Developers should prefer Lexical search over semantic retrieval when the use case requires exact keyword matching, deterministic behavior, or explainable results. Lexical search shines in scenarios where the literal text of the query must appear in the retrieved documents—such as searching for log messages, code snippets, configuration files, or compliance-related records. In these cases, semantic retrieval could introduce unwanted noise by returning conceptually similar but textually different results. Lexical search guarantees precision and reproducibility because it ranks results purely on observed word frequencies and positions, making it easy to understand and debug.
Another advantage of Lexical search appears when handling short or structured queries. For example, a user searching for “error 503,” “config.yaml,” or “index type: IVF_FLAT” benefits from exact matching rather than semantic expansion. Lexical search performs well here because it operates efficiently on discrete tokens and doesn’t require complex embeddings or neural models. Moreover, its inverted index structure allows for low-latency lookups even on massive datasets, making it ideal for applications like search logs, internal documentation systems, or e-commerce filters.
That said, developers often integrate Lexical search with Milvus for hybrid retrieval to gain the best of both worlds. Lexical search handles deterministic precision—identifying documents that contain the exact query terms—while Milvus performs semantic re-ranking or query expansion based on embeddings. But in purely factual or rule-based environments, such as source code search or legal text retrieval, Lexical search alone remains the more reliable option. Its simplicity, transparency, and efficiency make it the preferred foundation for applications where accuracy and control outweigh contextual inference.