Uncompressed vector embeddings

You can opt-out of using vector quantization to compress your vector data.

When creating the collection, you can choose not to use quantization through the collection definition:

from weaviate.classes.config import Configure, Property, DataTypeclient.collections.create(    name="MyCollection",    vector_config=Configure.Vectors.text2vec_openai(        quantizer=Configure.VectorIndex.Quantizer.none()    ),    properties=[        Property(name="title", data_type=DataType.TEXT),    ],)

import weaviate, { configure } from 'weaviate-client';

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

await client.Collections.Create(    new CollectionCreateParams    {        Name = "MyCollection",        Properties = [Property.Text("title")],        VectorConfig = Configure.Vector(            "default",            v => v.Text2VecTransformers(),            index: new VectorIndex.HNSW            {                Quantizer = new VectorIndex.Quantizers.None { },            }        ),    });

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 (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.

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

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