rembQuerying with RedisVL
In this notebook, we will explore more complex queries that can be performed with redisvl
Before running this notebook, be sure to
- Have installed
redisvland have that environment active for this notebook. - Have a running Redis instance with RediSearch > 2.4 running.
import pickle
from jupyterutils import table_print, result_print
# load in the example data and printing utils
data = pickle.load(open("hybrid_example_data.pkl", "rb"))
table_print(data)
user
age
job
credit_score
office_location
user_embedding
last_updated
john
18
engineer
high
-122.4194,37.7749
b'\xcd\xcc\xcc=\xcd\xcc\xcc=\x00\x00\x00?'
1741627789
derrick
14
doctor
low
-122.4194,37.7749
b'\xcd\xcc\xcc=\xcd\xcc\xcc=\x00\x00\x00?'
1741627789
nancy
94
doctor
high
-122.4194,37.7749
b'333?\xcd\xcc\xcc=\x00\x00\x00?'
1710696589
tyler
100
engineer
high
-122.0839,37.3861
b'\xcd\xcc\xcc=\xcd\xcc\xcc>\x00\x00\x00?'
1742232589
tim
12
dermatologist
high
-122.0839,37.3861
b'\xcd\xcc\xcc>\xcd\xcc\xcc>\x00\x00\x00?'
1739644189
taimur
15
CEO
low
-122.0839,37.3861
b'\x9a\x99\x19?\xcd\xcc\xcc=\x00\x00\x00?'
1742232589
joe
35
dentist
medium
-122.0839,37.3861
b'fff?fff?\xcd\xcc\xcc='
1742232589
schema = {
"index": {
"name": "user_queries",
"prefix": "user_queries_docs",
"storage_type": "hash", # default setting -- HASH
},
"fields": [
{"name": "user", "type": "tag"},
{"name": "credit_score", "type": "tag"},
{"name": "job", "type": "text"},
{"name": "age", "type": "numeric"},
{"name": "last_updated", "type": "numeric"},
{"name": "office_location", "type": "geo"},
{
"name": "user_embedding",
"type": "vector",
"attrs": {
"dims": 3,
"distance_metric": "cosine",
"algorithm": "flat",
"datatype": "float32"
}
}
],
}
from redisvl.index import SearchIndex
# construct a search index from the schema
index = SearchIndex.from_dict(schema, redis_url="redis://localhost:6379")
# create the index (no data yet)
index.create(overwrite=True)
# use the CLI to see the created index
!rvl index listall
13:00:56 [RedisVL] INFO Indices:
13:00:56 [RedisVL] INFO 1. user_queries
# load data to redis
keys = index.load(data)
index.info()['num_docs']
7
Hybrid Queries
Hybrid queries are queries that combine multiple types of filters. For example, you may want to search for a user that is a certain age, has a certain job, and is within a certain distance of a location. This is a hybrid query that combines numeric, tag, and geographic filters.
Tag Filters
Tag filters are filters that are applied to tag fields. These are fields that are not tokenized and are used to store a single categorical value.
from redisvl.query import VectorQuery
from redisvl.query.filter import Tag
t = Tag("credit_score") == "high"
v = VectorQuery(
vector=[0.1, 0.1, 0.5],
vector_field_name="user_embedding",
return_fields=["user", "credit_score", "age", "job", "office_location", "last_updated"],
filter_expression=t
)
results = index.query(v)
result_print(results)
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
# negation
t = Tag("credit_score") != "high"
v.set_filter(t)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
Performance Tip: For HNSW and SVS-VAMANA indexes, you can add runtime parameters to tune search performance:
# Example with runtime parameters for better recall
v = VectorQuery(
vector=[0.1, 0.1, 0.5],
vector_field_name="user_embedding",
return_fields=["user", "credit_score", "age"],
filter_expression=t,
ef_runtime=100, # HNSW: higher for better recall
search_window_size=40 # SVS-VAMANA: larger window for better recall
)
These parameters can be adjusted at query time without rebuilding the index. See the Advanced Queries guide for more details.
# use multiple tags as a list
t = Tag("credit_score") == ["high", "medium"]
v.set_filter(t)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
# use multiple tags as a set (to enforce uniqueness)
t = Tag("credit_score") == set(["high", "high", "medium"])
v.set_filter(t)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
What about scenarios where you might want to dynamically generate a list of tags? Have no fear. RedisVL allows you to do this gracefully without having to check for the empty case. The empty case is when you attempt to run a Tag filter on a field with no defined values to match:
Tag("credit_score") == []
An empty filter like the one above will yield a * Redis query filter which implies the base case -- there is no filter here to use.
