"""Real-time feedback observer for sending pipeline events to the frontend. This observer watches pipeline frames and sends relevant events (transcriptions, bot text, function calls, TTFB metrics) over WebSocket to provide real-time feedback in the UI. For frames with presentation timestamps (pts), like TTSTextFrame, we respect the timing by queuing them and sending at the appropriate time, similar to how base_output.py handles timed frames. Note: Node transition events are sent directly from PipecatEngine.set_node() rather than being observed here, to ensure precise timing at the moment of node changes. """ import asyncio import time from typing import TYPE_CHECKING, Awaitable, Callable, Optional, Set from loguru import logger if TYPE_CHECKING: from api.services.pipecat.in_memory_buffers import InMemoryLogsBuffer from pipecat.frames.frames import ( CancelFrame, EndFrame, FunctionCallInProgressFrame, FunctionCallResultFrame, InterimTranscriptionFrame, InterruptionFrame, MetricsFrame, StopFrame, TranscriptionFrame, TTSTextFrame, ) from pipecat.metrics.metrics import TTFBMetricsData from pipecat.observers.base_observer import BaseObserver, FramePushed from pipecat.processors.frame_processor import FrameDirection from pipecat.utils.time import nanoseconds_to_seconds class RealtimeFeedbackObserver(BaseObserver): """Observer that sends real-time transcription, bot response, and metrics via WebSocket. Observes pipeline frames and sends events for: - User transcriptions (interim and final) - Bot TTS text (with pts-based timing) - Function calls (start/end) - TTFB metrics (LLM generation time only - filters to processors containing "LLM") For frames with pts (presentation timestamp), we queue them and send at the appropriate time to sync with audio playback. Note: Node transitions are handled by PipecatEngine.set_node() callback. """ def __init__( self, ws_sender: Callable[[dict], Awaitable[None]], logs_buffer: Optional["InMemoryLogsBuffer"] = None, ): """ Args: ws_sender: Async function to send messages over WebSocket. Expected signature: async def send(message: dict) -> None logs_buffer: Optional InMemoryLogsBuffer to persist events for post-call analysis. """ super().__init__() self._ws_sender = ws_sender self._logs_buffer = logs_buffer self._frames_seen: Set[str] = set() # Clock/timing for pts-based frames (similar to base_output.py) self._clock_queue: Optional[asyncio.PriorityQueue] = None self._clock_task: Optional[asyncio.Task] = None self._clock_start_time: Optional[float] = ( None # Wall clock time when we started ) self._pts_start_time: Optional[int] = None # First pts value we saw async def _ensure_clock_task(self): """Create the clock task if it doesn't exist.""" if self._clock_queue is None: self._clock_queue = asyncio.PriorityQueue() self._clock_task = asyncio.create_task(self._clock_task_handler()) async def _cancel_clock_task(self): """Cancel the clock task and clear the queue. Called on interruption to discard any pending bot text that hasn't been sent yet. """ if self._clock_task: self._clock_task.cancel() try: await self._clock_task except asyncio.CancelledError: pass self._clock_task = None self._clock_queue = None # Reset timing references so next bot response starts fresh self._clock_start_time = None self._pts_start_time = None async def _handle_interruption(self): """Handle interruption by clearing queued bot text. Similar to base_output.py's handle_interruptions, we cancel the clock task and recreate it to discard pending frames. """ await self._cancel_clock_task() async def _clock_task_handler(self): """Process timed frames from the queue, respecting their presentation timestamps. Similar to base_output.py's _clock_task_handler, we wait until the frame's pts time has arrived before sending. """ while True: try: pts, _frame_id, message = await self._clock_queue.get() # Calculate when to send based on pts relative to our start time if ( self._clock_start_time is not None and self._pts_start_time is not None ): # Target time = start wall time + (frame pts - start pts) in seconds target_time = self._clock_start_time + nanoseconds_to_seconds( pts - self._pts_start_time ) current_time = time.time() if target_time > current_time: await asyncio.sleep(target_time - current_time) # Send the message await self._send_message(message) self._clock_queue.task_done() except asyncio.CancelledError: break except Exception as e: logger.debug(f"Clock task error: {e}") async def on_push_frame(self, data: FramePushed): """Process frames and send relevant ones to the client.""" frame = data.frame frame_direction = data.direction logger.trace(f"{self} Received Frame: {frame} Direction: {frame_direction}") # Handle pipeline termination - stop clock task if isinstance(frame, (EndFrame, CancelFrame, StopFrame)): await self._cancel_clock_task() return # Handle interruptions - clear any queued bot text if isinstance(frame, InterruptionFrame): await self._handle_interruption() return # Skip already processed frames (frames can be observed multiple times) if frame.id in self._frames_seen: return self._frames_seen.add(frame.id) # Handle user transcriptions (interim) if isinstance(frame, InterimTranscriptionFrame): await self._send_message( { "type": "rtf-user-transcription", "payload": { "text": frame.text, "final": False, "user_id": frame.user_id, "timestamp": frame.timestamp, }, } ) # Handle user transcriptions (final) elif isinstance(frame, TranscriptionFrame): await self._send_message( { "type": "rtf-user-transcription", "payload": { "text": frame.text, "final": True, "user_id": frame.user_id, "timestamp": frame.timestamp, }, } ) # Increment turn counter on final user transcription if self._logs_buffer: self._logs_buffer.increment_turn() # Handle bot TTS text - respect pts timing elif isinstance(frame, TTSTextFrame): message = { "type": "rtf-bot-text", "payload": { "text": frame.text, }, } # If frame has pts, queue it for timed delivery if frame.pts: # Initialize timing reference on first pts frame if self._pts_start_time is None: self._pts_start_time = frame.pts self._clock_start_time = time.time() await self._ensure_clock_task() await self._clock_queue.put((frame.pts, frame.id, message)) else: # No pts, send immediately await self._send_message(message) # Handle function call in progress elif ( isinstance(frame, FunctionCallInProgressFrame) and frame_direction == FrameDirection.DOWNSTREAM ): await self._send_message( { "type": "rtf-function-call-start", "payload": { "function_name": frame.function_name, "tool_call_id": frame.tool_call_id, }, } ) # Handle function call result elif ( isinstance(frame, FunctionCallResultFrame) and frame_direction == FrameDirection.DOWNSTREAM ): await self._send_message( { "type": "rtf-function-call-end", "payload": { "function_name": frame.function_name, "tool_call_id": frame.tool_call_id, "result": str(frame.result) if frame.result else None, }, } ) # Handle TTFB metrics - capture LLM generation time only elif isinstance(frame, MetricsFrame): # Check if this MetricsFrame contains TTFB data from an LLM processor for metric_data in frame.data: if isinstance(metric_data, TTFBMetricsData): # Only send TTFB if it's from an LLM processor if metric_data.processor and "LLM" in metric_data.processor: await self._send_message( { "type": "rtf-ttfb-metric", "payload": { "ttfb_seconds": metric_data.value, "processor": metric_data.processor, "model": metric_data.model, }, } ) async def _send_message(self, message: dict): """Send message via WebSocket AND append to logs buffer, handling errors gracefully.""" # Send via WebSocket try: await self._ws_sender(message) except Exception as e: # Log but don't fail - feedback is non-critical logger.debug(f"Failed to send real-time feedback message: {e}") # Also append to logs buffer if self._logs_buffer: try: await self._logs_buffer.append(message) except Exception as e: logger.error(f"Failed to append to logs buffer: {e}")