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https://github.com/trustgraph-ai/trustgraph.git
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120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
"""
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Simple token counter for each LLM response.
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"""
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from prometheus_client import Counter
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import json
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import logging
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from .. schema import TextCompletionResponse, Error
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from .. base import FlowProcessor, ConsumerSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "metering"
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class Processor(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id", default_ident)
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if not hasattr(__class__, "input_token_metric"):
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__class__.input_token_metric = Counter(
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'input_tokens', 'Input token count'
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)
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if not hasattr(__class__, "output_token_metric"):
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__class__.output_token_metric = Counter(
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'output_tokens', 'Output token count'
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)
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if not hasattr(__class__, "input_cost_metric"):
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__class__.input_cost_metric = Counter(
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'input_cost', 'Input cost'
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)
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if not hasattr(__class__, "output_cost_metric"):
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__class__.output_cost_metric = Counter(
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'output_cost', 'Output cost'
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)
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super(Processor, self).__init__(
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**params | {
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"id": id,
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}
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)
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self.register_config_handler(self.on_cost_config)
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self.register_specification(
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ConsumerSpec(
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name = "input",
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schema = TextCompletionResponse,
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handler = self.on_message,
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)
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)
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self.prices = {}
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self.config_key = "token-costs"
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# Load token costs from the config service
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async def on_cost_config(self, config, version):
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logger.info(f"Loading metering configuration version {version}")
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if self.config_key not in config:
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logger.warning(f"No key {self.config_key} in config")
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return
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config = config[self.config_key]
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self.prices = {
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k: json.loads(v)
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for k, v in config.items()
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}
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def get_prices(self, modelname):
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if modelname in self.prices:
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model = self.prices[modelname]
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return model["input_price"], model["output_price"]
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return None, None # Return None if model is not found
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async def on_message(self, msg, consumer, flow):
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v = msg.value()
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modelname = v.model
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num_in = v.in_token
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num_out = v.out_token
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__class__.input_token_metric.inc(num_in)
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__class__.output_token_metric.inc(num_out)
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model_input_price, model_output_price = self.get_prices(modelname)
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if model_input_price == None:
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cost_per_call = f"Model Not Found in Price list"
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else:
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cost_in = num_in * model_input_price
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cost_out = num_out * model_output_price
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cost_per_call = round(cost_in + cost_out, 6)
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__class__.input_cost_metric.inc(cost_in)
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__class__.output_cost_metric.inc(cost_out)
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logger.info(f"Input Tokens: {num_in}")
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logger.info(f"Output Tokens: {num_out}")
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logger.info(f"Cost for call: ${cost_per_call}")
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@staticmethod
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def add_args(parser):
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FlowProcessor.add_args(parser)
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def run():
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Processor.launch(default_ident, __doc__)
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