Lexical search contributes greatly to the explainability of hybrid retrieval systems by providing transparent and interpretable scoring mechanisms. Unlike vector similarity—which measures distances in a high-dimensional space that can be hard to interpret—Lexical search scores, such as those produced by BM25, are grounded in observable text features like term frequency and inverse document frequency. This makes it possible to explain why a document was retrieved or ranked highly, since the influence of each word and weight can be directly analyzed and visualized.
For example, if a hybrid retrieval system combines Milvus embeddings with BM25 scores, developers can easily show which keywords contributed most to the ranking. They can present explanations like “This document ranked higher because it contained the query term ‘database indexing’ five times and has shorter text length.” This level of interpretability is important for applications such as legal document search, healthcare records retrieval, or enterprise knowledge bases, where users must understand and trust the reasoning behind results. Lexical scores thus provide a human-understandable justification for part of the hybrid ranking.
In practice, combining Lexical explainability with vector semantics allows for both transparency and depth. Developers can show which words matched while also noting that semantically related concepts were captured through embeddings in Milvus. For instance, a result might not contain the query word “backup,” but vector similarity identifies it as relevant because it discusses “data redundancy.” This hybrid explainability gives users confidence in both exact matches and inferred meanings. Ultimately, Lexical search anchors hybrid retrieval systems in interpretability, ensuring that developers and users can trace, audit, and fine-tune how search relevance is determined.