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The standard RAG tutorial teaches a simple pattern: embed your documents, store them in a vector database, retrieve the top K, and feed them to the LLM. The vector engine is passive infrastructure. Put vectors in, get neighbors out. Configure once, forget about it forever.
This mental model is why most AI agents treat vector search as a black box. They can call the API...
Most vector search tutorials stop at single-vector embeddings: one document, one vector, one similarity score. That works for demos. It falls apart when your retrieval pipeline needs to capture fine-grained token-level interactions across text, images, and PDFs at production scale.
Until now, engineers who wanted to go deeper had to piece together scattered papers, blo...