For example, I want to develop skills such as weather inquiry, music search, restaurant search, voice command etc. For each skills, they have their own training data. Currently, in Rasa platform all examples are designed for one skill, i.e. multiple intents in a skill/task. To develop multiple-skill robot, how can I create and use models for different skills? Or, should I just mixed all training data together and treat all skills conceptually as one super skill? I am afraid the quality may not be good if all data are mixed together. Any suggestions on how to go about this? Thanks for the community.

yes, are you right @lingvisa .i also have same question.

I have been investigating the same problem. The way I see the problem is that, as designed, Rasa’s entity extraction is non-contextual. It tries to recognize entities, and then it classifies an utterance to an intent. The drawback is that EVERYTHING about human language is contextual. Think about all those times you’ve been confused about something someone said in a conversation until they provided the context. “Oh, you were referring back to that movie we watched last week. Now I understand.” Another user in a different forum on this site has done a POC of using micro-services. ( Providing conversation context to the NLU using microservices ) Conceptually, the system would be set up to first classify the utterance to an intent, and then forward it to a Rasa server instance that was trained on data only for that intent. Essentially, the system would be putting the utterance in context before analyzing it. The micro-service idea looks achievable, but a maintenance nightmare. I’d like to find an easier way to achieve the same goal.

Hi, Shotgun167, thank you for you sharing your experience. My question is more about a high-level issue, i.e. what’s the strategies to develop multiple-skill robot in Rasa platform? Some thoughts or advices would be really appreciated.

[image] lingvisa: what’s the strategies to develop multiple-skill robot in Rasa platform? Yeah, that’s kind of what I was trying to get at, but didn’t state very well. From my limited experience Rasa, in it’s current state, is not designed well for a multi-skill robot. An intent has an implied/assumed context and the context is encapsulated in the model. Rasa glosses over it all by lumping all intents into a single model, and therefore: The strategy question then simplifies to one of how do we route a question/skill/command/intent/etc to a model that encapsulates a context that can interpret it properly. I’m suggesting that Rasa needs a meta interpreter and a router to choose one of several models to do the understanding. Is there a way to accomplish this without a modification to Rasa, or running a multitude of Rasa instances?

I think there is some slight confusion here. You can absolutely use rasa_nlu for multiple intents (What you’re calling skills). Our team has had success with a couple separate intents that represent fundamentally different “skills”. As long as you supply the model with a healthy amount of training data for each intent, you’ll be fine. For a given utterance you can check what the model classifies the intent as in the “intent” and “intent_ranking” response when you test it.

Shotgun, I got your point. In addition to NLU, Core part also needs to accomodate such a request. The latest announcement on the merging NLU and Core may have an impact on this, but it would be great if Rasa team can share some thoughts on this.

The impression I got from Rasa at the NYC meetup was that the NLU+Core merger is purely administrative in nature. The actual software will go on as before.

Hi, @daxaxelrod , to me an intent is very different from a skill. Typically, a skill contains multiple intents, such as “thank_you”, “greet”, “faq”. If we build all very different skills (‘intents’ in your term) into one model, multiple issues probably arise. For instance, quality issue, and secondly, maintenance issue.

Basically what you need is a BoB (bot of bots), that will quickly figure out what the user wants and transfer to the appropriate bot. You can’t do this in Rasa natively, you’d have to hack it by creating N+1 bots (where N is the number of skills and 1 is the general bot) and then write custom code to transfer the tracker (conversation state) or just the slots (information the bot has collected) to the more specific bot.

What's the best way to develop Alexa-like multiple skills in Rasa platform?

Rasa Open Source

ridasaleem0 (Rida Saleem) April 22, 2021, 7:51pm 19

Hey did you find answers to its implementation?

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Multi-skilled Rasa bot Rasa Open Source	5	1190	September 28, 2020
Multiple developers Getting Started with Rasa	0	235	May 16, 2019
Multiple intent detection with slot tagging for each intent Rasa Open Source	5	1924	April 17, 2020
Providing conversation context to the NLU using microservices Tutorials, Resources & Videos	28	11561	February 21, 2022
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What's the best way to develop Alexa-like multiple skills in Rasa platform?

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