In the rapidly evolving landscape of AI-powered applications, the ability to process and understand documents has become increasingly crucial. Whether you’re dealing with PDFs, Word documents, or PowerPoint presentations, extracting meaningful insights from unstructured data is a challenge many developers face daily. In this post, we’ll explore how Apache Camel’s new AI components enable developers to build sophisticated RAG (Retrieval Augmented Generation) pipelines with minimal code. We’ll combine the power of Docling for document conversion with LangChain4j for AI orchestration, all orchestrated through Camel’s YAML DSL.
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CAMELAI
Apache Camel 4.15 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 You can now easier extend Camel via 3d-party dependencies via Java ServiceLoader using the ContextServicePlugin SPI. You can add custom sensitive keys to camel.main.additionalSensitiveKeywords which Camel will mask in logging. Camel JBang camel debug now also supports debugging Camel Quarkus applications, by executing camel debug pom.
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RELEASES
The Camel community announces the immediate availability of the Camel 4.15.0 release with 123 new features and improvements. The artifacts are published and ready for you to download from the Central Maven repository. For more details please take a look at the release notes. Many thanks to all who made this release possible. On behalf of the Camel PMC, Gregor Zurowski
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RELEASES