I propose an open plugin architecture that allows language models to access external data and capabilities through plugins. Language models today are limited to their training data, but real-world AI requires access to vast, disparate data sources. An open plugin architecture addresses this by enabling plugins for search and more. The architecture defines an interface for creating and installing plugins. Developers can then create plugins accessing proprietary data, open data, and online services. The plugins run externally but programmatically interact with the language model, feeding data and retrieving responses. This idea came from the need to extend language models beyond their training data. The open architecture means the capabilities of models can grow over time through the development of new plugins. This has the potential to make language models much more useful and scalable.

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