refactor: fixing linting issues

litellm/exceptions.py

@@ -76,8 +76,8 @@ class ContextWindowExceededError(BadRequestError):  # type: ignore
         self.llm_provider = llm_provider
         super().__init__(
             message=self.message, 
-            model=self.model, 
+            model=self.model, # type: ignore
-            llm_provider=self.llm_provider, 
+            llm_provider=self.llm_provider, # type: ignore
             response=response
         )  # Call the base class constructor with the parameters it needs
 
@@ -101,7 +101,7 @@ class APIError(APIError): # type: ignore
         self.model = model
         super().__init__(
             self.message,
-            request=request
+            request=request # type: ignore
         )
 
 # raised if an invalid request (not get, delete, put, post) is made

litellm/llms/aleph_alpha.py

@@ -5,7 +5,7 @@ import requests
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse, Choices, Message
+from litellm.utils import ModelResponse, Choices, Message, Usage
 import httpx
 
 class AlephAlphaError(Exception):
@@ -265,9 +265,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/anthropic.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time
 from typing import Callable, Optional
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 import litellm 
 from .prompt_templates.factory import prompt_factory, custom_prompt
 import httpx
@@ -167,9 +167,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/azure.py

@@ -7,11 +7,17 @@ from litellm import OpenAIConfig
 import httpx
 
 class AzureOpenAIError(Exception):
-    def __init__(self, status_code, message, request: httpx.Request, response: httpx.Response):
+    def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None):
         self.status_code = status_code
         self.message = message
+        if request:
             self.request = request
+        else:
+            self.request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+        if response:
             self.response = response
+        else:
+            self.response = httpx.Response(status_code=status_code, request=self.request)
         super().__init__(
             self.message
         )  # Call the base class constructor with the parameters it needs
@@ -136,7 +142,7 @@ class AzureChatCompletion(BaseLLM):
                     headers=headers,
                 )
                 if response.status_code != 200:
-                    raise AzureOpenAIError(status_code=response.status_code, message=response.text, request=response.request, response=response)
+                    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)
@@ -172,7 +178,7 @@ class AzureChatCompletion(BaseLLM):
                     method="POST"
                 ) as response: 
                     if response.status_code != 200:
-                        raise AzureOpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                        raise AzureOpenAIError(status_code=response.status_code, message=response.text)
                     
                     completion_stream = response.iter_lines()
                     streamwrapper = CustomStreamWrapper(completion_stream=completion_stream, model=model, custom_llm_provider="openai",logging_obj=logging_obj)
@@ -194,7 +200,7 @@ class AzureChatCompletion(BaseLLM):
                     method="POST"
                 ) as response: 
             if response.status_code != 200:
-                raise AzureOpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                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:

litellm/llms/base.py

@@ -1,9 +1,10 @@
 ## This is a template base class to be used for adding new LLM providers via API calls
 import litellm 
 import httpx, certifi, ssl
+from typing import Optional
 
 class BaseLLM:
-    _client_session = None
+    _client_session: Optional[httpx.Client] = None
     def create_client_session(self):
         if litellm.client_session: 
             _client_session = litellm.client_session

litellm/llms/baseten.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time
 from typing import Callable
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 
 class BasetenError(Exception):
     def __init__(self, status_code, message):
@@ -136,9 +136,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/bedrock.py

@@ -4,7 +4,7 @@ from enum import Enum
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse, get_secret
+from litellm.utils import ModelResponse, get_secret, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 import httpx
 
@@ -424,9 +424,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens = prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
     except BedrockError as e:
         exception_mapping_worked = True
@@ -497,6 +500,11 @@ def embedding(
         "total_tokens": input_tokens,
     }
 
-
+    usage = Usage(
+            prompt_tokens=input_tokens,
+            completion_tokens=0,
+            total_tokens=input_tokens + 0
+    )
+    model_response.usage = usage
 
     return model_response

litellm/llms/cohere.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time, traceback
 from typing import Callable, Optional
-from litellm.utils import ModelResponse, Choices, Message
+from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
 import httpx
 
