Text Embeddings

Added in v1.26.3

Databricks Embeddings with Weaviate

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.

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) serverless 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 Dataricks 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 API v4

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

# Recommended: save sensitive data as environment variables
databricks_token = os.getenv("DATABRICKS_TOKEN")
headers = {
    "X-Databricks-Token": databricks_token,
}

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,                       # `weaviate_url`: your Weaviate URL
    auth_credentials=Auth.api_key(weaviate_key),      # `weaviate_key`: your Weaviate API key
    headers=headers
)

# Work with Weaviate

client.close()

  API docs

JS/TS API v3

import weaviate from 'weaviate-client'

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

const client = await weaviate.connectToWeaviateCloud(
  'WEAVIATE_INSTANCE_URL',  // Replace with your instance URL
  {
    authCredentials: new weaviate.ApiKey('WEAVIATE_INSTANCE_APIKEY'),
    headers: {
      'X-Databricks-Token': databricksToken,
    }
  }
)

// Work with Weaviate

client.close()

  API docs

Go

// Set these environment variables
// WCD_HOSTNAME            your Weaviate instance hostname, excluding the scheme (https://)
// WCD_API_KEY          your Weaviate instance API key
// <PROVIDER>_APIKEY       your model provider API key (or token)

package main

import (
    "context"
    "fmt"
    "os"

    "github.com/weaviate/weaviate-go-client/v4/weaviate"
    "github.com/weaviate/weaviate-go-client/v4/weaviate/auth"
)

func main() {
    cfg := weaviate.Config{
        Host:       os.Getenv("WCD_HOSTNAME"),
        Scheme:     "https",
        AuthConfig: auth.ApiKey{Value: os.Getenv("WCD_API_KEY")},
        Headers: map[string]string{
            "X-Databricks-Token": os.Getenv("DATABRICKS_TOKEN"),
        },
    }

    client, err := weaviate.NewClient(cfg)
    if err != nil {
        fmt.Println(err)
    }

    // Work with Weaviate
}

  API docs

Configure the vectorizer

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

Python API v4

from weaviate.classes.config import Configure

databricks_vectorizer_endpoint = os.getenv("DATABRICKS_VECTORIZER_ENDPOINT")  # If saved as an environment variable

client.collections.create(
    "DemoCollection",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_databricks(
            endpoint=databricks_vectorizer_endpoint,  # Required for Databricks
            name="title_vector",
            source_properties=["title"],
        )
    ],
    # Additional parameters not shown
)

  API docs

JS/TS API v3

const databricksVectorizerEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || '';  // If saved as an environment variable

await client.collections.create({
  name: 'DemoCollection',
  properties: [
    {
      name: 'title',
      dataType: 'text' as const,
    },
  ],
  vectorizers: [
    weaviate.configure.vectorizer.text2VecDatabricks({
      endpoint: databricksVectorizerEndpoint,  // Required for Databricks
      name: 'title_vector',
      sourceProperties: ['title'],
    })
  ],
  // Additional parameters not shown
});

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    // Define the collection
    basicDatabricksVectorizerDef := &models.Class{
        Class: "DemoCollection",
        VectorConfig: map[string]models.VectorConfig{
            "title_vector": {
                Vectorizer: map[string]interface{}{
                    "text2vec-databricks": map[string]interface{}{
                        "sourceProperties": []string{"title"},
                        "endpoint":         "<databricks_vectorizer_endpoint>", // Required for Databricks
                    },
                },
            },
        },
    }

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

  API docs

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.

Data import

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

Python API v4

collection = client.collections.get("DemoCollection")

with collection.batch.dynamic() as batch:
    for src_obj in source_objects:
        weaviate_obj = {
            "title": src_obj["title"],
            "description": src_obj["description"],
        }

        # The model provider integration will automatically vectorize the object
        batch.add_object(
            properties=weaviate_obj,
            # vector=vector  # Optionally provide a pre-obtained vector
        )

  API docs

JS/TS API v3

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

let dataObjects = []

for (let srcObject of srcObjects) {
  dataObject.push({
    title: srcObject.title,
    description: srcObject.description,
  });
}

const response = await myCollection.data.insertMany(dataObjects);

console.log(response);

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    var sourceObjects = []map[string]string{
        // Objects not shown
    }

    // Convert items into a slice of models.Object
    objects := []models.PropertySchema{}
    for i := range sourceObjects {
        objects = append(objects, map[string]interface{}{
            // Populate the object with the data
        })
    }

    // Batch write items
    batcher := client.Batch().ObjectsBatcher()
    for _, dataObj := range objects {
        batcher.WithObjects(&models.Object{
            Class:      "DemoCollection",
            Properties: dataObj,
        })
    }

    // Flush
    batchRes, err := batcher.Do(ctx)

    // Error handling
    if 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)
                }
            }
        }
    }
}

  API docs

Re-use existing vectors

If you already have a compatible model vector available, you can provide it directly to Weaviate. This can be useful if you have already generated embeddings using the same model and want to use them in Weaviate, such as when migrating data from another system.

Searches

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

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 API v4

collection = client.collections.get("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"])

  API docs

JS/TS API v3

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

let result;

result = await myCollection.query.nearText(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    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)
}

  API docs

Hybrid search

What is a hybrid search?

A hybrid search performs a vector search and a keyword (BM25) search, before combining the results to return the best matching objects from the database.

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 API v4

collection = client.collections.get("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"])

  API docs

JS/TS API v3

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

result = await myCollection.query.hybrid(
  'A holiday film',  // The model provider integration will automatically vectorize the query
  {
    limit: 2,
  }
)

console.log(JSON.stringify(result.objects, null, 2));

  API docs

Go

// package, imports not shown

func main() {
    // Instantiation not shown

    ctx := context.Background()

    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)
}

  API docs

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 data guides show how to perform data operations (i.e. create, update, delete).
• The how-to: 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

Questions and feedback

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

• Technical questions
• Documentation feedback