Scalar Quantization (SQ)
Added in v1.26.0
Scalar quantization (SQ) is a vector compression technique that can reduce the size of a vector.
To use SQ, enable it in the collection definition, then add data to the collection.
Basic configuration
SQ must be enabled at collection creation time. You cannot enable SQ after you add data to a collection.
To enable SQ, set vector_index_config.
- Python Client v4
- Python Client v3
import weaviate.classes.config as wc
client.collections.create(
name="MyCollection",
vectorizer_config=wc.Configure.Vectorizer.text2vec_openai(),
vector_index_config=wc.Configure.VectorIndex.hnsw(
quantizer=wc.Configure.VectorIndex.Quantizer.sq()
),
)
class_definition = {
"class": "MyCollection",
"vectorizer": "text2vec-openai", # Can be any vectorizer
"vectorIndexType": "hnsw",
"vectorIndexConfig": {
"SQ": {
"enabled": True,
},
},
# Remainder not shown
}
client.schema.create_class(class_definition)
Custom configuration
To tune SQ, set these vectorIndexConfig parameters.
| Parameter | Type | Default | Details |
|---|---|---|---|
sq: enabled | boolean | false | Uses SQ when true. The Python client v4 does not use the enabled parameter. To enable SQ with the v4 client, set a quantizer in the collection definition. |
sq: rescoreLimit | integer | -1 | The minimum number of candidates to fetch before rescoring. |
sq: trainingLimit | integer | 100000 | The size of the training set to determine scalar bucket boundaries. |
sq: cache | boolean | false | Use the vector cache when true. |
vectorCacheMaxObjects | integer | 1e12 | Maximum number of objects in the memory cache. By default, this limit is set to one trillion (1e12) objects when a new collection is created. For sizing recommendations, see Vector cache considerations. |
- Python Client v4
- Python Client v3
import weaviate.classes.config as wc
client.collections.create(
name="MyCollection",
vectorizer_config=wc.Configure.Vectorizer.text2vec_openai(),
vector_index_config=wc.Configure.VectorIndex.hnsw(
distance_metric=wc.VectorDistances.COSINE,
vector_cache_max_objects=100000,
quantizer=wc.Configure.VectorIndex.Quantizer.sq(
rescore_limit=200,
training_limit=50000,
cache=True,
)
),
)
class_definition = {
"class": "MyCollection",
"vectorizer": "text2vec-openai", # Can be any vectorizer
"vectorIndexType": "hnsw",
"vectorIndexConfig": {
"SQ": {
"enabled": True,
"rescoreLimit": 200, # The minimum number of candidates to fetch before rescoring
"cache": True, # Default: False
},
"vectorCacheMaxObjects": 100000, # Cache size (used if `cache` enabled)
},
# Remainder not shown
}
client.schema.create_class(class_definition)
Multiple vectors
Added in v1.24.0
Collections support multiple named vectors.
Collections can have multiple named vectors. The vectors in a collection can have their own configurations, and compression must be enabled independently for each vector. Every vector is independent and can use PQ, BQ, SQ, or no compression.
Related pages
Questions and feedback
If you have any questions or feedback, let us know in the user forum.