diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md
index 6c638d6de..62a10b44d 100644
--- a/docs/my-website/docs/embedding/supported_embedding.md
+++ b/docs/my-website/docs/embedding/supported_embedding.md
@@ -1,3 +1,6 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
 # Embedding Models
 
 ## Quick Start
@@ -7,8 +10,81 @@ import os
 os.environ['OPENAI_API_KEY'] = ""
 response = embedding(model='text-embedding-ada-002', input=["good morning from litellm"])
 ```
+## Proxy Usage 
 
-### Input Params for `litellm.embedding()`
+**NOTE**
+For `vertex_ai`,
+```bash
+export GOOGLE_APPLICATION_CREDENTIALS="absolute/path/to/service_account.json"
+```
+
+### Add model to config 
+
+```yaml
+model_list:
+- model_name: textembedding-gecko
+  litellm_params:
+    model: vertex_ai/textembedding-gecko
+
+general_settings:
+  master_key: sk-1234
+```
+
+### Start proxy 
+
+```bash
+litellm --config /path/to/config.yaml 
+
+# RUNNING on http://0.0.0.0:8000
+```
+
+### Test 
+
+<Tabs>
+<TabItem value="curl" label="Curl">
+
+```bash
+curl --location 'http://0.0.0.0:8000/embeddings' \
+--header 'Authorization: Bearer sk-1234' \
+--header 'Content-Type: application/json' \
+--data '{"input": ["Academia.edu uses"], "model": "textembedding-gecko", "encoding_format": "base64"}'
+```
+
+</TabItem>
+<TabItem value="openai" label="OpenAI (python)">
+
+```python
+from openai import OpenAI
+client = OpenAI(
+  api_key="sk-1234",
+  base_url="http://0.0.0.0:8000"
+)
+
+client.embeddings.create(
+  model="textembedding-gecko",
+  input="The food was delicious and the waiter...",
+  encoding_format="float"
+)
+```
+</TabItem>
+<TabItem value="langchain" label="Langchain Embeddings">
+
+```python
+from langchain_openai import OpenAIEmbeddings
+
+embeddings = OpenAIEmbeddings(model="textembedding-gecko", openai_api_base="http://0.0.0.0:8000", openai_api_key="sk-1234")
+
+text = "This is a test document."
+
+query_result = embeddings.embed_query(text)
+
+print(f"VERTEX AI EMBEDDINGS")
+print(query_result[:5])
+```
+</TabItem>
+</Tabs>
+
+## Input Params for `litellm.embedding()`
 ### Required Fields
 
 - `model`: *string* - ID of the model to use. `model='text-embedding-ada-002'`