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Merge branch 'main' into litellm_response_cost_headers

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Krish Dholakia 2024-06-27 21:33:09 -07:00 • committed by GitHub
commit fa1cb7d903
124 changed files with 3705 additions and 150 deletions
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38
litellm/cost_calculator.py
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@ -428,7 +428,6 @@ def completion_cost(
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
prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
@ -468,6 +467,10 @@ def completion_cost(
"n", 1
) # openai default
else:
if model is None:
raise ValueError(
f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
)
if len(messages) > 0:
prompt_tokens = token_counter(model=model, messages=messages)
elif len(prompt) > 0:
@ -478,6 +481,15 @@ 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 None:
try:
_, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
except Exception as e:
verbose_logger.error(
"litellm.cost_calculator.py::completion_cost() - Error inferring custom_llm_provider - {}".format(
str(e)
)
)
if (
call_type == CallTypes.image_generation.value
or call_type == CallTypes.aimage_generation.value
@ -544,12 +556,7 @@ 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)
):
if custom_llm_provider is not None and custom_llm_provider == "vertex_ai":
# Calculate the prompt characters + response characters
if len("messages") > 0:
prompt_string = litellm.utils.get_formatted_prompt(
@ -559,14 +566,15 @@ def completion_cost(
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
)
if completion_response is not None and isinstance(
completion_response, ModelResponse
):
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,