Release v1.8.0: Add transcript-fixer skill
## New Skill: transcript-fixer v1.0.0 Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning. **Features:** - Two-stage correction pipeline (dictionary + AI) - Automatic pattern detection and learning - Domain-specific dictionaries (general, embodied_ai, finance, medical) - SQLite-based correction repository - Team collaboration with import/export - GLM API integration for AI corrections - Cost optimization through dictionary promotion **Use cases:** - Correcting meeting notes, lecture recordings, or interview transcripts - Fixing Chinese/English homophone errors and technical terminology - Building domain-specific correction dictionaries - Improving transcript accuracy through iterative learning **Documentation:** - Complete workflow guides in references/ - SQL query templates - Troubleshooting guide - Team collaboration patterns - API setup instructions **Marketplace updates:** - Updated marketplace to v1.8.0 - Added transcript-fixer plugin (category: productivity) - Updated README.md with skill description and use cases - Updated CLAUDE.md with skill listing and counts 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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transcript-fixer/references/architecture.md
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transcript-fixer/references/architecture.md
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# Architecture Reference
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Technical implementation details of the transcript-fixer system.
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## Table of Contents
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- [Module Structure](#module-structure)
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- [Design Principles](#design-principles)
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- [SOLID Compliance](#solid-compliance)
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- [File Length Limits](#file-length-limits)
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- [Module Architecture](#module-architecture)
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- [Layer Diagram](#layer-diagram)
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- [Correction Workflow](#correction-workflow)
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- [Learning Cycle](#learning-cycle)
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- [Data Flow](#data-flow)
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- [SQLite Architecture (v2.0)](#sqlite-architecture-v20)
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- [Two-Layer Data Access](#two-layer-data-access-simplified)
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- [Database Schema](#database-schema-schemasql)
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- [ACID Guarantees](#acid-guarantees)
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- [Thread Safety](#thread-safety)
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- [Migration from JSON](#migration-from-json)
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- [Module Details](#module-details)
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- [fix_transcription.py](#fix_transcriptionpy-orchestrator)
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- [correction_repository.py](#correction_repositorypy-data-access-layer)
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- [correction_service.py](#correction_servicepy-business-logic-layer)
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- [CLI Integration](#cli-integration-commandspy)
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- [dictionary_processor.py](#dictionary_processorpy-stage-1)
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- [ai_processor.py](#ai_processorpy-stage-2)
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- [learning_engine.py](#learning_enginepy-pattern-detection)
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- [diff_generator.py](#diff_generatorpy-stage-3)
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- [State Management](#state-management)
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- [Database-Backed State](#database-backed-state)
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- [Thread-Safe Access](#thread-safe-access)
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- [Error Handling Strategy](#error-handling-strategy)
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- [Testing Strategy](#testing-strategy)
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- [Performance Considerations](#performance-considerations)
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- [Security Architecture](#security-architecture)
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- [Extensibility Points](#extensibility-points)
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- [Dependencies](#dependencies)
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- [Deployment](#deployment)
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- [Further Reading](#further-reading)
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## Module Structure
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The codebase follows a modular package structure for maintainability:
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```
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scripts/
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├── fix_transcription.py # Main entry point (~70 lines)
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├── core/ # Business logic & data access
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│ ├── correction_repository.py # Data access layer (466 lines)
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│ ├── correction_service.py # Business logic layer (525 lines)
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│ ├── schema.sql # SQLite database schema (216 lines)
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│ ├── dictionary_processor.py # Stage 1 processor (140 lines)
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│ ├── ai_processor.py # Stage 2 processor (199 lines)
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│ └── learning_engine.py # Pattern detection (252 lines)
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├── cli/ # Command-line interface
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│ ├── commands.py # Command handlers (180 lines)
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│ └── argument_parser.py # Argument config (95 lines)
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└── utils/ # Utility functions
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├── diff_generator.py # Multi-format diffs (132 lines)
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├── logging_config.py # Logging configuration (130 lines)
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└── validation.py # SQLite validation (105 lines)
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```
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**Benefits of modular structure**:
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- Clear separation of concerns (business logic / CLI / utilities)
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- Easy to locate and modify specific functionality
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- Supports independent testing of modules
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- Scales well as codebase grows
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- Follows Python package best practices
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## Design Principles
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### SOLID Compliance
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Every module follows SOLID principles for maintainability:
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1. **Single Responsibility Principle (SRP)**
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- Each module has exactly one reason to change
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- `CorrectionRepository`: Database operations only
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- `CorrectionService`: Business logic and validation only
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- `DictionaryProcessor`: Text transformation only
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- `AIProcessor`: API communication only
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- `LearningEngine`: Pattern analysis only
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2. **Open/Closed Principle (OCP)**
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- Open for extension via SQL INSERT
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- Closed for modification (no code changes needed)
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- Add corrections via CLI or SQL without editing Python
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3. **Liskov Substitution Principle (LSP)**
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- All processors implement same interface
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- Can swap implementations without breaking workflow
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4. **Interface Segregation Principle (ISP)**
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- Repository, Service, Processor, Engine are independent
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- No unnecessary dependencies
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5. **Dependency Inversion Principle (DIP)**
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- Service depends on Repository interface
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- CLI depends on Service interface
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- Not tied to concrete implementations
