# Uncompressed vector embeddings :::callout{intent="info" title="Compression by Default"} Starting with `v1.33`, you can set a default quantization for new collections using the [`DEFAULT_QUANTIZATION`](../database-configuration/overview.md#DEFAULT_QUANTIZATION) environment variable. This variable is not set by default, meaning no quantization is applied unless you explicitly configure it. When set (e.g., to 8-bit [RQ quantization](compression-rq-compression.md)), all newly created collections will use that quantization setting. Note that once set on a collection, quantization can't be disabled. Default quantization won't be applied to a collection if the index type isn't supported (for example PQ and SQ aren't supported for the flat index). ::: You can opt-out of using vector quantization to compress your vector data. ## Disable compression for new collection When creating the collection, you can choose not to use quantization through the collection definition: :::code-group{sync="languages"} ```python title="Python" {6} from weaviate.classes.config import Configure, Property, DataType client.collections.create( name="MyCollection", vector_config=Configure.Vectors.text2vec_openai( quantizer=Configure.VectorIndex.Quantizer.none() ), properties=[ Property(name="title", data_type=DataType.TEXT), ], ) ``` ```typescript title="JS/TS" import weaviate, { configure } from 'weaviate-client'; ``` ```java title="Java" {3} client.collections.create("MyCollection", col -> col.vectorConfig(VectorConfig.text2vecTransformers(vc -> vc .quantization(Quantization.uncompressed()) )).properties(Property.text("title"))); ``` ```csharp title="C#" {11} 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 { }, } ), } ); ``` ::: ## Additional considerations ### Multiple vector embeddings (named vectors) Collections can have multiple [named vectors](../reference-configuration/collections.md#multiple-vector-embeddings-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](compression-pq-compression.md), [BQ](compression-bq-compression.md), [RQ](compression-rq-compression.md), [SQ](compression-sq-compression.md), or no compression. ### Multi-vector embeddings (ColBERT, ColPali, etc.) :::callout{intent="info" title="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](compression-pq-compression.md), [BQ](compression-bq-compression.md), [RQ](compression-rq-compression.md), [SQ](compression-sq-compression.md), 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. :::callout{intent="note" title="Multi-vector performance"} RQ supports multi-vector embeddings. Each token vector is rounded up to a multiple of 64 dimensions, which may result in less than 4x compression for very short vectors. This is a technical limitation that may be addressed in future versions. ::: ## Further resources - [Starter guides: Compression](../starter-guides/managing-resources-compression.md) - [Reference: Vector index](../reference-configuration/indexing-vector-index.md) - [Concepts: Vector quantization](../concepts/vector-quantization.md) - [Concepts: Vector index](../indexing/vector-index.md) ## Questions and feedback Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=