# PromptSuite 2.0 Implementation Summary

## Overview

Successfully implemented a complete redesign of PromptSuite according to the new requirements. The tool now creates multi-prompt datasets from single-prompt datasets using templates with variation specifications.

## ✅ Core Requirements Implemented

### 1. **New Input Format**
- ✅ Assumes data comes from tables (HuggingFace-compatible format)
- ✅ Supports CSV, JSON, pandas DataFrame, and HuggingFace Dataset inputs
- ✅ Requires task instruction (static across all rows)
- ✅ Uses string format templates with Python f-string syntax

### 2. **Template System**
- ✅ Python f-string compatibility with `{variable}` syntax
- ✅ Custom variation annotations: `{field:variation_type}`
- ✅ Supported variation types:
  - `semantic` - Meaning-preserving variations
  - `paraphrase` - Paraphrasing variations  
  - `non-semantic` - Formatting/punctuation variations
  - `lexical` - Word choice variations
  - `syntactic` - Sentence structure variations
  - `surface` - Surface-level formatting variations
- ✅ Template validation with clear error messages

### 3. **Command Line Interface**
- ✅ Minimal parameter design: `--template`, `--data`, `--instruction`
- ✅ Additional options: `--few-shot`, `--output`, `--max-variations-per-row`, etc.
- ✅ Multiple output formats: JSON, CSV, HuggingFace datasets
- ✅ Verbose mode, dry-run, validation-only modes
- ✅ Statistics reporting

### 4. **Dictionary-Based Input Handling**
- ✅ **Literals** (strings/numbers): Applied to entire dataset
- ✅ **Lists**: Applied per sample/row
- ✅ **Few-shot examples**: 
  - List of lists: Different examples per sample
  - Tuple: Same examples for entire dataset

### 5. **Technical Requirements**
- ✅ Full HuggingFace datasets compatibility
- ✅ Clean Python package structure for pip installation
- ✅ Minimal dependencies (pandas, datasets, click, pyyaml)
- ✅ Clear error messages for missing columns or invalid templates
- ✅ Pip-installable with entry point: `promptsuite`

## 📁 Package Structure

```
src/promptsuite/
├── core/               # Core logic and main classes
│   ├── api.py               # High-level Python API (PromptSuite)
│   ├── engine.py            # Main PromptSuiteEngine engine class
│   ├── template_parser.py   # Template parsing with variation annotations
│   ├── template_keys.py     # Template keys and constants
│   ├── models.py            # Data models and configurations
│   ├── exceptions.py        # Custom exceptions
│   └── __init__.py          # Core module exports
├── generation/          # Variation generation modules
├── augmentations/       # Text augmentation modules
├── validators/          # Template and data validators
├── utils/               # Utility functions
├── shared/              # Shared resources
├── ui/                  # Streamlit web interface
│   └── utils/           # UI utilities
├── examples/            # API usage examples
│   └── api_example.py   # API usage example
└── pyproject.toml           # Modern package configuration
README.md                # Comprehensive documentation
requirements.txt         # Dependencies
```

## 🚀 Key Features Delivered

### Template Parsing
- Regex-based field extraction from f-string templates
- Validation of variation types and template syntax
- Support for optional variation annotations
- Clear error reporting for malformed templates

### Variation Generation
- Combinatorial generation of all field variations
- Configurable maximum variations per field and total
- Smart handling of different input types (literals, lists, tuples)
- Metadata tracking for original values and variation counts

### CLI Interface
- Comprehensive command-line tool with help documentation
- Support for file input/output in multiple formats
- Validation and dry-run modes
- Statistics reporting and verbose output

### Python API
- Clean, intuitive API for programmatic use
- Full type hints and documentation
- Error handling with descriptive messages
- Statistics and metadata generation

## 📊 Example Usage

### Command Line
```bash
# Basic usage
promptsuite --template '{"instruction_template": "{instruction}: {question}", "question": ["paraphrase_with_llm"], "gold": "answer"}' \
               --data data.csv

# With few-shot examples and output
promptsuite --template '{"instruction_template": "{instruction}: {question}", "question": ["paraphrase_with_llm"], "gold": "answer", "few_shot": {"count": 2, "format": "fixed", "split": "all"}}' \
               --data data.csv \
               --output variations.json
```

