Build a Local RAG Pipeline With Ollama + pgvector — No API Keys, No Cloud

Retrieval-Augmented Generation is one of those ideas that sounds complex until you actually build it. At its core: shove documents into a vector database, embed a user query the same way, find the closest matches, and feed them to an LLM as context. That's it. The problem? Most tutorials wire this to OpenAI embeddings and Pinecone, meaning you pay per token and your data leaves your machine. Let's fix that. This guide builds a fully local RAG pipeline: Ollama for the LLM and embeddings PostgreSQL + pgvector as the vector store Python to glue it together 100% offline. No API keys. No cloud. What You Need Docker (for Postgres + pgvector) Ollama installed locally Python 3.11+ ~4 GB free RAM Pull the models first: ollama pull nomic-embed-text # 274MB embedding model ollama pull llama3.2 # ~2GB, fast on CPU Step 1: Spin Up pgvector pgvector adds a vector column type and similarity search operators to Postgres: docker run -d \ --name pgvector \ -e POSTGRES_PASSWORD = localrag \ -e POSTGRES_D