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This is a beta feature available in versions 0.25.0 and above.
Vector search finds rows whose embeddings are closest to a query vector. It is useful when relevance depends on semantic similarity rather than exact token matches. Today, many Postgres applications use the pgvector extension for similarity search. pgvector works well for many use cases, but separate vector indexes can struggle when a query also needs selective filters, text predicates, or frequent updates. The ParadeDB index supports pgvector’s vector type inside the same index that stores text and columnar data. This lets ParadeDB evaluate vector similarity, full-text predicates, filters, and ranking from one index-backed query path. ParadeDB vectors use ParadeDB-built index structures designed for fast nearest-neighbor retrieval at scale. They are independent from pgvector’s HNSW and IVFFlat indexes, while still using pgvector’s vector type. Because vectors live in the same index as text and filters, vector search can be combined with text search into a single ranking. See How Hybrid Search Works. For indexing, query syntax, and tuning options, see the Vector / Semantic Search reference.