diff --git a/docs/my-website/docs/completion/function_call.md b/docs/my-website/docs/completion/function_call.md
index 0d244c33996..8004a55d12b 100644
--- a/docs/my-website/docs/completion/function_call.md
+++ b/docs/my-website/docs/completion/function_call.md
@@ -270,7 +270,106 @@ ModelResponse(
 )
 ```
 
-## Deprecated - Function Calling with `functions`
+## Parallel Function Calling - Azure OpenAI
+```python
+# set Azure env variables
+import os
+os.environ['AZURE_API_KEY'] = "" # litellm reads AZURE_API_KEY from .env and sends the request
+os.environ['AZURE_API_BASE'] = "https://openai-gpt-4-test-v-1.openai.azure.com/"
+os.environ['AZURE_API_VERSION'] = "2023-07-01-preview"
+
+import litellm
+import json
+# Example dummy function hard coded to return the same weather
+# In production, this could be your backend API or an external API
+def get_current_weather(location, unit="fahrenheit"):
+    """Get the current weather in a given location"""
+    if "tokyo" in location.lower():
+        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
+    elif "san francisco" in location.lower():
+        return json.dumps({"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"})
+    elif "paris" in location.lower():
+        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
+    else:
+        return json.dumps({"location": location, "temperature": "unknown"})
+
+## Step 1: send the conversation and available functions to the model
+messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                },
+                "required": ["location"],
+            },
+        },
+    }
+]
+
+response = litellm.completion(
+    model="azure/chatgpt-functioncalling", # model = azure/<your-azure-deployment-name>
+    messages=messages,
+    tools=tools,
+    tool_choice="auto",  # auto is default, but we'll be explicit
+)
+print("\nLLM Response1:\n", response)
+response_message = response.choices[0].message
+tool_calls = response.choices[0].message.tool_calls
+print("\nTool Choice:\n", tool_calls)
+
+## Step 2 - Parse the Model Response and Execute Functions
+# Check if the model wants to call a function
+if tool_calls:
+    # Execute the functions and prepare responses
+    available_functions = {
+        "get_current_weather": get_current_weather,
+    }
+
+    messages.append(response_message)  # Extend conversation with assistant's reply
+
+    for tool_call in tool_calls:
+      print(f"\nExecuting tool call\n{tool_call}")
+      function_name = tool_call.function.name
+      function_to_call = available_functions[function_name]
+      function_args = json.loads(tool_call.function.arguments)
+      # calling the get_current_weather() function
+      function_response = function_to_call(
+          location=function_args.get("location"),
+          unit=function_args.get("unit"),
+      )
+      print(f"Result from tool call\n{function_response}\n")
+
+      # Extend conversation with function response
+      messages.append(
+          {
+              "tool_call_id": tool_call.id,
+              "role": "tool",
+              "name": function_name,
+              "content": function_response,
+          }
+      )
+
+## Step 3 - Second litellm.completion() call
+second_response = litellm.completion(
+    model="azure/chatgpt-functioncalling",
+    messages=messages,
+)
+print("Second Response\n", second_response)
+print("Second Response Message\n", second_response.choices[0].message.content)
+
+```
+
+## Deprecated - Function Calling with `completion(functions=functions)`
 ```python
 import os, litellm
 from litellm import completion