This article presents a complete, production-ready architecture for building a Retrieval-Augmented Generation (RAG) system specifically designed for small and medium-sized enterprises (SMEs). It moves beyond theoretical concepts to provide a practical, step-by-step guide covering the entire pipeline: data ingestion, chunking, embedding, vector storage, retrieval, and generation. The architecture emphasizes scalability, cost-effectiveness, and ease of maintenance, making it accessible for teams with limited resources. Key decisions such as choosing between different vector databases, optimizing retrieval strategies, and integrating with LLMs are discussed with concrete examples. For any SME looking to leverage the power of RAG without the overhead of a large tech stack, this guide offers a clear and actionable blueprint.
This article provides a comprehensive architecture for building an enterprise-level Retrieval-Augmented Generation (RAG) system tailored for small to medium enterprises. It covers the entire pipeline from data ingestion to retrieval and generation, emphasizing practical, scalable solutions. This is a valuable resource for teams looking to implement RAG without the resources of large tech companies.