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import logging
from dataclasses import dataclass
from typing import Dict, Optional
from datetime import datetime
logger = logging.getLogger(__name__)
@dataclass
class ModelPricing:
"""Pricing information for Azure OpenAI models."""
model_name: str
input_cost_per_1k_tokens: float # Cost per 1000 input tokens
output_cost_per_1k_tokens: float # Cost per 1000 output tokens
description: str
@dataclass
class TokenUsage:
"""Token usage statistics for a single API call."""
prompt_tokens: int
completion_tokens: int
total_tokens: int
model: str
timestamp: datetime
@dataclass
class CostAnalysis:
"""Cost analysis for document processing."""
total_input_tokens: int
total_output_tokens: int
total_cost: float
model_breakdown: Dict[str, Dict[str, float]] # {model: {"input_cost": x, "output_cost": y, "total_cost": z}}
processing_time: float
timestamp: datetime
class CostTracker:
"""Tracks token usage and calculates costs for Azure OpenAI API calls."""
# Hardcoded pricing for Azure OpenAI models (current as of 2024)
# Source: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
MODEL_PRICING = {
# Standard model names
"gpt-4o-mini": ModelPricing(
model_name="gpt-4o-mini",
input_cost_per_1k_tokens=0.00015, # $0.00015 per 1K input tokens
output_cost_per_1k_tokens=0.0006, # $0.0006 per 1K output tokens
description="GPT-4o Mini (O3 Mini)"
),
"gpt-4o": ModelPricing(
model_name="gpt-4o",
input_cost_per_1k_tokens=0.0025, # $0.0025 per 1K input tokens
output_cost_per_1k_tokens=0.01, # $0.01 per 1K output tokens
description="GPT-4o (O4)"
),
"gpt-35-turbo": ModelPricing(
model_name="gpt-35-turbo",
input_cost_per_1k_tokens=0.0005, # $0.0005 per 1K input tokens
output_cost_per_1k_tokens=0.0015, # $0.0015 per 1K output tokens
description="GPT-3.5 Turbo (O3)"
),
# Azure deployment names (custom names set in Azure)
"o3-mini": ModelPricing(
model_name="o3-mini",
input_cost_per_1k_tokens=0.00015, # $0.00015 per 1K input tokens
output_cost_per_1k_tokens=0.0006, # $0.0006 per 1K output tokens
description="O3 Mini (GPT-4o Mini)"
),
"o4-mini": ModelPricing(
model_name="o4-mini",
input_cost_per_1k_tokens=0.00015, # $0.00015 per 1K input tokens
output_cost_per_1k_tokens=0.0006, # $0.0006 per 1K output tokens
description="O4 Mini (GPT-4o Mini)"
),
"o3": ModelPricing(
model_name="o3",
input_cost_per_1k_tokens=0.0005, # $0.0005 per 1K input tokens
output_cost_per_1k_tokens=0.0015, # $0.0015 per 1K output tokens
description="O3 (GPT-3.5 Turbo)"
),
"o4": ModelPricing(
model_name="o4",
input_cost_per_1k_tokens=0.0025, # $0.0025 per 1K input tokens
output_cost_per_1k_tokens=0.01, # $0.01 per 1K output tokens
description="O4 (GPT-4o)"
),
# Alternative model names that might be used in Azure deployments
"gpt-4o-mini-2024-07-18": ModelPricing(
model_name="gpt-4o-mini-2024-07-18",
input_cost_per_1k_tokens=0.00015, # $0.00015 per 1K input tokens
output_cost_per_1k_tokens=0.0006, # $0.0006 per 1K output tokens
description="GPT-4o Mini (O3 Mini) - Latest"
),
"gpt-4o-2024-05-13": ModelPricing(
model_name="gpt-4o-2024-05-13",
input_cost_per_1k_tokens=0.0025, # $0.0025 per 1K input tokens
output_cost_per_1k_tokens=0.01, # $0.01 per 1K output tokens
description="GPT-4o (O4) - Latest"
),
"gpt-35-turbo-0125": ModelPricing(
model_name="gpt-35-turbo-0125",
input_cost_per_1k_tokens=0.0005, # $0.0005 per 1K input tokens
output_cost_per_1k_tokens=0.0015, # $0.0015 per 1K output tokens
description="GPT-3.5 Turbo (O3) - Latest"
),
}
def __init__(self):
self.usage_history: list[TokenUsage] = []
self.current_session_tokens = 0
self.current_session_cost = 0.0
def record_usage(self, prompt_tokens: int, completion_tokens: int, model: str) -> TokenUsage:
"""Record token usage from an API call."""
