Vectorizer and vector index config

Specify a vectorizer

Specify a vectorizer for a collection.

Additional information

Collection level settings override default values and general configuration parameters such as environment variables.

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "Article",
    vectorizer_config=Configure.Vectorizer.text2vec_openai(),
    properties=[  # properties configuration is optional
        Property(name="title", data_type=DataType.TEXT),
        Property(name="body", data_type=DataType.TEXT),
    ]
)

API docs

class_obj = {
    "class": "Article",
    "properties": [
        {
            "name": "title",
            "dataType": ["text"],
        },
    ],
    "vectorizer": "text2vec-openai"  # this could be any vectorizer
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

import { vectorizer, dataType } from 'weaviate-client';

await client.collections.create({
  name: 'Article',
  vectorizers: vectorizer.text2VecOpenAI(),
  properties: [
    { name: 'title', dataType: dataType.TEXT },
    { name: 'body', dataType: dataType.TEXT },
  ],
})

API docs

const classWithVectorizer = {
  class: 'Article',
  properties: [
    {
      name: 'title',
      dataType: ['text'],
    },
  ],
  vectorizer: 'text2vec-openai', // this could be any vectorizer
};

// Add the class to the schema
result = await client.schema.classCreator().withClass(classWithVectorizer).do();

// Additional configuration not shown
// Define the vectorizer in the WeaviateClass Builder
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .vectorizer("text2vec-openai") // Vectorize of your choic e.g. text2vec-openai or text2vec-cohere
    .build();
// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Vectorizer:  "text2vec-openai",
    Properties: []*models.Property{
      {
        Name:     "title",
        DataType: schema.DataTypeText.PropString(),
      },
      {
        Name:     "body",
        DataType: schema.DataTypeText.PropString(),
      },
    },
  }

API docs

Specify vectorizer settings

To configure how a vectorizer works (i.e. what model to use) with a specific collection, set the vectorizer parameters.

from weaviate.classes.config import Configure

client.collections.create(
    "Article",
    vectorizer_config=Configure.Vectorizer.text2vec_cohere(
        model="embed-multilingual-v2.0",
        vectorize_collection_name=True
    ),
)

API docs

class_obj = {
    "class": "Article",
    "vectorizer": "text2vec-cohere",  # this could be any vectorizer
    "moduleConfig": {
        "text2vec-cohere": {  # this must match the vectorizer used
            "vectorizeClassName": True,
            "model": "embed-multilingual-v2.0",
        }
    }
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

import { vectorizer } from 'weaviate-client';

await client.collections.create({
  name: 'Article',
  vectorizers: vectorizer.text2VecCohere({
    model: 'embed-multilingual-v2.0',
    vectorizeCollectionName: true,
  }),
})

API docs

const classWithModuleSettings = {
  class: 'Article',
  properties: [
    {
      name: 'title',
      dataType: ['text'],
    },
  ],
  vectorizer: 'text2vec-cohere', // this could be any vectorizer
  moduleConfig: {
    'text2vec-cohere': {
      // this must match the vectorizer used
      vectorizeClassName: true,
      model: 'embed-multilingual-v2.0',
    },
  },
};

// Add the class to the schema
result = await client.schema
  .classCreator()
  .withClass(classWithModuleSettings)
  .do();

// Additional configuration not shown
// Define the module settings
Map<String, Object> text2vecOpenAI = new HashMap<>();
Map<String, Object> text2vecOpenAISettings = new HashMap<>();
text2vecOpenAISettings.put("vectorizePropertyName", false);
text2vecOpenAISettings.put("model", "text-embedding-3-small"); // set the model of your choice e.g. //
                                                               // text-embedding-3-small
text2vecOpenAI.put("text2vec-openai", text2vecOpenAISettings);
Map<Object, Object> moduleConfig = new HashMap<>();
moduleConfig.put("text2vec-openai", text2vecOpenAI);

// Set the module configu in the WeaviateClass Builder
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .moduleConfig(moduleConfig) // Set the module config
    .build();

// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Vectorizer:  "text2vec-cohere",
    ModuleConfig: map[string]interface{}{
      "text2vec-cohere": map[string]interface{}{
        "model":              "embed-multilingual-v2.0",
        "vectorizeClassName": true,
      },
    },
  }

API docs

Define named vectors

Added in v1.24

You can define multiple named vectors per collection. This allows each object to be represented by multiple vector embeddings, each with its own vector index.

