Full-text filter and index are already available!
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!
Qdrant is designed as an efficient vector database, allowing for a quick search of the nearest neighbours. But, you may find yourself in need of applying some extra filtering on top of the semantic search. Up to version 0.10, Qdrant was offering support for keywords only. Since 0.10, there is a possibility to apply full-text constraints as well. There is a new type of filter that you can use to do that, also combined with every other filter type.
Using full-text filters without the payload index Full-text filters without the index created on a field will return only those entries which contain all the terms included in the query. That is effectively a substring match on all the individual terms but not a substring on a whole query.
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