Introduction In the just released Apache Camel 4.10 LTS, AI-related components have been further enhanced. Among others, three new components related to AI model serving have been added. 1 TorchServe component TensorFlow Serving component KServe component My previous article Apache Camel AI: Leverage power of AI with DJL component demonstrated how the DJL component can be used to perform AI model inference within the Camel routes. Starting from 4.10, in addition to the in-route inference by DJL, these new components will allow the Camel routes to invoke external model servers to perform inference.

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AI

Apache Camel 4.10 LTS has just been released. This release introduces a set of new features and noticeable improvements that we will cover in this blog post. Camel Core Added customize to RouteBuilder to make it easier to configure a specific Camel component / dataformat, service in a Java lambda style, such as follows: @Override public void configure() throws Exception { customize(KServeComponent.class, k -> { k.getConfiguration().setTarget("localhost:8888"); }); from("timer:kserve?repeatCount=1") .to("kserve:model/metadata?modelName=myModel") .log("${body}"); } This makes it possible for low-code users that want to have a single Java file with the Camel route and all the Java based configuration done entirely from the same configure method.

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RELEASES

Introduction In a previous blog post related to Artificial Intelligence with Camel, we introduced the resolution of AI services by interface. This feature brings Camel and Quarkus LangChain4j closer than ever so that it takes less code to invoke a LangChain4j AI service from a route. In this blog post, we would like to introduce a related feature that should be released in the next Camel Quarkus version. This time, we’ll do the same kind of operation, except that we will resolve the bean by its name.

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AI