fix(utils.py): fix cost calculation to handle tool input

litellm/proxy/proxy_server.py

@@ -366,6 +366,7 @@ async def track_cost_callback(
     global prisma_client
     try:
         # check if it has collected an entire stream response
+        print(f"kwargs stream: {kwargs.get('stream', None)} + complete streaming response: {kwargs.get('complete_streaming_response', None)}")
         if "complete_streaming_response" in kwargs:
             # for tracking streaming cost we pass the "messages" and the output_text to litellm.completion_cost 
             completion_response=kwargs["complete_streaming_response"]
@@ -377,16 +378,16 @@ async def track_cost_callback(
                 completion=output_text
             )
             print("streaming response_cost", response_cost)
-        # for non streaming responses
-        elif kwargs["stream"] is False: # regular response
+        elif kwargs["stream"] == False: # for non streaming responses
             input_text = kwargs.get("messages", "")
+            print(f"type of input_text: {type(input_text)}")
             if isinstance(input_text, list): 
                 response_cost = litellm.completion_cost(completion_response=completion_response, messages=input_text)
             elif isinstance(input_text, str):
                 response_cost = litellm.completion_cost(completion_response=completion_response, prompt=input_text)
             print(f"received completion response: {completion_response}")
             
-            print("regular response_cost", response_cost)
+            print(f"regular response_cost: {response_cost}")
         user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key", None)
         print(f"user_api_key - {user_api_key}; prisma_client - {prisma_client}")
         if user_api_key and prisma_client: 

litellm/tests/test_proxy_server_spend.py

@@ -0,0 +1,69 @@
+# import openai, json
+# client = openai.OpenAI(
+#     api_key="sk-1234",
+#     base_url="http://0.0.0.0:8000"
+# )
+
+# super_fake_messages = [
+#   {
+#     "role": "user",
+#     "content": "What's the weather like in San Francisco, Tokyo, and Paris?"
+#   },
+#   {
+#     "content": None,
+#     "role": "assistant",
+#     "tool_calls": [
+#       {
+#         "id": "1",
+#         "function": {
+#           "arguments": "{\"location\": \"San Francisco\", \"unit\": \"celsius\"}",
+#           "name": "get_current_weather"
+#         },
+#         "type": "function"
+#       },
+#       {
+#         "id": "2",
+#         "function": {
+#           "arguments": "{\"location\": \"Tokyo\", \"unit\": \"celsius\"}",
+#           "name": "get_current_weather"
+#         },
+#         "type": "function"
+#       },
+#       {
+#         "id": "3",
+#         "function": {
+#           "arguments": "{\"location\": \"Paris\", \"unit\": \"celsius\"}",
+#           "name": "get_current_weather"
+#         },
+#         "type": "function"
+#       }
+#     ]
+#   },
+#   {
+#     "tool_call_id": "1",
+#     "role": "tool",
+#     "name": "get_current_weather",
+#     "content": "{\"location\": \"San Francisco\", \"temperature\": \"90\", \"unit\": \"celsius\"}"
+#   },
+#   {
+#     "tool_call_id": "2",
+#     "role": "tool",
+#     "name": "get_current_weather",
+#     "content": "{\"location\": \"Tokyo\", \"temperature\": \"30\", \"unit\": \"celsius\"}"
+#   },
+#   {
+#     "tool_call_id": "3",
+#     "role": "tool",
+#     "name": "get_current_weather",
+#     "content": "{\"location\": \"Paris\", \"temperature\": \"50\", \"unit\": \"celsius\"}"
+#   }
+# ]
+
+# super_fake_response = client.chat.completions.create(
+#     model="gpt-3.5-turbo",
+#     messages=super_fake_messages,
+#     seed=1337,
+#     stream=False
+# )  # get a new response from the model where it can see the function response
+
+# print(json.dumps(super_fake_response.model_dump(), indent=4))

litellm/utils.py

@@ -1858,8 +1858,6 @@ def completion_cost(
         - If an error occurs during execution, the function returns 0.0 without blocking the user's execution path.
     """
     try:
-        if messages != []:
-            prompt = " ".join([message["content"] for message in messages])
         # Handle Inputs to completion_cost
         prompt_tokens = 0
         completion_tokens = 0
@@ -1869,6 +1867,9 @@ def completion_cost(
             completion_tokens = completion_response['usage']['completion_tokens']
             model = model or completion_response['model'] # check if user passed an override for model, if it's none check completion_response['model']
         else:
+            if len(messages) > 0:
+                prompt_tokens = token_counter(model=model, messages=messages)
+            elif len(prompt) > 0: 
                 prompt_tokens = token_counter(model=model, text=prompt)
             completion_tokens = token_counter(model=model, text=completion)