This article focuses on the best RAG (Retrieval-Augmented Generation) systems for local deployment on Windows, with Ollama support. Users with limited GPU memory but strong privacy concerns want to deploy efficient RAG solutions on their own computers. The author tested MaxKB, Openwebui, and Cherry Studio with embedding models like gte-qwen2-7b-instruct and Qwen3-Embedding-4B, but achieved mediocre results. The article invites the community to share comparative experiences to help users optimize their local AI deployments. This offers practical value for readers interested in local LLM applications, privacy protection, and cutting-edge technology evolution, especially those exploring AI solutions in resource-constrained environments.