As such, each named vector configuration can include its own vectorizer and vector index settings.

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "ArticleNV",
    vectorizer_config=[
        # Set a named vector with the "text2vec-cohere" vectorizer
        Configure.NamedVectors.text2vec_cohere(
            name="title",
            source_properties=["title"],                        # (Optional) Set the source property(ies)
            vector_index_config=Configure.VectorIndex.hnsw()    # (Optional) Set vector index options
        ),
        # Set another named vector with the "text2vec-openai" vectorizer
        Configure.NamedVectors.text2vec_openai(
            name="title_country",
            source_properties=["title", "country"],             # (Optional) Set the source property(ies)
            vector_index_config=Configure.VectorIndex.hnsw()    # (Optional) Set vector index options
        ),
        # Set a named vector for your own uploaded vectors
        Configure.NamedVectors.none(
            name="custom_vector",
            vector_index_config=Configure.VectorIndex.hnsw()    # (Optional) Set vector index options
        )
    ],
    properties=[  # Define properties
        Property(name="title", data_type=DataType.TEXT),
        Property(name="country", data_type=DataType.TEXT),
    ],
)

API docs

# Unfortunately, named vectors are not suppored in the v3 API / Python client.
# Please upgrade to the v4 API / Python client to use named vectors.

API docs Deprecated (v3)

import { vectorizer, dataType } from 'weaviate-client';

await client.collections.create({
  name: 'ArticleNV',

  vectorizers: [
    // Set a named vector with the "text2vec-cohere" vectorizer
    vectorizer.text2VecCohere({
      name: 'title',
      sourceProperties: ['title'],                      // (Optional) Set the source property(ies)
      vectorIndexConfig: configure.vectorIndex.hnsw()   // (Optional) Set the vector index configuration
    }),
    // Set a named vector with the "text2vec-openai" vectorizer
    vectorizer.text2VecOpenAI({
      name: 'title_country',
      sourceProperties: ['title','country'],            // (Optional) Set the source property(ies)
      vectorIndexConfig: configure.vectorIndex.hnsw()   // (Optional) Set the vector index configuration
    }),
    // Set a named vector for your own uploaded vectors
    vectorizer.none({
      name: 'custom_vector',
      vectorIndexConfig: configure.vectorIndex.hnsw()   // (Optional) Set the vector index configuration
    })
  ],

  properties: [
    { name: 'title', dataType: dataType.TEXT },
    { name: 'country', dataType: dataType.TEXT },
  ],
})

API docs

const classWithNamedVectors = {
  class: 'ArticleNV',
  vectorConfig: {
    // Set a named vector with the "text2vec-cohere" vectorizer
    title: {
      vectorizer: {
        'text2vec-cohere': {
          properties: ['title'], // (Optional) Set the source property(ies)
        },
      },
      vectorIndexType: 'hnsw',  // (Optional) Set the vector index type
      vectorIndexConfig: {}     // (Optional) Set the vector index configuration
    },
    // Set a named vector with the "text2vec-openai" vectorizer
    title_country: {
      vectorizer: {
        'text2vec-openai': {
          properties: ['title','country'],  // (Optional) Set the source property(ies)
        },
      },
      vectorIndexType: 'hnsw',  // (Optional) Set the vector index type
      vectorIndexConfig: {}     // (Optional) Set the vector index configuration
    },
    // Set a named vector for your own uploaded vectors
    custom_vector: {
      vectorizer: {
        'none': {}
      },
      vectorIndexType: 'hnsw',  // (Optional) Set the vector index type
      vectorIndexConfig: {}     // (Optional) Set the vector index configuration
    },
  },
  properties: [
    {
      name: 'title',
      dataType: ['text'],
    },
    {
      name: 'country',
      dataType: ['text'],
    },
  ],
};

