LLMCostCard#

class council.llm.LLMCostCard(input: float, output: float)[source]#

Bases: object

LLM cost per million token

get_costs(prompt_tokens: int, completion_tokens: int) → Tuple[float, float][source]#: Return tuple of (prompt_tokens_cost, completion_token_cost)

property input: float#: Cost per million input (prompt) tokens.

input_cost(tokens: int) → float[source]#: Get prompt_tokens_cost for a given amount of input tokens.

property output: float#: Cost per million output (completion) tokens.

output_cost(tokens: int) → float[source]#: Get completion_token_cost for a given amount of completion tokens.

LLMConsumptionCalculatorBase#

class council.llm.LLMConsumptionCalculatorBase(model: str)[source]#

Bases: ABC

Helper class to manage LLM consumptions.

abstract find_model_costs() → LLMCostCard | None[source]#: Get LLMCostCard for self to calculate cost consumptions.

format_kind(token_kind: TokenKind, cost: bool = False) → str[source]#: Format Consumption.kind - from ‘prompt’ to ‘{self.model}:prompt_tokens’

abstract get_consumptions(*args, **kwargs) → List[Consumption][source]#: Each calculator will implement with its own parameters.

get_default_consumptions(duration: float) → List[Consumption][source]#: 1 call and specified duration consumptions. To use when token info is not available

LLMCostManagerObject#

class council.llm.LLMCostManagerObject(kind: str, version: str, metadata: DataObjectMetadata, spec: T)[source]#

Bases: DataObject[LLMCostManagerSpec]

Helper class to instantiate an LLMCostManagerObject from a YAML file

get_cost_map(category: str) → Dict[str, LLMCostCard][source]#: Get cost mapping {model: LLMCostCard} for a given category

TokenKind#

class council.llm.TokenKind(value)[source]#

Bases: str, Enum

An enumeration.

prompt = 'prompt'#: Prompt tokens

completion = 'completion'#: Completion tokens

total = 'total'#: Total tokens

reasoning = 'reasoning'#: Reasoning tokens, specific for OpenAI o1 models

cache_creation_prompt = 'cache_creation_prompt'#: Cache creation prompt tokens, specific for Anthropic prompt caching

cache_read_prompt = 'cache_read_prompt'#: Cache read prompt tokens, specific for Anthropic and OpenAI prompt caching