> […] would have to convert 51,751 embeddings to blob data and then convert them back to arrays when searching which would take over 20 seconds. 0.4ms per embe

> […] would have to convert 51,751 embeddings to blob data and then convert them back to arrays when searching which would take over 20 seconds. 0.4ms per embedding is far longer than I’d have expected. I haven’t worked in this space just yet, so I lack knowledge and am curious: how big is each embedding? A few hundred numbers? The bottleneck here is Python, right? How fast is it with Chroma? —⁂— Search difficulties in the presence of intermingled metadata and data is not a new problem. I detest issue templates on GitHub because they’re putting everything into one freeform text field, and any keyword that’s part of the template becomes completely unsearchable, because every issue matches the query. This has bitten me more than a few times.