total_tokens = prompt_tokens + completion_tokens
usage = TokenUsage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=total_tokens,
model=model,
timestamp=datetime.now()
)
self.usage_history.append(usage)
self.current_session_tokens += total_tokens
# Calculate cost for this usage
cost = self._calculate_cost(prompt_tokens, completion_tokens, model)
self.current_session_cost += cost
logger.info(f"Recorded usage: {prompt_tokens} input + {completion_tokens} output = {total_tokens} total tokens "
f"for model {model}, cost: ${cost:.6f}")
return usage
def _calculate_cost(self, input_tokens: int, output_tokens: int, model: str) -> float:
"""Calculate cost for given token usage and model."""
if model not in self.MODEL_PRICING:
logger.warning(f"Unknown model pricing for {model}, using default pricing")
# Try to guess the model type based on the name
if "mini" in model.lower():
# Assume it's a mini model (cheapest)
model = "o3-mini"
elif "o4" in model.lower():
# Assume it's O4 (most expensive)
model = "o4"
elif "o3" in model.lower():
# Assume it's O3 (medium)
model = "o3"
else:
# Default to cheapest option
model = "o3-mini"
pricing = self.MODEL_PRICING[model]
input_cost = (input_tokens / 1000) * pricing.input_cost_per_1k_tokens
output_cost = (output_tokens / 1000) * pricing.output_cost_per_1k_tokens
return input_cost + output_cost
def get_session_summary(self) -> Dict[str, any]:
"""Get summary of current session usage."""
if not self.usage_history:
return {
"total_tokens": 0,
"total_cost": 0.0,
"model_breakdown": {},
"usage_count": 0
}
model_breakdown = {}
for usage in self.usage_history:
if usage.model not in model_breakdown:
model_breakdown[usage.model] = {
"input_tokens": 0,
"output_tokens": 0,
"total_tokens": 0,
"cost": 0.0,
"usage_count": 0
}
model_breakdown[usage.model]["input_tokens"] += usage.prompt_tokens
model_breakdown[usage.model]["output_tokens"] += usage.completion_tokens
model_breakdown[usage.model]["total_tokens"] += usage.total_tokens
model_breakdown[usage.model]["usage_count"] += 1
model_breakdown[usage.model]["cost"] += self._calculate_cost(
usage.prompt_tokens, usage.completion_tokens, usage.model
)
return {
"total_tokens": self.current_session_tokens,
"total_cost": self.current_session_cost,
"model_breakdown": model_breakdown,
"usage_count": len(self.usage_history)
}
def reset_session(self):
"""Reset current session statistics."""
self.usage_history = []
self.current_session_tokens = 0
self.current_session_cost = 0.0
logger.info("Cost tracker session reset")
def get_available_models(self) -> list[str]:
"""Get list of available models with pricing."""
return list(self.MODEL_PRICING.keys())
def get_model_info(self, model: str) -> Optional[ModelPricing]:
"""Get pricing information for a specific model."""
return self.MODEL_PRICING.get(model)
def add_deployment_pricing(self, deployment_name: str, model_type: str = "o3-mini"):
"""Add pricing for a custom deployment name by mapping it to an existing model type."""
if deployment_name in self.MODEL_PRICING:
return # Already exists
# Map deployment name to existing model pricing
if model_type in self.MODEL_PRICING:
base_pricing = self.MODEL_PRICING[model_type]
self.MODEL_PRICING[deployment_name] = ModelPricing(
model_name=deployment_name,
input_cost_per_1k_tokens=base_pricing.input_cost_per_1k_tokens,
output_cost_per_1k_tokens=base_pricing.output_cost_per_1k_tokens,
description=f"{deployment_name} ({base_pricing.description})"
)
logger.info(f"Added pricing for deployment {deployment_name} based on {model_type}")
else:
logger.warning(f"Unknown model type {model_type} for deployment {deployment_name}")
def guess_model_type(self, deployment_name: str) -> str:
"""Guess the model type based on deployment name."""
deployment_lower = deployment_name.lower()
if "mini" in deployment_lower:
return "o3-mini"
elif "o4" in deployment_lower:
return "o4"
elif "o3" in deployment_lower:
return "o3"
else:
return "o3-mini" # Default to cheapest
# Global cost tracker instance
cost_tracker = CostTracker() |