mirror of
https://github.com/BerriAI/litellm.git
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2488e4b45f
* feat(litellm_logging.py): update standard logging payload to include debug information for cost failures Also includes fixes for cohere rerank cost tracking + databricks llama2 model cost tracking Easier to repro cost failures and improve reliability in prod * fix(proxy_server.py): emit cost failure debug info for slack alerting Improves debug information for cost tracking failures, on slack alerting
66 lines
2.4 KiB
Python
66 lines
2.4 KiB
Python
"""
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Helper util for handling databricks-specific cost calculation
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- e.g.: handling 'dbrx-instruct-*'
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"""
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from typing import Tuple
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from litellm.types.utils import Usage
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from litellm.utils import get_model_info
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def cost_per_token(model: str, usage: Usage) -> 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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Input:
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- model: str, the model name without provider prefix
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- usage: LiteLLM Usage block, containing anthropic caching information
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Returns:
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Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
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"""
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base_model = model
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if model.startswith("databricks/dbrx-instruct") or model.startswith(
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"dbrx-instruct"
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):
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base_model = "databricks-dbrx-instruct"
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elif model.startswith("databricks/meta-llama-3.1-70b-instruct") or model.startswith(
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"meta-llama-3.1-70b-instruct"
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):
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base_model = "databricks-meta-llama-3-1-70b-instruct"
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elif model.startswith(
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"databricks/meta-llama-3.1-405b-instruct"
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) or model.startswith("meta-llama-3.1-405b-instruct"):
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base_model = "databricks-meta-llama-3-1-405b-instruct"
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elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
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"mixtral-8x7b-instruct-v0.1"
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):
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base_model = "databricks-mixtral-8x7b-instruct"
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elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
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"mixtral-8x7b-instruct-v0.1"
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):
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base_model = "databricks-mixtral-8x7b-instruct"
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elif model.startswith("databricks/bge-large-en") or model.startswith(
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"bge-large-en"
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):
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base_model = "databricks-bge-large-en"
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elif model.startswith("databricks/gte-large-en") or model.startswith(
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"gte-large-en"
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):
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base_model = "databricks-gte-large-en"
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elif model.startswith("databricks/llama-2-70b-chat") or model.startswith(
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"llama-2-70b-chat"
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):
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base_model = "databricks-llama-2-70b-chat"
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## GET MODEL INFO
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model_info = get_model_info(model=base_model, custom_llm_provider="databricks")
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## CALCULATE INPUT COST
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prompt_cost: float = usage["prompt_tokens"] * model_info["input_cost_per_token"]
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## CALCULATE OUTPUT COST
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completion_cost = usage["completion_tokens"] * model_info["output_cost_per_token"]
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return prompt_cost, completion_cost
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