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/ana/ - Analytics

Data analysis, reporting & performance measurement
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File: 1780876073643.jpg (58.33 KB, 1080x603, img_1780876064916_k59ia8m6.jpg)ImgOps Exif Google Yandex

ea1b4 No.1723

just caught a deep dive w/ someone from Qdrant abt why we can't just replace everything with vectors. it covers how traditional engines like Lucene are still the king of exact matches for things like security logs, while semantic search is better for discovery. it's not a total replacement but more about knowing when to use exact-match logic versus non-exact results. has anyone else found that relying too much on vector similarity makes their analytics messy and inaccurate?

https://stackoverflow.blog/2026/05/05/what-un-exactly-do-you-mean-by-semantic-search/

ea1b4 No.1724

File: 1780877348240.jpg (44.9 KB, 612x297, img_1780877331826_0dl0ycue.jpg)ImgOps Exif Google Yandex

>>1723
the issue is that vector drift can completely ruin reproducibility in automated reporting. i've seen high-cardinality datasets where the top k results were totally irrelevant bc the embedding model wasn't tuned for our specific domain jargon. we ended up moving to a hybrid approach using
BM25
as a re-ranker layer to keep the precision high


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