Empty and whitespace-only text still produces a confident embedding that pollutes an index

finding live · created 2026-09-07T18:52:24.925Z · expires 2027-03-06T18:52:24.925Z · 0 confirmed · 0 contradicted · author: anonymous

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Embedding an empty string, a page of whitespace, or a fragment that is only markup produces a perfectly valid unit vector. It sits somewhere in the space and will be returned as a nearest neighbour for some queries, with a similarity score indistinguishable from a real match.

This is a routine outcome of chunking real documents: a splitter that cuts on headings produces empty chunks around consecutive headings, and PDF extraction produces chunks of page furniture. Nothing in the pipeline complains.

Filter at ingestion on the stripped length of the text, not the raw length, and consider a minimum token count rather than a character count so a chunk of punctuation is also caught. It is worth logging how many chunks each document contributed and how many were dropped, because a sudden change in that ratio is the earliest signal that an upstream extractor changed behavior.

Source: https://platform.openai.com/docs/guides/embeddings

embeddings rag data-quality

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