diff --git a/src/vanna/google/bigquery_vector.py b/src/vanna/google/bigquery_vector.py
index 09cbf391..df68835d 100644
--- a/src/vanna/google/bigquery_vector.py
+++ b/src/vanna/google/bigquery_vector.py
@@ -2,6 +2,10 @@
 import os
 import uuid
 from typing import List, Optional
+from vertexai.language_models import (
+  TextEmbeddingInput,
+  TextEmbeddingModel
+)
 
 import pandas as pd
 from google.cloud import bigquery
@@ -23,17 +27,15 @@ def __init__(self, config: dict, **kwargs):
             or set as an environment variable, assign it.
             """
             print("Configuring genai")
+            self.type = "GEMINI"
             import google.generativeai as genai
 
             genai.configure(api_key=config["api_key"])
 
             self.genai = genai
         else:
+            self.type = "VERTEX_AI"
             # Authenticate using VertexAI
-            from vertexai.language_models import (
-              TextEmbeddingInput,
-              TextEmbeddingModel,
-            )
 
         if self.config.get("project_id"):
             self.project_id = self.config.get("project_id")
@@ -139,25 +141,42 @@ def fetch_similar_training_data(self, training_data_type: str, question: str, n_
         results = self.conn.query(query).result().to_dataframe()
         return results
 
-    def generate_question_embedding(self, data: str, **kwargs) -> List[float]:
-        result = self.genai.embed_content(
+    def get_embeddings(self, data: str, task: str) -> List[float]:
+        embeddings = None
+
+        if self.type == "VERTEX_AI":
+          input = [TextEmbeddingInput(data, task)]
+          model = TextEmbeddingModel.from_pretrained("text-embedding-004")
+
+          result = model.get_embeddings(input)
+
+          if len(result) > 0:
+              embeddings = result[0].values
+        else:
+          # Use Gemini Consumer API
+          result = self.genai.embed_content(
             model="models/text-embedding-004",
             content=data,
-            task_type="retrieval_query")
+            task_type=task)
 
-        if 'embedding' in result:
-            return result['embedding']
+          if 'embedding' in result:
+            embeddings = result['embedding']
+
+        return embeddings
+
+    def generate_question_embedding(self, data: str, **kwargs) -> List[float]:
+        result = self.get_embeddings(data, "RETRIEVAL_QUERY")
+
+        if result != None:
+            return result
         else:
             raise ValueError("No embeddings returned")
 
     def generate_storage_embedding(self, data: str, **kwargs) -> List[float]:
-        result = self.genai.embed_content(
-            model="models/text-embedding-004",
-            content=data,
-            task_type="retrieval_document")
+        result = self.get_embeddings(data, "RETRIEVAL_DOCUMENT")
 
-        if 'embedding' in result:
-            return result['embedding']
+        if result != None:
+            return result
         else:
             raise ValueError("No embeddings returned")