Build a Go RAG feeder that parses text, generates OpenAI embeddings, and stores them in Postgres with pgvector for Retrieval-Augmented Generation (RAG). Retrieval-Augmented Generation, RAG, solves this without retraining or fine-tuning anything: we store our own data as vectors, look up the entries closest to a question, and hand only those entries to the LLM as context. Here we build the feeder application: a small Go program that reads a set of fairy tales from a Markdown file, generates embeddings for each one using the OpenAI API, and stores them in Postgres with the pgvector extension.