This cycle enables:

Continuous system improvement
Accumulation of specialized training data
Evolution of processing capabilities

3. Core Principles¶

3.1 Modularity¶

Every system component should be:

Independently deployable
Clearly scoped in functionality
Accessible through well-defined interfaces
Composable into larger processing chains

3.2 Extensibility¶

The system supports extension through:

Abstract interfaces for core components
Plugin architecture for new processors
Configurable processing pipelines
Standardized metadata handling

3.3 Simplicity¶

Development prioritizes:

Clear, focused component responsibilities
Minimal dependencies between modules
Straightforward pipeline construction
Rapid prototyping capabilities

4. Component Framework¶

4.1 Base Interfaces¶

Core system interfaces define standard operations:

class ContentProcessor(ABC):
    """Base interface for content processing operations."""

    @abstractmethod
    def process(self, content: RawContent) -> ProcessedContent:
        """Transform input content using current processing capability."""
        pass

    @abstractmethod
    def generate_training_data(
        self, 
        raw: RawContent, 
        processed: ProcessedContent
    ) -> TrainingData:
        """Extract training data from processing results."""
        pass

4.2 Pipeline Architecture¶

Pipelines compose processors into workflows:

class ProcessingPipeline:
    """Orchestrates content processing workflow."""

    def __init__(
        self,
        processors: List[ContentProcessor],
        training_collector: Optional[TrainingCollector] = None
    ):
        self.processors = processors
        self.collector = training_collector

    def execute(
        self, 
        source: ContentSource
    ) -> Tuple[ProcessedContent, Optional[TrainingData]]:
        """Execute pipeline with optional training data collection."""
        pass

5. Implementation Guidelines¶

5.1 Development Phases¶

Development follows a staged approach:

Phase 1: Rapid Prototyping

Focus on core functionality
Minimal error handling
Basic logging
Proof of concept implementations

Phase 2: Stabilization

Enhanced error handling
Comprehensive logging
Performance optimization
Testing infrastructure

Phase 3: Production Hardening

Full error recovery
Performance monitoring
Production logging
Deployment automation

5.2 Code Standards¶

Code development prioritizes:

Strong typing
Clear documentation
Consistent naming
Modular design

5.3 Testing Strategy¶

Testing emphasis varies by phase:

Prototyping: Basic functionality validation
Stabilization: Comprehensive unit testing
Production: Full test coverage and integration testing

6. Current State and Future Directions¶

6.1 Implemented Components¶

Current system includes:

YouTube content acquisition
Audio transcription processing
Pattern-based text processing
Basic pipeline orchestration

6.2 Planned Enhancements¶

Near-term priorities:

Enhanced metadata handling
Improved pipeline composition
Training data collection framework
Model fine-tuning infrastructure

6.3 Future Capabilities¶

Long-term objectives:

Automated quality assessment
Advanced pipeline orchestration
Distributed processing support
Enhanced training data generation

7. Design Decisions and Tradeoffs¶

7.1 Current Design Choices¶

The system currently prioritizes:

Rapid prototype development
Simple pipeline construction
Clear component boundaries
Flexible processing flows

7.2 Design Tradeoffs¶

Key tradeoffs include:

Simplicity over comprehensive error handling
Flexibility over optimization
Rapid development over complete documentation
Basic logging over detailed instrumentation

8. Conclusion¶

The TNH Scholar system design provides a framework for AI-enhanced content processing with self-improvement capabilities. Its modular architecture and cyclical processing model enable continuous enhancement of processing capabilities while maintaining flexibility for varied content workflows.

Success metrics for this design include:

Ease of new pipeline creation
Quality of generated training data
Rate of processing improvement
System extensibility