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:01JMJJHE28ZW4F33ZNRKXRHYCS',
  'score': 1.8181817787737895,
  'vector_distance': '0',
  'user': 'john',
  'credit_score': 'high',
  'age': '18',
  'job': 'engineer',
  'office_location': '-122.4194,37.7749'},
 {'id': 'user_queries_docs:01JMJJHE2899024DYPXT6424N9',
  'score': 0.0,
  'vector_distance': '0',
  'user': 'derrick',
  'credit_score': 'low',
  'age': '14',
  'job': 'doctor',
  'office_location': '-122.4194,37.7749'},
 {'id': 'user_queries_docs:01JMJJPEYCQ89ZQW6QR27J72WT',
  'score': 1.8181817787737895,
  'vector_distance': '0',
  'user': 'john',
  'credit_score': 'high',
  'age': '18',
  'job': 'engineer',
  'office_location': '-122.4194,37.7749'},
 {'id': 'user_queries_docs:01JMJJPEYD544WB1TKDBJ3Z3J9',
  'score': 0.0,
  'vector_distance': '0',
  'user': 'derrick',
  'credit_score': 'low',
  'age': '14',
  'job': 'doctor',
  'office_location': '-122.4194,37.7749'},
 {'id': 'user_queries_docs:01JMJJHE28B5R6T00DH37A7KSJ',
  'score': 1.8181817787737895,
  'vector_distance': '0.109129190445',
  'user': 'tyler',
  'credit_score': 'high',
  'age': '100',
  'job': 'engineer',
  'office_location': '-122.0839,37.3861'},
 {'id': 'user_queries_docs:01JMJJPEYDPF9S5328WHCQN0ND',
  'score': 1.8181817787737895,
  'vector_distance': '0.109129190445',
  'user': 'tyler',
  'credit_score': 'high',
  'age': '100',
  'job': 'engineer',
  'office_location': '-122.0839,37.3861'},
 {'id': 'user_queries_docs:01JMJJHE28G5F943YGWMB1ZX1V',
  'score': 0.0,
  'vector_distance': '0.158808946609',
  'user': 'tim',
  'credit_score': 'high',
  'age': '12',
  'job': 'dermatologist',
  'office_location': '-122.0839,37.3861'},
 {'id': 'user_queries_docs:01JMJJPEYDKA9ARKHRK1D7KPXQ',
  'score': 0.0,
  'vector_distance': '0.158808946609',
  'user': 'tim',
  'credit_score': 'high',
  'age': '12',
  'job': 'dermatologist',
  'office_location': '-122.0839,37.3861'},
 {'id': 'user_queries_docs:01JMJJHE28NR7KF0EZEA433T2J',
  'score': 0.0,
  'vector_distance': '0.217882037163',
  'user': 'taimur',
  'credit_score': 'low',
  'age': '15',
  'job': 'CEO',
  'office_location': '-122.0839,37.3861'},
 {'id': 'user_queries_docs:01JMJJPEYD9EAVGJ2AZ8K9VX7Q',
  'score': 0.0,
  'vector_distance': '0.217882037163',
  'user': 'taimur',
  'credit_score': 'low',
  'age': '15',
  'job': 'CEO',
  'office_location': '-122.0839,37.3861'}]

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))

# 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))

# 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))

t = Tag("credit_score") == "high"
low = Num("age") >= 18
high = Num("age") <= 100

combined = t & low & high

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))

low = Num("age") < 18
high = Num("age") > 93

combined = low | high

v.set_filter(combined)
result_print(index.query(v))

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))

# just age and credit_score
combined = make_filter(age=18, credit="high")
v.set_filter(combined)
result_print(index.query(v))

# just age
combined = make_filter(age=18)
v.set_filter(combined)
result_print(index.query(v))

# no filters
combined = make_filter()
v.set_filter(combined)
result_print(index.query(v))

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)

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.")

4 records match the filter expression @credit_score:{low} for the given index.

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)

range_query.set_distance_threshold(0.1)

result_print(index.query(range_query))

is_engineer = Text("job") == "engineer"

range_query.set_filter(is_engineer)

result_print(index.query(range_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)

# 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])'

results = index.search(str(t))
for r in results.docs:
    print(r.__dict__)

{'id': 'user_queries_docs:01JMJJHE28G5F943YGWMB1ZX1V', 'payload': None, 'user': 'tim', 'age': '12', 'job': 'dermatologist', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '>>\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJHE28ZW4F33ZNRKXRHYCS', 'payload': None, 'user': 'john', 'age': '18', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '==\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJHE28B5R6T00DH37A7KSJ', 'payload': None, 'user': 'tyler', 'age': '100', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '=>\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJHE28EX13NEE7BGBM8FH3', 'payload': None, 'user': 'nancy', 'age': '94', 'job': 'doctor', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '333?=\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJPEYCQ89ZQW6QR27J72WT', 'payload': None, 'user': 'john', 'age': '18', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '==\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJPEYDAN0M3V7EQEVPS6HX', 'payload': None, 'user': 'nancy', 'age': '94', 'job': 'doctor', 'credit_score': 'high', 'office_location': '-122.4194,37.7749', 'user_embedding': '333?=\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJPEYDPF9S5328WHCQN0ND', 'payload': None, 'user': 'tyler', 'age': '100', 'job': 'engineer', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '=>\x00\x00\x00?'}
{'id': 'user_queries_docs:01JMJJPEYDKA9ARKHRK1D7KPXQ', 'payload': None, 'user': 'tim', 'age': '12', 'job': 'dermatologist', 'credit_score': 'high', 'office_location': '-122.0839,37.3861', 'user_embedding': '>>\x00\x00\x00?'}

# Cleanup
index.delete()