- 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_openai_embeddings.py

@@ -0,0 +1,51 @@
+from langchain_openai import OpenAIEmbeddings
+from typing import List, Any
+import time
+from common.utils.business_event_context import current_event
+
+
+class TrackedOpenAIEmbeddings(OpenAIEmbeddings):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        start_time = time.time()
+        result = super().embed_documents(texts)
+        end_time = time.time()
+
+        # Estimate token usage (OpenAI uses tiktoken for this)
+        import tiktoken
+        enc = tiktoken.encoding_for_model(self.model)
+        total_tokens = sum(len(enc.encode(text)) for text in texts)
+
+        metrics = {
+            'total_tokens': total_tokens,
+            'prompt_tokens': total_tokens,  # For embeddings, all tokens are prompt tokens
+            'completion_tokens': 0,
+            'time_elapsed': end_time - start_time,
+            'interaction_type': 'Embedding',
+            }
+        current_event.log_llm_metrics(metrics)
+
+        return result
+
+    def embed_query(self, text: str) -> List[float]:
+        start_time = time.time()
+        result = super().embed_query(text)
+        end_time = time.time()
+
+        # Estimate token usage
+        import tiktoken
+        enc = tiktoken.encoding_for_model(self.model)
+        total_tokens = len(enc.encode(text))
+
+        metrics = {
+            'total_tokens': total_tokens,
+            'prompt_tokens': total_tokens,
+            'completion_tokens': 0,
+            'time_elapsed': end_time - start_time,
+            'interaction_type': 'Embedding',
+        }
+        current_event.log_llm_metrics(metrics)
+
+        return result