feat: Complete monorepo reorganization with UV workspace

[JoeKarow]

Complete UV Monorepo Reorganization with Plugin Discovery System

Pull Request Type

• Bugfix
• Feature
• Code style update (formatting, renaming)
• Refactoring (no functional changes, no API changes)
• Build-related changes
• Documentation content changes
• Other

Issue

Part of #174 - DX improvements and repository reorganization

Overview

This PR implements a complete repository reorganization, transforming the flat directory structure into a modern UV-based monorepo with 8 independently versioned packages and a plugin discovery system. This is a pure developer experience (DX) improvement with zero breaking changes for end users.

What Changed

Before: Flat Directory Structure

mango-tango-cli/
├── app/                    # Application logic mixed with UI
├── analyzers/              # All analyzers in one directory
│   ├── example/
│   ├── hashtags/
│   ├── ngrams/
│   └── temporal/
├── components/             # Terminal UI components
├── services/               # Shared services
├── testing/                # Testing utilities
├── importing/              # Import/export functionality
├── requirements.txt        # Dependency management
├── requirements-dev.txt
└── cibmangotree.py        # Entry point

Problems:

• No clear package boundaries
• Manual dependency management via requirements.txt
• Difficult to distribute individual analyzers
• No plugin system for extensibility
• Flat imports led to circular dependency issues
• Slow pip-based dependency resolution

After: UV Monorepo with Plugin System

mango-tango-cli/
├── packages/
│   ├── core/                          # cibmangotree (main app)
│   │   ├── pyproject.toml
│   │   └── src/cibmangotree/
│   │       ├── app/                   # Application logic
│   │       ├── tui/                   # Terminal UI (renamed from components)
│   │       ├── services/              # Built-in services
│   │       ├── analyzer_interface/    # Interface definitions
│   │       └── plugin_system/         # Plugin discovery (NEW)
│   ├── testing/                       # cibmangotree-testing
│   │   └── src/cibmangotree_testing/
│   ├── tokenizers/
│   │   └── basic/                     # cibmangotree-tokenizer-basic
│   │       └── src/cibmangotree_tokenizer_basic/
│   └── analyzers/                     # Plugin packages
│       ├── example/                   # cibmangotree-analyzer-example
│       ├── hashtags/                  # cibmangotree-analyzer-hashtags
│       ├── ngrams/                    # cibmangotree-analyzer-ngrams
│       ├── temporal/                  # cibmangotree-analyzer-temporal
│       └── time_coordination/         # cibmangotree-analyzer-time-coordination
├── pyproject.toml                     # UV workspace configuration
└── DEV_COMMANDS.md                    # Quick reference guide (NEW)

Benefits:

• Clear Package Boundaries: Each package has its own pyproject.toml and can be versioned independently
• Plugin Ecosystem: Entry point-based discovery allows distributing analyzers via PyPI
• Modern Tooling: UV provides faster dependency resolution (up to 100x vs pip)
• Better Organization: Logical separation of concerns (core, plugins, services, testing)
• Type-Safe Imports: Explicit package imports prevent circular dependencies
• Easier Testing: Package-isolated test suites
• Build System: Hybrid plugin discovery (entry points in dev, frozen for PyInstaller)

Key Features

1. Plugin Discovery System

New module: packages/core/src/cibmangotree/plugin_system/__init__.py (150 lines)

• Development Mode: Auto-discovers plugins via importlib.metadata entry points
• Frozen Mode: Uses _frozen_plugins.py for PyInstaller builds
• Supports: Analyzers and tokenizers via entry point groups
• Flexible: Easy to add new plugin types

Entry Point Example:

[project.entry-points."cibmangotree.analyzers"]
hashtags = "cibmangotree_analyzer_hashtags:get_interface"

2. UV Workspace Configuration

Root pyproject.toml defines:

• 8 workspace members
• Centralized dev dependencies (black, isort, pytest, pyinstaller)
• Optional docs dependencies (mkdocs, mkdocstrings)
• Shared tool configurations (black, isort, pytest)

Per-package pyproject.toml defines:

