The first law of software complexity says that “a well-designed system will degrade into a badly designed system over time”. This law can be ruthless for open source projects receiving hundreds of contributions every month. That’s why projects must refactor code, evaluate APIs, review tests, and modernize code to leverage the latest and greatest features from Java. Camel Test Support One area in Camel that has had little attention until recently was our test support code.

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CAMELTEST

Chunking is a crucial aspect of data processing that can significantly impact retrieval quality, query latency, costs, and even the accuracy of Large Language Model (LLM) outputs. In this blog post, we’ll explore what chunking is, its importance, and how the new Chunking DSL in Apache Camel 4.8.0 improves data processing workflows. The Problem with Traditional Chunking Approaches Before Camel 4.8.0, applications using Camel would have to implement custom chunking logic or rely on external libraries.

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CAMELAI

Apache Camel AI is a set of components that allow various AI-related technologies to be integrated with Camel. Nowadays, LLMs such as OpenAI ChatGPT and Meta Llama are gaining a lot of attention, and many frameworks and tools are exploring ways to utilise them. Camel AI also includes the LangChain4j component suite, and there are already blog posts about how you can utilise LLMs using LangChain4j in the Camel Blog:

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CAMELAI