Local RAG Knowledge Assistant

Built a Retrieval-Augmented Generation (RAG) workflow using self-hosted n8n that

ingests documents from Google Drive, extracts and chunks text with a Recursive

Character Text Splitter, generates embeddings locally using LM Studio (OpenAI-

compatible API), stores vectors in ChromaDB, and enables document-grounded

question answering through semantic vector search.