mirror of
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94 lines
5.6 KiB
Python
94 lines
5.6 KiB
Python
#### What this does ####
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# On success + failure, log events to aispend.io
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import dotenv, os
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import requests
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dotenv.load_dotenv() # Loading env variables using dotenv
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import traceback
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import datetime
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model_cost = {
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"gpt-3.5-turbo": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
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"gpt-35-turbo": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002}, # azure model name
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"gpt-3.5-turbo-0613": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
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"gpt-3.5-turbo-0301": {"max_tokens": 4000, "input_cost_per_token": 0.0000015, "output_cost_per_token": 0.000002},
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"gpt-3.5-turbo-16k": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004},
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"gpt-35-turbo-16k": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004}, # azure model name
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"gpt-3.5-turbo-16k-0613": {"max_tokens": 16000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.000004},
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"gpt-4": {"max_tokens": 8000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.00006},
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"gpt-4-0613": {"max_tokens": 8000, "input_cost_per_token": 0.000003, "output_cost_per_token": 0.00006},
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"gpt-4-32k": {"max_tokens": 8000, "input_cost_per_token": 0.00006, "output_cost_per_token": 0.00012},
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"claude-instant-1": {"max_tokens": 100000, "input_cost_per_token": 0.00000163, "output_cost_per_token": 0.00000551},
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"claude-2": {"max_tokens": 100000, "input_cost_per_token": 0.00001102, "output_cost_per_token": 0.00003268},
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"text-bison-001": {"max_tokens": 8192, "input_cost_per_token": 0.000004, "output_cost_per_token": 0.000004},
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"chat-bison-001": {"max_tokens": 4096, "input_cost_per_token": 0.000002, "output_cost_per_token": 0.000002},
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"command-nightly": {"max_tokens": 4096, "input_cost_per_token": 0.000015, "output_cost_per_token": 0.000015},
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}
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class AISpendLogger:
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# Class variables or attributes
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def __init__(self):
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# Instance variables
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self.account_id = os.getenv("AISPEND_ACCOUNT_ID")
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self.api_key = os.getenv("AISPEND_API_KEY")
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def price_calculator(self, model, response_obj, start_time, end_time):
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# try and find if the model is in the model_cost map
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# else default to the average of the costs
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prompt_tokens_cost_usd_dollar = 0
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completion_tokens_cost_usd_dollar = 0
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if model in model_cost:
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prompt_tokens_cost_usd_dollar = model_cost[model]["input_cost_per_token"] * response_obj["usage"]["prompt_tokens"]
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completion_tokens_cost_usd_dollar = model_cost[model]["output_cost_per_token"] * response_obj["usage"]["completion_tokens"]
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elif "replicate" in model:
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# replicate models are charged based on time
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# llama 2 runs on an nvidia a100 which costs $0.0032 per second - https://replicate.com/replicate/llama-2-70b-chat
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model_run_time = end_time - start_time # assuming time in seconds
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cost_usd_dollar = model_run_time * 0.0032
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prompt_tokens_cost_usd_dollar = cost_usd_dollar / 2
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completion_tokens_cost_usd_dollar = cost_usd_dollar / 2
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else:
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# calculate average input cost
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input_cost_sum = 0
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output_cost_sum = 0
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for model in model_cost:
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input_cost_sum += model_cost[model]["input_cost_per_token"]
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output_cost_sum += model_cost[model]["output_cost_per_token"]
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avg_input_cost = input_cost_sum / len(model_cost.keys())
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avg_output_cost = output_cost_sum / len(model_cost.keys())
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prompt_tokens_cost_usd_dollar = model_cost[model]["input_cost_per_token"] * response_obj["usage"]["prompt_tokens"]
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completion_tokens_cost_usd_dollar = model_cost[model]["output_cost_per_token"] * response_obj["usage"]["completion_tokens"]
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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def log_event(self, model, response_obj, start_time, end_time, print_verbose):
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# Method definition
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try:
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print_verbose(f"AISpend Logging - Enters logging function for model {model}")
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url = f"https://aispend.io/api/v1/accounts/{self.account_id}/data"
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headers = {
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'Authorization': f'Bearer {self.api_key}',
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'Content-Type': 'application/json'
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}
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response_timestamp = datetime.datetime.fromtimestamp(int(response_obj["created"])).strftime('%Y-%m-%d')
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prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar = self.price_calculator(model, response_obj, start_time, end_time)
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prompt_tokens_cost_usd_cent = prompt_tokens_cost_usd_dollar * 100
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completion_tokens_cost_usd_cent = completion_tokens_cost_usd_dollar * 100
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data = [{
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"requests": 1,
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"requests_context": 1,
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"context_tokens": response_obj["usage"]["prompt_tokens"],
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"requests_generated": 1,
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"generated_tokens": response_obj["usage"]["completion_tokens"],
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"recorded_date": response_timestamp,
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"model_id": response_obj["model"],
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"generated_tokens_cost_usd_cent": prompt_tokens_cost_usd_cent,
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"context_tokens_cost_usd_cent": completion_tokens_cost_usd_cent
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}]
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print_verbose(f"AISpend Logging - final data object: {data}")
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except:
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# traceback.print_exc()
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print_verbose(f"AISpend Logging Error - {traceback.format_exc()}")
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pass
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