Hybrid search represents a paradigm shift in information retrieval. By combining traditional lexical (keyword-based) search with modern vector (semantic) search, we can achieve results that are both precise and contextually relevant.
Lexical search excels at exact matches — when a user searches for “PHP 8.3 migration guide”, lexical search finds documents containing those exact terms. However, it fails at understanding intent. A search for “how to upgrade my scripting language” won’t match documents about PHP migration.
Vector search solves this by encoding queries and documents into high-dimensional vector spaces using embedding models like E5-large-instruct. Semantically similar content clusters together, so “upgrade scripting language” lands near “PHP migration” in vector space.
The {!bool} query parser in Apache Solr combines both approaches in a single request. Lexical scores from edismax and KNN vector scores are summed, with configurable weights controlling the balance. Union mode surfaces hits from either signal; intersection mode requires both.
Key tuning parameters include: lexical_weight (0.1 = semantic-dominant, 1.0 = full lexical), vector_topk (candidate pool size), mm (minimum match), and quality_boost (content richness scoring).