workflow_test working except for text_input node

Update Dockerfile

.github/workflows/format.yaml

@@ -1,4 +1,4 @@
-name: lint
+name: format
 
 on:
   workflow_dispatch:
@@ -12,12 +12,12 @@ on:
       - "docs/**"
 
 concurrency:
-  group: build-lint-${{ github.event.pull_request.number || github.ref }}
+  group: build-format-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
 jobs:
-  autopep8:
-    name: "Formatting lints"
+  ruff-format:
+    name: "Formatting checker"
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repo
@@ -25,7 +25,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
       - name: Setup virtual environment
         run: |
           python -m venv .venv
@@ -34,11 +34,8 @@ jobs:
           source .venv/bin/activate
           python -m pip install --upgrade pip
           pip install -r dev-requirements.txt
-      - name: autopep8
-        id: autopep8
+      - name: Ruff formatter
+        id: ruff
         run: |
           source .venv/bin/activate
-          autopep8 --max-line-length 100 --exit-code -r -d --exclude "*_pb2.py" -a -a src/
-      - name: Fail if autopep8 requires changes
-        if: steps.autopep8.outputs.exit-code == 2
-        run: exit 1
+          ruff format --config line-length=100 --diff --exclude "*_pb2.py"

.github/workflows/publish_test.yaml

@@ -1,10 +1,6 @@
 name: publish-test
 
-on:
-  workflow_dispatch:
-  push:
-    branches:
-      - main
+on: workflow_dispatch
 
 jobs:
   build:
@@ -14,7 +10,6 @@ jobs:
       - name: Checkout repo
         uses: actions/checkout@v4
         with:
-          ref: ${{ github.event.inputs.gitref }}
           fetch-tags: true
           fetch-depth: 100
       - name: Set up Python

.github/workflows/tests.yaml

@@ -20,21 +20,24 @@ jobs:
     name: "Unit and Integration Tests"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - name: Checkout repo
+        uses: actions/checkout@v4
       - name: Set up Python
         id: setup_python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.10'
+          python-version: "3.10"
       - name: Cache virtual environment
         uses: actions/cache@v3
         with:
-          # We are hashing requirements-dev.txt and requirements-extra.txt which
-          # contain all dependencies needed to run the tests and examples.
-          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('linux-py3.10-requirements.txt') }}-${{ hashFiles('dev-requirements.txt') }}
+          # We are hashing dev-requirements.txt and test-requirements.txt which
+          # contain all dependencies needed to run the tests.
+          key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
           path: .venv
       - name: Install system packages
-        run: sudo apt-get install -y portaudio19-dev
+        id: install_system_packages
+        run: |
+          sudo apt-get install -y portaudio19-dev
       - name: Setup virtual environment
         run: |
           python -m venv .venv
@@ -42,8 +45,8 @@ jobs:
         run: |
           source .venv/bin/activate
           python -m pip install --upgrade pip
-          pip install -r linux-py3.10-requirements.txt -r dev-requirements.txt
+          pip install -r dev-requirements.txt -r test-requirements.txt
       - name: Test with pytest
         run: |
           source .venv/bin/activate
-          pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests
+          pytest --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests

