How it works
Have you ever wondered about the technology behind chatbots found on websites that interacts with users and answers FAQs? If you have ever looked for a solution to a problem about a service on any website and you happen to have to scroll through all the other irrelevant answers before finally getting the answer you wanted, then you'll agree with me it is time consuming. In this article, I will guide you on how to build a bot that can give precise answers about queries the users might have concerning the services on any company website as well as run automated tasks like booking a ticket, cancelling it, reschedule etc.
When the user opens the website, the web UI loads and appears at the bottom right corner of the screen. Once clicked, it displays a pre-configured welcome message. If the user types any questions related to the company, the AI model searches the pre-loaded PDF files containing every information about the company, if the answer is found, the bot gives the user a human friendly and summarized response. In our case, the bot will not only answer FAQs, but will help users book travel tickets, give the different destinations, let them choose between classic or VIP travel options with their different tariffs.
The technologies involved are:
- Amazon Lex
- AWS S3
- Amazon Bedrock
- Amazon Polly
- AWS IAM
Architecture
Till now all the intents are the automatically generated, meaning the bot knows nothing about our company, only expects specific data in order to treat the request, say if the destination city isn't in it's database, then it'll not accept any other answer. So we have to personalize the bot in order to accept our company data, say we only travel to few towns, our tarrifs are in local currency. My generated intents are:
- BookJourney
- CheckReservationDetails
- GetTravelStatus
- CancelJourney
- RescheduleJourney
- FallbackIntent
We are therefore going to add a knowledge base which Amazon Lex will interact with in order to give us answers specific to our company. In order to personalize the different intents to go into the knowledge base and look for the required information, we need to configure each of them:
Configure BookJourney intent
Click BookJourney inside the intents page and scroll down to slots
Then go to Advanced options and configure the following
Enhance this slot with the assisted slot resolution feature: enable
Runtime generative AI features: enable
Select model: Anthropic, Claude instant and save
Repeat the same activity for all the slots that have as type AMAZON.city or AMAZON.date, remember to save each time you enable the assisted slot resolution. Check for the intents and make sure they are configured to seek assistance from AI in order to manage the City and Date slots. This will help users give cities that are in the company data and for dates, the user can be prompted and they respond with either today, tomorrow or in two weeks and the model figures out the date based on the present date.
After finishing and saving the intents, go to Slot types and select ClassType
Click save slot type. Then Click on Build to make sure all changes are saved
Create S3 bucket and store company data
Go to the console and search S3 -> Create bucket
Bucket name: knowledge-base-bucket-xxx (xxx are random numbers to make the bucket name unique)
region: us-east-1
Upload data that has the data of the company, this include every service, cost etc that the company sells. In our case, lets use this text and convert to pdf and upload to our S3 bucket.
General Express Description:
Welcome to the General Express chatbot! We are dedicated to providing exceptional travel services across various destinations in Cameroon, with our headquarters located in Bafoussam. Our mission is to ensure a comfortable, convenient, and enjoyable travelexperience for all our customers. Key Features:
Travel Destinations and Pricing:
Our chatbot can assist you in exploring our travel routes and pricing options:
Bafoussam to Douala: 4000 XAF (Classic) | 6000 XAF (VIP)
Bafoussam to Yaoundé: 5000 XAF (Classic) | 6500 XAF (VIP)
Bafoussam to Bangante: 1000 XAF (Classic) | 1500 XAF (VIP)
Bafoussam to Bafang: 1000 XAF (Classic) | 1500 XAF (VIP)
Bafoussam to Bamenda: 2000 XAF (Classic) | 2500 XAF (VIP)
Bafoussam to Buea: 6000 XAF (Classic) | 7000 XAF (VIP)
Luggage Handling:
Our team will assess the value of your luggage before departure, ensuring safe and secure transport. Travel Schedules:
The chatbot provides information about our travel times:
Morning Journeys: 6 AM to 10 AM
Day Journeys: 11 AM to 5 PM
Night Journeys: 6 PM to 2 AM
Complimentary Meals:
Enjoy complimentary meals during your journey, enhancing your travel experience without any additional costs. Customer Service:
Our friendly and welcoming personnel are committed to ensuring your comfort and satisfaction throughout your travels. If you have any questions or need assistance, our chatbot is here to help!
Contact Information:
For further inquiries or to make a reservation, feel free to reach us at:
Phone: 600000000
Phone: 800000000
Conclusion:
The General Express chatbot is designed to make your travel
planning seamless and efficient. Whether you need information about
routes, pricing, or travel schedules, we are here to assist you at every step. Experience the joy of travel with General Express, where customer satisfaction is our top prior
Create a knowledge base for Amazon Bedrock
Go to the search bar in the console and search Amazon Bedrock -> Builder Tools -> Knowledge basses -> Create knowledge Base.
Provide the following details:
Knowledge base name: travel-assistant-knowledge-base
IAM: Create and use a new service role
choose data source: S3 -> Next
Configure data source:
data source name: travel-assistant-knowledge-base
Data source location: this account
S3 URI: Browse S3
choose: knowledge-base-bucket-xxx
next
Select embeddings model and configure vector store
Embeddings modele: Titan Embeddings G1 - Text v1.2
vector database: Quick create a new vector store = true
next
Review everything and create knowledge base. It will take few minutes to create. After that, we'll need to sync the knowledge base so that the LLM can read the pdf documents that are stored in our S3.
After the data source is created, click on sync for the LLM to read the PDF documents in our S3 bucket. If other information or services are added to the company, it just needs to be documented and uploaded to S3, not forgetting to sync the knowledge base.
Then click Add. Next configure the intent,
QnA configuration:
Select model: Anthropic Claude instant
Knowledge store: Knowledge base for Amazon bedrock
Knowledge base for Amazon Bedrock Id: (return to knowledge base console and copy the knowledge base id we created earlier, it has 10 characters)
click save intent
The saved intent should appear to the left of the screen amongst the intents.
Woow, we have our bot answer questions based on the knowledge base information. Next we can try to book a ticket:
I will just type in Book a ticket, the model will identify the intent and ask the relevant questions in order to book the ticket
Looking at the Inspect dashboard, the bot asks questions and populates it, this can be used to trigger lambda functions to carryout the desired task in the backend.
Now we need to integrate it to our website by building a web UI chat icon.
Build a WebUI and attach to the website
Create a version
On the Lex console select the bot we created and select bot versions on the left panel and create a new version. This creates a snapshot of what we just built. It will create Version 1
Under the section Getting started with the AWS CloudFormation deployment option we choose the region where we want to create the stack which should also be the same region where the bot is located(North Virginia) and click launch stack. Search the following fields and populate them with the following:
Lex V2 Bot configuration parameters:
LexV2BotId: XXXXXXX (Replace X with the bot id you copied)
LexV2BotAliasId: XXXXXXX (Replace with the alias Id)
Acknowledge the two radio buttons under Capabilities and create the stack.
It will open a cloud formation console, wait for it to finish creating.
Copy the code snippet and go to the index.html file of the running website and paste it in the body part of the html code. Do necessary updates on the website to display the newly configured index.html file. In my case, it is a static website hosted in an S3 bucket. So i will upload the index.html to the S3.
Now open the website, you should see the chatbot open to the bottom right side of the screen
And voila!! We have successfully integrated a chatbot to our website.
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