Use approximate nearest-neighbour algorithms to search semantic space — the retrieval engine that makes RAG actually work.
Five passes over the same idea, each from a different angle. Do them in order, or jump to whichever you need.
Vector search translates the fuzzy concept of "meaning similarity" into a geometric operation: find the K nearest embeddings to a query embedding. ANN algorithms like HNSW, IVF, and DiskANN each make different trade-offs between recall, latency, memory, and index build time. Choosing the right one for your data scale and freshness requirements is a core engineering decision.
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