CHANGELOG.md

@@ -1,10 +1,628 @@
 # Changelog
 
-All notable changes to **pipecat** will be documented in this file.
+All notable changes to **Pipecat** will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+
+- Added `AssemblyAISTTService` and corresponding foundational examples
+  `07o-interruptible-assemblyai.py` and `13d-assemblyai-transcription.py`.
+
+- Added a foundational example for Gladia transcription:
+  `13c-gladia-transcription.py`
+
+### Fixed
+
+- Fixed `enable_usage_metrics` to control LLM/TTS usage metrics separately
+  from `enable_metrics`.
+
+## [0.0.46] - 2024-10-19
+
+### Added
+
+- Added `audio_passthrough` parameter to `STTService`. If enabled it allows
+  audio frames to be pushed downstream in case other processors need them.
+
+- Added input parameter options for `PlayHTTTSService` and
+  `PlayHTHttpTTSService`.
+
+### Changed
+
+- Moved `SileroVAD` audio processor to `processors.audio.vad`.
+
+- Module `utils.audio` is now `audio.utils`. A new `resample_audio` function has
+  been added.
+
+- `PlayHTTTSService` now uses PlayHT websockets instead of HTTP requests.
+
+- The previous `PlayHTTTSService` HTTP implementation is now
+  `PlayHTHttpTTSService`.
+
+- `PlayHTTTSService` and `PlayHTHttpTTSService` now use a `voice_engine` of
+  `PlayHT3.0-mini`, which allows for multi-lingual support.
+
+- Renamed `OpenAILLMServiceRealtimeBeta` to `OpenAIRealtimeBetaLLMService` to
+  match other services.
+
+### Deprecated
+
+- `LLMUserResponseAggregator` and `LLMAssistantResponseAggregator` are
+  mostly deprecated, use `OpenAILLMContext` instead.
+
+- The `vad` package is now deprecated and `audio.vad` should be used
+  instead. The `avd` package will get removed in a future release.
+
+### Fixed
+
+- Fixed an issue that would cause an error if no VAD analyzer was passed to
+  `LiveKitTransport` params.
+
+- Fixed `SileroVAD` processor to support interruptions properly.
+
+### Other
+
+- Added `examples/foundational/07-interruptible-vad.py`. This is the same as
+  `07-interruptible.py` but using the `SileroVAD` processor instead of passing
+  the `VADAnalyzer` in the transport.
+
+## [0.0.45] - 2024-10-16
+
+### Changed
+
+- Metrics messages have moved out from the transport's base output into RTVI.
+
+## [0.0.44] - 2024-10-15
+
+### Added
+
+- Added support for OpenAI Realtime API with the new
+  `OpenAILLMServiceRealtimeBeta` processor.
+  (see https://platform.openai.com/docs/guides/realtime/overview)
+
+- Added `RTVIBotTranscriptionProcessor` which will send the RTVI
+  `bot-transcription` protocol message. These are TTS text aggregated (into
+  sentences) messages.
+
+- Added new input params to the `MarkdownTextFilter` utility. You can set
+  `filter_code` to filter code from text and `filter_tables` to filter tables
+  from text.
+
+- Added `CanonicalMetricsService`. This processor uses the new
+  `AudioBufferProcessor` to capture conversation audio and later send it to
+  Canonical AI.
+  (see https://canonical.chat/)
+
+- Added `AudioBufferProcessor`. This processor can be used to buffer mixed user and
+  bot audio. This can later be saved into an audio file or processed by some
+  audio analyzer.
+
+- Added `on_first_participant_joined` event to `LiveKitTransport`.
+
+### Changed
+
+- LLM text responses are now logged properly as unicode characters.
+
+- `UserStartedSpeakingFrame`, `UserStoppedSpeakingFrame`,
+  `BotStartedSpeakingFrame`, `BotStoppedSpeakingFrame`, `BotSpeakingFrame` and
+  `UserImageRequestFrame` are now based from `SystemFrame`
+
+### Fixed
+
+- Merge `RTVIBotLLMProcessor`/`RTVIBotLLMTextProcessor` and
+  `RTVIBotTTSProcessor`/`RTVIBotTTSTextProcessor` to avoid out of order issues.
+
+- Fixed an issue in RTVI protocol that could cause a `bot-llm-stopped` or
+  `bot-tts-stopped` message to be sent before a `bot-llm-text` or `bot-tts-text`
+  message.
+
+- Fixed `DeepgramSTTService` constructor settings not being merged with default
+  ones.
+
+- Fixed an issue in Daily transport that would cause tasks to be hanging if
+  urgent transport messages were being sent from a transport event handler.
+
+- Fixed an issue in `BaseOutputTransport` that would cause `EndFrame` to be
+  pushed downed too early and call `FrameProcessor.cleanup()` before letting the
+  transport stop properly.
+
+## [0.0.43] - 2024-10-10
+
+### Added
+
+- Added a new util called `MarkdownTextFilter` which is a subclass of a new
+  base class called `BaseTextFilter`. This is a configurable utility which
+  is intended to filter text received by TTS services.
+
+- Added new `RTVIUserLLMTextProcessor`. This processor will send an RTVI
+  `user-llm-text` message with the user content's that was sent to the LLM.
+
+### Changed
+
+- `TransportMessageFrame` doesn't have an `urgent` field anymore, instead
+  there's now a `TransportMessageUrgentFrame` which is a `SystemFrame` and
+  therefore skip all internal queuing.
+
+- For TTS services, convert inputted languages to match each service's language
+  format
+
+### Fixed
+
+- Fixed an issue where changing a language with the Deepgram STT service
+  wouldn't apply the change. This was fixed by disconnecting and reconnecting
+  when the language changes.
+
+## [0.0.42] - 2024-10-02
+
+### Added
+
+- `SentryMetrics` has been added to report frame processor metrics to
+  Sentry. This is now possible because `FrameProcessorMetrics` can now be passed
+  to `FrameProcessor`.
+
+- Added Google TTS service and corresponding foundational example
+  `07n-interruptible-google.py`
+
+- Added AWS Polly TTS support and `07m-interruptible-aws.py` as an example.
+
