A chatbot, like several human customer support rep, wants knowledge about your enterprise and merchandise with a view to reply to prospects with the right info. What’s an environment friendly technique to hook up your knowledge to a chat software with out vital knowledge engineering? On this weblog, I’ll show how one can construct a Fb Messenger chatbot to assist customers discover trip leases utilizing CSV knowledge on Airbnb leases.
Providers Used
We are going to use the next companies to implement our chatbot:
Loading the Airbnb Knowledge into Rockset
Airbnb knowledge is on the market in CSV format for various cities and is split into itemizing, evaluation, and calendar knowledge. From the Rockset console, I uploaded these information for Amsterdam into three totally different collections (airbnb_listings, airbnb_reviews, and airbnb_calendar). You can too add knowledge for different cities into these collections if wanted.
Alternatively, chances are you’ll place your knowledge in an Amazon S3 bucket and create a group that repeatedly syncs with the info 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 undertaking I created right here.
app.js creates a HTTP server utilizing Categorical. The server handles GET requests to confirm webhooks and POST requests to reply to person messages.
// Accepts POST requests on the /webhook endpoint
app.put up('/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
With a purpose to perceive person messages and questions, I’ve built-in Dialogflow (apiai) in my undertaking. Dialogflow permits you to create intents and extract meanings out of phrases utilizing machine studying. To hook up with Dialogflow out of your software you want to use Shopper entry token to create the shopper object.
I created an intent rentalcity and skilled it with just a few phrases to extract the requested metropolis and date for the rental and the variety of folks within the get together. Equally, I created just a few extra intents to reply follow-up requests utilizing contexts. The Dialogflow undertaking export is included within the recipes github repository.
Interacting with the Chatbot
We now have the bot arrange and a mechanism to know customers’ requests. All we want now could be a technique to translate the requests into significant responses. Utilizing Rockset’s Node.js shopper, 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, worth, 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.worth
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 very best variety of evaluations obtainable on the given date.
To get extra info for this itemizing, the person 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 person also can 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 person varieties in subsequent. This makes use of the offset SQL command to get the subsequent consequence.
Abstract
We now have a data-driven chatbot that understands customers’ requests for trip leases and responds utilizing Airbnb itemizing and calendar knowledge! Its means to supply immediate replies leveraging quick SQL queries on the Airbnb CSV knowledge additional enriches the shopper expertise. Plus it was comparatively straightforward to attach the chatbot to the underlying Airbnb knowledge set. The complete means of constructing the chatbot took me lower than a day, from loading the dataset into Rockset and writing the chatbot to establishing interactions to reply with the related info.