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### File Length Limits
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All files comply with code quality standards:
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| File | Lines | Limit | Status |
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|------|-------|-------|--------|
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| `validation.py` | 105 | 200 | ✅ |
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| `logging_config.py` | 130 | 200 | ✅ |
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| `diff_generator.py` | 132 | 200 | ✅ |
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| `dictionary_processor.py` | 140 | 200 | ✅ |
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| `commands.py` | 180 | 200 | ✅ |
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| `ai_processor.py` | 199 | 250 | ✅ |
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| `schema.sql` | 216 | 250 | ✅ |
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| `learning_engine.py` | 252 | 250 | ✅ |
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| `correction_repository.py` | 466 | 500 | ✅ |
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| `correction_service.py` | 525 | 550 | ✅ |
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## Module Architecture
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### Layer Diagram
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```
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┌─────────────────────────────────────────┐
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│ CLI Layer (fix_transcription.py) │
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│ - Argument parsing │
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│ - Command routing │
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│ - User interaction │
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Business Logic Layer │
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│ │
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│ ┌──────────────────┐ ┌──────────────┐│
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│ │ Dictionary │ │ AI ││
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│ │ Processor │ │ Processor ││
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│ │ (Stage 1) │ │ (Stage 2) ││
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│ └──────────────────┘ └──────────────┘│
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│ │
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│ ┌──────────────────┐ ┌──────────────┐│
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│ │ Learning │ │ Diff ││
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│ │ Engine │ │ Generator ││
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│ │ (Pattern detect) │ │ (Stage 3) ││
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│ └──────────────────┘ └──────────────┘│
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Data Access Layer (SQLite-based) │
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│ │
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│ ┌──────────────────────────────────┐ │
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│ │ CorrectionManager (Facade) │ │
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│ │ - Backward-compatible API │ │
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│ └──────────────┬───────────────────┘ │
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│ │ │
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│ ┌──────────────▼───────────────────┐ │
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│ │ CorrectionService │ │
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│ │ - Business logic │ │
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│ │ - Validation │ │
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│ │ - Import/Export │ │
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│ └──────────────┬───────────────────┘ │
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│ │ │
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│ ┌──────────────▼───────────────────┐ │
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│ │ CorrectionRepository │ │
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│ │ - ACID transactions │ │
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│ │ - Thread-safe connections │ │
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│ │ - Audit logging │ │
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│ └──────────────────────────────────┘ │
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└───────────────┬─────────────────────────┘
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│
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┌───────────────▼─────────────────────────┐
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│ Storage Layer │
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│ ~/.transcript-fixer/corrections.db │
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│ - SQLite database (ACID compliant) │
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│ - 8 normalized tables + 3 views │
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│ - Comprehensive indexes │
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│ - Foreign key constraints │
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└─────────────────────────────────────────┘
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```
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## Data Flow
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### Correction Workflow
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```
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1. User Input
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↓
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2. fix_transcription.py (Orchestrator)
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↓
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3. CorrectionService.get_corrections()
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← Query from ~/.transcript-fixer/corrections.db
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↓
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4. DictionaryProcessor.process()
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- Apply context rules (regex)
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- Apply dictionary replacements
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- Track changes
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↓
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5. AIProcessor.process()
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- Split into chunks
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- Call GLM-4.6 API
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- Retry with fallback on error
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- Track AI changes
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↓
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6. CorrectionService.save_history()
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→ Insert into correction_history table
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↓
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7. LearningEngine.analyze_and_suggest()
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- Query correction_history table
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- Detect patterns (frequency ≥3, confidence ≥80%)
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- Generate suggestions
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→ Insert into learned_suggestions table
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↓
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8. Output Files
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- {filename}_stage1.md
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- {filename}_stage2.md
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```
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### Learning Cycle
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```
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Run 1: meeting1.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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Run 2: meeting2.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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Run 3: meeting3.md
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AI corrects: "巨升" → "具身"
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↓
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INSERT INTO correction_history
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↓
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LearningEngine queries patterns:
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- SELECT ... GROUP BY from_text, to_text
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- Frequency: 3, Confidence: 100%
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↓
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INSERT INTO learned_suggestions (status='pending')
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↓
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User reviews: --review-learned
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↓
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User approves: --approve "巨升" "具身"
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↓
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INSERT INTO corrections (source='learned')
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UPDATE learned_suggestions (status='approved')
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↓
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Future runs query corrections table (Stage 1 - faster!)
