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This is what we use: BERT sentence transformers to generate the embeddings (we used Universal Sentence Encoder before that and it was good too), and ElasticSear
by Xenoamorphous 3y ago
This is what we use: BERT sentence transformers to generate the embeddings (we used Universal Sentence Encoder before that and it was good too), and ElasticSearch for storage, which has a dense vector data type. It also has a cosineSimilarity function to run searches.
- fzliu 3y agoI would not use Elastic for vector search due to its architectural limitations and poor performance when conducting vector search. https://zilliz.com/benchmark https://zilliz.com/benchmark
- Xenoamorphous 3y agoI should’ve said that we were already using it for actual search where embeddings similarity is just one component of the overall score. For pure vector stuff a dedicated solution will be faster.
- dunefox 3y agoWhat about Solr?