Implement Sentry instrumentation for performance and error tracking

src/pipecat/processors/frame_processor.py

@@ -14,18 +14,14 @@
     EndFrame,
     ErrorFrame,
     Frame,
-    MetricsFrame,
     StartFrame,
     StartInterruptionFrame,
     StopInterruptionFrame,
     SystemFrame)
 from pipecat.metrics.metrics import (
     LLMTokenUsage,
-    LLMUsageMetricsData,
-    MetricsData,
-    ProcessingMetricsData,
-    TTFBMetricsData,
-    TTSUsageMetricsData)
+    MetricsData)
+from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics
 from pipecat.utils.utils import obj_count, obj_id
 
 from loguru import logger
@@ -35,79 +31,13 @@ class FrameDirection(Enum):
     DOWNSTREAM = 1
     UPSTREAM = 2
 
-
-class FrameProcessorMetrics:
-    def __init__(self, name: str):
-        self._core_metrics_data = MetricsData(processor=name)
-        self._start_ttfb_time = 0
-        self._start_processing_time = 0
-        self._should_report_ttfb = True
-
-    def _processor_name(self):
-        return self._core_metrics_data.processor
-
-    def _model_name(self):
-        return self._core_metrics_data.model
-
-    def set_core_metrics_data(self, data: MetricsData):
-        self._core_metrics_data = data
-
-    async def start_ttfb_metrics(self, report_only_initial_ttfb):
-        if self._should_report_ttfb:
-            self._start_ttfb_time = time.time()
-            self._should_report_ttfb = not report_only_initial_ttfb
-
-    async def stop_ttfb_metrics(self):
-        if self._start_ttfb_time == 0:
-            return None
-
-        value = time.time() - self._start_ttfb_time
-        logger.debug(f"{self._processor_name()} TTFB: {value}")
-        ttfb = TTFBMetricsData(
-            processor=self._processor_name(),
-            value=value,
-            model=self._model_name())
-        self._start_ttfb_time = 0
-        return MetricsFrame(data=[ttfb])
-
-    async def start_processing_metrics(self):
-        self._start_processing_time = time.time()
-
-    async def stop_processing_metrics(self):
-        if self._start_processing_time == 0:
-            return None
-
-        value = time.time() - self._start_processing_time
-        logger.debug(f"{self._processor_name()} processing time: {value}")
-        processing = ProcessingMetricsData(
-            processor=self._processor_name(), value=value, model=self._model_name())
-        self._start_processing_time = 0
-        return MetricsFrame(data=[processing])
-
-    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
-        logger.debug(
-            f"{self._processor_name()} prompt tokens: {tokens.prompt_tokens}, completion tokens: {tokens.completion_tokens}")
-        value = LLMUsageMetricsData(
-            processor=self._processor_name(),
-            model=self._model_name(),
-            value=tokens)
-        return MetricsFrame(data=[value])
-
-    async def start_tts_usage_metrics(self, text: str):
-        characters = TTSUsageMetricsData(
-            processor=self._processor_name(),
-            model=self._model_name(),
-            value=len(text))
-        logger.debug(f"{self._processor_name()} usage characters: {characters.value}")
-        return MetricsFrame(data=[characters])
-
-
 class FrameProcessor:
 
     def __init__(
             self,
             *,
             name: str | None = None,
+            metrics: FrameProcessorMetrics | None = None,
             sync: bool = True,
             loop: asyncio.AbstractEventLoop | None = None,
             **kwargs):
@@ -129,7 +59,8 @@ def __init__(
         self._report_only_initial_ttfb = False
 
         # Metrics
-        self._metrics = FrameProcessorMetrics(name=self.name)
+        self._metrics = metrics or FrameProcessorMetrics()
+        self._metrics.set_processor_name(self.name)
 
         # Every processor in Pipecat should only output frames from a single
         # task. This avoid problems like audio overlapping. System frames are

src/pipecat/processors/metrics/frame_processor_metrics.py

@@ -0,0 +1,79 @@
+import time
+
+from pipecat.frames.frames import MetricsFrame
+from pipecat.metrics.metrics import (
+    LLMTokenUsage,
+    LLMUsageMetricsData,
+    MetricsData,
+    ProcessingMetricsData,
+    TTFBMetricsData,
+    TTSUsageMetricsData)
+
+from loguru import logger
+
+class FrameProcessorMetrics:
+    def __init__(self):
+        self._start_ttfb_time = 0
+        self._start_processing_time = 0
+        self._should_report_ttfb = True
+
+    def _processor_name(self):
+        return self._core_metrics_data.processor
+
+    def _model_name(self):
+        return self._core_metrics_data.model
+
+    def set_core_metrics_data(self, data: MetricsData):
+        self._core_metrics_data = data
+
+    def set_processor_name(self, name: str):
+        self._core_metrics_data = MetricsData(processor=name)
+
+    async def start_ttfb_metrics(self, report_only_initial_ttfb):
+        if self._should_report_ttfb:
+            self._start_ttfb_time = time.time()
+            self._should_report_ttfb = not report_only_initial_ttfb
+
+    async def stop_ttfb_metrics(self):
+        if self._start_ttfb_time == 0:
+            return None
+
+        value = time.time() - self._start_ttfb_time
+        logger.debug(f"{self._processor_name()} TTFB: {value}")
+        ttfb = TTFBMetricsData(
+            processor=self._processor_name(),
+            value=value,
+            model=self._model_name())
+        self._start_ttfb_time = 0
+        return MetricsFrame(data=[ttfb])
+
+    async def start_processing_metrics(self):
+        self._start_processing_time = time.time()
+
+    async def stop_processing_metrics(self):
+        if self._start_processing_time == 0:
+            return None
+
+        value = time.time() - self._start_processing_time
+        logger.debug(f"{self._processor_name()} processing time: {value}")
+        processing = ProcessingMetricsData(
+            processor=self._processor_name(), value=value, model=self._model_name())
+        self._start_processing_time = 0
+        return MetricsFrame(data=[processing])
+
+    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
+        logger.debug(
+            f"{self._processor_name()} prompt tokens: {tokens.prompt_tokens}, completion tokens: {tokens.completion_tokens}")
+        value = LLMUsageMetricsData(
+            processor=self._processor_name(),
+            model=self._model_name(),
+            value=tokens)
+        return MetricsFrame(data=[value])
+
+    async def start_tts_usage_metrics(self, text: str):
+        characters = TTSUsageMetricsData(
+            processor=self._processor_name(),
+            model=self._model_name(),
+            value=len(text))
+        logger.debug(f"{self._processor_name()} usage characters: {characters.value}")
+        return MetricsFrame(data=[characters])

