Question Answering as a Service with Cohere and Qdrant
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load.
With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
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.
Read more in the article by Kacper Łukawski.