# gracefully fallback to "*" filter if empty case
empty_case = Tag("credit_score") == []
v.set_filter(empty_case)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
Numeric Filters
Numeric filters are filters that are applied to numeric fields and can be used to isolate a range of values for a given field.
from redisvl.query.filter import Num
numeric_filter = Num("age").between(15, 35)
v.set_filter(numeric_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
# exact match query
numeric_filter = Num("age") == 14
v.set_filter(numeric_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
# negation
numeric_filter = Num("age") != 14
v.set_filter(numeric_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
Timestamp Filters
In redis all times are stored as an epoch time numeric however, this class allows you to filter with python datetime for ease of use.
from redisvl.query.filter import Timestamp
from datetime import datetime
dt = datetime(2025, 3, 16, 13, 45, 39, 132589)
print(f'Epoch comparison: {dt.timestamp()}')
timestamp_filter = Timestamp("last_updated") > dt
v.set_filter(timestamp_filter)
result_print(index.query(v))
Epoch comparison: 1742147139.132589
vector_distance
user
credit_score
age
job
office_location
last_updated
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
from redisvl.query.filter import Timestamp
from datetime import datetime
dt = datetime(2025, 3, 16, 13, 45, 39, 132589)
print(f'Epoch comparison: {dt.timestamp()}')
timestamp_filter = Timestamp("last_updated") < dt
v.set_filter(timestamp_filter)
result_print(index.query(v))
Epoch comparison: 1742147139.132589
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
from redisvl.query.filter import Timestamp
from datetime import datetime
dt_1 = datetime(2025, 1, 14, 13, 45, 39, 132589)
dt_2 = datetime(2025, 3, 16, 13, 45, 39, 132589)
print(f'Epoch between: {dt_1.timestamp()} - {dt_2.timestamp()}')
timestamp_filter = Timestamp("last_updated").between(dt_1, dt_2)
v.set_filter(timestamp_filter)
result_print(index.query(v))
Epoch between: 1736880339.132589 - 1742147139.132589
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
Text Filters
Text filters are filters that are applied to text fields. These filters are applied to the entire text field. For example, if you have a text field that contains the text "The quick brown fox jumps over the lazy dog", a text filter of "quick" will match this text field.
from redisvl.query.filter import Text
# exact match filter -- document must contain the exact word doctor
text_filter = Text("job") == "doctor"
v.set_filter(text_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
# negation -- document must not contain the exact word doctor
negate_text_filter = Text("job") != "doctor"
v.set_filter(negate_text_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
# wildcard match filter
wildcard_filter = Text("job") % "doct*"
v.set_filter(wildcard_filter)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
# fuzzy match filter
fuzzy_match = Text("job") % "%%engine%%"
v.set_filter(fuzzy_match)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
# conditional -- match documents with job field containing engineer OR doctor
conditional = Text("job") % "engineer|doctor"
v.set_filter(conditional)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
# gracefully fallback to "*" filter if empty case
empty_case = Text("job") % ""
v.set_filter(empty_case)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
last_updated
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
Use raw query strings as input. Below we use the ~ flag to indicate that the full text query is optional. We also choose the BM25 scorer and return document scores along with the result.
v.set_filter("(~(@job:engineer))")
v.scorer("BM25").with_scores()
index.query(v)
[{'id': 'user_queries_docs:01JY4J5VC91SV4C91BM4D0FCV2',
'score': 0.9090908893868948,
'vector_distance': '0',
'user': 'john',
'credit_score': 'high',
'age': '18',
'job': 'engineer',
'office_location': '-122.4194,37.7749',
'last_updated': '1741627789'},
{'id': 'user_queries_docs:01JY4J5VC90DRSFJ0WKXXN49JT',
'score': 0.0,
'vector_distance': '0',
'user': 'derrick',
'credit_score': 'low',
'age': '14',
'job': 'doctor',
'office_location': '-122.4194,37.7749',
'last_updated': '1741627789'},
{'id': 'user_queries_docs:01JY4J5VC9QTPMCD60YP40Q6PW',
'score': 0.9090908893868948,
'vector_distance': '0.109129190445',
'user': 'tyler',
'credit_score': 'high',
'age': '100',
'job': 'engineer',
'office_location': '-122.0839,37.3861',
'last_updated': '1742232589'},
{'id': 'user_queries_docs:01JY4J5VC9FW7QQNJKDJ4Z7PRG',
'score': 0.0,
'vector_distance': '0.158808946609',
'user': 'tim',
'credit_score': 'high',
'age': '12',
'job': 'dermatologist',
'office_location': '-122.0839,37.3861',
'last_updated': '1739644189'},
{'id': 'user_queries_docs:01JY4J5VC940DJ9F47EJ6KN2MH',
'score': 0.0,
'vector_distance': '0.217882037163',
'user': 'taimur',
'credit_score': 'low',
'age': '15',
'job': 'CEO',
'office_location': '-122.0839,37.3861',
'last_updated': '1742232589'},
{'id': 'user_queries_docs:01JY4J5VC9D53KQD7ZTRP14KCE',
'score': 0.0,
'vector_distance': '0.266666650772',
'user': 'nancy',
'credit_score': 'high',
'age': '94',
'job': 'doctor',
'office_location': '-122.4194,37.7749',
'last_updated': '1710696589'},
{'id': 'user_queries_docs:01JY4J5VC9806MD90GBZNP0MNY',
'score': 0.0,
'vector_distance': '0.653301358223',
'user': 'joe',
'credit_score': 'medium',
'age': '35',
'job': 'dentist',
'office_location': '-122.0839,37.3861',
'last_updated': '1742232589'}]
Geographic Filters
Geographic filters are filters that are applied to geographic fields. These filters are used to find results that are within a certain distance of a given point. The distance is specified in kilometers, miles, meters, or feet. A radius can also be specified to find results within a certain radius of a given point.
