The ability to efficiently locate relevant information from massive datasets is a primary determinant of a RAG system's success, especially in production environments. As systems scale, the retrieval component often becomes a critical performance consideration. This chapter focuses on the strategies required to build and optimize distributed retrieval systems capable of handling such demands.

We will examine methods for scaling vector search through sharding, replication, and sophisticated indexing. You will learn about implementing distributed dense retrieval and optimizing its performance. The discussion will extend to hybrid search approaches, combining the strengths of dense and sparse retrievers at scale. Furthermore, we will cover graph-based retrieval techniques, multi-vector and ColBERT-style architectures for enhanced retrieval, the design of advanced re-ranking pipelines in distributed settings, and approaches for near real-time indexing. The chapter includes a practical segment on setting up a sharded vector index, reinforcing the discussed principles.