Batch Jobs for Data Processing: RAG systems require extensive data processing to retrieve and process external knowledge sources. Kubernetes' Batch Jobs efficiently manage this by running short-lived pods that execute these tasks to completion.
Stateful Sets for Persistent Data: The Stateful Service pattern ensures that pods for RAG models have a stable storage backend, which is crucial for maintaining the state of the external knowledge base.
Services for Load Balancing: Kubernetes' Service object allows RAG systems to distribute network traffic to pods, providing a stable entry point and handling high loads during real-time data retrieval.
Init Containers for Setup: Before a RAG pod can serve requests, it might need data from external sources. Init Containers can prepare the environment, pulling in the necessary datasets to be readily available for the RAG system.
Sidecars for Auxiliary Tasks: Auxiliary containers can run alongside the main RAG model container, handling tasks like logging, monitoring, or updates to the knowledge base without affecting the primary application.
Operators for Automation: Kubernetes operators can manage complex RAG applications, automating tasks such as scaling, updates, and maintenance.
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