Vector indexes in Redis: algorithms, hybrid search & scaling

Redis has integrated vector index capabilities into its platform, enabling high-dimensional similarity searches essential for applications like support chatbots and retrieval augmented generation (RAG) pipelines. Vector indexes, such as those supported by Redis 8, facilitate rapid retrieval of semantically similar data by transforming data into vector embeddings represented as arrays of floating-point numbers. Redis offers three index algorithms—FLAT, HNSW, and SVS-VAMANA—each tailored for different scales and accuracy needs, with HNSW being the most commonly deployed in production environments due to its efficient multi-layer graph structure. Additionally, Redis 8.4 introduced hybrid search capabilities via the FT.HYBRID command, allowing for combined vector and full-text searches with improved performance for distributed workloads. Redis also supports various numeric types for vector fields and provides two mechanisms for vector search: through the Redis Query Engine for complex queries and Vector Sets for simpler, native use cases. This versatility makes Redis a robust choice for building scalable, AI-powered applications by consolidating multiple functionalities like caching, session management, and operational data storage within a unified platform.

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