50-Line RAG Pipeline: ChromaDB + Embeddings + Anthropic
Build a working RAG system in Python using ChromaDB for storage, SentenceTransformers for semantic search embeddings, and Anthropic for generation—answers questions from unseen docs via retrieval + prompting.
Grasp RAG by Building and Running It
RAG (Retrieval-Augmented Generation) becomes intuitive not from diagrams but from executing code that queries unseen documents—like a paper the model never trained on—and gets accurate answers. Skip CRUD or Hello World; this 50-line pipeline is your essential first Python AI project for day-one production relevance. It demonstrates semantic search retrieving relevant chunks, then feeding them into an LLM via a tuned system prompt for grounded responses.
Core Mechanics: Semantic Search + Prompting
RAG relies on two elements: (1) semantic search via embeddings (using SentenceTransformers) stored in ChromaDB vector database for fast retrieval of contextually similar document chunks; (2) an effective system prompt that injects retrieved content into the LLM (Anthropic) to generate answers without hallucination. Provide your documents as input, embed them once, query semantically, and output synthesized responses—bypassing the LLM's static training data.