fix text completion response from codestral

litellm/llms/text_completion_codestral.py

@@ -257,7 +257,7 @@ class CodestralTextCompletion(BaseLLM):
             original_response=response.text,
             additional_args={"complete_input_dict": data},
         )
-        print_verbose(f"raw model_response: {response.text}")
+        print_verbose(f"codestral api: raw model_response: {response.text}")
         ## RESPONSE OBJECT
         if response.status_code != 200:
             raise TextCompletionCodestralError(
@@ -269,7 +269,44 @@ class CodestralTextCompletion(BaseLLM):
         except:
             raise TextCompletionCodestralError(message=response.text, status_code=422)
 
-        _response = litellm.TextCompletionResponse(**completion_response)
+        _original_choices = completion_response.get("choices", [])
+        _choices: List[litellm.utils.TextChoices] = []
+        for choice in _original_choices:
+            # This is what 1 choice looks like from codestral API
+            # {
+            #     "index": 0,
+            #     "message": {
+            #     "role": "assistant",
+            #     "content": "\n assert is_odd(1)\n assert",
+            #     "tool_calls": null
+            #     },
+            #     "finish_reason": "length",
+            #     "logprobs": null
+            #     }
+            _finish_reason = None
+            _index = 0
+            _text = None
+            _logprobs = None
+
+            _choice_message = choice.get("message", {})
+            _choice = litellm.utils.TextChoices(
+                finish_reason=choice.get("finish_reason"),
+                index=choice.get("index"),
+                text=_choice_message.get("content"),
+                logprobs=choice.get("logprobs"),
+            )
+
+            _choices.append(_choice)
+
+        _response = litellm.TextCompletionResponse(
+            id=completion_response.get("id"),
+            choices=_choices,
+            created=completion_response.get("created"),
+            model=completion_response.get("model"),
+            usage=completion_response.get("usage"),
+            stream=False,
+            object=completion_response.get("object"),
+        )
         return _response
 
     def completion(

litellm/main.py

@@ -355,9 +355,10 @@ async def acompletion(
             else:
                 response = init_response  # type: ignore
 
-            if custom_llm_provider == "text-completion-openai" and isinstance(
+            if (
-                response, TextCompletionResponse
+                custom_llm_provider == "text-completion-openai"
-            ):
+                or custom_llm_provider == "text-completion-codestral"
+            ) and isinstance(response, TextCompletionResponse):
                 response = litellm.OpenAITextCompletionConfig().convert_to_chat_model_response_object(
                     response_object=response,
                     model_response_object=litellm.ModelResponse(),
@@ -3458,7 +3459,9 @@ def embedding(
 
 ###### Text Completion ################
 @client
-async def atext_completion(*args, **kwargs):
+async def atext_completion(
+    *args, **kwargs
+) -> Union[TextCompletionResponse, TextCompletionStreamWrapper]:
     """
     Implemented to handle async streaming for the text completion endpoint
     """

litellm/tests/test_text_completion.py

@@ -4100,6 +4100,9 @@ async def test_completion_codestral_fim_api():
         # Add any assertions here to check the response
         print(response)
 
+        assert response.choices[0].text is not None
+        assert len(response.choices[0].text) > 0
+
         # cost = litellm.completion_cost(completion_response=response)
         # print("cost to make mistral completion=", cost)
         # assert cost > 0.0