Hybrid indexing means maintaining more than one type of index over the same data so that different query types can each be served efficiently. A common example is keeping a vector graph index for semantic similarity alongside an inverted keyword index for exact-term matching, both built over the same documents.
This is what makes hybrid search practical. To combine dense vector retrieval with sparse keyword retrieval, the database needs both a structure optimised for nearest-neighbour search and one optimised for term lookups. By maintaining both, it can run each kind of query against the index best suited to it, then fuse the results into a single ranking.
Hybrid indexing can also refer to combining multiple vector index strategies — for instance pairing a coarse cluster-based index with a fine graph index, or mixing approximate and exact execution depending on filter selectivity. In all cases the principle is the same: rather than forcing one index to serve every query, the system keeps complementary structures so each query takes the most efficient path, at the cost of extra storage and maintenance.