Once every chunk is tokenized, BM25Okapi builds the index — computing document lengths, average document length, and IDF scores for every unique term in the corpus. This happens once at startup. At query time, bm25_search tokenizes the incoming query the same way, calls get_scores to compute a BM25 relevance score for every chunk in parallel, then sorts and returns the top-k results. The sanity check at the bottom runs a test query to confirm the index is working before we move on to the embedding retriever.
I run this Dokploy instance on Hetzner and my experience has been really positive. The pricing is unbeatable, even with the recent increase, and it’s been rock solid for me. Really, with the Dokploy instance, there’s nothing stopping me from packing up and going somewhere else. Having that kind of freedom is very nice. But I’m more than happy to stick with Hetzner.
，详情可参考有道翻译
for all applications: business and scientific. The name symbolized that System/360 covered
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