Rasa

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.