fix(main.py): route openai calls to /completion when text_completion is True

litellm/llms/openai.py

@@ -1576,6 +1576,7 @@ class OpenAITextCompletion(BaseLLM):
                 response = openai_client.completions.create(**data)  # type: ignore
 
                 response_json = response.model_dump()
+
                 ## LOGGING
                 logging_obj.post_call(
                     input=prompt,

litellm/main.py

@@ -1078,6 +1078,91 @@ def completion(
                         "api_base": api_base,
                     },
                 )
+        elif (
+            custom_llm_provider == "text-completion-openai"
+            or (text_completion is True and custom_llm_provider == "openai")
+            or "ft:babbage-002" in model
+            or "ft:davinci-002" in model  # support for finetuned completion models
+        ):
+            openai.api_type = "openai"
+
+            api_base = (
+                api_base
+                or litellm.api_base
+                or get_secret("OPENAI_API_BASE")
+                or "https://api.openai.com/v1"
+            )
+
+            openai.api_version = None
+            # set API KEY
+
+            api_key = (
+                api_key
+                or litellm.api_key
+                or litellm.openai_key
+                or get_secret("OPENAI_API_KEY")
+            )
+
+            headers = headers or litellm.headers
+
+            ## LOAD CONFIG - if set
+            config = litellm.OpenAITextCompletionConfig.get_config()
+            for k, v in config.items():
+                if (
+                    k not in optional_params
+                ):  # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in
+                    optional_params[k] = v
+            if litellm.organization:
+                openai.organization = litellm.organization
+
+            if (
+                len(messages) > 0
+                and "content" in messages[0]
+                and type(messages[0]["content"]) == list
+            ):
+                # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content']
+                # https://platform.openai.com/docs/api-reference/completions/create
+                prompt = messages[0]["content"]
+            else:
+                prompt = " ".join([message["content"] for message in messages])  # type: ignore
+
+            ## COMPLETION CALL
+            _response = openai_text_completions.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                api_key=api_key,
+                api_base=api_base,
+                acompletion=acompletion,
+                client=client,  # pass AsyncOpenAI, OpenAI client
+                logging_obj=logging,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                timeout=timeout,  # type: ignore
+            )
+
+            if (
+                optional_params.get("stream", False) == False
+                and acompletion == False
+                and text_completion == False
+            ):
+                # convert to chat completion response
+                _response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
+                    response_object=_response, model_response_object=model_response
+                )
+
+            if optional_params.get("stream", False) or acompletion == True:
+                ## LOGGING
+                logging.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=_response,
+                    additional_args={"headers": headers},
+                )
+            response = _response
+
         elif (
             model in litellm.open_ai_chat_completion_models
             or custom_llm_provider == "custom_openai"
@@ -1164,89 +1249,6 @@ def completion(
                     original_response=response,
                     additional_args={"headers": headers},
                 )
-        elif (
-            custom_llm_provider == "text-completion-openai"
-            or "ft:babbage-002" in model
-            or "ft:davinci-002" in model  # support for finetuned completion models
-        ):
-            openai.api_type = "openai"
-
-            api_base = (
-                api_base
-                or litellm.api_base
-                or get_secret("OPENAI_API_BASE")
-                or "https://api.openai.com/v1"
-            )
-
-            openai.api_version = None
-            # set API KEY
-
-            api_key = (
-                api_key
-                or litellm.api_key
-                or litellm.openai_key
-                or get_secret("OPENAI_API_KEY")
-            )
-
-            headers = headers or litellm.headers
-
-            ## LOAD CONFIG - if set
-            config = litellm.OpenAITextCompletionConfig.get_config()
-            for k, v in config.items():
-                if (
-                    k not in optional_params
-                ):  # completion(top_k=3) > openai_text_config(top_k=3) <- allows for dynamic variables to be passed in
-                    optional_params[k] = v
-            if litellm.organization:
-                openai.organization = litellm.organization
-
-            if (
-                len(messages) > 0
-                and "content" in messages[0]
-                and type(messages[0]["content"]) == list
-            ):
-                # text-davinci-003 can accept a string or array, if it's an array, assume the array is set in messages[0]['content']
-                # https://platform.openai.com/docs/api-reference/completions/create
-                prompt = messages[0]["content"]
-            else:
-                prompt = " ".join([message["content"] for message in messages])  # type: ignore
-
-            ## COMPLETION CALL
-            _response = openai_text_completions.completion(
-                model=model,
-                messages=messages,
-                model_response=model_response,
-                print_verbose=print_verbose,
-                api_key=api_key,
-                api_base=api_base,
-                acompletion=acompletion,
-                client=client,  # pass AsyncOpenAI, OpenAI client
-                logging_obj=logging,
-                optional_params=optional_params,
-                litellm_params=litellm_params,
-                logger_fn=logger_fn,
-                timeout=timeout,  # type: ignore
-            )
-
-            if (
-                optional_params.get("stream", False) == False
-                and acompletion == False
-                and text_completion == False
-            ):
-                # convert to chat completion response
-                _response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
-                    response_object=_response, model_response_object=model_response
-                )
-
-            if optional_params.get("stream", False) or acompletion == True:
-                ## LOGGING
-                logging.post_call(
-                    input=messages,
-                    api_key=api_key,
-                    original_response=_response,
-                    additional_args={"headers": headers},
-                )
-            response = _response
         elif (
             "replicate" in model
             or custom_llm_provider == "replicate"

