Neural Search

At enterprise scale, vector search hits a hard physical wall: RAM capacity. Storing 100 million document chunks at 1,536 dimensions in full 32-bit floating-point precision demands over 600 gigabytes of raw memory before allocating a single byte for graph indexes like HNSW. When infrastructure bills outpace retrieval budgets, scaling requires algorithmic compression rather than throwing more high-memory nodes at the cluster. ...

The architecture of Retrieval-Augmented Generation (RAG) systems is undergoing a fundamental transformation, driven by the failure of traditional retrieval pipelines to maintain semantic coherence across fragmented document segments. For years, the industry standard relied on “early chunking” prior to vector embedding, severing the connective tissue of long-form text, isolating pronouns from referents and stripping local segments of their governing global context. This analysis deconstructs Late Chunking, a methodology pioneered by Jina AI and Voyage AI that reverses the traditional pipeline order to preserve full document semantics within individual chunk embeddings. ...