from redisvl.query.filter import Geo, GeoRadius
# within 10 km of San Francisco office
geo_filter = Geo("office_location") == GeoRadius(-122.4194, 37.7749, 10, "km")
v.set_filter(geo_filter)
result_print(index.query(v))
score
vector_distance
user
credit_score
age
job
office_location
last_updated
0.4545454446934474
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.4545454446934474
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.4545454446934474
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
# within 100 km Radius of San Francisco office
geo_filter = Geo("office_location") == GeoRadius(-122.4194, 37.7749, 100, "km")
v.set_filter(geo_filter)
result_print(index.query(v))
score
vector_distance
user
credit_score
age
job
office_location
last_updated
0.4545454446934474
0
john
high
18
engineer
-122.4194,37.7749
1741627789
0.4545454446934474
0
derrick
low
14
doctor
-122.4194,37.7749
1741627789
0.4545454446934474
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.4545454446934474
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.4545454446934474
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.4545454446934474
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
1710696589
0.4545454446934474
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
# not within 10 km Radius of San Francisco office
geo_filter = Geo("office_location") != GeoRadius(-122.4194, 37.7749, 10, "km")
v.set_filter(geo_filter)
result_print(index.query(v))
score
vector_distance
user
credit_score
age
job
office_location
last_updated
0.0
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
1742232589
0.0
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
1739644189
0.0
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
1742232589
0.0
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
1742232589
Combining Filters
In this example, we will combine a numeric filter with a tag filter. We will search for users that are between the ages of 20 and 30 and have a job of "engineer".
Intersection ("and")
t = Tag("credit_score") == "high"
low = Num("age") >= 18
high = Num("age") <= 100
ts = Timestamp("last_updated") > datetime(2025, 3, 16, 13, 45, 39, 132589)
combined = t & low & high & ts
v = VectorQuery([0.1, 0.1, 0.5],
"user_embedding",
return_fields=["user", "credit_score", "age", "job", "office_location"],
filter_expression=combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
Union ("or")
The union of two queries is the set of all results that are returned by either of the two queries. The union of two queries is performed using the | operator.
low = Num("age") < 18
high = Num("age") > 93
combined = low | high
v.set_filter(combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0
derrick
low
14
doctor
-122.4194,37.7749
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
Dynamic Combination
There are often situations where you may or may not want to use a filter in a given query. As shown above, filters will except the None type and revert to a wildcard filter essentially returning all results.
The same goes for filter combinations which enables rapid reuse of filters in requests with different parameters as shown below. This removes the need for a number of "if-then" conditionals to test for the empty case.
def make_filter(age=None, credit=None, job=None):
flexible_filter = (
(Num("age") > age) &
(Tag("credit_score") == credit) &
(Text("job") % job)
)
return flexible_filter
# all parameters
combined = make_filter(age=18, credit="high", job="engineer")
v.set_filter(combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
# just age and credit_score
combined = make_filter(age=18, credit="high")
v.set_filter(combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
# just age
combined = make_filter(age=18)
v.set_filter(combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
# no filters
combined = make_filter()
v.set_filter(combined)
result_print(index.query(v))
vector_distance
user
credit_score
age
job
office_location
0
john
high
18
engineer
-122.4194,37.7749
0
derrick
low
14
doctor
-122.4194,37.7749
0.109129190445
tyler
high
100
engineer
-122.0839,37.3861
0.158808946609
tim
high
12
dermatologist
-122.0839,37.3861
0.217882037163
taimur
low
15
CEO
-122.0839,37.3861
0.266666650772
nancy
high
94
doctor
-122.4194,37.7749
0.653301358223
joe
medium
35
dentist
-122.0839,37.3861
Non-vector Queries
In some cases, you may not want to run a vector query, but just use a FilterExpression similar to a SQL query. The FilterQuery class enable this functionality. It is similar to the VectorQuery class but solely takes a FilterExpression.
