fzowl · fzowl · Sep 8, 2026
diff --git a/PACKAGES.md b/PACKAGES.md
@@ -40,7 +40,7 @@ A BOM is provided that can be used to define the versions of all Semantic Kernel
 : Provides a connector that can be used to interact with the OpenAI API.
 
 `semantickernel-aiservices-voyageai`
-: Provides connectors for VoyageAI's embedding and reranking services, including text embeddings, contextualized embeddings, multimodal embeddings, and document reranking.
+: Provides connectors for VoyageAI by MongoDB embedding and reranking services, including text embeddings, contextualized embeddings, multimodal embeddings, and document reranking.
 
 ## Example Configurations
 
@@ -75,9 +75,9 @@ POM XML for a simple project that uses OpenAI.
 </project>
 ```
 
-### Example: VoyageAI Embeddings and Reranking
+### Example: VoyageAI by MongoDB Embeddings and Reranking
 
-POM XML for a project that uses VoyageAI for embeddings and reranking.
+POM XML for a project that uses VoyageAI by MongoDB for embeddings and reranking.
 
 ```xml
 

diff --git a/aiservices/voyageai/pom.xml b/aiservices/voyageai/pom.xml
@@ -9,8 +9,8 @@
     </parent>
 
     <artifactId>semantickernel-aiservices-voyageai</artifactId>
-    <name>Semantic Kernel VoyageAI Services</name>
-    <description>VoyageAI services for Semantic Kernel</description>
+    <name>Semantic Kernel VoyageAI by MongoDB Services</name>
+    <description>VoyageAI by MongoDB services for Semantic Kernel</description>
 
     <dependencies>
         <dependency>

diff --git a/...es/voyageai/contextualizedembedding/VoyageAIContextualizedEmbeddingGenerationService.java b/...es/voyageai/contextualizedembedding/VoyageAIContextualizedEmbeddingGenerationService.java
@@ -19,14 +19,20 @@
 import java.util.stream.Collectors;
 
 /**
- * VoyageAI contextualized embedding generation service.
+ * VoyageAI by MongoDB contextualized embedding generation service.
  * Generates embeddings that capture both local chunk details and global document-level metadata.
- * Supports models like voyage-3.
+ * Supports models like voyage-context-4 (voyage-context-3 is the previous generation).
  */
 public final class VoyageAIContextualizedEmbeddingGenerationService implements TextEmbeddingGenerationService {
 
     private static final Logger LOGGER = LoggerFactory.getLogger(VoyageAIContextualizedEmbeddingGenerationService.class);
 
+    /**
+     * Target chunk size in tokens used when the backend auto-chunks flat document
+     * inputs. VoyageAI allows values up to 32K tokens.
+     */
+    private static final int AUTO_CHUNK_SIZE = 32000;
+
     private final VoyageAIClient client;
     private final String modelId;
     private final String serviceId;
@@ -35,7 +41,7 @@ public final class VoyageAIContextualizedEmbeddingGenerationService implements T
      * Creates a new instance of VoyageAI contextualized embedding generation service.
      *
      * @param client    VoyageAI client
-     * @param modelId   Model ID (e.g., "voyage-3")
+     * @param modelId   Model ID (e.g., "voyage-context-4")
      * @param serviceId Optional service ID
      */
     public VoyageAIContextualizedEmbeddingGenerationService(
@@ -88,18 +94,23 @@ public Mono<List<Embedding>> generateContextualizedEmbeddingsAsync(List<List<Str
             "contextualizedembeddings",
             request,
             VoyageAIModels.ContextualizedEmbeddingResponse.class)
-            .map(response -> {
-                List<Embedding> embeddings = new ArrayList<>();
-                // Parse nested data structure: {"data":[{"data":[{"embedding":[...]}]}]}
-                for (VoyageAIModels.ContextualizedEmbeddingDataList dataList : response.getData()) {
-                    for (VoyageAIModels.EmbeddingDataItem item : dataList.getData()) {
-                        embeddings.add(new Embedding(item.getEmbedding()));
-                    }
-                }
+            .map(this::parseEmbeddings);
+    }
 
-                LOGGER.debug("Received {} contextualized embeddings from VoyageAI", embeddings.size());
-                return embeddings;
-            });
+    /**
+     * Parses the nested contextualized embedding response
+     * ({@code {"data":[{"data":[{"embedding":[...]}]}]}}) into a flat list of embeddings.
+     */
+    private List<Embedding> parseEmbeddings(VoyageAIModels.ContextualizedEmbeddingResponse response) {
+        List<Embedding> embeddings = new ArrayList<>();
+        for (VoyageAIModels.ContextualizedEmbeddingDataList dataList : response.getData()) {
+            for (VoyageAIModels.EmbeddingDataItem item : dataList.getData()) {
+                embeddings.add(new Embedding(item.getEmbedding()));
+            }
+        }
+
+        LOGGER.debug("Received {} contextualized embeddings from VoyageAI by MongoDB", embeddings.size());
+        return embeddings;
     }
 
     /**
@@ -122,7 +133,10 @@ public Mono<Embedding> generateEmbeddingAsync(String data) {
 
