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Cache plus-white minus-white

Build better experiences that grow with you—with accessible, enterprise-grade caching built by the devs who brought you open source Redis.

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res11 = r.json().set("newbike", "$", ["Deimos", {"crashes": 0}, None])
print(res11) # >>> True

res12 = r.json().get("newbike", "$")
print(res12) # >>> ['["Deimos", { "crashes": 0 }, null]']

res13 = r.json().get("newbike", "$[1].crashes")
print(res13) # >>> ['0']

res14 = r.json().delete("newbike", "$.[-1]")
print(res14) # >>> [1]

res15 = r.json().get("newbike", "$")
print(res15) # >>> [['Deimos', {'crashes': 0}]]

Vector plus-white minus-white

The best results are the results you were searching for. Make your AI app smarter and faster with streamlined document search, recommendation systems, semantic caching, and Retrieval Augmented Generation (RAG).

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# Create a vector index using the HNSW algorithm, 768 dimension length, and inner product distance metric
> FT.CREATE idx-videos ON HASH PREFIX 1 video: SCHEMA content_vector VECTOR HNSW 6 TYPE FLOAT32 DIM 768 DISTANCE_METRIC IP content TEXT metadata TEXT

# Add a video vector with metadata
> HSET video:0 content_vector "\xa4q\t=\xc1\xdes\xbdZ$ (integer) 3

# Search for videos using a similar vector and the K-nearest neighbors algorithm
> FT.SEARCH idx-videos "* => [KNN 3 @content_vector $vector AS vector_score]" RETURN 3 metadata content vector_score SORTBY vector_score LIMIT 0 3 PARAMS 2 vector "\b[\xb7;\x81\x12\x9c\xbc\xc6!...\xfe

Database plus-white minus-white

Use Redis as your NoSQL database to build fast, reliable apps that make five-9s uptime look easy.

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# Create an index. In this example, all JSON documents with the key prefix 'user:' will be indexed.
rs = r.ft("idx:users")
rs.create_index(
schema,
definition=IndexDefinition(
prefix=["user:"], index_type=IndexType.JSON
)
)

# Use JSON.SET to set each user value at the specified path.
r.json().set("user:1", Path.root_path(), user1)
r.json().set("user:2", Path.root_path(), user2)
r.json().set("user:3", Path.root_path(), user3)

# Find the user Paul and filter the results by age.
res = rs.search(
Query("Paul @age:[30 40]")
)

# Result:
# {1 total, docs: [Document {'id': 'user:3', 'payload': None, 'json': '{"name":"Paul Zamir","email":"paul.zamir@example.com","age":35,"city":"Tel Aviv"}'}]}

# b'OK'
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Find what you need to get started

Quick Start

Redis as an in-memory data structure store

Quick Start

Redis as a document database

Quick Start

Redis as a vector database

Cheat sheet

Redis commands cheat sheet

Tutorial

Perform vector search using Redis in NodeJS


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Customer stories

Finance

Flowdesk implements Redis for real-time analytics, achieving 10x faster query performance and 50% reduction in infrastructure costs.

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SOFTWARE

Docugami uses Redis to easily store, search, and update vector embeddings at scale.

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Security

Ekata uses Auto Tiering, which requires 70% less of the expensive DRAM storage they needed before Redis.

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Customer stories

Mobile gaming

Redis allows Niantic to use less overhead to balance their server load and offer great player experiences.

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Customer stories

Retail

Ulta implements Redis to enhance customer experience, achieving 50% reduction in response time and scaling to handle more than 300,000 transactions per second.

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Customer stories

Hospitality

iFood boosts performance and scalability with Redis, achieving a 50% reduction in latency and a 3x increase in throughput for its food delivery platform.

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