An analysis of the final solution

Finding an immediate fix for the problem is very satisfying no matter how many times this process occurs.

An analysis of the final solution

Lex Amazon They try to have a low learning curve and, at the same time, a strong expressive power. For diverse reasons, at Tryolabs we have focused on Api. Our impression was that LUIS and Watson propose a framework a little more complex and eventually more powerful than what we needed.

Regarding the Amazon Lex, we did not get access to the Limited Preview at the time of writing this post.

We are not going to exhaustively compare Api. When you model a chatbot, you understand immediately that one of the hardest parts, if not the hardest of all, is the modeling of the conversation flow.

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It is this that defines, basically, the behavior of the chatbot. Intents creates links between what a user says and what action should be taken by the bot. Contexts are string values, useful for differentiating requests which might have different meaning depending on previous requests.

At the same time, the matching of an intent can create and delete contexts. When the user has indicated the pizza type, size, etc.


This mechanism of intents and contexts allows to create state machines that model large and complex flows. However, you cannot model that an intent can be matched only if a certain context is not present.

This is a current limitation of Api. Entities You can define your own entities and use those proposed by the platform. Slot-Filling Capabilities This is a key point of Api.

Slot-filling allows you to indicate, for a given intent, what are the fields that play a role and if they are mandatory or not. This is great since you do not have to deal with missing information since it is done on the Api.

In the example above, Api. Server Size Coding Of course, to define the full logic of your chatbot you will need to add some custom coding on the server side. A very useful feature is that the result sent to Api. You can decide which intents are going to call the webhook and if the webhook is going to be called during the slot-filling processing.

This combination is a powerful and flexible tool to customize your chatbot behavior.


Slot-filling is an integrated feature.This rule modifies the testing procedures approved for analysis and sampling under the Clean Water Act. The changes adopted in this final rule fall into the following categories: New and revised EPA methods (including new and/or revised methods published by voluntary consensus standard bodies.

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An analysis of the final solution

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Modeling and Simulation

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The chatbot industry is still in its early days, but growing very fast. What at first may have looked like a fad or a marketing strategy, is becoming a real need.

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