Refactor language_to_soniox_language to use resolve_language + LANGUAGE_MAP
pattern consistent with other services. Fix resolve_language fallback to use
str(language) instead of language.value so plain strings don't crash.
The Inworld WS TTS plugin previously relied on the base TTS service's 3-second AUDIO_CONTEXT_TIMEOUT to detect when audio was done, then sent close_context in on_audio_context_completed. This added unnecessary latency before TTSStoppedFrame was emitted.
The original implementation likely borrowed this idea from the 11labs' impelementation. But it's likely better to mirror the Cartesia plugin pattern where on_audio_context_completed is a no-op because the server signals completion directly.
Now close_context is sent in on_turn_context_completed (right after flush_context), so the server responds with contextClosed immediately after the last audio byte. The existing receive handler already calls remove_audio_context on contextClosed, which exits the audio context handler cleanly.
The default model for OpenAILLMService and AzureLLMService was still set
to gpt-4o. Restored it to gpt-4.1. Also, removed hardcoded gpt-4o/gpt-4o-mini
model references from examples so they pick up the new default.
* Add ServiceSwitcherStrategyFailover for automatic error-based service switching
Introduce a strategy hierarchy: ServiceSwitcherStrategy (base) →
ServiceSwitcherStrategyManual (handles ManuallySwitchServiceFrame) →
ServiceSwitcherStrategyFailover (adds error-based failover). ServiceSwitcher
now defaults to ServiceSwitcherStrategyManual with strategy_type optional.
Non-fatal ErrorFrames are forwarded to the strategy via handle_error().
* Move metadata request into _set_active_if_available
Requesting metadata is part of making a service active, so it belongs
alongside setting _active_service and firing on_service_switched. This
removes the duplicate queue_frame calls from ServiceSwitcher push_frame
and process_frame.
* Add system_instruction parameter to run_inference
Allow callers to provide a custom system instruction directly when calling
run_inference, without having to construct provider-specific context objects.
For OpenAI, the instruction is prepended as a system message (preserving
existing messages). For Anthropic, Google, and AWS Bedrock, it overrides the
single system field with a warning when an existing system instruction is
present in the context.
* Use system_instruction parameter in _generate_summary
Pass the summarization prompt via run_inference's system_instruction
parameter instead of embedding it as a system message in the context.
* Add changelog for #3968
Enhanced the logic for extracting the system message in the traced_llm decorator to support LLMContext via adapter and handle exceptions gracefully. This improves compatibility with different context types and ensures better tracing information.
