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Architecture Documentation

This directory contains comprehensive architecture documentation for the JuDDGES system, featuring detailed Mermaid diagrams and conceptual explanations.

Overview

JuDDGES is a complex legal AI system with multiple interconnected components. These documents provide visual and conceptual understanding of how the system works, how components interact, and how data flows through the pipeline.

Documentation Structure

Core Architecture Documents

System Architecture

Purpose: High-level overview of the entire JuDDGES system

What You'll Learn:

  • Overall system architecture and main components
  • External integrations (Weaviate, HuggingFace, DVC)
  • Technology stack and component interactions
  • Scalability and security considerations

Diagrams:

  • High-level system architecture
  • Component interaction patterns
  • Technology stack relationships

Best For: Getting the big picture, understanding system boundaries, planning integrations

Data Flow Pipeline

Purpose: Detailed visualization of data transformation journey

What You'll Learn:

  • 9-stage data processing pipeline
  • Data format transformations (PDF → Text → Vectors → Predictions)
  • Parallel processing architecture
  • Processing metrics and performance optimization

Diagrams:

  • Complete data flow pipeline (Stage 1-9)
  • Data format evolution
  • Parallel execution paths
  • Data volume flow (Sankey diagram)
  • Error handling and recovery

Best For: Understanding data processing, debugging pipeline issues, optimizing performance

Weaviate Integration

Purpose: Vector database architecture and operations

What You'll Learn:

  • Weaviate infrastructure and deployment
  • Schema design for legal documents and chunks
  • Query patterns (semantic, hybrid, RAG)
  • Performance optimization strategies

Diagrams:

  • Integration architecture
  • Collection schemas (class diagrams)
  • Data ingestion pipeline
  • Query architecture (sequence diagrams)
  • Hybrid search components
  • Docker deployment

Best For: Working with vector database, implementing search, schema design

Model Training Flow

Purpose: Training and inference workflow visualization

What You'll Learn:

  • Complete training pipeline from data to checkpoints
  • PEFT/LoRA fine-tuning strategy
  • Multi-model training matrix
  • Inference pipeline with context retrieval
  • Optimization techniques and hardware requirements

Diagrams:

  • Training architecture (end-to-end)
  • LoRA parameter-efficient fine-tuning
  • Multi-model training matrix
  • Inference pipeline
  • Optimization strategies
  • Deployment options
  • Hardware requirements by model size

Best For: Training models, understanding fine-tuning, planning deployments

Component Relationships

Purpose: Module dependencies and interaction patterns

What You'll Learn:

  • JuDDGES module structure and dependencies
  • Class relationships and inheritance
  • Configuration hierarchy
  • Error propagation paths
  • Testing structure

Diagrams:

  • High-level component architecture
  • Detailed module dependencies
  • Class relationship diagrams
  • Data flow dependencies
  • Configuration hierarchy
  • Error handling flows
  • Testing dependencies
  • Package dependencies

Best For: Development, debugging, understanding codebase structure, refactoring

Project Overview

Purpose: Introduction to JuDDGES mission and capabilities

What You'll Learn:

  • Mission statement and research vision
  • Key features and target domains
  • Project consortium and collaborators
  • Technology stack
  • Getting started resources

Best For: New users, stakeholders, understanding project goals

How to Use This Documentation

For Developers

  1. Start with System Architecture for the big picture
  2. Review Component Relationships to understand code structure
  3. Dive into Data Flow Pipeline when working with data processing
  4. Consult Model Training Flow for training and inference tasks
  5. Reference Weaviate Integration for database operations

For Researchers

  1. Begin with Project Overview to understand the project
  2. Study System Architecture for technical understanding
  3. Examine Data Flow Pipeline for data processing methodology
  4. Review Model Training Flow for reproducibility details

For System Architects

  1. Review System Architecture for overall design
  2. Analyze Component Relationships for dependencies
  3. Study Weaviate Integration for scalability patterns
  4. Examine Data Flow Pipeline for bottleneck identification

For Data Engineers

  1. Focus on Data Flow Pipeline for end-to-end understanding
  2. Study Weaviate Integration for data storage patterns
  3. Review System Architecture for data source integrations

Diagram Legend

All diagrams in this documentation use consistent styling:

  • Blue boxes (#e3f2fd): Input sources, raw data
  • Green boxes (#e8f5e9): Output results, completed stages
  • Purple boxes (#f3e5f5): Databases, persistent storage
  • Orange boxes (#fff3e0): Configuration, orchestration, decisions
  • Amber boxes (#ffe0b2): Model artifacts, checkpoints
  • Red boxes (#ffebee): Predictions, evaluation results, errors

Mermaid Diagram Types Used

  • Flowcharts: Process flows and decision trees
  • Sequence Diagrams: Component interactions over time
  • Class Diagrams: Object relationships and schema
  • State Diagrams: Error handling and state transitions
  • Graph Diagrams: Component dependencies
  • Sankey Diagrams: Data volume flows

Cross-References

External Resources

Contributing

When adding or updating architecture documentation:

  1. Use Mermaid diagrams: All diagrams should be in Mermaid format
  2. Follow style guide: Use consistent colors and diagram types
  3. Maintain cross-references: Link related documents
  4. Update this README: Add new documents to the structure
  5. Test diagrams: Ensure they render correctly in GitHub and MkDocs

See Documentation Style Guide for detailed standards.

Questions?

Last Updated: 2025-10-11 Version: 1.0 Maintainer: JuDDGES Documentation Team