"Vector search and applications" by Andrey Vasnetsov, CTO at 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!
Andrey Vasnetsov, Co-founder and CTO at Qdrant has shared about vector search and applications with Learn NLP Academy.
The video record of the discussion is available on YouTube by this link 🔗
He covered the following topics:
- Qdrant search engine and Quaterion similarity learning framework;
- Similarity learning to multimodal settings;
- Elastic search embeddings vs vector search engines;
- Support for multiple embeddings;
- Fundraising and VC discussions;
- Vision for vector search evolution;
- Finetuning for out of domain.
