diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md
index 9a8cd90a1f..a0e243a656 100644
--- a/docs/my-website/docs/providers/vertex.md
+++ b/docs/my-website/docs/providers/vertex.md
@@ -440,20 +440,12 @@ Use Vertex AI Context Caching
 1. Add model to config.yaml
 ```yaml
 model_list:
-  # used for /chat/completions, /completions, /embeddings endpoints
   - model_name: gemini-1.5-pro-001
     litellm_params:
       model: vertex_ai_beta/gemini-1.5-pro-001
       vertex_project: "project-id"
       vertex_location: "us-central1"
-      vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
-
-# used for the /cachedContent and vertexAI native endpoints
-default_vertex_config:
-  vertex_project: "adroit-crow-413218"
-  vertex_location: "us-central1"
-  vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
-
+      vertex_credentials: "/path/to/service_account.json" # [OPTIONAL] Do this OR `!gcloud auth application-default login` - run this to add vertex credentials to your env
 ```
 
 2. Start Proxy 
@@ -463,71 +455,45 @@ $ litellm --config /path/to/config.yaml
 ```
 
 3. Make Request!
-We make the request in two steps:
-- Create a cachedContents object
-- Use the cachedContents object in your /chat/completions 
-
-**Create a cachedContents object**
-
-First, create a cachedContents object by calling the Vertex `cachedContents` endpoint. The LiteLLM proxy forwards the `/cachedContents` request to the VertexAI API.
 
 ```python
-import httpx
+import datetime
+import openai
+import vertexai
+from vertexai.generative_models import Content, Part
+from vertexai.preview import caching
+from vertexai.preview.generative_models import GenerativeModel
 
-# Set Litellm proxy variables
-LITELLM_BASE_URL = "http://0.0.0.0:4000"
-LITELLM_PROXY_API_KEY = "sk-1234"
-
-httpx_client = httpx.Client(timeout=30)
-
-print("Creating cached content")
-create_cache = httpx_client.post(
-    url=f"{LITELLM_BASE_URL}/vertex-ai/cachedContents",
-    headers={"Authorization": f"Bearer {LITELLM_PROXY_API_KEY}"},
-    json={
-        "model": "gemini-1.5-pro-001",
-        "contents": [
-            {
-                "role": "user",
-                "parts": [{
-                    "text": "This is sample text to demonstrate explicit caching." * 4000
-                }]
-            }
-        ],
-    }
+# use Vertex AI SDK to create CachedContent
+vertexai.init(project="adroit-crow-413218", location="us-central1")
+print("creating cached content")
+contents_here: list[Content] = [
+    Content(role="user", parts=[Part.from_text("huge string of text here" * 10000)])
+]
+cached_content = caching.CachedContent.create(
+    model_name="gemini-1.5-pro-001",
+    contents=contents_here,
+    expire_time=datetime.datetime(2024, 8, 10),
 )
 
-print("Response from create_cache:", create_cache)
-create_cache_response = create_cache.json()
-print("JSON from create_cache:", create_cache_response)
-cached_content_name = create_cache_response["name"]
-```
-
-**Use the cachedContents object in your /chat/completions request to VertexAI**
-
-```python
-import openai
-
-# Set Litellm proxy variables
-LITELLM_BASE_URL = "http://0.0.0.0:4000"
-LITELLM_PROXY_API_KEY = "sk-1234"
-
-client = openai.OpenAI(api_key=LITELLM_PROXY_API_KEY, base_url=LITELLM_BASE_URL)
 
+# use OpenAI SDK to send a request to LiteLLM Proxy
+# base_url is litellm proxy server and api_key is api key to litellm proxy
+client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
 response = client.chat.completions.create(
     model="gemini-1.5-pro-001",
-    max_tokens=8192,
     messages=[
         {
             "role": "user",
-            "content": "What is the sample text about?",
+            "content": "hello!",
         },
     ],
-    temperature=0.7,
-    extra_body={"cached_content": cached_content_name},  # Use the cached content
+    temperature="0.7",
+    extra_body={"cached_content": cached_content.resource_name},
 )
 
-print("Response from proxy:", response)
+print("response from proxy", response)
+
 ```
 
 </TabItem>