How to check a RAG vector index on its own, in four layers from the chunks to the answer, with runnable Python and 25 evaluation tools compared.
Validating a vector database built for RAG: four layers of checks
How to check a RAG vector index on its own, in four layers from the chunks to the answer, with runnable Python and 25 evaluation tools compared.
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The 0.998 ANN-versus-exact check is useful because it isolates index loss from relevance quality. I would make ties at the top-ten boundary explicit in that comparison, especially once quantization produces more equal scores. Two searches can return different document IDs with identical boundary scores without one having lost a closer neighbour.
Keep the strict ID-overlap number, but add the score gap for each missed exact result and a tie-aware diagnostic with a stated tolerance. That separates genuine approximation error from arbitrary tie ordering, while preserving your distinction between finding nearest vectors and finding answer-bearing evidence.