Adds `enable_prompt_caching` setting to `AWSBedrockLLMSettings`. When
enabled, appends `cachePoint` markers to system prompts and tool
definitions in ConverseStream requests.
This can reduce TTFT by up to 85% for multi-turn conversations where
the system prompt stays constant (e.g. voice agents, chat assistants).
Follows the same pattern as `AnthropicLLMService.enable_prompt_caching`.
Usage:
```python
llm = AWSBedrockLLMService(
settings=AWSBedrockLLMSettings(
model="au.anthropic.claude-haiku-4-5-20251001-v1:0",
enable_prompt_caching=True,
),
)
```
See: https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html
The location and project_id fields were deprecated since 0.0.90 in
favor of direct __init__ parameters. Now that InputParams is removed,
project_id is required and location defaults to "us-east4" directly
in the signature.
Override _update_settings in CartesiaTTSService to flush the current
audio context and assign a new turn context ID when voice, model, or
language settings change. This prevents Context has closed errors
from Cartesia API, which locks these parameters per context.
Remove the deprecated text_aggregator parameter from TTSService,
CartesiaTTSService, and RimeTTSService, and the deprecated text_filter
parameter from TTSService. Users should use LLMTextProcessor before
the TTS service instead. Update the voice-switching example to use
LLMTextProcessor with PatternPairAggregator.
Change the default from 10s to None so deferred function calls can run
indefinitely when no timeout is configured. Only create the timeout
task when a timeout is actually provided (per-call or service-level).
Add SmallestSTTService using the Pulse WebSocket API for real-time
transcription. Includes SmallestSTTSettings dataclass, 32-language
support with resolve_language fallback, VAD-driven finalize signal,
and SMALLEST_TTFS_P99 latency constant.
Also adds X-Source and X-Pipecat-Version headers to Smallest STT
and TTS WebSocket connections.
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.
Now that LLMContextFrame is the only frame that provides a context,
remove the intermediate `context = None` / `if context:` pattern
and handle context processing directly in the isinstance branch.
