Introduction

Conversational AI is no longer a niche hobby; enterprises use it for customer support, lead qualification, and internal tooling. Rasa Open Source gives you a production‑ready stack that runs on‑premise, lets you keep data private, and offers full Python extensibility. In this article we’ll walk through a complete Rasa project from scratch, covering environment setup, NLU training, story‑driven dialogue management, custom actions, testing, and deployment. By the end you’ll have a working chatbot that can greet users, answer FAQs, and fetch dynamic data from an external API.

Actionable Insight – Start every Rasa project in its own virtual environment. It isolates dependencies and makes CI/CD pipelines deterministic.

Prerequisites

Requirement