Text Embeddings

Weaviate Cloud only

Configure a Weaviate vector index to use a Weaviate Embeddings model, and Weaviate will generate embeddings for various operations using the specified model and your Weaviate API key. This feature is called the vectorizer.

At import time, Weaviate generates text object embeddings and saves them into the index. For vector and hybrid search operations, Weaviate converts text queries into embeddings.

Embedding integration illustration

Requirements

To use Weaviate Embeddings, you need a Weaviate Cloud instance with a Weaviate client library that supports Weaviate Embeddings.

API credentials

Your Weaviate Cloud credentials are automatically used to authorize your access to Weaviate Embeddings.

Python

import weaviate
from weaviate.classes.init import Auth
import os

# Best practice: store your credentials in environment variables
weaviate_url = os.getenv("WEAVIATE_URL")
weaviate_key = os.getenv("WEAVIATE_API_KEY")

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                     # Weaviate URL: "REST Endpoint" in Weaviate Cloud console
    auth_credentials=Auth.api_key(weaviate_key),  # Weaviate API key: "ADMIN" API key in Weaviate Cloud console
)

print(client.is_ready())  # Should print: `True`

# Work with Weaviate

client.close()

JavaScript/TypeScript

import weaviate from 'weaviate-client'

// Best practice: store your credentials in environment variables
const weaviateUrl = process.env.WEAVIATE_URL as string;        // Weaviate URL: "REST Endpoint" in Weaviate Cloud console
const weaviateApiKey = process.env.WEAVIATE_API_KEY as string; // Weaviate API key: "ADMIN" API key in Weaviate Cloud console

const client = await weaviate.connectToWeaviateCloud(
  weaviateUrl,  
  {
    authCredentials: new weaviate.ApiKey(weaviateApiKey),  
  }
)

// Work with Weaviate

client.close()

Java

// Best practice: store your credentials in environment variablesString weaviateUrl = System.getenv("WEAVIATE_URL");String weaviateApiKey = System.getenv("WEAVIATE_API_KEY");WeaviateClient client = WeaviateClient.connectToWeaviateCloud(weaviateUrl, // Replace with your    // Weaviate Cloud URL    weaviateApiKey // Replace with your Weaviate Cloud key);System.out.println(client.isReady()); // Should print: `True`client.close(); // Free up resources

// Best practice: store your credentials in environment variablesstring weaviateUrl = Environment.GetEnvironmentVariable("WEAVIATE_URL");string weaviateApiKey = Environment.GetEnvironmentVariable("WEAVIATE_API_KEY");using var client = await Connect.Cloud(    weaviateUrl, // Replace with your Weaviate Cloud URL    weaviateApiKey // Replace with your Weaviate Cloud key);var meta = await client.GetMeta();Console.WriteLine(meta.Version);

Configure the vectorizer

Configure a Weaviate index as follows to use a Weaviate Embeddings model:

Python

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=[        Configure.Vectors.text2vec_weaviate(            name="title_vector",            source_properties=["title"]        )    ],    # Additional parameters not shown)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecWeaviate({        name: 'title_vector',        sourceProperties: ['title'],      },    ),  ],  // Additional parameters not shown});

// Define the collectionbasicWeaviateVectorizerDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-weaviate": map[string]interface{}{},      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(basicWeaviateVectorizerDef).Do(ctx)if err != nil {  panic(err)}

Java

client.collections.create("DemoCollection",
    col -> col
        .vectorConfig(
            VectorConfig.text2vecWeaviate("title_vector", c -> c.sourceProperties("title")))
        .properties(Property.text("title"), Property.text("description")));

await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v => v.Text2VecWeaviate(),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);

Select a model

You can specify one of the available models for the vectorizer to use, as shown in the following configuration example.

Python

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=[        Configure.Vectors.text2vec_weaviate(            name="title_vector",            source_properties=["title"],            model="Snowflake/snowflake-arctic-embed-l-v2.0"        )    ],    # Additional parameters not shown)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecWeaviate({      name: 'title_vector',      sourceProperties: ['title'],      model: 'Snowflake/snowflake-arctic-embed-l-v2.0',    }),  ],  // Additional parameters not shown});

// Define the collectionweaviateVectorizerWithModelDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-weaviate": map[string]interface{}{          "model": "arctic-embed-l-v2.0",        },      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(weaviateVectorizerWithModelDef).Do(ctx)if err != nil {  panic(err)}

Java

client.collections
    .create("DemoCollection",
        col -> col
            .vectorConfig(VectorConfig.text2vecWeaviate("title_vector",
                c -> c.sourceProperties("title")
                    .model("Snowflake/snowflake-arctic-embed-l-v2.0")))
            .properties(Property.text("title"), Property.text("description")));

await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v => v.Text2VecWeaviate(model: "Snowflake/snowflake-arctic-embed-l-v2.0"),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);

You can specify one of the available models for Weaviate to use. The default model is used if no model is specified.

Vectorization behavior

Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects.