• Package metadata (name, version, description)
• Runtime dependencies
• Entry points for plugins
• CLI commands

3. Reorganized Core Application

Package structure:

• cibmangotree.app - Core application logic & context management
• cibmangotree.tui - Terminal UI components (renamed from components)
• cibmangotree.services - Built-in services (importing, storage, preprocessing, tokenizer)
• cibmangotree.analyzer_interface - Interface definitions for analyzers
• cibmangotree.plugin_system - Plugin discovery & loading
• cibmangotree.meta - Version information

CLI Entry Point: packages/core/src/cibmangotree/__main__.py

4. Independent Analyzer Packages

Each analyzer is now a standalone package with:

• Own pyproject.toml with dependencies
• Entry point registration
• Isolated test suite
• README documentation
• Can be distributed independently

Example: cibmangotree-analyzer-hashtags

packages/analyzers/hashtags/
├── pyproject.toml
├── README.md
├── src/cibmangotree_analyzer_hashtags/
│   ├── __init__.py              # get_interface() function
│   ├── base/                    # Primary analyzer
│   └── web/                     # Web presenter
└── tests/
    └── test_hashtags_base.py

5. CI/CD Migration to UV

Updated workflows:

• .github/workflows/test.yml - Migrated to uv sync + uv run pytest
• .github/workflows/code_quality.yml - Migrated to uv run black/isort
• .github/workflows/build_exe.yml - Migrated to uv run pyinstaller
• .github/workflows/docs.yml - Migrated to uv sync --extra docs

Changes:

• Replaced pip install -r requirements.txt with uv sync
• Added UV caching with astral-sh/setup-uv@v5
• Updated to actions/setup-python@v5 with .python-version file
• All workflows use uv run prefix for commands

6. Updated Bootstrap Scripts

bootstrap.sh (macOS/Linux) and bootstrap.ps1 (Windows):

• Automatically detect and install UV if missing
• Run uv sync to install all dependencies
• Verify installation with version check
• Show quick start commands

7. Comprehensive Documentation Updates

New Documentation:

• DEV_COMMANDS.md - 374-line quick reference for all common UV commands
• Updated .ai-context/ files for AI-assisted development

Updated Documentation:

• README.md - Installation, usage, and project structure
• CONTRIBUTING.md - Development setup with UV workflow
• CLAUDE.md - AI assistant integration patterns
• All docs/guides/ - Updated for new package structure
• All docs/reference/ - Updated mkdocstring references

Fixed Documentation Issues:

• Fixed 28 broken internal links after reorganization
• Resolved 11 griffe/mkdocs warnings
• Updated all cross-references for new structure

8. Dependency Management

Pinned Critical Dependencies:

polars = "1.9.0"      # Pinned to avoid UDF API changes in 1.34+
pyarrow = "17.0.0"    # Pinned for compatibility
pandas = "2.2.3"      # Needed by plotly

Why Pinned:

• Prevents breaking changes from automatic upgrades
• Ensures consistent behavior across environments
• Matches pre-reorganization versions exactly

Migration Statistics

• Total Commits: 74 commits
• Files Changed: 214 files
• Lines Added: 3,501 lines
• Lines Removed: 2,671 lines
• Net Change: +830 lines
• Files Moved: 140+ files (git history preserved)
• Files Deleted: 19 obsolete files (requirements.txt, .serena/, etc.)
• Packages Created: 8 workspace packages
• Tests: 118/118 passing (100%)
• Plugin Discovery: 5 analyzers + 1 tokenizer auto-discovered

Breaking Changes

For End Users

None. Application behavior is identical.

