fix(main.py): support cost calculation for text completion streaming object

litellm/main.py

@@ -3114,6 +3114,70 @@ def config_completion(**kwargs):
             "No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`"
         )
 
+def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]=None):
+    id = chunks[0]["id"]
+    object = chunks[0]["object"]
+    created = chunks[0]["created"]
+    model = chunks[0]["model"]
+    system_fingerprint = chunks[0].get("system_fingerprint", None)
+    finish_reason = chunks[-1]["choices"][0]["finish_reason"]
+    logprobs = chunks[-1]["choices"][0]["logprobs"]
+
+    response = {
+        "id": id,
+        "object": object,
+        "created": created,
+        "model": model,
+        "system_fingerprint": system_fingerprint,
+        "choices": [
+            {
+            "text": None,
+            "index": 0,
+            "logprobs": logprobs,
+            "finish_reason": finish_reason
+            }
+        ],
+        "usage": {
+            "prompt_tokens": None,
+            "completion_tokens": None,
+            "total_tokens": None
+        }
+    }
+    content_list = []
+    for chunk in chunks:
+        choices = chunk["choices"]
+        for choice in choices:
+            if choice is not None and hasattr(choice, "text") and choice.get("text") is not None:
+                _choice = choice.get("text")
+                content_list.append(_choice)
+
+    # Combine the "content" strings into a single string || combine the 'function' strings into a single string
+    combined_content = "".join(content_list)
+
+    # Update the "content" field within the response dictionary
+    response["choices"][0]["text"] = combined_content
+
+    if len(combined_content) > 0:
+        completion_output = combined_content
+    else:
+        completion_output = ""
+    # # Update usage information if needed
+    try:
+        response["usage"]["prompt_tokens"] = token_counter(
+            model=model, messages=messages
+        )
+    except:  # don't allow this failing to block a complete streaming response from being returned
+        print_verbose(f"token_counter failed, assuming prompt tokens is 0")
+        response["usage"]["prompt_tokens"] = 0
+    response["usage"]["completion_tokens"] = token_counter(
+        model=model,
+        text=combined_content,
+        count_response_tokens=True,  # count_response_tokens is a Flag to tell token counter this is a response, No need to add extra tokens we do for input messages
+    )
+    response["usage"]["total_tokens"] = (
+        response["usage"]["prompt_tokens"] + response["usage"]["completion_tokens"]
+    )
+    return response
 
 def stream_chunk_builder(chunks: list, messages: Optional[list] = None):
     id = chunks[0]["id"]
@@ -3121,6 +3185,8 @@ def stream_chunk_builder(chunks: list, messages: Optional[list] = None):
     created = chunks[0]["created"]
     model = chunks[0]["model"]
     system_fingerprint = chunks[0].get("system_fingerprint", None)
+    if isinstance(chunks[0]["choices"][0], litellm.utils.TextChoices): # route to the text completion logic 
+        return stream_chunk_builder_text_completion(chunks=chunks, messages=messages)
     role = chunks[0]["choices"][0]["delta"]["role"]
     finish_reason = chunks[-1]["choices"][0]["finish_reason"]
 

litellm/tests/test_text_completion.py

@@ -2936,18 +2936,21 @@ async def test_async_text_completion_chat_model_stream():
             stream=True,
             max_tokens=10,
         )
-        print(f"response: {response}")
 
         num_finish_reason = 0
+        chunks = [] 
         async for chunk in response:
             print(chunk)
+            chunks.append(chunk)
             if chunk["choices"][0].get("finish_reason") is not None:
                 num_finish_reason += 1
-                print("finish_reason", chunk["choices"][0].get("finish_reason"))
 
         assert (
             num_finish_reason == 1
         ), f"expected only one finish reason. Got {num_finish_reason}"
+        response_obj = litellm.stream_chunk_builder(chunks=chunks)
+        cost = litellm.completion_cost(completion_response=response_obj)
+        assert cost > 0
     except Exception as e:
         pytest.fail(f"GOT exception for gpt-3.5 In streaming{e}")
 

litellm/utils.py

@@ -558,6 +558,13 @@ class TextChoices(OpenAIObject):
     def __setitem__(self, key, value):
         # Allow dictionary-style assignment of attributes
         setattr(self, key, value)
+    
+    def json(self, **kwargs):
+        try:
+            return self.model_dump()  # noqa
+        except:
+            # if using pydantic v1
+            return self.dict()
 
 
 class TextCompletionResponse(OpenAIObject):