litellm/tests/test_text_completion.py

@@ -1,24 +1,31 @@
-import sys, os, asyncio
+import asyncio
+import os
+import sys
 import traceback
+
 from dotenv import load_dotenv
 
 load_dotenv()
-import os, io
+import io
+import os
 
 sys.path.insert(
     0, os.path.abspath("../..")
 )  # Adds the parent directory to the system path
+from unittest.mock import MagicMock, patch
+
 import pytest
+
 import litellm
 from litellm import (
-    embedding,
+    RateLimitError,
-    completion,
-    text_completion,
-    completion_cost,
-    atext_completion,
     TextCompletionResponse,
+    atext_completion,
+    completion,
+    completion_cost,
+    embedding,
+    text_completion,
 )
-from litellm import RateLimitError
 
 litellm.num_retries = 3
 
@@ -4082,9 +4089,10 @@ async def test_async_text_completion_chat_model_stream():
 async def test_completion_codestral_fim_api():
     try:
         litellm.set_verbose = True
-        from litellm._logging import verbose_logger
         import logging
 
+        from litellm._logging import verbose_logger
+
         verbose_logger.setLevel(level=logging.DEBUG)
         response = await litellm.atext_completion(
             model="text-completion-codestral/codestral-2405",
@@ -4113,9 +4121,10 @@ async def test_completion_codestral_fim_api():
 @pytest.mark.asyncio
 async def test_completion_codestral_fim_api_stream():
     try:
-        from litellm._logging import verbose_logger
         import logging
 
+        from litellm._logging import verbose_logger
+
         litellm.set_verbose = False
 
         # verbose_logger.setLevel(level=logging.DEBUG)
@@ -4145,3 +4154,47 @@ async def test_completion_codestral_fim_api_stream():
         # assert cost > 0.0
     except Exception as e:
         pytest.fail(f"Error occurred: {e}")
+
+
+def mock_post(*args, **kwargs):
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.headers = {"Content-Type": "application/json"}
+    mock_response.model_dump.return_value = {
+        "id": "cmpl-7a59383dd4234092b9e5d652a7ab8143",
+        "object": "text_completion",
+        "created": 1718824735,
+        "model": "Sao10K/L3-70B-Euryale-v2.1",
+        "choices": [
+            {
+                "index": 0,
+                "text": ") might be faster than then answering, and the added time it takes for the",
+                "logprobs": None,
+                "finish_reason": "length",
+                "stop_reason": None,
+            }
+        ],
+        "usage": {"prompt_tokens": 2, "total_tokens": 18, "completion_tokens": 16},
+    }
+    return mock_response
+
+
+def test_completion_vllm():
+    """
+    Asserts a text completion call for vllm actually goes to the text completion endpoint
+    """
+    from openai import OpenAI
+
+    client = OpenAI(api_key="my-fake-key")
+
+    with patch.object(client.completions, "create", side_effect=mock_post) as mock_call:
+        response = text_completion(
+            model="openai/gemini-1.5-flash",
+            prompt="ping",
+            client=client,
+        )
+        print(response)
+
+        assert response.usage.prompt_tokens == 2
+
+        mock_call.assert_called_once()