Weaviate's integration with Databricks' APIs allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate collection](#configure-collection) to use a generative AI model with Databricks. Weaviate will perform retrieval augmented generation (RAG) using the specified endpoint and your Databricks token. More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the Databricks generative model to generate outputs. ![RAG integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_databricks_rag.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the Databricks generative AI integration (`generative-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. ::: ### API credentials 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 ``` ::: ## Configure collection :::callout{intent="info" title="Generative model integration mutability"} A collection's `generative` model integration configuration is mutable from `v1.25.23`, `v1.26.8` and `v1.27.1`. See [this section](../how-to-manage-collections/generative-reranker-models.md#update-the-generative-model-integration) for details on how to update the collection configuration. ::: [Configure a Weaviate collection](../how-to-manage-collections/generative-reranker-models.md#specify-a-generative-model-integration) to use a Databricks generative AI endpoint as follows: :::code-group{sync="languages"} ```python title="Python" {6} from weaviate.classes.config import Configure databricks_generative_endpoint = os.getenv("DATABRICKS_GENERATIVE_ENDPOINT") client.collections.create( "DemoCollection", generative_config=Configure.Generative.databricks(endpoint=databricks_generative_endpoint) # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {5-7} const databricksGenerativeEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || ''; // If saved as an environment variable await client.collections.create({ name: 'DemoCollection', generative: weaviate.configure.generative.databricks({ endpoint: databricksGenerativeEndpoint, // Required for Databricks }), // Additional parameters not shown }); ``` ::: This will configure Weaviate to use the generative AI model served through the endpoint you specify. ### Generative parameters Configure the following generative parameters to customize the model behavior. :::code-group{sync="languages"} ```python title="Python" {6-13} from weaviate.classes.config import Configure databricks_generative_endpoint = os.getenv("DATABRICKS_GENERATIVE_ENDPOINT") client.collections.create( "DemoCollection", generative_config=Configure.Generative.databricks( endpoint=databricks_generative_endpoint # # These parameters are optional # max_tokens=500, # temperature=0.7, # top_p=0.7, # top_k=0.1 ) # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" const databricksGenerativeEndpoint = process.env.DATABRICKS_VECTORIZER_ENDPOINT || ''; // If saved as an environment variable ``` ::: For further details on model parameters, see the [Databricks documentation](https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html#chat-task). ## Select a model at runtime Aside from setting the default model provider when creating the collection, you can also override it at query time. :::code-group{sync="languages"} ```python title="Python" {9-16} from weaviate.classes.config import Configure from weaviate.classes.generate import GenerativeConfig collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", limit=2, grouped_task="Write a tweet promoting these two movies", generative_provider=GenerativeConfig.databricks( # # These parameters are optional # max_tokens=500, # temperature=0.7, # top_p=0.7, # top_k=0.1 ), # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" import { generativeParameters } from 'weaviate-client'; ``` ::: ## 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](#api-credentials) above. ## Retrieval augmented generation After configuring the generative AI integration, perform RAG operations, either with the [single prompt](#single-prompt) or [grouped task](#grouped-task) method. ### Single prompt ![Single prompt RAG integration generates individual outputs per search result](/assets/docs/weaviate/model-providers/_includes/integration_databricks_rag_single.png) To generate text for each object in the search results, use the single prompt method. The example below generates outputs for each of the `n` search results, where `n` is specified by the `limit` parameter. When creating a single prompt query, use braces `{}` to interpolate the object properties you want Weaviate to pass on to the language model. For example, to pass on the object's `title` property, include `{title}` in the query. :::code-group{sync="languages"} ```python title="Python" {5-6} collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", # The model provider integration will automatically vectorize the query single_prompt="Translate this into French: {title}", limit=2 ) for obj in response.objects: print(obj.properties["title"]) print(f"Generated output: {obj.generated}") # Note that the generated output is per object ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ### Grouped task ![Grouped task RAG integration generates one output for the set of search results](/assets/docs/weaviate/model-providers/_includes/integration_databricks_rag_grouped.png) To generate one text for the entire set of search results, use the grouped task method. In other words, when you have `n` search results, the generative model generates one output for the entire group. :::code-group{sync="languages"} ```python title="Python" {5-6} collection = client.collections.use("DemoCollection") response = collection.generate.near_text( query="A holiday film", # The model provider integration will automatically vectorize the query grouped_task="Write a fun tweet to promote readers to check out these films.", limit=2 ) print(f"Generated output: {response.generative.text}") # Note that the generated output is per query for obj in response.objects: print(obj.properties["title"]) ``` ```typescript title="JavaScript/TypeScript" let response; const myCollection = client.collections.use("DemoCollection"); ``` ::: ## Further resources ### Other integrations - [Databricks embedding models + Weaviate](databricks-embeddings.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. ### References - [Databricks foundation model documentation](https://docs.databricks.com/en/machine-learning/foundation-models/api-reference.html) ## Questions and feedback Have a question or feedback? Here's how to reach us. ::::card-grid :::card{title="Community Forum" href="https://forum.weaviate.io/c/support" icon="messages-square"} Ask questions and connect with other developers on our **Community forum**. ::: :::card{title="Support" href="/guides/support-overview" icon="life-buoy"} Weaviate Cloud user or customer? Find the right channel on the **Support page**. ::: :::: ## Related pages - [Agents](./agents-index.md) - [AI-assisted Weaviate code generation](./ai-assisted-vibe-coding-index.md) - [APIs](./apis-index.md) - [Authorization and authentication](./authorization-and-authentication-index.md) - [Benchmarks](./benchmarks-index.md) - [Best practices](./best-practices-index.md) - [Client libraries](./clients-index.md) - [Client Libraries / SDKs](./client-libraries-index.md) - [Cloud](./cloud-index.md) - [Cloud account management](./cloud-account-management-index.md) # Agent Instructions This portal answers questions programmatically. 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=