# Query and Filter Data

```json metadata
{
  "schema_version": 2,
  "title": "Query and Filter Data",
  "description": "",
  "categories": null,
  "tableOfContents": {"sections":[{"id":"prerequisites","title":"Prerequisites"},{"id":"what-youll-learn","title":"What You'll Learn"},{"children":[{"id":"tag-filters","title":"Tag Filters"},{"id":"numeric-filters","title":"Numeric Filters"},{"id":"timestamp-filters","title":"Timestamp Filters"},{"id":"text-filters","title":"Text Filters"},{"id":"geographic-filters","title":"Geographic Filters"}],"id":"complex-filtering","title":"Complex Filtering"},{"children":[{"id":"intersection-and","title":"Intersection (\"and\")"},{"id":"union-or","title":"Union (\"or\")"},{"id":"dynamic-combination","title":"Dynamic Combination"}],"id":"combining-filters","title":"Combining Filters"},{"id":"non-vector-queries","title":"Non-vector Queries"},{"id":"count-queries","title":"Count Queries"},{"id":"range-queries","title":"Range Queries"},{"children":[{"id":"raw-redis-query-string","title":"Raw Redis Query String"}],"id":"advanced-query-modifiers","title":"Advanced Query Modifiers"},{"id":"next-steps","title":"Next Steps"},{"id":"cleanup","title":"Cleanup"}]}

,
  "codeExamples": []
}
```



This guide covers the filtering capabilities in RedisVL, including tag, numeric, text, geo, and timestamp filters. You'll learn how to combine filters to create complex queries that narrow down search results precisely.

## Prerequisites

Before you begin, ensure you have:
- Installed RedisVL: `pip install redisvl`
- A running Redis instance ([Redis 8+](https://redis.io/downloads/) or [Redis Cloud](https://redis.io/cloud))

## What You'll Learn

By the end of this guide, you will be able to:
- Create and apply tag, numeric, text, and geo filters
- Combine multiple filters using AND/OR logic
- Use FilterQuery for non-vector searches
- Execute CountQuery to get matching record counts
- Apply RangeQuery for distance-based vector searches


```python
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)
```


<table><tr><th>user</th><th>age</th><th>job</th><th>credit_score</th><th>office_location</th><th>user_embedding</th><th>last_updated</th></tr><tr><td>john</td><td>18</td><td>engineer</td><td>high</td><td>-122.4194,37.7749</td><td>b'\xcd\xcc\xcc=\xcd\xcc\xcc=\x00\x00\x00?'</td><td>1741627789</td></tr><tr><td>derrick</td><td>14</td><td>doctor</td><td>low</td><td>-122.4194,37.7749</td><td>b'\xcd\xcc\xcc=\xcd\xcc\xcc=\x00\x00\x00?'</td><td>1741627789</td></tr><tr><td>nancy</td><td>94</td><td>doctor</td><td>high</td><td>-122.4194,37.7749</td><td>b'333?\xcd\xcc\xcc=\x00\x00\x00?'</td><td>1710696589</td></tr><tr><td>tyler</td><td>100</td><td>engineer</td><td>high</td><td>-122.0839,37.3861</td><td>b'\xcd\xcc\xcc=\xcd\xcc\xcc>\x00\x00\x00?'</td><td>1742232589</td></tr><tr><td>tim</td><td>12</td><td>dermatologist</td><td>high</td><td>-122.0839,37.3861</td><td>b'\xcd\xcc\xcc>\xcd\xcc\xcc>\x00\x00\x00?'</td><td>1739644189</td></tr><tr><td>taimur</td><td>15</td><td>CEO</td><td>low</td><td>-122.0839,37.3861</td><td>b'\x9a\x99\x19?\xcd\xcc\xcc=\x00\x00\x00?'</td><td>1742232589</td></tr><tr><td>joe</td><td>35</td><td>dentist</td><td>medium</td><td>-122.0839,37.3861</td><td>b'fff?fff?\xcd\xcc\xcc='</td><td>1742232589</td></tr></table>



```python
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"
            }

        }
    ],
}
```


```python
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)
```


```python
# load data to redis
keys = index.load(data)
```


```python
index.info()['num_docs']
```




