# AbstractLLM Development Checklist

This checklist outlines key tasks and considerations when implementing the AbstractLLM package. It's designed to ensure thorough and high-quality implementation, especially when using LLM-assisted development.

## Project Structure

- [ ] Create basic directory structure
- [ ] Set up gitignore file
- [ ] Set up virtual environment
- [ ] Initialize README.md
- [ ] Create LICENSE file (MIT recommended)
- [ ] Configure setup.py for packaging

## Core Implementation

### Interface

- [ ] Define AbstractLLMInterface class in interface.py
- [ ] Implement constructor with config handling
- [ ] Add abstract generate() method
- [ ] Add get_capabilities() method
- [ ] Add set_config() and get_config() methods
- [ ] Ensure clear method documentation

### Factory

- [ ] Create provider mapping in factory.py
- [ ] Implement create_llm() factory function
- [ ] Add error handling for unsupported providers
- [ ] Add import handling for missing dependencies

### Utilities

- [ ] Set up logging module in utils/logging.py
- [ ] Implement log_request() function
- [ ] Implement log_response() function
- [ ] Add setup_logging() function
- [ ] Document logging formats and levels

## Provider Implementations

### OpenAI Provider

- [ ] Implement OpenAIProvider class in providers/openai.py
- [ ] Handle API key management (from config or environment)
- [ ] Implement generate() method with OpenAI API
- [ ] Add system prompt support
- [ ] Document model parameters
- [ ] Implement error handling for API errors
- [ ] Override get_capabilities() method

### Anthropic Provider

- [ ] Implement AnthropicProvider class in providers/anthropic.py
- [ ] Handle API key management
- [ ] Implement generate() method with Anthropic API
- [ ] Add system prompt support
- [ ] Document model parameters
- [ ] Implement error handling for API errors
- [ ] Override get_capabilities() method

### Ollama Provider

- [ ] Implement OllamaProvider class in providers/ollama.py
- [ ] Handle base URL configuration
- [ ] Implement generate() method with Ollama API
- [ ] Add system prompt support if available
- [ ] Document model parameters
- [ ] Implement error handling for HTTP errors
- [ ] Override get_capabilities() method

### Hugging Face Provider

- [ ] Implement HuggingFaceProvider class in providers/huggingface.py
- [ ] Add lazy model loading mechanism
- [ ] Handle model configuration (precision, device)
- [ ] Implement generate() method with local inference
- [ ] Add system prompt support
- [ ] Document model parameters
- [ ] Implement error handling for model loading/inference
- [ ] Override get_capabilities() method

## Package Integration

- [ ] Set up __init__.py to export key interfaces
- [ ] Define proper version number
- [ ] Organize imports for clean public API
- [ ] Configure provider namespace

## Testing

- [ ] Create unit tests for AbstractLLMInterface
- [ ] Create unit tests for factory function
- [ ] Create unit tests for each provider
- [ ] Add tests with mock responses
- [ ] Add tests for error handling
- [ ] Create integration tests with real API calls (when possible)

## Documentation

- [ ] Update README.md with installation instructions
- [ ] Add basic usage examples for each provider
- [ ] Document configuration options
- [ ] Create usage guide with advanced patterns
- [ ] Add API reference documentation
- [ ] Include provider-specific notes

## Packaging

- [ ] Finalize setup.py with all dependencies
- [ ] Configure extras_require for optional dependencies
- [ ] Set up proper version constraints
- [ ] Verify package installs correctly
- [ ] Ensure all imports work after installation
- [ ] Test with different Python versions

## Final Checks

- [ ] Run linter on all code
- [ ] Verify docstrings on all public methods
- [ ] Check for hardcoded values that should be configurable
- [ ] Verify error messages are clear and helpful
- [ ] Test with different Python versions
- [ ] Check memory usage with large models (HuggingFace)
- [ ] Verify proper cleanup of resources

## Release Preparation

- [ ] Update version number
- [ ] Create release notes
- [ ] Tag release in git
- [ ] Build distribution package
- [ ] Upload to PyPI
- [ ] Verify installation from PyPI works correctly
- [ ] Create GitHub release