- Keep old parameter name for backward compatibility
- Add deprecation warning when old parameter is used
- Automatically migrate old parameter value to new min_turn_silence parameter
- Exclude deprecated parameter from WebSocket URL to avoid sending it to API
- New parameter takes precedence if both are set
- Update 13d-assemblyai-transcription.py to explicitly use u3-rt-pro model
- Update 55d-update-settings-assemblyai-stt.py to demonstrate keyterms updates instead of language updates
- Add helpful logging to show before/after keyterms boosting effect
- Use difficult names (Xiomara, Saoirse, Krzystof) to demonstrate boosting effectiveness
- Add "beta feature" note to custom prompt warning
- Rename min_end_of_turn_silence_when_confident parameter to min_turn_silence across all AssemblyAI code
- Update documentation, examples, and test files to use new parameter name
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.
The update-docs workflow intermittently failed with "Input required and
not supplied: token" because pull_request events from fork PRs don't
have access to repository secrets. Switching to pull_request_target
runs the workflow in the base repo's context, ensuring secrets are
always available. This is safe since the workflow only runs on
already-merged PRs.
- 07o-interruptible-assemblyai.py: Basic example using Pipecat VAD mode
- 07o-interruptible-assemblyai-stt.py: Advanced example using STT-controlled
turn detection with comprehensive documentation on u3-rt-pro features
(turn detection tuning, prompt-based enhancement, speaker diarization)
The request_finalize() method in STTService is synchronous (sets a flag),
but was being called with await in the VAD turn endpoint handling code.
This caused "object NoneType can't be used in 'await' expression" errors.
Also includes automatic formatting improvements from ruff.
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
- Remove unused Mapping import
- Remove info logs at initialization (connection params)
- Remove info logs in _handle_transcription (transcript details, text sent to LLM)
- Remove info logs in _build_ws_url (WebSocket URL and params)
- Keep debug logs (less verbose, appropriate for development)
u3-rt-pro guarantees SpeechStarted is always sent before transcripts,
so the fallback UserStartedSpeakingFrame broadcast is never needed.
This ensures clean pairing of UserStarted/StoppedSpeakingFrame:
- Start: Always from _handle_speech_started
- Stop: Always from _handle_transcription on final turn
- Add request_finalize() before sending ForceEndpoint in Pipecat mode
- Keep confirm_finalize() when receiving formatted finals in Pipecat mode
- Remove confirm_finalize() from STT mode (use finalized=True instead)
This follows Pipecat's two-step finalization pattern where request_finalize()
is called when sending a finalize request to the STT service, and
confirm_finalize() is called when receiving confirmation back.
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.
