Vector Search Fundamentals That Every RAG System Designer Should Know

Retrieval-Augmented Generation (RAG) systems rely on vector search techniques that trade a small degree of accuracy for significant gains in speed and scalability. The HNSW algorithm, one of the most widely used Approximate Nearest Neighbor methods, organizes vectors as nodes in a hierarchical graph to reach target results with far fewer distance calculations than brute-force search. Choosing the right similarity metric matters too — cosine similarity suits text embeddings where direction encodes meaning, while L2 distance is more appropriate when vector magnitude is also significant, as in image features. Metadata filtering allows further refinement of search results by applying conditions like category or date, either before or after the ANN search step, each approach carrying its own accuracy and performance trade-offs. Modern vector databases such as Pinecone, Weaviate, and Qdrant incorporate these techniques to help developers balance recall, query speed, and infrastructure cost when building RAG pipelines.