A Deep Dive into Vector Stores: Classifying the Backbone of Retrieval-Augmented Generation

Vector stores represent a crucial building block for Retrieval-Augmented Generation (RAG), efficiently storing and retrieving high-dimensional embeddings to ensure relevance and accuracy for generative AI applications. This paper introduces a classification scheme that categorizes vector stores into four main classes of systems: lightweight and local solutions, open-source and distributed platforms, cloud-native and commercial services, and semantic/contextual search-oriented systems. We discuss the architectures, capabilities, strengths, weaknesses, and use cases of one representative vector database in each category: FAISS, Milvus, Pinecone, and Weaviate. Practical guidelines on the implementation are presented, focused on optimization techniques, strategies for data management, and considerations on security. Comparative insights enable practitioners to align the selection of the vector store with the workflow of RAG solutions. Future trends are explored, such as hybrid search and explainability.