llmmaster 0.1.1

A unified interface for interacting with multiple LLMs.

LLM Master

LLM Master is a Python library that provides a unified interface for interacting with multiple Large Language Models (LLMs) from different providers. It supports concurrent execution of multiple LLMs and easy management of API keys and model configurations.

Supported LLM Providers

Text-to-Text Models

• Anthropic (Claude)
• Google (Gemini)
• Groq
• OpenAI (GPT)
• Perplexity

Text-to-Image Models

• OpenAI (Dall-E)
• Stable Diffusion (comming soon)

Text-to-Speech models and more models will be covered!

Features

• Concurrent execution of multiple LLMs
• Easy configuration of API keys through environment variables
• Support for various models from each provider
• Customizable generation parameters
  • Required parameters: provider and prompt
  • Optional parameters: model and particular parameters for different model
• Thread-based execution for improved performance

Installation

To use LLM Master, you need to install the library. You can do this using pip:

pip install llmmaster

Relevant packages will also be installed.

Usage

1. Set up your API keys as environment variables:

For Mac/Linux,

export ANTHROPIC_API_KEY="your_anthropic_key"
export GEMINI_API_KEY="your_gemini_key"
export GROQ_API_KEY="your_groq_key"
export OPENAI_API_KEY="your_openai_key"
export PERPLEXITY_API_KEY="your_perplexity_key"

For Windows,

SET ANTHROPIC_API_KEY=your_anthropic_key
SET GEMINI_API_KEY=your_gemini_key
SET GROQ_API_KEY=your_groq_key
SET OPENAI_API_KEY=your_openai_key
SET PERPLEXITY_API_KEY=your_perplexity_key

2. Use cases

• Using single LLM

from llmmaster import LLMMaster

# Create an instance of LLMMaster
llmmaster = LLMMaster()

# Configure LLM instance
llmmaster.summon({
    "openai_instance": llmmaster.pack_parameters(
        provider="openai",
        model="gpt-4o",
        prompt="Hello, what's the weather like today?",
        max_tokens=100,
        temperature=0.7
    )
})

# Run LLM
llmmaster.run()

# Get results
results = llmmaster.results
print(results["openai_instance"])

# Clear instances
llmmaster.dismiss()

• Using multiple LLMs simultaneously

llmmaster.summon({
    "openai_instance": llmmaster.pack_parameters(
        provider="openai",
        prompt="Summarize the main ideas of quantum computing."
    ),
    "anthropic_instance": llmmaster.pack_parameters(
        provider="anthropic",
        prompt="Explain the concept of artificial general intelligence."
    )
})

llmmaster.run()

results = llmmaster.results
print(results["openai_instance"])
print(results["anthropic_instance"])

Notes

• Please comply with the terms of service for each provider's API.
• Securely manage your API keys and be careful not to commit them to public repositories.
• There is a limit (32) to the number of LLM instances that can be created at once.
• Input parameters are not strictly checked by the rules defined by each provider. You may face an error due to some wrong paramer or combination of parameters.

Customization

• You can easily adjust default models by updating the dictionary in config.py and creating a new thread class for the provider.
• You can also run an individual provider LLM without using LLMMaster but each defined class.

Contributing

Contributions to LLM Master are welcome! Please feel free to submit a Pull Request and bug reports and feature requests through GitHub Issues.

License

This project is licensed under the MIT License.

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This project is under development. New features and support for additional providers may be added. Please check the repository for the latest information.