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Additional Authors: Tolulope Olatunbosun, Phil Romero & Mike Mason, Los Alamos National Laboratory
Retrieval Augmented Generation (RAG) systems improve the response relevance of LLMs (Large Language Models) by limiting the context to a document corpus. RAG systems have seen broad deployment as document summarization engines and AI chatbots. However, deploying these systems often assumes a privileged and “cloudy” environment with multi-container orchestration (i.e. docker compose) and unfettered internet access to pull resources (e.g. software, data, and models) on-the-fly. As an alternative, we leveraged Charliecloud’s NVIDIA GPU support capabilities to deploy a RAG chatbot in an unprivileged HPC environment where resources are pre-staged. We demonstrate the deployment of AI Chatbots using Charliecloud on a variety of hardware and software versioning.
