two version of R2R are here HEAD master

1 files changed, 78 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/fireworks_ai/cost_calculator.py b/.venv/lib/python3.12/site-packages/litellm/llms/fireworks_ai/cost_calculator.py
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/fireworks_ai/cost_calculator.py
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+"""
+For calculating cost of fireworks ai serverless inference models.
+"""
+
+from typing import Tuple
+
+from litellm.types.utils import Usage
+from litellm.utils import get_model_info
+
+
+# Extract the number of billion parameters from the model name
+# only used for together_computer LLMs
+def get_base_model_for_pricing(model_name: str) -> str:
+    """
+    Helper function for calculating together ai pricing.
+
+    Returns:
+    - str: model pricing category if mapped else received model name
+    """
+    import re
+
+    model_name = model_name.lower()
+
+    # Check for MoE models in the form <number>x<number>b
+    moe_match = re.search(r"(\d+)x(\d+)b", model_name)
+    if moe_match:
+        total_billion = int(moe_match.group(1)) * int(moe_match.group(2))
+        if total_billion <= 56:
+            return "fireworks-ai-moe-up-to-56b"
+        elif total_billion <= 176:
+            return "fireworks-ai-56b-to-176b"
+
+    # Check for standard models in the form <number>b
+    re_params_match = re.search(r"(\d+)b", model_name)
+    if re_params_match is not None:
+        params_match = str(re_params_match.group(1))
+        params_billion = float(params_match)
+
+        # Determine the category based on the number of parameters
+        if params_billion <= 16.0:
+            return "fireworks-ai-up-to-16b"
+        elif params_billion <= 80.0:
+            return "fireworks-ai-16b-80b"
+
+    # If no matches, return the original model_name
+    return "fireworks-ai-default"
+
+
+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
+    """
+    ## check if model mapped, else use default pricing
+    try:
+        model_info = get_model_info(model=model, custom_llm_provider="fireworks_ai")
+    except Exception:
+        base_model = get_base_model_for_pricing(model_name=model)
+
+        ## GET MODEL INFO
+        model_info = get_model_info(
+            model=base_model, custom_llm_provider="fireworks_ai"
+        )
+
+    ## 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