PentaRAG: Large-Scale Intelligent Knowledge Retrieval for Enterprise LLM Applications

Abstract

Enterprise deployments of large-language model (LLM) demand continuously changing document collections with sub-second latency and predictable GPU cost requirements that classical Retrieval-Augmented Generation (RAG) pipelines only partially satisfy. We present PentaRAG, a five-layer module that routes each query through two instant caches (fixed key-value and semantic), a memory-recall mode that exploits the LLM's own weights, an adaptive session memory, and a conventional retrieval-augmentation layer. Implemented with Mistral-8B, Milvus and vLLM, the system can answer most repeated or semantically similar questions from low-latency caches while retaining full retrieval for novel queries. On the TriviaQA domain, LoRA fine-tuning combined with the memory-recall layer raises answer similarity by approximately 8% and factual correctness by approximately 16% over the base model. Under a nine-session runtime simulation, cache warming reduces mean latency from several seconds to well below one second and shifts traffic toward the fast paths. Resource-efficiency tests show that PentaRAG cuts average GPU time to 0.248 seconds per query, roughly half that of a naive RAG baseline, and sustains an aggregate throughput of approximately 100,000 queries per second on our setup. These results demonstrate that a layered routing strategy can deliver freshness, speed, and efficiency simultaneously in production-grade RAG systems.