Pass realtime_service_mode=RealtimeServiceModeConfig() through every
realtime LLM service example (base, async-tool, video, text-output,
persistent-context, update-settings, MCP) so context aggregation uses
the new realtime-mode semantics instead of relying on local VAD as a
workaround.
Where examples previously wired SileroVADAnalyzer into
LLMUserAggregatorParams to coax turn frames out of services that don't
emit them server-side (AWS Nova Sonic, Ultravox, Gemini Live), the local
VAD is now removed. realtime_service_mode keeps context writes correct
without it, and the Phase 1.5 server-side InterruptionFrame fixes for
Nova Sonic and Ultravox keep the bot from talking past the user when
they barge in.
Transcript-logging event handlers move from on_user_turn_stopped /
on_assistant_turn_stopped to on_user_message_added /
on_assistant_message_added, which carry the finalized text in realtime
mode (the turn-stopped events fire before the message is finalized, so
their `content` is None in that mode).
For services that don't emit user-turn frames (Gemini Live, AWS Nova
Sonic, Ultravox) the example now carries a Tier 1 comment block that
spells out which downstream processors won't activate, how to add local
VAD if needed, and the caveat that locally-generated turn boundaries
are a heuristic that may diverge from server-side ground truth.
Adds examples/realtime/realtime-openai-local-vad.py, a new variant of
the OpenAI Realtime example that disables OpenAI's server-side turn
detection and drives turn boundaries locally — useful when you want a
turn analyzer like LocalSmartTurnV3 to decide when the user is done
speaking. Server-emitted turn frames are still preferred when available.
The Gemini Live local-VAD variant already existed; it's been updated in
place rather than rewritten.
Remove `examples/` from the `pyrightconfig.json` ignore list and fix
the resulting type errors across all example files. Common fixes:
- Required API keys: `os.getenv("X")` -> `os.environ["X"]` so the
return type is `str` rather than `str | None`, and misconfiguration
fails fast.
- Narrow `LLMContextMessage` union members with `isinstance(..., dict)`
before dict-style access.
- `assert isinstance(params.llm, ...)` before calling service-specific
methods that aren't on the base `LLMService`.
- Guard optional frame fields (e.g. `LLMSearchResponseFrame.search_result`)
before use.
Example files like openai.py shadow installed packages when Python adds the
script directory to sys.path. Prepend the parent folder name to each example
file (e.g. openai.py -> function-calling-openai.py). Also split
thinking-and-mcp/ into separate mcp/ and thinking/ directories.
Move 304 examples from a flat numbered directory into 14 descriptive
subfolders: getting-started, services (speech + function-calling),
transcription, vision, realtime, persistent-context,
context-summarization, update-settings (stt/tts/llm), turn-management,
thinking-and-mcp, transports, video-avatar, video-processing, and
features.
Strip numbered prefixes from filenames (e.g. 07c-interruptible-deepgram.py
becomes services/speech/deepgram.py) since the folder context makes them
redundant. Keep numbered prefixes only in getting-started/ where ordering
matters.
Update eval script paths and README to match the new structure.
