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?
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.
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.