Author: Tyler Band

Building a full-featured Angular browser/mobile user interface requires a very specific skill set. There is a vast array of tools, frameworks, and platforms, and each requires a steep learning curve.

An open-source project, ApiLogicServer, has released a technical preview that combines GenAI-powered microservice automation with Ontimize, an Angular UI framework. While the promise of a complete running application is the goal of GenAI-enabled microservice, the developer will still need to interact with the generated components to create a finished web application.  

Let's use ChatGPT to build a REST API Microservice for a budgeting application. This needs to support multi-tenant security and include actual spending matched against budget categories.  Of course, a Google sheet or Excel would be the simple answer. However, I wanted a multi-user cloud solution and to use the new open-source REST API microservice platform API Logic Server (ALS). Our microservice needs an SQL database, an ORM, a server, REST API, react-admin UI, and a docker container.  

AI Design of the Data Model

I started by asking ChatGPT 3.5 to generate a budget application data model. 

Apache Kafka has emerged as a clear leader in corporate architecture for moving from data at rest (DB transactions) to event streaming. There are many presentations that explain how Kafka works and how to scale this technology stack (either on-premise or cloud). Building a microservice using ChatGPT to consume messages and enrich, transform, and persist is the next phase of this project. In this example, we will be consuming input from an IoT device (RaspberryPi) which sends a JSON temperature reading every few seconds.

Consume a Message

As each Kafka event message is produced (and logged), a Kafka microservice consumer is ready to handle each message. I asked ChatGPT to generate some Python code, and it gave me the basics to poll and read from the named "topic." What I got was a pretty good start to consume a topic, key, and JSON payload. The ChatGPT created code to persist this to a database using SQLAlchemy. I then wanted to transform the JSON payload and use API Logic Server (ALS - an open source project on GitHub) rules to unwarp the JSON, validate, calculate, and produce a new set of message payloads based on the source temperature outside a given range.

I have been using a new open-source platform, API Logic Server (an open-source project on GitHub) to deliver API microservices for a client. I wanted to build a complete mobile banking API from start to finish based on the old TPC benchmark.  This includes declarative business logic (a.k.a. spreadsheet-like rules), security, react-admin UI, and an Open API (Swagger) documentation.

API Logic Server (ALS) creates executable projects that you can extend in your IDE.   It is an open-source Python platform based on SQLAlchemy 2.0, Flask, safrs-JSON API, react-admin, and LogicBank (a declarative spreadsheet-like rules engine).