Scaling Low‑Latency RAG Systems with Vector Databases and Distributed Memory Caching
Introduction Retrieval‑augmented generation (RAG) has quickly become the de‑facto pattern for building conversational agents, question‑answering services, and enterprise knowledge assistants. By coupling a large language model (LLM) with a searchable knowledge base, RAG systems can produce answers that are both grounded in factual data and adaptable to new information without retraining the model. The biggest operational challenge, however, is latency. Users expect sub‑second responses even when the underlying knowledge base contains billions of vectors. Achieving that performance requires a careful blend of: ...