--- title: "Locally Hosted ImageBind Embeddings + Weaviate" description: "Weaviate's integration with the Meta ImageBind library allows you to access its capabilities directly from Weaviate. The ImageBind model supports multiple modalities (text, image, audio, video, thermal, IMU and depth)." url: https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal --- # Locally Hosted ImageBind Embeddings + Weaviate Weaviate's integration with the Meta ImageBind library allows you to access its capabilities directly from Weaviate. The ImageBind model supports multiple modalities (text, image, audio, video, thermal, IMU and depth). [Configure a Weaviate vector index](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#configure-the-vectorizer) to use the ImageBind integration, and [configure the Weaviate instance](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#weaviate-configuration) with a model image, and Weaviate will generate embeddings for various operations using the specified model in the ImageBind inference container. This feature is called the *vectorizer*. At [import time](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#data-import), Weaviate generates multimodal object embeddings and saves them into the index. For [vector](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#vector-near-text-search) and [hybrid](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#hybrid-search) search operations, Weaviate converts queries of one or more modalities into embeddings. [Multimodal search operations](https://docs.weaviate.io/weaviate/model-providers/imagebind/embeddings-multimodal#vector-near-media-search) are also supported. ![Embedding integration illustration](https://docs.weaviate.io/assets/images/integration_imagebind_embedding-5d2f163e429283951df6c97cbc55776e.png) ## Requirements ### Weaviate configuration Your Weaviate instance must be configured with the ImageBind multimodal vectorizer integration (`multi2vec-bind`) module. **For Weaviate Cloud (WCD) users** This integration is not available for Weaviate Cloud (WCD) instances, as it requires spinning up a container with the ImageBind model. #### Enable the integration module - Check the [cluster metadata](https://docs.weaviate.io/deploy/configuration/status#cluster-metadata) to verify if the module is enabled. - Follow the [how-to configure modules](https://docs.weaviate.io/weaviate/configuration/modules) guide to enable the module in Weaviate. #### Configure the integration To use this integration, you must configure the container image of the ImageBind model, and the inference endpoint of the containerized model. The following example shows how to configure the ImageBind integration in Weaviate: #### Docker #### Docker Option 1: Use a pre-configured `docker-compose.yml` file Follow the instructions on the [Weaviate Docker installation configurator](https://docs.weaviate.io/deploy/installation-guides/docker-installation#configurator) to download a pre-configured `docker-compose.yml` file with a selected model #### Docker Option 2: Add the configuration manually Alternatively, add the configuration to the `docker-compose.yml` file manually as in the example below. ```yaml services: weaviate: # Other Weaviate configuration environment: ENABLE_MODULES: multi2vec-bind # Enable this module BIND_INFERENCE_API: http://multi2vec-bind:8080 # Set the inference API endpoint multi2vec-bind: # Set the name of the inference container mem_limit: 12g image: cr.weaviate.io/semitechnologies/multi2vec-bind:imagebind environment: ENABLE_CUDA: 0 # Set to 1 to enable ``` - `BIND_INFERENCE_API` environment variable sets the inference API endpoint - `multi2vec-bind` is the name of the inference container - `image` is the container image - `ENABLE_CUDA` environment variable enables GPU usage #### Kubernetes Configure the ImageBind integration in Weaviate by adding or updating the `multi2vec-bind` module in the `modules` section of the Weaviate Helm chart values file. For example, modify the `values.yaml` file as follows: ```yaml modules: multi2vec-bind: enabled: true tag: imagebind repo: semitechnologies/multi2vec-bind registry: cr.weaviate.io envconfig: enable_cuda: true ``` See the [Weaviate Helm chart](https://github.com/weaviate/weaviate-helm/blob/master/weaviate/values.yaml) for an example of the `values.yaml` file including more configuration options. ### Credentials As this integration runs a local container with the ImageBind model, no additional credentials (e.g. API key) are required. Connect to Weaviate as usual, such as in the examples below. **Python** ```py import weaviate client = weaviate.connect_to_local() # Work with Weaviate client.close() ``` **JavaScript/TypeScript** ```ts import weaviate from '@weaviate/node' const client = await weaviate.connectToLocal() // Work with Weaviate client.close() ``` ## Configure the vectorizer [Configure a Weaviate index](https://docs.weaviate.io/weaviate/manage-collections/vector-config#specify-a-vectorizer) as follows to use an ImageBind embedding model: **Python** ```py 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_bind( name="title_vector", # Define the fields to be used for the vectorization - using image_fields, text_fields, video_fields image_fields=[ Multi2VecField(name="poster", weight=0.9) ], text_fields=[ Multi2VecField(name="title", weight=0.1) ] ) ], # Additional parameters not shown ) ``` **JavaScript/TypeScript** ```ts await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, { name: 'poster', dataType: 'blob' as const, }, ], vectorizers: [ weaviate.configure.vectors.multi2VecClip({ name: 'title_vector', imageFields: [ { name: 'poster', weight: 0.9, }, ], textFields: [ { name: 'title', weight: 0.1, }, ], }), ], // Additional parameters not shown }); ``` There is only one ImageBind model