Source code for langchain_community.callbacks.openai_info

"""Callback Handler that prints to std out."""

import threading
from typing import Any, Dict, List

from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.outputs import LLMResult

MODEL_COST_PER_1K_TOKENS = {
    # GPT-4o input
    "gpt-4o": 0.005,
    "gpt-4o-2024-05-13": 0.005,
    # GPT-4o output
    "gpt-4o-completion": 0.015,
    "gpt-4o-2024-05-13-completion": 0.015,
    # GPT-4 input
    "gpt-4": 0.03,
    "gpt-4-0314": 0.03,
    "gpt-4-0613": 0.03,
    "gpt-4-32k": 0.06,
    "gpt-4-32k-0314": 0.06,
    "gpt-4-32k-0613": 0.06,
    "gpt-4-vision-preview": 0.01,
    "gpt-4-1106-preview": 0.01,
    "gpt-4-0125-preview": 0.01,
    "gpt-4-turbo-preview": 0.01,
    "gpt-4-turbo": 0.01,
    "gpt-4-turbo-2024-04-09": 0.01,
    # GPT-4 output
    "gpt-4-completion": 0.06,
    "gpt-4-0314-completion": 0.06,
    "gpt-4-0613-completion": 0.06,
    "gpt-4-32k-completion": 0.12,
    "gpt-4-32k-0314-completion": 0.12,
    "gpt-4-32k-0613-completion": 0.12,
    "gpt-4-vision-preview-completion": 0.03,
    "gpt-4-1106-preview-completion": 0.03,
    "gpt-4-0125-preview-completion": 0.03,
    "gpt-4-turbo-preview-completion": 0.03,
    "gpt-4-turbo-completion": 0.03,
    "gpt-4-turbo-2024-04-09-completion": 0.03,
    # GPT-3.5 input
    # gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024.
    # Switches to gpt-3.5-turbo-0125 after.
    "gpt-3.5-turbo": 0.0015,
    "gpt-3.5-turbo-0125": 0.0005,
    "gpt-3.5-turbo-0301": 0.0015,
    "gpt-3.5-turbo-0613": 0.0015,
    "gpt-3.5-turbo-1106": 0.001,
    "gpt-3.5-turbo-instruct": 0.0015,
    "gpt-3.5-turbo-16k": 0.003,
    "gpt-3.5-turbo-16k-0613": 0.003,
    # GPT-3.5 output
    # gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024.
    # Switches to gpt-3.5-turbo-0125 after.
    "gpt-3.5-turbo-completion": 0.002,
    "gpt-3.5-turbo-0125-completion": 0.0015,
    "gpt-3.5-turbo-0301-completion": 0.002,
    "gpt-3.5-turbo-0613-completion": 0.002,
    "gpt-3.5-turbo-1106-completion": 0.002,
    "gpt-3.5-turbo-instruct-completion": 0.002,
    "gpt-3.5-turbo-16k-completion": 0.004,
    "gpt-3.5-turbo-16k-0613-completion": 0.004,
    # Azure GPT-35 input
    "gpt-35-turbo": 0.0015,  # Azure OpenAI version of ChatGPT
    "gpt-35-turbo-0301": 0.0015,  # Azure OpenAI version of ChatGPT
    "gpt-35-turbo-0613": 0.0015,
    "gpt-35-turbo-instruct": 0.0015,
    "gpt-35-turbo-16k": 0.003,
    "gpt-35-turbo-16k-0613": 0.003,
    # Azure GPT-35 output
    "gpt-35-turbo-completion": 0.002,  # Azure OpenAI version of ChatGPT
    "gpt-35-turbo-0301-completion": 0.002,  # Azure OpenAI version of ChatGPT
    "gpt-35-turbo-0613-completion": 0.002,
    "gpt-35-turbo-instruct-completion": 0.002,
    "gpt-35-turbo-16k-completion": 0.004,
    "gpt-35-turbo-16k-0613-completion": 0.004,
    # Others
    "text-ada-001": 0.0004,
    "ada": 0.0004,
    "text-babbage-001": 0.0005,
    "babbage": 0.0005,
    "text-curie-001": 0.002,
    "curie": 0.002,
    "text-davinci-003": 0.02,
    "text-davinci-002": 0.02,
    "code-davinci-002": 0.02,
    # Fine Tuned input
    "babbage-002-finetuned": 0.0016,
    "davinci-002-finetuned": 0.012,
    "gpt-3.5-turbo-0613-finetuned": 0.003,
    "gpt-3.5-turbo-1106-finetuned": 0.003,
    "gpt-3.5-turbo-0125-finetuned": 0.003,
    # Fine Tuned output
    "babbage-002-finetuned-completion": 0.0016,
    "davinci-002-finetuned-completion": 0.012,
    "gpt-3.5-turbo-0613-finetuned-completion": 0.006,
    "gpt-3.5-turbo-1106-finetuned-completion": 0.006,
    "gpt-3.5-turbo-0125-finetuned-completion": 0.006,
    # Azure Fine Tuned input
    "babbage-002-azure-finetuned": 0.0004,
    "davinci-002-azure-finetuned": 0.002,
    "gpt-35-turbo-0613-azure-finetuned": 0.0015,
    # Azure Fine Tuned output
    "babbage-002-azure-finetuned-completion": 0.0004,
    "davinci-002-azure-finetuned-completion": 0.002,
    "gpt-35-turbo-0613-azure-finetuned-completion": 0.002,
    # Legacy fine-tuned models
    "ada-finetuned-legacy": 0.0016,
    "babbage-finetuned-legacy": 0.0024,
    "curie-finetuned-legacy": 0.012,
    "davinci-finetuned-legacy": 0.12,
}


