The Apache Camel website now generates markdown versions of all documentation pages following the llms.txt specification. This makes our documentation easily accessible to Large Language Models (LLMs) and AI coding assistants. What is llms.txt? The llms.txt specification is a standardized format that helps LLMs discover and consume website content efficiently. Similar to how robots.txt guides web crawlers and sitemap.xml helps search engines, llms.txt provides a structured entry point for AI systems to understand and access documentation.
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TOOLING
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