diff --git a/packages/components/nodes/embeddings/AWSBedrockEmbedding/AWSBedrockEmbedding.ts b/packages/components/nodes/embeddings/AWSBedrockEmbedding/AWSBedrockEmbedding.ts
index 2b8fc8060..5c5f6352b 100644
--- a/packages/components/nodes/embeddings/AWSBedrockEmbedding/AWSBedrockEmbedding.ts
+++ b/packages/components/nodes/embeddings/AWSBedrockEmbedding/AWSBedrockEmbedding.ts
@@ -19,7 +19,7 @@ class AWSBedrockEmbedding_Embeddings implements INode {
     constructor() {
         this.label = 'AWS Bedrock Embeddings'
         this.name = 'AWSBedrockEmbeddings'
-        this.version = 4.0
+        this.version = 5.0
         this.type = 'AWSBedrockEmbeddings'
         this.icon = 'aws.svg'
         this.category = 'Embeddings'
@@ -53,11 +53,40 @@ class AWSBedrockEmbedding_Embeddings implements INode {
                 description: 'If provided, will override model selected from Model Name option',
                 type: 'string',
                 optional: true
+            },
+            {
+                label: 'Cohere Input Type',
+                name: 'inputType',
+                type: 'options',
+                description:
+                    'Specifies the type of input passed to the model. Required for cohere embedding models v3 and higher. <a target="_blank" href="https://docs.cohere.com/reference/embed">Official Docs</a>',
+                options: [
+                    {
+                        label: 'search_document',
+                        name: 'search_document',
+                        description: 'Use this to encode documents for embeddings that you store in a vector database for search use-cases'
+                    },
+                    {
+                        label: 'search_query',
+                        name: 'search_query',
+                        description: 'Use this when you query your vector DB to find relevant documents.'
+                    },
+                    {
+                        label: 'classification',
+                        name: 'classification',
+                        description: 'Use this when you use the embeddings as an input to a text classifier'
+                    },
+                    {
+                        label: 'clustering',
+                        name: 'clustering',
+                        description: 'Use this when you want to cluster the embeddings.'
+                    }
+                ],
+                optional: true
             }
         ]
     }
 
-    //@ts-ignore
     loadMethods = {
         async listModels(): Promise<INodeOptionsValue[]> {
             return await getModels(MODEL_TYPE.EMBEDDING, 'AWSBedrockEmbeddings')
@@ -71,6 +100,11 @@ class AWSBedrockEmbedding_Embeddings implements INode {
         const iRegion = nodeData.inputs?.region as string
         const iModel = nodeData.inputs?.model as string
         const customModel = nodeData.inputs?.customModel as string
+        const inputType = nodeData.inputs?.inputType as string
+
+        if (iModel.startsWith('cohere') && !inputType) {
+            throw new Error('Input Type must be selected for Cohere models.')
+        }
 
         const obj: BedrockEmbeddingsParams = {
             model: customModel ? customModel : iModel,
@@ -97,20 +131,27 @@ class AWSBedrockEmbedding_Embeddings implements INode {
 
         const model = new BedrockEmbeddings(obj)
 
-        // Avoid Illegal Invocation
         model.embedQuery = async (document: string): Promise<number[]> => {
-            return await embedText(document, client, iModel)
+            if (iModel.startsWith('cohere')) {
+                const embeddings = await embedTextCohere([document], client, iModel, inputType)
+                return embeddings[0]
+            } else {
+                return await embedTextTitan(document, client, iModel)
+            }
         }
 
         model.embedDocuments = async (documents: string[]): Promise<number[][]> => {
-            return Promise.all(documents.map((document) => embedText(document, client, iModel)))
+            if (iModel.startsWith('cohere')) {
+                return await embedTextCohere(documents, client, iModel, inputType)
+            } else {
+                return Promise.all(documents.map((document) => embedTextTitan(document, client, iModel)))
+            }
         }
         return model
     }
 }
 
-const embedText = async (text: string, client: BedrockRuntimeClient, model: string): Promise<number[]> => {
-    // replace newlines, which can negatively affect performance.
+const embedTextTitan = async (text: string, client: BedrockRuntimeClient, model: string): Promise<number[]> => {
     const cleanedText = text.replace(/\n/g, ' ')
 
     const res = await client.send(
@@ -132,4 +173,26 @@ const embedText = async (text: string, client: BedrockRuntimeClient, model: stri
     }
 }
 
+const embedTextCohere = async (texts: string[], client: BedrockRuntimeClient, model: string, inputType: string): Promise<number[][]> => {
+    const cleanedTexts = texts.map((text) => text.replace(/\n/g, ' '))
+
+    const command = {
+        modelId: model,
+        body: JSON.stringify({
+            texts: cleanedTexts,
+            input_type: inputType,
+            truncate: 'END'
+        }),
+        contentType: 'application/json',
+        accept: 'application/json'
+    }
+    const res = await client.send(new InvokeModelCommand(command))
+    try {
+        const body = new TextDecoder().decode(res.body)
+        return JSON.parse(body).embeddings
+    } catch (e) {
+        throw new Error('An invalid response was returned by Bedrock.')
+    }
+}
+
 module.exports = { nodeClass: AWSBedrockEmbedding_Embeddings }