Weaviate's integration with Databricks' APIs allows you to access models hosted on their platform directly from Weaviate. [Configure a Weaviate vector index](#configure-the-vectorizer) 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](#data-import), Weaviate generates text object embeddings and saves them into the index. For [vector](#vector-near-text-search) and [hybrid](#hybrid-search) search operations, Weaviate converts text queries into embeddings. ![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_databricks_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Databricks vectorizer integration (`text2vec-databricks`) module. :::accordion{title="For Weaviate Cloud (WCD) users"} This integration is enabled by default on Weaviate Cloud (WCD) instances. ::: :::accordion{title="For self-hosted users"} - Check the [cluster metadata](../monitoring-and-logging/status.md#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](../how-to-configure-weaviate/modules.md) 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](https://docs.databricks.com/en/dev-tools/auth/pat.html) 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. :::code-group{sync="languages"} ```python title="Python" # Recommended: save sensitive data as environment variables databricks_token = os.getenv("DATABRICKS_TOKEN") ``` ```typescript title="JavaScript/TypeScript" const databricksToken = process.env.DATABRICKS_TOKEN || ''; // Replace with your inference API key ``` ```goraw title="Go" "X-Databricks-Token": os.Getenv("DATABRICKS_TOKEN"), ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) to use a Databricks serving model endpoint by setting the vectorizer as follows: :::code-group{sync="languages"} ```python title="Python" {8-14} import os from weaviate.classes.config import Configure databricks_vectorizer_endpoint = os.getenv("DATABRICKS_VECTORIZER_ENDPOINT") # If saved as an environment variable client.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 ) ``` ```typescript title="JavaScript/TypeScript" {11-17} 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.vectors.text2VecDatabricks({ endpoint: databricksVectorizerEndpoint, // Required for Databricks name: 'title_vector', sourceProperties: ['title'], }) ], // Additional parameters not shown }); ``` ```goraw title="Go" {1-20} // Define the collection basicDatabricksVectorizerDef := &models.Class{ Class: "DemoCollection", VectorConfig: map[string]models.VectorConfig{ "title_vector": { Vectorizer: map[string]interface{}{ "text2vec-databricks": map[string]interface{}{ "properties": []string{"title"}, "endpoint": "", // Required for Databricks }, }, }, }, } // add the collection err = 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](https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#embedding-request). ## 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](#databricks-personal-access-token) above. ## Data import After configuring the vectorizer, [import data](../how-to-manage-objects/import.md) into Weaviate. Weaviate generates embeddings for text objects using [the specified model](#vectorizer-parameters). :::code-group{sync="languages"} ```python title="Python" {13-20} 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.") break failed_objects = collection.batch.failed_objects if failed_objects: print(f"Number of failed imports: {len(failed_objects)}") print(f"First failed object: {failed_objects[0]}") ``` ```typescript title="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." } ]; ``` ```goraw title="Go" {9-44} 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.Object objects := []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 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) } } } } ``` ::: :::callout{intent="tip" title="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. ![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_databricks_embedding_search.png) ### Vector (near text) search When you perform a [vector search](../how-to-query-search/similarity.md#search-with-text), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ```goraw title="Go" {1-9} 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 :::callout{intent="info" title="What is a hybrid search?"} A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](../how-to-query-search/hybrid.md) to return the best matching objects from the database. ::: When you perform a [hybrid search](../how-to-query-search/hybrid.md), 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`. :::code-group{sync="languages"} ```python title="Python" {3-6} 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"]) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ```goraw title="Go" {1-9} 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](databricks-generative.md). ### 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](../how-to-manage-collections/index.md) and [How-to: Manage objects](../how-to-manage-objects/index.md) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](../how-to-query-search/index.md) 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](https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html) ## Questions and feedback Have a question or feedback? 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To receive a synthesized, source-cited answer instead of crawling page by page, append the `?ask=` query parameter to any page URL on this site: /guides/quickstart?ask=how+do+I+authenticate Optional parameters: - `&goal=` steers the answer toward your objective (e.g. `&goal=write+a+python+client`). - `&version=