The basic elements involved in designing the flow of interaction are utterances, intents, and slots. Its deep learning functionalities is what enables chatbots to identify the intent behind a particular question, understand the context, and give back an appropriate response. This is passed onto Lex which finally delivers it to the user on the appĪmazon Lex is a conversational interface framework that’s used to design a chatbot’s understanding of natural questions asked and ability to carry forward a conversation.The final response is again translated back to the original language by AWS Translate.This is passed onto AWS Lambda which queries the PostgreSQL database to find the right answer.Lex understands the user query and identifies the intent or the data that the user wants.AWS Translate converts the query to English, and passes it on to Amazon Lex.This is passed onto AWS Lambda which identifies the location where the query originates, to identify the language.The user query input in the chabot hit the API Gateway.Here’s how the complete chatbot workflow was designed to operate: This was primarily because the client was already using AWS Cloud for hosting services and it made sense to build new solutions within the same ecosystem. The chatbots were made available as web and iOS applications, and built entirely using AWS products. These bots worked on an “asked and answered” approach where the client could simply ask a query and the bot would analyze all necessary data to give a clear answer. The solution was to build enterprise chatbots that could deliver the same insights without taking up too much time or effort on the part of the client stakeholders. ![]() However, these were not easily accessible and involved some effort before stakeholders could extract relevant insights. You can include slot variables in the prompt, which Lex fills with what the user said (to the best of its knowledge, at least).The client had a set of data visualization dashboards that tracked asset performance data in real time. Lex supports this, under the “Confirmation prompt” settings. This is all the configuration your bot needs, but most users like to see a confirmation prompt before the action is taken, both for peace of mind and to ensure the bot hasn’t screwed something up. You can do this by surrounding the slot name with brackets in the utterance definition: an appointment on If a user says “I would like to book an appointment tomorrow,” you can cut out the extra step and consider that slot fulfilled. Otherwise, some people may get stuck.Īdditionally, you can integrate slots directly into the utterances. A good rule of thumb, though, is to include the types in the slot prompt so that the user knows the options. You can also limit your custom slot type to only exact words and synonyms, if you want it to be more strict. Lex expands your slot values to include similar responses that you may get from users in the real world. ![]() For example, if you offer a few different kinds of appointments, you can add them in your own type. For example, if you’re asking the user for the date of their appointment, you may write something along the lines of “Which day would you like to book your appointment for?” Each slot comes with its own prompt, which is shown or read to the user.
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