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"""Cost tracking service for Azure OpenAI and Document Intelligence."""
from dataclasses import dataclass
from typing import Dict, Optional, List
import logging
from datetime import datetime
import pandas as pd
logger = logging.getLogger(__name__)
@dataclass
class TokenCosts:
"""Costs per 1M tokens for different models."""
GPT41: float = 2.0 # $10 per 1M input tokens for GPT-4.1
GPT41_OUTPUT: float = 8.0 # $30 per 1M output tokens for GPT-4.1
@dataclass
class DocumentIntelligenceCosts:
"""Costs for Document Intelligence."""
READ: float = 1.5 # $1.5 per 1,000 pages
LAYOUT: float = 10.0 # $10 per 1,000 pages
CUSTOM: float = 30.0 # $30 per 1,000 pages
@dataclass
class LLMCall:
"""Represents a single LLM call with its details."""
description: str
input_tokens: int
output_tokens: int
timestamp: datetime = datetime.now()
@property
def input_cost(self) -> float:
return (self.input_tokens / 1_000_000) * TokenCosts.GPT41
@property
def output_cost(self) -> float:
return (self.output_tokens / 1_000_000) * TokenCosts.GPT41_OUTPUT
@property
def total_cost(self) -> float:
return self.input_cost + self.output_cost
class CostTracker:
"""Tracks costs for Azure OpenAI and Document Intelligence usage."""
def __init__(self):
self.token_costs = TokenCosts()
self.di_costs = DocumentIntelligenceCosts()
self.current_file_costs = {
"input_tokens": 0,
"output_tokens": 0,
"di_pages": 0,
"di_operations": {} # Track different DI operations
}
self.total_costs = {
"input_tokens": 0,
"output_tokens": 0,
"di_pages": 0,
"di_operations": {}
}
self.llm_input_tokens = 0
self.llm_output_tokens = 0
self.di_pages = 0
self.llm_calls: List[LLMCall] = [] # Track individual LLM calls
def reset_current_file(self):
"""Reset costs for current file."""
self.current_file_costs = {
"input_tokens": 0,
"output_tokens": 0,
"di_pages": 0,
"di_operations": {}
}
self.llm_calls = [] # Reset LLM calls for new file
def add_tokens(self, input_tokens: int, output_tokens: int, model: str = "GPT41"):
"""Add tokens for current file and total."""
self.current_file_costs["input_tokens"] += input_tokens
self.current_file_costs["output_tokens"] += output_tokens
self.total_costs["input_tokens"] += input_tokens
self.total_costs["output_tokens"] += output_tokens
logger.info(f"Added tokens - Input: {input_tokens}, Output: {output_tokens} for model {model}")
def add_di_operation(self, operation: str, pages: int):
"""Add Document Intelligence operation costs."""
if operation not in self.current_file_costs["di_operations"]:
self.current_file_costs["di_operations"][operation] = 0
self.total_costs["di_operations"][operation] = 0
self.current_file_costs["di_operations"][operation] += pages
self.current_file_costs["di_pages"] += pages
self.total_costs["di_operations"][operation] += pages
self.total_costs["di_pages"] += pages
logger.info(f"Added DI operation - {operation}: {pages} pages")
def calculate_current_file_costs(self) -> Dict:
"""Calculate costs for current file."""
costs = {
"openai": {
"input_tokens": self.current_file_costs["input_tokens"],
"output_tokens": self.current_file_costs["output_tokens"],
"input_cost": (self.current_file_costs["input_tokens"] / 1_000_000) * self.token_costs.GPT41,
"output_cost": (self.current_file_costs["output_tokens"] / 1_000_000) * self.token_costs.GPT41_OUTPUT,
"total_cost": 0,
"calls": [call.__dict__ for call in self.llm_calls] # Include detailed call information
},
"document_intelligence": {
"total_pages": self.current_file_costs["di_pages"],
"operations": {},
"total_cost": 0
}
}
# Calculate OpenAI total cost
costs["openai"]["total_cost"] = costs["openai"]["input_cost"] + costs["openai"]["output_cost"]
# Calculate Document Intelligence costs
for operation, pages in self.current_file_costs["di_operations"].items():
cost = (pages / 1000) * getattr(self.di_costs, operation.upper(), self.di_costs.READ)
costs["document_intelligence"]["operations"][operation] = {
"pages": pages,
"cost": cost
}
costs["document_intelligence"]["total_cost"] += cost
return costs
def get_total_costs(self) -> Dict:
"""Get total costs across all files."""
