- Introduction of dynamic Retrievers & Specialists

- Introduction of dynamic Processors
- Introduction of caching system
- Introduction of a better template manager
- Adaptation of ModelVariables to support dynamic Processors / Retrievers / Specialists
- Start adaptation of chat client

common/langchain/tracked_transcription.py

@@ -0,0 +1,77 @@
+# common/langchain/tracked_transcription.py
+from typing import Any, Optional, Dict
+import time
+from openai import OpenAI
+from common.utils.business_event_context import current_event
+
+
+class TrackedOpenAITranscription:
+    """Wrapper for OpenAI transcription with metric tracking"""
+
+    def __init__(self, api_key: str, **kwargs: Any):
+        """Initialize with OpenAI client settings"""
+        self.client = OpenAI(api_key=api_key)
+        self.model = kwargs.get('model', 'whisper-1')
+
+    def transcribe(self,
+                   file: Any,
+                   model: Optional[str] = None,
+                   language: Optional[str] = None,
+                   prompt: Optional[str] = None,
+                   response_format: Optional[str] = None,
+                   temperature: Optional[float] = None,
+                   duration: Optional[int] = None) -> str:
+        """
+        Transcribe audio with metrics tracking
+
+        Args:
+            file: Audio file to transcribe
+            model: Model to use (defaults to whisper-1)
+            language: Optional language of the audio
+            prompt: Optional prompt to guide transcription
+            response_format: Response format (json, text, etc)
+            temperature: Sampling temperature
+            duration: Duration of audio in seconds for metrics
+
+        Returns:
+            Transcription text
+        """
+        start_time = time.time()
+
+        try:
+            # Create transcription options
+            options = {
+                "file": file,
+                "model": model or self.model,
+            }
+            if language:
+                options["language"] = language
+            if prompt:
+                options["prompt"] = prompt
+            if response_format:
+                options["response_format"] = response_format
+            if temperature:
+                options["temperature"] = temperature
+
+            response = self.client.audio.transcriptions.create(**options)
+
+            # Calculate metrics
+            end_time = time.time()
+
+            # Token usage for transcriptions is based on audio duration
+            metrics = {
+                'total_tokens': duration or 600,  # Default to 10 minutes if duration not provided
+                'prompt_tokens': 0,  # For transcriptions, all tokens are completion
+                'completion_tokens': duration or 600,
+                'time_elapsed': end_time - start_time,
+                'interaction_type': 'ASR',
+            }
+            current_event.log_llm_metrics(metrics)
+
+            # Return text from response
+            if isinstance(response, str):
+                return response
+            return response.text
+
+        except Exception as e:
+            raise Exception(f"Transcription failed: {str(e)}")