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initial commit for new pipecat architecture

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Aleix Conchillo Flaqué
2024-04-24 18:29:24 -07:00
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docs/README.md
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# Daily AI SDK Docs
# Pipecat Docs
## [Architecture Overview](architecture.md)
Learn about the thinking behind the SDK's design.
Learn about the thinking behind the framework's design.
## [A Frame's Progress](frame-progress.md)
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## [Example Code](examples/)
The repo includes several example apps in the `examples` directory. The docs explain how they work.
The repository includes several example apps in the `examples` directory. The docs explain how they work.
## [API Reference](api/)

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docs/architecture.md
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# Daily AI SDK Architecture Guide
# Pipecat architecture guide
## Frames
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## Pipelines
Pipelines are lists of frame processors that read from a source queue and send the processed frames to a sink queue. A very simple pipeline might chain an LLM frame processor to a text-to-speech frame processor, with a transport's send queue as its sync. Placing LLM message frames on the pipeline's source queue will cause the LLM's response to be spoken. See example #2 for an implementation of this.
Pipelines are lists of frame processors linked together. Frame processors can push frames upstream or downstream to their peers. A very simple pipeline might chain an LLM frame processor to a text-to-speech frame processor, with a transport as an output.
## Transports
Transports provide a receive queue, which is input from "the outside world", and a sink queue, which is data that will be sent "to the outside world". The `LocalTransportService` does this with the local camera, mic, display and speaker. The `DailyTransportService` does this with a WebRTC session joined to a Daily.co room.
Transports provide input and output frame processors to receive or send frames respectively. For example, the `DailyTransport` does this with a WebRTC session joined to a Daily.co room.