llm-blocks 0.3.5

Simple interface for creating and managing LLM chains

LLM Blocks

LLM Blocks is a Python library that provides a flexible and easy-to-use interface for interacting with OpenAI's GPT models. It provides a set of classes and methods to handle different types of interactions with the model, such as chat, template, and streamed responses.

📚 Table of Contents

• Why Use LLM Blocks
• Repo Structure
• Installation
• Usage
• Testing
• Contributing

🚀 Why Use LLM Blocks

LLM Blocks simplifies the process of interacting with OpenAI's GPT models. It provides a set of classes and methods that abstract away the complexity of the underlying API calls, allowing you to focus on what matters most - building your application. Whether you're building a chatbot, a code generator, or any other application that leverages AI, LLM Blocks can help you get there faster.

📂 Repo Structure

llm_blocks
├── blocks.py
├── block_factory.py
├── __init__.py
├── requirements.dev.txt
tests
└── test_blocks.py

💻 Installation

To install LLM Blocks, you can use pip:

pip install llm_blocks

🎯 Usage

Here's a simple example of how to use LLM Blocks:

from llm_blocks import block_factory

# Create a block
block = block_factory.get('block')

# Execute the block with some content
response = block.execute("Hello, world!")
# or execute like a function
response = block("Hello, world!")

# Print the response
print(response)

🧪 Testing

To run the tests, navigate to the root directory of the project and run:

python -m unittest discover tests

🤝 Contributing

Contributions are welcome! Please read our contributing guidelines to get started.

📝 License

This project is licensed under the terms of the MIT license. See the LICENSE file for details.

📧 Contact

If you have any questions, feel free to reach out to us at [e-mail].