Binary Quantization (BQ)

Compression by Default

Starting with v1.33, Weaviate enables 8-bit RQ quantization by default when creating new collections to ensure efficient resource utilization and faster performance. This behavior can be changed through the DEFAULT_QUANTIZATION environment variable. Note that once enabled, quantization can't be disabled for a collection. Default quantization only applies for the HNSW vector index type.

Binary quantization (BQ) is a vector compression technique that can reduce the size of a vector.

To use BQ, enable it as shown below and add data to the collection.

Additional information

• How to set the index type

Enable compression for new collection

BQ can be enabled at collection creation time through the collection definition:

PythonJavaScript/TypeScriptGoJava v6 (Beta)JavaC# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Configure

client.collections.create(
    name="MyCollection",
    vector_config=Configure.Vectors.text2vec_openai(
        name="default",
        quantizer=Configure.VectorIndex.Quantizer.bq(),
    ),
)

import { configure } from 'weaviate-client';

const collection = await client.collections.create({
  name: 'MyCollection',
  vectorizers: weaviate.configure.vectors.selfProvided({
    vectorIndexConfig: weaviate.configure.vectorIndex.hnsw({
      quantizer: weaviate.configure.vectorIndex.quantizer.bq(),
    })
  })
})

// Define the configuration for BQ. Setting 'enabled' to true
bq_config := map[string]interface{}{
  "enabled": true,
}

// Define the class schema
class := &models.Class{
  Class:      className,
  Vectorizer: "text2vec-openai",
  // Assign the BQ configuration to the vector index config
  VectorIndexConfig: map[string]interface{}{
  	"bq": bq_config,
  },
}

// Create the collection in Weaviate
err = client.Schema().ClassCreator().
  WithClass(class).
  Do(context.Background())

client.collections.create("MyCollection",
    col -> col.vectorConfig(VectorConfig.text2vecTransformers(vc -> vc
        .quantization(Quantization.bq())
    )).properties(Property.text("title")));

WeaviateClass myCollection = WeaviateClass.builder()
    .className("MyCollection")
    .vectorizer("text2vec-openai")
    .vectorIndexConfig(VectorIndexConfig.builder()
        .bq(BQConfig.builder()
            .enabled(true)
            .build())
        .build())
    .properties(Arrays.asList(
        Property.builder()
            .name("title")
            .dataType(Arrays.asList(DataType.TEXT))
            .build()))
    .build();

Result<Boolean> createResult = client.schema().classCreator()
    .withClass(myCollection)
    .run();

await client.Collections.Create(new CollectionConfig
{
    Name = "MyCollection",
    Properties = [Property.Text("title")],
    VectorConfig = new VectorConfig(
        "default",
        new Vectorizer.Text2VecTransformers(),
        new VectorIndex.HNSW
        {
            Quantizer = new VectorIndex.Quantizers.BQ()
        }
    )
});

Enable compression for existing collection

Added in v1.31

The ability to enable BQ compression after collection creation was added in Weaviate v1.31.

BQ can also be enabled for an existing collection by updating the collection definition:

PythonGoJava v6 (Beta)JavaC# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Reconfigure

collection = client.collections.use("MyCollection")
collection.config.update(
    vector_config=Reconfigure.Vectors.update(
        name="default",
        vector_index_config=Reconfigure.VectorIndex.flat(
            quantizer=Reconfigure.VectorIndex.Quantizer.bq(
                rescore_limit=20,
            ),
        ),
    )
)

// Get the existing collection configuration
class, err := client.Schema().ClassGetter().
  WithClassName(className).Do(context.Background())

if err != nil {
  log.Fatalf("get class for vec idx cfg update: %v", err)
}

// Get the current vector index configuration
cfg := class.VectorIndexConfig.(map[string]interface{})

// Add BQ configuration to enable binary quantization
cfg["bq"] = map[string]interface{}{
  "enabled":      true,
  "rescoreLimit": 200,
  "cache":        true,
}

// Update the class configuration
class.VectorIndexConfig = cfg

// Apply the updated configuration to the collection
err = client.Schema().ClassUpdater().
  WithClass(class).Do(context.Background())

if err != nil {
  log.Fatalf("update class to use bq: %v", err)
}

CollectionHandle<Map<String, Object>> collection =
    client.collections.use("MyCollection");
collection.config.update(c -> c.vectorConfig(VectorConfig
    .text2vecTransformers(vc -> vc.quantization(Quantization.bq()))));

WeaviateClass updatedCollection = WeaviateClass.builder()
    .className("MyCollection")
    .description("Updated collection with BQ compression")
    .properties(Arrays.asList(
        Property.builder()
            .name("title")
            .dataType(Arrays.asList(DataType.TEXT))
            .build()))
    .vectorizer("text2vec-openai")
    .vectorIndexConfig(VectorIndexConfig.builder()
        .bq(BQConfig.builder()
            .enabled(true)
            .rescoreLimit(10L)
            .build())
        .build())
    .build();

