/groq-prompt-engineer-toolkit

⚡Powered by Groq LPUs and the latest (July 2024) Llama 3 Models to generate high-quality engineered prompts and datasets for fine-tuning AI models. Try this free Streamlit application designed to help you become a prompt engineering and fine-tuning pro.

Primary LanguagePythonMIT LicenseMIT

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⚡Powered by Groq's cutting-edge LPUs and Meta's Powerful Open Source Llama 3 models

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The Groq AI - Prompt Engineering Toolkit is a powerful Streamlit, Python and Groq AI powered application designed to streamline your AI prompt engineering and fine-tuning dataset workflows and to assist you in becoming a Prompt Engineeering Pro! Harness the power of Groq's cutting-edge LPUs and Meta's Powerful Open Source Llama 3 models to generate high-quality prompts and create synthectic datasets for fine-tuning AI models.

✨ Key Features

  • Prompt Generation: Craft effective prompts for a wide range of tasks, from creative writing to code generation.
  • Test Data Generation: Create synthetic datasets for fine-tuning your AI models, ensuring they perform optimally.
  • Multi-Model Support: Choose from various Groq Llama 3 models to leverage different capabilities.
  • User-Friendly Interface: Intuitive Streamlit interface makes the app accessible to both beginners and experienced users.

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🛠️ Tools: Streamlit - Langsmith - Python - Conda - Git

🎈Streamlit: Rapid AI Frontend User Interface (UI) Application Development

Streamlit provides an intuitive framework for building interactive web applications with minimal code, allowing us to focus on delivering a seamless user experience. logo-streamlit

🔍 Langsmith: LLM Application Lifecycle Management

LangSmith is a tool for observing, debugging, dataset creation, cost analysis and improving your AI/LLM applications.

Get you Langsmith API Key here https://smith.langchain.com/

Key features include:

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  • 🐞 Real-time debugging and performance optimization
  • 👥 Collaboration tools for sharing chain traces
  • 📝 Hub for crafting, versioning, and commenting on prompts
  • 🏷️ Annotation Queues for human labeling and feedback
  • 📊 Dataset creation for evaluations, few-shot prompting, and fine-tuning
  • 🧪 Comprehensive testing and evaluation capabilities, including AI-assisted evaluation
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🚀 Quickstart

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Download and Install Python

https://www.python.org/downloads/macos/

https://www.python.org/downloads/windows/

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Download and Install Git

https://git-scm.com/download/mac

https://git-scm.com/download/win

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Download and Install Conda

We recommend using conda for easy and secure environment management

Download it from https://docs.conda.io/en/latest/miniconda.html.

Scroll down on the miniconda page *** to the "Latest Miniconda installer links" section to download for Windows, MacOs and Linux ***

1. Set Up Your Environment

  1. Create a secure Conda Environment:

    conda create -n gpe-env python=3.12 
conda activate gpe-env

  2. Install Dependencies:

    pip install -r requirements.txt

2. Get and Save Your API Keys

  1. Groq API Key: This special key allows you to tap into Groq's powerful AI models. Get your free key at https://groq.com/developers.
  • How to use the Groq API Key: Enter this special key in the left side of the Streamlit frontend UI in order to use the app.

  1. Create a .env File: In your project's folder, create a new text file named .env.

  2. Add Your LANGSMITH/LANGCHAIN API Key: Open the .env file and paste in your Langsmith API key:

    LANGCHAIN_TRACING_V2=true
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com" 
LANGCHAIN_API_KEY="your api key goes here" #
LANGCHAIN_PROJECT="groq-prompts"

    Keep this file and your API Keys safe and don't share it!

3. Launch the App

  1. Navigate to the Project Directory:

    cd /path/to/your/project

  2. Run the Streamlit App:

    streamlit run v1.95-groq-prompt-engineer.py

Your app will open in your web browser, ready for you to start exploring!

🚀 Usage

1. Generate Prompts

  1. Enter Your Question or Task: Describe the task you want the AI to perform (e.g., "Write a short story about a time traveler who meets their younger self.").
  2. Add Variables (Optional): Provide specific details or constraints (e.g., "topic: time travel, audience: young adults, tone: suspenseful").
  3. Click "Generate Prompt": The app will generate a prompt tailored to your input.
  4. Download Options: Download the prompt as a TXT or JSONL file for later use.

2. Generate Test Data

  1. Enter Topic or Text: Provide a topic or text as a basis for generating conversation pairs (e.g., "The ethics of artificial intelligence").
  2. Specify Number of Pairs: Choose how many conversation pairs you want to generate (e.g., 20).
  3. Click "Generate Test Data": The app will create a JSON or JSONL file containing the generated conversation pairs.

💡 Tips

  • Be Specific: The more specific your task descriptions and analysis prompts, the better the results.
  • Experiment with Variables: Try different combinations of input variables to fine-tune your prompts.
  • Iterate and Refine: Don't be afraid to experiment and refine your prompts based on the generated results.

🙏 Acknowledgements

  • Groq: For the powerful and versatile Llama 3 language models.
  • Meta's Llama 3: The Brainpower Behind the Magic
  • Streamlit: For making it easy to build interactive web applications.
  • Langchain's Langsmith: Tracing and Observability for LLMs Tracing and observing the behavior of large language models (LLMs)s.

🛠️ Under the Hood: Technical Deep Dive

Let's explore the key technologies and techniques that power this application.

1. Llama 3: The Brainpower Behind the Magic

Llama 3 is a family of large language models (LLMs) developed by Meta. These models are trained on massive datasets of text and code, enabling them to perform a wide range of tasks, including:

  • Text Generation: Write stories, poems, articles, and more.
  • Code Generation: Generate code in various programming languages.
  • Translation: Translate text between languages.
  • Question Answering: Provide informative answers to questions.
  • Summarization: Condense large amounts of text into concise summaries.

This app leverages the power of Llama 3 to generate prompts and create fine-tuning test data.

2. Langsmith: Tracing and Observability for LLMs

This application integrates with LangSmith, a framework developed by LangChain for tracing and observing the behavior of large language models (LLMs). LangSmith allows developers to gain insights into how their LLMs are performing, identify potential issues, and improve the overall quality of their AI applications.

3. Streamlit: Building Interactive User Interfaces

Streamlit is a Python library that makes it incredibly easy to create interactive web applications for data science and machine learning. Its intuitive API and focus on simplicity allow developers to quickly build and deploy powerful apps without the need for extensive front end user inteface web development knowledge.

This app leverages Streamlit to provide a user-friendly interface for interacting with the Groq Llama 3 models and managing your prompt engineering and fine-tuning workflows.

4. Putting It All Together: The Workflow

Here's a high-level overview of how the app works:

  1. User Input: You provide a task description or topic for test data generation.
  2. Prompt Generation (if applicable): The app uses Groq's Llama 3 to generate a prompt based on your input.
  3. Test Data Generation (if applicable): The app uses Groq's Llama 3 to generate conversation pairs for fine-tuning your AI models.
  4. Output and Download: The app displays the generated prompts or test data, and provides download options for convenient storage and reuse.

This integration of Groq's Llama 3, LangSmith, and Streamlit empowers you to harness the power of AI for your prompt engineering and fine-tuning tasks.

🙌 Contributing

I welcome contributions from the community! Here's how you can get involved:

  1. Fork the Repository: Click the "Fork" button at the top right of this page.

  2. Create a New Branch: Make your changes in a separate branch to keep things organized.

    git checkout -b feature/your-feature-name

  3. Commit Your Changes: Add clear and concise commit messages to explain your work.

    git commit -m "Add your descriptive commit message here"

  4. Push to Your Fork: Send your changes to your forked repository on GitHub.

    git push origin feature/your-feature-name

  5. Open a Pull Request: Submit a pull request to the main repository, describing your changes and their benefits.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 Learn More

Want to dive deeper into the technologies behind this project? Here are some helpful resources:

🚀 Let's Build the Future of AI Together!

We believe this project is a stepping stone towards a more accessible and powerful future for AI development. Join us on this exciting journey!

  • Star this Repository: Show your support and help others discover this project.
  • Share Your Creations: We'd love to see what you build using this app! Share your projects and ideas with the community.
  • Contribute and Collaborate: Let's work together to make this project even better!

Let's unlock the potential of AI together!

🚀 Get Started Today!

Ready to unleash the power of Groq's Llama 3 for your AI prompt engineering and fine-tuning tasks?

  1. Clone this repository:

    git clone https://github.com/your-username/your-repository-name.git

  2. Follow the Quickstart guide above to set up your environment and configure your API key.

  3. Start exploring the app and see what you can create!

🙋‍♀️ Need Help? Have Questions?

We're here to support you on your AI journey. Feel free to reach out if you encounter any issues or have questions about the app.

  • Open an Issue: Report bugs or suggest new features by opening an issue on the GitHub repository.
  • Join the Community: Connect with other users and developers in our community forum (link to be added soon).

🙏 Acknowledgements

  • Thank you to the awesome teams at Groq, Meta's LLama 3, Streamlit and Langchain!!!: I extend my profound gratitude to the amazing teams who have made these project possible:

  • Groq LPU's: The fastest inference on the planet.

  • Meta's Llama 3: The Brainpower Behind the Magic

  • Streamlit: For creating an intuitive and user-friendly framework for building web applications.

  • LangChains Langsmith: For developing the LangSmith tracing and observability framework.

Happy Prompt Engineering! Gregory Kennedy