Build a local RAG pipeline in 30 lines of Python (no Docker, no API keys)

Most RAG tutorials start with "spin up Docker" and "get your API key." This one starts with pip install . The problem Retrieval-Augmented Generation (RAG) is the standard way to ground LLM answers in your own data. But the typical setup looks like this: Spin up a Docker container for your vector database Sign up for an API and grab your keys Configure connection strings, authentication, ports Write 100+ lines of glue code That is a lot of infrastructure for what is conceptually simple: embed text, store vectors, search by similarity. What if you could skip all of that? The 30-line local RAG pipeline Here is a complete RAG pipeline. No Docker. No API keys. No cloud. Just Python. pip install velesdb sentence-transformers from sentence_transformers import SentenceTransformer from velesdb import Database # Load embedding model (runs locally, no API key) model = SentenceTransformer ( " all-MiniLM-L6-v2 " ) # Open a local database (just a folder on disk) db = Database ( " ./rag_data " ) coll