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fac3b2ee42
* fix: fix type-checking errors * fix: fix additional type-checking errors * fix: additional type-checking error fixes * fix: fix additional type-checking errors * fix: additional type-check fixes * fix: fix all type-checking errors + add pyright to ci/cd * fix: fix incorrect import * ci(config.yml): use mypy on ci/cd * fix: fix type-checking errors in utils.py * fix: fix all type-checking errors on main.py * fix: fix mypy linting errors * fix(anthropic/cost_calculator.py): fix linting errors * fix: fix mypy linting errors * fix: fix linting errors
59 lines
2 KiB
Python
59 lines
2 KiB
Python
"""
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Helper util for handling anthropic-specific cost calculation
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- e.g.: prompt caching
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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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## GET MODEL INFO
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model_info = get_model_info(model=model, custom_llm_provider="anthropic")
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## CALCULATE INPUT COST
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### Cost of processing (non-cache hit + cache hit) + Cost of cache-writing (cache writing)
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prompt_cost = 0.0
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### PROCESSING COST
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non_cache_hit_tokens = usage.prompt_tokens
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cache_hit_tokens = 0
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if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
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cache_hit_tokens = usage.prompt_tokens_details.cached_tokens
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non_cache_hit_tokens = non_cache_hit_tokens - cache_hit_tokens
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prompt_cost = float(non_cache_hit_tokens) * model_info["input_cost_per_token"]
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_cache_read_input_token_cost = model_info.get("cache_read_input_token_cost")
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if (
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_cache_read_input_token_cost is not None
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and usage.prompt_tokens_details
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and usage.prompt_tokens_details.cached_tokens
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):
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prompt_cost += (
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float(usage.prompt_tokens_details.cached_tokens)
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* _cache_read_input_token_cost
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)
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### CACHE WRITING COST
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_cache_creation_input_token_cost = model_info.get("cache_creation_input_token_cost")
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if _cache_creation_input_token_cost is not None:
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prompt_cost += (
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float(usage._cache_creation_input_tokens) * _cache_creation_input_token_cost
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)
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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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