     /**
      * Generates embeddings for the given texts.
-     * Each text is treated as a separate document for contextualized embeddings.
+     * Each text is treated as a separate document and chunked by the VoyageAI by MongoDB
+     * backend. The {@code contextualizedembeddings} API is called with a flat list of
+     * documents, {@code enable_auto_chunking=true} and {@code chunk_size=32000}
+     * (which requires {@code input_type="document"}).
      *
      * @param data The texts to generate embeddings for
      * @return A Mono that completes with the list of embeddings
@@ -133,13 +147,22 @@ public Mono<List<Embedding>> generateEmbeddingsAsync(List<String> data) {
             return Mono.just(Collections.emptyList());
         }
 
-        // Convert each string to a single-element list for contextualized embeddings
-        List<List<String>> inputs = new ArrayList<>();
-        for (String text : data) {
-            inputs.add(Arrays.asList(text));
-        }
+        LOGGER.debug("Generating contextualized embeddings for {} documents (auto-chunking, "
+            + "chunk_size={}) using model {}", data.size(), AUTO_CHUNK_SIZE, modelId);
+
+        VoyageAIModels.ContextualizedEmbeddingRequest request =
+            new VoyageAIModels.ContextualizedEmbeddingRequest();
+        request.setFlatInputs(data);
+        request.setModel(modelId);
+        request.setInputType("document");
+        request.setEnableAutoChunking(true);
+        request.setChunkSize(AUTO_CHUNK_SIZE);
 
-        return generateContextualizedEmbeddingsAsync(inputs);
+        return client.sendRequestAsync(
+            "contextualizedembeddings",
+            request,
+            VoyageAIModels.ContextualizedEmbeddingResponse.class)
+            .map(this::parseEmbeddings);
     }
 
     /**
@@ -173,7 +196,7 @@ public Builder withClient(VoyageAIClient client) {
         /**
          * Sets the model ID.
          *
-         * @param modelId Model ID (e.g., "voyage-3")
+         * @param modelId Model ID (e.g., "voyage-context-4")
          * @return This builder
          */
         public Builder withModelId(String modelId) {

diff --git a/...i/src/main/java/com/microsoft/semantickernel/aiservices/voyageai/core/VoyageAIClient.java b/...i/src/main/java/com/microsoft/semantickernel/aiservices/voyageai/core/VoyageAIClient.java
@@ -18,7 +18,7 @@
 import java.util.concurrent.TimeUnit;
 
 /**
- * HTTP client for VoyageAI API.
+ * HTTP client for the VoyageAI by MongoDB API.
  */
 public final class VoyageAIClient {
     private static final Logger LOGGER = LoggerFactory.getLogger(VoyageAIClient.class);

diff --git a/...i/src/main/java/com/microsoft/semantickernel/aiservices/voyageai/core/VoyageAIModels.java b/...i/src/main/java/com/microsoft/semantickernel/aiservices/voyageai/core/VoyageAIModels.java
@@ -8,7 +8,7 @@
 import java.util.List;
 
 /**
- * VoyageAI API request and response models.
+ * VoyageAI by MongoDB API request and response models.
  */
 public class VoyageAIModels {
 
@@ -122,6 +122,7 @@ public void setUsage(EmbeddingUsage usage) {
     /**
      * Embedding data item.
      */
+    @JsonIgnoreProperties(ignoreUnknown = true)
     public static class EmbeddingDataItem {
         @JsonProperty("object")
         private String object;
@@ -306,8 +307,10 @@ public void setRelevanceScore(double relevanceScore) {
      */
     @JsonInclude(JsonInclude.Include.NON_NULL)
     public static class ContextualizedEmbeddingRequest {
+        // Holds either a flat List&lt;String&gt; (when auto-chunking is enabled) or a
+        // nested List&lt;List&lt;String&gt;&gt; of pre-chunked documents.
         @JsonProperty("inputs")
-        private List<List<String>> inputs;
+        private Object inputs;
 
         @JsonProperty("model")
         private String model;
@@ -324,16 +327,48 @@ public static class ContextualizedEmbeddingRequest {
         @JsonProperty("output_dtype")
         private String outputDtype;
 
+        @JsonProperty("enable_auto_chunking")
+        private Boolean enableAutoChunking;
+
+        @JsonProperty("chunk_size")
+        private Integer chunkSize;
+
         @SuppressFBWarnings("EI_EXPOSE_REP")
-        public List<List<String>> getInputs() {
+        public Object getInputs() {
             return inputs;
         }
 
-        @SuppressFBWarnings("EI_EXPOSE_REP2")
+        /**
+         * Sets pre-chunked inputs: one inner list of chunks per document.
+         */
         public void setInputs(List<List<String>> inputs) {
             this.inputs = inputs;
         }
 
