知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
"""
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Helper util for handling azure openai-specific cost calculation
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- e.g.: prompt caching, audio tokens
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"""
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from typing import Optional, Tuple
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
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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(
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model: str,
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usage: Usage,
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response_time_ms: Optional[float] = 0.0,
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service_tier: Optional[str] = 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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Input:
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- model: str, the model name without provider prefix
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- usage: LiteLLM Usage block, containing caching and audio token 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="azure")
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## Speech / Audio cost calculation (cost per second for TTS models)
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if (
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"output_cost_per_second" in model_info
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and model_info["output_cost_per_second"] is not None
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and response_time_ms is not None
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):
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verbose_logger.debug(
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f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
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)
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## COST PER SECOND ##
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prompt_cost = 0.0
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completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
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return prompt_cost, completion_cost
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## Use generic cost calculator for all other cases
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## This properly handles: text tokens, audio tokens, cached tokens, reasoning tokens, etc.
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return generic_cost_per_token(
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model=model,
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usage=usage,
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custom_llm_provider="azure",
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service_tier=service_tier,
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)
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