mirror of
https://github.com/dograh-hq/dograh.git
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chore: fix tracing for text chat mode
This commit is contained in:
Abhishek Kumar
parent
e23cce444f
commit
08a2435ba5
31 changed files with 1753 additions and 597 deletions
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@ -27,6 +27,15 @@ from typing import TYPE_CHECKING, Awaitable, Callable, Optional, Set
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from loguru import logger
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from api.services.pipecat.realtime_feedback_events import (
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build_bot_text_event,
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build_function_call_end_event,
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build_function_call_start_event,
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build_pipeline_error_event,
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build_ttfb_metric_event,
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build_user_transcription_event,
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)
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if TYPE_CHECKING:
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from api.services.pipecat.in_memory_buffers import InMemoryLogsBuffer
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@ -211,29 +220,23 @@ class RealtimeFeedbackObserver(BaseObserver):
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# Handle user transcriptions (interim) - WebSocket only
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elif isinstance(frame, InterimTranscriptionFrame):
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await self._send_ws(
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{
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"type": RealtimeFeedbackType.USER_TRANSCRIPTION.value,
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"payload": {
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"text": frame.text,
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"final": False,
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"user_id": frame.user_id,
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"timestamp": frame.timestamp,
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},
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}
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build_user_transcription_event(
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text=frame.text,
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final=False,
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user_id=frame.user_id,
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timestamp=frame.timestamp,
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)
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)
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# Handle user transcriptions (final) - WebSocket only
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# Complete turn text is persisted via register_turn_handlers
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elif isinstance(frame, TranscriptionFrame):
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await self._send_ws(
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{
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"type": RealtimeFeedbackType.USER_TRANSCRIPTION.value,
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"payload": {
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"text": frame.text,
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"final": True,
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"user_id": frame.user_id,
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"timestamp": frame.timestamp,
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},
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}
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build_user_transcription_event(
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text=frame.text,
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final=True,
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user_id=frame.user_id,
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timestamp=frame.timestamp,
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)
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)
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# Handle engine-queued speech (transition/tool messages) marked for
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# log persistence. The downstream TTSTextFrame(s) from the TTS service
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@ -241,23 +244,13 @@ class RealtimeFeedbackObserver(BaseObserver):
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# to avoid word-level log entries from word-timestamp providers.
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elif isinstance(frame, TTSSpeakFrame):
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if getattr(frame, "persist_to_logs", False):
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await self._append_to_buffer(
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{
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"type": RealtimeFeedbackType.BOT_TEXT.value,
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"payload": {"text": frame.text},
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}
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)
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await self._append_to_buffer(build_bot_text_event(text=frame.text))
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# Handle bot TTS text - respect pts timing, WebSocket only
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# Complete turn text is persisted via register_turn_handlers,
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# except for frames explicitly flagged persist_to_logs (e.g. recording
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# transcripts from play_audio) which bypass the aggregator path.
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elif isinstance(frame, TTSTextFrame):
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message = {
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"type": RealtimeFeedbackType.BOT_TEXT.value,
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"payload": {
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"text": frame.text,
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},
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}
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message = build_bot_text_event(text=frame.text)
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# If frame has pts, queue it for timed delivery
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if frame.pts:
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@ -280,13 +273,10 @@ class RealtimeFeedbackObserver(BaseObserver):
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and frame_direction == FrameDirection.DOWNSTREAM
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):
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await self._send_message(
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{
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"type": RealtimeFeedbackType.FUNCTION_CALL_START.value,
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"payload": {
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"function_name": frame.function_name,
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"tool_call_id": frame.tool_call_id,
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},
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}
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build_function_call_start_event(
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function_name=frame.function_name,
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tool_call_id=frame.tool_call_id,
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)
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)
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# Handle function call result
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elif (
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@ -294,14 +284,11 @@ class RealtimeFeedbackObserver(BaseObserver):
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and frame_direction == FrameDirection.DOWNSTREAM
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):
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await self._send_message(
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{
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"type": RealtimeFeedbackType.FUNCTION_CALL_END.value,
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"payload": {
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"function_name": frame.function_name,
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"tool_call_id": frame.tool_call_id,
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"result": str(frame.result) if frame.result else None,
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},
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}
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build_function_call_end_event(
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function_name=frame.function_name,
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tool_call_id=frame.tool_call_id,
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result=frame.result,
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)
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)
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# Handle TTFB metrics - capture LLM generation time only
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elif isinstance(frame, MetricsFrame):
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@ -311,47 +298,42 @@ class RealtimeFeedbackObserver(BaseObserver):
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# Only send TTFB if it's from an LLM processor
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if metric_data.processor and "LLM" in metric_data.processor:
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await self._send_message(
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{
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"type": RealtimeFeedbackType.TTFB_METRIC.value,
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"payload": {
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"ttfb_seconds": metric_data.value,
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"processor": metric_data.processor,
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"model": metric_data.model,
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},
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}
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build_ttfb_metric_event(
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ttfb_seconds=metric_data.value,
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processor=metric_data.processor,
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model=metric_data.model,
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)
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)
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# Handle pipeline errors
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elif isinstance(frame, ErrorFrame):
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processor_name = str(frame.processor) if frame.processor else None
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payload = {
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"error": frame.error,
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"fatal": frame.fatal,
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"processor": processor_name,
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}
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extra_payload: dict[str, object] = {}
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# Surface structured fields when the underlying exception carries
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# them (e.g. google.genai APIError: code=1008, status=None,
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# message="Your project has been denied access...").
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exc = frame.exception
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if exc is not None:
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exc_type = type(exc).__name__
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payload["exception_type"] = exc_type
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payload["exception_message"] = str(exc)
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extra_payload["exception_type"] = exc_type
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extra_payload["exception_message"] = str(exc)
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for attr in ("code", "status", "message", "details"):
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value = getattr(exc, attr, None)
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if value is None or attr in payload:
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if value is None or attr in extra_payload:
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continue
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try:
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# Ensure the value is JSON-serializable; fall back
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# to str() for opaque objects (e.g. raw response).
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json.dumps(value)
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payload[attr] = value
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extra_payload[attr] = value
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except (TypeError, ValueError):
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payload[attr] = str(value)
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extra_payload[attr] = str(value)
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await self._send_message(
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{
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"type": RealtimeFeedbackType.PIPELINE_ERROR.value,
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"payload": payload,
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}
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build_pipeline_error_event(
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error=frame.error,
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fatal=frame.fatal,
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processor=processor_name,
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extra_payload=extra_payload or None,
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)
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)
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async def _send_ws(self, message: dict):
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@ -401,14 +383,11 @@ def register_turn_log_handlers(
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logs_buffer.increment_turn()
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try:
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await logs_buffer.append(
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{
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"type": RealtimeFeedbackType.USER_TRANSCRIPTION.value,
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"payload": {
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"text": message.content,
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"final": True,
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"timestamp": message.timestamp,
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},
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}
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build_user_transcription_event(
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text=message.content,
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final=True,
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timestamp=message.timestamp,
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)
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)
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except Exception as e:
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logger.error(f"Failed to append user turn to logs buffer: {e}")
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@ -418,13 +397,10 @@ def register_turn_log_handlers(
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if message.content:
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try:
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await logs_buffer.append(
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{
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"type": RealtimeFeedbackType.BOT_TEXT.value,
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"payload": {
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"text": message.content,
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"timestamp": message.timestamp,
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},
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}
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build_bot_text_event(
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text=message.content,
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timestamp=message.timestamp,
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)
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)
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except Exception as e:
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logger.error(f"Failed to append assistant turn to logs buffer: {e}")
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