Rasa

• Open Source Conversational AI

Overview Rasa is an open-source framework for building contextual assistants and chatbots. Unlike visual flow builders (like Botpress), Rasa is "code-first" and uses machine learning to manage dialogue, allowing for more flexible, non-linear conversations.

Architecture

1. Rasa NLU Turning text into structure.

• Intent Classification: "I want pizza" -> intent: order_food
• Entity Extraction: "large pepperoni" -> size: large, topping: pepperoni

2. Rasa Core (Dialogue Management) Deciding what to do next. Rather than if/else flowcharts, Rasa uses "Stories" (training data) to teach a machine learning model how to respond. It can handle interruptions and context switching naturally.

Files

• nlu.yml: Examples of intents.
• stories.yml: Example conversation flows.
• domain.yml: List of all intents, entities, slots, and responses.

Action Server Rasa communicates with an external "Action Server" (usually Python) to execute custom code (API calls, DB lookups).

class ActionCheckWeather(Action):
    def run(self, dispatcher, tracker, domain):
        city = tracker.get_slot("city")
        temp = get_weather(city)
        dispatcher.utter_message(text=f"It is {temp} in {city}")
        return []

Privacy Rasa is self-hosted (no data leaves your server), making it popular in healthcare and banking.