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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", "$")
print(res15) # >>> [['Deimos', {'crashes': 0}]]The best results are the results you were searching for. Make your AI app smarter and faster with streamlined document search, recommendation systems, semantic caching, and Retrieval Augmented Generation (RAG).
# Create a vector index using the HNSW algorithm, 768 dimension length, and inner product distance metric
> FT.CREATE idx-videos ON HASH PREFIX 1 video: SCHEMA content_vector VECTOR HNSW 6 TYPE FLOAT32 DIM 768 DISTANCE_METRIC IP content TEXT metadata TEXT
# Add a video vector with metadata
> HSET video:0 content_vector "\xa4q\t=\xc1\xdes\xbdZ$<\xbd\xd5\xc1\x99<b\xf0\xf2<x[...\xf8<" content "SUMMARY:\nThe video discusses the limitations of MySQL at scale and introduces Redis Enterprise" metadata "{\"id\":\"FQzlq91g7mg\",\"link\":\"https://www.youtube.com/watch?v=FQzlq91g7mg\",\"title\":\"Redis + MySQL in 60 Seconds\"}"
(integer) 3
# Search for videos using a similar vector and the K-nearest neighbors algorithm
> FT.SEARCH idx-videos "* => [KNN 3 @content_vector $vector AS vector_score]" RETURN 3 metadata content vector_score SORTBY vector_score LIMIT 0 3 PARAMS 2 vector "\b[\xb7;\x81\x12\x9c\xbc\xc6!...\xfe<" DIALECT 2Use Redis as your NoSQL database to build fast, reliable apps that make five-9s uptime look easy.
# Connect to Redis
r = Redis(host='localhost', port=6379, decode_responses=True)
# Create a search index on documents with prefix "doc:"
try:
r.ft("idx:docs").create_index(
fields=[
TextField("$.title", as_name="title"),
TextField("$.content", as_name="content"),
TagField("$.tags", as_name="tags")
],
definition=IndexDefinition(prefix=["doc:"], index_type=IndexType.JSON)
)
except Exception as e:
print(f"Index creation skipped: {e}")
# Add some documents
doc1 = {
"title": "Redis JSON Guide",
"content": "Learn how to use Redis with JSON and search.",
"tags": "redis,guide,search"
}
doc2 = {
"title": "Introduction to Vector Search",
"content": "Explore semantic search with Redis.",
"tags": "vector,search,redis"
}
doc3 = {
"title": "Caching Best Practices",
"content": "Speed up your apps using Redis caching.",
"tags": "cache,performance,redis"
}
r.json().set("doc:1", Path.root_path(), doc1)
r.json().set("doc:2", Path.root_path(), doc2)
r.json().set("doc:3", Path.root_path(), doc3)
# Search for documents that mention "search"
query = Query("search")
res = r.ft("idx:docs").search(query)
# Print matching documents
for doc in res.docs:
print(f"{doc.id}:\n Title: {doc.title}\n Tags: {doc.tags}\n")Flowdesk implements Redis for real-time analytics, achieving 10x faster query performance and 50% reduction in infrastructure costs.
Ekata uses Auto Tiering, which requires 70% less of the expensive DRAM storage they needed before Redis.
Redis allows Niantic to use less overhead to balance their server load and offer great player experiences.
Ulta implements Redis to enhance customer experience, achieving 50% reduction in response time and scaling to handle more than 300,000 transactions per second.
iFood boosts performance and scalability with Redis, achieving a 50% reduction in latency and a 3x increase in throughput for its food delivery platform.