Member-only story
Most RAG tutorials show you how to call an embedding API and do a similarity search. That is the easy 20%. The hard 80% is the architecture around it: how you chunk documents, how you prevent retrieved chunks from being redundant, how you expose this as a reliable HTTP API, and how you reason about the latency budget at each stage.
This article builds the complete stack from scratch in pure Python — no FastAPI or pgvector required to run the code, but the architecture maps exactly to what you would deploy with those tools. Every component is a drop-in replacement: swap the TF-IDF vectors for sentence-transformers, the SQLite store for pgvector, and the mock LLM for Claude.
Disclaimer: The opinions expressed in this article are my own and do not represent the views of Google. This content is based solely on publicly available information.
Architecture Overview #
Before writing any code, it helps to see how the five components fit together and where the critical design decisions live. The diagram below maps the full request path from document ingestion to answer generation.
A production RAG API has five components in a sequential pipeline: