PromptSuite: A Task-Agnostic Framework for Multi-Prompt Generation

Overview

PromptSuite transforms your single-prompt datasets into rich multi-prompt datasets by applying various types of variations specified in your templates. It supports HuggingFace-compatible datasets and provides both a command-line interface and a modern web UI.

📚 Documentation

• 📖 Complete API Guide - Python API reference and examples
• 🏗️ Developer Documentation - For contributors and developers

Installation

From PyPI (Recommended)

pip install promptsuite

From GitHub (Latest)

pip install git+https://github.com/eliyahabba/PromptSuite.git

From Source

git clone https://github.com/eliyahabba/PromptSuite.git
cd PromptSuite
pip install -e .

Quick Start

Command Line Interface

promptsuite --template '{"instruction": "{instruction}: {text}", "text": ["paraphrase_with_llm"], "gold": "label"}' \
               --data data.csv --max-variations-per-row 50

Streamlit Interface

Launch the modern Streamlit interface for an intuitive experience:

# If installed via pip
promptsuite-ui

# From project root (development)
python src/promptsuite/ui/main.py

The web UI provides:

• 📁 Step 1: Upload data or use sample datasets
• 🔧 Step 2: Build templates with smart suggestions
• ⚡ Step 3: Generate variations with real-time progress and export results

Python API

from promptsuite import PromptSuite
import pandas as pd

# Initialize
ps = PromptSuite()

# Load data
data = [{"question": "What is 2+2?", "answer": "4"}]
ps.load_dataframe(pd.DataFrame(data))

# Configure template
template = {
  'instruction': 'Please answer the following questions.',
  'prompt format': 'Q: {question}\nA: {answer}',
  'question': ['typos and noise'],
}
ps.set_template(template)

# Generate variations
ps.configure(max_rows=2, variations_per_field=3)
variations = ps.generate(verbose=True)

# Export results
ps.export("output.json", format="json")

For more detailed examples of API usage, refer to the examples/ directory.

📖 Code Examples

Sentiment Analysis

import pandas as pd
from promptsuite import PromptSuite

data = pd.DataFrame({
  'text': ['I love this movie!', 'This book is terrible.'],
  'label': ['positive', 'negative']
})

template = {
  'instruction': 'Classify the sentiment',
  'instruction_variations': ['paraphrase_with_llm'],
  'prompt format': f"Text: {text}\nSentiment: {label}",
  'text': ['typos and noise'],
}

ps = PromptSuite()
ps.load_dataframe(data)
ps.set_template(template)
ps.configure(
  variations_per_field=3,
  max_variations_per_row=2,
  random_seed=42,
  api_platform="TogetherAI",  # or "OpenAI", "Anthropic", "Google", "Cohere"
  model_name="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
)
variations = ps.generate(verbose=True)

Question Answering with Few-shot

template = {
  'instruction': 'Answer the question:\nQuestion: {question}\nAnswer: {answer}',
  'instruction_variations': ['paraphrase_with_llm'],
  'question': ['semantic'],
  'gold': 'answer',
  'few_shot': {
    'count': 2,
    'format': 'same_examples__synchronized_order_variations',
    'split': 'train'
  }
}

ps = PromptSuite()
ps.load_dataframe(qa_data)
ps.set_template(template)
ps.configure(
  variations_per_field=2,
  api_platform="TogetherAI",  # or "OpenAI", "Anthropic", "Google", "Cohere"
  model_name="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
)
variations = ps.generate(verbose=True)

Multiple Choice with Few-shot

import pandas as pd
from promptsuite import PromptSuite

data = pd.DataFrame({
    'question': [
        'What is the largest planet in our solar system?',
        'Which chemical element has the symbol O?',
        'What is the fastest land animal?',
        'What is the smallest prime number?',
        'Which continent is known as the "Dark Continent"?'
    ],
    'options': [
        'Earth, Jupiter, Mars, Venus',
        'Oxygen, Gold, Silver, Iron',
        'Lion, Cheetah, Horse, Leopard',
        '1, 2, 3, 0',
        'Asia, Africa, Europe, Australia'
    ],
    'answer': [1, 0, 1, 1, 1],
    'subject': ['Astronomy', 'Chemistry', 'Biology', 'Mathematics', 'Geography']
})

template = {
    'prompt format': 'Question: {question}\nOptions: {options}\nAnswer:',
    'prompt format variations': ['format structure'],
    'options': ['shuffle', 'enumerate'],
    'gold': {
        'field': 'answer',
        'type': 'index',
        'options_field': 'options'
    },
    'few_shot': {
        'count': 2,
        'format': 'same_examples__synchronized_order_variations',
        'split': 'train'
    }
}

ps = PromptSuite()
ps.load_dataframe(data)
ps.set_template(template)
ps.configure(max_rows=5, variations_per_field=1)
variations = ps.generate(verbose=True)
for v in variations:
    print(v['prompt'])