+- Added InputParams to Azure TTS service.
+
+- Added `LivekitTransport` (audio-only for now).
+
+- RTVI 0.2.0 is now supported.
+
+- All `FrameProcessors` can now register event handlers.
+
+```
+tts = SomeTTSService(...)
+
+@tts.event_handler("on_connected"):
+async def on_connected(processor):
+  ...
+```
+
+- Added `AsyncGeneratorProcessor`. This processor can be used together with a
+  `FrameSerializer` as an async generator. It provides a `generator()` function
+  that returns an `AsyncGenerator` and that yields serialized frames.
+
+- Added `EndTaskFrame` and `CancelTaskFrame`. These are new frames that are
+  meant to be pushed upstream to tell the pipeline task to stop nicely or
+  immediately respectively.
+
+- Added configurable LLM parameters (e.g., temperature, top_p, max_tokens, seed)
+  for OpenAI, Anthropic, and Together AI services along with corresponding
+  setter functions.
+
+- Added `sample_rate` as a constructor parameter for TTS services.
+
+- Pipecat has a pipeline-based architecture. The pipeline consists of frame
+  processors linked to each other. The elements traveling across the pipeline
+  are called frames.
+
+  To have a deterministic behavior the frames traveling through the pipeline
+  should always be ordered, except system frames which are out-of-band
+  frames. To achieve that, each frame processor should only output frames from a
+  single task.
+
+  In this version all the frame processors have their own task to push
+  frames. That is, when `push_frame()` is called the given frame will be put
+  into an internal queue (with the exception of system frames) and a frame
+  processor task will push it out.
+
+- Added pipeline clocks. A pipeline clock is used by the output transport to
+  know when a frame needs to be presented. For that, all frames now have an
+  optional `pts` field (prensentation timestamp). There's currently just one
+  clock implementation `SystemClock` and the `pts` field is currently only used
+  for `TextFrame`s (audio and image frames will be next).
+
+- A clock can now be specified to `PipelineTask` (defaults to
+  `SystemClock`). This clock will be passed to each frame processor via the
+  `StartFrame`.
+
+- Added `CartesiaHttpTTSService`.
+
+- `DailyTransport` now supports setting the audio bitrate to improve audio
+  quality through the `DailyParams.audio_out_bitrate` parameter. The new
+  default is 96kbps.
+
+- `DailyTransport` now uses the number of audio output channels (1 or 2) to set
+  mono or stereo audio when needed.
+
+- Interruptions support has been added to `TwilioFrameSerializer` when using
+  `FastAPIWebsocketTransport`.
+
+- Added new `LmntTTSService` text-to-speech service.
+  (see https://www.lmnt.com/)
+
+- Added `TTSModelUpdateFrame`, `TTSLanguageUpdateFrame`, `STTModelUpdateFrame`,
+  and `STTLanguageUpdateFrame` frames to allow you to switch models, language
+  and voices in TTS and STT services.
+
+- Added new `transcriptions.Language` enum.
+
+### Changed
+
+- Context frames are now pushed downstream from assistant context aggregators.
+
+- Removed Silero VAD torch dependency.
+
+- Updated individual update settings frame classes into a single
+  `ServiceUpdateSettingsFrame` class.
+
+- We now distinguish between input and output audio and image frames. We
+  introduce `InputAudioRawFrame`, `OutputAudioRawFrame`, `InputImageRawFrame`
+  and `OutputImageRawFrame` (and other subclasses of those). The input frames
+  usually come from an input transport and are meant to be processed inside the
+  pipeline to generate new frames. However, the input frames will not be sent
+  through an output transport. The output frames can also be processed by any
+  frame processor in the pipeline and they are allowed to be sent by the output
+  transport.
+
+- `ParallelTask` has been renamed to `SyncParallelPipeline`. A
+  `SyncParallelPipeline` is a frame processor that contains a list of different
+  pipelines to be executed concurrently. The difference between a
+  `SyncParallelPipeline` and a `ParallelPipeline` is that, given an input frame,
+  the `SyncParallelPipeline` will wait for all the internal pipelines to
+  complete. This is achieved by making sure the last processor in each of the
+  pipelines is synchronous (e.g. an HTTP-based service that waits for the
+  response).
+
+- `StartFrame` is back a system frame to make sure it's processed immediately by
+  all processors. `EndFrame` stays a control frame since it needs to be ordered
+  allowing the frames in the pipeline to be processed.
+
+- Updated `MoondreamService` revision to `2024-08-26`.
+
+- `CartesiaTTSService` and `ElevenLabsTTSService` now add presentation
+  timestamps to their text output. This allows the output transport to push the
+  text frames downstream at almost the same time the words are spoken. We say
+  "almost" because currently the audio frames don't have presentation timestamp
+  but they should be played at roughly the same time.
+
+- `DailyTransport.on_joined` event now returns the full session data instead of
+  just the participant.
+
+- `CartesiaTTSService` is now a subclass of `TTSService`.
+
+- `DeepgramSTTService` is now a subclass of `STTService`.
+
+- `WhisperSTTService` is now a subclass of `SegmentedSTTService`. A
+  `SegmentedSTTService` is a `STTService` where the provided audio is given in a
+  big chunk (i.e. from when the user starts speaking until the user stops
+  speaking) instead of a continous stream.
+
+### Fixed
+
+- Fixed OpenAI multiple function calls.
+
+- Fixed a Cartesia TTS issue that would cause audio to be truncated in some
