LLM-Blocks :building_construction:

LLM-Blocks is a Python library that provides a simple interface for creating and managing Language Learning Model (LLM) chains. It is designed to make it easy to interact with OpenAI's GPT-3.5-turbo model, allowing you to create completions and generate responses in a chat-like format.

:book: Table of Contents

• Why Use LLM-Blocks
• Repo Structure
• Installation
• Usage
• Contributing
• License

:question: Why Use LLM-Blocks

LLM-Blocks is designed to simplify the process of creating and managing LLM chains. It provides a high-level interface that abstracts away the complexities of interacting with the OpenAI API, allowing you to focus on creating engaging and interactive chat experiences. Whether you're building a chatbot, a virtual assistant, or any other application that requires conversational AI, LLM-Blocks can help you get there faster.

:deciduous_tree: Repo Structure

.
├── .gitignore
├── llm_blocks
│   ├── blocks.py
│   └── __init__.py
├── requirements.txt
├── setup.py
├── test.ipynb
└── turbo_docs.toml

:gear: Installation

LLM-Blocks can be installed via pip:

pip install llm-blocks

:rocket: Usage

Here's an example of how to use the StreamBlock and BatchBlock classes in LLM-Blocks:

from llm_blocks import blocks

template = "Hello, {name}!"

# Create a StreamBlock
block = blocks.StreamBlock(template=template)
response_generator = block(name="World")
block.display(response_generator)

# Create a BatchBlock
# Example of how to use the non-defualt model
block = blocks.BatchBlock(template=template, model_name="gpt-4")
response = block(name="World")
block.display(response)

:handshake: Contributing

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