redis-vector-search
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, EFCONSTRUCTION, EFRUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/redis/agent-skills --skill redis-vector-search- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads redis-vector-search automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
VECTORfield inFT.CREATE(raw RQE) or a RedisVLIndexSchema. - 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:
DIM— the model's output dimensionality (e.g. 1536 for OpenAItext-embedding-3-small). A mismatch produces silent garbage.DISTANCE_METRIC—COSINEfor normalized text embeddings (the common case),IPfor unnormalized inner-product,L2for raw Euclidean.TYPE/datatype— usuallyFLOAT32. UseFLOAT16or quantized variants only when memory cost is a hard constraint.
Raw RQE:
FT.CREATE idx:docs ON HASH PREFIX 1 doc:
SCHEMA
content TEXT
embedding VECTOR HNSW 6
TYPE FLOAT32
DIM 1536
DISTANCE_METRIC COSINE
RedisVL:
schema = IndexSchema.from_dict({
"index": {"name": "idx:docs", "prefix": "doc:"},
"fields": [
{"name": "content", "type": "text"},
{"name": "embedding", "type": "vector", "attrs": {
"dims": 1536, "algorithm": "HNSW",
"datatype": "FLOAT32", "distance_metric": "COSINE",
}},
]
})
See references/index-creation.md for redis-py and RedisVL variants.
2. HNSW vs FLAT
| Algorithm | Speed | Accuracy | Memory | Best for |
|---|---|---|---|---|
| HNSW | Fast (approximate) | ~95%+ recall (tunable) | Higher | Large datasets (>10k vectors), latency-sensitive |
| FLAT | Slow (exact) | 100% | Lower | Small datasets (<10k), accuracy-critical |
Default to HNSW for any production-scale workload. Tuning levers:
M— connections per node (16–64). Higher = better recall, more memory.EF_CONSTRUCTION— build-time graph quality (100–500). Higher = better index, slower build.EF_RUNTIME— query-time candidate-list size. Higher = better recall, slower queries.
Use FLAT when the corpus is small and you need exact results (e.g. semantic dedup over a few thousand items).
See references/algorithm-choice.md.
3. Hybrid search — filter before vector
Apply attribute filters (TAG / NUMERIC) so the engine narrows the search space before the vector comparison. Don't fetch a wide result set and then filter client-side — that's slower and less accurate.
from redisvl.query import VectorQuery
from redisvl.query.filter import Num, Tag
filters = (Tag("category") == "technology") & (Num("date") >= 2024)
query = VectorQuery(
vector=query_embedding,
vector_field_name="embedding",
return_fields=["content", "category", "date"],
num_results=10,
filter_expression=filters,
)
results = index.query(query)
For text + vector fusion (BM25-weighted text scoring combined with vector similarity), use HybridQuery on Redis ≥ 8.4 with redis-py ≥ 7.1, or AggregateHybridQuery on older Redis. That's a different "hybrid" from filtered vector search above.
See references/hybrid-search.md.
4. RAG pattern
Standard pipeline: embed the user query → vector search Redis → pass top-K context to the LLM.
# Index documents with embeddings
records = [{"content": doc.content,
"embedding": embed_model.encode(doc.content).tolist(),
"source": doc.source}
for doc in documents]
index.load(records)
# Retrieve relevant context for a user question
q_emb = embed_model.encode(user_question)
results = index.query(VectorQuery(
vector=q_emb,
vector_field_name="embedding",
return_fields=["content", "source"],
num_results=5,
))
# Generate with retrieved context
context = "\n".join(r["content"] for r in results)
response = llm.generate(f"Context: {context}\n\nQuestion: {user_question}")
Practical tips:
- Match metric to model. Most modern text embedding models pair best with
COSINE. - Chunk long documents before indexing — retrieval over 200–500-token chunks usually beats indexing whole pages.
- Batch inserts with
index.load([...])instead of one call per record. - Pre-filter with attributes (tenant, recency, document type) before the vector search.
See references/rag-pattern.md.
References
FAQ
What does the redis-vector-search skill do?
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, EFCONSTRUCTION, EFRUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
How do I install redis-vector-search?
Run npx skills add https://github.com/redis/agent-skills --skill redis-vector-search in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/redis-vector-search/ (personal) or .claude/skills/redis-vector-search/ (project).
Which AI agents can use redis-vector-search?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is redis-vector-search safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains redis-vector-search?
It is published by redis in redis/agent-skills, and it is an official skill from the maker of the technology it covers. It has 415 recorded installs.
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