In a world where personalized digital experiences are key, AI chatbots are becoming essential tools for customer interaction and support. In this blog, I’ll guide you through how I used Mendix and AWS Bedrock to build a personalized AI chatbot that draws context from a knowledge base. All in just one hour.
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Whether you’re looking to automate customer service or create an internal knowledge assistant, this blog will show how easy it can be to integrate advanced AI with the low-code platform Mendix.
Using the AI Bot Starter App template, I was able to rapidly create the structure of my chatbot application. The starter app comes with built-in features, which significantly reduced the time spent on initial configuration. This allowed me to focus on customizing the bot and integrating it with AI.
With the app structure in place, the next step was to create a knowledge base for the chatbot to pull from. I uploaded our Squad Apps Coding Conventions document to an S3 bucket, which served as the contextual data for the knowledge base. I then connected it to AWS Bedrock using Titan Text Embeddings v2 to convert text into vector representations. This allowed the chatbot to understand user queries in a more natural and intelligent way.
For efficient search and retrieval, I used OpenSearch Serverless as the vector database. This ensured that when a user asked a question, the chatbot could identify the most relevant chunks of information from the stored knowledge base.
With everything set up, I tested the chatbot within the Mendix application. Not only did it respond accurately based on the context from the knowledge base, it also showed which specific chunk of data was used to generate the response. This transparency allowed me to verify that the correct information was being retrieved.
In just one hour, I was able to build a personalized AI chatbot that leverages a knowledge base using Mendix and AWS Bedrock. The combination of these platforms makes it simple to deploy AI-driven applications that can provide contextual, relevant responses to users, whether for customer service or internal tools.
This project highlights how quickly you can bring AI-powered solutions to life using low-code platforms and cloud-based AI services like AWS Bedrock.
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