b700cfac64
- Removed Portkey from the equation, and defined explicit monitoring using Langchain native code - Optimization of Business Event logging
28 lines
913 B
Python
28 lines
913 B
Python
import time
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from common.utils.business_event_context import current_event
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def tracked_transcribe(client, *args, **kwargs):
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start_time = time.time()
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# Extract the file and model from kwargs if present, otherwise use defaults
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file = kwargs.get('file')
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model = kwargs.get('model', 'whisper-1')
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duration = kwargs.pop('duration', 600)
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result = client.audio.transcriptions.create(*args, **kwargs)
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end_time = time.time()
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# Token usage for transcriptions is actually the duration in seconds we pass, as the whisper model is priced per second transcribed
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metrics = {
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'total_tokens': duration,
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'prompt_tokens': 0, # For transcriptions, all tokens are considered "completion"
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'completion_tokens': duration,
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'time_elapsed': end_time - start_time,
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'interaction_type': 'ASR',
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}
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current_event.log_llm_metrics(metrics)
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return result
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