Keyword Search

Conventional keyword search matches user query words to document words using an inverted index data structure for efficient matching and ranking by relevancy.

Vector searches have proven to be useful for handling free-text queries, as opposed to the traditional keyword-based search. However, developing a vector search based on word embeddings from a pre-trained model has limitations when it comes to adapting to custom domains. While keyword search can adapt to new domains they are inherently unsuitable for free-text query. How can we combine both these and implement an hybrid search?