+  cases.
+
+- Fixed a `BaseOutputTransport` issue that would stop audio and video rendering
+  tasks (after receiving and `EndFrame`) before the internal queue was emptied,
+  causing the pipeline to finish prematurely.
+
+- `StartFrame` should be the first frame every processor receives to avoid
+  situations where things are not initialized (because initialization happens on
+  `StartFrame`) and other frames come in resulting in undesired behavior.
+
+### Performance
+
+- `obj_id()` and `obj_count()` now use `itertools.count` avoiding the need of
+  `threading.Lock`.
+
+### Other
+
+- Pipecat now uses Ruff as its formatter (https://github.com/astral-sh/ruff).
+
+## [0.0.41] - 2024-08-22
+
+### Added
+
+- Added `LivekitFrameSerializer` audio frame serializer.
+
+### Fixed
+
+- Fix `FastAPIWebsocketOutputTransport` variable name clash with subclass.
+
+- Fix an `AnthropicLLMService` issue with empty arguments in function calling.
+
+### Other
+
+- Fixed `studypal` example errors.
+
+## [0.0.40] - 2024-08-20
+
+### Added
+
+- VAD parameters can now be dynamicallt updated using the
+  `VADParamsUpdateFrame`.
+
+- `ErrorFrame` has now a `fatal` field to indicate the bot should exit if a
+  fatal error is pushed upstream (false by default). A new `FatalErrorFrame`
+  that sets this flag to true has been added.
+
+- `AnthropicLLMService` now supports function calling and initial support for
+  prompt caching.
+  (see https://www.anthropic.com/news/prompt-caching)
+
+- `ElevenLabsTTSService` can now specify ElevenLabs input parameters such as
+  `output_format`.
+
+- `TwilioFrameSerializer` can now specify Twilio's and Pipecat's desired sample
+  rates to use.
+
+- Added new `on_participant_updated` event to `DailyTransport`.
+
+- Added `DailyRESTHelper.delete_room_by_name()` and
+  `DailyRESTHelper.delete_room_by_url()`.
+
+- Added LLM and TTS usage metrics. Those are enabled when
+  `PipelineParams.enable_usage_metrics` is True.
+
+- `AudioRawFrame`s are now pushed downstream from the base output
+  transport. This allows capturing the exact words the bot says by adding an STT
+  service at the end of the pipeline.
+
+- Added new `GStreamerPipelineSource`. This processor can generate image or
+  audio frames from a GStreamer pipeline (e.g. reading an MP4 file, and RTP
+  stream or anything supported by GStreamer).
+
+- Added `TransportParams.audio_out_is_live`. This flag is False by default and
+  it is useful to indicate we should not synchronize audio with sporadic images.
+
+- Added new `BotStartedSpeakingFrame` and `BotStoppedSpeakingFrame` control
+  frames. These frames are pushed upstream and they should wrap
+  `BotSpeakingFrame`.
+
+- Transports now allow you to register event handlers without decorators.
+
+### Changed
+
+- Support RTVI message protocol 0.1. This includes new messages, support for
+  messages responses, support for actions, configuration, webhooks and a bunch
+  of new cool stuff.
+  (see https://docs.rtvi.ai/)
+
+- `SileroVAD` dependency is now imported via pip's `silero-vad` package.
+
+- `ElevenLabsTTSService` now uses `eleven_turbo_v2_5` model by default.
+
+- `BotSpeakingFrame` is now a control frame.
+
+- `StartFrame` is now a control frame similar to `EndFrame`.
+
+- `DeepgramTTSService` now is more customizable. You can adjust the encoding and
+  sample rate.
+
+### Fixed
+
+- `TTSStartFrame` and `TTSStopFrame` are now sent when TTS really starts and
+  stops. This allows for knowing when the bot starts and stops speaking even
+  with asynchronous services (like Cartesia).
+
+- Fixed `AzureSTTService` transcription frame timestamps.
+
+- Fixed an issue with `DailyRESTHelper.create_room()` expirations which would
+  cause this function to stop working after the initial expiration elapsed.
+
+- Improved `EndFrame` and `CancelFrame` handling. `EndFrame` should end things
+  gracefully while a `CancelFrame` should cancel all running tasks as soon as
+  possible.
+
+- Fixed an issue in `AIService` that would cause a yielded `None` value to be
+  processed.
+
+- RTVI's `bot-ready` message is now sent when the RTVI pipeline is ready and
+  a first participant joins.
+
+- Fixed a `BaseInputTransport` issue that was causing incoming system frames to
+  be queued instead of being pushed immediately.
+
+- Fixed a `BaseInputTransport` issue that was causing start/stop interruptions
+  incoming frames to not cancel tasks and be processed properly.
+
+### Other
+
+- Added `studypal` example (from to the Cartesia folks!).
+
+- Most examples now use Cartesia.
+
+- Added examples `foundational/19a-tools-anthropic.py`,
+  `foundational/19b-tools-video-anthropic.py` and
+  `foundational/19a-tools-togetherai.py`.
+
+- Added examples `foundational/18-gstreamer-filesrc.py` and
+  `foundational/18a-gstreamer-videotestsrc.py` that show how to use
+  `GStreamerPipelineSource`
+
+- Remove `requests` library usage.
+
+- Cleanup examples and use `DailyRESTHelper`.
+
+## [0.0.39] - 2024-07-23
+
+### Fixed
+
+- Fixed a regression introduced in 0.0.38 that would cause Daily transcription
+  to stop the Pipeline.
+
+## [0.0.38] - 2024-07-23
+
+### Added
+
+- Added `force_reload`, `skip_validation` and `trust_repo` to `SileroVAD` and
+  `SileroVADAnalyzer`. This allows caching and various GitHub repo validations.
+
+- Added `send_initial_empty_metrics` flag to `PipelineParams` to request for
+  initial empty metrics (zero values). True by default.
+
+### Fixed
+
+- Fixed initial metrics format. It was using the wrong keys name/time instead of
+  processor/value.
+
+- STT services should be using ISO 8601 time format for transcription frames.
+
+- Fixed an issue that would cause Daily transport to show a stop transcription
+  error when actually none occurred.
+
+## [0.0.37] - 2024-07-22
+
+### Added
+
+- Added `RTVIProcessor` which implements the RTVI-AI standard.
+  See https://github.com/rtvi-ai
+
+- Added `BotInterruptionFrame` which allows interrupting the bot while talking.
+
+- Added `LLMMessagesAppendFrame` which allows appending messages to the current
+  LLM context.
+
+- Added `LLMMessagesUpdateFrame` which allows changing the LLM context for the
+  one provided in this new frame.
+
+- Added `LLMModelUpdateFrame` which allows updating the LLM model.
+
+- Added `TTSSpeakFrame` which causes the bot say some text. This text will not
+  be part of the LLM context.
+
+- Added `TTSVoiceUpdateFrame` which allows updating the TTS voice.
+
+### Removed
+
+- We remove the `LLMResponseStartFrame` and `LLMResponseEndFrame` frames. These
+  were added in the past to properly handle interruptions for the
+  `LLMAssistantContextAggregator`. But the `LLMContextAggregator` is now based
+  on `LLMResponseAggregator` which handles interruptions properly by just
+  processing the `StartInterruptionFrame`, so there's no need for these extra
+  frames any more.
+
+### Fixed
+
+- Fixed an issue with `StatelessTextTransformer` where it was pushing a string
+  instead of a `TextFrame`.
+
+- `TTSService` end of sentence detection has been improved. It now works with
+  acronyms, numbers, hours and others.
+
+- Fixed an issue in `TTSService` that would not properly flush the current
+  aggregated sentence if an `LLMFullResponseEndFrame` was found.
+
+### Performance
+
+- `CartesiaTTSService` now uses websockets which improves speed. It also
+  leverages the new Cartesia contexts which maintains generated audio prosody
+  when multiple inputs are sent, therefore improving audio quality a lot.
+
+## [0.0.36] - 2024-07-02
+
+### Added
+
+- Added `GladiaSTTService`.
+  See https://docs.gladia.io/chapters/speech-to-text-api/pages/live-speech-recognition
+
+- Added `XTTSService`. This is a local Text-To-Speech service.
+  See https://github.com/coqui-ai/TTS
+
+- Added `UserIdleProcessor`. This processor can be used to wait for any
+  interaction with the user. If the user doesn't say anything within a given
+  timeout a provided callback is called.
+
+- Added `IdleFrameProcessor`. This processor can be used to wait for frames
+  within a given timeout. If no frame is received within the timeout a provided
+  callback is called.
+
+- Added new frame `BotSpeakingFrame`. This frame will be continuously pushed
+  upstream while the bot is talking.
+
+- It is now possible to specify a Silero VAD version when using `SileroVADAnalyzer`
+  or `SileroVAD`.
+
+- Added `AysncFrameProcessor` and `AsyncAIService`. Some services like
+  `DeepgramSTTService` need to process things asynchronously. For example, audio
+  is sent to Deepgram but transcriptions are not returned immediately. In these
+  cases we still require all frames (except system frames) to be pushed
+  downstream from a single task. That's what `AsyncFrameProcessor` is for. It
+  creates a task and all frames should be pushed from that task. So, whenever a
+  new Deepgram transcription is ready that transcription will also be pushed
+  from this internal task.
+
+- The `MetricsFrame` now includes processing metrics if metrics are enabled. The
+  processing metrics indicate the time a processor needs to generate all its
+  output. Note that not all processors generate these kind of metrics.
+
+### Changed
+
+- `WhisperSTTService` model can now also be a string.
+
+- Added missing \* keyword separators in services.
+
+### Fixed
+
+- `WebsocketServerTransport` doesn't try to send frames anymore if serializers
+  returns `None`.
+
+- Fixed an issue where exceptions that occurred inside frame processors were
+  being swallowed and not displayed.
+
+- Fixed an issue in `FastAPIWebsocketTransport` where it would still try to send
+  data to the websocket after being closed.
+
+### Other
+
+- Added Fly.io deployment example in `examples/deployment/flyio-example`.
+
+- Added new `17-detect-user-idle.py` example that shows how to use the new
+  `UserIdleProcessor`.
+
+## [0.0.35] - 2024-06-28
+
+### Changed
+
+- `FastAPIWebsocketParams` now require a serializer.
+
+- `TwilioFrameSerializer` now requires a `streamSid`.
+
+### Fixed
+
+- Silero VAD number of frames needs to be 512 for 16000 sample rate or 256 for
+  8000 sample rate.
+
+## [0.0.34] - 2024-06-25
+
+### Fixed
+
+- Fixed an issue with asynchronous STT services (Deepgram and Azure) that could
+  interruptions to ignore transcriptions.
+
+- Fixed an issue introduced in 0.0.33 that would cause the LLM to generate
+  shorter output.
+
+## [0.0.33] - 2024-06-25
+
+### Changed
+
+- Upgraded to Cartesia's new Python library 1.0.0. `CartesiaTTSService` now
+  expects a voice ID instead of a voice name (you can get the voice ID from
+  Cartesia's playground). You can also specify the audio `sample_rate` and
+  `encoding` instead of the previous `output_format`.
+
+### Fixed
+
+- Fixed an issue with asynchronous STT services (Deepgram and Azure) that could
+  cause static audio issues and interruptions to not work properly when dealing
+  with multiple LLMs sentences.
+
+- Fixed an issue that could mix new LLM responses with previous ones when
+  handling interruptions.
+
+- Fixed a Daily transport blocking situation that occurred while reading audio
+  frames after a participant left the room. Needs daily-python >= 0.10.1.
+
 ## [0.0.32] - 2024-06-22
 
 ### Added
@@ -18,7 +636,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added new `TwilioFrameSerializer`. This is a new serializer that knows how to
   serialize and deserialize audio frames from Twilio.
 
-- Added Daily transport event: `on_dialout_answered`.  See
+- Added Daily transport event: `on_dialout_answered`. See
   https://reference-python.daily.co/api_reference.html#daily.EventHandler
 
 - Added new `AzureSTTService`. This allows you to use Azure Speech-To-Text.
@@ -258,7 +876,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added Daily transport support for dial-in use cases.
 
 - Added Daily transport events: `on_dialout_connected`, `on_dialout_stopped`,
-  `on_dialout_error` and `on_dialout_warning`.  See
+  `on_dialout_error` and `on_dialout_warning`. See
   https://reference-python.daily.co/api_reference.html#daily.EventHandler
 
 ## [0.0.21] - 2024-05-22

README.md

@@ -4,8 +4,7 @@
 
 # Pipecat
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Discord](https://img.shields.io/discord/1239284677165056021
-)](https://discord.gg/pipecat)
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
 `pipecat` is a framework for building voice (and multimodal) conversational agents. Things like personal coaches, meeting assistants, [story-telling toys for kids](https://storytelling-chatbot.fly.dev/), customer support bots, [intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0), and snarky social companions.
 
@@ -39,7 +38,7 @@ pip install "pipecat-ai[option,...]"
 
