Implementing RAG from Scratch with Python, Qdrant, and Docling (techlife.blog)

This post walks through implementing Retrieval-Augmented Generation (RAG) from scratch using Python, Docling for smart chunking, sentence-transformers for embeddings, and Qdrant as the vector store. It breaks RAG into concrete steps: split documents into “smallest meaningful pieces” (not just by length or whitespace) using a chunker that preserves metadata; encode chunks with an embedding model in batches (example uses sentence-transformers/all-MiniLM-L6-v2, batch_size=32, convert_to_numpy=True) and attach vectors to each chunk; persist these vectors to a vector DB (Qdrant shown, but OpenSearch/Chroma/pgvector also viable); and at query time, embed the incoming text and retrieve nearest vectors by cosine similarity. Code snippets show chunk creation, batch embedding, and query-to-vector search, including Qdrant’s scoring interpretation (relevance approaches 1). The guide is significant because it demystifies RAG with practical, reproducible patterns and concrete heuristics: use 256–512 token chunks with 50–100 token overlap to balance context and noise, store embeddings as lists in chunk metadata, and prefer multilingual embedding models (paraphrase-multilingual-MiniLM-L12-v2 or LaBSE) for non-English content. It highlights common next steps for production systems—hybrid BM25+semantic search, reranking, and alternative chunking strategies—making it a useful blueprint for engineers building semantically-aware retrieval pipelines.