Unless specified otherwise in the collection definition, the default behavior is to:

Only vectorize properties that use the text or text[] data type (unless skipped)
Sort properties in alphabetical (a-z) order before concatenating values
If vectorizePropertyName is true (false by default) prepend the property name to each property value
Join the (prepended) property values with spaces
Prepend the class name (unless vectorizeClassName is false)
Convert the produced string to lowercase

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

from weaviate.classes.config import Configureclient.collections.create(    "DemoCollection",    vector_config=[        Configure.Vectors.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)

JavaScript/TypeScript

await client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.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});

// Define the collectionweaviateVectorizerArcticEmbedMV15 := &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 collectionerr = client.Schema().ClassCreator().WithClass(weaviateVectorizerArcticEmbedMV15).Do(ctx)if err != nil {  panic(err)}

Java

client.collections.create("DemoCollection",
    col -> col.vectorConfig(VectorConfig.text2vecWeaviate("title_vector",
        c -> c.sourceProperties("title").model("Snowflake/snowflake-arctic-embed-m-v1.5")
    // .inferenceUrl(null)
    // .dimensions(0)
    )).properties(Property.text("title"), Property.text("description")));

await client.Collections.Create(
    new CollectionCreateParams
    {
        Name = "DemoCollection",
        VectorConfig = new VectorConfigList
        {
            Configure.Vector(
                "title_vector",
                v =>
                    v.Text2VecWeaviate(
                        model: "Snowflake/snowflake-arctic-embed-m-v1.5"
                    // baseURL: null,
                    // dimensions: 0
                    ),
                sourceProperties: ["title"]
            ),
        },
        Properties = [Property.Text("title"), Property.Text("description")],
    }
);

Data import

After configuring the vectorizer, import data into Weaviate. Weaviate generates embeddings for text objects using the specified model.

Python

source_objects = [    {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},    {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},    {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},    {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},    {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."}]collection = client.collections.use("DemoCollection")with collection.batch.fixed_size(batch_size=200) as batch:    for src_obj in source_objects:        # The model provider integration will automatically vectorize the object        batch.add_object(            properties={                "title": src_obj["title"],                "description": src_obj["description"],            },            # vector=vector  # Optionally provide a pre-obtained vector        )        if batch.number_errors > 10:            print("Batch import stopped due to excessive errors.")            breakfailed_objects = collection.batch.failed_objectsif failed_objects:    print(f"Number of failed imports: {len(failed_objects)}")    print(f"First failed object: {failed_objects[0]}")

JavaScript/TypeScript

let srcObjects = [
  { title: "The Shawshank Redemption", description: "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places." },
  { title: "The Godfather", description: "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga." },
  { title: "The Dark Knight", description: "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City." },
  { title: "Jingle All the Way", description: "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve." },
  { title: "A Christmas Carol", description: "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption." }
];

var sourceObjects = []map[string]string{  {"title": "The Shawshank Redemption", "description": "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."},  {"title": "The Godfather", "description": "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."},  {"title": "The Dark Knight", "description": "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."},  {"title": "Jingle All the Way", "description": "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."},  {"title": "A Christmas Carol", "description": "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."},}// Convert items into a slice of models.Objectobjects := []models.PropertySchema{}for i := range sourceObjects {  objects = append(objects, map[string]interface{}{    // Populate the object with the data    "title":       sourceObjects[i]["title"],    "description": sourceObjects[i]["description"],  })}// Batch write itemsbatcher := client.Batch().ObjectsBatcher()for _, dataObj := range objects {  batcher.WithObjects(&models.Object{    Class:      "DemoCollection",    Properties: dataObj,  })}// FlushbatchRes, err := batcher.Do(ctx)// Error handlingif err != nil {  panic(err)}for _, res := range batchRes {  if res.Result.Errors != nil {    for _, err := range res.Result.Errors.Error {      if err != nil {        fmt.Printf("Error details: %v\n", *err)        panic(err.Message)      }    }  }}

Java

// Define the source objects
List<Map<String, Object>> sourceObjects = List.of(Map.of("title", "The Shawshank Redemption",
    "description",
    "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places."),
    Map.of("title", "The Godfather", "description",
        "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga."),
    Map.of("title", "The Dark Knight", "description",
        "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City."),
    Map.of("title", "Jingle All the Way", "description",
        "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve."),
    Map.of("title", "A Christmas Carol", "description",
        "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption."));

// Get a handle to the collection
CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");

// Insert the data using insertMany
InsertManyResponse response = collection.data.insertMany(sourceObjects.toArray(new Map[0]));

// Check for errors
if (!response.errors().isEmpty()) {
  System.err.printf("Number of failed imports: %d\n", response.errors().size());
  System.err.printf("First failed object error: %s\n", response.errors().get(0));
} else {
  System.out.printf("Successfully inserted %d objects.\n", response.uuids().size());
}