Example Output Format

A typical output from ps.generate() or the exported JSON file looks like this (for a multiple choice template):

[
  {
    "prompt": "Answer the following multiple choice question:\nQuestion: What is 2+2?\nOptions: 3, 4, 5, 6\nAnswer:",
    "original_row_index": 1,
    "variation_count": 1,
    "template_config": {
      "instruction": "Answer the following multiple choice question:\nQuestion: {question}\nOptions: {options}\nAnswer: {answer}",
      "options": ["shuffle"],
      "gold": {
        "field": "answer",
        "type": "index",
        "options_field": "options"
      },
      "few_shot": {
        "count": 1,
        "format": "same_examples__synchronized_order_variations",
        "split": "train"
      }
    },
    "field_values": {
      "options": "3, 4, 5, 6"
    },
    "gold_updates": {
      "answer": "1"
    },
    "conversation": [
      {
        "role": "user",
        "content": "Answer the following multiple choice question:\nQuestion: What is 2+2?\nOptions: 3, 4, 5, 6\nAnswer:"
      },
      {
        "role": "assistant",
        "content": "1"
      },
      {
        "role": "user",
        "content": "Answer the following multiple choice question:\nQuestion: What is the capital of France?\nOptions: London, Berlin, Paris, Madrid\nAnswer:"
      }
    ]
  }
]

📖 Detailed Guide

Data Loading

# CSV
ps.load_csv('data.csv')

# JSON
ps.load_json('data.json')

# HuggingFace
ps.load_dataset('squad', split='train[:100]')

# DataFrame
ps.load_dataframe(df)

Generation Options

ps.configure(
    max_rows=10,                    # How many data rows to use
    variations_per_field=3,         # Variations per field (default: 3)
    max_variations_per_row=50,      # Cap on total variations per row
    random_seed=42,                 # For reproducibility
    api_platform="TogetherAI",      # or "OpenAI", "Anthropic", "Google", "Cohere"
    model_name="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
)

Export Formats

# JSON - Full data with metadata
ps.export("output.json", format="json")

# CSV - Flattened for spreadsheets
ps.export("output.csv", format="csv")

# TXT - Plain prompts only
ps.export("output.txt", format="txt")

Web UI Interface

PromptSuite 2.0 includes a modern, interactive web interface built with Streamlit.

The UI guides you through a simple 3-step workflow:

1. Upload Data: Load your dataset (CSV/JSON) or use built-in samples. Preview and validate your data before continuing.
2. Build Template: Create or select a prompt template, with smart suggestions based on your data. See a live preview of your template.
3. Generate & Export: Configure generation settings, run the variation process, and export your results in various formats.

The Streamlit UI is the easiest way to explore, test, and generate prompt variations visually.

🤖 Supported AI Platforms

PromptSuite supports multiple AI platforms with automatic dependency detection:

Core Platforms (Always Available)

• TogetherAI: Open-source models (Llama, Mistral, etc.)
• OpenAI: GPT models (GPT-4, GPT-3.5, etc.)

Extended Platforms (Optional Dependencies)

• Anthropic: Claude models (claude-3-haiku, claude-3-sonnet, claude-3-opus)
• Google: Gemini models (gemini-1.5-flash, gemini-1.5-pro)
• Cohere: Command models (command-r-plus, command-r)

Installation

Install optional platform dependencies:

pip install -r requirements-optional.txt

Or install specific platforms:

pip install anthropic          # For Anthropic/Claude
pip install google-generativeai # For Google/Gemini
pip install cohere             # For Cohere

API Keys

Set environment variables for the platforms you want to use:

export TOGETHER_API_KEY="your_together_key"
export OPENAI_API_KEY="your_openai_key"
export ANTHROPIC_API_KEY="your_anthropic_key"
export GOOGLE_API_KEY="your_google_key"
export COHERE_API_KEY="your_cohere_key"

Platform Selection

# Automatic platform detection
from promptsuite.shared.model_client import get_supported_platforms, is_platform_available

available_platforms = [p for p in get_supported_platforms() if is_platform_available(p)]
print(f"Available platforms: {available_platforms}")

# Use different platforms
ps.configure(api_platform="OpenAI", model_name="gpt-4o-mini")
ps.configure(api_platform="Anthropic", model_name="claude-3-haiku-20240307")
ps.configure(api_platform="Google", model_name="gemini-1.5-flash")

Adding New Platforms

See Platform Integration Guide for instructions on adding support for additional AI platforms.

🔧 Advanced Features

Performance Optimization

PromptSuite automatically optimizes performance by pre-generating variations for shared fields:

• Instruction variations (instruction variations) are generated once and reused across all data rows
• Prompt format variations (prompt format variations) are generated once and reused across all data rows

This optimization is especially important for LLM-based augmenters like paraphrase_with_llm that would otherwise run the same API calls repeatedly for identical text.

Gold Field Configuration

Simple format (for text answers):

'gold': 'answer'  # Just the column name

Advanced format (for index-based answers):

'gold': {
    'field': 'answer',
    'type': 'index',        # Answer is an index
    'options_field': 'options'  # Column with the options
}

Few-Shot Configuration

Few-shot examples can be configured with different sampling strategies. The format names clearly indicate what varies across data rows and variations:

Format naming convention: <examples_strategy>__<order_strategy>

• Examples strategy: Whether examples are the same or different across data rows
• Order strategy: Whether the order of examples varies across variations

Examples:

# same_examples__no_variations
# Row 1: [Example A, Example B]
# Row 2: [Example A, Example B]  # Same examples, no variations

# same_examples__synchronized_order_variations  
# Row 1, Variation 1: [Example A, Example B]
# Row 1, Variation 2: [Example B, Example A]
# Row 2, Variation 1: [Example A, Example B]  # Same order as Row 1, Variation 1
# Row 2, Variation 2: [Example B, Example A]  # Same order as Row 1, Variation 2

# different_examples__same_shuffling_order_across_rows
# Row 1, Variation 1: [Example A, Example B]
# Row 1, Variation 2: [Example A, Example B]  # Same examples for this row
# Row 2, Variation 1: [Example C, Example D]  # Different examples
# Row 2, Variation 2: [Example C, Example D]  # Same examples for this row

# different_examples__different_order_per_variation
# Row 1, Variation 1: [Example A, Example B]
# Row 1, Variation 2: [Example C, Example D]  # Different examples
# Row 2, Variation 1: [Example E, Example F]  # Different examples
# Row 2, Variation 2: [Example G, Example H]  # Different examples

Example:

"few_shot": {
    "count": 2,                    # Number of examples to use
    "format": "same_examples__synchronized_order_variations",   # Sampling strategy
    "split": "train",              # Use only training data for examples
    "filter_by": "category",       # NEW: Optional column name to filter few-shot examples by
    "fallback_strategy": "global"  # NEW: What to do if not enough examples in filtered category ('global' or 'strict')
}

Few-Shot Filtering with filter_by and fallback_strategy:

This new feature allows you to control which few-shot examples are selected based on metadata columns (e.g., 'category', 'difficulty').

• filter_by: Specify a column name in your dataset (e.g., "category", "subject") to ensure that few-shot examples are chosen from the same category as the current data row.
• fallback_strategy: Defines the behavior when there aren't enough few-shot examples within the filtered category:
  • "global": (Default) If there aren't enough examples in the specified filter_by category, the system will sample the remaining required examples from the entire dataset (globally), ignoring the category filter for those additional examples. This ensures that the requested count of few-shot examples is always met.
  • "strict": If there aren't enough examples in the specified filter_by category, the system will only use the examples available within that category. It will not pull examples from other categories, and if the count cannot be met from the category, it will raise an FewShotDataInsufficientError. This is useful for strict domain-specific few-shot requirements.

This feature is useful when you want each test question to have unique few-shot examples for context, but don't need multiple variations of the few-shot examples themselves.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.