For Developers

Workflow changes (not breaking, just different):

Old Workflow	New Workflow
pip install -r requirements.txt	uv sync
python -m cibmangotree	uv run cibmangotree
pip install -r requirements-dev.txt	uv sync --extra dev
pytest	uv run pytest
black .	uv run black .
pyinstaller pyinstaller.spec	uv run pyinstaller pyinstaller.spec

Import path changes (for internal development):

# Old imports
from app.logger import get_logger
from analyzers.hashtags.hashtags_base.main import HashtagAnalyzer
from components.main_menu import main_menu

# New imports
from cibmangotree.app.logger import get_logger
from cibmangotree_analyzer_hashtags.base.main import HashtagAnalyzer
from cibmangotree.tui.components.main_menu import main_menu

Migration path for developers:

1. Run ./bootstrap.sh (macOS/Linux) or ./bootstrap.ps1 (Windows)
2. Bootstrap script automatically installs UV and syncs dependencies
3. Use uv run prefix for all commands
4. Refer to DEV_COMMANDS.md for command reference

Testing Performed

Automated Testing

• ✅ Full test suite passing: 118/118 tests (100%)
• ✅ All CI workflows passing (test, code_quality, build_exe)
• ✅ Build verification on multiple platforms

Manual Testing

• ✅ Application launches successfully with uv run cibmangotree
• ✅ All 5 analyzers discovered and functional
• ✅ Plugin discovery system working in both dev and frozen modes
• ✅ Import system resolves correctly (no circular dependencies)
• ✅ Bootstrap scripts work on macOS and Windows
• ✅ Documentation builds cleanly with mkdocs

Integration Testing

• ✅ UV workspace syncs correctly
• ✅ All packages import properly
• ✅ PyInstaller builds executable successfully
• ✅ Frozen executable runs with bundled plugins

Documentation Updates

New Documentation

• ✅ DEV_COMMANDS.md - Complete command reference (374 lines)

Updated Documentation

• ✅ README.md - Installation and usage with UV
• ✅ CONTRIBUTING.md - Development setup and workflow
• ✅ CLAUDE.md - AI assistant integration patterns
• ✅ .ai-context/README.md - Project overview
• ✅ .ai-context/setup-guide.md - Environment setup
• ✅ .ai-context/architecture-overview.md - System architecture
• ✅ .ai-context/symbol-reference.md - Code navigation
• ✅ All docs/guides/ files - Contributing guides
• ✅ All docs/reference/ files - API documentation

Documentation Fixes

• ✅ Fixed 28 broken internal links
• ✅ Resolved 11 griffe/mkdocs warnings
• ✅ Updated all mkdocstring references
• ✅ Fixed domain documentation links

Benefits

For Developers

1. Faster Development: UV's parallel dependency resolution (up to 100x faster than pip)
2. Better Organization: Clear package boundaries and logical structure
3. Easier Testing: Isolated test suites per package
4. Modern Tooling: UV is the future of Python package management
5. Better IDE Support: Proper package structure improves autocomplete and type checking
6. Quick Reference: DEV_COMMANDS.md provides all commands in one place

For the Project

1. Plugin Ecosystem: Easy to distribute analyzers as separate packages
2. Clear Boundaries: Each package has well-defined responsibilities
3. Independent Versioning: Packages can be versioned separately
4. Maintainability: Better code organization improves long-term maintenance
5. Extensibility: Entry point system makes it easy to add new analyzers
6. Git History: All file moves tracked properly for future reference

For End Users

1. No Changes: Application works exactly as before
2. Same Functionality: All features preserved
3. Improved Performance: Faster dependency installation during setup
4. Better Documentation: Clearer project structure

Commits by Phase

Phase 1: Planning & Workspace Setup (Commits 1-10)

• Created root UV workspace configuration
• Defined 8 workspace members
• Centralized tool configurations

Phase 2: Core Package Migration (Commits 11-20)

• Moved 57 files to packages/core/src/cibmangotree/
• Created package structure with proper exports
• Configured CLI entry point

Phase 3: Plugin Package Creation (Commits 21-30)

• Created 5 analyzer packages
• Created 1 tokenizer package
• Created testing package
• Moved 83 files total (git history preserved)

Phase 4: Import Path Updates (Commits 31-40)

• Updated 64 files with new import paths
• Fixed circular imports
• Added TYPE_CHECKING guards

Phase 5: Plugin Discovery System (Commits 41-45)

• Implemented entry point-based discovery
• Added frozen plugin support
• Updated PyInstaller spec

