RASA: Difference between revisions
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Open source machine learning tools for developers to build, improve, and deploy text-and voice-based chatbots and assistants. (cit. RASA github home). | "''Open source machine learning tools for developers to build, improve, and deploy text-and voice-based chatbots and assistants''". (cit. [https://github.com/RasaHQ/ RASA github home page]). | ||
RASA is probably the most important tool to develop "task-oriented" conversational | RASA is probably the most important open-source tool to develop "task-oriented" conversational applications. Despite the RASA official statement, the original project has not conceived to manage voice interactions, but just [[chatbots]] with some support to GUI/buttons. | ||
RASA architecture consist in two main components: | |||
RASA | * ''RASA NLU'' is based upon [https://rasa.com/blog/introducing-dual-intent-and-entity-transformer-diet-state-of-the-art-performance-on-a-lightweight-architecture/ DIET algorithm], a a refined state of the art intent/entities "classifier | ||
* ''RASA Core'' (now called ''RASA Dialog Manager''), based on [https://rasa.com/blog/unpacking-the-ted-policy-in-rasa-open-source/ TED policy], a machine learning algorithm, to manage multi-turn dialogs, escaping the traditional state-machine based way, but instead allowing conversation developers to insert "''stories",'' set of of intents-actions sequences (conversation examples). With [https://rasa.com/blog/were-a-step-closer-to-getting-rid-of-intents/ end-to-end training], developers program the conversational agent [[dialog manager]] giving end-to-end turn-taking examples (the stories). | |||
RASA owned in few years now, a huge open community of developers and researchers. It's probably the biggest open source project to develop on-premise "production-ready" complex dialog systems. All the development ecosystem is around the [[Python]] programming language. | |||
== References == | == References == | ||
* Home page: https://rasa.com/ | |||
* Github: https://github.com/RasaHQ/ | |||
* Community forum: https://forum.rasa.com/ | |||
Latest revision as of 08:38, 10 December 2021
"Open source machine learning tools for developers to build, improve, and deploy text-and voice-based chatbots and assistants". (cit. RASA github home page).
RASA is probably the most important open-source tool to develop "task-oriented" conversational applications. Despite the RASA official statement, the original project has not conceived to manage voice interactions, but just chatbots with some support to GUI/buttons.
RASA architecture consist in two main components:
- RASA NLU is based upon DIET algorithm, a a refined state of the art intent/entities "classifier
- RASA Core (now called RASA Dialog Manager), based on TED policy, a machine learning algorithm, to manage multi-turn dialogs, escaping the traditional state-machine based way, but instead allowing conversation developers to insert "stories", set of of intents-actions sequences (conversation examples). With end-to-end training, developers program the conversational agent dialog manager giving end-to-end turn-taking examples (the stories).
RASA owned in few years now, a huge open community of developers and researchers. It's probably the biggest open source project to develop on-premise "production-ready" complex dialog systems. All the development ecosystem is around the Python programming language.
References[edit | edit source]
- Home page: https://rasa.com/
- Github: https://github.com/RasaHQ/
- Community forum: https://forum.rasa.com/