Use start_offset_secs (offset from StartFrame) on ProcessorStartupTiming
instead of a wall-clock timestamp. Reports keep a single start_time
anchor for dashboard visualization. Remove _mono_to_wall conversion.
Switch ProcessorStartupTiming, StartupTimingReport, and
TransportTimingReport from dataclasses to Pydantic BaseModel. Add
start_time (Unix timestamp) fields and wall clock conversion for
monotonic observer timestamps.
Add BotConnectedFrame (SystemFrame) pushed by SFU transports (Daily,
LiveKit, HeyGen, Tavus) when the bot joins the room. Replace the
on_transport_readiness_measured event with on_transport_timing_report
which includes both bot_connected_secs and client_connected_secs.
Introduce ClientConnectedFrame (SystemFrame) pushed by all transports
when a client connects. StartupTimingObserver uses this to measure
transport readiness — the time from StartFrame to first client
connection — via a new on_transport_readiness_measured event.
Tracks how long each processor start method takes during pipeline
startup by measuring StartFrame arrive/leave deltas. Emits a timing
report via the on_startup_timing_report event and auto-logs a summary.
Internal pipeline processors are excluded from reports by default.
The Whisper-based ONNX model expects 16 kHz audio, but the
_predict_endpoint method had five hardcoded references to 16000 without
checking the actual pipeline sample rate. When running at 8 kHz (e.g.
Twilio telephony), audio was fed to the feature extractor at the wrong
rate, causing the model to perceive speech at 2x speed with shifted
formant frequencies and produce incorrect end-of-turn predictions.
Add automatic resampling via numpy interpolation before feature
extraction and replace all hardcoded sample rate values with a
_MODEL_SAMPLE_RATE constant. Also fix the WAV debug logger to write
files with the correct sample rate header.
Fixes#3844
When filter_incomplete_user_turns is enabled and an LLMMessagesUpdateFrame
replaces the context via set_messages(), the turn completion instructions
system message was lost. This caused the LLM to stop emitting turn
completion markers. Re-inject the instructions after set_messages() to
fix this.
Allow pushing frames upstream through the pipeline by passing
FrameDirection.UPSTREAM. Downstream frames use the existing push queue,
while upstream frames are pushed directly from the pipeline sink.
The ServiceSettings refactor (PR #3714) changed self._settings from
dicts to dataclass subclasses, but tracing code still used .items(),
in containment, and subscript access, causing AttributeError on
every traced call. Use given_fields() for iteration and attribute
access for named fields.
Replace _rtvi_external instance variable with a local prepend_rtvi flag
since it is only used during __init__ to decide whether to prepend the
RTVIProcessor to the pipeline.
When the user places an RTVIProcessor inside their pipeline and provides
a custom RTVIObserver subclass in observers, PipelineTask correctly
detects both and logs "skipping default ones." However it then
unconditionally prepends self._rtvi to the pipeline, causing the
processor to appear twice in the frame chain.
Track whether the RTVIProcessor was found externally (inside the user
pipeline) vs created internally. Only prepend it when created internally.
Fixes#3867
Even when summarization_timeout is explicitly set to None, use a
DEFAULT_SUMMARIZATION_TIMEOUT (120s) fallback so the LLM call can
never hang indefinitely. Applied in both LLMService and the dedicated
LLM path in LLMContextSummarizer.
The dedicated LLM logic lived in LLMAssistantAggregator, creating two
code paths and requiring the aggregator to call a private LLMService
method. Move it into the summarizer which already owns the config and
summarization lifecycle, keeping the aggregator handler as a single-line
upstream push.
Adds a configurable summarization_timeout (default 120s) that cancels
summary generation if the LLM hangs. On timeout, an error result is
returned so _summarization_in_progress resets and future
summarizations are unblocked.
Adds an field to LLMContextSummarizationConfig that allows
routing summarization to a separate LLM service (e.g., Gemini Flash)
instead of the pipeline's primary model. This avoids paying for
expensive inference when compressing context in long-running sessions.
Allows applications to customize how the summary is wrapped when
injected into context (e.g., XML tags, custom delimiters) so system
prompts can distinguish summaries from live conversation.
Add deprecation warnings to start_processing_metrics() and
stop_processing_metrics() on FrameProcessorMetrics and FrameProcessor.
Mark ProcessingMetricsData as deprecated in docstring. All existing
behavior is preserved — the warnings inform users that these will be
removed in a future version.
