diff --git a/docs/my-website/docs/pass_through/vertex_ai.md b/docs/my-website/docs/pass_through/vertex_ai.md
index 53ae410913e..bfc1bc98b84 100644
--- a/docs/my-website/docs/pass_through/vertex_ai.md
+++ b/docs/my-website/docs/pass_through/vertex_ai.md
@@ -1,3 +1,7 @@
+import Image from '@theme/IdealImage';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # [BETA] Vertex AI Endpoints (Pass-Through)
 
 Pass-through endpoints for Vertex AI - call provider-specific endpoint, in native format (no translation).
@@ -40,16 +44,120 @@ litellm --config /path/to/config.yaml
 
 #### 3. Test it 
 
-```shell
-curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:countTokens \
--H "Content-Type: application/json" \
--H "Authorization: Bearer sk-1234" \
--d '{"instances":[{"content": "gm"}]}'
+```python
+import vertexai
+from google.auth.credentials import Credentials
+from vertexai.generative_models import GenerativeModel
+
+LITELLM_PROXY_API_KEY = "sk-1234"
+LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"
+
+import datetime
+
+
+class CredentialsWrapper(Credentials):
+    def __init__(self, token=None):
+        super().__init__()
+        self.token = token
+        self.expiry = None  # or set to a future date if needed
+
+    def refresh(self, request):
+        pass
+
+    def apply(self, headers, token=None):
+        headers["Authorization"] = f"Bearer {self.token}"
+
+    @property
+    def expired(self):
+        return False  # Always consider the token as non-expired
+
+    @property
+    def valid(self):
+        return True  # Always consider the credentials as valid
+
+
+credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
+
+vertexai.init(
+    project="adroit-crow-413218",
+    location="us-central1",
+    api_endpoint=LITELLM_PROXY_BASE,
+    credentials=credentials,
+    api_transport="rest",
+    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+)
+
+model = GenerativeModel("gemini-1.5-flash-001")
+
+response = model.generate_content(
+    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
+)
+
+print(response.text)
 ```
+
 ## Usage Examples
 
 ### Gemini API (Generate Content)
 
+<Tabs>
+<TabItem value="py" label="Vertex Python SDK">
+
+```python
+import vertexai
+from google.auth.credentials import Credentials
+from vertexai.generative_models import GenerativeModel
+
+LITELLM_PROXY_API_KEY = "sk-1234"
+LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"
+
+import datetime
+
+
+class CredentialsWrapper(Credentials):
+    def __init__(self, token=None):
+        super().__init__()
+        self.token = token
+        self.expiry = None  # or set to a future date if needed
+
+    def refresh(self, request):
+        pass
+
+    def apply(self, headers, token=None):
+        headers["Authorization"] = f"Bearer {self.token}"
+
+    @property
+    def expired(self):
+        return False  # Always consider the token as non-expired
+
+    @property
+    def valid(self):
+        return True  # Always consider the credentials as valid
+
+
+credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
+
+vertexai.init(
+    project="adroit-crow-413218",
+    location="us-central1",
+    api_endpoint=LITELLM_PROXY_BASE,
+    credentials=credentials,
+    api_transport="rest",
+    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+)
+
+model = GenerativeModel("gemini-1.5-flash-001")
+
+response = model.generate_content(
+    "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
+)
+
+print(response.text)
+```
+
+</TabItem>
+<TabItem value="Curl" label="Curl">
+
 ```shell
 curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-001:generateContent \
   -H "Content-Type: application/json" \
@@ -57,8 +165,77 @@ curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.5-flash-0
   -d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
 ```
 
+</TabItem>
+</Tabs>
+
+
 ### Embeddings API
 
+<Tabs>
+<TabItem value="py" label="Vertex Python SDK">
+
+```python
+from typing import List, Optional
+from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
+import vertexai
+from google.auth.credentials import Credentials
+from vertexai.generative_models import GenerativeModel
+
+LITELLM_PROXY_API_KEY = "sk-1234"
+LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex-ai"
+
+import datetime
+
+
+class CredentialsWrapper(Credentials):
+    def __init__(self, token=None):
+        super().__init__()
+        self.token = token
+        self.expiry = None  # or set to a future date if needed
+
+    def refresh(self, request):
+        pass
+
+    def apply(self, headers, token=None):
+        headers["Authorization"] = f"Bearer {self.token}"
+
+    @property
+    def expired(self):
+        return False  # Always consider the token as non-expired
+
+    @property
+    def valid(self):
+        return True  # Always consider the credentials as valid
+
+
+credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
+
+vertexai.init(
+    project="adroit-crow-413218",
+    location="us-central1",
+    api_endpoint=LITELLM_PROXY_BASE,
+    credentials=credentials,
+    api_transport="rest",
+    request_metadata=[("Authorization", f"Bearer {LITELLM_PROXY_API_KEY}")],
+
+
+def embed_text(
+    texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
+    task: str = "RETRIEVAL_DOCUMENT",
+    model_name: str = "text-embedding-004",
+    dimensionality: Optional[int] = 256,
+) -> List[List[float]]:
+    """Embeds texts with a pre-trained, foundational model."""
+    model = TextEmbeddingModel.from_pretrained(model_name)
+    inputs = [TextEmbeddingInput(text, task) for text in texts]
+    kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
+    embeddings = model.get_embeddings(inputs, **kwargs)
+    return [embedding.values for embedding in embeddings]
+```
+</TabItem>
+
+<TabItem value="curl" label="Curl">
+
 ```shell
 curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-gecko@001:predict \
   -H "Content-Type: application/json" \
@@ -66,6 +243,10 @@ curl http://localhost:4000/vertex-ai/publishers/google/models/textembedding-geck
   -d '{"instances":[{"content": "gm"}]}'
 ```
 
+</TabItem>
+
+</Tabs>
+
 ### Imagen API
 
 ```shell