Building a Perplexity Clone for Local LLMs in 50 Lines of Python

Your local LLM is smart but blind — it can't see the internet. Here's how to give it eyes, a filter, and a citation engine. This is a hands-on tutorial. We'll install a library, run a real query, break down every stage of what happens inside, and look at the actual output your LLM receives. By the end, you'll have a working pipeline that turns any local model (Ollama, LM Studio, anything with a text input) into something that searches the web, reads pages, ranks the results, and generates a structured prompt with inline citations — like a self-hosted Perplexity. Background: If you want to understand the architecture this is based on, I wrote a deep dive into how Perplexity actually works — the five-stage RAG pipeline, hybrid retrieval on Vespa.ai, Cerebras-accelerated inference, the citation integrity problems. This tutorial is the practical counterpart. Repo: github.com/KazKozDev/production_rag_pipeline What We're Building A pipeline that does this: Your question ↓ Search (Bing + Duck