Description
Wiggle provides a flexible and modular library for chaining multiple Language Learning Models (LLMs), integrating context from various sources like vector databases, and efficiently processing large or complex data by partitioning tasks across nodes and integrating results. The framework is designed to support both large models (e.g., GPT-4) and smaller models (e.g., LLaMA 3.1), ensuring scalability, modularity, and efficiency.
Wiggle tries to be a good Go citizen. It is a library more than a framework despite being called a framework. It has batteries but does not require they are used. The core of Wiggle is a set of defined interfaces. Entire applications can be written by simple using the interfaces to define your Node structure. However, most all of the Node and supporting types have implementations available in the nlib directory. Depending on the task being worked on a mix of predefined structures and domain specific ones generally works best.
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