[docs]def standardize_model_name( model_name: str, is_completion: bool = False, ) -> str: """ Standardize the model name to a format that can be used in the OpenAI API. Args: model_name: Model name to standardize. is_completion: Whether the model is used for completion or not. Defaults to False. Returns: Standardized model name. """ model_name = model_name.lower() if ".ft-" in model_name: model_name = model_name.split(".ft-")[0] + "-azure-finetuned" if ":ft-" in model_name: model_name = model_name.split(":")[0] + "-finetuned-legacy" if "ft:" in model_name: model_name = model_name.split(":")[1] + "-finetuned" if is_completion and ( model_name.startswith("gpt-4") or model_name.startswith("gpt-3.5") or model_name.startswith("gpt-35") or ("finetuned" in model_name and "legacy" not in model_name) ): return model_name + "-completion" else: return model_name
[docs]def get_openai_token_cost_for_model( model_name: str, num_tokens: int, is_completion: bool = False ) -> float: """ Get the cost in USD for a given model and number of tokens. Args: model_name: Name of the model num_tokens: Number of tokens. is_completion: Whether the model is used for completion or not. Defaults to False. Returns: Cost in USD. """ model_name = standardize_model_name(model_name, is_completion=is_completion) if model_name not in MODEL_COST_PER_1K_TOKENS: raise ValueError( f"Unknown model: {model_name}. Please provide a valid OpenAI model name." "Known models are: " + ", ".join(MODEL_COST_PER_1K_TOKENS.keys()) ) return MODEL_COST_PER_1K_TOKENS[model_name] * (num_tokens / 1000)
[docs]class OpenAICallbackHandler(BaseCallbackHandler): """Callback Handler that tracks OpenAI info.""" total_tokens: int = 0 prompt_tokens: int = 0 completion_tokens: int = 0 successful_requests: int = 0 total_cost: float = 0.0
[docs] def __init__(self) -> None: super().__init__() self._lock = threading.Lock()
def __repr__(self) -> str: return ( f"Tokens Used: {self.total_tokens}\n" f"\tPrompt Tokens: {self.prompt_tokens}\n" f"\tCompletion Tokens: {self.completion_tokens}\n" f"Successful Requests: {self.successful_requests}\n" f"Total Cost (USD): ${self.total_cost}" ) @property def always_verbose(self) -> bool: """Whether to call verbose callbacks even if verbose is False.""" return True
[docs] def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any ) -> None: """Print out the prompts.""" pass
[docs] def on_llm_new_token(self, token: str, **kwargs: Any) -> None: """Print out the token.""" pass
[docs] def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Collect token usage.""" if response.llm_output is None: return None if "token_usage" not in response.llm_output: with self._lock: self.successful_requests += 1 return None # compute tokens and cost for this request token_usage = response.llm_output["token_usage"] completion_tokens = token_usage.get("completion_tokens", 0) prompt_tokens = token_usage.get("prompt_tokens", 0) model_name = standardize_model_name(response.llm_output.get("model_name", "")) if model_name in MODEL_COST_PER_1K_TOKENS: completion_cost = get_openai_token_cost_for_model( model_name, completion_tokens, is_completion=True ) prompt_cost = get_openai_token_cost_for_model(model_name, prompt_tokens) else: completion_cost = 0 prompt_cost = 0 # update shared state behind lock with self._lock: self.total_cost += prompt_cost + completion_cost self.total_tokens += token_usage.get("total_tokens", 0) self.prompt_tokens += prompt_tokens self.completion_tokens += completion_tokens self.successful_requests += 1
def __copy__(self) -> "OpenAICallbackHandler": """Return a copy of the callback handler.""" return self def __deepcopy__(self, memo: Any) -> "OpenAICallbackHandler": """Return a deep copy of the callback handler.""" return self