A Service-Oriented Modular Architecture for Domain-Adaptive Retrieval-Augmented Generation
Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) have emerged as a practical approach for building knowledge-grounded conversational systems. As these systems are increasingly deployed in data-centric environments, the need for systems that support multi-domain adaptability becomes critical. However, deploying RAG systems across heterogeneous domains remains challenging, as retrieval performance and response quality depend heavily on the choice of embedding models, retrieval strategies, vector stores and language model backends. Existing RAG toolkits either focus on algorithmic benchmarking with limited support for stateful, multi-user deployment or on application development with limited support for component-level experimentation and observability, forcing practitioners to choose between research flexibility and production readiness. We propose a deployment-oriented, modular RAG architecture that enables controlled experimentation across domains while incorporating stateful system services. Pipeline components are independently configurable and extensible as the RAG ecosystem evolves. A dedicated system services layer provides persistent storage, session-aware interaction and role-based access control. In addition, integrated latency instrumentation enables fine-grained performance profiling alongside quality evaluation. We implement and evaluate the system across a Sanskrit philosophical corpus and a biomedical abstract corpus to investigate RAG behavior across heterogeneous domains. Experimental results show that identical configurations can exhibit substantially different retrieval and generation characteristics across domains. Multi-turn evaluation demonstrates that session-aware interaction preserves conversational relevance across turns, while latency profiling reveals non-obvious performance interactions between pipeline stages. Collectively, these findings highlight the importance of modularity, statefulness and observability in the design of RAG systems intended for both deployment and cross-domain research.