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Showing posts with the label rabbitmq

Containers & Kubernetes in Windows Server 2025 or RedHat EL(RHEL)

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Windows Server vs RedHat for Modern Hosting Windows Server (including 2025) is traditionally strong for legacy, .NET Framework , Active Directory , and Hyper-V virtualization. Hyper-V is a hypervisor — meaning it is designed to run full Virtual Machines . Each VM boots its own full OS. This is great for old workloads or isolating entire OS instances, but it is heavy for modern microservices. RedHat Enterprise Linux ( RHEL ) and its family (CentOS, Rocky, AlmaLinux) were designed much closer to the container ecosystem . Linux was the birthplace of Docker , containers , and Kubernetes . The kernel features containers depend on (cgroups, namespaces) were invented in Linux. That is why RedHat integrates better with container runtimes (containerd, CRI-O) and Kubernetes cluster nodes. So if someone asks “which is better for containers? Windows or RedHat?” — the modern industry answer is: RedHat or any Linux distro is the natural native home of containers . The Scenario: I want...

Improving LLMs CAG with accumulative knowledge using RabbitMQ, Ollama and Gemma3

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Improving LLMs CAG with accumulative knowledge using RabbitMQ, Ollama and Gemma3 Why RabbitMQ ? Thinking about an LLM that will receive a lot of messages from everywhere means that you need to keep track of its messages and responses so the context could be understood on the whole conversation. Remember that it can't be just a stream every time; sometimes users would appear after days or months, and the sense of knowing the person should be stored. Now, storing is just one part of the solution for retaining context. We all have heard about RAG and CAG, but what happens if I need to store the information in real-time? You know, users keep sending messages. So the saving and retrieving messages would be blocking the AI's response time. Therefore, I figured why not use a queue system that notifies a consumer about new messages, stores them in a DB, so t...