refactor(openai.py): moving openai text completion calls to http

litellm/main.py

@@ -49,7 +49,7 @@ from .llms import (
     palm,
     vertex_ai,
     maritalk)
-from .llms.openai import OpenAIChatCompletion
+from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
 from .llms.azure import AzureChatCompletion
 from .llms.prompt_templates.factory import prompt_factory, custom_prompt, function_call_prompt
 import tiktoken
@@ -73,6 +73,7 @@ from litellm.utils import (
 ####### ENVIRONMENT VARIABLES ###################
 dotenv.load_dotenv()  # Loading env variables using dotenv
 openai_chat_completions = OpenAIChatCompletion()
+openai_text_completions = OpenAITextCompletion()
 azure_chat_completions = AzureChatCompletion()
 ####### COMPLETION ENDPOINTS ################
 
@@ -498,14 +499,8 @@ def completion(
             )
         elif (
             custom_llm_provider == "text-completion-openai"
-            or model in litellm.open_ai_text_completion_models
             or "ft:babbage-002" in model
             or "ft:davinci-002" in model  # support for finetuned completion models
-            # NOTE: Do NOT add custom_llm_provider == "openai". 
-            # this will break hosted vllm/proxy calls. 
-            # see: https://docs.litellm.ai/docs/providers/vllm#calling-hosted-vllm-server. 
-            # VLLM expects requests to call openai.ChatCompletion we need those requests to always 
-            # call openai.ChatCompletion
         ):
             # print("calling custom openai provider")
             openai.api_type = "openai"
@@ -558,43 +553,22 @@ def completion(
                 },
             )
             ## COMPLETION CALL
-            response = openai.Completion.create(
-                model=model, 
-                prompt=prompt,
-                headers=headers,
-                api_key = api_key,
-                api_base=api_base,
-                **optional_params
-            )
-            if "stream" in optional_params and optional_params["stream"] == True:
-                response = CustomStreamWrapper(response, model, custom_llm_provider="text-completion-openai", logging_obj=logging)
-                return response
-            ## LOGGING
-            logging.post_call(
-                input=prompt,
+            model_response = openai_text_completions.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
                 api_key=api_key,
-                original_response=response,
-                additional_args={
-                    "openai_organization": litellm.organization,
-                    "headers": headers,
-                    "api_base": openai.api_base,
-                    "api_type": openai.api_type,
-                },
+                api_base=api_base,
+                logging_obj=logging,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn
             )
-            ## RESPONSE OBJECT
-            model_response._hidden_params["original_response"] = response # track original response, if users make a litellm.text_completion() request, we can return the original response
-            choices_list = []
-            for idx, item in enumerate(response["choices"]):
-                if len(item["text"]) > 0: 
-                    message_obj = Message(content=item["text"])
-                else: 
-                    message_obj = Message(content=None)
-                choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
-                choices_list.append(choice_obj)
-            model_response["choices"] = choices_list
-            model_response["created"] = response.get("created", time.time())
-            model_response["model"] = model
-            model_response["usage"] = response.get("usage", 0)
+            
+            if "stream" in optional_params and optional_params["stream"] == True:
+                response = CustomStreamWrapper(model_response, model, custom_llm_provider="text-completion-openai", logging_obj=logging)
+                return response
             response = model_response
         elif (
             "replicate" in model or