Semantic Question Answering as a Service

What it does

End-to-end Question Answering system for the biomedical data with SaaS tools: Cohere co.embed API and Qdrant

Bi-encoders are probably the most efficient way of setting up a semantic Question Answering system. This architecture relies on the same neural model that creates vector embeddings for both questions and answers. The assumption is, both question and answer should have representations close to each other in the latent space. It should be like that because they should both describe the same semantic concept. That doesn’t apply to answers like “Yes” or “No” though, but standard FAQ-like problems are a bit easier as there is typically an overlap between both texts. Not necessarily in terms of wording, but in their semantics. And yeah, you need to bring your own…from qdrant.tech

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