mirror of
https://github.com/trustgraph-ai/trustgraph.git
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ef1b8b5a13
* Bump version * Added Prom metrics to metering, added dashboard * Update YAMLs * Add $ on axis * Tweak dashboard
101 lines
2.9 KiB
Python
101 lines
2.9 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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from . pricelist import price_list
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from .. schema import TextCompletionResponse, Error
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from .. schema import text_completion_response_queue
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from .. log_level import LogLevel
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from .. base import Consumer
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = text_completion_response_queue
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default_subscriber = module
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class Processor(Consumer):
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def __init__(self, **params):
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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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input_queue = params.get("input_queue", default_input_queue)
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subscriber = params.get("subscriber", default_subscriber)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"subscriber": subscriber,
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"input_schema": TextCompletionResponse,
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}
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)
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def get_prices(self, prices, modelname):
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for model in prices["price_list"]:
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if model["model_name"] == 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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def handle(self, msg):
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v = msg.value()
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modelname = v.model
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling response {id}...", flush=True)
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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(price_list, 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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print(f"Input Tokens: {num_in}", flush=True)
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print(f"Output Tokens: {num_out}", flush=True)
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print(f"Cost for call: ${cost_per_call}", flush=True)
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@staticmethod
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def add_args(parser):
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Consumer.add_args(
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parser, default_input_queue, default_subscriber,
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)
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def run():
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Processor.start(module, __doc__)
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