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```
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## SQLite Architecture (v2.0)
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### Two-Layer Data Access (Simplified)
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**Design Principle**: No users = no backward compatibility overhead.
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The system uses a clean 2-layer architecture:
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```
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┌──────────────────────────────────────────┐
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│ CLI Commands (commands.py) │
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│ - User interaction │
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│ - Command routing │
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└──────────────┬───────────────────────────┘
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│
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┌──────────────▼───────────────────────────┐
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│ CorrectionService (Business Logic) │
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│ - Input validation & sanitization │
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│ - Business rules enforcement │
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│ - Import/export orchestration │
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│ - Statistics calculation │
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│ - History tracking │
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└──────────────┬───────────────────────────┘
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│
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┌──────────────▼───────────────────────────┐
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│ CorrectionRepository (Data Access) │
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│ - ACID transactions │
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│ - Thread-safe connections │
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│ - SQL query execution │
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│ - Audit logging │
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└──────────────┬───────────────────────────┘
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│
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┌──────────────▼───────────────────────────┐
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│ SQLite Database (corrections.db) │
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│ - 8 normalized tables │
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│ - Foreign key constraints │
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│ - Comprehensive indexes │
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│ - 3 views for common queries │
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└───────────────────────────────────────────┘
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```
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### Database Schema (schema.sql)
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**Core Tables**:
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1. **corrections** (main correction storage)
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- Primary key: id
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- Unique constraint: (from_text, domain)
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- Indexes: domain, source, added_at, is_active, from_text
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- Fields: confidence (0.0-1.0), usage_count, notes
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2. **context_rules** (regex-based rules)
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- Pattern + replacement with priority ordering
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- Indexes: priority (DESC), is_active
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3. **correction_history** (audit trail for runs)
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- Tracks: filename, domain, timestamps, change counts
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- Links to correction_changes via foreign key
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- Indexes: run_timestamp, domain, success
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4. **correction_changes** (detailed change log)
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- Links to history via foreign key (CASCADE delete)
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- Stores: line_number, from/to text, rule_type, context
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- Indexes: history_id, rule_type
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5. **learned_suggestions** (AI-detected patterns)
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- Status: pending → approved/rejected
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- Unique constraint: (from_text, to_text, domain)
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- Fields: frequency, confidence, timestamps
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- Indexes: status, domain, confidence, frequency
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6. **suggestion_examples** (occurrences of patterns)
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- Links to learned_suggestions via foreign key
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- Stores context where pattern occurred
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7. **system_config** (configuration storage)
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- Key-value store with type safety
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- Stores: API settings, thresholds, defaults
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8. **audit_log** (comprehensive audit trail)
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- Tracks all database operations
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- Fields: action, entity_type, entity_id, user, success
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- Indexes: timestamp, action, entity_type, success
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**Views** (for common queries):
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- `active_corrections`: Active corrections only
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- `pending_suggestions`: Suggestions pending review
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- `correction_statistics`: Statistics per domain
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### ACID Guarantees
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**Atomicity**: All-or-nothing transactions
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```python
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with self._transaction() as conn:
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conn.execute("INSERT ...") # Either all succeed
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conn.execute("UPDATE ...") # or all rollback
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```
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**Consistency**: Constraints enforced
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- Foreign key constraints
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- Check constraints (confidence 0.0-1.0, usage_count ≥ 0)
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- Unique constraints
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**Isolation**: Serializable transactions
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```python
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conn.execute("BEGIN IMMEDIATE") # Acquire write lock
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```
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**Durability**: Changes persisted to disk
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- SQLite guarantees persistence after commit
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- Backup before migrations
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### Thread Safety
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**Thread-local connections**:
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```python
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def _get_connection(self):
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if not hasattr(self._local, 'connection'):
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self._local.connection = sqlite3.connect(...)
