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Add model to meter metrics (#589)

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cybermaggedon 2025-12-10 11:34:48 +00:00 • committed by GitHub
parent 727b6bc9d6
commit 2b5ba68f00
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1 changed files with 20 additions and 23 deletions
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43
trustgraph-flow/trustgraph/metering/counter.py
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@ -20,24 +20,18 @@ class Processor(FlowProcessor):
id = params.get("id", default_ident)
if not hasattr(__class__, "input_token_metric"):
__class__.input_token_metric = Counter(
'input_tokens', 'Input token count'
if not hasattr(__class__, "token_metric"):
__class__.token_metric = Counter(
'tokens',
'Token count',
['model', 'direction']
)
if not hasattr(__class__, "output_token_metric"):
__class__.output_token_metric = Counter(
'output_tokens', 'Output token count'
)
if not hasattr(__class__, "input_cost_metric"):
__class__.input_cost_metric = Counter(
'input_cost', 'Input cost'
)
if not hasattr(__class__, "output_cost_metric"):
__class__.output_cost_metric = Counter(
'output_cost', 'Output cost'
if not hasattr(__class__, "cost_metric"):
__class__.cost_metric = Counter(
'cost',
'Cost in USD',
['model', 'direction']
)
super(Processor, self).__init__(
@ -87,12 +81,13 @@ class Processor(FlowProcessor):
v = msg.value()
modelname = v.model
num_in = v.in_token
num_out = v.out_token
modelname = v.model or "unknown"
num_in = v.in_token or 0
num_out = v.out_token or 0
__class__.input_token_metric.inc(num_in)
__class__.output_token_metric.inc(num_out)
# Increment token metrics with model and direction labels
__class__.token_metric.labels(model=modelname, direction="input").inc(num_in)
__class__.token_metric.labels(model=modelname, direction="output").inc(num_out)
model_input_price, model_output_price = self.get_prices(modelname)
@ -103,9 +98,11 @@ class Processor(FlowProcessor):
cost_out = num_out * model_output_price
cost_per_call = round(cost_in + cost_out, 6)
__class__.input_cost_metric.inc(cost_in)
__class__.output_cost_metric.inc(cost_out)
# Increment cost metrics with model and direction labels
__class__.cost_metric.labels(model=modelname, direction="input").inc(cost_in)
__class__.cost_metric.labels(model=modelname, direction="output").inc(cost_out)
logger.info(f"Model: {modelname}")
logger.info(f"Input Tokens: {num_in}")
logger.info(f"Output Tokens: {num_out}")
logger.info(f"Cost for call: ${cost_per_call}")