@@ -186,9 +186,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding(

litellm/llms/huggingface_restapi.py

@@ -6,7 +6,7 @@ import httpx, requests
 import time
 import litellm
 from typing import Callable, Dict, List, Any
-from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper
+from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, Usage
 from typing import Optional
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
@@ -381,9 +381,12 @@ def completion(
 
             model_response["created"] = time.time()
             model_response["model"] = model
-            model_response.usage.completion_tokens = completion_tokens
+            usage = Usage(
-            model_response.usage.prompt_tokens = prompt_tokens
+                prompt_tokens=prompt_tokens,
-            model_response.usage.total_tokens = prompt_tokens + completion_tokens
+                completion_tokens=completion_tokens,
+                total_tokens=prompt_tokens + completion_tokens
+            )
+            model_response.usage = usage
             model_response._hidden_params["original_response"] = completion_response
             return model_response
     except HuggingfaceError as e: 

litellm/llms/maritalk.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time, traceback
 from typing import Callable, Optional, List
-from litellm.utils import ModelResponse, Choices, Message
+from litellm.utils import ModelResponse, Choices, Message, Usage
 import litellm
 
 class MaritalkError(Exception):
@@ -145,9 +145,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding(

litellm/llms/nlp_cloud.py

@@ -5,7 +5,7 @@ import requests
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 
 class NLPCloudError(Exception):
     def __init__(self, status_code, message):
@@ -171,9 +171,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/oobabooga.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time
 from typing import Callable, Optional
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 class OobaboogaError(Exception):
@@ -111,9 +111,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/openai.py

@@ -2,16 +2,22 @@ from typing import Optional, Union
 import types
 import httpx
 from .base import BaseLLM
-from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, convert_to_model_response_object
+from litellm.utils import ModelResponse, Choices, Message, CustomStreamWrapper, convert_to_model_response_object, Usage
 from typing import Callable, Optional
 import aiohttp
 
 class OpenAIError(Exception):
-    def __init__(self, status_code, message, request: httpx.Request, response: httpx.Response):
+    def __init__(self, status_code, message, request: Optional[httpx.Request]=None, response: Optional[httpx.Response]=None):
         self.status_code = status_code
         self.message = message
+        if request:
             self.request = request
+        else:
+            self.request = httpx.Request(method="POST", url="https://api.openai.com/v1")
+        if response:
             self.response = response
+        else:
+            self.response = httpx.Response(status_code=status_code, request=self.request)
         super().__init__(
             self.message
         )  # Call the base class constructor with the parameters it needs
@@ -264,13 +270,13 @@ class OpenAIChatCompletion(BaseLLM):
                   model: str
     ):
         with self._client_session.stream(
-                    url=f"{api_base}",
+                    url=f"{api_base}", # type: ignore
                     json=data,
                     headers=headers,
                     method="POST" 
                 ) as response: 
                     if response.status_code != 200:
-                        raise OpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                        raise OpenAIError(status_code=response.status_code, message=response.text()) # type: ignore
                     
                     completion_stream = response.iter_lines()
                     streamwrapper = CustomStreamWrapper(completion_stream=completion_stream, model=model, custom_llm_provider="openai",logging_obj=logging_obj)
@@ -292,7 +298,7 @@ class OpenAIChatCompletion(BaseLLM):
                     method="POST"
                 ) as response: 
             if response.status_code != 200:
-                raise OpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                raise OpenAIError(status_code=response.status_code, message=response.text()) # type: ignore
             
             streamwrapper = CustomStreamWrapper(completion_stream=response.aiter_lines(), model=model, custom_llm_provider="openai",logging_obj=logging_obj)
             async for transformed_chunk in streamwrapper:
@@ -383,7 +389,7 @@ class OpenAITextCompletion(BaseLLM):
         try: 
             ## RESPONSE OBJECT
             if response_object is None or model_response_object is None:
-                raise ValueError(message="Error in response object format")
+                raise ValueError("Error in response object format")
             choice_list=[]
             for idx, choice in enumerate(response_object["choices"]): 
                 message = Message(content=choice["text"], role="assistant")
@@ -406,11 +412,11 @@ class OpenAITextCompletion(BaseLLM):
             raise e
 
     def completion(self, 
-               model: Optional[str]=None,
+               model_response: ModelResponse,    
-               messages: Optional[list]=None,
+               api_key: str,
-               model_response: Optional[ModelResponse]=None,
+               model: str,
+               messages: list,
                print_verbose: Optional[Callable]=None,
-               api_key: Optional[str]=None,
                api_base: Optional[str]=None,
                logging_obj=None,
                acompletion: bool = False,
@@ -449,7 +455,7 @@ class OpenAITextCompletion(BaseLLM):
                 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)
                 else:
-                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model)
+                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, prompt=prompt, api_key=api_key, logging_obj=logging_obj, model=model) # type: ignore
             elif optional_params.get("stream", False):
                 return self.streaming(logging_obj=logging_obj, api_base=api_base, data=data, headers=headers, model_response=model_response, model=model)
             else:
@@ -459,7 +465,7 @@ class OpenAITextCompletion(BaseLLM):
                     headers=headers,
                 )
                 if response.status_code != 200:
-                    raise OpenAIError(status_code=response.status_code, message=response.text, request=self._client_session.request, response=response)
+                    raise OpenAIError(status_code=response.status_code, message=response.text)
                 
                 ## LOGGING
                 logging_obj.post_call(
@@ -521,7 +527,7 @@ class OpenAITextCompletion(BaseLLM):
                     method="POST"
                 ) as response: 
                     if response.status_code != 200:
-                        raise OpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                        raise OpenAIError(status_code=response.status_code, message=response.text) 
                     
                     streamwrapper = CustomStreamWrapper(completion_stream=response.iter_lines(), model=model, custom_llm_provider="text-completion-openai",logging_obj=logging_obj)
                     for transformed_chunk in streamwrapper:
@@ -542,7 +548,7 @@ class OpenAITextCompletion(BaseLLM):
                     method="POST"
                 ) as response: 
             if response.status_code != 200:
-                raise OpenAIError(status_code=response.status_code, message=response.text(), request=self._client_session.request, response=response)
+                raise OpenAIError(status_code=response.status_code, message=response.text)
             
             streamwrapper = CustomStreamWrapper(completion_stream=response.aiter_lines(), model=model, custom_llm_provider="text-completion-openai",logging_obj=logging_obj)
             async for transformed_chunk in streamwrapper:

litellm/llms/palm.py

@@ -3,7 +3,7 @@ import json
 from enum import Enum
 import time
 from typing import Callable, Optional
-from litellm.utils import ModelResponse, get_secret, Choices, Message
+from litellm.utils import ModelResponse, get_secret, Choices, Message, Usage
 import litellm
 import sys
 
@@ -157,9 +157,12 @@ def completion(
 
     model_response["created"] = time.time()
     model_response["model"] = "palm/" + model
-    model_response.usage.completion_tokens = completion_tokens
+    usage = Usage(
-    model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-    model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+    model_response.usage = usage
     return model_response
 
 def embedding():

litellm/llms/petals.py

@@ -5,7 +5,7 @@ import requests
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 class PetalsError(Exception):
@@ -176,9 +176,12 @@ def completion(
 
     model_response["created"] = time.time()
     model_response["model"] = model
-    model_response.usage.completion_tokens = completion_tokens
+    usage = Usage(
-    model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-    model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+    model_response.usage = usage
     return model_response
 
 def embedding():

litellm/llms/replicate.py

@@ -3,7 +3,7 @@ import json
 import requests
 import time
 from typing import Callable, Optional
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 import litellm 
 import httpx
 
@@ -261,9 +261,12 @@ def completion(
         prompt_tokens = len(encoding.encode(prompt))
         completion_tokens = len(encoding.encode(model_response["choices"][0]["message"].get("content", "")))
         model_response["model"] = "replicate/" + model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 

litellm/llms/sagemaker.py

@@ -5,7 +5,7 @@ import requests
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse, get_secret
+from litellm.utils import ModelResponse, get_secret, Usage
 import sys
 from copy import deepcopy
 import httpx
@@ -172,9 +172,12 @@ def completion(
 
     model_response["created"] = time.time()
     model_response["model"] = model
-    model_response.usage.completion_tokens = completion_tokens
+    usage = Usage(
-    model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-    model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+    model_response.usage = usage
     return model_response
 
 def embedding():

litellm/llms/together_ai.py

@@ -5,7 +5,7 @@ import requests
 import time
 from typing import Callable, Optional
 import litellm
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 
 class TogetherAIError(Exception):
@@ -182,9 +182,12 @@ def completion(
             model_response.choices[0].finish_reason = completion_response["output"]["choices"][0]["finish_reason"]
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def embedding():

litellm/llms/vertex_ai.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time
 from typing import Callable, Optional
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 import litellm
 
 class VertexAIError(Exception):
@@ -150,10 +150,12 @@ def completion(
     completion_tokens = len(
         encoding.encode(model_response["choices"][0]["message"].get("content", ""))
     )
-
+    usage = Usage(
-    model_response.usage.completion_tokens = completion_tokens
+            prompt_tokens=prompt_tokens,
-    model_response.usage.prompt_tokens = prompt_tokens
+            completion_tokens=completion_tokens,
-    model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            total_tokens=prompt_tokens + completion_tokens
+        )
+    model_response.usage = usage
     return model_response
 
 

litellm/llms/vllm.py

@@ -4,7 +4,7 @@ from enum import Enum
 import requests
 import time
 from typing import Callable, Any
-from litellm.utils import ModelResponse
+from litellm.utils import ModelResponse, Usage
 from .prompt_templates.factory import prompt_factory, custom_prompt
 llm = None
 class VLLMError(Exception):
@@ -90,9 +90,12 @@ def completion(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         return model_response
 
 def batch_completions(
@@ -170,9 +173,12 @@ def batch_completions(
 
         model_response["created"] = time.time()
         model_response["model"] = model
-        model_response.usage.completion_tokens = completion_tokens
+        usage = Usage(
-        model_response.usage.prompt_tokens = prompt_tokens
+            prompt_tokens=prompt_tokens,
-        model_response.usage.total_tokens = prompt_tokens + completion_tokens
+            completion_tokens=completion_tokens,
+            total_tokens=prompt_tokens + completion_tokens
+        )
+        model_response.usage = usage
         final_outputs.append(model_response)
     return final_outputs
 

litellm/main.py

@@ -12,6 +12,7 @@ from typing import Any
 from functools import partial
 import dotenv, traceback, random, asyncio, time, contextvars
 from copy import deepcopy
+import httpx
 import litellm
 from litellm import (  # type: ignore
     client,
@@ -838,14 +839,14 @@ def completion(
             )
             ## COMPLETION CALL
             openai.api_key = api_key # set key for deep infra 
+            openai.base_url = api_base # use the deepinfra api base
             try:
-                response = openai.ChatCompletion.create(
+                response = openai.chat.completions.create(
-                    model=model,
+                    model=model, # type: ignore
-                    messages=messages,
+                    messages=messages, # type: ignore
-                    api_base=api_base, # use the deepinfra api base
+                    api_type="openai", # type: ignore
-                    api_type="openai",
+                    api_version=api_version, # type: ignore
-                    api_version=api_version, # default None
+                    **optional_params, # type: ignore
-                    **optional_params,
                 )
             except Exception as e:
                 ## LOGGING - log the original exception returned
@@ -932,7 +933,7 @@ def completion(
         elif model in litellm.openrouter_models or custom_llm_provider == "openrouter":
             openai.api_type = "openai"
             # not sure if this will work after someone first uses another API
-            openai.api_base = (
+            openai.base_url = (
                 litellm.api_base
                 if litellm.api_base is not None
                 else "https://openrouter.ai/api/v1"
@@ -963,9 +964,9 @@ def completion(
             logging.pre_call(input=messages, api_key=openai.api_key, additional_args={"complete_input_dict": data, "headers": headers})
             ## COMPLETION CALL
             if headers:
-                response = openai.ChatCompletion.create(
+                response = openai.chat.completions.create(
-                    headers=headers,
+                    headers=headers, # type: ignore
-                    **data,
+                    **data, # type: ignore
                 )
             else:
                 openrouter_site_url = get_secret("OR_SITE_URL")
@@ -976,11 +977,11 @@ def completion(
                 # if openrouter_app_name is None, set it to liteLLM
                 if openrouter_app_name is None:
                     openrouter_app_name = "liteLLM"
-                response = openai.ChatCompletion.create(
+                response = openai.chat.completions.create( # type: ignore
-                    headers={
+                    extra_headers=httpx.Headers({ # type: ignore
-                        "HTTP-Referer": openrouter_site_url,  # To identify your site
+                        "HTTP-Referer": openrouter_site_url,  # type: ignore
-                        "X-Title": openrouter_app_name,  # To identify your app
+                        "X-Title": openrouter_app_name,  # type: ignore
-                    },
+                    }), # type: ignore
                     **data,
                 )
             ## LOGGING
@@ -1961,7 +1962,7 @@ def text_completion(
                     futures = [executor.submit(process_prompt, i, individual_prompt) for i, individual_prompt in enumerate(prompt)]
                     for i, future in enumerate(concurrent.futures.as_completed(futures)):
                         responses[i] = future.result()
-                    text_completion_response["choices"] = responses
+                    text_completion_response.choices = responses 
 
