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weaviate · Dec 10, 2024 · 1698615 · 1698615
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diff --git a/developers/weaviate/model-providers/_includes/provider.vectorizer.py b/developers/weaviate/model-providers/_includes/provider.vectorizer.py
@@ -934,6 +934,101 @@
 # clean up
 client.collections.delete("DemoCollection")
 
+# START BasicMMVectorizerVoyageAI
+from weaviate.classes.config import Configure
+
+client.collections.create(
+    "DemoCollection",
+    # highlight-start
+    properties=[
+        Property(name="title", data_type=DataType.TEXT),
+        Property(name="poster", data_type=DataType.BLOB),
+    ],
+    vectorizer_config=[
+        Configure.NamedVectors.multi2vec_voyageai(
+            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)
+            ],
+        )
+    ],
+    # highlight-end
+    # Additional parameters not shown
+)
+# END BasicMMVectorizerVoyageAI
+
+# clean up
+client.collections.delete("DemoCollection")
+
+# START MMVectorizerVoyageAICustomModel
+from weaviate.classes.config import Configure
+
+client.collections.create(
+    "DemoCollection",
+    # highlight-start
+    properties=[
+        Property(name="title", data_type=DataType.TEXT),
+        Property(name="poster", data_type=DataType.BLOB),
+    ],
+    vectorizer_config=[
+        Configure.NamedVectors.multi2vec_voyageai(
+            name="title_vector",
+            model="voyage-multimodal-3",
+            # 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)
+            ],
+        )
+    ],
+    # highlight-end
+    # Additional parameters not shown
+)
+# END MMVectorizerVoyageAICustomModel
+
+# clean up
+client.collections.delete("DemoCollection")
+
+# START FullMMVectorizerVoyageAI
+from weaviate.classes.config import Configure
+
+client.collections.create(
+    "DemoCollection",
+    # highlight-start
+    properties=[
+        Property(name="title", data_type=DataType.TEXT),
+        Property(name="poster", data_type=DataType.BLOB),
+    ],
+    vectorizer_config=[
+        Configure.NamedVectors.multi2vec_voyageai(
+            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="voyage-multimodal-3",
+            # truncate="true",  # "false"
+            # base_url="<custom_voyageai_url>"
+        )
+    ],
+    # highlight-end
+    # Additional parameters not shown
+)
+# END FullMMVectorizerVoyageAI
+
+# clean up
+client.collections.delete("DemoCollection")
+
 # START BasicVectorizerWeaviate
 from weaviate.classes.config import Configure
 

diff --git a/developers/weaviate/model-providers/_includes/provider.vectorizer.ts b/developers/weaviate/model-providers/_includes/provider.vectorizer.ts
@@ -1034,6 +1034,116 @@ await client.collections.create({
 // Clean up
 await client.collections.delete('DemoCollection');
 
+// START BasicMMVectorizerVoyageAI
+await client.collections.create({
+  name: "DemoCollection",
+  // highlight-start
+  properties: [
+    {
+      name: 'title',
+      dataType: weaviate.configure.dataType.TEXT,
+    },
+    {
+      name: 'poster',
+      dataType: weaviate.configure.dataType.BLOB,
+    },
+  ],
+  vectorizers: [
+    weaviate.configure.vectorizer.multi2VecVoyageAI({
+      name: "title_vector",
+      // Define the fields to be used for the vectorization - using imageFields, textFields
+      imageFields: [{
+        name: "poster",
+        weight: 0.9
+      }],
+      textFields: [{
+        name: "title",
+        weight: 0.1
+      }]
+    })],
+    // highlight-end
+    // Additional parameters not shown
+})
+// END BasicMMVectorizerVoyageAI
+
+// Clean up
+await client.collections.delete('DemoCollection');
+
+// START MMVectorizerVoyageAICustomModel
+await client.collections.create({
+  name: "DemoCollection",
+  // highlight-start
+  properties: [
+    {
+      name: 'title',
+      dataType: weaviate.configure.dataType.TEXT,
+    },
+    {
+      name: 'poster',
+      dataType: weaviate.configure.dataType.BLOB,
+    },
+  ],
+  vectorizers: [
+    weaviate.configure.vectorizer.multi2VecVoyageAI({
+      name: "title_vector",
+      model: "voyage-multimodal-3",
+      // Define the fields to be used for the vectorization - using imageFields, textFields
+      imageFields: [{
+        name: "poster",
+        weight: 0.9
+      }],
+      textFields: [{
+        name: "title",
+        weight: 0.1
+      }]
+    })],
+    // highlight-end
+    // Additional parameters not shown
+})
+// END MMVectorizerVoyageAICustomModel
+
+// Clean up
+await client.collections.delete('DemoCollection');
+
+// START FullMMVectorizerVoyageAI
+await client.collections.create({
+  name: "DemoCollection",
+  // highlight-start
+  properties: [
+    {
+      name: 'title',
+      dataType: weaviate.configure.dataType.TEXT,
+    },
+    {
+      name: 'poster',
+      dataType: weaviate.configure.dataType.BLOB,
+    },
+  ],
+  vectorizers: [
+    weaviate.configure.vectorizer.multi2VecVoyageAI({
+      name: "title_vector",
+      // Define the fields to be used for the vectorization - using imageFields, textFields
+      imageFields: [{
+        name: "poster",
+        weight: 0.9
+      }],
+      textFields: [{
+        name: "title",
+        weight: 0.1
+      }],
+      // Further options
+      // model: "voyage-multimodal-3",
+      // truncate: "true",  // "false"
+      // baseURL: "<custom_voyageai_url>"
+    })],
+    // highlight-end
+    // Additional parameters not shown
+})
+// END FullMMVectorizerVoyageAI
+
+// Clean up
+await client.collections.delete('DemoCollection');
+
 // START BasicVectorizerWeaviate
 await client.collections.create({
   name: 'DemoCollection',

