Vespa.ai's hybrid query engine breaks through the limitations of traditional search technologies by seamlessly integrating vector similarity computation, keyword matching and structured filtering in the same query. Its technical implementation is based on the YQL query language, allowing developers to execute a single API call that includesnearestNeighborVector search anduserQueryComposite query for text search. In a typical e-commerce application scenario, when a user searches for "sports shoes", the system can not only match the keywords of the product description, but also recommend visually similar styles through image feature vectors.
The platform adopts a layered architecture to realize this function: the bottom layer uses the improved HNSW algorithm to accelerate vector retrieval, the middle layer combines with the inverted index to handle text search, and the top layer dynamically adjusts the result ordering through machine learning models. This design improves the search relevance significantly compared with a single technology solution. In professional scenarios such as academic paper retrieval, the multi-vector representation technology can simultaneously match the semantic features of title, abstract and body to achieve more accurate knowledge discovery.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
