two version of R2R are here HEAD master

1 files changed, 61 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py
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+"""
+Helper util for handling azure openai-specific cost calculation
+- e.g.: prompt caching
+"""
+
+from typing import Optional, Tuple
+
+from litellm._logging import verbose_logger
+from litellm.types.utils import Usage
+from litellm.utils import get_model_info
+
+
+def cost_per_token(
+    model: str, usage: Usage, response_time_ms: Optional[float] = 0.0
+) -> 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
+    """
+    ## GET MODEL INFO
+    model_info = get_model_info(model=model, custom_llm_provider="azure")
+    cached_tokens: Optional[int] = None
+    ## CALCULATE INPUT COST
+    non_cached_text_tokens = usage.prompt_tokens
+    if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
+        cached_tokens = usage.prompt_tokens_details.cached_tokens
+        non_cached_text_tokens = non_cached_text_tokens - cached_tokens
+    prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
+
+    ## CALCULATE OUTPUT COST
+    completion_cost: float = (
+        usage["completion_tokens"] * model_info["output_cost_per_token"]
+    )
+
+    ## Prompt Caching cost calculation
+    if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
+        # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
+        prompt_cost += cached_tokens * (
+            model_info.get("cache_read_input_token_cost", 0) or 0
+        )
+
+    ## Speech / Audio cost calculation
+    if (
+        "output_cost_per_second" in model_info
+        and model_info["output_cost_per_second"] is not None
+        and response_time_ms is not None
+    ):
+        verbose_logger.debug(
+            f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
+        )
+        ## COST PER SECOND ##
+        prompt_cost = 0
+        completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
+
+    return prompt_cost, completion_cost