How we speed up filtered vector search with ACORN

Vector embeddings have significantly advanced search capabilities, especially for large datasets, and Weaviate's vector and hybrid search functions support applications like recommendation engines and e-commerce. The complexity of searches that combine traditional filters with vector similarity poses technical challenges, such as deciding whether to filter or search first. Weaviate introduced the ACORN (ANN Constraint-Optimized Retrieval Network) strategy to address these challenges, offering a filter-agnostic solution that maintains graph connectivity without predefined filters. ACORN enhances search efficiency by using a two-hop neighborhood expansion, ensuring performance even when filters and queries have low correlation. Weaviate's implementation of ACORN includes adaptive two-hop expansion, smart entry point handling, and compatibility with existing HNSW indexes, resulting in performance improvements up to tenfold in challenging scenarios. This makes filtered vector search systems more robust and reliable, and ACORN is available in Weaviate 1.27 and newer versions.