System Architecture

Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto pattern for building LLM‑powered applications that need up‑to‑date, factual, or domain‑specific knowledge. In a classic RAG pipeline, a user query is first retrieved from a knowledge store (often a vector database) and then generated by a large language model (LLM) conditioned on those retrieved passages. While the basic flow works well for offline or batch workloads, many production scenarios—customer‑support chatbots, real‑time recommendation engines, autonomous IoT devices, and AR/VR assistants—require sub‑second latency, high availability, and privacy‑preserving inference at the edge. Achieving these goals with a single monolithic retrieval layer is challenging: ...

Introduction Large language models (LLMs) have transformed how we build conversational assistants, code generators, and, increasingly, autonomous agents that can plan, act, and learn without human supervision. The most visible limitation of current LLM‑driven agents is the context window: a fixed‑size token buffer (e.g., 8 k, 32 k, or 128 k tokens) that the model can attend to at inference time. When an agent operates over days, weeks, or months, the amount of relevant information quickly exceeds this window. ...