- 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/llm_metrics_handler.py

@@ -0,0 +1,49 @@
+import time
+from langchain.callbacks.base import BaseCallbackHandler
+from typing import Dict, Any, List
+from langchain.schema import LLMResult
+from common.utils.business_event_context import current_event
+from flask import current_app
+
+
+class LLMMetricsHandler(BaseCallbackHandler):
+    def __init__(self):
+        self.total_tokens: int = 0
+        self.prompt_tokens: int = 0
+        self.completion_tokens: int = 0
+        self.start_time: float = 0
+        self.end_time: float = 0
+        self.total_time: float = 0
+
+    def reset(self):
+        self.total_tokens = 0
+        self.prompt_tokens = 0
+        self.completion_tokens = 0
+        self.start_time = 0
+        self.end_time = 0
+        self.total_time = 0
+
+    def on_llm_start(self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) -> None:
+        self.start_time = time.time()
+
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        self.end_time = time.time()
+        self.total_time = self.end_time - self.start_time
+
+        usage = response.llm_output.get('token_usage', {})
+        self.prompt_tokens += usage.get('prompt_tokens', 0)
+        self.completion_tokens += usage.get('completion_tokens', 0)
+        self.total_tokens = self.prompt_tokens + self.completion_tokens
+
+        metrics = self.get_metrics()
+        current_event.log_llm_metrics(metrics)
+        self.reset()  # Reset for the next call
+
+    def get_metrics(self) -> Dict[str, int | float]:
+        return {
+            'total_tokens': self.total_tokens,
+            'prompt_tokens': self.prompt_tokens,
+            'completion_tokens': self.completion_tokens,
+            'time_elapsed': self.total_time,
+            'interaction_type': 'LLM',
+        }