Lexical search handles exact keyword matches through its core reliance on word-level tokenization and inverted indexes. When a user submits a query, the system breaks it into tokens—usually words or stems—and searches for documents that contain those tokens in their indexed form. This process makes Lexical search highly efficient at finding exact matches because it directly maps query terms to document occurrences. Algorithms like BM25 or TF-IDF then rank results based on how frequently those words appear and how rare they are across the corpus, ensuring that more focused and unique matches score higher.
This exact-match behavior is especially useful for applications that require precision, such as legal text search, source code search, or log file analysis. For instance, a query for “error code 502” should only retrieve documents explicitly mentioning that code, not semantically similar phrases like “server failure.” Lexical search ensures this by treating each word literally. It also supports phrase matching and Boolean operators (AND, OR, NOT), allowing developers to define strict retrieval logic. This deterministic nature makes results explainable and reproducible, which is valuable for enterprise and compliance-heavy use cases.
In modern hybrid systems, Lexical search still plays a vital role even when vector databases like Milvus are used. It serves as a precision filter, ensuring that the retrieved candidates explicitly reference the query’s keywords before semantic expansion happens. After this stage, embeddings can be used for re-ranking or supplementing results that may not match exactly but are conceptually related. By handling exact matches at the start, Lexical search maintains precision and performance, creating a stable foundation for semantic search layers to build upon.