 Your project may or may not need these, so they're made available as optional requirements. Here is a list:
 
-- **AI services**: `anthropic`, `azure`, `deepgram`, `google`, `fal`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`
+- **AI services**: `anthropic`, `assemblyai`, `aws`, `azure`, `deepgram`, `gladia`, `google`, `fal`, `lmnt`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`, `xtts`
 - **Transports**: `local`, `websocket`, `daily`
 
 ## Code examples
@@ -49,56 +48,56 @@ Your project may or may not need these, so they're made available as optional re
 
 ## A simple voice agent running locally
 
-Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [ElevenLabs](https://elevenlabs.io/) for text-to-speech.
+Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [Cartesia](https://cartesia.ai/) for text-to-speech.
 
 ```python
-#app.py
-
 import asyncio
-import aiohttp
 
 from pipecat.frames.frames import EndFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 async def main():
-  async with aiohttp.ClientSession() as session:
-    # Use Daily as a real-time media transport (WebRTC)
-    transport = DailyTransport(
-      room_url=...,
-      token=...,
-      "Bot Name",
-      DailyParams(audio_out_enabled=True))
+  # Use Daily as a real-time media transport (WebRTC)
+  transport = DailyTransport(
+    room_url=...,
+    token=...,
+    bot_name="Bot Name",
+    params=DailyParams(audio_out_enabled=True))
 
-    # Use Eleven Labs for Text-to-Speech
-    tts = ElevenLabsTTSService(
-      aiohttp_session=session,
-      api_key=...,
-      voice_id=...,
-      )
+  # Use Cartesia for Text-to-Speech
+  tts = CartesiaTTSService(
+    api_key=...,
+    voice_id=...
+  )
 
-    # Simple pipeline that will process text to speech and output the result
-    pipeline = Pipeline([tts, transport.output()])
+  # Simple pipeline that will process text to speech and output the result
+  pipeline = Pipeline([tts, transport.output()])
 
-    # Create Pipecat processor that can run one or more pipelines tasks
-    runner = PipelineRunner()
+  # Create Pipecat processor that can run one or more pipelines tasks
+  runner = PipelineRunner()
 
-    # Assign the task callable to run the pipeline
-    task = PipelineTask(pipeline)
+  # Assign the task callable to run the pipeline
+  task = PipelineTask(pipeline)
 
-    # Register an event handler to play audio when a
-    # participant joins the transport WebRTC session
-    @transport.event_handler("on_participant_joined")
-    async def on_new_participant_joined(transport, participant):
-      participant_name = participant["info"]["userName"] or ''
-      # Queue a TextFrame that will get spoken by the TTS service (Eleven Labs)
-      await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
+  # Register an event handler to play audio when a
+  # participant joins the transport WebRTC session
+  @transport.event_handler("on_first_participant_joined")
+  async def on_first_participant_joined(transport, participant):
+    participant_name = participant.get("info", {}).get("userName", "")
+    # Queue a TextFrame that will get spoken by the TTS service (Cartesia)
+    await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))
 
-    # Run the pipeline task
-    await runner.run(task)
+  # Register an event handler to exit the application when the user leaves.
+  @transport.event_handler("on_participant_left")
+  async def on_participant_left(transport, participant, reason):
+    await task.queue_frame(EndFrame())
+
+  # Run the pipeline task
+  await runner.run(task)
 
 if __name__ == "__main__":
   asyncio.run(main())
@@ -112,7 +111,6 @@ python app.py
 
 Daily provides a prebuilt WebRTC user interface. Whilst the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
 
-
 ## WebRTC for production use
 
 WebSockets are fine for server-to-server communication or for initial development. But for production use, you’ll need client-server audio to use a protocol designed for real-time media transport. (For an explanation of the difference between WebSockets and WebRTC, see [this post.](https://www.daily.co/blog/how-to-talk-to-an-llm-with-your-voice/#webrtc))
@@ -125,15 +123,12 @@ Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://
 
 Voice Activity Detection &mdash; very important for knowing when a user has finished speaking to your bot. If you are not using press-to-talk, and want Pipecat to detect when the user has finished talking, VAD is an essential component for a natural feeling conversation.
 
-Pipecast makes use of WebRTC VAD by default when using a WebRTC transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.
+Pipecat makes use of WebRTC VAD by default when using a WebRTC transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.
 
 ```shell
 pip install pipecat-ai[silero]
 ```
 
-The first time your run your bot with Silero, startup may take a while whilst it downloads and caches the model in the background. You can check the progress of this in the console.
-
-
 ## Hacking on the framework itself
 
 _Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
@@ -146,20 +141,20 @@ source venv/bin/activate
 From the root of this repo, run the following:
 
 ```shell
-pip install -r dev-requirements.txt -r {env}-requirements.txt
+pip install -r dev-requirements.txt
 python -m build
 ```
 
-This builds the package. To use the package locally (eg to run sample files), run
+This builds the package. To use the package locally (e.g. to run sample files), run
 
 ```shell
-pip install --editable .
+pip install --editable ".[option,...]"
 ```
 
 If you want to use this package from another directory, you can run:
 
 ```shell
-pip install path_to_this_repo
+pip install "path_to_this_repo[option,...]"
 ```
 
 ### Running tests
@@ -167,27 +162,29 @@ pip install path_to_this_repo
 From the root directory, run:
 