+        /**
+         * Sets a flat list of documents to be chunked by the backend. Requires
+         * {@code enable_auto_chunking=true} and {@code input_type="document"}.
+         */
+        public void setFlatInputs(List<String> inputs) {
+            this.inputs = inputs;
+        }
+
+        public Boolean getEnableAutoChunking() {
+            return enableAutoChunking;
+        }
+
+        public void setEnableAutoChunking(Boolean enableAutoChunking) {
+            this.enableAutoChunking = enableAutoChunking;
+        }
+
+        public Integer getChunkSize() {
+            return chunkSize;
+        }
+
+        public void setChunkSize(Integer chunkSize) {
+            this.chunkSize = chunkSize;
+        }
+
         public String getModel() {
             return model;
         }

diff --git a/...aiservices/voyageai/multimodalembedding/VoyageAIMultimodalEmbeddingGenerationService.java b/...aiservices/voyageai/multimodalembedding/VoyageAIMultimodalEmbeddingGenerationService.java
@@ -19,9 +19,9 @@
 import java.util.stream.Collectors;
 
 /**
- * VoyageAI multimodal embedding generation service.
+ * VoyageAI by MongoDB multimodal embedding generation service.
  * Generates embeddings for text, images, or interleaved text and images.
- * Supports the voyage-multimodal-3 model.
+ * Supports models like voyage-multimodal-3.5, voyage-multimodal-3.
  * <p>
  * Constraints:
  * - Maximum 1,000 inputs per request
@@ -41,7 +41,7 @@ public final class VoyageAIMultimodalEmbeddingGenerationService implements TextE
      * Creates a new instance of VoyageAI multimodal embedding generation service.
      *
      * @param client    VoyageAI client
-     * @param modelId   Model ID (e.g., "voyage-multimodal-3")
+     * @param modelId   Model ID (e.g., "voyage-multimodal-3.5")
      * @param serviceId Optional service ID
      */
     public VoyageAIMultimodalEmbeddingGenerationService(
@@ -196,7 +196,7 @@ public Builder withClient(VoyageAIClient client) {
         /**
          * Sets the model ID.
          *
-         * @param modelId Model ID (e.g., "voyage-multimodal-3")
+         * @param modelId Model ID (e.g., "voyage-multimodal-3.5")
          * @return This builder
          */
         public Builder withModelId(String modelId) {

diff --git a/.../microsoft/semantickernel/aiservices/voyageai/reranking/VoyageAITextRerankingService.java b/.../microsoft/semantickernel/aiservices/voyageai/reranking/VoyageAITextRerankingService.java
@@ -17,8 +17,8 @@
 import java.util.stream.Collectors;
 
 /**
- * VoyageAI implementation of {@link TextRerankingService}.
- * Supports models like rerank-2, rerank-2-lite.
+ * VoyageAI by MongoDB implementation of {@link TextRerankingService}.
+ * Supports models like rerank-3, rerank-3-lite, rerank-2.5, rerank-2.5-lite.
  */
 public final class VoyageAITextRerankingService implements TextRerankingService {
 
@@ -33,7 +33,7 @@ public final class VoyageAITextRerankingService implements TextRerankingService
      * Creates a new instance of VoyageAI text reranking service.
      *
      * @param client    VoyageAI client
-     * @param modelId   Model ID (e.g., "rerank-2")
+     * @param modelId   Model ID (e.g., "rerank-3")
      * @param serviceId Optional service ID
      * @param topK      Optional top K results to return
      */
@@ -140,7 +140,7 @@ public Builder withClient(VoyageAIClient client) {
         /**
          * Sets the model ID.
          *
-         * @param modelId Model ID (e.g., "rerank-2")
+         * @param modelId Model ID (e.g., "rerank-3")
          * @return This builder
          */
         public Builder withModelId(String modelId) {

diff --git a/...antickernel/aiservices/voyageai/textembedding/VoyageAITextEmbeddingGenerationService.java b/...antickernel/aiservices/voyageai/textembedding/VoyageAITextEmbeddingGenerationService.java
@@ -18,8 +18,8 @@
 import java.util.stream.Collectors;
 
 /**
- * VoyageAI implementation of {@link TextEmbeddingGenerationService}.
- * Supports models like voyage-3-large, voyage-3.5, voyage-code-3, voyage-finance-2, voyage-law-2.
+ * VoyageAI by MongoDB implementation of {@link TextEmbeddingGenerationService}.
+ * Supports models like voyage-4-large, voyage-4, voyage-4-lite, voyage-code-4, voyage-finance-2, voyage-law-2.
  */
 public final class VoyageAITextEmbeddingGenerationService implements TextEmbeddingGenerationService {
 
@@ -33,7 +33,7 @@ public final class VoyageAITextEmbeddingGenerationService implements TextEmbeddi
      * Creates a new instance of VoyageAI text embedding generation service.
      *
      * @param client    VoyageAI client
-     * @param modelId   Model ID (e.g., "voyage-3-large")
+     * @param modelId   Model ID (e.g., "voyage-4-large")
      * @param serviceId Optional service ID
      */
     public VoyageAITextEmbeddingGenerationService(
@@ -143,7 +143,7 @@ public Builder withClient(VoyageAIClient client) {
         /**
          * Sets the model ID.
          *
-         * @param modelId Model ID (e.g., "voyage-3-large")
+         * @param modelId Model ID (e.g., "voyage-4-large")
          * @return This builder
          */
         public Builder withModelId(String modelId) {