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283 lines
9.7 KiB
Python
283 lines
9.7 KiB
Python
"""Callback Handler that prints to std out."""
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import threading
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from typing import Any, Dict, List
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.messages import AIMessage
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from langchain_core.outputs import ChatGeneration, LLMResult
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MODEL_COST_PER_1K_TOKENS = {
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# GPT-4o input
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"gpt-4o": 0.005,
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"gpt-4o-2024-05-13": 0.005,
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# GPT-4o output
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"gpt-4o-completion": 0.015,
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"gpt-4o-2024-05-13-completion": 0.015,
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# GPT-4 input
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"gpt-4": 0.03,
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"gpt-4-0314": 0.03,
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"gpt-4-0613": 0.03,
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"gpt-4-32k": 0.06,
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"gpt-4-32k-0314": 0.06,
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"gpt-4-32k-0613": 0.06,
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"gpt-4-vision-preview": 0.01,
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"gpt-4-1106-preview": 0.01,
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"gpt-4-0125-preview": 0.01,
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"gpt-4-turbo-preview": 0.01,
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"gpt-4-turbo": 0.01,
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"gpt-4-turbo-2024-04-09": 0.01,
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# GPT-4 output
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"gpt-4-completion": 0.06,
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"gpt-4-0314-completion": 0.06,
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"gpt-4-0613-completion": 0.06,
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"gpt-4-32k-completion": 0.12,
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"gpt-4-32k-0314-completion": 0.12,
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"gpt-4-32k-0613-completion": 0.12,
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"gpt-4-vision-preview-completion": 0.03,
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"gpt-4-1106-preview-completion": 0.03,
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"gpt-4-0125-preview-completion": 0.03,
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"gpt-4-turbo-preview-completion": 0.03,
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"gpt-4-turbo-completion": 0.03,
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"gpt-4-turbo-2024-04-09-completion": 0.03,
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# GPT-3.5 input
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# gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024.
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# Switches to gpt-3.5-turbo-0125 after.
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"gpt-3.5-turbo": 0.0015,
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"gpt-3.5-turbo-0125": 0.0005,
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"gpt-3.5-turbo-0301": 0.0015,
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"gpt-3.5-turbo-0613": 0.0015,
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"gpt-3.5-turbo-1106": 0.001,
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"gpt-3.5-turbo-instruct": 0.0015,
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"gpt-3.5-turbo-16k": 0.003,
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"gpt-3.5-turbo-16k-0613": 0.003,
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# GPT-3.5 output
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# gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024.
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# Switches to gpt-3.5-turbo-0125 after.
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"gpt-3.5-turbo-completion": 0.002,
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"gpt-3.5-turbo-0125-completion": 0.0015,
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"gpt-3.5-turbo-0301-completion": 0.002,
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"gpt-3.5-turbo-0613-completion": 0.002,
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"gpt-3.5-turbo-1106-completion": 0.002,
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"gpt-3.5-turbo-instruct-completion": 0.002,
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"gpt-3.5-turbo-16k-completion": 0.004,
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"gpt-3.5-turbo-16k-0613-completion": 0.004,
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# Azure GPT-35 input
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"gpt-35-turbo": 0.0015, # Azure OpenAI version of ChatGPT
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"gpt-35-turbo-0301": 0.0015, # Azure OpenAI version of ChatGPT
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"gpt-35-turbo-0613": 0.0015,
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"gpt-35-turbo-instruct": 0.0015,
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"gpt-35-turbo-16k": 0.003,
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"gpt-35-turbo-16k-0613": 0.003,
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# Azure GPT-35 output
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"gpt-35-turbo-completion": 0.002, # Azure OpenAI version of ChatGPT
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"gpt-35-turbo-0301-completion": 0.002, # Azure OpenAI version of ChatGPT
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"gpt-35-turbo-0613-completion": 0.002,
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"gpt-35-turbo-instruct-completion": 0.002,
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"gpt-35-turbo-16k-completion": 0.004,
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"gpt-35-turbo-16k-0613-completion": 0.004,
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# Others
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"text-ada-001": 0.0004,
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"ada": 0.0004,
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"text-babbage-001": 0.0005,
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"babbage": 0.0005,
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"text-curie-001": 0.002,
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"curie": 0.002,
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"text-davinci-003": 0.02,
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"text-davinci-002": 0.02,
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"code-davinci-002": 0.02,
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# Fine Tuned input
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"babbage-002-finetuned": 0.0016,
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"davinci-002-finetuned": 0.012,
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"gpt-3.5-turbo-0613-finetuned": 0.003,
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"gpt-3.5-turbo-1106-finetuned": 0.003,
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"gpt-3.5-turbo-0125-finetuned": 0.003,
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# Fine Tuned output
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"babbage-002-finetuned-completion": 0.0016,
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"davinci-002-finetuned-completion": 0.012,
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"gpt-3.5-turbo-0613-finetuned-completion": 0.006,
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"gpt-3.5-turbo-1106-finetuned-completion": 0.006,
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"gpt-3.5-turbo-0125-finetuned-completion": 0.006,
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# Azure Fine Tuned input
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"babbage-002-azure-finetuned": 0.0004,
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"davinci-002-azure-finetuned": 0.002,
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"gpt-35-turbo-0613-azure-finetuned": 0.0015,
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# Azure Fine Tuned output
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"babbage-002-azure-finetuned-completion": 0.0004,
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"davinci-002-azure-finetuned-completion": 0.002,
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"gpt-35-turbo-0613-azure-finetuned-completion": 0.002,
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# Legacy fine-tuned models
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"ada-finetuned-legacy": 0.0016,
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"babbage-finetuned-legacy": 0.0024,
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"curie-finetuned-legacy": 0.012,
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"davinci-finetuned-legacy": 0.12,
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}
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def standardize_model_name(
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model_name: str,
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is_completion: bool = False,
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) -> str:
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"""
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Standardize the model name to a format that can be used in the OpenAI API.
