A single function Jev-like wrapper for LLMs, including vision models

A new technical proposal introduces a lightweight, single-function wrapper designed to simplify interactions with Large Language Models (LLMs) and vision-capable models. Inspired by the Jev architecture, this approach aims to streamline the integration process for developers by providing a unified interface for handling multimodal inputs and outputs. By abstracting the complexities of various model APIs into a single, cohesive function, the wrapper seeks to reduce boilerplate code and improve maintainability in AI-driven applications. The project highlights the growing trend toward simplifying LLM orchestration, allowing developers to switch between different models—including those with vision capabilities—with minimal configuration changes. This tool is particularly useful for those looking to implement rapid prototyping or build robust pipelines that require consistent interaction patterns across diverse AI architectures. The implementation details and usage examples are available for those interested in adopting this simplified integration pattern for their own machine learning projects.