diff --git a/litellm/llms/vertex_ai/vertex_embeddings/bge.py b/litellm/llms/vertex_ai/vertex_embeddings/bge.py
new file mode 100644
index 00000000000..401f7ebd907
--- /dev/null
+++ b/litellm/llms/vertex_ai/vertex_embeddings/bge.py
@@ -0,0 +1,100 @@
+"""
+Vertex AI BGE (BAAI General Embedding) Configuration
+
+BGE models deployed on Vertex AI require different input format:
+- Use "prompt" instead of "content" as the input field
+"""
+
+from typing import List, Optional, Union
+
+from .types import (
+    EmbeddingParameters,
+    TaskType,
+    TextEmbeddingBGEInput,
+    VertexEmbeddingRequest,
+)
+
+
+class VertexBGEConfig:
+    """
+    Configuration and transformation logic for BGE models on Vertex AI.
+    
+    BGE (BAAI General Embedding) models use a different request format
+    where the input field is named "prompt" instead of "content".
+    """
+
+    @staticmethod
+    def is_bge_model(model: str) -> bool:
+        """
+        Check if the model is a BGE (BAAI General Embedding) model.
+        
+        Args:
+            model: The model name
+            
+        Returns:
+            bool: True if the model is a BGE model
+        """
+        return "bge" in model.lower()
+
+    @staticmethod
+    def transform_request(
+        input: Union[list, str], optional_params: dict, model: str
+    ) -> VertexEmbeddingRequest:
+        """
+        Transforms an OpenAI request to a Vertex BGE embedding request.
+        
+        BGE models use "prompt" instead of "content" as the input field.
+        
+        Args:
+            input: The input text(s) to embed
+            optional_params: Optional parameters for the request
+            model: The model name
+            
+        Returns:
+            VertexEmbeddingRequest: The transformed request
+        """
+        vertex_request: VertexEmbeddingRequest = VertexEmbeddingRequest()
+        vertex_text_embedding_input_list: List[TextEmbeddingBGEInput] = []
+        task_type: Optional[TaskType] = optional_params.get("task_type")
+        title = optional_params.get("title")
+
+        if isinstance(input, str):
+            input = [input]
+
+        for text in input:
+            embedding_input = VertexBGEConfig._create_embedding_input(
+                prompt=text, task_type=task_type, title=title
+            )
+            vertex_text_embedding_input_list.append(embedding_input)
+
+        vertex_request["instances"] = vertex_text_embedding_input_list
+        vertex_request["parameters"] = EmbeddingParameters(**optional_params)
+
+        return vertex_request
+
+    @staticmethod
+    def _create_embedding_input(
+        prompt: str,
+        task_type: Optional[TaskType] = None,
+        title: Optional[str] = None,
+    ) -> TextEmbeddingBGEInput:
+        """
+        Creates a TextEmbeddingBGEInput object for BGE models.
+
+        BGE models use "prompt" instead of "content" as the input field.
+
+        Args:
+            prompt: The prompt to be embedded
+            task_type: The type of task to be performed
+            title: The title of the document to be embedded
+
+        Returns:
+            TextEmbeddingBGEInput: A TextEmbeddingBGEInput object
+        """
+        text_embedding_input = TextEmbeddingBGEInput(prompt=prompt)
+        if task_type is not None:
+            text_embedding_input["task_type"] = task_type
+        if title is not None:
+            text_embedding_input["title"] = title
+        return text_embedding_input
+