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"""
Helper util for handling databricks-specific cost calculation
- e.g.: handling 'dbrx-instruct-*'
"""
from typing import Tuple
from litellm.types.utils import Usage
from litellm.utils import get_model_info
def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
"""
Calculates the cost per token for a given model, prompt tokens, and completion tokens.
Input:
- model: str, the model name without provider prefix
- usage: LiteLLM Usage block, containing anthropic caching information
Returns:
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
"""
base_model = model
if model.startswith("databricks/dbrx-instruct") or model.startswith(
"dbrx-instruct"
):
base_model = "databricks-dbrx-instruct"
elif model.startswith("databricks/meta-llama-3.1-70b-instruct") or model.startswith(
"meta-llama-3.1-70b-instruct"
):
base_model = "databricks-meta-llama-3-1-70b-instruct"
elif model.startswith(
"databricks/meta-llama-3.1-405b-instruct"
) or model.startswith("meta-llama-3.1-405b-instruct"):
base_model = "databricks-meta-llama-3-1-405b-instruct"
elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
"mixtral-8x7b-instruct-v0.1"
):
base_model = "databricks-mixtral-8x7b-instruct"
elif model.startswith("databricks/mixtral-8x7b-instruct-v0.1") or model.startswith(
"mixtral-8x7b-instruct-v0.1"
):
base_model = "databricks-mixtral-8x7b-instruct"
elif model.startswith("databricks/bge-large-en") or model.startswith(
"bge-large-en"
):
base_model = "databricks-bge-large-en"
elif model.startswith("databricks/gte-large-en") or model.startswith(
"gte-large-en"
):
base_model = "databricks-gte-large-en"
elif model.startswith("databricks/llama-2-70b-chat") or model.startswith(
"llama-2-70b-chat"
):
base_model = "databricks-llama-2-70b-chat"
## GET MODEL INFO
model_info = get_model_info(model=base_model, custom_llm_provider="databricks")
## CALCULATE INPUT COST
prompt_cost: float = usage["prompt_tokens"] * model_info["input_cost_per_token"]
## CALCULATE OUTPUT COST
completion_cost = usage["completion_tokens"] * model_info["output_cost_per_token"]
return prompt_cost, completion_cost
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