Duration: 2:14

0 views

0

This upload helps get started w/ Rasa framework fast.

i. Rasa is an open-source conversational AI framework that allows developers to build, deploy, and improve AI-powered chatbots and virtual assistants. The architecture of Rasa consists of several components that work together to enable natural language understanding, dialogue management, and response generation. Here's an overview of the architecture of Rasa:

1. **Rasa NLU (Natural Language Understanding)**:
- Rasa NLU is responsible for understanding and extracting meaning from user messages. It processes user input and extracts intents (the user's goal or intention) and entities (relevant pieces of information) from the text.
- Rasa NLU uses machine learning models (such as intent classifiers and entity extractors) to perform these tasks. Developers can train custom models using their own data or pre-trained models provided by Rasa.

2. **Rasa Core (Dialogue Management)**:
- Rasa Core is responsible for managing the dialogue flow and deciding how the assistant should respond to user messages based on the current conversation context.
- Rasa Core uses a machine learning-based approach called reinforcement learning to learn from user interactions and improve over time. It maintains a dialogue state tracker to keep track of the conversation history and context.
- Developers define the dialogue flow and behavior of the assistant using a set of rules or a machine learning-based policy model.

3. **Rasa Action Server**:
- The Rasa Action Server is responsible for executing actions or tasks in response to user messages. Actions can include sending messages, querying external APIs, accessing databases, or performing any other custom logic.
- Developers define custom actions and their corresponding logic in the action server. Rasa Core communicates with the action server to trigger and execute actions during the conversation.

4. **Training Pipeline**:
- The training pipeline is a series of components and algorithms used to train the machine learning models in Rasa NLU and Rasa Core.
- Developers can configure the training pipeline to include different components such as tokenizers, featurizers, intent classifiers, entity extractors, and policy models. They can also customize the pipeline based on their specific requirements and data.

5. **Integration Channels**:
- Rasa supports integration with various messaging platforms and channels, allowing developers to deploy their assistants on websites, mobile apps, social media platforms, and other communication channels.
- Rasa provides built-in integrations for popular platforms such as Slack, Facebook Messenger, WhatsApp, and more. Developers can also build custom integrations using Rasa's API and SDKs.

Overall, the architecture of Rasa is designed to be flexible, modular, and customizable, allowing developers to build sophisticated conversational AI applications tailored to their specific use cases and requirements.