Metadata Filtering: Boost Vector Search Precision at Scale

This post details the critical role of metadata filtering in enhancing vector search precision, especially as datasets grow. It explains the difference between pre-filtering and post-filtering, and where each breaks down at scale. The post introduces hybrid queries, specifically mentioning Reciprocal Rank Fusion (RRF) and Redis's FT.HYBRID command, as a way to combine keyword search and vector similarity. It emphasizes the benefits of keeping filters, text indexes, and vectors within a single engine to avoid data synchronization issues and maintain consistency. Best practices for metadata filtering, such as limiting filterable indexes and treating tenant/permission filters as security controls, are also provided.