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v0.0.29

pipecat-ai/pipecatv0.0.29Jun 7, 2024by aconchillo

AI Summary

Introduced a base pipeline class, function calling capabilities, and detailed metrics tracking for TTFB and TTS services.

Key Highlights

  • Added BasePipeline class and MetricsFrame for TTFB reporting.
  • Added FunctionFilter for custom frame filtering.
  • Added LLMService.register_function() for function calling.
  • Added enable_metrics to PipelineParams.

New Features

  • Pipeline base class
  • Function calling
  • Metrics framework

Full Release Notes

### Added

- Added a new `FunctionFilter`. This filter will let you filter frames based on a given function, except system messages which should never be filtered.

- Added `FrameProcessor.can_generate_metrics()` method to indicate if a processor can generate metrics. In the future this might get an extra argument to ask for a specific type of metric.

- Added `BasePipeline`. All pipeline classes should be based on this class. All subclasses should implement a `processors_with_metrics()` method that returns a list of all `FrameProcessor`s in the pipeline that can generate metrics.

- Added `enable_metrics` to `PipelineParams`.

- Added `MetricsFrame`. The `MetricsFrame` will report different metrics in the system. Right now, it can report TTFB (Time To First Byte) values for different services, that is the time spent between the arrival of a `Frame` to the processor/service until the first `DataFrame` is pushed downstream. If metrics are enabled an intial `MetricsFrame` with all the services in the pipeline will be sent.

- Added TTFB metrics and debug logging for TTS services.

### Changed

- Moved `ParallelTask` to `pipecat.pipeline.parallel_task`.

### Fixed

- Fixed PlayHT TTS service to work properly async.