src/pipecat/processors/metrics/sentry.py

@@ -0,0 +1,52 @@
+import time
+from loguru import logger
+
+try:
+    import sentry_sdk
+    sentry_available = sentry_sdk.is_initialized()
+    if not sentry_available:
+        logger.debug("Sentry SDK not initialized. Sentry features will be disabled.")
+except ImportError:
+    sentry_available = False
+    logger.debug("Sentry SDK not installed. Sentry features will be disabled.")
+
+from pipecat.processors.metrics.frame_processor_metrics import FrameProcessorMetrics
+
+class SentryMetrics(FrameProcessorMetrics):
+    def __init__(self):
+        super().__init__()
+        self._ttfb_metrics_span = None
+        self._processing_metrics_span = None
+
+    async def start_ttfb_metrics(self, report_only_initial_ttfb):
+        if self._should_report_ttfb:
+            self._start_ttfb_time = time.time()
+            if sentry_available:
+                self._ttfb_metrics_span = sentry_sdk.start_span(
+                    op="ttfb", 
+                    description=f"TTFB for {self._processor_name()}",
+                    start_timestamp=self._start_ttfb_time
+                )
+                logger.debug(f"Sentry Span ID: {self._ttfb_metrics_span.span_id} Description: {self._ttfb_metrics_span.description} started.")
+            self._should_report_ttfb = not report_only_initial_ttfb
+
+    async def stop_ttfb_metrics(self):
+        stop_time = time.time()
+        if sentry_available:
+            self._ttfb_metrics_span.finish(end_timestamp=stop_time)
+
+    async def start_processing_metrics(self):
+        self._start_processing_time = time.time()
+        if sentry_available:
+            self._processing_metrics_span = sentry_sdk.start_span(
+                op="processing", 
+                description=f"Processing for {self._processor_name()}",
+                start_timestamp=self._start_processing_time
+            )
+            logger.debug(f"Sentry Span ID: {self._processing_metrics_span.span_id} Description: {self._processing_metrics_span.description} started.")
+
+
+    async def stop_processing_metrics(self):
+        stop_time = time.time()
+        if sentry_available:
+            self._processing_metrics_span.finish(end_timestamp=stop_time)

src/pipecat/services/deepgram.py

@@ -124,6 +124,7 @@ def __init__(self,
                      smart_format=True,
                      punctuate=True,
                      profanity_filter=True,
+                     vad_events=False,
                  ),
                  **kwargs):
         super().__init__(**kwargs)
@@ -134,6 +135,15 @@ def __init__(self,
             api_key, config=DeepgramClientOptions(url=url, options={"keepalive": "true"}))
         self._connection: AsyncListenWebSocketClient = self._client.listen.asyncwebsocket.v("1")
         self._connection.on(LiveTranscriptionEvents.Transcript, self._on_message)
+        if self.vad_enabled:
+            self._connection.on(LiveTranscriptionEvents.SpeechStarted, self._on_speech_started)
+
+    @property
+    def vad_enabled(self):
+        return self._live_options.vad_events
+
+    def can_generate_metrics(self) -> bool:
+        return self.vad_enabled
 
     async def set_model(self, model: str):
         await super().set_model(model)
@@ -161,9 +171,7 @@ async def cancel(self, frame: CancelFrame):
         await self._disconnect()
 
     async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        await self.start_processing_metrics()
         await self._connection.send(audio)
-        await self.stop_processing_metrics()
         yield None
 
     async def _connect(self):
@@ -176,6 +184,10 @@ async def _disconnect(self):
         if self._connection.is_connected:
             await self._connection.finish()
             logger.debug(f"{self}: Disconnected from Deepgram")
+
+    async def _on_speech_started(self, *args, **kwargs):
+        await self.start_ttfb_metrics()
+        await self.start_processing_metrics()
 
     async def _on_message(self, *args, **kwargs):
         result: LiveResultResponse = kwargs["result"]
@@ -188,7 +200,9 @@ async def _on_message(self, *args, **kwargs):
             language = result.channel.alternatives[0].languages[0]
             language = Language(language)
         if len(transcript) > 0:
+            await self.stop_ttfb_metrics()
             if is_final:
                 await self.push_frame(TranscriptionFrame(transcript, "", time_now_iso8601(), language))
+                await self.stop_processing_metrics()
             else:
                 await self.push_frame(InterimTranscriptionFrame(transcript, "", time_now_iso8601(), language))