Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lexical and vector rank

Redis Search Single source of guidance for Redis Search — the retrieval surface that spans lexical, numeric, geo, JSON path, and vector queries. Vector fields are part of the same FT.CREATE machinery as TEXT/TAG/NUMERIC fields, and FT.HYBRID blends lexical and vector ranking in one command, so this skill covers them together. When to apply Creating, modifying, or reviewing a Redis Search index ( FT.CREATE , FT.ALTER ). Writing or optimizing FT.SEARCH , FT.AGGREGATE , or FT.HYBRID queries. Picking between TEXT , TAG , NUMERIC , GEO , GEOSHAPE , VECTOR , or JSON path fields. Defining a VECTOR field, choosing HNSW vs FLAT, tuning HNSW parameters. Building a retrieval augmented generation (RAG) pipeline. Rolling out a new index schema without downtime. Troubleshooting empty results, slow queries, or tokenization issues with FT.EXPLAIN , FT.PROFILE , FT.INFO . 1. Pick the right command Three query commands. Reach for the narrowest one that fits. Command When to use Mental model Minimum Redis FT.SEARCH Document retrieval, ranked or sorted. Best default. Returns matching docs directly. 2.0 (module) / 8.0 (built in) FT.AGGREGATE Faceting, computed fields, custom output shape, analytics. Declarative pipeline: LOAD , APPLY , GROUPBY , REDUCE , SORTBY . 2.0 / 8.0 FT.HYBRID Blend lexical (BM25) with vector similarity, with configurable fusion. Pipeline with explicit SEARCH + VSIM legs and a COMBINE fusion stage. 8.4.0 For Redis < 8.4 the lexical+vector blend is approximated with FT.SEARCH pre filter + = [KNN ...] . See [references/command selection.md](references/command selection.md) and [references/hybrid search.md](references/hybrid search.md). 2. Schema basics — FT.CREATE FT.CREATE indexes Hash or JSON documents matching a PREFIX . Always set PREFIX . Use DIALECT 2 (the default since Redis 8; required for vector queries). Pick the narrowest field type that supports your access pattern: Field type Use when Notes TEXT Full text search Tokenized + stemmed; not for exact match TAG Exact match / filtering Add SORTABLE UNF for fastest tag queries NUMERIC Range queries, sorting Prices, counts, timestamps GEO Lat/long points Stores, users GEOSHAPE Polygon / area queries Delivery zones, regions VECTOR Similarity search HNSW or FLAT; see §4 JSON $.path AS alias Nested JSON fields ON JSON ; see [references/json indexing.md](references/json indexing.md) The classic mistake is TEXT for a category or status field "because it's a string" — TAG is roughly 10× faster for exact match filtering. See [references/index creation.md](references/index creation.md), [references/field types.md](references/field types.md), [references/dialect.md](references/dialect.md), [references/ft create options.md](references/ft create options.md), [references/json indexing.md](references/json indexing.md). 3. Common queries Narrow with filters; return only what you need. Operators worth remembering: space = AND, = OR, = NOT, ~ = optional (scoring boost), = {$weight: N} = boost. Escape hyphens and special characters inside TAG values ( @sku:{ABC\\ 123} ). See [references/query syntax.md](references/query syntax.md) and [references/search syntax primitives.md](references/search syntax primitives.md) for the DSL vocabulary. For tokenization gotchas (stemming, stopwords, language) see [references/text tokenization.md](references/text tokenization.md). For result shaping ( SORTBY , RETURN , HIGHLIGHT , SUMMARIZE , NOCONTENT ) see [references/result shaping.md](references/result shaping.md). For performance levers (pre filters, SORTABLE fields, tight RETURN , FT.PROFILE ) see [references/query optimization.md](references/query optimization.md). 