fix(azure.py): use openai client sdk for handling sync+async calling

litellm/llms/azure.py

@@ -4,8 +4,9 @@ from .base import BaseLLM
 from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, convert_to_model_response_object
 from typing import Callable, Optional
 from litellm import OpenAIConfig
-import litellm
+import litellm, json
 import httpx
+from openai import AzureOpenAI, AsyncAzureOpenAI
 
 class AzureOpenAIError(Exception):
     def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None):
@@ -73,8 +74,6 @@ class AzureOpenAIConfig(OpenAIConfig):
                          top_p)
 
 class AzureChatCompletion(BaseLLM):
-    _client_session: Optional[httpx.Client] = None
-    _aclient_session: Optional[httpx.AsyncClient] = None
 
     def __init__(self) -> None:
         super().__init__()
@@ -110,17 +109,12 @@ class AzureChatCompletion(BaseLLM):
             self._client_session = self.create_client_session()
         exception_mapping_worked = False
         try:
-            if headers is None:
-                headers = self.validate_environment(api_key=api_key, azure_ad_token=azure_ad_token)
 
             if model is None or messages is None:
                 raise AzureOpenAIError(status_code=422, message=f"Missing model or messages")
-            # Ensure api_base ends with a trailing slash
-            if not api_base.endswith('/'):
-                api_base += '/'
         
-            api_base = api_base + f"openai/deployments/{model}/chat/completions?api-version={api_version}"
             data = {
+                "model": model,
                 "messages": messages, 
                 **optional_params
             }
@@ -137,41 +131,34 @@ class AzureChatCompletion(BaseLLM):
             )
             if acompletion is True: 
                 if optional_params.get("stream", False):
-                    return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
+                    return self.async_streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token)
                 else:
-                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response)
+                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, api_key=api_key, api_version=api_version, model=model, azure_ad_token=azure_ad_token)
             elif "stream" in optional_params and optional_params["stream"] == True:
-                return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
+                return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model, api_key=api_key, api_version=api_version, azure_ad_token=azure_ad_token)
             else:
-                response = self._client_session.post(
+                azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
-                    url=api_base,
+                response = azure_client.chat.completions.create(**data) # type: ignore
-                    json=data,
+                return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response)
-                    headers=headers,
-                    timeout=litellm.request_timeout
-                )
-                if response.status_code != 200:
-                    raise AzureOpenAIError(status_code=response.status_code, message=response.text)
-                    
-                ## RESPONSE OBJECT
-                return convert_to_model_response_object(response_object=response.json(), model_response_object=model_response)
         except AzureOpenAIError as e: 
             exception_mapping_worked = True
             raise e
         except Exception as e: 
             raise e
     
-    async def acompletion(self, api_base: str, data: dict, headers: dict, model_response: ModelResponse): 
+    async def acompletion(self, 
-       if self._aclient_session is None:
+                          api_key: str, 
-           self._aclient_session = self.create_aclient_session()
+                          api_version: str, 
-       client = self._aclient_session
+                          model: str, 
+                          api_base: str, 
+                          data: dict, 
+                          headers: dict, 
+                          model_response: ModelResponse,
+                          azure_ad_token: Optional[str]=None, ): 
        try:
-            response = await client.post(api_base, json=data, headers=headers, timeout=litellm.request_timeout) 
+            azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
-            response_json = response.json()
+            response = await azure_client.chat.completions.create(**data) 
-            if response.status_code != 200:
+            return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response)
-                raise AzureOpenAIError(status_code=response.status_code, message=response.text, request=response.request, response=response)
-            
-            ## RESPONSE OBJECT
-            return convert_to_model_response_object(response_object=response_json, model_response_object=model_response)
        except Exception as e: 
            if isinstance(e,httpx.TimeoutException):
                 raise AzureOpenAIError(status_code=500, message="Request Timeout Error")
@@ -183,49 +170,33 @@ class AzureChatCompletion(BaseLLM):
     def streaming(self,
                   logging_obj,
                   api_base: str, 
+                  api_key: str,
+                  api_version: str, 
                   data: dict, 
                   headers: dict, 
                   model_response: ModelResponse, 
-                  model: str
+                  model: str,
+                  azure_ad_token: Optional[str]=None, 
     ):
-        if self._client_session is None:
+        azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
-            self._client_session = self.create_client_session()
+        response = azure_client.chat.completions.create(**data)
-        with self._client_session.stream(
+        streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj)
-                    url=f"{api_base}",
-                    json=data,
-                    headers=headers,
-                    method="POST",
-                    timeout=litellm.request_timeout
-                ) as response: 
-                    if response.status_code != 200:
-                        raise AzureOpenAIError(status_code=response.status_code, message="An error occurred while streaming")
-                    
-                    completion_stream = response.iter_lines()
-                    streamwrapper = CustomStreamWrapper(completion_stream=completion_stream, model=model, custom_llm_provider="azure",logging_obj=logging_obj)
         for transformed_chunk in streamwrapper:
             yield transformed_chunk
 
