Could you please share the code you mentioned? This will help in identifying why the events are only being generated for the final turn and not for each turn in the conversation.

part 2: async def _end_interview(self): """End the interview with a farewell message.""" farewell = self.interview_questions.get_farewell_message() # Log interview end if self.interview_logger.start_times.get("interview"): total_duration = time.time() - self.interview_logger.start_times["interview"] self.interview_logger.log_interview_end( total_duration, len(self.interview_questions.questions) ) await self.session.generate_reply(instructions=f"Say: {farewell}") @function_tool async def process_user_response( self, context: RunContext, user_response: str, question_number: int, is_follow_up: bool = False ): """Process the user's response and determine next action.""" # Debug: Log function tool call logger.info(f"🔧 FUNCTION TOOL CALLED: process_user_response") logger.info(f" User response: {user_response[:100]}...") logger.info(f" Question number: {question_number}") logger.info(f" Is follow-up: {is_follow_up}") print(f"🔧 FUNCTION TOOL CALLED: process_user_response - {user_response[:50]}...") # Log user response start (this is called when user starts speaking) if not self.user_response_start_time: self.user_response_start_time = time.time() self.interview_logger.log_user_response_start(question_number) # Log that turn detection should be active self.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="user_response_start", confidence=0.5, # Initial confidence when user starts success=True, details="User started responding, turn detection should be active" ) # Log user response end if self.user_response_start_time: duration = time.time() - self.user_response_start_time self.interview_logger.log_user_response_end( question_number, user_response, duration ) self.user_response_start_time = None # Reset for next response # Log turn detection success if self.turn_detection_start_time: turn_detection_duration = time.time() - self.turn_detection_start_time self.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="user_finished", confidence=self.last_turn_detection_confidence or 0.9, success=True, details=f"Turn detected after {turn_detection_duration:.2f}s" ) else: # Log turn detection failure (no start time recorded) self.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="user_finished", confidence=0.0, success=False, details="No turn detection start time recorded - turn detection may not be working" ) # Determine next action if is_follow_up and self.interview_questions.can_ask_follow_up(): # Ask another follow-up await self._ask_follow_up_question(user_response) else: # Move to next main question await self._move_to_next_question() return {"status": "processed", "next_action": "question"} def prewarm(proc: JobProcess): """Preload models for faster startup.""" logger.info("Preloading VAD model...") proc.userdata["vad"] = silero.VAD.load() logger.info("VAD model loaded successfully") async def entrypoint(ctx: JobContext): """Main entry point for the interview agent.""" # Set up telemetry for debugging from livekit.agents.telemetry import set_tracer_provider from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import ConsoleSpanExporter, BatchSpanProcessor # Set up console telemetry to see traces trace_provider = TracerProvider() trace_provider.add_span_processor(BatchSpanProcessor(ConsoleSpanExporter())) set_tracer_provider(trace_provider) # Set up logging context ctx.log_context_fields = { "room": ctx.room.name, "agent_type": "interview_agent" } logger.info(f"Starting interview agent session for room: {ctx.room.name}") logger.info("🔍 Telemetry enabled - will show traces for debugging") # Create agent instance for logging agent = InterviewAgent() # Create agent session with optimized settings for low latency session = AgentSession( vad=ctx.proc.userdata["vad"], stt=deepgram.STT( model="nova-3", language="en", # Optimize for speed interim_results=True, punctuate=True, smart_format=True ), llm=openai.LLM( model="gpt-4o-mini", # Optimize for speed temperature=0.7 ), tts=openai.TTS( voice="alloy", # Optimize for speed speed=1.0 ), # Use LiveKit's turn detection for natural conversation flow turn_detection=EnglishModel(), # Allow interruptions for more natural flow allow_interruptions=True