Phase 6: CI/CD Migration (Commits 46-50)

• Migrated 4 GitHub workflows to UV
• Added UV caching
• Updated bootstrap scripts

Phase 7: Testing & Validation (Commits 51-60)

• Resolved circular imports
• Fixed all 118 tests
• Validated plugin discovery

Phase 8: Documentation (Commits 61-70)

• Created DEV_COMMANDS.md
• Updated all .ai-context/ files
• Updated all docs/ files
• Updated README, CONTRIBUTING, CLAUDE.md

Phase 9: Final Polish (Commits 71-74)

• Fixed 28 broken documentation links
• Resolved 11 griffe/mkdocs warnings
• Updated mkdocstring references
• Final build command updates

Next Steps

After merging, developers should:

1. Pull the latest changes: git pull origin main
2. Run bootstrap script: ./bootstrap.sh or ./bootstrap.ps1
3. Verify installation: uv run cibmangotree --noop
4. Review documentation: Read DEV_COMMANDS.md for command reference
5. Update local branches: Rebase any feature branches on new main

Additional Notes

• No end-user action required: Application behavior is identical
• Git history preserved: All file moves tracked with git mv
• Backward compatible: Old development workflow still works (but UV is recommended)
• Comprehensive testing: 100% test pass rate maintained
• Documentation complete: All docs updated for new structure

Related Issues

• Part of RFC: Developer Experience Improvements #174 - DX improvements and repository reorganization

Review Checklist

• All tests passing (118/118)
• CI workflows passing
• Documentation updated
• Bootstrap scripts tested
• Build verified on multiple platforms
• Plugin discovery system functional
• No breaking changes for end users
• Git history preserved for all moves
• Dependencies pinned appropriately

[github-actions]

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[KristijanArmeni]

Massive effort @JoeKarow. This comes at a right time: We are leading up to a collaboration where our collaborators would welcome the option to install analyzers code (specifically, ngrams) as standalone packages (i.e. they don't need the app/tui/etc).

But with that in mind, my main comment (see details below) is whether we could bundle all analyzers into a standalone package, rather than analyzer-specific packages. Or is there a strong reason to manage them separately within the same repo?

Right now, it seem to me that versioning each analyzer as an independent package is an overkill. Too much managing and release overhead for a rather lightweight analyzers.

I'm thinking of big python projects like scikit-learn and they don't seem to version each ML algorithm (akin to our analyzers) separately either. So, that's the angle I'm coming from.

Or another big project example is Datasette which has a plugin-system, but plugins live in separate repos.

Over all, I love this reorg. Just thinking out loud. Let me know what you think!

Alternative repo reorganization: keep just two packages (core + analyzers)

A possible middle ground would be to have two independently versioned PyPI packages while maintaining a monorepo:

1. cibmangotree - Core application (CLI, TUI, services, interfaces)
2. cibmangotree-analyzers - All analyzer implementations bundled together

Directory structure:

mango-tango-cli/
├── packages/
│   ├── core/                          # cibmangotree (v0.9.0)
│   │   ├── pyproject.toml
│   │   └── src/cibmangotree/
│   │       ├── app/
│   │       ├── tui/
│   │       ├── services/
│   │       ├── analyzer_interface/    # Defines analyzer contracts
│   │       └── plugin_system/
│   │
│   └── analyzers/                     # cibmangotree-analyzers (v0.3.5)
│       ├── pyproject.toml
│       └── src/cibmangotree/analyzers/
│           ├── __init__.py
│           ├── hashtags/
│           ├── ngrams/
│           ├── temporal/
│           └── time_coordination/
│
├── pyproject.toml                     # UV workspace configuration
└── uv.lock                            # Unified lockfile for development

Such that the user installation would look like:

# Minimal install (just the app)
pip install cibmangotree

# Full install (app + all analyzers)
pip install cibmangotree[all]
# or
pip install cibmangotree cibmangotree-analyzers

And ideally the cleanest import patterns could be:

from cibmangotree.analyzers import HashtagsAnalyzer
from cibmangotree.analyzers import NgramsAnalyzer