Redis Vector Search

Guidance for storing and searching embeddings in Redis. Covers index configuration, algorithm selection, hybrid filtering, and the RAG retrieval pattern with RedisVL.

When to apply

• Defining a VECTOR field in FT.CREATE (raw RQE) or a RedisVL IndexSchema.
• Choosing HNSW vs FLAT and tuning HNSW parameters.
• Adding category, date, or tenant filters to a vector query.
• Building a retrieval-augmented generation (RAG) pipeline on top of Redis.

This skill builds on the redis-query-engine skill — vector fields live inside RQE indexes and share the same FT.CREATE / FT.SEARCH machinery.

1. Configure the vector index properly

Three settings must match the embedding model: