# Tagmatic Implementation Tasks

## Phase 1: Project Setup & Foundation

### 1.1 Environment Setup
- [x] Initialize project with `uv init`
- [x] Configure `pyproject.toml` with project metadata, dependencies, and build settings
- [x] Set up `.gitignore` for Python projects
- [x] Create MIT license file
- [x] Set up basic project directory structure

### 1.2 Development Infrastructure
- [x] Configure pre-commit hooks (black, isort, flake8, mypy)
- [ ] Set up GitHub Actions for CI/CD
- [x] Configure pytest for testing
- [x] Set up coverage reporting
- [ ] Create development scripts in `scripts/` directory

## Phase 2: Core Library Implementation

### 2.1 Category System (`src/tagmatic/core/category.py`)
- [x] Implement `Category` class with name, description, and optional examples
- [x] Implement `CategorySet` class for managing collections of categories
- [x] Add validation for category definitions
- [x] Support for JSON/YAML serialization/deserialization
- [x] Add type hints and docstrings

### 2.2 Prompt Templates (`src/tagmatic/prompts/templates.py`)
- [x] Create base prompt template for classification
- [x] Implement dynamic prompt generation based on categories
- [x] Add support for custom prompt templates
- [x] Optimize prompts for different LLM providers
- [x] Include few-shot examples when available

### 2.3 Classification Engine (`src/tagmatic/core/classifier.py`)
- [x] Implement main `Classifier` class
- [x] Accept LangChain LLM objects as input
- [x] Implement basic classification logic using LangChain's structured output
- [x] Add confidence scoring
- [x] Handle edge cases and errors gracefully
- [x] Support for batch classification

### 2.4 Voting Classifier (integrated in `src/tagmatic/providers/base.py` and `src/tagmatic/core/classifier.py`)
- [x] Implement voting mechanism with configurable rounds (odd numbers only)
- [x] Majority vote selection logic
- [x] Confidence aggregation from multiple votes
- [x] Tie-breaking strategies
- [x] Performance optimization for multiple calls

### 2.5 Utilities (`src/tagmatic/utils/`)
- [x] Configuration management (`config.py`)
- [x] Input validation (`validation.py`)
- [x] Common helper functions
- [x] Error handling and custom exceptions

### 2.6 Package Initialization (`src/tagmatic/__init__.py`)
- [x] Export main classes and functions
- [x] Version management
- [x] Clean public API

## Phase 3: Testing Suite

### 3.1 Unit Tests
- [x] Test `Category` and `CategorySet` classes
- [x] Test prompt template generation
- [x] Test classification logic with mock LLM responses
- [x] Test voting classifier with various scenarios
- [x] Test configuration and validation utilities

### 3.2 Integration Tests
- [x] End-to-end classification tests
- [x] Test with different LangChain LLM providers
- [ ] Performance benchmarks
- [x] Error handling scenarios

### 3.3 Test Data & Fixtures
- [x] Create sample datasets for testing
- [x] Mock LLM responses for consistent testing
- [x] Edge case test scenarios

## Phase 4: Documentation & Examples

### 4.1 Core Documentation
- [x] Comprehensive README.md with quick start guide
- [x] API documentation with docstrings
- [x] Installation instructions
- [x] Configuration guide

### 4.2 Examples (`examples/`)
- [x] Basic usage example (`basic_usage.py`)
- [x] Voting classifier example (`voting_classifier.py`)
- [ ] Advanced configuration example
- [ ] Batch processing example

### 4.3 Tutorial Notebook (`examples/tutorial.ipynb`)
- [x] Step-by-step tutorial with real data
- [x] Different use cases (sentiment, topic classification, etc.)
- [x] Performance comparison with/without voting
- [x] Best practices and tips

### 4.4 Advanced Documentation
- [ ] Contributing guidelines
- [ ] Code of conduct
- [ ] Issue templates
- [ ] Pull request templates

## Phase 5: Quality Assurance & Polish

### 5.1 Code Quality
- [ ] Ensure 100% type hint coverage
- [ ] Achieve >90% test coverage
- [ ] Code review and refactoring
- [ ] Performance optimization
- [ ] Memory usage optimization

### 5.2 Documentation Review
- [ ] Proofread all documentation
- [ ] Verify all examples work correctly
- [ ] Check API consistency
- [ ] Update docstrings and comments

### 5.3 Package Preparation
- [ ] Verify package builds correctly
- [ ] Test installation from PyPI test server
- [ ] Prepare release notes
- [ ] Tag version for release

## Phase 6: Release & Community

### 6.1 Initial Release
- [ ] Publish to PyPI
- [ ] Create GitHub release
- [ ] Announce on relevant communities
- [ ] Set up issue tracking

### 6.2 Community Building
- [ ] Respond to initial feedback
- [ ] Create roadmap for future features
- [ ] Set up contribution workflow
- [ ] Monitor usage and gather metrics

## Implementation Priority

### High Priority (MVP)
1. Category system
2. Basic classifier with LangChain integration
3. Voting classifier
4. Basic tests
5. README and basic examples

### Medium Priority
1. Comprehensive test suite
2. Tutorial notebook
3. Advanced configuration
4. Performance optimization

### Low Priority (Future Releases)
1. Advanced features (batch processing, streaming)
2. Additional prompt templates
3. Performance benchmarks
4. Community features

## Technical Decisions Made

1. **LangChain Integration**: Use LangChain LLM objects directly for provider agnosticism
2. **Project Structure**: Standard Python package structure with `src/` layout
3. **Build Tool**: Use `uv` for modern Python dependency management
4. **Testing**: pytest with comprehensive coverage
5. **Documentation**: Combination of README, docstrings, and Jupyter notebooks
6. **License**: MIT for maximum adoption
7. **Type Safety**: Full type hints throughout the codebase

## Success Criteria

- [x] Library can classify text into user-defined categories
- [x] Voting classifier improves accuracy over single classification
- [x] Works with any LangChain-compatible LLM
- [x] Easy to install and use (< 10 lines of code for basic usage)
- [x] Comprehensive documentation and examples
- [ ] >90% test coverage (comprehensive test suite implemented, coverage needs verification)
- [x] Professional code quality standards
