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HyDE could be a challenger to SBERT !

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The problem:

SBERT models https://www.sbert.net/ are trained to build a similarity embedding between a query and a passage.
The problem is that queries and passages are very different in length, form and semantic.

A solution with HyDE ?
Why not instead match a generated query passage from a model like OpenAI #GPT to passages in a vector database like Weaviate: compare the generated query passage embedding to (near) all database embeddings?

Another module for Weaviate in preparation?

WPSOLR + Weaviate: https://www.wpsolr.com/guide/configuration-step-by-step-schematic/configure-your-indexes/create-weaviate-index/

#wpsolr #weaviate ##nlp #gpt #vectordatabase #vectorsearch #sbert

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