Lexical search integrates with vector similarity search through a hybrid retrieval architecture that combines keyword precision with semantic understanding. The process typically starts with a Lexical search using an algorithm like BM25 to retrieve documents that explicitly match the user’s query terms. These initial results are ranked according to term frequency, inverse document frequency, and document length normalization. Once this candidate set is retrieved, each document and the query are represented as embeddings stored in a vector database such as Milvus, which then computes semantic similarity scores.
Integration between the two systems can occur at different stages of the pipeline. One common approach is “Lexical-first,” where Lexical search acts as a filter to reduce the search space before vector similarity computation. This improves performance by limiting the number of embeddings that Milvus must compare. Another approach is “parallel retrieval,” where both Lexical and vector searches run independently, and their scores are later combined through a weighted fusion model. Developers can tune the weights to adjust how much influence each method has, depending on whether the task prioritizes precision or conceptual similarity.
This integrated method is especially useful for retrieval-augmented generation (RAG) systems, enterprise document search, and customer support applications. Lexical search ensures that key terms—such as specific names or codes—are not missed, while Milvus brings in documents with relevant meanings that lack exact word matches. The combination produces more balanced, contextually aware results. By integrating Lexical and vector search, developers can achieve the best of both retrieval paradigms: deterministic precision from Lexical indexes and semantic flexibility from vector embeddings.