ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter9/browser-use-rpa/browser-use/browser_use/tokens/views.py

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+from datetime import datetime
+from typing import Any, TypeVar
+
+from pydantic import BaseModel, Field
+
+from browser_use.llm.views import ChatInvokeUsage
+
+T = TypeVar('T', bound=BaseModel)
+
+
+class TokenUsageEntry(BaseModel):
+	"""Single token usage entry"""
+
+	model: str
+	timestamp: datetime
+	usage: ChatInvokeUsage
+
+
+class TokenCostCalculated(BaseModel):
+	"""Token cost"""
+
+	new_prompt_tokens: int
+	new_prompt_cost: float
+
+	prompt_read_cached_tokens: int | None
+	prompt_read_cached_cost: float | None
+
+	prompt_cached_creation_tokens: int | None
+	prompt_cache_creation_cost: float | None
+	"""Anthropic only: The cost of creating the cache."""
+
+	completion_tokens: int
+	completion_cost: float
+
+	@property
+	def prompt_cost(self) -> float:
+		return self.new_prompt_cost + (self.prompt_read_cached_cost or 0) + (self.prompt_cache_creation_cost or 0)
+
+	@property
+	def total_cost(self) -> float:
+		return (
+			self.new_prompt_cost
+			+ (self.prompt_read_cached_cost or 0)
+			+ (self.prompt_cache_creation_cost or 0)
+			+ self.completion_cost
+		)
+
+
+class ModelPricing(BaseModel):
+	"""Pricing information for a model"""
+
+	model: str
+	input_cost_per_token: float | None
+	output_cost_per_token: float | None
+
+	cache_read_input_token_cost: float | None
+	cache_creation_input_token_cost: float | None
+
+	max_tokens: int | None
+	max_input_tokens: int | None
+	max_output_tokens: int | None
+
+
+class CachedPricingData(BaseModel):
+	"""Cached pricing data with timestamp"""
+
+	timestamp: datetime
+	data: dict[str, Any]
+
+
+class ModelUsageStats(BaseModel):
+	"""Usage statistics for a single model"""
+
+	model: str
+	prompt_tokens: int = 0
+	completion_tokens: int = 0
+	total_tokens: int = 0
+	cost: float = 0.0
+	invocations: int = 0
+	average_tokens_per_invocation: float = 0.0
+
+
+class ModelUsageTokens(BaseModel):
+	"""Usage tokens for a single model"""
+
+	model: str
+	prompt_tokens: int
+	prompt_cached_tokens: int
+	completion_tokens: int
+	total_tokens: int
+
+
+class UsageSummary(BaseModel):
+	"""Summary of token usage and costs"""
+
+	total_prompt_tokens: int
+	total_prompt_cost: float
+
+	total_prompt_cached_tokens: int
+	total_prompt_cached_cost: float
+
+	total_completion_tokens: int
+	total_completion_cost: float
+	total_tokens: int
+	total_cost: float
+	entry_count: int
+
+	by_model: dict[str, ModelUsageStats] = Field(default_factory=dict)