// Add the class to the schema
result = await client.schema
  .classCreator()
  .withClass(classWithNamedVectors)
  .do();

// Additional configuration not shown
// Define the vectorizers configurations
Map<String, Object> text2vecOpenAI = new HashMap<>();
Map<String, Object> text2vecOpenAISettings = new HashMap<>();
text2vecOpenAISettings.put("properties", new String[] { "name" });
text2vecOpenAI.put("text2vec-openai", text2vecOpenAISettings);

Map<String, Object> text2vecCohere = new HashMap<>();
Map<String, Object> text2vecCohereSettings = new HashMap<>();
text2vecCohereSettings.put("properties", new String[] { "body" });
text2vecCohere.put("text2vec-cohere", text2vecCohereSettings);

// Define the vector configurations
Map<String, WeaviateClass.VectorConfig> vectorConfig = new HashMap<>();
vectorConfig.put("name_vector", WeaviateClass.VectorConfig.builder()
    .vectorIndexType("hnsw")
    .vectorizer(text2vecOpenAI)
    .build());
vectorConfig.put("body_vector", WeaviateClass.VectorConfig.builder()
    .vectorIndexType("hnsw")
    .vectorizer(text2vecCohere)
    .build());

// Define the vectorizers in the WeaviateClass Builder
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .vectorConfig(vectorConfig)
    .build();
// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "ArticleNV",
    Description: "Collection of articles with named vectors",
    Properties: []*models.Property{
      {
        Name:     "title",
        DataType: schema.DataTypeText.PropString(),
      },
      {
        Name:     "country",
        DataType: schema.DataTypeText.PropString(),
      },
    },
    VectorConfig: map[string]models.VectorConfig{
      "title": {
        Vectorizer: map[string]interface{}{
          "text2vec-openai": map[string]interface{}{
            "sourceProperties": []string{"title"},
          },
        },
        VectorIndexType: "hnsw",
      },
      "title_country": {
        Vectorizer: map[string]interface{}{
          "text2vec-openai": map[string]interface{}{
            "sourceProperties": []string{"title", "country"},
          },
        },
        VectorIndexType: "hnsw",
      },
      "custom_vector": {
        Vectorizer: map[string]interface{}{
          "none": map[string]interface{}{},
        },
        VectorIndexType: "hnsw",
      },
    },
  }

API docs

Add new named vectors

Added in v1.31

Named vectors can be added to existing collection definitions with named vectors. (This is not possible for collections without named vectors.)

Python Client v4
JS/TS Client v3
Java
Go

from weaviate.classes.config import Configure

articles = client.collections.get("Article")

articles.config.add_vector(
    vector_config=Configure.NamedVectors.text2vec_cohere(
        name="body_vector",
        source_properties=["body"],
    )
)

API docs

// TS support coming soon

// Java support coming soon

// Go support coming soon

Objects aren't automatically revectorized

Adding a new named vector to the collection definition won't trigger vectorization for existing objects. Only new or updated objects will receive embeddings for the newly added named vector definition.

Define multi-vector embeddings (e.g. ColBERT, ColPali)

Added in v1.29, v1.30

Multi-vector embeddings, also known as multi-vectors, represent a single object with multiple vectors, i.e. a 2-dimensional matrix. Multi-vectors are currently only available for HNSW indexes for named vectors. To use multi-vectors, enable it for the appropriate named vector.

Python Client v4
Java

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        # Example 1 - Use a model integration
        # The factory function will automatically enable multi-vector support for the HNSW index
        Configure.NamedVectors.text2colbert_jinaai(
            name="jina_colbert",
            source_properties=["text"],
        ),
        # Example 2 - User-provided multi-vector representations
        # Must explicitly enable multi-vector support for the HNSW index
        Configure.NamedVectors.none(
            name="custom_multi_vector",
            vector_index_config=Configure.VectorIndex.hnsw(
                multi_vector=Configure.VectorIndex.MultiVector.multi_vector()
            ),
        ),
    ],
    properties=[
        Property(name="text", data_type=DataType.TEXT)
    ]
    # Additional parameters not shown
)

API docs

// Define collection properties
Property textProperty = Property.builder()
    .name("text")
    .description("Text content for ColBERT vectorization")
    .dataType(Arrays.asList(DataType.TEXT))
    .build();

// Define the vectorizers configurations for named vectors
Map<String, Object> text2colbertJinaAI = new HashMap<>();
Map<String, Object> text2colbertSettings = new HashMap<>();
text2colbertSettings.put("properties", new String[] { "text" });
text2colbertJinaAI.put("text2colbert-jinaai", text2colbertSettings);

// Configure multi-vector for custom vectors
Map<String, Object> noneVectorizer = new HashMap<>();
noneVectorizer.put("none", new Object());

// Create multi-vector config for custom vectors
VectorIndexConfig customMultiVectorConfig = VectorIndexConfig.builder()
    .multiVector(MultiVectorConfig.builder().build()) // Enable multi-vector with default settings
    .build();

// Define the vector configurations
Map<String, WeaviateClass.VectorConfig> vectorConfig = new HashMap<>();

// Example 1: ColBERT vectorizer
vectorConfig.put("jina_colbert", WeaviateClass.VectorConfig.builder()
    .vectorIndexType("hnsw")
    .vectorizer(text2colbertJinaAI)
    .build());

// Example 2: User-provided multi-vector representations
vectorConfig.put("custom_multi_vector", WeaviateClass.VectorConfig.builder()
    .vectorIndexType("hnsw")
    .vectorizer(noneVectorizer)
    .vectorIndexConfig(customMultiVectorConfig)
    .build());

// Create the collection with multi-vector configuration
WeaviateClass multiVecClass = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(textProperty))
    .vectorConfig(vectorConfig)
    .build();

// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(multiVecClass)
    .run();

API docs

Use quantization and encoding to compress your vectors

Multi-vector embeddings use up more memory than single vector embeddings. You can use vector quantization and encoding to compress them and reduce memory usage.

Set vector index type

The vector index type can be set for each collection at creation time, between hnsw, flat and dynamic index types.

from weaviate.classes.config import Configure, Property, DataType

client.collections.create(
    "Article",
    vectorizer_config=Configure.Vectorizer.text2vec_openai(),
    vector_index_config=Configure.VectorIndex.hnsw(),  # Use the HNSW index
    # vector_index_config=Configure.VectorIndex.flat(),  # Use the FLAT index
    # vector_index_config=Configure.VectorIndex.dynamic(),  # Use the DYNAMIC index
    properties=[
        Property(name="title", data_type=DataType.TEXT),
        Property(name="body", data_type=DataType.TEXT),
    ]
)

API docs

class_obj = {
    'class': 'Article',
    'properties': [
        {
            'name': 'title',
            'dataType': ['text'],
        },
    ],
    'vectorizer': 'text2vec-openai',  # this could be any vectorizer
    "vectorIndexType": "hnsw",  # or "flat" or "dynamic"
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

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

await client.collections.create({
  name: 'Article',
  vectorizers: vectorizer.text2VecOpenAI({
    vectorIndexConfig: configure.vectorIndex.hnsw(),  // Use HNSW
    // vectorIndexConfig: configure.vectorIndex.flat(),  // Use Flat
    // vectorIndexConfig: configure.vectorIndex.dynamic(),  // Use Dynamic
  }),
  properties: [
    { name: 'title', dataType: dataType.TEXT },
    { name: 'body', dataType: dataType.TEXT },
  ],
})

