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.
- Add InterruptionFrame handling with stop_all_metrics()
- Add processing metrics (start/stop) at response boundaries
- Fix agent transcript handling for voice and text modalities:
- Voice mode: push LLMTextFrame (append_to_context=False) and
TTSTextFrame for deltas, skip duplicated final text
- Text mode: push LLMTextFrame with proper response lifecycle,
no TTSTextFrame (downstream TTS handles audio)
- Add output_medium parameter to AgentInputParams and OneShotInputParams
- Improve TTFB measurement using VAD speech end time
- Update example with user turn strategies and transcript events
- Add text-only output example (50a-ultravox-realtime-text.py)
Move the sentence vs token aggregation concern into text aggregators
so all text flows through them regardless of mode. This enables
pattern detection and tag handling to work in TOKEN mode.
- Add TextAggregationMode enum (SENTENCE, TOKEN) as the user-facing
TTS setting, separate from the internal AggregationType
- Add TOKEN mode support to Simple, SkipTags, and PatternPair aggregators
- Add text_aggregation_mode parameter to TTSService and all TTS subclasses
- Deprecate aggregate_sentences in favor of text_aggregation_mode
- Merge TTSService._process_text_frame() into a single codepath
Add TextAggregationMetricsData measuring the time from the first LLM
token to the first complete sentence, representing the latency cost of
sentence aggregation in the TTS pipeline.
- Wire up passing speed setting to Groq, even though only a value of 1.0 is supported today
- Update the 55y example to switch voices instead of changing speed
- Add a 55zn example to exercise runtime updates of Groq STT
The only (rare) exception—where a service directly still needs to directly call `self._sync_model_name_to_metrics()`—is when the model name need to be "pulled" from another field (or nested field) in settings up to settings.model on a settings update. This only occurs in Deepgram services, where we use the voice as the model name.
This change has the side-effect of bringing model name to metrics for a number of services that were accidentally omitting it before.
This was added in 31daa889e8, but only
to `RimeTTSService`, not to `RimeNonJsonTTSService. Bringing these
to parity means that users switching between the two, with the same
inputs, have more consistent vocalization behaviors.
