Weaviate's integration with NVIDIA's APIs allows you to access their models' capabilities directly from Weaviate. [Configure a Weaviate vector index](#configure-the-vectorizer) to use an NVIDIA embedding model, and Weaviate will generate embeddings for various operations using the specified model and your NVIDIA NIM API key. This feature is called the _vectorizer_. At [import time](#data-import), Weaviate generates multimodal 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. [Multimodal search operations](#vector-near-media-search) are also supported. ![Embedding integration illustration](/assets/docs/weaviate/model-providers/_includes/integration_nvidia_embedding.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the NVIDIA vectorizer integration (`multi2vec-nvidia`) 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 NVIDIA NIM API key to Weaviate for this integration. Go to [NVIDIA](https://build.nvidia.com/) to sign up and obtain an API key. Provide the API key to Weaviate using one of the following methods: - Set the `NVIDIA_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 nvidia_key = os.getenv("NVIDIA_API_KEY") ``` ```typescript title="JavaScript/TypeScript" const nvidiaApiKey = process.env.NVIDIA_API_KEY || ''; // Replace with your inference API key ``` ::: ## Configure the vectorizer [Configure a Weaviate index](../how-to-manage-collections/vector-config.md#specify-a-vectorizer) as follows to use an NVIDIA embedding model: :::code-group{sync="languages"} ```python title="Python" {5-20} from weaviate.classes.config import Configure, DataType, Multi2VecField, Property client.collections.create( "DemoCollection", properties=[ Property(name="title", data_type=DataType.TEXT), Property(name="poster", data_type=DataType.BLOB), ], vector_config=[ Configure.Vectors.multi2vec_nvidia( name="title_vector", # Define the fields to be used for the vectorization - using image_fields, text_fields image_fields=[ Multi2VecField(name="poster", weight=0.9) ], text_fields=[ Multi2VecField(name="title", weight=0.1) ], ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" await client.collections.create({ name: 'DemoCollection', vectorizers: [ weaviate.configure.vectors.multi2VecNvidia({ name: 'title_vector', imageFields: [{ name: "poster", weight: 0.9 }], textFields: [{ name: "title", weight: 0.1 }], }) ], // Additional parameters not shown }) ``` ::: ### Select a model You can specify one of the [available models](#available-models) for the vectorizer to use, as shown in the following configuration example. :::code-group{sync="languages"} ```python title="Python" {5-21} from weaviate.classes.config import Configure, DataType, Multi2VecField, Property client.collections.create( "DemoCollection", properties=[ Property(name="title", data_type=DataType.TEXT), Property(name="poster", data_type=DataType.BLOB), ], vector_config=[ Configure.Vectors.multi2vec_nvidia( name="title_vector", model="nvidia/nvclip", # Define the fields to be used for the vectorization - using image_fields, text_fields image_fields=[ Multi2VecField(name="poster", weight=0.9) ], text_fields=[ Multi2VecField(name="title", weight=0.1) ], ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" await client.collections.create({ name: 'DemoCollection', vectorizers: [ weaviate.configure.vectors.multi2VecNvidia({ name: 'title_vector', model: "nvidia/nv-embed-v1", imageFields: [{ name: "poster", weight: 0.9 }], textFields: [{ name: "title", weight: 0.1 }], }) ], // Additional parameters not shown }) ``` ::: You can [specify](#vectorizer-parameters) one of the [available models](#available-models) for Weaviate to use. The [default model](#available-models) is used if no model is specified. :::accordion{title="Vectorization behavior"} Weaviate follows the collection configuration and a set of predetermined rules to vectorize objects. Unless specified otherwise in the collection definition, the default behavior is to: - Only vectorize properties that use the `text` or `text[]` data type (unless [skipped](../how-to-manage-collections/vector-config.md#property-level-settings)) - Sort properties in alphabetical (a-z) order before concatenating values - If `vectorizePropertyName` is `true` (`false` by default) prepend the property name to each property value - Join the (prepended) property values with spaces - Prepend the class name (unless `vectorizeClassName` is `false`) - Convert the produced string to lowercase ::: ### Vectorizer parameters The following examples show how to configure NVIDIA-specific options. :::code-group{sync="languages"} ```python title="Python" {5-22} from weaviate.classes.config import Configure, DataType, Multi2VecField, Property client.collections.create( "DemoCollection", properties=[ Property(name="title", data_type=DataType.TEXT), Property(name="poster", data_type=DataType.BLOB), ], vector_config=[ Configure.Vectors.multi2vec_nvidia( name="title_vector", # Define the fields to be used for the vectorization - using image_fields, text_fields image_fields=[ Multi2VecField(name="poster", weight=0.9) ], text_fields=[ Multi2VecField(name="title", weight=0.1) ], # Further options # model="nvidia/nvclip", ) ], # Additional parameters not shown ) ``` ```typescript title="JavaScript/TypeScript" await client.collections.create({ name: 'DemoCollection', vectorizers: [ weaviate.configure.vectors.multi2VecNvidia({ name: 'title_vector', model: "nvidia/nv-embed-v1", imageFields: [{ name: "poster", weight: 0.9 }], textFields: [{ name: "title", weight: 0.1 }], // Further options }) ], // Additional parameters not shown }) ``` ::: For further details on model parameters, see the [NVIDIA NIM API documentation](https://docs.api.nvidia.com/nim/reference/retrieval-apis). ## 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. :::code-group{sync="languages"} ```python title="Python" {11-15} collection = client.collections.use("DemoCollection") with collection.batch.fixed_size(batch_size=200) as batch: for src_obj in source_objects: poster_b64 = url_to_base64(src_obj["poster_path"]) weaviate_obj = { "title": src_obj["title"], "poster": poster_b64 # Add the image in base64 encoding } # The model provider integration will automatically vectorize the object batch.add_object( properties=weaviate_obj, # vector=vector # Optionally provide a pre-obtained vector ) ``` ```typescript title="JavaScript/TypeScript" const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) ``` ::: :::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 NVIDIA model. ![Embedding integration at search illustration](/assets/docs/weaviate/model-providers/_includes/integration_nvidia_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) ``` ::: ### 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#change-the-fusion-method) 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) ``` ::: ### Vector (near media) search When you perform a media search such as a [near image search](../how-to-query-search/similarity.md#search-with-image), Weaviate converts the query into an embedding using the specified model and returns the most similar objects from the database. To perform a near media search such as near image search, convert the media query into a base64 string and pass it to the search query. The query below returns the `n` most similar objects to the input image from the database, set by `limit`. :::code-group{sync="languages"} ```python title="Python" def url_to_base64(url): import requests import base64 image_response = requests.get(url) content = image_response.content return base64.b64encode(content).decode("utf-8") ``` ```typescript title="JavaScript/TypeScript" const base64String = 'SOME_BASE_64_REPRESENTATION'; result = await myCollection.query.nearImage( base64String, // The model provider integration will automatically vectorize the query { limit: 2, } ) console.log(JSON.stringify(result.objects, null, 2)); ``` ::: ## References ### Available models You can use any multimodal embedding model [on NVIDIA NIM APIs](https://build.nvidia.com/models) with Weaviate. The default model is `nvidia/nvclip`. ## Further resources ### Other integrations - [NVIDIA text embedding models + Weaviate](nvidia-embeddings.md). - [NVIDIA generative models + Weaviate](nvidia-generative.md). - [NVIDIA reranker models + Weaviate](nvidia-reranker.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 - [NVIDIA NIM API documentation](https://docs.api.nvidia.com/nim/) ## 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=