API docs

const classWithIndexType = {
  class: 'Article',
  properties: [
    {
      name: 'title',
      dataType: ['text'],
    },
  ],
  vectorizer: 'text2vec-openai', // this could be any vectorizer
  vectorIndexType: 'flat', // or 'hnsw', or 'dynamic'
  vectorIndexConfig: {
    bq: {
      enabled: true, // Enable BQ compression. Default: False
      rescoreLimit: 200, // The minimum number of candidates to fetch before rescoring. Default: -1 (No limit)
      cache: true, // Enable use of vector cache. Default: False
    },
    vectorCacheMaxObjects: 100000, // Cache size if `cache` enabled. Default: 1000000000000
  },
};

// Add the class to the schema
result = await client.schema.classCreator().withClass(classWithIndexType).do();

// Additional configuration not shown
// Define the index type in the WeaviateClass Builder
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .vectorizer("text2vec-openai")
    .vectorIndexType("hnsw") // set the vector index of your choice e.g. hnsw, flat...
    .build();
// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Properties: []*models.Property{
      {
        Name:     "title",
        DataType: schema.DataTypeText.PropString(),
      },
      {
        Name:     "country",
        DataType: schema.DataTypeText.PropString(),
      },
    },
    Vectorizer:      "text2vec-openai",
    VectorIndexType: "hnsw", // Or "flat", "dynamic"
  }

API docs

Additional information

Read more about index types & compression in Concepts: Vector index.

Set vector index parameters

Set vector index parameters such as compression and filter strategy through collection configuration. Some parameters can be updated later after collection creation.

Filter strategy parameter

Was added in v1.27

from weaviate.classes.config import Configure, Property, DataType, VectorDistances, VectorFilterStrategy

client.collections.create(
    "Article",
    # Additional configuration not shown
    vector_index_config=Configure.VectorIndex.hnsw(
        quantizer=Configure.VectorIndex.Quantizer.bq(),
        ef_construction=300,
        distance_metric=VectorDistances.COSINE,
        filter_strategy=VectorFilterStrategy.SWEEPING  # or ACORN (Available from Weaviate v1.27.0)
    ),
)

API docs

class_obj = {
    'class': 'Article',
    # Additional configuration not shown
    "vectorIndexType": "flat",
    "vectorIndexConfig": {
        "bq": {
            "enabled": True,  # Enable BQ compression. Default: False
            "rescoreLimit": 200,  # The minimum number of candidates to fetch before rescoring. Default: -1 (No limit)
            "cache": True,  # Enable use of vector cache. Default: False
        },
        "vectorCacheMaxObjects": 100000,  # Cache size if `cache` enabled. Default: 1000000000000
        "filterStrategy": "sweeping"  # or "acorn" (Available from Weaviate v1.27.0)
    }
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

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

await client.collections.create({
  name: 'Article',
  // Additional configuration not shown
  vectorizers: vectorizer.text2VecCohere({
    vectorIndexConfig: configure.vectorIndex.flat({
      quantizer: configure.vectorIndex.quantizer.bq({
        rescoreLimit: 200,
        cache: true
      }),
      vectorCacheMaxObjects: 100000
    })
  })
})

API docs

const classWithIndexParams = {
  class: 'Article',
  // Additional configuration not shown
  vectorIndexType: 'flat', // or `hnsw`
  vectorIndexConfig: {
    bq: {
      enabled: true, // Enable BQ compression. Default: False
      rescoreLimit: 200, // The minimum number of candidates to fetch before rescoring. Default: -1 (No limit)
      cache: true, // Enable use of vector cache. Default: False
    },
    vectorCacheMaxObjects: 100000, // Cache size if `cache` enabled. Default: 1000000000000
  },
};

// Add the class to the schema
result = await client.schema.classCreator().withClass(classWithIndexType).do();

// Additional configuration not shown
// Define the VectorIndexConfig with compression
VectorIndexConfig createBqIndexConfig = VectorIndexConfig.builder()
    .bq(BQConfig.builder()
        .enabled(true)
        .rescoreLimit(123L)
        .cache(true)
        .build())
    .vectorCacheMaxObjects(100000L)
    .build();

WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .vectorIndexType("flat") // set the vector index of your choice e.g. hnsw, flat...
    .vectorIndexConfig(createBqIndexConfig)
    .vectorizer("text2vec-openai")
    .build();

// Add the collection to the schema
Result<Boolean> result = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Properties: []*models.Property{
      {
        Name:     "title",
        DataType: schema.DataTypeText.PropString(),
      },
      {
        Name:     "country",
        DataType: schema.DataTypeText.PropString(),
      },
    },
    Vectorizer:      "text2vec-openai",
    VectorIndexType: "hnsw",
    VectorIndexConfig: map[string]interface{}{
      "bq": map[string]interface{}{
        "enabled": true,
      },
      "efConstruction": 300,
      "distance":       "cosine",
      "filterStrategy": "acorn",
    },
  }

API docs

Additional information

Read more about index types & compression in Concepts: Vector index.

Property-level settings

Configure individual properties in a collection. Each property can have it's own configuration. Here are some common settings:

Vectorize the property
Vectorize the property name
Set a tokenization type

from weaviate.classes.config import Configure, Property, DataType, Tokenization

client.collections.create(
    "Article",
    vectorizer_config=Configure.Vectorizer.text2vec_cohere(),

    properties=[
        Property(
            name="title",
            data_type=DataType.TEXT,
            vectorize_property_name=True,  # Use "title" as part of the value to vectorize
            tokenization=Tokenization.LOWERCASE,  # Use "lowecase" tokenization
            description="The title of the article."  # Optional description
        ),
        Property(
            name="body",
            data_type=DataType.TEXT,
            skip_vectorization=True,  # Don't vectorize this property
            tokenization=Tokenization.WHITESPACE  # Use "whitespace" tokenization
        ),
    ]
)

API docs

class_obj = {
    "class": "Article",
    "vectorizer": "text2vec-huggingface",  # this could be any vectorizer
    "properties": [
        {
            "name": "title",
            "dataType": ["text"],
            "moduleConfig": {
                "text2vec-huggingface": {  # this must match the vectorizer used
                    "vectorizePropertyName": True,
                    "tokenization": "lowercase"
                }
            }
        },
        {
            "name": "body",
            "dataType": ["text"],
            "moduleConfig": {
                "text2vec-huggingface": {  # this must match the vectorizer used
                    "skip": True,  # Don't vectorize body
                    "tokenization": "whitespace"
                }
            }
        },
    ],
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

import { vectorizer, dataType, tokenization } from 'weaviate-client';

const newCollection = await client.collections.create({
  name: 'Article',
  vectorizers: vectorizer.text2VecHuggingFace(),
  properties: [
    {
      name: 'title',
      dataType: dataType.TEXT,
      vectorizePropertyName: true,
      tokenization: tokenization.LOWERCASE // or 'lowercase'
    },
    {
      name: 'body',
      dataType: dataType.TEXT,
      skipVectorization: true,
      tokenization: tokenization.WHITESPACE // or 'whitespace'
    },
  ],
})

API docs

const classWithPropModuleSettings = {
  class: 'Article',
  vectorizer: 'text2vec-huggingface', // this could be any vectorizer
  properties: [
    {
      name: 'title',
      dataType: ['text'],
      moduleConfig: {
        'text2vec-huggingface': {
          // this must match the vectorizer used
          vectorizePropertyName: true,
          tokenization: 'lowercase', // Use "lowercase" tokenization
        },
      },
    },
    {
      name: 'body',
      dataType: ['text'],
      moduleConfig: {
        'text2vec-huggingface': {
          // this must match the vectorizer used
          skip: true, // Don't vectorize this property
          tokenization: 'whitespace', // Use "whitespace" tokenization
        },
      },
    },
  ],
};

// Add the class to the schema
result = await client.schema
  .classCreator()
  .withClass(classWithPropModuleSettings)
  .do();

Property titleProperty = Property.builder()
    .name("title")
    .description("title of the article")
    .dataType(Arrays.asList(DataType.TEXT))
    .tokenization(Tokenization.WORD)
    .build();

