These messages are developer instructions to the assistant (e.g. "Please
introduce yourself to the user"), not simulated user input. The
"developer" role is semantically correct for this purpose.
- Speechmatics: move config build after super().__init__ and settings
delta so turn_detection_mode (e.g. ADAPTIVE) takes effect
- Google STT: fix example passing bare Language enum instead of list
- Google TTS: add missing explicit defaults for all custom settings fields
- Soniox: fix accidental tuple wrapping of STT service in example
- Speechmatics examples: fix system->user role in kick-off messages
- Deepgram Flux: move tag from settings to __init__ (billing metadata)
- ElevenLabs STT: default tag_audio_events to None (use API default)
- Fal STT: simplify language default handling
- Google TTS: rename GoogleStreamTTSSettings to GoogleTTSSettings
Add `system_instruction` field to `LLMSettings` so it is runtime-updatable via settings.
For Google (GoogleLLMService, GoogleVertexLLMService), deprecate the init-time arg since it was already shipped. For Anthropic, AWS Bedrock, and OpenAI, remove the init-time arg entirely since it was never shipped.
Still need to handle realtime services (OpenAI Realtime, Grok Realtime, Gemini Live).
Thinking, sometimes called "extended thinking" or "reasoning", is an LLM process where the model takes some additional time before giving an answer. It's useful for complex tasks that may require some level of planning and structured, step-by-step reasoning. The model can output its thoughts (or thought summaries, depending on the model) in addition to the answer. The thoughts are usually pretty granular and not really suitable for being spoken out loud in a conversation, but can be useful for logging or prompt debugging.
Here's what's added:
1. New typed input parameters for Google and Anthropic LLMs that control the models' thinking behavior (like how much thinking to do, and whether to output thoughts or thought summaries).
2. New frames for representing thoughts output by LLMs.
3. A generic mechanism for associating extra LLM-specific data with a function call in context, used specifically to support Google's function-call-related "thought signatures", which are necessary to ensure thinking continuity between function calls in a chain (where the model thinks, makes a function call, thinks some more, etc.)
4. A generic mechanism for recording LLM thoughts to context, used specifically to support Anthropic, whose thought signatures are expected to appear alongside the text of the thoughts within assistant context messages.
5. An expansion of `TranscriptProcessor` to process LLM thoughts in addition to user and assistant utterances.
With all these examples updated, we no longer need dedicated examples illustrating `LLMContext`, so they're removed.
Here’s where we *don’t* yet use `LLMContext` and associated machinery:
- Realtime services: OpenAI Realtime, Gemini Live, and AWS Nova Sonic (support coming soon)
- `GoogleLLMOpenAIBetaService` (it’s deprecated, so we didn’t bother adding support)
- `LLMLogObserver` (support coming soon)
- `GatedOpenAILLMContextAggregator` (support coming soon)
- `LangchainProcessor` (support coming soon)
- `Mem0MemoryService` (support coming soon)
- Examples that use LLM-specific tools definitions as opposed to `ToolsSchema` (these will be updated soon)
- Examples that rely `GoogleLLMContext.upgrade_to_google` (TBD what to do with these)
Examples that use `LLMLogObserver`:
- 30-
Examples that use `GatedOpenAILLMContextAggregator`:
- 22-
Examples that use `LangchainProcessor`:
- 07b-
Examples that use `Mem0MemoryService`:
- 37-
Examples that need updating to use `ToolsSchema`:
- 15-
- 15a-
- 20a-
- 20c-
- 20d-
- 22b-
- 22c-
- 33-
- 36-
Examples that use `GoogleLLMContext.upgrade_to_google`:
- 22d-
- 25-