// Define the source objects
var sourceObjects = new[]
{
    new
    {
        title = "The Shawshank Redemption",
        description = "A wrongfully imprisoned man forms an inspiring friendship while finding hope and redemption in the darkest of places.",
    },
    new
    {
        title = "The Godfather",
        description = "A powerful mafia family struggles to balance loyalty, power, and betrayal in this iconic crime saga.",
    },
    new
    {
        title = "The Dark Knight",
        description = "Batman faces his greatest challenge as he battles the chaos unleashed by the Joker in Gotham City.",
    },
    new
    {
        title = "Jingle All the Way",
        description = "A desperate father goes to hilarious lengths to secure the season's hottest toy for his son on Christmas Eve.",
    },
    new
    {
        title = "A Christmas Carol",
        description = "A miserly old man is transformed after being visited by three ghosts on Christmas Eve in this timeless tale of redemption.",
    },
};

// Get a handle to the collection
var collection = client.Collections.Use("DemoCollection");

// Insert the data using insertMany
var response = await collection.Data.InsertMany(sourceObjects);

// Check for errors
if (response.HasErrors)
{
    Console.WriteLine($"Number of failed imports: {response.Errors.Count()}");
    Console.WriteLine($"First failed object error: {response.Errors.First().Message}");
}
else
{
    Console.WriteLine($"Successfully inserted {response.Objects.Count()} objects.");
}

Searches

Once the vectorizer is configured, Weaviate will perform vector and hybrid search operations using the specified WED model.

Embedding integration at search illustration

Vector (near text) search

When you perform a vector search, Weaviate converts the text query into an embedding using the specified model and returns the most similar objects from the database.

The query below returns the n most similar objects from the database, set by limit.

Python

collection = client.collections.use("DemoCollection")response = collection.query.near_text(    query="A holiday film",  # The model provider integration will automatically vectorize the query    limit=2)for obj in response.objects:    print(obj.properties["title"])

JavaScript/TypeScript

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

nearTextResponse, err := client.GraphQL().Get().  WithClassName("DemoCollection").  WithFields(    graphql.Field{Name: "title"},  ).  WithNearText(client.GraphQL().NearTextArgBuilder().    WithConcepts([]string{"A holiday film"})).  WithLimit(2).  Do(ctx)if err != nil {  panic(err)}fmt.Printf("%v", nearTextResponse)

Java

CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");var response = collection.query.nearText("A holiday film", // The model provider integration will automatically vectorize the query    q -> q.limit(2).returnMetadata(Metadata.DISTANCE));for (var o : response.objects()) {  System.out.println(o.properties().get("title"));}

var collection = client.Collections.Use("DemoCollection");var response = await collection.Query.NearText(    "A holiday film", // The model provider integration will automatically vectorize the query    limit: 2,    returnMetadata: MetadataOptions.Distance);foreach (var o in response.Objects){    Console.WriteLine(o.Properties["title"]);}

Hybrid search

When you perform a hybrid search, Weaviate converts the text query into an embedding using the specified model and returns the best scoring objects from the database.

The query below returns the n best scoring objects from the database, set by limit.

Python

collection = client.collections.use("DemoCollection")response = collection.query.hybrid(    query="A holiday film",  # The model provider integration will automatically vectorize the query    limit=2)for obj in response.objects:    print(obj.properties["title"])

JavaScript/TypeScript

const collectionName = 'DemoCollection'
const myCollection = client.collections.use(collectionName)

hybridResponse, err := client.GraphQL().Get().  WithClassName("DemoCollection").  WithFields(    graphql.Field{Name: "title"},  ).  WithHybrid(client.GraphQL().HybridArgumentBuilder().    WithQuery("A holiday film")).  WithLimit(2).  Do(ctx)if err != nil {  panic(err)}fmt.Printf("%v", hybridResponse)

Java

CollectionHandle<Map<String, Object>> collection = client.collections.use("DemoCollection");QueryResponse<Map<String, Object>> response = collection.query.hybrid("A holiday film", // The model provider integration will automatically vectorize the query    q -> q.limit(2).returnMetadata(Metadata.DISTANCE));for (var o : response.objects()) {  System.out.println(o.properties().get("title"));}

var collection = client.Collections.Use("DemoCollection");var response = await collection.Query.Hybrid(    "A holiday film", // The model provider integration will automatically vectorize the query    limit: 2,    returnMetadata: MetadataOptions.Distance);foreach (var o in response.Objects){    Console.WriteLine(o.Properties["title"]);}

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

`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

Further resources

Code examples

Once the integrations are configured at the collection, the data management and search operations in Weaviate work identically to any other collection. See the following model-agnostic examples:

The How-to: Manage collections and How-to: Manage objects guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them).
The How-to: Query & Search guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation.

Multimodal embeddings

Looking to embed document images instead of text? See Weaviate Embeddings: Multimodal for visual document retrieval without OCR or preprocessing.

References

Weaviate Embeddings Documentation
Weaviate Embeddings Models

Pricing

Weaviate Embeddings models are charged based on token usage. For more pricing information, see the Weaviate Cloud pricing page.

Questions and feedback

Have a question or feedback? Here's how to reach us.

Community Forum

Ask questions and connect with other developers on our Community forum.

Support

Weaviate Cloud user or customer? Find the right channel on the Support page.

Requirements

API credentials

Configure the vectorizer

Select a model

Vectorizer parameters

Data import

Searches

Vector (near text) search

Hybrid search

Available models

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

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

Further resources

Code examples

Multimodal embeddings

References

Pricing

Questions and feedback

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

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