LLMPromptKit is a comprehensive library for managing, versioning, testing, and evaluating prompts for Large Language Models (LLMs). It provides a structured framework to help data scientists and developers create, optimize, and maintain high-quality prompts.

Features

Prompt Management: Create, update, and organize prompts with metadata and tags
Version Control: Track prompt changes over time with full version history
A/B Testing: Compare different prompt variations to find the most effective one
Evaluation Framework: Measure prompt quality with customizable metrics
Advanced Templating: Create dynamic prompts with variables, conditionals, and loops
Command-line Interface: Easily integrate into your workflow
Hugging Face Integration: Seamlessly test prompts with thousands of open-source models

Hugging Face Integration

LLMPromptKit includes a powerful integration with Hugging Face models, allowing you to:

Test prompts with thousands of open-source models
Run evaluations with models like FLAN-T5, GPT-2, and others
Compare prompt performance across different model architectures
Access specialized models for tasks like translation, summarization, and question answering

python

from llmpromptkit import PromptManager, PromptTesting
from llmpromptkit.integrations.huggingface import get_huggingface_callback

# Initialize components
prompt_manager = PromptManager()
testing = PromptTesting(prompt_manager)

# Get a HuggingFace callback
hf_callback = get_huggingface_callback(
    model_name="google/flan-t5-base", 
    task="text2text-generation"
)

# Run tests with the model
test_results = await testing.run_test_cases(prompt_id="your_prompt_id", llm_callback=hf_callback)

Documentation

For detailed documentation, see the docs directory:

Installation

bash

pip install llmpromptkit

Quick Start

from llmpromptkit import PromptManager, VersionControl, PromptTesting, Evaluator

# Initialize components
prompt_manager = PromptManager()
version_control = VersionControl(prompt_manager)
testing = PromptTesting(prompt_manager)
evaluator = Evaluator(prompt_manager)

# Create a prompt
prompt = prompt_manager.create(
    content="Summarize the following text: {text}",
    name="Simple Summarization",
    description="A simple prompt for text summarization",
    tags=["summarization", "basic"]
)

# Create a new version
version_control.commit(
    prompt_id=prompt.id,
    commit_message="Initial version"
)

# Update the prompt
prompt_manager.update(
    prompt.id,
    content="Please provide a concise summary of the following text in 2-3 sentences: {text}"
)

# Commit the updated version
version_control.commit(
    prompt_id=prompt.id,
    commit_message="Improved prompt with length guidance"
)

# Create a test case
test_case = testing.create_test_case(
    prompt_id=prompt.id,
    input_vars={"text": "Lorem ipsum dolor sit amet..."},
    expected_output="This is a summary of the text."
)

# Define an LLM callback for testing
async def llm_callback(prompt, vars):
    # In a real scenario, this would call an actual LLM API
    return "This is a summary of the text."

# Run the test case
import asyncio
test_result = asyncio.run(testing.run_test_case(
    test_case_id=test_case.id,
    llm_callback=llm_callback
))

# Evaluate a prompt with multiple inputs
evaluation_result = asyncio.run(evaluator.evaluate_prompt(
    prompt_id=prompt.id,
    inputs=[{"text": "Sample text 1"}, {"text": "Sample text 2"}],
    llm_callback=llm_callback
))

print(f"Evaluation metrics: {evaluation_result['aggregated_metrics']}")

Command-line Interface
LLMPromptKit comes with a powerful CLI for managing prompts:

# Create a prompt
llmpromptkit prompt create "Summarization" --content "Summarize: {text}" --tags "summarization,basic"

# List all prompts
llmpromptkit prompt list

# Create a new version
llmpromptkit version commit <prompt_id> --message "Updated prompt"

# Run tests
llmpromptkit test run-all <prompt_id> --llm openai

Advanced Usage
Advanced Templating
LLMPromptKit supports advanced templating with conditionals and loops:

from llmpromptkit import PromptTemplate

template = PromptTemplate("""
{system_message}

{for example in examples}
Input: {example.input}
Output: {example.output}
{endfor}

Input: {input}
Output:
""")

rendered = template.render(
    system_message="You are a helpful assistant.",
    examples=[
        {"input": "Hello", "output": "Hi there!"},
        {"input": "How are you?", "output": "I'm doing well, thanks!"}
    ],
    input="What's the weather like?"
)

Custom Evaluation Metrics
Create custom metrics to evaluate prompt performance:
from llmpromptkit import EvaluationMetric, Evaluator

class CustomMetric(EvaluationMetric):
    def __init__(self):
        super().__init__("custom_metric", "My custom evaluation metric")
    
    def compute(self, generated_output, expected_output=None, **kwargs):
        # Custom logic to score the output
        return score  # A float between 0 and 1

# Register the custom metric
evaluator = Evaluator(prompt_manager)
evaluator.register_metric(CustomMetric())

Use Cases

Prompt Development: Iteratively develop and refine prompts with version control
Prompt Optimization: A/B test different prompt variations to find the most effective approach
Quality Assurance: Ensure prompt quality with automated testing and evaluation
Team Collaboration: Share and collaborate on prompts with a centralized management system
Production Deployment: Maintain consistent prompt quality in production applications

License
MIT License

## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.

## Author
Biswanath Roul - [GitHub](https://github.com/biswanathroul)

biswanathroul/llmpromptkit

LLMPromptKit: LLM Prompt Management System

Features

Hugging Face Integration

Documentation

Installation

biswanathroul/
llmpromptkit