feat(llm_cost_calc/google.py): do character based cost calculation for vertex ai

Calculate cost for vertex ai responses using characters in query/response

 Closes https://github.com/BerriAI/litellm/issues/4165

litellm/cost_calculator.py

@@ -6,6 +6,9 @@ from typing import List, Literal, Optional, Tuple, Union
 import litellm
 import litellm._logging
 from litellm import verbose_logger
+from litellm.litellm_core_utils.llm_cost_calc.google import (
+    cost_per_character as google_cost_per_character,
+)
 from litellm.litellm_core_utils.llm_cost_calc.google import (
     cost_per_token as google_cost_per_token,
 )
@@ -23,8 +26,8 @@ from litellm.utils import (
 
 
 def _cost_per_token_custom_pricing_helper(
-    prompt_tokens=0,
-    completion_tokens=0,
+    prompt_tokens: float = 0,
+    completion_tokens: float = 0,
     response_time_ms=None,
     ### CUSTOM PRICING ###
     custom_cost_per_token: Optional[CostPerToken] = None,
@@ -52,6 +55,9 @@ def cost_per_token(
     response_time_ms=None,
     custom_llm_provider: Optional[str] = None,
     region_name=None,
+    ### CHARACTER PRICING ###
+    prompt_characters: float = 0,
+    completion_characters: float = 0,
     ### CUSTOM PRICING ###
     custom_cost_per_token: Optional[CostPerToken] = None,
     custom_cost_per_second: Optional[float] = None,
@@ -64,6 +70,8 @@ def cost_per_token(
         prompt_tokens (int): The number of tokens in the prompt.
         completion_tokens (int): The number of tokens in the completion.
         response_time (float): The amount of time, in milliseconds, it took the call to complete.
+        prompt_characters (float): The number of characters in the prompt. Used for vertex ai cost calculation.
+        completion_characters (float): The number of characters in the completion response. Used for vertex ai cost calculation.
         custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
         custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
         custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
@@ -127,7 +135,16 @@ def cost_per_token(
 
     # see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
     print_verbose(f"Looking up model={model} in model_cost_map")
-    if custom_llm_provider == "vertex_ai" or custom_llm_provider == "gemini":
+    if custom_llm_provider == "vertex_ai":
+        return google_cost_per_character(
+            model=model_without_prefix,
+            custom_llm_provider=custom_llm_provider,
+            prompt_characters=prompt_characters,
+            completion_characters=completion_characters,
+            prompt_tokens=prompt_tokens,
+            completion_tokens=completion_tokens,
+        )
+    elif custom_llm_provider == "gemini":
         return google_cost_per_token(
             model=model_without_prefix,
             custom_llm_provider=custom_llm_provider,
@@ -378,7 +395,9 @@ def completion_cost(
             model = "dall-e-2"  # for dall-e-2, azure expects an empty model name
         # Handle Inputs to completion_cost
         prompt_tokens = 0
+        prompt_characters = 0
         completion_tokens = 0
+        completion_characters = 0
         custom_llm_provider = None
         if completion_response is not None:
             # get input/output tokens from completion_response
@@ -495,6 +514,30 @@ def completion_cost(
                 f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
             )
 
+        if (
+            custom_llm_provider is not None
+            and custom_llm_provider == "vertex_ai"
+            and completion_response is not None
+            and isinstance(completion_response, ModelResponse)
+        ):
+            # Calculate the prompt characters + response characters
+            if len("messages") > 0:
+                prompt_string = litellm.utils.get_formatted_prompt(
+                    data={"messages": messages}, call_type="completion"
+                )
+            else:
+                prompt_string = ""
+
+            prompt_characters = litellm.utils._count_characters(text=prompt_string)
+
+            completion_string = litellm.utils.get_response_string(
+                response_obj=completion_response
+            )
+
+            completion_characters = litellm.utils._count_characters(
+                text=completion_string
+            )
+
         (
             prompt_tokens_cost_usd_dollar,
             completion_tokens_cost_usd_dollar,
@@ -507,6 +550,8 @@ def completion_cost(
             region_name=region_name,
             custom_cost_per_second=custom_cost_per_second,
             custom_cost_per_token=custom_cost_per_token,
+            prompt_characters=prompt_characters,
+            completion_characters=completion_characters,
         )
         _final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
         print_verbose(