     async def async_streaming(self, 
                           logging_obj,
                           api_base: str, 
+                          api_key: str, 
+                          api_version: str, 
                           data: dict, 
                           headers: dict, 
                           model_response: ModelResponse, 
-                          model: str):
+                          model: str,
-        if self._aclient_session is None:
+                          azure_ad_token: Optional[str]=None):
-           self._aclient_session = self.create_aclient_session()
+        azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
-        client = self._aclient_session
+        response = await azure_client.chat.completions.create(**data)
-        async with client.stream(
+        streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj)
-                    url=f"{api_base}",
-                    json=data,
-                    headers=headers,
-                    method="POST",
-                    timeout=litellm.request_timeout
-                ) as response: 
-            if response.status_code != 200:
-                raise AzureOpenAIError(status_code=response.status_code, message=response.text)
-            
-            streamwrapper = CustomStreamWrapper(completion_stream=response.aiter_lines(), model=model, custom_llm_provider="azure",logging_obj=logging_obj)
         async for transformed_chunk in streamwrapper:
             yield transformed_chunk
 
@@ -234,23 +205,17 @@ class AzureChatCompletion(BaseLLM):
                 input: list,
                 api_key: str,
                 api_base: str,
-                azure_ad_token: str,
                 api_version: str,
                 logging_obj=None,
                 model_response=None,
-                optional_params=None,):
+                optional_params=None,
+                azure_ad_token: Optional[str]=None):
         super().embedding()
         exception_mapping_worked = False
         if self._client_session is None:
             self._client_session = self.create_client_session()
         try: 
-            headers = self.validate_environment(api_key, azure_ad_token=azure_ad_token)
+            azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token)
-            # Ensure api_base ends with a trailing slash
-            if not api_base.endswith('/'):
-                api_base += '/'
-
-            api_base = api_base + f"openai/deployments/{model}/embeddings?api-version={api_version}"
-            model = model
             data = {
                 "model": model,
                 "input": input,
@@ -264,9 +229,7 @@ class AzureChatCompletion(BaseLLM):
                     additional_args={"complete_input_dict": data},
                 )
             ## COMPLETION CALL            
-            response = self._client_session.post(
+            response = azure_client.embeddings.create(**data) # type: ignore
-                api_base, headers=headers, json=data, timeout=litellm.request_timeout
-            )
             ## LOGGING
             logging_obj.post_call(
                     input=input,
@@ -275,9 +238,7 @@ class AzureChatCompletion(BaseLLM):
                     original_response=response,
                 )
 
-            if response.status_code!=200:
+            embedding_response = json.loads(response.model_dump_json()) 
-                raise AzureOpenAIError(message=response.text, status_code=response.status_code)
-            embedding_response = response.json() 
             output_data = []
             for idx, embedding in enumerate(embedding_response["data"]):
                 output_data.append(

litellm/tests/test_async_fn.py

@@ -36,7 +36,7 @@ def test_sync_response_anyscale():
 
 # test_sync_response_anyscale()
 
-def test_async_response():
+def test_async_response_openai():
     import asyncio
     litellm.set_verbose = True
     async def test_get_response():
@@ -44,13 +44,27 @@ def test_async_response():
         messages = [{"content": user_message, "role": "user"}]
         try:
             response = await acompletion(model="gpt-3.5-turbo", messages=messages)
-            # response = await response
             print(f"response: {response}")
         except Exception as e:
             pytest.fail(f"An exception occurred: {e}")
 
     asyncio.run(test_get_response())
-test_async_response()
+
+def test_async_response_azure():
+    import asyncio
+    litellm.set_verbose = True
+    async def test_get_response():
+        user_message = "Hello, how are you?"
+        messages = [{"content": user_message, "role": "user"}]
+        try:
+            response = await acompletion(model="azure/chatgpt-v-2", messages=messages)
+            print(f"response: {response}")
+        except Exception as e:
+            pytest.fail(f"An exception occurred: {e}")
+
+    asyncio.run(test_get_response())
+
+
 def test_async_anyscale_response():
     import asyncio
     litellm.set_verbose = True
@@ -73,7 +87,7 @@ def test_get_response_streaming():
         messages = [{"content": user_message, "role": "user"}]
         try:
             litellm.set_verbose = True
-            response = await acompletion(model="gpt-3.5-turbo", messages=messages, stream=True)
+            response = await acompletion(model="azure/chatgpt-v-2", messages=messages, stream=True)
             print(type(response))
 
             import inspect

litellm/tests/test_completion.py

@@ -481,7 +481,7 @@ def test_completion_openai_litellm_key():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
-test_completion_openai_litellm_key()
+# test_completion_openai_litellm_key()
 
 def test_completion_openrouter1():
     try:
@@ -562,6 +562,8 @@ def test_completion_azure():
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
 
+test_completion_azure()
+
 def test_azure_openai_ad_token():
     # this tests if the azure ad token is set in the request header
     # the request can fail since azure ad tokens expire after 30 mins, but the header MUST have the azure ad token

litellm/tests/test_embedding.py

@@ -20,7 +20,7 @@ def test_openai_embedding():
         # print(f"response: {str(response)}")
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-test_openai_embedding()
+# test_openai_embedding()
 
 def test_openai_azure_embedding_simple():
     try:

litellm/tests/test_loadtest_router.py

@@ -1,69 +1,69 @@
-# import sys, os
+import sys, os
-# import traceback
+import traceback
-# from dotenv import load_dotenv
+from dotenv import load_dotenv
-# import copy
+import copy
 
-# load_dotenv()
+load_dotenv()
-# sys.path.insert(
+sys.path.insert(
-#     0, os.path.abspath("../..")
+    0, os.path.abspath("../..")
-# )  # Adds the parent directory to the system path
+)  # Adds the parent directory to the system path
-# import asyncio
+import asyncio
-# from litellm import Router, Timeout
+from litellm import Router, Timeout
 
 
-# async def call_acompletion(semaphore, router: Router, input_data):
+async def call_acompletion(semaphore, router: Router, input_data):
-#     async with semaphore:
+    async with semaphore:
-#         try:
+        try:
-#             # Use asyncio.wait_for to set a timeout for the task
+            # Use asyncio.wait_for to set a timeout for the task
-#             response = await router.acompletion(**input_data)
+            response = await router.acompletion(**input_data)
-#             # Handle the response as needed
+            # Handle the response as needed
-#             return response
+            return response
-#         except Timeout:
+        except Timeout:
-#             print(f"Task timed out: {input_data}")
+            print(f"Task timed out: {input_data}")
-#             return None  # You may choose to return something else or raise an exception
+            return None  # You may choose to return something else or raise an exception
 
