Lexical search supports filtering in vector databases by providing a precise and efficient way to narrow down the candidate set before vector similarity computations. In a hybrid search pipeline, Lexical filtering typically happens first: it applies keyword matches or structured conditions to limit which documents or vectors are considered for semantic comparison. For example, if a query asks for “Python tutorials in data engineering,” Lexical search might filter documents that contain “Python” or belong to the “data engineering” category before passing them to the vector search layer in Milvus.
This pre-filtering approach significantly improves system performance. Vector similarity search involves comparing embeddings in high-dimensional space, which can be computationally expensive when applied to an entire dataset. By using Lexical search to filter based on metadata, tags, or key phrases, developers reduce the number of embeddings that Milvus must process, thereby cutting latency and resource usage. For instance, a system might use Lexical filters to select only “technical blog” entries from millions of documents, and then let Milvus perform similarity ranking within that subset.
Beyond efficiency, Lexical filtering also adds control and interpretability to hybrid retrieval. Developers can design Boolean filters, range queries, or field-specific conditions to enforce strict constraints before vector retrieval runs. This makes the system both fast and explainable—Lexical search dictates which documents are eligible, while Milvus determines which among them are semantically most relevant. Together, they create a layered retrieval strategy where Lexical filtering ensures correctness and Milvus ensures contextual depth, resulting in search systems that are both scalable and intelligent.