Result<Boolean> updateResult = client.schema().classUpdater()
    .withClass(updatedCollection)
    .run();

await collection.Config.Update(c =>
{
    var vectorConfig = c.VectorConfig["default"];
    vectorConfig.VectorIndexConfig.UpdateHNSW(h => h.Quantizer = new VectorIndex.Quantizers.BQ());
});

BQ parameters

The following parameters are available for BQ compression, under vectorIndexConfig:

Parameter	Type	Default	Details
bq : enabled	boolean	false	Enable BQ. Weaviate uses binary quantization (BQ) compression when true. The Python client does not use the enabled parameter. To enable BQ with the v4 client, set a quantizer in the collection definition.
bq : rescoreLimit	integer	-1	The minimum number of candidates to fetch before rescoring.
bq : cache	boolean	false	Whether to cache the vectors in memory. (only when using the flat vector index type)
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.

For example:

PythonJavaScript/TypeScriptGoJava v6 (Beta)JavaC# (Beta)

  API docs

More infoCode snippets in the documentation reflect the latest client library and Weaviate Database version. Check the Release notes for specific versions.

If a snippet doesn't work or you have feedback, please open a GitHub issue.

from weaviate.classes.config import Configure

client.collections.create(
    name="MyCollection",
    vector_config=Configure.Vectors.text2vec_openai(
        name="default",
        quantizer=Configure.VectorIndex.Quantizer.bq(rescore_limit=200, cache=True),
        vector_index_config=Configure.VectorIndex.flat(
            vector_cache_max_objects=100000,
        ),
    ),
)

import { configure } from 'weaviate-client';

const collection = await client.collections.create({
  name: 'MyCollection',
  vectorizers: weaviate.configure.vectors.selfProvided({
    vectorIndexConfig: weaviate.configure.vectorIndex.hnsw({
      quantizer: weaviate.configure.vectorIndex.quantizer.bq({
        cache: true,     // Enable caching
        rescoreLimit: 200, // The minimum number of candidates to fetch before rescoring
      }),
      vectorCacheMaxObjects: 10000 // Cache size (used if `cache` enabled)
    })
  })
})

// Define a custom configuration for BQ
bq_with_options_config := map[string]interface{}{
  "enabled":      true,
  "rescoreLimit": 200,  // The minimum number of candidates to fetch before rescoring
  "cache":        true, // Enable caching of binary quantized vectors
}

// Define the class schema with the custom BQ config and other HNSW settings
class_with_options := &models.Class{
  Class:      className,
  Vectorizer: "text2vec-openai",
  VectorIndexConfig: map[string]interface{}{
  	"bq": bq_with_options_config,
  	"distance":              "cosine", // Set the distance metric for HNSW
  	"vectorCacheMaxObjects": 100000,   // Configure the vector cache
  },
}

// Create the collection in Weaviate
err = client.Schema().ClassCreator().
  WithClass(class_with_options).
  Do(context.Background())

client.collections.create("MyCollection",
    col -> col.vectorConfig(VectorConfig.text2vecTransformers(vc -> vc
        .quantization(Quantization.bq(q -> q.cache(true).rescoreLimit(200)))
        .vectorIndex(Hnsw.of(c -> c.vectorCacheMaxObjects(100000)))
    )).properties(Property.text("title")));

WeaviateClass myCollection = WeaviateClass.builder()
    .className("MyCollection")
    .vectorizer("text2vec-openai")
    .vectorIndexConfig(VectorIndexConfig.builder()
        .bq(BQConfig.builder()
            .enabled(true)
            .rescoreLimit(20L)
            .build())
        .build())
    .properties(Arrays.asList(
        Property.builder()
            .name("title")
            .dataType(Arrays.asList(DataType.TEXT))
            .build()))
    .build();

Result<Boolean> createResult = client.schema().classCreator()
    .withClass(myCollection)
    .run();

await client.Collections.Create(new CollectionConfig
{
    Name = "MyCollection",
    Properties = [Property.Text("title")],
    VectorConfig = new VectorConfig(
        "default",
        new Vectorizer.Text2VecTransformers(),
        new VectorIndex.HNSW
        {
            VectorCacheMaxObjects = 100000,
            Quantizer = new VectorIndex.Quantizers.BQ
            {
                Cache = true,
                RescoreLimit = 200
            }
        }
    )
});

Additional considerations

Multiple vector embeddings (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, RQ, SQ, or no compression.

Multi-vector embeddings (ColBERT, ColPali, etc.)

Added in v1.30

Multi-vector embeddings (implemented through models like ColBERT, ColPali, or ColQwen) represent each object or query using multiple vectors instead of a single vector. Just like with single vectors, multi-vectors support PQ, BQ, RQ, SQ, or no compression.

During the initial search phase, compressed vectors are used for efficiency. However, when computing the MaxSim operation, uncompressed vectors are utilized to ensure more precise similarity calculations. This approach balances the benefits of compression for search efficiency with the accuracy of uncompressed vectors during final scoring.

Further resources

• Starter guides: Compression
• Reference: Vector index
• Concepts: Vector quantization
• Concepts: Vector index

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