Topic · 6 stories
6 stories tagged vectors-plainly on AI Tamer, newest first.
Vectors, Plainly · Part 6 of 6
The parts tutorials skip: sharding and write freshness, memory and disk-based indexes, where a GPU actually pays off, and the security and compliance questions a vector store raises.
Vectors, Plainly · Part 5 of 6
Retrieval-augmented generation is an architecture, and the vector database is one box in it. The whole loop, the RAG variants worth knowing, reranking, and when a knowledge graph beats similarity.
Vectors, Plainly · Part 4 of 6
Retrieval quality is decided by the embedding model, the dimensions and the chunking, and it is only known once you measure recall. How to choose, how to test, and where vector search earns its place.
Vectors, Plainly · Part 3 of 6
A vector index has no row-level security by default, and a missing filter leaks data without an error. The isolation levels, what each engine offers, and how to lay out collections, namespaces and metadata.
Vectors, Plainly · Part 2 of 6
How HNSW graphs, IVF cells and product quantization make nearest-neighbour search fast, and how filters, keyword fusion and reranking change what a query returns.
Vectors, Plainly · Part 1 of 6
A vector database answers “what is closest in meaning to this?” instead of “which rows match?”. Here is the machinery: embeddings, distance, and why you never design the fields.
