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fix(utils.py): allow passing in custom pricing to completion_cost as params
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3 changed files with 94 additions and 4 deletions
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@ -35,6 +35,7 @@ from dataclasses import (
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import litellm._service_logger # for storing API inputs, outputs, and metadata
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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from litellm.caching import DualCache
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from litellm.types.utils import CostPerToken
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oidc_cache = DualCache()
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@ -4222,6 +4223,29 @@ def token_counter(
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return num_tokens
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def _cost_per_token_custom_pricing_helper(
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prompt_tokens=0,
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completion_tokens=0,
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response_time_ms=None,
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### CUSTOM PRICING ###
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custom_cost_per_token: Optional[CostPerToken] = None,
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custom_cost_per_second: Optional[float] = None,
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) -> Optional[Tuple[float, float]]:
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"""Internal helper function for calculating cost, if custom pricing given"""
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if custom_cost_per_token is None and custom_cost_per_second is None:
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return None
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if custom_cost_per_token is not None:
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input_cost = custom_cost_per_token["input_cost_per_token"] * prompt_tokens
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output_cost = custom_cost_per_token["output_cost_per_token"] * completion_tokens
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return input_cost, output_cost
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elif custom_cost_per_second is not None:
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output_cost = custom_cost_per_second * response_time_ms / 1000 # type: ignore
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return 0, output_cost
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return None
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def cost_per_token(
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model="",
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prompt_tokens=0,
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@ -4229,7 +4253,10 @@ def cost_per_token(
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response_time_ms=None,
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custom_llm_provider=None,
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region_name=None,
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):
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### CUSTOM PRICING ###
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custom_cost_per_token: Optional[CostPerToken] = None,
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custom_cost_per_second: Optional[float] = None,
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) -> Tuple[float, float]:
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"""
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Calculates the cost per token for a given model, prompt tokens, and completion tokens.
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@ -4237,13 +4264,28 @@ def cost_per_token(
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model (str): The name of the model to use. Default is ""
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prompt_tokens (int): The number of tokens in the prompt.
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completion_tokens (int): The number of tokens in the completion.
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response_time (float): The amount of time, in milliseconds, it took the call to complete.
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custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
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custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
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custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
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Returns:
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tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
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"""
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## CUSTOM PRICING ##
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response_cost = _cost_per_token_custom_pricing_helper(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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response_time_ms=response_time_ms,
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custom_cost_per_second=custom_cost_per_second,
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custom_cost_per_token=custom_cost_per_token,
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)
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if response_cost is not None:
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return response_cost[0], response_cost[1]
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# given
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prompt_tokens_cost_usd_dollar = 0
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completion_tokens_cost_usd_dollar = 0
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prompt_tokens_cost_usd_dollar: float = 0
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completion_tokens_cost_usd_dollar: float = 0
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model_cost_ref = litellm.model_cost
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model_with_provider = model
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if custom_llm_provider is not None:
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@ -4406,6 +4448,9 @@ def completion_cost(
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size=None,
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quality=None,
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n=None, # number of images
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### CUSTOM PRICING ###
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custom_cost_per_token: Optional[CostPerToken] = None,
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custom_cost_per_second: Optional[float] = None,
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) -> float:
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"""
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Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm.
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@ -4418,6 +4463,8 @@ def completion_cost(
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prompt (str): Optional. The input prompt passed to the llm
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completion (str): Optional. The output completion text from the llm
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total_time (float): Optional. (Only used for Replicate LLMs) The total time used for the request in seconds
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custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
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custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
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Returns:
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float: The cost in USD dollars for the completion based on the provided parameters.
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@ -4427,7 +4474,6 @@ def completion_cost(
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Note:
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- For custom pricing, see this - https://docs.litellm.ai/docs/proxy/custom_pricing
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- If completion_response is provided, the function extracts token information and the model name from it.
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- If completion_response is not provided, the function calculates token counts based on the model and input text.
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- The cost is calculated based on the model, prompt tokens, and completion tokens.
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@ -4562,6 +4608,8 @@ def completion_cost(
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custom_llm_provider=custom_llm_provider,
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response_time_ms=total_time,
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region_name=region_name,
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custom_cost_per_second=custom_cost_per_second,
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custom_cost_per_token=custom_cost_per_token,
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)
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_final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
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print_verbose(
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