AI Skills
redis-vector-search

redis-vector-search

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Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.

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更新于 2026/9/8
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名称
redis-vector-search
描述

Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.

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: