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Groq AI

Version

maintained - yes contributions - welcome

Groq AI

Overview

The Groq AI Toolkit makes it easy to use Groq's breakthrough LPU Inference Engine for creating chatbots and generating text with near-real-time responses (800 tokens/sec). It's designed for everyone, from beginners to experienced developers, allowing quick addition of AI features to projects with simple commands. While it offers simplicity and lightweight integration, it doesn't compromise on power; experienced developers can access the full suite of advanced options available via the API, ensuring robust customization and control. This toolkit is perfect for those looking to efficiently tap into advanced AI without getting bogged down in technical details, yet it still provides the depth needed for complex project requirements.

Key Features

  • Conversational AI: Create interactive, real-time chat experiences (chatbots) or AI assistants.
  • Text Generation: Produce coherent and contextually relevant text and answers from simple prompts.
  • Ultra-Fast Performance: Achieve near-real-time responses with an impressive speed of 800 tokens per second.
  • Highly Customizable: Tailor settings like streaming, JSON outputs, system prompts and more to suit your specific requirements.
  • Lightweight Integration: Efficiently designed with minimal dependencies, requiring only the requests package for core functionality.

Prerequisites

  • Python 3.x
  • An API key from Groq AI

Dependencies

The following Python packages are required:

  • requests: For making HTTP requests to Groq's API.

The following Python packages are optional:

  • python-dotenv: For managing API keys and other environment variables.

Installation

To use the Groq AI Toolkit, clone the repository to your local machine and install the required Python packages.

Clone the repository:

git clone https://github.com/RMNCLDYO/groq-ai-toolkit.git

Navigate to the repositories folder:

cd groq-ai-toolkit

Install the required dependencies:

pip install -r requirements.txt

Configuration

  1. Obtain an API key from Groq AI.

  2. You have three options for managing your API key:

    Click here to view the API key configuration options

    • Setting it as an environment variable on your device (recommended for everyday use)

      • Navigate to your terminal.
      • Add your API key like so: 
        export GROQ_API_KEY=your_api_key

      This method allows the API key to be loaded automatically when using the wrapper or CLI.

    • Using an .env file (recommended for development):

      • Install python-dotenv if you haven't already: pip install python-dotenv.
      • Create a .env file in the project's root directory.
      • Add your API key to the .env file like so: 
        GROQ_API_KEY=your_api_key

      This method allows the API key to be loaded automatically when using the wrapper or CLI, assuming you have python-dotenv installed and set up correctly.

    • Direct Input:

      • If you prefer not to use a .env file, you can directly pass your API key as an argument to the CLI or the wrapper functions.

        CLI

        --api_key "your_api_key"

        Wrapper

        api_key="your_api_key"

      This method requires manually inputting your API key each time you initiate an API call, ensuring flexibility for different deployment environments.

Usage

The Groq AI Toolkit can be used in two different modes: Chat and Text. Each mode is designed for specific types of interactions with Groq's open models.

Chat Mode

Chat mode is intended for chatting with an AI model (similar to a chatbot) or building conversational applications.

Example Usage

CLI

python cli.py --chat

Wrapper

from groq import Chat

Chat().run()

An executable version of this example can be found here. (You must move this file to the root folder before running the program.)

Text Mode

Text mode is suitable for generating text content based on a provided prompt.

Example Usage

CLI

python cli.py --text --prompt "Explain the importance of low latency LLMs."

Wrapper

from groq import Text

Text().run(prompt="Explain the importance of low latency LLMs.")

An executable version of this example can be found here. (You must move this file to the root folder before running the program.)

Advanced Configuration

CLI and Wrapper Options

Description CLI Flags CLI Usage Wrapper Usage
Enable chat mode -c, --chat --chat See mode usage above.
Enable text mode -t, --text --text See mode usage above.
User prompt -p, --prompt --prompt "Explain the importance of low latency LLMs." prompt="Explain the importance of low latency LLMs."
API key for authentication -a, --api_key --api_key "your_api_key" api_key="your_api_key"
Model name -m, --model --model "llama-3.1-8b-instant" model="llama-3.1-8b-instant"
System prompt (instructions) -sp, --system_prompt --system_prompt "You are a helpful assistant." system_prompt="You are a helpful assistant."
Enable streaming mode -st, --stream --stream stream=True
Enable json mode -js, --json --json json=True
Sampling temperature -tm, --temperature --temperature 0.7 temperature=0.7
Maximum number of tokens to generate -mt, --max_tokens --max_tokens 1024 max_tokens=1024
Nucleus sampling threshold -tp, --top_p --top_p 0.9 top_p=0.9
Seed used for sampling -sd, -seed --seed 123456789 seed=123456789
Stop sequence for completion -ss, --stop --stop "\n" stop="\n"

To exit the program at any time, you can type exit or quit. This command works similarly whether you're interacting with the program via the CLI or through the Python wrapper ensuring that you can easily and safely conclude your work with the Groq AI Toolkit without having to resort to interrupt signals or forcibly closing the terminal or command prompt.

Available Models

Model Max Tokens
llama-3.1-70b-versatile (Preview) 131072
llama-3.1-8b-instant (Preview) 131072
llava-v1.5-7b-4096-preview 4096
llama-guard-3-8b 8192
llama3-70b-8192 8192
llama3-8b-8192 8192
llama2-70b-4096 4096
mixtral-8x7b-32768 32768
gemma-7b-it 8192
gemma2-9b-it 8192

Note

While in Preview, Groq is limiting 3.1 models to max tokens of 8k.

Contributing

Contributions are welcome!

Please refer to CONTRIBUTING.md for detailed guidelines on how to contribute to this project.

Reporting Issues

Encountered a bug? We'd love to hear about it. Please follow these steps to report any issues:

  1. Check if the issue has already been reported.
  2. Use the Bug Report template to create a detailed report.
  3. Submit the report here.

Your report will help us make the project better for everyone.

Feature Requests

Got an idea for a new feature? Feel free to suggest it. Here's how:

  1. Check if the feature has already been suggested or implemented.
  2. Use the Feature Request template to create a detailed request.
  3. Submit the request here.

Your suggestions for improvements are always welcome.

Versioning and Changelog

Stay up-to-date with the latest changes and improvements in each version:

  • CHANGELOG.md provides detailed descriptions of each release.

Security

Your security is important to us. If you discover a security vulnerability, please follow our responsible disclosure guidelines found in SECURITY.md. Please refrain from disclosing any vulnerabilities publicly until said vulnerability has been reported and addressed.

License

Licensed under the MIT License. See LICENSE for details.