Building a RAG system from scratch

The author shares their experience in solving the problem of searching through a large archive of personal text notes accumulated over many years and stored in an unstructured format. Instead of manually organizing files, the article proposes using modern technologies to automate the search process. It explores the development of a Retrieval-Augmented Generation (RAG) system, which allows for the efficient indexing of local documents and retrieving information using language models. The author details the technical implementation, from selecting tools to architectural decisions that transform a chaotic file storage into an intelligent knowledge base. This approach demonstrates how machine learning methods can be applied to everyday personal information management tasks, making searches through old archives fast and context-aware. The article is useful for developers interested in the practical aspects of implementing LLMs and building search systems based on vector data representations.