Text Embeddings

Weaviate's integration with Databricks' APIs allows you to access models hosted on their platform directly from Weaviate.

Configure a Weaviate vector index to use a Databricks embedding model, and Weaviate will generate embeddings for various operations using the specified endpoint and your Databricks token. 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

Weaviate configuration

Your Weaviate instance must be configured with the Databricks vectorizer integration (text2vec-databricks) module.

For Weaviate Cloud (WCD) users

This integration is enabled by default on Weaviate Cloud (WCD) instances.

For self-hosted users

Check the cluster metadata to verify if the module is enabled.
Follow the how-to configure modules guide to enable the module in Weaviate.

Databricks Personal Access Token

You must provide a valid Databricks Personal Access Token (PAT) to Weaviate for this integration. Refer to the Databricks documentation for instructions on generating your PAT in your workspace.

Provide the Databricks token to Weaviate using one of the following methods:

Set the DATABRICKS_TOKEN environment variable that is available to Weaviate.
Provide the token at runtime, as shown in the examples below.

Python

# Recommended: save sensitive data as environment variables
databricks_token = os.getenv("DATABRICKS_TOKEN")

JavaScript/TypeScript

const databricksToken = process.env.DATABRICKS_TOKEN || '';  // Replace with your inference API key

"X-Databricks-Token": os.Getenv("DATABRICKS_TOKEN"),

Configure the vectorizer

Configure a Weaviate index to use a Databricks serving model endpoint by setting the vectorizer as follows:

Python

import osfrom weaviate.classes.config import Configuredatabricks_vectorizer_endpoint = os.getenv("DATABRICKS_VECTORIZER_ENDPOINT")  # If saved as an environment variableclient.collections.create(    "DemoCollection",    vector_config=[        Configure.Vectors.text2vec_databricks(            endpoint=databricks_vectorizer_endpoint,  # Required for Databricks            name="title_vector",            source_properties=["title"],        )    ],    # Additional parameters not shown)

JavaScript/TypeScript

const databricksVectorizerEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || '';  // If saved as an environment variableawait client.collections.create({  name: 'DemoCollection',  properties: [    {      name: 'title',      dataType: 'text' as const,    },  ],  vectorizers: [    weaviate.configure.vectors.text2VecDatabricks({      endpoint: databricksVectorizerEndpoint,  // Required for Databricks      name: 'title_vector',      sourceProperties: ['title'],    })  ],  // Additional parameters not shown});

// Define the collectionbasicDatabricksVectorizerDef := &models.Class{  Class: "DemoCollection",  VectorConfig: map[string]models.VectorConfig{    "title_vector": {      Vectorizer: map[string]interface{}{        "text2vec-databricks": map[string]interface{}{          "properties": []string{"title"},          "endpoint":   "<databricks_vectorizer_endpoint>", // Required for Databricks        },      },    },  },}// add the collectionerr = client.Schema().ClassCreator().WithClass(basicDatabricksVectorizerDef).Do(ctx)if err != nil {  panic(err)}

This will configure Weaviate to use the vectorizer served through the endpoint you specify.

Vectorizer parameters

endpoint: The URL of the embedding model hosted on Databricks.
instruction: An optional instruction to pass to the embedding model.

For further details on model parameters, see the Databricks documentation.

Header parameters

You can provide the token as well as some optional parameters at runtime through additional headers in the request. The following headers are available:

X-Databricks-Token: The Databricks API token.
X-Databricks-Endpoint: The endpoint to use for the Databricks model.
X-Databricks-User-Agent: The user agent to use for the Databricks model.

Any additional headers provided at runtime will override the existing Weaviate configuration.

Provide the headers as shown in the API credentials examples above.

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)      }    }  }}

Searches

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

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)

References

Further resources

Other integrations

Databricks generative models + Weaviate.

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.

External resources

Databricks Foundation model REST API reference

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