Engineer Builds Personal RAG System to Unify Scattered Digital Knowledge

A software engineer has begun developing a Retrieval-Augmented Generation (RAG) system from first principles, aiming to create a unified, searchable knowledge layer across tools like Notion, Slack, Gmail, Logseq, and Markdown files. The project was inspired by frustration with fragmented personal knowledge and tools like NotebookLM that require separate, manually managed workspaces. RAG systems work by retrieving relevant context from a personal datastore and feeding it alongside a user query to a large language model, enabling more accurate and personalized responses. The proposed architecture breaks down into core components including source connectors, an ingestion pipeline, data processing, and knowledge storage optimized for fast retrieval. The engineer is documenting the build process as a multi-part series, with the goal of creating what he describes as a personal Google Search across one's entire digital life.