fromredisvl.queryimportVectorQueryfromredisvl.query.filterimportTagt=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)

# negationt=Tag("credit_score")!="high"v.set_filter(t)result_print(index.query(v))

# use multiple tags as a listt=Tag("credit_score")==["high","medium"]v.set_filter(t)result_print(index.query(v))

# 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))

# gracefully fallback to "*" filter if empty caseempty_case=Tag("credit_score")==[]v.set_filter(empty_case)result_print(index.query(v))

fromredisvl.query.filterimportNumnumeric_filter=Num("age").between(15,35)v.set_filter(numeric_filter)result_print(index.query(v))

# exact match querynumeric_filter=Num("age")==14v.set_filter(numeric_filter)result_print(index.query(v))

# negationnumeric_filter=Num("age")!=14v.set_filter(numeric_filter)result_print(index.query(v))

fromredisvl.query.filterimportTimestampfromdatetimeimportdatetimedt=datetime(2025,3,16,13,45,39,132589)print(f'Epoch comparison:{dt.timestamp()}')timestamp_filter=Timestamp("last_updated")>dtv.set_filter(timestamp_filter)result_print(index.query(v))

Epoch comparison: 1742147139.132589

fromredisvl.query.filterimportTimestampfromdatetimeimportdatetimedt=datetime(2025,3,16,13,45,39,132589)print(f'Epoch comparison:{dt.timestamp()}')timestamp_filter=Timestamp("last_updated")<dtv.set_filter(timestamp_filter)result_print(index.query(v))

Epoch comparison: 1742147139.132589

fromredisvl.query.filterimportTimestampfromdatetimeimportdatetimedt_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

fromredisvl.query.filterimportText# exact match filter -- document must contain the exact word doctortext_filter=Text("job")=="doctor"v.set_filter(text_filter)result_print(index.query(v))

# negation -- document must not contain the exact word doctornegate_text_filter=Text("job")!="doctor"v.set_filter(negate_text_filter)result_print(index.query(v))

# wildcard match filterwildcard_filter=Text("job")%"doct*"v.set_filter(wildcard_filter)result_print(index.query(v))

# fuzzy match filterfuzzy_match=Text("job")%"%%engine%%"v.set_filter(fuzzy_match)result_print(index.query(v))

# conditional -- match documents with job field containing engineer OR doctorconditional=Text("job")%"engineer|doctor"v.set_filter(conditional)result_print(index.query(v))

# gracefully fallback to "*" filter if empty caseempty_case=Text("job")%""v.set_filter(empty_case)result_print(index.query(v))

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'}]

fromredisvl.query.filterimportGeo,GeoRadius# within 10 km of San Francisco officegeo_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 officegeo_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 officegeo_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")>=18high=Num("age")<=100ts=Timestamp("last_updated")>datetime(2025,3,16,13,45,39,132589)combined=t&low&high&tsv=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")<18high=Num("age")>93combined=low|highv.set_filter(combined)result_print(index.query(v))

defmake_filter(age=None,credit=None,job=None):flexible_filter=((Num("age")>age)&(Tag("credit_score")==credit)&(Text("job")%job))returnflexible_filter

# all parameterscombined=make_filter(age=18,credit="high",job="engineer")v.set_filter(combined)result_print(index.query(v))

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

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

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

# check out the complex query from abovestr(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")>=18high=Num("age")<=100combined=t&low&highstr(combined)

'((@credit_score:{high} @age:[18 +inf]) @age:[-inf 100])'

results=index.search(str(t))forrinresults.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'}

# Cleanupindex.delete()