    7



## Complex Filtering

Complex filtering allows you to combine multiple types of filters in your queries. 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 complex filtering 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.


```python
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)
str(v)
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>





    '@credit_score:{high}=>[KNN 10 @user_embedding $vector AS vector_distance] RETURN 7 user credit_score age job office_location last_updated vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 10'




```python
v.query_string()
```




    '@credit_score:{high}=>[KNN 10 @user_embedding $vector AS vector_distance]'




```python
# negation
t = Tag("credit_score") != "high"

v.set_filter(t)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


**Performance Tip:** For HNSW and SVS-VAMANA indexes, you can add runtime parameters to tune search performance:

```python
# 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](https://redis.io/docs/latest/advanced_queries) for more details.


```python
# use multiple tags as a list
t = Tag("credit_score") == ["high", "medium"]

v.set_filter(t)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
# 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))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


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.


```python
# gracefully fallback to "*" filter if empty case
empty_case = Tag("credit_score") == []

v.set_filter(empty_case)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


### 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.


```python
from redisvl.query.filter import Num

numeric_filter = Num("age").between(15, 35)

v.set_filter(numeric_filter)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
# exact match query
numeric_filter = Num("age") == 14

v.set_filter(numeric_filter)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr></table>



```python
# negation
numeric_filter = Num("age") != 14

v.set_filter(numeric_filter)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


### 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. 


```python
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: 1742129139.132589



<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
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: 1742129139.132589



<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>



```python
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: 1736858739.132589 - 1742129139.132589



<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr></table>


### 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.


```python
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))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>



```python
# 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))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
# wildcard match filter
wildcard_filter = Text("job") % "doct*"

v.set_filter(wildcard_filter)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>



```python
# fuzzy match filter
fuzzy_match = Text("job") % "%%engine%%"

v.set_filter(fuzzy_match)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
# conditional -- match documents with job field containing engineer OR doctor
conditional = Text("job") % "engineer|doctor"

v.set_filter(conditional)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>



```python
# gracefully fallback to "*" filter if empty case
empty_case = Text("job") % ""

v.set_filter(empty_case)
result_print(index.query(v))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


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.


```python
v.set_filter("(~(@job:engineer))")
v.scorer("BM25").with_scores()

index.query(v)
```




    [{'id': 'user_queries_docs:01KHKHSW68SH7A1AT1RDG5FC1A',
      'score': 1.8181817787737895,
      '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:01KHKHSW68SH7A1AT1RDG5FC1B',
      '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:01KHKHSW68SH7A1AT1RDG5FC1D',
      'score': 1.8181817787737895,
      '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:01KHKHSW69W3GWRFZADZM7XMYV',
      'score': 0.0,
      'vector_distance': '0.158808887005',
      'user': 'tim',
      'credit_score': 'high',
      'age': '12',
      'job': 'dermatologist',
      'office_location': '-122.0839,37.3861',
      'last_updated': '1739644189'},
     {'id': 'user_queries_docs:01KHKHSW69W3GWRFZADZM7XMYW',
      'score': 0.0,
      'vector_distance': '0.217881977558',
      'user': 'taimur',
      'credit_score': 'low',
      'age': '15',
      'job': 'CEO',
      'office_location': '-122.0839,37.3861',
      'last_updated': '1742232589'},
     {'id': 'user_queries_docs:01KHKHSW68SH7A1AT1RDG5FC1C',
      '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:01KHKHSW69W3GWRFZADZM7XMYX',
      '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.


```python
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))
```


<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.4545454446934474</td><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr></table>



```python
# 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))
```


<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.4545454446934474</td><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td><td>1741627789</td></tr><tr><td>0.4545454446934474</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.4545454446934474</td><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.4545454446934474</td><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td><td>1710696589</td></tr><tr><td>0.4545454446934474</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>



```python
# 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))
```


<table><tr><th>score</th><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th><th>last_updated</th></tr><tr><td>0.0</td><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td><td>1739644189</td></tr><tr><td>0.0</td><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td><td>1742232589</td></tr><tr><td>0.0</td><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td><td>1742232589</td></tr></table>


## Combining Filters

This example combines a numeric filter with a tag filter to search for users between ages 20 and 30 who have the job "engineer".

### Intersection ("and")


