Introduction This has been several blog posts now where we have learned about how to use generative AI for data extraction from a Camel route. Starting from the initial inception, we have then focused a lot on how to best combine Camel and Quarkus LangChain4j. In this blog post, we will reap the benefit of this great combination to improve the accuracy of our data extraction almost for free. Almost for free really?
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CAMELAI
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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CAMELAI
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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