4. Vector basics Three vector settings have to match the embedding model exactly: DIM — output dimensionality (e.g. 1536 for OpenAI text embedding 3 small ). Mismatch produces silent garbage. DISTANCE METRIC — COSINE for normalized text embeddings (common case), IP for unnormalized inner product, L2 for raw Euclidean. TYPE — usually FLOAT32 . Use FLOAT16 or quantized variants only when memory is the binding constraint. Algorithm Speed Accuracy Memory Use for HNSW Fast (approximate) ~95%+ recall (tunable) Higher Production: 10k vectors, latency sensitive FLAT Slow (exact) 100% Lower Small corpora (<10k), exact match required HNSW tuning levers: M (16–64, connections per node), EF CONSTRUCTION (100–500, build quality), EF RUNTIME (query time candidate list). See [references/vector query.md](references/vector query.md), [references/algorithm choice.md](references/algorithm choice.md). 5. Hybrid retrieval Two distinct patterns get called "hybrid." Pick by intent. Filter then vector (any Redis version) — apply attribute filters so the engine narrows the search space before the vector comparison. Lexical + vector fusion (Redis ≥ 8.4) — blend BM25 text scoring with vector similarity, fuse with RRF or LINEAR . Use FT.HYBRID (see §1). Don't fetch a wide unfiltered result and filter client side — slower and less accurate. See [references/hybrid search.md](references/hybrid search.md). 6. Aggregations and shaping FT.AGGREGATE is the declarative result shaping command. Build a pipeline of stages. Common stages: LOAD , APPLY (computed fields), FILTER (post query), GROUPBY + REDUCE ( SUM , COUNT , AVG , FIRST VALUE , TOLIST ), SORTBY , LIMIT . For long running result sets use WITHCURSOR + FT.CURSOR READ to page server side. See [references/aggregate pipeline.md](references/aggregate pipeline.md) and [references/aggregate cursors.md](references/aggregate cursors.md). 7. RAG pattern Standard pipeline: embed the query, vector search Redis, pass top K context to the LLM. Practical tips: Match the metric to the embedding model (almost always COSINE for normalized text models). Chunk long documents (200–500 token chunks usually beat indexing whole pages). Batch inserts rather than one call per record. Pre filter with attributes (tenant, recency, document type) before the vector search — see §5. Re rank at the top of the funnel if precision matters more than recall. See [references/rag pattern.md](references/rag pattern.md). 8. Operations Zero downtime schema changes: keep app queries pointed at an alias and swap the underlying index. Useful management commands: FT.INFO , FT.DROPINDEX , FT. LIST , FT.ALIASADD/UPDATE/DEL . See [references/index management.md](references/index management.md). Debug empty or slow queries with FT.EXPLAIN (shows how the query was parsed) and FT.PROFILE (shows execution stats). See [references/debugging.md](references/debugging.md). 9. Client examples Inline examples in this SKILL.md are CLI / RESP form — the wire protocol every client serializes to. For idiomatic snippets in a specific client: redis py (Python, raw client): [references/clients/python redis py.md](references/clients/python redis py.md) Jedis (Java): [references/clients/java jedis.md](references/clients/java jedis.md) RedisVL (Python, higher level SDK on top of redis py): [references/clients/python redisvl.md](references/clients/python redisvl.md) Other clients (Lettuce, node redis, go redis, NRedisStack, .NET) translate the same CLI form; coverage is tracked as a follow up. References [Redis: Search and query](https://redis.io/docs/latest/develop/interact/search and query/) [Redis: Vectors](https://redis.io/docs/latest/develop/ai/search and query/vectors/) [Redis: Query syntax](https://redis.io/docs/latest/develop/interact/search and query/query/) [Redis: Query dialects](https://redis.io/docs/latest/develop/interact/search and query/advanced concepts/dialects/) [Redis: RAG quickstart](https://redis.io/docs/latest/develop/get started/rag/) [FT.CREATE](https://redis.io/docs/latest/commands/ft.create/) · [FT.SEARCH](https://redis.io/docs/latest/commands/ft.search/) · [FT.AGGREGATE](https://redis.io/docs/latest/commands/ft.aggregate/) · [FT.HYBRID](https://redis.io/docs/latest/commands/ft.hybrid/) [RedisVL documentation](https://docs.redisvl.com/en/latest/)

redis-search