Building a RAG Chatbot with React/Angular, Node.js, pgvector, and OpenAI

Most chatbots are lying to your users. Not maliciously — but when a user asks "why does useEffect run twice in React?" and the bot confidently gives a generic answer that has nothing to do with your course content, that’s a failure. 👉 The bot doesn’t know your product. It just sounds like it does. So I built a RAG (Retrieval-Augmented Generation) chatbot that answers using real data instead of guessing. 👉 Full Details: https://jsden.com/projects/rag-chatbot The Problem Basic chatbot: User → LLM → Response Issues: Hallucinations Generic answers No product awareness The Fix: RAG New flow: User → Retrieve → Context → LLM → Response Instead of guessing, the model answers using your data. Tech Stack Frontend: React / Angular Backend: Node.js (Express) DB: PostgreSQL + pgvector Embeddings: OpenAI LLM: GPT-4 How It Works 1. Chunking function chunkText(text, size = 500) { const chunks = []; for (let i = 0; i < text.length; i += size) { chunks.push(text.slice(i, i + size)); } return chunks