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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Introduction In this blog post, we’ll explore how Apache Camel JBang’s Infrastructure Command can help you rapidly prototype end-to-end integration scenarios and adapt to changing requirements. We’ll walk through a realistic development scenario where requirements evolve over time, demonstrating how Camel’s flexibility makes it an ideal choice for proof-of-concept development. Camel JBang Infrastructure: Your Prototyping Toolkit We already know and love Camel JBang (if you don’t, check out Claus Ibsen’s YouTube channel for excellent tutorials).

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HOWTOS

Introduction In the previous blog posts (camel-tensorflow-serving and camel-torchserve), we discussed the recent release of Apache Camel 4.10 LTS, which introduced three new AI model serving components. 1 TorchServe component TensorFlow Serving component KServe component We previously wrote about the TorchServe and TensorFlow Serving components. This post introduces the KServe component, concluding the series. KServe Component KServe is a platform for serving AI models on Kubernetes. KServe defines an API protocol enabling clients to perform health checks, retrieve metadata, and run inference on model servers.

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