FT.SEARCH searches the index with the provided query, and returns thespecified values. Note that FT.SEARCH returns results for the entire cluster, not just the node that is queried.

For details on query syntax, seeQuery syntax.

Syntax

FT.SEARCH index query  [NOCONTENT]  [TIMEOUT timeout]  [PARAMS nargs name value [ name value ...]]  [RETURN num field [AS alias] [ field [AS alias] ... ]]  [LIMIT offset num]  DIALECT 2

• index (required): This index you want to query.
• query (required): This is your query. For details on query syntax, seeQuery syntax.
• NOCONTENT (optional): This returns just the document IDs, and excludes thecontent.
• TIMEOUT (optional): Lets you set a timeout value for the search command.
• PARAMS (optional): The number of key value pairs times two.
• RETURN (optional): Specifies the fields you want to retrieve from your documents, along with any aliases for the returned values. By default, all fields are returned unless theNOCONTENT option is set, in which case no fields are returned. If num is set to 0, it behaves the same asNOCONTENT.
• LIMIT (optional): Lets you choose pagination with an offset anda number count. If you don't use this parameter, the default isLIMIT 0 10,which returns at most 10 keys.
• DIALECT 2 (optional): Specifies your dialect. The only supported dialectis dialect 2.

Command return

• This command returns an array or an error message. The elements of thereturned array represent the best matched results of the query. Each arrayelement has the following:

• The entry hash key

• An array of the following:

  • Key value: [$score_as ] score_value
  • Distance value
  • Attribute name
  • Vector value

  IfNOCONTENT is used, the array elements consist of just the document IDs.

Example #1: Simple vector search query

For this example, assume we're building a property searching index where customers can search properties based on some features. Assume we have a list of properties with the following attributes:

• Description - vector embedding for given property.
• Other fields - each property can have other metadata as well. However, for simplicity, other fields are ignored in this example.

At first, we create an HNSW index with the description as a vector field using theFT.CREATE command:

FT.CREATE idx SCHEMA description VECTOR HNSW 6 TYPE FLOAT32 DIM 3 DISTANCE_METRIC L2

Now we can insert a few properties (this can be done prior to index creation as well) using the HSET command:

HSET p1 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?"HSET p2 description "\x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00\x00"HSET p3 description "\x00\x00\x80?\x00\x00\x00\x00\x00\x00\x00\x00"HSET p4 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?"HSET p5 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?"

Now we can perform queries using the FT.SEARCH command. The following query returns up to five of the most similar properties to the provided query vector:

FT.SEARCH idx "*=>[KNN 5 @description $query_vector]" PARAMS 2 query_vector "\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00" DIALECT 2

Returned result:

 1) (integer) 5 2) p5 3) 1) __description_score    2) 1.6400001049    3) description    4) \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80? 4) p4 5) 1) __description_score    2) 1.6400001049    3) description    4) \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80? 6) p2 7) 1) __description_score    2) 1.6400001049    3) description    4) \x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00\x00 8) p1 9) 1) __description_score    2) 1.6400001049    3) description    4) \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?10) p311) 1) __description_score    2) 0.0399999953806    3) description    4) \x00\x00\x80?\x00\x00\x00\x00\x00\x00\x00\x00

Code Sample

# Before running, ensure you have installed redis-py:#    pip install redisimport redisclient = redis.cluster.RedisCluster(host='your_server_host', port=6379)result = client.execute_command('FT.SEARCH', 'idx', '*=>[KNN 5 @description $query_vector]', 'PARAMS', '2', 'query_vector', '"\xcd\xccL?\x00\x00\x00\x00"', 'DIALECT', '2')print(result)

# Before running, ensure you have installed ioredis:#   npm install ioredisconst Redis = require("ioredis");const redis = new Redis.Cluster([{  port: 6379,  host: "your_server_host",},]);redis.call("FT.SEARCH", "idx", "*=>[KNN 5 @description $query_vector]", "PARAMS", "2", "query_vector", "\xcd\xccL?\x00\x00\x00\x00\x00\x00", "DIALECT", "2").then(result => {console.log(result);redis.disconnect();});

# Before running, ensure you have install redis-cliredis-cli -h your_server_host -p 6379 FT.SEARCH idx "(*)=>[KNN 5 @description $query_vector]" PARAMS 2 query_vector "\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00" DIALECT 2

Example #2: Vector search withhybrid queries

For this example we will do ahybrid query by using an additional two attributes named city and price:

• Description - vector embedding for given property.
• City - name of the city.
• Price - cost of the property.

At first, we create an index with the description as a vector field, city as a tag field, price as a numeric field:

FT.CREATE idx SCHEMA description VECTOR HNSW 6 TYPE FLOAT32 DIM 3 DISTANCE_METRIC L2 city TAG price NUMERIC

Now, we can insert a few properties (this can be done prior to index creation as well):

HSET p1 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?" city "NEW YORK" price 500000HSET p2 description "\x00\x00\x00\x00\x00\x00\x80?\x00\x00\x00\x00" city "NEW JERSEY" price 400000HSET p3 description "\x00\x00\x80?\x00\x00\x00\x00\x00\x00\x00\x00" city "BANGALORE" price 60000HSET p4 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?" city "NEW YORK" price 600000HSET p5 description "\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?" city "BANGALORE" price 75000

Now, we can perform queries. The following query returns up to five of the most similar properties to the provided query vector, filtering only to those in BANGALORE priced less than 100000:

FT.SEARCH idx "(@city:{BANGALORE} @price:[-inf 100000])=>[KNN 5 @description $query_vector]" PARAMS 2 query_vector "\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00" DIALECT 2

Returned result:

1) (integer) 22) p53) 1) __description_score   2) 1.6400001049   3) city   4) BANGALORE   5) price   6) 75000   7) description   8) \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x80?4) p35) 1) __description_score   2) 0.0399999953806   3) city   4) BANGALORE   5) price   6) 60000   7) description   8) \x00\x00\x80?\x00\x00\x00\x00\x00\x00\x00\x00

Refer toQuery syntax for details on the filter query format.

Code Sample

# Before running, ensure you have installed redis-py:#   pip install redisimport redisclient = redis.cluster.RedisCluster(host='your_server_host', port=6379)result = client.execute_command('FT.SEARCH', 'idx', '(@city:{BANGALORE} @price:[-inf 100000])=>[KNN 5 @description $query_vector]', 'PARAMS', '2', 'query_vector', '"\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00"', 'DIALECT', '2')print(result)

# Before running, ensure you have installed ioredis:#   npm install ioredisconst Redis = require("ioredis");const redis = new Redis.Cluster([{  port: 6379,  host: "your_server_host",},]);redis.call("FT.SEARCH", "idx", "(@city:{BANGALORE} @price:[-inf 100000])=>[KNN 5 @description $query_vector]", "PARAMS", "2", "query_vector", "\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00", "DIALECT", "2").then(result => {console.log(result);redis.disconnect();});

# Before running, ensure you have install redis-cliredis-cli -h your_server_host -p 6379 FT.SEARCH idx "(@city:{BANGALORE} @price:[-inf 100000])=>[KNN 5 @description $query_vector]" PARAMS 2 query_vector "\xcd\xccL?\x00\x00\x00\x00\x00\x00\x00\x00" DIALECT 2