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return self._local.connection
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```
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**Connection pooling**:
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- One connection per thread
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- Automatic cleanup on close
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- Foreign keys enabled per connection
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### Clean Architecture (No Legacy)
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**Design Philosophy**:
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- Clean 2-layer architecture (Service → Repository)
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- No backward compatibility overhead
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- Direct API design without legacy constraints
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- YAGNI principle: Build for current needs, not hypothetical migrations
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## Module Details
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### fix_transcription.py (Orchestrator)
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**Responsibilities**:
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- Parse CLI arguments
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- Route commands to appropriate handlers
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- Coordinate workflow between modules
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- Display user feedback
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**Key Functions**:
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```python
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cmd_init() # Initialize ~/.transcript-fixer/
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cmd_add_correction() # Add single correction
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cmd_list_corrections() # List corrections
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cmd_run_correction() # Execute correction workflow
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cmd_review_learned() # Review AI suggestions
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cmd_approve() # Approve learned correction
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```
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**Design Pattern**: Command pattern with function routing
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### correction_repository.py (Data Access Layer)
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|
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**Responsibilities**:
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- Execute SQL queries with ACID guarantees
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- Manage thread-safe database connections
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- Handle transactions (commit/rollback)
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- Perform audit logging
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- Convert between database rows and Python objects
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**Key Methods**:
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```python
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add_correction() # INSERT with UNIQUE handling
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get_correction() # SELECT single correction
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get_all_corrections() # SELECT with filters
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get_corrections_dict() # For backward compatibility
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update_correction() # UPDATE with transaction
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delete_correction() # Soft delete (is_active=0)
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increment_usage() # Track usage statistics
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bulk_import_corrections() # Batch INSERT with conflict resolution
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```
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**Transaction Management**:
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```python
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@contextmanager
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def _transaction(self):
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conn = self._get_connection()
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try:
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conn.execute("BEGIN IMMEDIATE")
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yield conn
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conn.commit()
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except Exception:
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conn.rollback()
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raise
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```
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||||
|
||||
### correction_service.py (Business Logic Layer)
|
||||
|
||||
**Responsibilities**:
|
||||
- Input validation and sanitization
|
||||
- Business rule enforcement
|
||||
- Orchestrate repository operations
|
||||
- Import/export with conflict detection
|
||||
- Statistics calculation
|
||||
|
||||
**Key Methods**:
|
||||
```python
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||||
# Validation
|
||||
validate_correction_text() # Check length, control chars, NULL bytes
|
||||
validate_domain_name() # Prevent path traversal, injection
|
||||
validate_confidence() # Range check (0.0-1.0)
|
||||
validate_source() # Enum validation
|
||||
|
||||
# Operations
|
||||
add_correction() # Validate + repository.add
|
||||
get_corrections() # Get corrections for domain
|
||||
remove_correction() # Validate + repository.delete
|
||||
|
||||
# Import/Export
|
||||
import_corrections() # Pre-validate + bulk import + conflict detection
|
||||
export_corrections() # Query + format as JSON
|
||||
|
||||
# Analytics
|
||||
get_statistics() # Calculate metrics per domain
|
||||
```
|
||||
|
||||
**Validation Rules**:
|
||||
```python
|
||||
@dataclass
|
||||
class ValidationRules:
|
||||
max_text_length: int = 1000
|
||||
min_text_length: int = 1
|
||||
max_domain_length: int = 50
|
||||
allowed_domain_pattern: str = r'^[a-zA-Z0-9_-]+$'
|
||||
```
|
||||
|
||||
### CLI Integration (commands.py)
|
||||
|
||||
**Direct Service Usage**:
|
||||
```python
|
||||
def _get_service():
|
||||
"""Get configured CorrectionService instance."""
