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Composite vector indexes combine vector similarity search with scalar filtering to enable efficient Filtered Approximate Nearest Neighbor (FANN) queries. Unlike traditional approaches that retrieve many vector-similar candidates before applying filters, composite indexes integrate scalar fields (like nutritional values) directly into the index structure, enabling early pruning and reducing wasted computation. Using a grocery recommendation system as an example, the post demonstrates how embeddings capture semantic meaning while scalar filters enforce constraints, allowing queries to find items that are both semantically similar and meet specific criteria. The implementation uses Couchbase's vector indexing with FAISS-based quantization methods, where vector fields are explicitly marked in the index definition alongside scalar fields for optimized ANN search.

Filtered ANN Search With Composite Vector Indexes

Before We Get to FANN, Let’s Build Intuition