part 3: ) # Set up metrics collection from livekit.agents.voice import MetricsCollectedEvent from livekit.agents import metrics usage_collector = metrics.UsageCollector() # Track metrics for latency calculation eou_metrics = {} llm_metrics = {} tts_metrics = {} @session.on("metrics_collected") def _on_metrics_collected(ev: MetricsCollectedEvent): # Use LiveKit's recommended log_metrics helper metrics.log_metrics(ev.metrics) # Track metrics by type for latency calculation metrics_type = getattr(ev.metrics, 'type', 'unknown') speech_id = getattr(ev.metrics, 'speech_id', None) if metrics_type == 'eou_metrics' and speech_id: eou_metrics[speech_id] = ev.metrics logger.info(f"🎯 EOU METRICS CAPTURED: {ev.metrics}") elif metrics_type == 'llm_metrics' and speech_id: llm_metrics[speech_id] = ev.metrics logger.info(f"🧠 LLM METRICS CAPTURED: {ev.metrics}") elif metrics_type == 'tts_metrics' and speech_id: tts_metrics[speech_id] = ev.metrics logger.info(f"🔊 TTS METRICS CAPTURED: {ev.metrics}") # Calculate total latency when we have all three metrics for a speech_id if speech_id in eou_metrics and speech_id in llm_metrics and speech_id in tts_metrics: eou = eou_metrics[speech_id] llm = llm_metrics[speech_id] tts = tts_metrics[speech_id] # Calculate total latency according to LiveKit formula total_latency = ( getattr(eou, 'end_of_utterance_delay', 0) + getattr(llm, 'ttft', 0) + getattr(tts, 'ttfb', 0) ) logger.info(f"⚡ TOTAL LATENCY CALCULATION for {speech_id}:") logger.info(f" EOU delay: {getattr(eou, 'end_of_utterance_delay', 0):.3f}s") logger.info(f" LLM ttft: {getattr(llm, 'ttft', 0):.3f}s") logger.info(f" TTS ttfb: {getattr(tts, 'ttfb', 0):.3f}s") logger.info(f" TOTAL: {total_latency:.3f}s") # Log detailed metrics for analysis agent.interview_logger.log_turn_detection_metrics( metrics={ "type": "latency_analysis", "speech_id": speech_id, "total_latency": total_latency, "eou_metrics": { "end_of_utterance_delay": getattr(eou, 'end_of_utterance_delay', 0), "transcription_delay": getattr(eou, 'transcription_delay', 0), "on_user_turn_completed_delay": getattr(eou, 'on_user_turn_completed_delay', 0) }, "llm_metrics": { "duration": getattr(llm, 'duration', 0), "ttft": getattr(llm, 'ttft', 0), "completion_tokens": getattr(llm, 'completion_tokens', 0), "tokens_per_second": getattr(llm, 'tokens_per_second', 0) }, "tts_metrics": { "duration": getattr(tts, 'duration', 0), "ttfb": getattr(tts, 'ttfb', 0), "audio_duration": getattr(tts, 'audio_duration', 0), "characters_count": getattr(tts, 'characters_count', 0) } } ) # Enhanced turn detection logging for other metrics metrics_str = str(ev.metrics).lower() if 'turn' in metrics_str or 'detection' in metrics_str: logger.info(f"🎯 TURN DETECTION METRIC: {ev.metrics}") # Log detailed turn detection metrics agent.interview_logger.log_turn_detection_metrics( metrics={ "type": getattr(ev.metrics, 'type', 'unknown'), "label": getattr(ev.metrics, 'label', 'unknown'), "timestamp": getattr(ev.metrics, 'timestamp', time.time()), "raw_metrics": str(ev.metrics) } ) # Extract confidence if available if hasattr(ev.metrics, 'confidence'): agent.last_turn_detection_confidence = ev.metrics.confidence elif hasattr(ev.metrics, 'value') and isinstance(ev.metrics.value, (int, float)): agent.last_turn_detection_confidence = float(ev.metrics.value) usage_collector.collect(ev.metrics) # Add turn detection event handlers logger.info("🔧 Registering turn_detected event handler") @session.on("turn_detected") def _on_turn_detected(ev): """Handle turn detection events.""" logger.info(f"🎯 TURN DETECTED: {ev}") print(f"🎯 TURN DETECTED: {ev}") # Immediate console feedback # Record turn detection start time agent.turn_detection_start_time = time.time() # Log successful turn detection current_question = agent.interview_questions.get_current_question() question_number = current_question.number if current_question else 0 agent.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="turn_detected", confidence=getattr(ev, 'confidence', 0.9), success=True, details=f"Turn detected: {ev}" ) @session.on("turn_detection_failed") def _on_turn_detection_failed(ev): """Handle turn detection failure events.""" logger.warning(f"❌ TURN DETECTION FAILED: {ev}") current_question = agent.interview_questions.get_current_question() question_number = current_question.number if current_question else 0 agent.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="turn_detection_failed", confidence=getattr(ev, 'confidence', 0.0), success=False, details=f"Turn detection failed: {ev}" ) @session.on("turn_detection_timeout") def _on_turn_detection_timeout(ev): """Handle turn detection timeout events.""" logger.warning(f"⏰ TURN DETECTION TIMEOUT: {ev}") current_question = agent.interview_questions.get_current_question() question_number = current_question.number if current_question else 0 timeout_duration = getattr(ev, 'timeout_duration', 0.0) agent.interview_logger.log_turn_detection_timeout( question_number=question_number, timeout_duration=timeout_duration, last_confidence=agent.last_turn_detection_confidence ) # Add session event handlers for turn detection model status logger.info("🔧 Registering user_state_changed event handler") @session.on("user_state_changed") def _on_user_state_changed(ev): """Handle user state change events.""" logger.info(f"👤 USER STATE CHANGED: {ev}") print(f"👤 USER STATE CHANGED: {ev}") # Immediate console feedback current_question = agent.interview_questions.get_current_question() question_number = current_question.number if current_question else 0 agent.interview_logger.log_turn_detection_attempt( question_number=question_number, attempt_type="user_state_changed", confidence=0.9, success=True, details=f"User state changed: {ev}" ) @session.on("session_started") def _on_session_started(ev): """Handle session start events.""" logger.info(f"🚀 Session started: {ev}") agent.interview_logger.log_turn_detection_model_event( "session_started", {"event": str(ev), "status": "active"} ) @session.on("session_ended") def _on_session_ended(ev): """Handle session end events.""" logger.info(f"🏁 Session ended: {ev}") agent.interview_logger.log_turn_detection_model_event( "session_ended", {"event": str(ev), "status": "inactive"} ) # Add error handling for turn detection model @session.on("error") def _on_error(ev): """Handle error events.""" error_msg = str(ev) logger.error(f"❌ Session error: {error_msg}") # Check if it's a turn detection related error if 'turn' in error_msg.lower() or 'detection' in error_msg.lower(): agent.interview_logger.log_turn_detection_model_event( "error", {"error_message": error_msg, "event": str(ev)} ) else: agent.interview_logger.log_error( "session_error", error_msg ) async def log_final_usage(): summary = usage_collector.get_summary() logger.info(f"Final usage summary: {summary}") ctx.add_shutdown_callback(log_final_usage) # Start the session await session.start( agent=agent, room=ctx.room ) if name == "__main__": cli.run_app( WorkerOptions( entrypoint_fnc=entrypoint, prewarm_fnc=prewarm ) )

actually that's it. i thought it would need to be broken up into 6 parts, but it only needed 3