from redisvl.query import FilterQuery
has_low_credit = Tag("credit_score") == "low"
filter_query = FilterQuery(
return_fields=["user", "credit_score", "age", "job", "location"],
filter_expression=has_low_credit
)
results = index.query(filter_query)
result_print(results)
user
credit_score
age
job
derrick
low
14
doctor
taimur
low
15
CEO
Count Queries
In some cases, you may need to use a FilterExpression to execute a CountQuery that simply returns the count of the number of entities in the pertaining set. It is similar to the FilterQuery class but does not return the values of the underlying data.
from redisvl.query import CountQuery
has_low_credit = Tag("credit_score") == "low"
filter_query = CountQuery(filter_expression=has_low_credit)
count = index.query(filter_query)
print(f"{count} records match the filter expression {str(has_low_credit)} for the given index.")
2 records match the filter expression @credit_score:{low} for the given index.
Range Queries
Range Queries are a useful method to perform a vector search where only results within a vector distance_threshold are returned. This enables the user to find all records within their dataset that are similar to a query vector where "similar" is defined by a quantitative value.
from redisvl.query import RangeQuery
range_query = RangeQuery(
vector=[0.1, 0.1, 0.5],
vector_field_name="user_embedding",
return_fields=["user", "credit_score", "age", "job", "location"],
distance_threshold=0.2
)
# same as the vector query or filter query
results = index.query(range_query)
result_print(results)
vector_distance
user
credit_score
age
job
0
john
high
18
engineer
0
derrick
low
14
doctor
0.109129190445
tyler
high
100
engineer
0.158808946609
tim
high
12
dermatologist
We can also change the distance threshold of the query object between uses if we like. Here we will set distance_threshold==0.1. This means that the query object will return all matches that are within 0.1 of the query object. This is a small distance, so we expect to get fewer matches than before.
range_query.set_distance_threshold(0.1)
result_print(index.query(range_query))
vector_distance
user
credit_score
age
job
0
john
high
18
engineer
0
derrick
low
14
doctor
Range queries can also be used with filters like any other query type. The following limits the results to only include records with a job of engineer while also being within the vector range (aka distance).
is_engineer = Text("job") == "engineer"
range_query.set_filter(is_engineer)
result_print(index.query(range_query))
vector_distance
user
credit_score
age
job
0
john
high
18
engineer
Advanced Query Modifiers
See all modifier options available on the query API docs: https://redis.io/docs/latest/develop/ai/redisvl/api/query
# Sort by a different field and change dialect
v = VectorQuery(
vector=[0.1, 0.1, 0.5],
vector_field_name="user_embedding",
return_fields=["user", "credit_score", "age", "job", "office_location"],
num_results=5,
filter_expression=is_engineer
).sort_by("age", asc=False).dialect(3)
result = index.query(v)
result_print(result)
vector_distance
age
user
credit_score
job
office_location
0.109129190445
100
tyler
high
engineer
-122.0839,37.3861
0
18
john
high
engineer
-122.4194,37.7749
Raw Redis Query String
Sometimes it's helpful to convert these classes into their raw Redis query strings.
# check out the complex query from above
str(v)
'@job:("engineer")=>[KNN 5 @user_embedding $vector AS vector_distance] RETURN 6 user credit_score age job office_location vector_distance SORTBY age DESC DIALECT 3 LIMIT 0 5'
t = Tag("credit_score") == "high"
str(t)
'@credit_score:{high}'
t = Tag("credit_score") == "high"
low = Num("age") >= 18
high = Num("age") <= 100
combined = t & low & high
str(combined)
'((@credit_score:{high} @age:[18 +inf]) @age:[-inf 100])'
The RedisVL SearchIndex class exposes a search() method which is a simple wrapper around the FT.SEARCH API. Provide any valid Redis query string.
results = index.search(str(t))
for r in results.docs:
print(r.__dict__)
{'id': 'user_queries_docs:01JY4J5VC91SV4C91BM4D0FCV2', 'payload': None, 'user': 'john', 'age': '18', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '==\x00\x00\x00?', 'last_updated': '1741627789'}
{'id': 'user_queries_docs:01JY4J5VC9D53KQD7ZTRP14KCE', 'payload': None, 'user': 'nancy', 'age': '94', 'job': 'doctor', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '333?=\x00\x00\x00?', 'last_updated': '1710696589'}
{'id': 'user_queries_docs:01JY4J5VC9QTPMCD60YP40Q6PW', 'payload': None, 'user': 'tyler', 'age': '100', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '=>\x00\x00\x00?', 'last_updated': '1742232589'}
{'id': 'user_queries_docs:01JY4J5VC9FW7QQNJKDJ4Z7PRG', 'payload': None, 'user': 'tim', 'age': '12', 'job': 'dermatologist', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '>>\x00\x00\x00?', 'last_updated': '1739644189'}
# Cleanup
index.delete()