Retrieval-Augmented Generation (RAG) has emerged as a critical framework for building question-answering systems that leverage private or proprietary data, avoiding reliance on third-party AI services. This article explores the technical architecture and implementation strategies for deploying a scalable and observable RAG service, emphasizing the role of generative AI infrastructure, Kubernetes, and CNCF tools such as Cluster Autoscaler and Multicluster Fleet Manager. The focus is on balancing performance, cost-efficiency, and observability while addressing the challenges of dynamic resource management and model optimization.
Retrieval-Augmented Generation (RAG) combines large language models (LLMs) with retrieval systems to enhance the accuracy of responses by incorporating relevant data from external sources. This approach is particularly valuable for applications requiring domain-specific knowledge or private data access. The architecture integrates Kubernetes as the container orchestration platform, enabling dynamic scaling and efficient resource management. Cluster Autoscaler (e.g., Luna) ensures automatic adjustment of compute resources based on workload demands, while CNCF tools like Multicluster Fleet Manager provide centralized orchestration across multiple Kubernetes clusters. Container Management Platforms such as Kubernetes also facilitate the deployment of distributed inference frameworks like VLLM and Ray, which optimize memory usage and throughput for large-scale workloads.
The system leverages Kubernetes for scalable deployment, with Cluster Autoscaler dynamically adjusting GPU and CPU resources. For example, clusters can scale from 4k to 4 GPUs within seconds, ensuring cost-efficiency during peak loads. Multicluster Fleet Manager enables unified management of multiple clusters, simplifying operations across heterogeneous environments.
Phoenix, an open-source observability tool, automates instrumentation to track critical metrics such as input/output token counts, inference latency, and RAG-retrieved data fragments. This provides real-time insights into system performance and helps identify bottlenecks. Parameters like context length and tensor parallelism are fine-tuned to balance accuracy and latency, with context length initially set to 128k but later adjusted to 4k due to excessive inference delays.
The system integrates VLLM for distributed inference and Ray for parallel processing, supporting high throughput with low memory overhead. SQL agents convert natural language queries into semantic SQL queries, while RAG agents retrieve relevant data from vector databases. A machine learning classifier (e.g., random forest) routes queries to the appropriate agent based on training data from internal sources like Jira and Zendesk.
This architecture demonstrates how generative AI infrastructure, Kubernetes, and CNCF tools can be combined to build scalable and observable RAG services. By leveraging Cluster Autoscaler for dynamic resource management, Phoenix for observability, and distributed frameworks like VLLM and Ray, the system achieves high performance while maintaining cost-efficiency. Key lessons include prioritizing synthetic data for initial training, fine-tuning model parameters iteratively, and adopting Python for its flexibility in integrating diverse components. For teams aiming to deploy private RAG systems, this approach provides a robust foundation for balancing scalability, observability, and operational efficiency.