Developer builds RAG system from scratch in Python without frameworks

A developer has published a detailed walkthrough on building a Retrieval-Augmented Generation (RAG) system in Python without relying on high-level frameworks like LangChain. RAG allows large language models to answer questions about private or current documents by retrieving relevant text snippets before generating a response. The tutorial covers the full pipeline, including chunking documents into semantically meaningful pieces, converting text into vector embeddings using a sentence transformer model, and performing similarity-based retrieval. The guide also addresses hybrid search combining vector and keyword methods, re-ranking results, and attaching source citations to answers. The author emphasizes that chunking strategy is the most critical decision in a RAG pipeline, recommending 100–250 word chunks with 10–20% overlap and preserved metadata.