The best way to Construct a Fb Messenger Chatbot Powered by Quick SQL on CSV

A chatbot, like several human customer support rep, wants information about your corporation and merchandise so as to reply to clients with the proper data. What’s an environment friendly strategy to hook up your information to a chat software with out important information engineering? On this weblog, I’ll exhibit how one can construct a Fb Messenger chatbot to assist customers discover trip leases utilizing CSV information on Airbnb leases.

Companies Used

We are going to use the next companies to implement our chatbot:

Loading the Airbnb Information into Rockset

Airbnb information is out there in CSV format for various cities and is split into itemizing, assessment, and calendar information. From the Rockset console, I uploaded these recordsdata for Amsterdam into three completely different collections (airbnb_listings, airbnb_reviews, and airbnb_calendar). You may also add information for different cities into these collections if wanted.

Alternatively, it’s possible you’ll place your information in an Amazon S3 bucket and create a set that constantly syncs with the information you add to the bucket.

Writing the Fb Messenger Bot

Utilizing the messenger bot tutorial, I created a Fb web page and webhooks to obtain occasions from the chatbot. You may consult with and familiarize your self with the Node.js mission I created right here.

app.js creates a HTTP server utilizing Categorical. The server handles GET requests to confirm webhooks and POST requests to answer consumer messages.

// Accepts POST requests on the /webhook endpoint
app.submit('/webhook', (req, res) => {
  let physique = req.physique;
  if (physique.object === 'web page') {
    physique.entry.forEach(perform(entry) {
        let occasion = entry.messaging[0];
        if (occasion.message && occasion.message.textual content) {
            // deal with the message
            handleMessage(occasion);
        }
    });
    // Return a '200 OK' response
    res.standing(200).ship('EVENT_RECEIVED');
  } else {
    // Return a '404 Not Discovered'
    res.sendStatus(404);
  }
});

handleMessage.js interacts with Dialogflow and queries Rockset to reply customers’ inquiries.

Utilizing Pure Language Processing with Dialogflow

In an effort to perceive consumer messages and questions, I’ve built-in Dialogflow (apiai) in my mission. Dialogflow means that you can create intents and extract meanings out of phrases utilizing machine studying. To hook up with Dialogflow out of your software that you must use Consumer entry token to create the consumer object.

I created an intent rentalcity and educated it with a number of phrases to extract the requested metropolis and date for the rental and the variety of individuals within the social gathering. Equally, I created a number of extra intents to reply follow-up requests utilizing contexts. The Dialogflow mission export is included within the recipes github repository.

Interacting with the Chatbot

We’ve the bot arrange and a mechanism to grasp customers’ requests. All we want now could be a strategy to translate the requests into significant responses. Utilizing Rockset’s Node.js consumer, I’ll question the the collections created in step one.

Let’s begin interacting with the chatbot.

The intent to seek out listings makes use of the next SQL question:

with listings as (
    choose id, identify, value, property_type
    from airbnb_listings
    the place decrease(airbnb_listings.metropolis) like :metropolis 
        and airbnb_listings.accommodates::int >= :quantity
    order by airbnb_listings.number_of_reviews desc
)
choose listings.id, listings.identify, listings.property_type, listings.value
from listings, airbnb_calendar
the place airbnb_calendar.date = :date and airbnb_calendar.obtainable = :avail
    and airbnb_calendar.listing_id = listings.id
restrict 1

This SQL question makes use of two collections airbnb_listings and airbnb_calendar to extract the rental with the best variety of evaluations obtainable on the given date.

To get extra data for this itemizing, the consumer can reply with particulars.

To reply this we fetch the abstract from the gathering airbnb_listings for the listing_id returned within the earlier question.

choose abstract from airbnb_listings the place id = :listing_id

The consumer can even request the most recent evaluations for this itemizing by replying present evaluations.

The SQL question to get the evaluations for this itemizing:

choose feedback, date
from airbnb_reviews
the place listing_id = :listing_id
order by date desc
restrict 3

To take a look at one other itemizing, the consumer sorts in subsequent. This makes use of the offset SQL command to get the subsequent outcome.

Abstract

We now have a data-driven chatbot that understands customers’ requests for trip leases and responds utilizing Airbnb itemizing and calendar information! Its capability to supply on the spot replies leveraging quick SQL queries on the Airbnb CSV information additional enriches the shopper expertise. Plus it was comparatively simple to attach the chatbot to the underlying Airbnb information set. Your entire means of constructing the chatbot took me lower than a day, from loading the dataset into Rockset and writing the chatbot to organising interactions to reply with the related data.