오픈 소스 Redis 개발자가 만든 접근성 좋은 엔터프라이즈급 캐싱을 통해 더 나은 경험을 만들고 함께 성장하세요.
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", "$")
print(res15) # >>> [['Deimos', {'crashes': 0}]]가장 좋은 결과는 당신이 추구하던 결과입니다. 간소화된 문서 검색, 추천 시스템, 시맨틱 캐싱, 검색 증강 생성(RAG)을 통해 AI 앱을 더 똑똑하고 빠르게 만들어보세요.
# Create a vector index using the HNSW algorithm, 768 dimension length, and inner product distance metric
> FT.CREATE idx-videos ON HASH PREFIX 1 video: SCHEMA content_vector VECTOR HNSW 6 TYPE FLOAT32 DIM 768 DISTANCE_METRIC IP content TEXT metadata TEXT
# Add a video vector with metadata
> HSET video:0 content_vector "\xa4q\t=\xc1\xdes\xbdZ$<\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 $vector AS vector_score]" RETURN 3 metadata content vector_score SORTBY vector_score LIMIT 0 3 PARAMS 2 vector "\b[\xb7;\x81\x12\x9c\xbc\xc6!...\xfe<" DIALECT 2Redis를 NoSQL 데이터베이스로 사용하여 99.999%의 가동 시간을 쉽게 달성하는 빠르고 안정적인 앱을 만들어 보세요.
# Create an index. In this example, all JSON documents with the key prefix 'user:' will be indexed.
rs = r.ft("idx:users")
rs.create_index(
schema,
definition=IndexDefinition(
prefix=["user:"], index_type=IndexType.JSON
)
)
# Use JSON.SET to set each user value at the specified path.
r.json().set("user:1", Path.root_path(), user1)
r.json().set("user:2", Path.root_path(), user2)
r.json().set("user:3", Path.root_path(), user3)
# Find the user Paul and filter the results by age.
res = rs.search(
Query("Paul @age:[30 40]")
)
# Result:
# {1 total, docs: [Document {'id': 'user:3', 'payload': None, 'json': '{"name":"Paul Zamir","email":"paul.zamir@example.com","age":35,"city":"Tel Aviv"}'}]}
# b'OK'
res11 = r.json().set("newbike", "$", ["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}]]
