Documentation Index
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get_model_encoding
def get_model_encoding(value_for_tiktoken: str):
- value_for_tiktoken: Model value for tiktoken.
Returns:
tiktoken.Encoding: Model encoding.
BaseTokenCounter
class BaseTokenCounter(ABC):
count_tokens_from_messages
def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
- messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
Returns:
int: Number of tokens in the messages.
encode
def encode(self, text: str):
- text (str): The text to encode.
Returns:
List[int]: List of token IDs.
decode
def decode(self, token_ids: List[int]):
- token_ids (List[int]): List of token IDs to decode.
Returns:
str: Decoded text.
OpenAITokenCounter
class OpenAITokenCounter(BaseTokenCounter):
init
def __init__(self, model: UnifiedModelType):
- model (UnifiedModelType): Model type for which tokens will be counted.
count_tokens_from_messages
def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
- messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
Returns:
int: Number of tokens in the messages.
_count_tokens_from_image
def _count_tokens_from_image(self, image: Image.Image, detail: OpenAIVisionDetailType):
"auto"
resolution model will be treated as :obj:"high". All images with
:obj:"low" detail cost 85 tokens each. Images with :obj:"high" detail
are first scaled to fit within a 2048 x 2048 square, maintaining their
aspect ratio. Then, they are scaled such that the shortest side of the
image is 768px long. Finally, we count how many 512px squares the image
consists of. Each of those squares costs 170 tokens. Another 85 tokens are
always added to the final total. For more details please refer to OpenAI
vision docs
Parameters:
- image (PIL.Image.Image): Image to count number of tokens.
- detail (OpenAIVisionDetailType): Image detail type to count number of tokens.
Returns:
int: Number of tokens for the image given a detail type.
encode
def encode(self, text: str):
- text (str): The text to encode.
Returns:
List[int]: List of token IDs.
decode
def decode(self, token_ids: List[int]):
- token_ids (List[int]): List of token IDs to decode.
Returns:
str: Decoded text.
AnthropicTokenCounter
class AnthropicTokenCounter(BaseTokenCounter):
init
def __init__(
self,
model: str,
api_key: Optional[str] = None,
base_url: Optional[str] = None
):
- model (str): The name of the Anthropic model being used.
- api_key (Optional[str], optional): The API key for authenticating with the Anthropic service. If not provided, it will use the ANTHROPIC_API_KEY environment variable. (default: :obj:
None)
- base_url (Optional[str], optional): The URL of the Anthropic service. If not provided, it will use the default Anthropic URL. (default: :obj:
None)
count_tokens_from_messages
def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
- messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
Returns:
int: Number of tokens in the messages.
encode
def encode(self, text: str):
- text (str): The text to encode.
Returns:
List[int]: List of token IDs.
decode
def decode(self, token_ids: List[int]):
- token_ids (List[int]): List of token IDs to decode.
Returns:
str: Decoded text.
LiteLLMTokenCounter
class LiteLLMTokenCounter(BaseTokenCounter):
init
def __init__(self, model_type: UnifiedModelType):
- model_type (UnifiedModelType): Model type for which tokens will be counted.
token_counter
completion_cost
def completion_cost(self):
count_tokens_from_messages
def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
- messages (List[OpenAIMessage]): Message list with the chat history in LiteLLM API format.
Returns:
int: Number of tokens in the messages.
calculate_cost_from_response
def calculate_cost_from_response(self, response: dict):
- response (dict): The completion response from LiteLLM.
Returns:
float: The cost of the completion call in USD.
encode
def encode(self, text: str):
- text (str): The text to encode.
Returns:
List[int]: List of token IDs.
decode
def decode(self, token_ids: List[int]):
- token_ids (List[int]): List of token IDs to decode.
Returns:
str: Decoded text.
MistralTokenCounter
class MistralTokenCounter(BaseTokenCounter):
init
def __init__(self, model_type: ModelType):
- model_type (ModelType): Model type for which tokens will be counted.
count_tokens_from_messages
def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
- messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
Returns:
int: Total number of tokens in the messages.
_convert_response_from_openai_to_mistral
def _convert_response_from_openai_to_mistral(self, openai_msg: OpenAIMessage):
- openai_msg (OpenAIMessage): An individual message with OpenAI format.
Returns:
ChatCompletionRequest: The converted message in Mistral’s request
format.
encode
def encode(self, text: str):
- text (str): The text to encode.
Returns:
List[int]: List of token IDs.
decode
def decode(self, token_ids: List[int]):
- token_ids (List[int]): List of token IDs to decode.
Returns:
str: Decoded text.