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Args:
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model_name: Model name to standardize.
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is_completion: Whether the model is used for completion or not.
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Defaults to False.
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Returns:
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Standardized model name.
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"""
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model_name = model_name.lower()
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if ".ft-" in model_name:
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model_name = model_name.split(".ft-")[0] + "-azure-finetuned"
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if ":ft-" in model_name:
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model_name = model_name.split(":")[0] + "-finetuned-legacy"
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if "ft:" in model_name:
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model_name = model_name.split(":")[1] + "-finetuned"
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if is_completion and (
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model_name.startswith("gpt-4")
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or model_name.startswith("gpt-3.5")
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or model_name.startswith("gpt-35")
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or ("finetuned" in model_name and "legacy" not in model_name)
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):
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return model_name + "-completion"
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else:
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return model_name
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def get_openai_token_cost_for_model(
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model_name: str, num_tokens: int, is_completion: bool = False
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) -> float:
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"""
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Get the cost in USD for a given model and number of tokens.
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Args:
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model_name: Name of the model
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num_tokens: Number of tokens.
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is_completion: Whether the model is used for completion or not.
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Defaults to False.
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Returns:
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Cost in USD.
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"""
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model_name = standardize_model_name(model_name, is_completion=is_completion)
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if model_name not in MODEL_COST_PER_1K_TOKENS:
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raise ValueError(
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f"Unknown model: {model_name}. Please provide a valid OpenAI model name."
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"Known models are: " + ", ".join(MODEL_COST_PER_1K_TOKENS.keys())
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)
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return MODEL_COST_PER_1K_TOKENS[model_name] * (num_tokens / 1000)
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class OpenAICallbackHandler(BaseCallbackHandler):
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"""Callback Handler that tracks OpenAI info."""
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total_tokens: int = 0
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prompt_tokens: int = 0
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completion_tokens: int = 0
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successful_requests: int = 0
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total_cost: float = 0.0
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def __init__(self) -> None:
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super().__init__()
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self._lock = threading.Lock()
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def __repr__(self) -> str:
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return (
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f"Tokens Used: {self.total_tokens}\n"
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f"\tPrompt Tokens: {self.prompt_tokens}\n"
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f"\tCompletion Tokens: {self.completion_tokens}\n"
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f"Successful Requests: {self.successful_requests}\n"
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f"Total Cost (USD): ${self.total_cost}"
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)
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@property
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def always_verbose(self) -> bool:
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"""Whether to call verbose callbacks even if verbose is False."""
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return True
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def on_llm_start(
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self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
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) -> None:
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"""Print out the prompts."""
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pass
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
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"""Print out the token."""
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pass
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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"""Collect token usage."""
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# Check for usage_metadata (langchain-core >= 0.2.2)
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try:
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generation = response.generations[0][0]
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except IndexError:
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generation = None
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if isinstance(generation, ChatGeneration):
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try:
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message = generation.message
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if isinstance(message, AIMessage):
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usage_metadata = message.usage_metadata
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else:
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usage_metadata = None
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except AttributeError:
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usage_metadata = None
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else:
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usage_metadata = None
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if usage_metadata:
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token_usage = {"total_tokens": usage_metadata["total_tokens"]}
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completion_tokens = usage_metadata["output_tokens"]
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prompt_tokens = usage_metadata["input_tokens"]
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if response.llm_output is None:
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# model name (and therefore cost) is unavailable in
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# streaming responses
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model_name = ""
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else:
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model_name = standardize_model_name(
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response.llm_output.get("model_name", "")
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)
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else:
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if response.llm_output is None:
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return None
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if "token_usage" not in response.llm_output:
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with self._lock:
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self.successful_requests += 1
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return None
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# compute tokens and cost for this request
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token_usage = response.llm_output["token_usage"]
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completion_tokens = token_usage.get("completion_tokens", 0)
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prompt_tokens = token_usage.get("prompt_tokens", 0)
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model_name = standardize_model_name(
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response.llm_output.get("model_name", "")
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)
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if model_name in MODEL_COST_PER_1K_TOKENS:
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completion_cost = get_openai_token_cost_for_model(
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model_name, completion_tokens, is_completion=True
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)
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prompt_cost = get_openai_token_cost_for_model(model_name, prompt_tokens)
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else:
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completion_cost = 0
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prompt_cost = 0
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# update shared state behind lock
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with self._lock:
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self.total_cost += prompt_cost + completion_cost
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self.total_tokens += token_usage.get("total_tokens", 0)
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self.prompt_tokens += prompt_tokens
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self.completion_tokens += completion_tokens
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self.successful_requests += 1
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def __copy__(self) -> "OpenAICallbackHandler":
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"""Return a copy of the callback handler."""
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return self
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def __deepcopy__(self, memo: Any) -> "OpenAICallbackHandler":
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"""Return a deep copy of the callback handler."""
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return self
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