available. **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](https://docs.weaviate.io/weaviate/manage-collections/vector-config#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 collection name if `vectorizeClassName` is `true` (the default differs by integration — check this page's parameter table) Since `v1.27`, the string is sent to the model as is. Earlier versions lowercase it first, and you can restore that behavior with the [`LOWERCASE_VECTORIZATION_INPUT`](https://docs.weaviate.io/deploy/configuration/env-vars#module-specific) environment variable. ### Vectorizer parameters The ImageBind vectorizer supports multiple modalities (text, image, audio, video, thermal, IMU and depth). One or more of these can be specified in the vectorizer configuration as shown. **Python** ```py 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), Property(name="sound", data_type=DataType.BLOB), Property(name="video", data_type=DataType.BLOB), ], vector_config=[ Configure.Vectors.multi2vec_bind( name="title_vector", # Define the fields to be used for the vectorization image_fields=[ Multi2VecField(name="poster", weight=0.7) ], text_fields=[ Multi2VecField(name="title", weight=0.1) ], audio_fields=[ Multi2VecField(name="sound", weight=0.1) ], video_fields=[ Multi2VecField(name="video", weight=0.1) ], # depth, IMU and thermal fields are also available ) ], # Additional parameters not shown ) ``` **JavaScript/TypeScript** ```ts await client.collections.create({ name: 'DemoCollection', properties: [ { name: 'title', dataType: 'text' as const, }, { name: 'poster', dataType: 'blob' as const, }, { name: 'sound', dataType: 'blob' as const, }, { name: 'video', dataType: 'blob' as const, }, ], vectorizers: [ weaviate.configure.vectors.multi2VecBind({ name: 'title_vector', imageFields: [ { name: 'poster', weight: 0.7, }, ], textFields: [ { name: 'title', weight: 0.1, }, ], audioFields: [ { name: 'sound', weight: 0.1, }, ], videoFields: [ { name: 'video', weight: 0.1, }, ], // depth, IMU and thermal fields are also available }), ], }); ``` ## Data import After configuring the vectorizer, [import data](https://docs.weaviate.io/weaviate/manage-objects/import) into Weaviate. Weaviate generates embeddings for the objects using the specified model. **Python** ```py 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 ) ``` **JavaScript/TypeScript** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(collectionName) let multiModalObjects = new Array(); for (let mmSrcObject of mmSrcObjects) { multiModalObjects.push({ title: mmSrcObject.title, poster: mmSrcObject.poster, // Add the image in base64 encoding }); } // The model provider integration will automatically vectorize the object const mmResponse = await myCollection.data.insertMany(multiModalObjects); console.log(mmResponse); ``` > [!TIP] > > **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 ImageBind model. ![Embedding integration at search illustration](https://docs.weaviate.io/assets/images/integration_imagebind_embedding_search-308acf79d0cf25506ecf453a2176ccf3.png) ### Vector (near text) search When you perform a [vector search](https://docs.weaviate.io/weaviate/search/similarity#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`. **Python** ```py 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"]) ``` **JavaScript/TypeScript** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(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)); ``` ### Hybrid search > [!NOTE] > > **What is a hybrid search?** > > A hybrid search performs a vector search and a keyword (BM25) search, before [combining the results](https://docs.weaviate.io/weaviate/search/hybrid) to return the best matching objects from the database. When you perform a [hybrid search](https://docs.weaviate.io/weaviate/search/hybrid), 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** ```py 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"]) ``` **JavaScript/TypeScript** ```ts const collectionName = 'DemoCollection' const myCollection = client.collections.use(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)); ``` ### Vector (near media) search When you perform a media search such as a [near image search](https://docs.weaviate.io/weaviate/search/similarity#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`. **Python** ```py 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") collection = client.collections.use("DemoCollection") query_b64 = url_to_base64(src_img_path) response = collection.query.near_image( near_image=query_b64, limit=2, return_properties=["title", "release_date", "tmdb_id", "poster"] # To include the poster property in the response (`blob` properties are not returned by default) ) for obj in response.objects: print(obj.properties["title"]) ``` **JavaScript/TypeScript** ```ts 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)); ``` You can perform similar searches for other media types such as audio, video, thermal, IMU, and depth, by using an equivalent search query for the respective media type. ## References ### Available models There is only one ImageBind model available. ## Further resources ### 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](https://docs.weaviate.io/weaviate/manage-collections) and [How-to: Manage objects](https://docs.weaviate.io/weaviate/manage-objects) guides show how to perform data operations (i.e. create, read, update, delete collections and objects within them). - The [How-to: Query & Search](https://docs.weaviate.io/weaviate/search) guides show how to perform search operations (i.e. vector, keyword, hybrid) as well as retrieval augmented generation. ### Model licenses Review the license for the model on the [ImageBind page](https://github.com/facebookresearch/ImageBind). It is your responsibility to evaluate whether the terms of its license(s), if any, are appropriate for your intended use. ### External resources - [ImageBind GitHub page](https://github.com/facebookresearch/ImageBind)