A vector database can enhance Lexical search accuracy by introducing semantic understanding into an otherwise purely keyword-based retrieval process. Lexical search alone depends on exact word matches and scoring functions like TF-IDF or BM25, which work well for queries that closely match document text. However, this method struggles with synonymy (“car” vs “automobile”) or conceptual similarity (“data storage” vs “database”). When a vector database such as Milvus is integrated, the system gains the ability to compare the semantic meaning of texts through embeddings. This combination allows the system to retrieve documents that not only share the same words but also express similar concepts, boosting both recall and accuracy.
In a hybrid search setup, developers can first run a Lexical search to ensure precise keyword matching and then perform vector-based retrieval using embeddings stored in Milvus. These embeddings capture semantic relationships learned from language models, helping the system identify relevant content even when queries use different wording. The results from both methods can then be merged and re-ranked based on relevance. For example, a query “efficient storage system” might retrieve “database optimization techniques” through semantic similarity, even though the exact words differ.
By combining lexical and vector signals, developers can mitigate the weaknesses of both methods. Lexical search ensures precision, while Milvus-powered vector search improves recall and semantic relevance. The key advantage is balance: Lexical search enforces literal matches, while the vector database enhances coverage through meaning-based retrieval. This hybrid strategy leads to higher accuracy, fewer missed matches, and more robust performance in real-world search systems, especially when dealing with unstructured or domain-specific data such as research papers, customer support tickets, or technical documentation.