docs(custom_callback.md): add async failure + streaming logging events to docs

https://github.com/BerriAI/litellm/issues/1125

docs/my-website/docs/observability/custom_callback.md

@@ -4,7 +4,9 @@
 You can create a custom callback class to precisely log events as they occur in litellm. 
 
 ```python
+import litellm
 from litellm.integrations.custom_logger import CustomLogger
+from litellm import completion, acompletion
 
 class MyCustomHandler(CustomLogger):
     def log_pre_api_call(self, model, messages, kwargs): 
@@ -21,14 +23,38 @@ class MyCustomHandler(CustomLogger):
 
     def log_failure_event(self, kwargs, response_obj, start_time, end_time): 
         print(f"On Failure")
+    
+    #### ASYNC #### - for acompletion/aembeddings
+    
+    async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Streaming")
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Success")
+
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Success")
 
 customHandler = MyCustomHandler()
 
 litellm.callbacks = [customHandler]
+
+## sync 
 response = completion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}],
                               stream=True)
 for chunk in response: 
     continue
+
+
+## async
+import asyncio 
+
+def async completion():
+    response = await acompletion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}],
+                              stream=True)
+    async for chunk in response: 
+        continue
+asyncio.run(completion())
 ```
 
 ## Callback Functions
@@ -87,6 +113,41 @@ print(response)
 
 ## Async Callback Functions 
 
+We recommend using the Custom Logger class for async.
+
+```python
+from litellm.integrations.custom_logger import CustomLogger
+from litellm import acompletion 
+
+class MyCustomHandler(CustomLogger):
+    #### ASYNC #### 
+    
+    async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Streaming")
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Success")
+
+    async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
+        print(f"On Async Failure")
+
+import asyncio 
+customHandler = MyCustomHandler()
+
+litellm.callbacks = [customHandler]
+
+def async completion():
+    response = await acompletion(model="gpt-3.5-turbo", messages=[{ "role": "user", "content": "Hi 👋 - i'm openai"}],
+                              stream=True)
+    async for chunk in response: 
+        continue
+asyncio.run(completion())
+```
+
+**Functions**
+
+If you just want to pass in an async function for logging. 
+
 LiteLLM currently supports just async success callback functions for async completion/embedding calls. 
 
 ```python
@@ -117,9 +178,6 @@ asyncio.run(test_chat_openai())
 :::info
 
 We're actively trying to expand this to other event types. [Tell us if you need this!](https://github.com/BerriAI/litellm/issues/1007)
-
-
-
 :::
 
 ## What's in kwargs? 

litellm/integrations/custom_logger.py

@@ -28,6 +28,8 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback
 
     def log_failure_event(self, kwargs, response_obj, start_time, end_time): 
         pass
+
+    #### ASYNC #### 
     
     async def async_log_stream_event(self, kwargs, response_obj, start_time, end_time):
         pass
@@ -41,7 +43,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callback
     async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
         pass
 
-    #### CALL HOOKS #### 
+    #### CALL HOOKS - proxy only #### 
     """
     Control the modify incoming / outgoung data before calling the model
     """