spring-view-component VS interfAIce

publishing{ publications { create("Maven") { from(components["java"]) groupId = "de.tschuehly" artifactId = "spring-view-component-thymeleaf" description = "Create server rendered components with thymeleaf" } withType { pom { packaging = "jar" name.set("spring-view-component-thymeleaf") description.set("Spring ViewComponent Thymeleaf") url.set("https://github.com/tschuehly/spring-view-component/") inceptionYear.set("2023") licenses { license { name.set("MIT license") url.set("https://opensource.org/licenses/MIT") } } developers { developer { id.set("tschuehly") name.set("Thomas Schuehly") email.set("[email protected]") } } scm { connection.set("scm:git:[email protected]:tschuehly/spring-view-component.git") developerConnection.set("scm:git:ssh:[email protected]:tschuehly/spring-view-component.git") url.set("https://github.com/tschuehly/spring-view-component") } } } } repositories { maven { url = layout.buildDirectory.dir("staging-deploy").get().asFile.toURI() } } }

interfAIce brings "Functional Prompt Engineering" into your applications, allowing you to define GPT tasks as normal interface methods and use them as though they were local components. Say goodbye to the process of crafting lengthy explanations to describe response formats to GPT. The auto-generated proxies of interfAIce efficiently handle the prompting, encoding, and decoding for you. The given video demonstrates interfAIce in action. If you find this interesting, you can find everything you need on our Github repo (https://github.com/mscheong01/interfAIce)