ARCHITECTING RETAIL-SCALE VECTOR STORE SYSTEMS FOR AGENTIC GENERATIVE AI

Modern retail-facing generative AI systems increasingly rely on retrievalaugmented generation (RAG) and agentic execution models to deliver accurate, context-aware responses across product catalogs, technical documentation, and operational metadata.At retail scale, these systems must support semantic retrieval over heterogeneous and continuously evolving knowledge while maintaining predictable latency and operational reliability.This paper presents a big data driven architecture for vector store systems designed to support retail-scale agentic generative AI.The architecture integrates batchoriented ingestion pipelines with incremental updates, document-aware chunking, multimodal embedding generation, and…