 
-# async def main():
+async def main():
-#     # Initialize the Router
+    # Initialize the Router
-#     model_list= [{
+    model_list= [{
-#             "model_name": "gpt-3.5-turbo", 
+            "model_name": "gpt-3.5-turbo", 
-#             "litellm_params": {
+            "litellm_params": {
-#                 "model": "gpt-3.5-turbo", 
+                "model": "gpt-3.5-turbo", 
-#                 "api_key": os.getenv("OPENAI_API_KEY"), 
+                "api_key": os.getenv("OPENAI_API_KEY"), 
-#             },
+            },
-#         }, {
+        }, {
-#             "model_name": "gpt-3.5-turbo", 
+            "model_name": "gpt-3.5-turbo", 
-#             "litellm_params": {
+            "litellm_params": {
-#                 "model": "azure/chatgpt-v-2", 
+                "model": "azure/chatgpt-v-2", 
-#                 "api_key": os.getenv("AZURE_API_KEY"), 
+                "api_key": os.getenv("AZURE_API_KEY"), 
-#                 "api_base": os.getenv("AZURE_API_BASE"),
+                "api_base": os.getenv("AZURE_API_BASE"),
-#                 "api_version": os.getenv("AZURE_API_VERSION")
+                "api_version": os.getenv("AZURE_API_VERSION")
-#             },
+            },
-#         }, {
+        }, {
-#             "model_name": "gpt-3.5-turbo", 
+            "model_name": "gpt-3.5-turbo", 
-#             "litellm_params": {
+            "litellm_params": {
-#                 "model": "azure/chatgpt-functioncalling", 
+                "model": "azure/chatgpt-functioncalling", 
-#                 "api_key": os.getenv("AZURE_API_KEY"), 
+                "api_key": os.getenv("AZURE_API_KEY"), 
-#                 "api_base": os.getenv("AZURE_API_BASE"),
+                "api_base": os.getenv("AZURE_API_BASE"),
-#                 "api_version": os.getenv("AZURE_API_VERSION")
+                "api_version": os.getenv("AZURE_API_VERSION")
-#             },
+            },
-#         }]
+        }]
-#     router = Router(model_list=model_list, num_retries=3, timeout=10)
+    router = Router(model_list=model_list, num_retries=3, timeout=10)
 
-#     # Create a semaphore with a capacity of 100
+    # Create a semaphore with a capacity of 100
-#     semaphore = asyncio.Semaphore(100)
+    semaphore = asyncio.Semaphore(100)
 
-#     # List to hold all task references
+    # List to hold all task references
-#     tasks = []
+    tasks = []
 
-#     # Launch 1000 tasks
+    # Launch 1000 tasks
-#     for _ in range(1000):
+    for _ in range(1000):
-#         task = asyncio.create_task(call_acompletion(semaphore, router, {"model": "gpt-3.5-turbo", "messages": [{"role":"user", "content": "Hey, how's it going?"}]}))
+        task = asyncio.create_task(call_acompletion(semaphore, router, {"model": "gpt-3.5-turbo", "messages": [{"role":"user", "content": "Hey, how's it going?"}]}))
-#         tasks.append(task)
+        tasks.append(task)
 
-#     # Wait for all tasks to complete
+    # Wait for all tasks to complete
-#     responses = await asyncio.gather(*tasks)
+    responses = await asyncio.gather(*tasks)
-#     # Process responses as needed
+    # Process responses as needed
-#     print(f"NUMBER OF COMPLETED TASKS: {len(responses)}")
+    print(f"NUMBER OF COMPLETED TASKS: {len(responses)}")
-# # Run the main function
+# Run the main function
-# asyncio.run(main())
+asyncio.run(main())

litellm/tests/test_streaming.py

@@ -374,7 +374,7 @@ def test_completion_azure_stream():
         print(f"completion_response: {complete_response}")
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
-# test_completion_azure_stream() 
+test_completion_azure_stream() 
 
 def test_completion_claude_stream():
     try:

litellm/utils.py

@@ -4505,6 +4505,7 @@ class CustomStreamWrapper:
             text = "" 
             is_finished = False
             finish_reason = None
+            if len(str_line.choices) > 0: 
                 if str_line.choices[0].delta.content is not None:
                     text = str_line.choices[0].delta.content
                 if str_line.choices[0].finish_reason:
@@ -4642,15 +4643,6 @@ class CustomStreamWrapper:
                 completion_obj["content"] = response_obj["text"]
                 if response_obj["is_finished"]: 
                     model_response.choices[0].finish_reason = response_obj["finish_reason"]
-            elif self.custom_llm_provider and self.custom_llm_provider == "azure": 
-                response_obj = self.handle_azure_chunk(chunk)
-                completion_obj["content"] = response_obj["text"]
-                print_verbose(f"response_obj: {response_obj}")
-                print_verbose(f"completion obj content: {completion_obj['content']}")
-                print_verbose(f"len(completion_obj['content']: {len(completion_obj['content'])}")
-                if response_obj["is_finished"]: 
-                    model_response.choices[0].finish_reason = response_obj["finish_reason"]
-                    print_verbose(f"model_response finish reason 2: {model_response.choices[0].finish_reason}")
             elif self.custom_llm_provider and self.custom_llm_provider == "maritalk":
                 response_obj = self.handle_maritalk_chunk(chunk)
                 completion_obj["content"] = response_obj["text"]