docs(routing.md): add proxy with multiple azure deployments to docs

docs/my-website/docs/routing.md

@@ -56,6 +56,8 @@ print(response)
 
 ## Redis Queue 
 
+In production, we use Redis to track usage across multiple Azure deployments.
+
 ```python
 router = Router(model_list=model_list, 
                 redis_host=os.getenv("REDIS_HOST"), 
@@ -63,4 +65,52 @@ router = Router(model_list=model_list,
                 redis_port=os.getenv("REDIS_PORT"))
 
 print(response)
-```
+```
+
+## Handle Multiple Azure Deployments via OpenAI Proxy Server
+
+#### 1. Clone repo 
+```shell
+git clone https://github.com/BerriAI/litellm.git
+```
+
+#### 2. Add Azure/OpenAI deployments to `secrets_template.toml`
+```python 
+[model."gpt-3.5-turbo"]
+model_list = [{ # list of model deployments 
+    "model_name": "gpt-3.5-turbo", # openai model name 
+    "litellm_params": { # params for litellm completion/embedding call 
+        "model": "azure/chatgpt-v-2", 
+        "api_key": "my-azure-api-key-1",
+        "api_version": "my-azure-api-version-1",
+        "api_base": "my-azure-api-base-1"
+    },
+    "tpm": 240000,
+    "rpm": 1800
+}, { # list of model deployments 
+    "model_name": "gpt-3.5-turbo", # openai model name 
+    "litellm_params": { # params for litellm completion/embedding call 
+        "model": "azure/chatgpt-function-calling", 
+        "api_key": "my-azure-api-key-2",
+        "api_version": "my-azure-api-version-2",
+        "api_base": "my-azure-api-base-2"
+    },
+    "tpm": 240000,
+    "rpm": 1800
+}, {
+    "model_name": "gpt-3.5-turbo", # openai model name 
+    "litellm_params": { # params for litellm completion/embedding call 
+        "model": "gpt-3.5-turbo", 
+        "api_key": "sk-...",
+    },
+    "tpm": 1000000,
+    "rpm": 9000
+}]
+```
+
+#### 3. Run with Docker Image
+```shell
+docker build -t litellm . && docker run -p 8000:8000 litellm
+```
+
+