Choose a model

On this page, you can find a list of pre-trained models designed specifically for enterprise retrieval tasks in English and other languages. Additional models and features will be added in the future, so please check back regularly for updates.

How to choose the right model?

Here are some simple recommendations on when you should use a specific model:

• Snowflake/snowflake-arctic-embed-m-v1.5
  Best for datasets that are primarily in English with text lengths typically under 512 tokens.
• Snowflake/snowflake-arctic-embed-l-v2.0
  Ideal for datasets that include multiple languages or require longer context (up to 8192 tokens). This model is optimized for robust performance on both English and multilingual retrieval tasks.

Below, you can find a complete list of all available models.

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Available models

Snowflake/snowflake-arctic-embed-l-v2.0 (default)

• A 568M parameter, 1024-dimensional model for multilingual enterprise retrieval tasks.
• Trained with Matryoshka Representation Learning to allow vector truncation with minimal loss.
• Quantization-friendly: Using scalar quantization and 256 dimensions provides 99% of unquantized, full-precision performance.
• Read more at the Snowflake blog, and the Hugging Face model card
• Allowable dimensions: 1024 (default), 256

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Snowflake/snowflake-arctic-embed-m-v1.5

• A 109M parameter, 768-dimensional model for enterprise retrieval tasks in English.
• Trained with Matryoshka Representation Learning to allow vector truncation with minimal loss.
• Quantization-friendly: Using scalar quantization and 256 dimensions provides 99% of unquantized, full-precision performance.
• Read more at the Snowflake blog, and the Hugging Face model card
• Allowable dimensions: 768 (default), 256

Input truncation

Currently, input exceeding the model's context windows is truncated from the right (i.e. the end of the input).

Vectorizer parameters

• model (optional): The name of the model to use for embedding generation.
• dimensions (optional): The number of dimensions to use for the generated embeddings.
• base_url (optional): The base URL for the Weaviate Embeddings service. (Not required in most cases.)

The following examples show how to configure Weaviate Embeddings-specific options.

Python API v4

from weaviate.classes.config import Configure

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_weaviate(
            name="title_vector",
            source_properties=["title"],
            model="Snowflake/snowflake-arctic-embed-m-v1.5",
            # Further options
            # dimensions=256
            # base_url="<custom_weaviate_embeddings_url>",
        )
    ],
    # Additional parameters not shown
)

  API docs

JS/TS API v3

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecWeaviate({
        name: 'title_vector',
        sourceProperties: ['title'],
        model: 'Snowflake/snowflake-arctic-embed-m-v1.5',
        // Further options
        // dimensions: 256,
        // baseURL: '<custom_weaviate_embeddings_url>',
      },
    ),
  ],
  // Additional parameters not shown
});

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    // Define the collection
    weaviateVectorizerArcticEmbedMV15 := &models.Class{
        Class: "DemoCollection",
        VectorConfig: map[string]models.VectorConfig{
            "title_vector": {
                Vectorizer: map[string]interface{}{
                    "text2vec-weaviate": map[string]interface{}{
                        "model":      "Snowflake/snowflake-arctic-embed-m-v1.5",
                        "dimensions": 256, // Or 768
                        "base_url":   "<custom_weaviate_url>",
                    },
                },
            },
        },
    }

    // add the collection
    err = client.Schema().ClassCreator().WithClass(weaviateVectorizerArcticEmbedMV15).Do(ctx)
    if err != nil {
        panic(err)
    }
}

  API docs

Java

Map<String, Object> text2vecWeaviate = new HashMap<>();
Map<String, Object> text2vecWeaviateSettings = new HashMap<>();

text2vecWeaviateSettings.put("properties", new String[]{"title"});
text2vecWeaviateSettings.put("model", new String[]{"Snowflake/snowflake-arctic-embed-m-v1.5"});
text2vecWeaviateSettings.put("dimensions", new Integer[]{768});  // 768, 256
text2vecWeaviateSettings.put("base_url", new String[]{"<custom_weaviate_url>"});
text2vecWeaviate.put("text2vec-weaviate", text2vecWeaviateSettings);

// Define the vector configurations
Map<String, WeaviateClass.VectorConfig> vectorConfig = new HashMap<>();
vectorConfig.put("title_vector", WeaviateClass.VectorConfig.builder()
  .vectorIndexType("hnsw")
  .vectorizer(text2vecWeaviate)
  .build());

// Create the collection "DemoCollection"
WeaviateClass clazz = WeaviateClass.builder()
  .className("DemoCollection")
  .vectorConfig(vectorConfig)
  .build();

Result<Boolean> result = client.schema().classCreator().withClass(clazz).run();

  API docs

Additional resources

• Weaviate Embeddings: Overview
• Weaviate Embeddings: Quickstart
• Weaviate Embeddings: Administration
• Model provider integrations: Weaviate Embeddings

Support & feedback

For help with Serverless Cloud, Enterprise Cloud, and Bring Your Own Cloud accounts, contact Weaviate support directly to open a support ticket. To add a support plan, contact Weaviate sales.

If you have any questions or feedback, let us know in the user forum.

Technical questions

If you have questions feel free to post on our Community forum.

Documentation feedback

Leave feedback by opening a GitHub issue.