diff --git a/developers/weaviate/model-providers/cohere/embeddings-multimodal.md b/developers/weaviate/model-providers/cohere/embeddings-multimodal.md
@@ -306,7 +306,7 @@ The query below returns the `n` most similar objects to the input image from the
 
 ### Other integrations
 
-- [Cohere text embeddings models + Weaviate](./embeddings.md).
+- [Cohere text embedding models + Weaviate](./embeddings.md).
 - [Cohere generative models + Weaviate](./generative.md).
 - [Cohere reranker models + Weaviate](./reranker.md).
 

diff --git a/developers/weaviate/model-providers/index.md b/developers/weaviate/model-providers/index.md
@@ -30,7 +30,7 @@ This enables an enhanced developed experience, such as the ability to:
 | [OctoAI (Deprecated)](./octoai/index.md) | [Text](./octoai/embeddings.md) | [Text](./octoai/generative.md) | - |
 | [OpenAI](./openai/index.md) | [Text](./openai/embeddings.md) | [Text](./openai/generative.md) | - |
 | [Azure OpenAI](./openai-azure/index.md) | [Text](./openai-azure/embeddings.md) | [Text](./openai-azure/generative.md) | - |
-| [Voyage AI](./voyageai/index.md) | [Text](./voyageai/embeddings.md) | - | [Reranker](./voyageai/reranker.md) |
+| [Voyage AI](./voyageai/index.md) | [Text](./voyageai/embeddings.md), [Multimodal](./voyageai/embeddings-multimodal.md) | - | [Reranker](./voyageai/reranker.md) |
 | [Weaviate](./weaviate/index.md) | [Text](./weaviate/embeddings.md) | - | - |
 
 #### Enable all API-based modules

diff --git a/developers/weaviate/model-providers/voyageai/embeddings-multimodal.md b/developers/weaviate/model-providers/voyageai/embeddings-multimodal.md
@@ -6,11 +6,6 @@ image: og/docs/integrations/provider_integrations_voyageai.jpg
 # tags: ['model providers', 'voyageai', 'embeddings']
 ---
 
-# VoyageAI Multimodal Embeddings with Weaviate
-
-:::info Added in `1.25.25`, `1.26.9` and `v1.27.2`
-:::
-
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import FilteredTextBlock from '@site/src/components/Documentation/FilteredTextBlock';
@@ -19,13 +14,18 @@ import TSConnect from '!!raw-loader!../_includes/provider.connect.ts';
 import PyCode from '!!raw-loader!../_includes/provider.vectorizer.py';
 import TSCode from '!!raw-loader!../_includes/provider.vectorizer.ts';
 
+# VoyageAI Multimodal Embeddings with Weaviate
+
+:::info Added in `1.27.8`
+:::
+
 Weaviate's integration with VoyageAI's APIs allows you to access their models' capabilities directly from Weaviate.
 
 [Configure a Weaviate vector index](#configure-the-vectorizer) to use a VoyageAI embedding model, and Weaviate will generate embeddings for various operations using the specified model and your VoyageAI 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.
+This integration supports CLIP-like, single-modality inputs from text or images, into a multimodal embedding space.
 
-Important to note that the integration only supports CLIP-like multimodality. For more advanced usage with interleaved text & images, please use the [Bring your own vectors](../../starter-guides/custom-vectors.mdx) setup.
+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](../_includes/integration_voyageai_embedding.png)
 
@@ -305,7 +305,7 @@ The query below returns the `n` most similar objects to the input image from the
 
 ### Other integrations
 
-- [VoyageAI text embeddings models + Weaviate](./embeddings.md).
+- [VoyageAI text embedding models + Weaviate](./embeddings.md).
 - [VoyageAI reranker models + Weaviate](./reranker.md).
 
 ### Code examples

diff --git a/developers/weaviate/model-providers/voyageai/embeddings.md b/developers/weaviate/model-providers/voyageai/embeddings.md
@@ -362,6 +362,7 @@ The query below returns the `n` best scoring objects from the database, set by `
 
 ### Other integrations
 
+- [Voyage AI multimodal embedding embeddings models + Weaviate](./embeddings-multimodal.md)
 - [Voyage AI reranker models + Weaviate](./embeddings.md).
 
 ### Code examples

diff --git a/developers/weaviate/model-providers/voyageai/reranker.md b/developers/weaviate/model-providers/voyageai/reranker.md
@@ -170,6 +170,7 @@ Any search in Weaviate can be combined with a reranker to perform reranking oper
 ### Other integrations
 
 - [Voyage AI embedding models + Weaviate](./embeddings.md).
+- [Voyage AI multimodal embedding embeddings models + Weaviate](./embeddings-multimodal.md)
 
 ### Code examples