系统开发代写｜Assignment 1: Dining Concierge Assistant

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Customer Service is a core service for a lot of businesses around the world and it is getting disrupted at the moment by Natural Language Processingpowered applications. In this first assignment you will implement a serverless, microservice-driven web application. Specifically, you will build a Dining Concierge chatbot that sends you restaurant suggestions given a set of preferences that you provide the chatbot with through conversation.

Outline:

This assignment has the following requirements:

https://github.com/001000001/aics-columbias2018/blob/master/aics-swagger.yamlUse http://editor.swagger.io/ to visualize this file

Gateway

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-importapi.html

Create a Lambda function (LF0) that performs the chat operation Use the request/response model (interfaces) specified in the API specification above

https://docs.aws.amazon.com/apigateway/latest/dev

eloperguide/how-to-cors.html

which you can use in your frontend. It will take care of calling your API, as well as session signing the API calls — an important security feature

https://docs.aws.amazon.com/apigateway/latest/developerguide/how-to-generate-sdk-javascript.html

Build a Dining Concierge chatbot using Amazon Lex.

https://docs.aws.amazon.com/lex/latest/dg/gettingstarted.html

1.Implement at least the following three intents:

Integrate the Lex chatbot into your chat API

1. Use the AWS SDK to call your Lex chatbot from the APILambda (LF0).

2.When the API receives a request, you should 1. extract the text message from the API request, 2. send it to your Lex chatbot, 3. wait for the response, 4. send back the response from Lex as the API response.

Use the Yelp API to collect 5,000+ random restaurants from

Manhattan.

1.Yelp API

2.DynamoDB (a noSQL database)

3.Note: you can perform this scraping from your computer or

You only need to store RestaurantID and Cuisine for each restaurant