### Python API
```python
from promptsuite import PromptSuite
import pandas as pd

data = pd.DataFrame({
    'question': ['What is 2+2?', 'What color is the sky?'],
    'options': ['A)3 B)4 C)5', 'A)Red B)Blue C)Green']
})

template = {
    'instruction_template': '{instruction}: {question}\nOptions: {options}',
    'instruction': ['semantic'],
    'question': ['paraphrase_with_llm'],
    'options': ['rewording'],
    'gold': 'answer'
}

ps = PromptSuite()
ps.load_dataframe(data)
ps.set_template(template)
ps.configure(max_rows=2, variations_per_field=3)
variations = ps.generate(verbose=True)
```

## Minimal Example (No gold, no few_shot)

```python
import pandas as pd
from promptsuite import PromptSuite

data = pd.DataFrame({
    'question': ['What is 2+2?', 'What is the capital of France?'],
    'answer': ['4', 'Paris']
})

template = {
    'instruction_template': 'Q: {question}\nA: {answer}',
    'question': ['rewording']
}

ps = PromptSuite()
ps.load_dataframe(data)
ps.set_template(template)
ps.configure(max_rows=2, variations_per_field=2)
variations = ps.generate(verbose=True)
print(variations)
```

## 🔄 Backward Compatibility

- ✅ Old `main.py` shows deprecation warnings
- ✅ Clear migration instructions provided
- ✅ Maintains project structure for existing users

## ✅ Testing & Validation

- ✅ Comprehensive test suite covering all major functionality
- ✅ Template parsing validation tests
- ✅ File I/O tests with multiple formats
- ✅ Few-shot example handling tests
- ✅ CLI functionality tests
- ✅ API integration tests

## 📈 Performance & Scalability

- ✅ Configurable maximum variations to control output size
- ✅ Efficient combinatorial generation with early stopping
- ✅ Memory-efficient processing of large datasets
- ✅ Optional HuggingFace datasets integration for large-scale data

## 🎯 Edge Cases Handled

- ✅ Missing columns in data with clear error messages
- ✅ Invalid variation types with helpful suggestions
- ✅ Malformed templates with specific error reporting
- ✅ Empty or insufficient few-shot examples
- ✅ Different input data formats (CSV, JSON, DataFrame, dict)
- ✅ Output directory creation for file saving

## 📦 Installation & Distribution

- ✅ Pip-installable package with `pip install -e .`
- ✅ Entry point for CLI: `promptsuite`
- ✅ Proper dependency management
- ✅ Development dependencies for testing and linting

## 🔧 Implementation Details

### Core Architecture
- **PromptSuite**: High-level interface for easy programmatic usage
- **PromptSuiteEngine**: Main engine class (in core/engine.py)
- **TemplateParser**: Handles f-string parsing and validation
- **VariationGenerator**: Generates variations based on type specifications (in generation/)
- **CLI**: Click-based command-line interface
- **Streamlit UI**: Modern web interface with step-by-step workflow

### Variation Types Implemented
1. **Semantic**: Meaning-preserving transformations
2. **Paraphrase**: Sentence restructuring while maintaining meaning
3. **Non-semantic**: Formatting, capitalization, punctuation changes
4. **Lexical**: Word choice and synonym substitutions
5. **Syntactic**: Sentence structure modifications
6. **Surface**: Whitespace, formatting, and visual changes

## Augmenters and Variation Types

PromptSuiteEngine supports a variety of augmenters for prompt variation:
- `format_structure` (`FORMAT_STRUCTURE_VARIATION`): Semantic-preserving format changes (separators, casing, field order)
- `typos and noise` (`TYPOS_AND_NOISE_VARIATION`): Injects typos, random case, whitespace, and punctuation noise
- `enumerate` (`ENUMERATE_VARIATION`): Adds enumeration to list fields (1. 2. 3. 4., A. B. C. D., roman, etc.)
- `paraphrase_with_llm`, `context`, `shuffle`, `multidoc`, and more
- `rewording`: Deprecated, kept for backward compatibility (now maps to `typos and noise`)

See the main README and API guide for template examples using these augmenters.

## 🎉 Deliverables Summary

1. ✅ **Updated codebase** with new architecture
2. ✅ **CLI tool** with template parsing (`promptsuite` command)
3. ✅ **Documentation** with comprehensive usage examples
4. ✅ **Setup.py** for pip installation
5. ✅ **Test suite** validating all functionality
6. ✅ **Example data and scripts** for demonstration
7. ✅ **Backward compatibility** warnings for migration

The implementation successfully meets all requirements and provides a robust, scalable solution for generating multi-prompt datasets from single-prompt datasets using template-based variation specifications. 