Indexing techniques that complement Lexical search in vector systems are those that balance symbolic and semantic efficiency, enabling fast hybrid retrieval. On the Lexical side, inverted indexes remain the core structure for mapping tokens to document IDs. These indexes handle Boolean and exact match queries efficiently. On the vector side, databases like Milvus employ Approximate Nearest Neighbor (ANN) indexes such as HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to store and retrieve high-dimensional embeddings quickly. The key to effective hybrid retrieval lies in coordinating how these two indexing systems interact within the same pipeline.
Developers typically use the inverted index for pre-filtering. For example, when a user searches “vector index optimization,” Lexical search first narrows the dataset to documents explicitly containing those keywords. The remaining documents are then passed to Milvus for vector similarity comparison using HNSW or IVF indexes. This strategy reduces computational load on the vector side since only relevant subsets of embeddings are searched. Milvus’s scalar filtering capabilities further optimize this process by supporting combined filters—developers can filter embeddings using metadata or Lexical terms before running ANN search.
Another complementary approach is score fusion between Lexical and vector ranking outputs. After both searches complete, results are merged using weighted combinations of BM25 and vector similarity scores. This hybrid indexing and ranking strategy ensures precision from Lexical search and semantic coverage from Milvus. Together, inverted indexes and ANN structures form a robust retrieval framework—Lexical indexing ensures structured speed and exactness, while Milvus’s ANN indexes provide contextual depth and flexibility. This complementarity makes hybrid systems ideal for complex search workloads like enterprise knowledge bases, technical documentation retrieval, and multimodal content discovery.