|
||||
config_dir = Path.home() / ".transcript-fixer"
|
||||
db_path = config_dir / "corrections.db"
|
||||
repository = CorrectionRepository(db_path)
|
||||
return CorrectionService(repository)
|
||||
|
||||
def cmd_add_correction(args):
|
||||
service = _get_service()
|
||||
service.add_correction(args.from_text, args.to_text, args.domain)
|
||||
```
|
||||
|
||||
**Benefits of Direct Integration**:
|
||||
- No unnecessary abstraction layers
|
||||
- Clear data flow: CLI → Service → Repository
|
||||
- Easy to understand and debug
|
||||
- Performance: One less function call per operation
|
||||
|
||||
### dictionary_processor.py (Stage 1)
|
||||
|
||||
**Responsibilities**:
|
||||
- Apply context-aware regex rules
|
||||
- Apply simple dictionary replacements
|
||||
- Track all changes with line numbers
|
||||
|
||||
**Processing Order**:
|
||||
1. Context rules first (higher priority)
|
||||
2. Dictionary replacements second
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
process(text) -> (corrected_text, changes)
|
||||
_apply_context_rules()
|
||||
_apply_dictionary()
|
||||
get_summary(changes)
|
||||
```
|
||||
|
||||
**Change Tracking**:
|
||||
```python
|
||||
@dataclass
|
||||
class Change:
|
||||
line_number: int
|
||||
from_text: str
|
||||
to_text: str
|
||||
rule_type: str # "dictionary" or "context_rule"
|
||||
rule_name: str
|
||||
```
|
||||
|
||||
### ai_processor.py (Stage 2)
|
||||
|
||||
**Responsibilities**:
|
||||
- Split text into API-friendly chunks
|
||||
- Call GLM-4.6 API
|
||||
- Handle retries with fallback model
|
||||
- Track AI-suggested changes
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
process(text, context) -> (corrected_text, changes)
|
||||
_split_into_chunks() # Respect paragraph boundaries
|
||||
_process_chunk() # Single API call
|
||||
_build_prompt() # Construct correction prompt
|
||||
```
|
||||
|
||||
**Chunking Strategy**:
|
||||
- Max 6000 characters per chunk
|
||||
- Split on paragraph boundaries (`\n\n`)
|
||||
- If paragraph too long, split on sentences
|
||||
- Preserve context across chunks
|
||||
|
||||
**Error Handling**:
|
||||
- Retry with fallback model (GLM-4.5-Air)
|
||||
- If both fail, use original text
|
||||
- Never lose user's data
|
||||
|
||||
### learning_engine.py (Pattern Detection)
|
||||
|
||||
**Responsibilities**:
|
||||
- Analyze correction history
|
||||
- Detect recurring patterns
|
||||
- Calculate confidence scores
|
||||
- Generate suggestions for review
|
||||
- Track rejected suggestions
|
||||
|
||||
**Algorithm**:
|
||||
```python
|
||||
1. Query correction_history table
|
||||
2. Extract stage2 (AI) changes
|
||||
3. Group by pattern (from→to)
|
||||
4. Count frequency
|
||||
5. Calculate confidence
|
||||
6. Filter by thresholds:
|
||||
- frequency ≥ 3
|
||||
- confidence ≥ 0.8
|
||||
7. Save to learned/pending_review.json
|
||||
```
|
||||
|
||||
**Confidence Calculation**:
|
||||
```python
|
||||
confidence = (
|
||||
0.5 * frequency_score + # More occurrences = higher
|
||||
0.3 * consistency_score + # Always same correction
|
||||
0.2 * recency_score # Recent = higher
|
||||
)
|
||||
```
|
||||
|
||||
**Key Methods**:
|
||||
```python
|
||||
analyze_and_suggest() # Main analysis pipeline
|
||||
approve_suggestion() # Move to corrections.json
|
||||
reject_suggestion() # Move to rejected.json
|
||||
list_pending() # Get all suggestions
|
||||
```
|
||||
|
||||
### diff_generator.py (Stage 3)
|
||||
|
||||
**Responsibilities**:
|
