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# 使用 HNSW 算法、768 维度长度和内积距离度量创建向量索引
> 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

# 添加具有元数据的视频向量
> 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

# 使用类似的向量和 K 最近邻算法搜索视频
> 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 2

# 在“users:*”上创建索引
> FT.CREATE user-idx ON JSON PREFIX 1 users: SCHEMA $.user.name AS name TEXT $.user.hobbies AS hobbies TAG $.user.age as age NUMERIC
“OK”

# 添加要索引的 JSON 文档
> JSON.SET users:1 $ ‘{“user”:{“name”:”John Smith”,”hobbies”:[“sports”,”computers”],”age”:23}}’
“OK”

# 搜索所有名为“John”的用户文档
> FT.SEARCH user-idx ‘@name:(John)’
1) “1”
2) “users:1”
3) 1) “$”
2) “{\”user\”:{\”name\”:\”John Smith\”,\”hobbies\”:[\”sports\”,\”computers\”],\”age\”:23}}”

# 搜索名为“John”且爱好为“sports”或“writing”且年龄在 20 到 30 之间的人员
> FT.SEARCH user-idx ‘@name:(John) @hobbies:{sports | writing} @age:[20 30]’
1) “1”
2) “users:1”

3) 1) “$” 2) “{\”user\”:{\”name\”:\”John Smith\”,\”hobbies\”:[\”sports\”,\”computers\”],\”age\”:23}}”