- Improvements on audio processing to limit CPU and memory usage

- Removed Portkey from the equation, and defined explicit monitoring using Langchain native code
- Optimization of Business Event logging

common/langchain/tracked_transcribe.py

@@ -0,0 +1,27 @@
+import time
+from common.utils.business_event_context import current_event
+
+
+def tracked_transcribe(client, *args, **kwargs):
+    start_time = time.time()
+
+    # Extract the file and model from kwargs if present, otherwise use defaults
+    file = kwargs.get('file')
+    model = kwargs.get('model', 'whisper-1')
+    duration = kwargs.pop('duration', 600)
+
+    result = client.audio.transcriptions.create(*args, **kwargs)
+    end_time = time.time()
+
+    # Token usage for transcriptions is actually the duration in seconds we pass, as the whisper model is priced per second transcribed
+
+    metrics = {
+        'total_tokens': duration,
+        'prompt_tokens': 0,  # For transcriptions, all tokens are considered "completion"
+        'completion_tokens': duration,
+        'time_elapsed': end_time - start_time,
+        'interaction_type': 'ASR',
+    }
+    current_event.log_llm_metrics(metrics)
+
+    return result