```python
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))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr></table>


### 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.


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

combined = low | high

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


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr></table>


### 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.




```python
def make_filter(age=None, credit=None, job=None):
    flexible_filter = (
        (Num("age") > age) &
        (Tag("credit_score") == credit) &
        (Text("job") % job)
    )
    return flexible_filter

```


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


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr></table>



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


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr></table>



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


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td></tr></table>



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


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th><th>office_location</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td><td>-122.4194,37.7749</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td><td>-122.0839,37.3861</td></tr><tr><td>0.217881977558</td><td>taimur</td><td>low</td><td>15</td><td>CEO</td><td>-122.0839,37.3861</td></tr><tr><td>0.266666650772</td><td>nancy</td><td>high</td><td>94</td><td>doctor</td><td>-122.4194,37.7749</td></tr><tr><td>0.653301358223</td><td>joe</td><td>medium</td><td>35</td><td>dentist</td><td>-122.0839,37.3861</td></tr></table>


## Non-vector Queries

When you need to query without vector similarity (similar to a SQL WHERE clause), use the ``FilterQuery`` class. It accepts a ``FilterExpression`` and returns matching records.


```python
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)
```


<table><tr><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>derrick</td><td>low</td><td>14</td><td>doctor</td></tr><tr><td>taimur</td><td>low</td><td>15</td><td>CEO</td></tr></table>


## Count Queries

Use ``CountQuery`` with a ``FilterExpression`` to get the count of matching records without retrieving the actual data.


```python
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.


```python
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)
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td></tr><tr><td>0.109129190445</td><td>tyler</td><td>high</td><td>100</td><td>engineer</td></tr><tr><td>0.158808887005</td><td>tim</td><td>high</td><td>12</td><td>dermatologist</td></tr></table>


The distance threshold can be changed between queries. Setting ``distance_threshold=0.1`` returns only matches within 0.1 distance of the query vector, resulting in fewer but more similar matches.


```python
range_query.set_distance_threshold(0.1)

result_print(index.query(range_query))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td></tr><tr><td>0</td><td>derrick</td><td>low</td><td>14</td><td>doctor</td></tr></table>


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).


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

range_query.set_filter(is_engineer)

result_print(index.query(range_query))
```


<table><tr><th>vector_distance</th><th>user</th><th>credit_score</th><th>age</th><th>job</th></tr><tr><td>0</td><td>john</td><td>high</td><td>18</td><td>engineer</td></tr></table>


## Advanced Query Modifiers

See all modifier options available on the query API docs: https://redis.io/docs/latest/develop/ai/redisvl/api/query


```python
# 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)
```


<table><tr><th>vector_distance</th><th>age</th><th>user</th><th>credit_score</th><th>job</th><th>office_location</th></tr><tr><td>0.109129190445</td><td>100</td><td>tyler</td><td>high</td><td>engineer</td><td>-122.0839,37.3861</td></tr><tr><td>0</td><td>18</td><td>john</td><td>high</td><td>engineer</td><td>-122.4194,37.7749</td></tr></table>


### Raw Redis Query String

Sometimes it's helpful to convert these classes into their raw Redis query strings.


```python
# 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'




```python
t = Tag("credit_score") == "high"

str(t)
```




    '@credit_score:{high}'




```python
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.


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

    {'id': 'user_queries_docs:01KHKHSW68SH7A1AT1RDG5FC1A', '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:01KHKHSW68SH7A1AT1RDG5FC1C', '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:01KHKHSW68SH7A1AT1RDG5FC1D', '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:01KHKHSW69W3GWRFZADZM7XMYV', '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'}


## Next Steps

Now that you understand filtering in RedisVL, explore these related guides:

- [Use Advanced Query Types](https://redis.io/docs/latest/advanced_queries) - Learn about TextQuery, HybridQuery, and MultiVectorQuery
- [Cache LLM Responses](https://redis.io/docs/latest/llmcache) - Use filters with semantic caching for multi-user scenarios
- [Write SQL Queries for Redis](https://redis.io/docs/latest/sql_to_redis_queries) - Use SQL-like syntax for Redis queries

## Cleanup


```python
index.delete()
```