Property bodyProperty = Property.builder()
    .name("body")
    .description("body of the article")
    .dataType(Arrays.asList(DataType.TEXT))
    .tokenization(Tokenization.LOWERCASE)
    .build();

// Add the defined properties to the collection
WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .description("Article collection Description...")
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .build();

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

API docs

  vTrue := true
  vFalse := false

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    Properties: []*models.Property{
      {
        Name:            "title",
        DataType:        schema.DataTypeText.PropString(),
        Tokenization:    "lowercase",
        IndexFilterable: &vTrue,
        IndexSearchable: &vFalse,
        ModuleConfig: map[string]interface{}{
          "text2vec-cohere": map[string]interface{}{
            "vectorizePropertyName": true,
          },
        },
      },
      {
        Name:            "body",
        DataType:        schema.DataTypeText.PropString(),
        Tokenization:    "whitespace",
        IndexFilterable: &vTrue,
        IndexSearchable: &vTrue,
        ModuleConfig: map[string]interface{}{
          "text2vec-cohere": map[string]interface{}{
            "vectorizePropertyName": false,
          },
        },
      },
    },
    Vectorizer: "text2vec-cohere",
  }

API docs

Specify a distance metric

If you choose to bring your own vectors, you should specify the distance metric.

from weaviate.classes.config import Configure, VectorDistances

client.collections.create(
    "Article",
    vector_index_config=Configure.VectorIndex.hnsw(
        distance_metric=VectorDistances.COSINE
    ),
)

API docs

class_obj = {
    "class": "Article",
    "vectorIndexConfig": {
        "distance": "cosine",
    },
}

client.schema.create_class(class_obj)

API docs Deprecated (v3)

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

await client.collections.create({
  name: 'Article',
  vectorizers: vectorizer.text2VecOllama({
    vectorIndexConfig: configure.vectorIndex.hnsw({
      distanceMetric: vectorDistances.COSINE // or 'cosine'
    })
  })
})

API docs

const classWithDistance = {
  class: 'Article',
  vectorIndexConfig: {
    distance: 'cosine',
  },
};

// Add the class to the schema
result = await client.schema.classCreator().withClass(classWithDistance).do();

// Additional configuration not shown
VectorIndexConfig vectorIndexConfig = VectorIndexConfig.builder()
    .distance(DistanceType.DOT) // Define Distance Type e.g. Dot, Cosine, hamming...
    .build();

WeaviateClass articleCollection = WeaviateClass.builder()
    .className(collectionName)
    .properties(Arrays.asList(titleProperty, bodyProperty))
    .vectorIndexConfig(vectorIndexConfig)
    .build();

Result<Boolean> classResult = client.schema().classCreator()
    .withClass(articleCollection)
    .run();

API docs

  articleClass := &models.Class{
    Class:       "Article",
    Description: "Collection of articles",
    VectorIndexConfig: map[string]interface{}{
      "distance": "cosine",
    },
  }

API docs

Additional information

For details on the configuration parameters, see the following:

Further resources

Questions and feedback

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.

Additional resources

Need help?

Vectorizer and vector index config

Specify a vectorizer

Specify vectorizer settings

Define named vectors

Add new named vectors

Define multi-vector embeddings (e.g. ColBERT, ColPali)

Set vector index type

Set vector index parameters

Property-level settings

Specify a distance metric

Further resources

Questions and feedback

Additional resources

Need help?

Specify a vectorizer​

Specify vectorizer settings​

Define named vectors​

Add new named vectors​

Define multi-vector embeddings (e.g. ColBERT, ColPali)​

Set vector index type​

Set vector index parameters​

Property-level settings​

Specify a distance metric​

Further resources​

Questions and feedback​

Specify a vectorizer

Specify vectorizer settings

Define named vectors

Add new named vectors

Define multi-vector embeddings (e.g. ColBERT, ColPali)

Set vector index type

Set vector index parameters

Property-level settings

Specify a distance metric

Further resources

Questions and feedback