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* build(model_prices_and_context_window.json): add gemini-1.5-flash context caching * fix(context_caching/transformation.py): just use last identified cache point Fixes https://github.com/BerriAI/litellm/issues/6738 * fix(context_caching/transformation.py): pick first contiguous block - handles system message error from google Fixes https://github.com/BerriAI/litellm/issues/6738 * fix(vertex_ai/gemini/): track context caching tokens * refactor(gemini/): place transformation.py inside `chat/` folder make it easy for user to know we support the equivalent endpoint * fix: fix import * refactor(vertex_ai/): move vertex_ai cost calc inside vertex_ai/ folder make it easier to see cost calculation logic * fix: fix linting errors * fix: fix circular import * feat(gemini/cost_calculator.py): support gemini context caching cost calculation generifies anthropic's cost calculation function and uses it across anthropic + gemini * build(model_prices_and_context_window.json): add cost tracking for gemini-1.5-flash-002 w/ context caching Closes https://github.com/BerriAI/litellm/issues/6891 * docs(gemini.md): add gemini context caching architecture diagram make it easier for user to understand how context caching works * docs(gemini.md): link to relevant gemini context caching code * docs(gemini/context_caching): add readme in github, make it easy for dev to know context caching is supported + where to go for code * fix(llm_cost_calc/utils.py): handle gemini 128k token diff cost calc scenario * fix(deepseek/cost_calculator.py): support deepseek context caching cost calculation * test: fix test
21 lines
587 B
Python
21 lines
587 B
Python
"""
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Cost calculator for DeepSeek Chat models.
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Handles prompt caching scenario.
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"""
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from typing import Tuple
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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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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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Follows the same logic as Anthropic's cost per token calculation.
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"""
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return generic_cost_per_token(
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model=model, usage=usage, custom_llm_provider="deepseek"
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)
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