The biggest challenge for enterprises in the rapidly evolving world of Generative AI isn’t just building “smarter” LLMs or agents — it’s securely connecting that AI to the decades of business logic and data locked away in enterprise systems. How do you let an AI agent interact with your Salesforce data, your Kafka topics, or your internal databases without rewriting everything or creating a massive security hole? It turns out the answer may already be running in your organization.
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AI
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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AI
© Paul Klee - Kunstsammlung Nordrhein-Westfalen Here we are with Camel K 2.8.0. We are please to announce the general availability of a new Camel K version. There are a few new exciting features we want to share within this release. Git branch/tag/commit In version 2.7.0 we announced the possibility to build a Camel application directly from Git source. We have worked in this release to include the possibility to specify a branch, tag or generically a commit to the configuration.
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