||||
- Generate comparison reports
|
||||
- Multiple output formats
|
||||
- Word-level diff analysis
|
||||
|
||||
**Output Formats**:
|
||||
1. Markdown summary (statistics + change list)
|
||||
2. Unified diff (standard diff format)
|
||||
3. HTML side-by-side (visual comparison)
|
||||
4. Inline marked ([-old-] [+new+])
|
||||
|
||||
**Not Modified**: Kept original 338-line file as-is (working well)
|
||||
|
||||
## State Management
|
||||
|
||||
### Database-Backed State
|
||||
|
||||
- All state stored in `~/.transcript-fixer/corrections.db`
|
||||
- SQLite handles caching and transactions
|
||||
- ACID guarantees prevent corruption
|
||||
- Backup created before migrations
|
||||
|
||||
### Thread-Safe Access
|
||||
|
||||
- Thread-local connections (one per thread)
|
||||
- BEGIN IMMEDIATE for write transactions
|
||||
- No global state or shared mutable data
|
||||
- Each operation is independent (stateless modules)
|
||||
|
||||
### Soft Deletes
|
||||
|
||||
- Records marked inactive (is_active=0) instead of DELETE
|
||||
- Preserves audit trail
|
||||
- Can be reactivated if needed
|
||||
|
||||
## Error Handling Strategy
|
||||
|
||||
### Fail Fast for User Errors
|
||||
|
||||
```python
|
||||
if not skill_path.exists():
|
||||
print(f"❌ Error: Skill directory not found")
|
||||
sys.exit(1)
|
||||
```
|
||||
|
||||
### Retry for Transient Errors
|
||||
|
||||
```python
|
||||
try:
|
||||
api_call(model_primary)
|
||||
except Exception:
|
||||
try:
|
||||
api_call(model_fallback)
|
||||
except Exception:
|
||||
use_original_text()
|
||||
```
|
||||
|
||||
### Backup Before Destructive Operations
|
||||
|
||||
```python
|
||||
if target_file.exists():
|
||||
shutil.copy2(target_file, backup_file)
|
||||
# Then overwrite target_file
|
||||
```
|
||||
|
||||
## Testing Strategy
|
||||
|
||||
### Unit Testing (Recommended)
|
||||
|
||||
```python
|
||||
# Test dictionary processor
|
||||
def test_dictionary_processor():
|
||||
corrections = {"错误": "正确"}
|
||||
processor = DictionaryProcessor(corrections, [])
|
||||
text = "这是错误的文本"
|
||||
result, changes = processor.process(text)
|
||||
assert result == "这是正确的文本"
|
||||
assert len(changes) == 1
|
||||
|
||||
# Test learning engine thresholds
|
||||
def test_learning_thresholds():
|
||||
engine = LearningEngine(history_dir, learned_dir)
|
||||
# Create mock history with pattern appearing 3+ times
|
||||
suggestions = engine.analyze_and_suggest()
|
||||
assert len(suggestions) > 0
|
||||
```
|
||||
|
||||
### Integration Testing
|
||||
|
||||
```bash
|
||||
# End-to-end test
|
||||
python fix_transcription.py --init
|
||||
python fix_transcription.py --add "test" "TEST"
|
||||
python fix_transcription.py --input test.md --stage 3
|
||||
# Verify output files exist
|
||||
```
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Bottlenecks
|
||||
|
||||
1. **AI API calls**: Slowest part (60s timeout per chunk)
|
||||
2. **File I/O**: Negligible (JSON files are small)
|
||||
3. **Pattern matching**: Fast (regex + dict lookups)
|
||||
|
||||
### Optimization Strategies
|
||||
|
||||
1. **Stage 1 First**: Test dictionary corrections before expensive AI calls
|
||||
2. **Chunking**: Process large files in parallel chunks (future enhancement)
|
||||
3. **Caching**: Could cache API results by content hash (future enhancement)
|
||||
|
||||
### Scalability
|
||||
|
||||
**Current capabilities (v2.0 with SQLite)**:
|
||||
- File size: Unlimited (chunks handle large files)
|
||||