                 return text_completion_response
     # else:
@@ -2012,10 +2013,10 @@ def moderation(input: str, api_key: Optional[str]=None):
                 get_secret("OPENAI_API_KEY")
             )
     openai.api_key = api_key
-    openai.api_type = "open_ai"
+    openai.api_type = "open_ai" # type: ignore
     openai.api_version = None
-    openai.api_base = "https://api.openai.com/v1"
+    openai.base_url = "https://api.openai.com/v1"
-    response = openai.Moderation.create(input)
+    response = openai.moderations.create(input=input)
     return response
 
 ####### HELPER FUNCTIONS ################

litellm/proxy/tests/test_async.py

@@ -2,9 +2,9 @@
 # it makes async Completion requests with streaming
 import openai
 
-openai.api_base = "http://0.0.0.0:8000"
+openai.base_url = "http://0.0.0.0:8000"
 openai.api_key = "temp-key"
-print(openai.api_base)
+print(openai.base_url)
 
 async def test_async_completion():
     response = await openai.Completion.acreate(

litellm/tests/test_exceptions.py

@@ -1,8 +1,4 @@
-try:
+from openai import AuthenticationError, BadRequestError, RateLimitError, OpenAIError
-    from openai import AuthenticationError, BadRequestError, RateLimitError, OpenAIError
-except: 
-    from openai.error import AuthenticationError, InvalidRequestError, RateLimitError, OpenAIError
-
 import os
 import sys
 import traceback

litellm/timeout.py

@@ -17,10 +17,7 @@ from concurrent import futures
 from inspect import iscoroutinefunction
 from functools import wraps
 from threading import Thread
-try: 
+from openai import Timeout
-    from openai import Timeout
-except:
-    from openai.error import Timeout
 
 
 def timeout(timeout_duration: float = 0.0, exception_to_raise=Timeout):

litellm/utils.py

@@ -39,12 +39,8 @@ from .integrations.weights_biases import WeightsBiasesLogger
 from .integrations.custom_logger import CustomLogger
 from .integrations.langfuse import LangFuseLogger
 from .integrations.litedebugger import LiteDebugger
-try:
+from openai import OpenAIError as OriginalError
-    from openai import OpenAIError as OriginalError
+from openai._models import BaseModel as OpenAIObject
-    from openai._models import BaseModel as OpenAIObject
-except:
-    from openai.error import OpenAIError as OriginalError
-    from openai.openai_object import OpenAIObject
 from .exceptions import (
     AuthenticationError,
     BadRequestError,
@@ -354,6 +350,22 @@ class TextChoices(OpenAIObject):
         else:
             self.logprobs = logprobs
         
+    def __contains__(self, key):
+        # Define custom behavior for the 'in' operator
+        return hasattr(self, key)
+
+    def get(self, key, default=None):
+        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
+        return getattr(self, key, default)
+    
+    def __getitem__(self, key):
+        # Allow dictionary-style access to attributes
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        # Allow dictionary-style assignment of attributes
+        setattr(self, key, value)
+
 class TextCompletionResponse(OpenAIObject):
     """
     {
@@ -399,6 +411,22 @@ class TextCompletionResponse(OpenAIObject):
         self._hidden_params = {} # used in case users want to access the original model response
         super(TextCompletionResponse, self).__init__(**params)
     
+    def __contains__(self, key):
+        # Define custom behavior for the 'in' operator
+        return hasattr(self, key)
+
+    def get(self, key, default=None):
+        # Custom .get() method to access attributes with a default value if the attribute doesn't exist
+        return getattr(self, key, default)
+    
+    def __getitem__(self, key):
+        # Allow dictionary-style access to attributes
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        # Allow dictionary-style assignment of attributes
+        setattr(self, key, value)
+
 ############################################################
 def print_verbose(print_statement):
     if litellm.set_verbose: