Build a RAG System in 50 Lines of Python

Retrieval-Augmented Generation (RAG) sounds complex, but the core idea is simple: give your LLM access to your own documents. Here's how to build one in 50 lines. What is RAG? Instead of relying solely on the LLM's training data, RAG retrieves relevant documents first, then feeds them as context to the LLM. This means your AI can answer questions about YOUR data. The Setup pip install openai chromadb sentence-transformers The Code import chromadb from sentence_transformers import SentenceTransformer import openai # 1. Initialize embedding model and vector DB embedder = SentenceTransformer ( " all-MiniLM-L6-v2 " ) client = chromadb . Client () collection = client . create_collection ( " docs " ) # 2. Add your documents docs = [ " Python 3.12 introduced type parameter syntax. " , " FastAPI is built on Starlette and Pydantic. " , " Docker containers share the host OS kernel. " , " PostgreSQL supports JSONB for document storage. " , " Redis can be used as a message broker with Pub/Sub. " ,