return {
"openai": {
"input_tokens": self.total_costs["input_tokens"],
"output_tokens": self.total_costs["output_tokens"],
"input_cost": (self.total_costs["input_tokens"] / 1_000_000) * self.token_costs.GPT41,
"output_cost": (self.total_costs["output_tokens"] / 1_000_000) * self.token_costs.GPT41_OUTPUT,
"total_cost": 0
},
"document_intelligence": {
"total_pages": self.total_costs["di_pages"],
"operations": {},
"total_cost": 0
}
}
def add_llm_tokens(self, input_tokens, output_tokens, description: str = "LLM Call"):
"""Add tokens for an LLM call with a description."""
self.llm_input_tokens += input_tokens
self.llm_output_tokens += output_tokens
# Also update the current file costs
self.current_file_costs["input_tokens"] += input_tokens
self.current_file_costs["output_tokens"] += output_tokens
self.total_costs["input_tokens"] += input_tokens
self.total_costs["output_tokens"] += output_tokens
# Add to LLM calls list
self.llm_calls.append(LLMCall(
description=description,
input_tokens=input_tokens,
output_tokens=output_tokens
))
def add_di_pages(self, num_pages):
self.di_pages += num_pages
# Also update the current file costs
self.current_file_costs["di_pages"] += num_pages
self.total_costs["di_pages"] += num_pages
# Add to LAYOUT operation by default since we're using layout analysis
if "LAYOUT" not in self.current_file_costs["di_operations"]:
self.current_file_costs["di_operations"]["LAYOUT"] = 0
self.total_costs["di_operations"]["LAYOUT"] = 0
self.current_file_costs["di_operations"]["LAYOUT"] += num_pages
self.total_costs["di_operations"]["LAYOUT"] += num_pages
def total_cost(self):
"""Calculate total cost using the same pricing as calculate_current_file_costs."""
costs = self.calculate_current_file_costs()
return costs["openai"]["total_cost"] + costs["document_intelligence"]["total_cost"]
def get_detailed_costs_table(self) -> pd.DataFrame:
"""Return a DataFrame of detailed costs."""
if not self.llm_calls:
return pd.DataFrame()
# Create list of dictionaries for DataFrame
rows = []
for call in self.llm_calls:
rows.append({
'Description': call.description,
'Input Tokens': call.input_tokens,
'Output Tokens': call.output_tokens,
'Input Cost': f"${call.input_cost:.4f}",
'Output Cost': f"${call.output_cost:.4f}",
'Total Cost': f"${call.total_cost:.4f}"
})
# Calculate totals
total_input = sum(call.input_tokens for call in self.llm_calls)
total_output = sum(call.output_tokens for call in self.llm_calls)
total_input_cost = sum(call.input_cost for call in self.llm_calls)
total_output_cost = sum(call.output_cost for call in self.llm_calls)
total_cost = total_input_cost + total_output_cost
# Add total row
rows.append({
'Description': 'TOTAL',
'Input Tokens': total_input,
'Output Tokens': total_output,
'Input Cost': f"${total_input_cost:.4f}",
'Output Cost': f"${total_output_cost:.4f}",
'Total Cost': f"${total_cost:.4f}"
})
# Create DataFrame
df = pd.DataFrame(rows)
# Set column order
df = df[['Description', 'Input Tokens', 'Output Tokens', 'Input Cost', 'Output Cost', 'Total Cost']]
return df |