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

This blog is based on experiments done about extracting structured data into its structured counterpart. More precisely, in this post, we’ll give directions about how to convert a conversation transcript into a Java object. Introduction Reading articles like this over the net, it seems that folks have a lot of unstructured data at the disposal while not being able to take advantage on it. So probably, in the future we might expect to deal more and more with unstructured data extraction in integration flow.

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CAMELAI

This blog shows how Apache Camel can help integrate multiple systems with an AI model, in particular, the camel-whatsapp component is used to build a chat on WhatsApp; so that a user can easily communicate with the LLM (large Language Model) via WhatsApp. Overview The objective is the following, I’d like to have specific conversations about some topic, in this case, how to contribute to Apache Camel, with an LLM via WhatsApp.

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