skills/redis-search/references/field-types.md

# Choose the Correct Field Type

Each field type has different capabilities and performance characteristics. Use the narrowest type that supports your access pattern — `TAG` is roughly 10× faster than `TEXT` for exact-match filtering, and `NUMERIC SORTABLE` is the only fast path for range sorts.

| Field Type | Use When | Notes |
|------------|----------|-------|
| `TEXT` | Full-text search needed | Tokenized, stemmed; **not** for exact match |
| `TAG` | Exact match, filtering | Faster than TEXT; add `SORTABLE UNF` for fastest tag queries |
| `NUMERIC` | Range queries, sorting | Prices, counts, timestamps |
| `GEO` | Lat/long point queries | Single points (stores, users) |
| `GEOSHAPE` | Polygon / area queries | Delivery zones, regions |
| `VECTOR` | Similarity search | HNSW or FLAT; see [algorithm-choice.md](algorithm-choice.md) |

**Correct:** Use TAG for exact matching (Bicycle dataset).

```
FT.CREATE idx:bicycle ON HASH PREFIX 1 bicycle:
    SCHEMA
        model        TEXT WEIGHT 2.0
        description  TEXT
        brand        TAG
        condition    TAG
        price        NUMERIC SORTABLE

# Query: exact-match TAG filter on brand
FT.SEARCH idx:bicycle "@brand:{Velorim} @condition:{new}" DIALECT 2
```

**Incorrect:** Using TEXT when you don't need full-text features.

```
# Overkill: TEXT for brand/condition adds unnecessary tokenization
FT.CREATE idx:bicycle ON HASH PREFIX 1 bicycle:
    SCHEMA
        model       TEXT
        brand       TEXT
        condition   TEXT
```

**Correct:** Use GEO for points, GEOSHAPE for areas.

```
# GEO for point locations (stores, users)
FT.CREATE idx:bicycle ON HASH PREFIX 1 bicycle:
    SCHEMA
        store_location GEO

# GEOSHAPE for areas (delivery zones, boundaries)
FT.CREATE idx:zones ON JSON PREFIX 1 zone:
    SCHEMA
        $.boundary AS boundary GEOSHAPE
```

For JSON-path fields (`$.path AS alias`), see [json-indexing.md](json-indexing.md). For vector fields, see [algorithm-choice.md](algorithm-choice.md).

## Client mirrors

```python
# redis-py — STEP_START field_types
# Mirrors doctests/search_quickstart.py
from redis import Redis
from redis.commands.search.field import TextField, TagField, NumericField, GeoField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType

r = Redis()
schema = (
    TextField("model", weight=2.0),
    TextField("description"),
    TagField("brand"),
    TagField("condition"),
    NumericField("price", sortable=True),
    GeoField("store_location"),
)
r.ft("idx:bicycle").create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.HASH))
# STEP_END
```

```java
// Jedis — STEP_START field_types
// Mirrors SearchQuickstartExample.java
import redis.clients.jedis.UnifiedJedis;
import redis.clients.jedis.search.FTCreateParams;
import redis.clients.jedis.search.IndexDataType;
import redis.clients.jedis.search.schemafields.*;

try (UnifiedJedis jedis = new UnifiedJedis("redis://localhost:6379")) {
    jedis.ftCreate("idx:bicycle",
        FTCreateParams.createParams().on(IndexDataType.HASH).prefix("bicycle:"),
        TextField.of("model").weight(2.0),
        TextField.of("description"),
        TagField.of("brand"),
        TagField.of("condition"),
        NumericField.of("price").sortable(),
        GeoField.of("store_location"));
}
// STEP_END
```

## Upstream sources

- redis-py: [`doctests/search_quickstart.py`](https://github.com/redis/redis-py/blob/master/doctests/search_quickstart.py)
- Jedis: [`SearchQuickstartExample.java`](https://github.com/redis/jedis/blob/master/src/test/java/io/redis/examples/SearchQuickstartExample.java)
- Reference: [Redis Search Field Types](https://redis.io/docs/latest/develop/interact/search-and-query/indexing/geoindex/)