AMAZON CHATBOT CODE

The business logic defined atop Lambda governed how the raw data from the database is computed and interpreted. Static: Simple introductory messages or questions, which can be answered without querying the database.īusiness-logic based: All questions around asset performance are answered by querying the database, extracting the answer, re-formatting it into a user-understandable format, and passing it to the user via Lex.įor the client, all asset performance and associated data was stored in PostgreSQL. Based on these, Lambda would activate to generate an appropriate answer. It also automatically manages all the resources required to manage and scale your code.įor this project, the user intent and slot values gathered by Lex were the triggers for Lambda.

AMAZON CHATBOT CODE

AWS LambdaĪWS Lambda is a serverless computing platform that can run code in response to event triggers. For the client, Srijan used Amazon Cognito and AWS Identity and Access Manager (IAM) for creating users, storing user data, creating user groups and access control based on authorization. Amazon CognitoĪmazon Cognito is a solution to securely manage user sign-up, sign-in and access controls within an application. This allowed the chatbot to accurately qualify questions, and also spot new slot values when they occur. Training data around slot values and possible permutation and combinations of slot values were also fed into Lex. All of these-region, site, date range - can be defined as slot values for the “equipment switched on” intent. They were necessary for questions that need additional data to be answered correctly.įor example, if the question is, “How many of a particular equipment are switched on?”, the chatbot needed to ask follow-up questions on the region, site, time frame etc.

Slots: These were values that help qualify a particular utterance or intent.

This key intent is passed forward and processed to extract the relevant answer. This is the exact information or piece of data that the used wants to know.

Intent: Lex was trained to strip down each utterance to a basic intent.

Additionally, deep learning capabilities allowed Lex to extrapolate from the given variations of a question, and expand it’s vocabulary to understand newer variations of a question.

This training data helped Lex understand the range of possible natural language questions. This data, along with data on several variations of every question was fed into Lex.

Utterances: These were sample questions that a user could possibly ask the chatbots.

For this particular project, here’s how these elements were defined: 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. The client had a set of data visualization dashboards that tracked asset performance data in real time.

Amazon chatbot