MiniChain  by srush

A tiny library for coding with large language models, MiniChain offers a lightweight alternative to complex prompt chaining frameworks. It's designed for developers and researchers who need to quickly prototype and integrate LLM capabilities into their applications, providing a more digestible approach to prompt engineering and LLM orchestration.

How It Works

MiniChain utilizes a functional, graph-based approach to LLM interactions. Prompts are defined as Python functions, which can be chained together to form complex workflows. This design allows for automatic debugging, error handling, and visualization of the prompt execution graph. It supports Jinja templating for prompt separation and offers built-in visualization via Gradio.

Quick Start & Requirements

• Install: pip install minichain
• Prerequisites: OPENAI_API_KEY environment variable.
• Demo: MiniChain Zoo

Highlighted Details

• Supports various LLM backends including OpenAI, Hugging Face, and Google Search.
• Implements advanced LLM patterns like Retrieval-Augmented QA, Chain-of-Thought, and Interleaved Code.
• Features automatic output parsing to typed Python objects (e.g., JSON).
• Includes a visualization system for debugging and understanding prompt chains.

Maintenance & Community

The project appears to be actively maintained by its creator, srush. Community interaction channels are not explicitly mentioned in the README.

Licensing & Compatibility

The README does not explicitly state a license. Given the nature of open-source projects, users should verify the license before commercial use or integration into closed-source products.

Limitations & Caveats

MiniChain does not provide built-in agents, tools, or explicit memory management classes, recommending external libraries or custom implementations for these features. Document and embedding management is also left to the user, suggesting libraries like Hugging Face Datasets.