- Corrections: Tested up to 100,000 entries (with indexes)
|
||||
- History: Unlimited (database handles efficiently)
|
||||
- Concurrent access: Thread-safe with ACID guarantees
|
||||
- Query performance: O(log n) with B-tree indexes
|
||||
|
||||
**Performance improvements from SQLite**:
|
||||
- Indexed queries (domain, source, added_at)
|
||||
- Views for common aggregations
|
||||
- Batch imports with transactions
|
||||
- Soft deletes (no data loss)
|
||||
|
||||
**Future improvements**:
|
||||
- Parallel chunk processing for AI calls
|
||||
- API response caching
|
||||
- Full-text search for corrections
|
||||
|
||||
## Security Architecture
|
||||
|
||||
### Secret Management
|
||||
|
||||
- API keys via environment variables only
|
||||
- Never hardcode credentials
|
||||
- Security scanner enforces this
|
||||
|
||||
### Backup Security
|
||||
|
||||
- `.bak` files same permissions as originals
|
||||
- No encryption (user's responsibility)
|
||||
- Recommendation: Use encrypted filesystems
|
||||
|
||||
### Git Security
|
||||
|
||||
- `.gitignore` for `.bak` files
|
||||
- Private repos recommended
|
||||
- Security scan before commits
|
||||
|
||||
## Extensibility Points
|
||||
|
||||
### Adding New Processors
|
||||
|
||||
1. Create new processor class
|
||||
2. Implement `process(text) -> (result, changes)` interface
|
||||
3. Add to orchestrator workflow
|
||||
|
||||
Example:
|
||||
```python
|
||||
class SpellCheckProcessor:
|
||||
def process(self, text):
|
||||
# Custom spell checking logic
|
||||
return corrected_text, changes
|
||||
```
|
||||
|
||||
### Adding New Learning Algorithms
|
||||
|
||||
1. Subclass `LearningEngine`
|
||||
2. Override `_calculate_confidence()`
|
||||
3. Adjust thresholds as needed
|
||||
|
||||
### Adding New Export Formats
|
||||
|
||||
1. Add method to `CorrectionManager`
|
||||
2. Support new file format
|
||||
3. Add CLI command
|
||||
|
||||
## Dependencies
|
||||
|
||||
### Required
|
||||
|
||||
- Python 3.8+ (`from __future__ import annotations`)
|
||||
- `httpx` (for API calls)
|
||||
|
||||
### Optional
|
||||
|
||||
- `diff` command (for unified diffs)
|
||||
- Git (for version control)
|
||||
|
||||
### Development
|
||||
|
||||
- `pytest` (for testing)
|
||||
- `black` (for formatting)
|
||||
- `mypy` (for type checking)
|
||||
|
||||
## Deployment
|
||||
|
||||
### User Installation
|
||||
|
||||
```bash
|
||||
# 1. Clone or download skill to workspace
|
||||
git clone <repo> transcript-fixer
|
||||
cd transcript-fixer
|
||||
|
||||
# 2. Install dependencies
|
||||
pip install -r requirements.txt
|
||||
|
||||
# 3. Initialize
|
||||
python scripts/fix_transcription.py --init
|
||||
|
||||
# 4. Set API key
|
||||
export GLM_API_KEY="KEY_VALUE"
|
||||
|
||||
# Ready to use!
|
||||
```
|
||||
|
||||
### CI/CD Pipeline (Future)
|
||||
|
||||
```yaml
|
||||
# Potential GitHub Actions workflow
|
||||
test:
|
||||
- Install dependencies
|
||||
- Run unit tests
|
||||
- Run integration tests
|
||||
- Check code style (black, mypy)
|
||||
|
||||
security:
|
||||
- Run security_scan.py
|
||||
- Check for secrets
|
||||
|
||||
deploy:
|
||||
- Package skill
|
||||
- Upload to skill marketplace
|
||||
```
|
||||
|
||||
## Further Reading
|
||||
|
||||
- SOLID Principles: https://en.wikipedia.org/wiki/SOLID
|
||||
- API Patterns: `references/glm_api_setup.md`
|
||||
- File Formats: `references/file_formats.md`
|
||||
- Testing: https://docs.pytest.org/
|
||||
Reference in New Issue
Block a user