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trax/docs/reports/01-repository-inventory.md

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Checkpoint 1: Repository Inventory Report

Current State Analysis - Trax Project

1. Project Structure

The Trax project is currently a minimal skeleton with uv package management properly configured.

What Exists ✅

  • uv Configuration: Proper pyproject.toml with uv tooling
  • Documentation: Basic CLAUDE.md (97 lines) and AGENTS.md (163 lines) - both well under 600 LOC limit
  • Config System: Centralized configuration inheriting from root .env
  • Testing Setup: pytest with coverage configured
  • Code Quality: Black, Ruff, MyPy configured with strict settings
  • Python 3.11+: Modern Python with type checking
  • Virtual Environment: .venv directory configured

What's Missing ❌

  • No actual media processing code yet
  • No transcript services implemented
  • No caching layer
  • No database/models
  • No API endpoints
  • No export functionality
  • No batch processing system

2. Key Components to Migrate from YouTube Summarizer

Based on analysis and priorities, here are the critical components to bring over:

🔥 Priority 1: Caching Architecture

90% cost reduction achieved in YouTube Summarizer

  • Multi-layer caching system:
    • EmbeddingCacheService (24h TTL, LZ4 compression)
    • MultiAgentCacheService (7d TTL, $0.015/analysis saved)
    • RAGQueryCacheService (6h TTL, 2+ second savings)
    • PromptComplexityCacheService (30d TTL, 95% accuracy)
  • UnifiedCacheOrchestrator for cross-cache optimization
  • SQLite-based with connection pooling
  • Smart cache warming and resource allocation

🎯 Priority 2: Transcription Service

20-70x faster transcription achieved

  • FasterWhisperTranscriptService with M3 optimizations
    • distil-large-v3 model (best speed/accuracy tradeoff)
    • Smart chunking for large files (10-minute segments)
    • Audio preprocessing (16kHz mono conversion)
    • VAD (Voice Activity Detection) optimization
  • Enhanced transcript storage with compression (68.6% space savings)
  • YouTube integration with fallback strategies
  • Batch processing support

📦 Priority 3: Export & Formatting

Critical for data persistence

  • Multi-format export (SRT, VTT, TXT, JSON, PDF)
  • Template-driven formatting with Jinja2
  • Batch export system for multiple files
  • Professional formatting with metadata
  • Safe filename generation with collision avoidance

⚡ Priority 4: Performance Optimizations

Essential patterns that worked

  • Database Registry Pattern (prevents SQLAlchemy conflicts)
  • Async pipeline patterns throughout
  • Connection pooling for database and external services
  • Smart compression (68.6% space savings for transcripts)
  • Protocol-based design for component swapping

3. Technical Debt & Failed Patterns to Avoid

❌ Patterns That Failed

  • React Frontend Complexity: Eventually decommissioned in favor of headless API
  • Simultaneous Frontend/Backend Development: Caused integration issues
  • Over-engineering: Features without clear value added complexity
  • Documentation Bloat: Grew beyond 600 LOC limits causing context issues
  • Mock-heavy Testing: Unrealistic test scenarios
  • Streaming Transcription: Unreliable, download-first approach won

✅ Patterns That Worked

  • Backend-first development: Get data layer right before UI
  • Database modification checklist: Prevented breaking changes
  • Test runner system: 229 tests with 0.2s discovery time
  • Registry pattern for SQLAlchemy: Solved relationship conflicts
  • Multi-layer caching: Massive cost and performance benefits
  • Real test files: Caught actual edge cases
  • Protocol-based services: Easy refactoring and swapping

4. Migration Risk Assessment

Low Risk Components ✅

  • Config system: Already compatible with uv and inheritance model
  • Caching services: Self-contained, easy to port
  • Export functionality: Modular design, simple migration
  • Testing patterns: Can adopt test runner system directly

Medium Risk Components ⚠️

  • Transcription service: Needs audio dependencies (FFmpeg, etc.)
  • Database patterns: Requires Alembic setup and careful migration
  • Batch processing: Needs queue design from scratch

High Risk Components ❌

  • None identified - starting fresh avoids legacy issues

5. Configuration Analysis

Current src/config.py provides:

  • Centralized configuration with root .env inheritance
  • API key management for multiple AI services
  • Path management for project directories
  • Validation methods for required keys
  • Service availability detection

Ready for Extension with:

  • Database configuration
  • Media processing settings
  • Transcription parameters
  • Caching configuration
  • Export paths

6. Development Environment Status

Component Status Action Needed
Python 3.11+ ✅ Ready None
uv package manager ✅ Configured Install dependencies
PostgreSQL ❌ Not setup Install and configure
FFmpeg ❌ Not verified Install for audio processing
Test infrastructure ⚠️ Basic Add real test files
CI/CD ❌ None Setup GitHub Actions

7. Immediate Requirements

To begin development, we need:

  1. Dependencies Installation:

    • Core: sqlalchemy, alembic, psycopg2-binary
    • Transcription: faster-whisper, yt-dlp, ffmpeg-python
    • AI: openai (for DeepSeek), aiohttp
    • Testing: Real audio/video test files

  2. Directory Structure:

    • src/services/ for business logic
    • src/models/ for database models
    • src/agents/rules/ for consistency rules
    • tests/fixtures/ for real test files

  3. Database Setup:

    • PostgreSQL installation
    • Initial schema design
    • Alembic configuration

Summary

Current State: Trax is a clean skeleton with proper uv configuration, ready for development.

Migration Path: Clear path exists for migrating priority components from YouTube Summarizer.

No Blocking Issues: No significant technical debt or conflicts identified.

Ready to Proceed: With systematic development following the established patterns.

Generated: 2024
Status: COMPLETE
Next: Historical Context Report