Weaviate's integration with OpenAI-style APIs allows you to access KubeAI models' directly from Weaviate. [Configure a Weaviate collection](#configure-collection) to use a generative AI model with KubeAI. Weaviate will perform retrieval augmented generation (RAG) using the specified model. More specifically, Weaviate will perform a search, retrieve the most relevant objects, and then pass them to the KubeAI generative model to generate outputs. ![RAG integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_kubeai_rag.png) ## Requirements ### KubeAI configuration KubeAI must be deployed in a Kubernetes cluster with an embedding model. For more specific instructions, see this [KubeAI deployment guide](https://www.kubeai.org/tutorials/weaviate/#kubeai-configuration). ### Weaviate configuration Your Weaviate instance must be configured with the OpenAI generative AI integration (`generative-openai`) 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 The OpenAI integration requires an API key value. To use KubeAI, provide any value for the API key, as this value is not used by KubeAI. Provide the API key to Weaviate using one of the following methods: - Set the `OPENAI_APIKEY` environment variable that is available to Weaviate. - Provide the API key at runtime, as shown in the examples below. :::code-group{sync="languages"} ```python title="Python" # Recommended: save sensitive data as environment variables openai_key = os.getenv("OPENAI_API_KEY") ``` ```typescript title="JavaScript/TypeScript" const openaiApiKey = process.env.OPENAI_API_KEY || ''; // Replace with your inference API key ``` ::: ## Configure collection Configure Weaviate to use a KubeAI generative AI model: :::code-group{sync="languages"} ```python title="Python" {5-15} from weaviate.classes.config import Configure client.collections.create( "DemoCollection", generative_config=Configure.Generative.openai( # Setting the model and base_url is required model="gpt-3.5-turbo", base_url="http://kubeai/openai", # Your private KubeAI API endpoint # These parameters are optional # frequency_penalty=0, # max_tokens=500, # presence_penalty=0, # temperature=0.7, # top_p=0.7, ) # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" {3-13} await client.collections.create({ name: 'DemoCollection', generative: weaviate.configure.generative.openAI({ // Setting the model and base_url is required model: 'gpt-3.5-turbo', baseURL: 'http://kubeai/openai', // These parameters are optional // frequencyPenalty: 0, // maxTokens: 500, // presencePenalty: 0, // temperature: 0.7, // topP: 0.7, }), // Additional parameters not shown }); ``` ::: Any model that is supported by vLLM or Ollama can be used with KubeAI. Refer to the [KubeAI docs on model management](https://www.kubeai.org/how-to/install-models/) for more information on available models and how to configure them. ## 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" {9-20} 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.openai( # Setting the model and base_url is required model="gpt-3.5-turbo", base_url="http://kubeai/openai", # Your private KubeAI API endpoint # These parameters are optional # frequency_penalty=0, # max_tokens=500, # presence_penalty=0, # temperature=0.7, # top_p=0.7, ), # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" import { generativeParameters } from 'weaviate-client'; ``` ::: ## 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_kubeai_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_kubeai_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 - [KubeAI embedding models + Weaviate](kubeai-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. ### External resources - [KubeAI documentation](https://www.kubeai.org/) - [KubeAI documentation on model management](https://www.kubeai.org/how-to/install-models/) ## Questions and feedback Have a question or feedback? 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