 ```shell
-pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests
+pytest --doctest-modules --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
 ```
 
 ## Setting up your editor
 
-This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting.
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
 
 ### Emacs
 
-You can use [use-package](https://github.com/jwiegley/use-package) to install [py-autopep8](https://codeberg.org/ideasman42/emacs-py-autopep8) package and configure `autopep8` arguments:
+You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
 
 ```elisp
-(use-package py-autopep8
+(use-package lazy-ruff
   :ensure t
-  :defer t
-  :hook ((python-mode . py-autopep8-mode))
+  :hook ((python-mode . lazy-ruff-mode))
   :config
-  (setq py-autopep8-options '("-a" "-a", "--max-line-length=100")))
+  (setq lazy-ruff-format-command "ruff format --config line-length=100")
+  (setq lazy-ruff-only-format-block t)
+  (setq lazy-ruff-only-format-region t)
+  (setq lazy-ruff-only-format-buffer t))
 ```
 
-`autopep8` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
+`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
 
 ```elisp
 (use-package pyvenv-auto
@@ -200,18 +197,14 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [p
 ### Visual Studio Code
 
 Install the
-[autopep8](https://marketplace.visualstudio.com/items?itemName=ms-python.autopep8) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, enable formatting on save and configure `autopep8` arguments:
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, enable formatting on save and configure `ruff` arguments:
 
 ```json
 "[python]": {
-    "editor.defaultFormatter": "ms-python.autopep8",
+    "editor.defaultFormatter": "charliermarsh.ruff",
     "editor.formatOnSave": true
 },
-"autopep8.args": [
-    "-a",
-    "-a",
-    "--max-line-length=100"
-],
+"ruff.format.args": ["--config", "line-length=100"]
 ```
 
 ## Getting help

dev-requirements.txt

@@ -1,8 +1,8 @@
-autopep8~=2.1.0
 build~=1.2.1
 grpcio-tools~=1.62.2
 pip-tools~=7.4.1
-pyright~=1.1.367
-pytest~=8.2.0
-setuptools~=69.5.1
+pyright~=1.1.376
+pytest~=8.3.2
+ruff~=0.6.7
+setuptools~=72.2.0
 setuptools_scm~=8.1.0

dot-env.template

@@ -1,6 +1,11 @@
 # Anthropic
 ANTHROPIC_API_KEY=...
 
+# AWS
+AWS_SECRET_ACCESS_KEY=...
+AWS_ACCESS_KEY_ID=...
+AWS_REGION=...
+
 # Azure
 AZURE_SPEECH_REGION=...
 AZURE_SPEECH_API_KEY=...
@@ -27,6 +32,13 @@ FAL_KEY=...
 # Fireworks
 FIREWORKS_API_KEY=...
 
+# Gladia
+GLADIA_API_KEY=...
+
+# LMNT
+LMNT_API_KEY=...
+LMNT_VOICE_ID=...
+
 # PlayHT
 PLAY_HT_USER_ID=...
 PLAY_HT_API_KEY=...

examples/README.md

@@ -41,6 +41,7 @@ Next, follow the steps in the README for each demo.
 | [Patient intake](patient-intake)             | A chatbot that can call functions in response to user input.                                                                               | Deepgram, ElevenLabs, OpenAI, Daily, Daily Prebuilt UI            |
 | [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                    | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [Twilio Chatbot](twilio-chatbot)             | A chatbot that connects to an incoming phone call from Twilio.                                                                             | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
+| [studypal](studypal)                         | A chatbot to have a conversation about any article on the web                                                                              |                                                                   |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

examples/canonical-metrics/.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+recordings/
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+runpod.toml

examples/canonical-metrics/Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.10-bullseye
+RUN mkdir /app
+COPY *.py /app/
+COPY requirements.txt /app/
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["python3", "server.py"]

examples/canonical-metrics/README.md

@@ -0,0 +1,37 @@
+# Simple Chatbot
+
+<img src="image.png" width="420px">
+
+This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.
+
+See a video of it in action: https://x.com/kwindla/status/1778628911817183509
+
+And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416
+
+ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
+
+## Build and test the Docker image
+
+```
+docker build -t chatbot .
+docker run --env-file .env -p 7860:7860 chatbot
+```

examples/canonical-metrics/bot.py

@@ -0,0 +1,146 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+import uuid
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.services.canonical import CanonicalMetricsService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            aiohttp_session=session,
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        """
+        CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
+        call completion, CanonicalMetrics will send the audio buffer to Canonical for
+        analysis. Visit https://voice.canonical.chat to learn more.
+        """
+        audio_buffer_processor = AudioBufferProcessor()
+        canonical = CanonicalMetricsService(
+            audio_buffer_processor=audio_buffer_processor,
+            aiohttp_session=session,
+            api_key=os.getenv("CANONICAL_API_KEY"),
+            api_url=os.getenv("CANONICAL_API_URL"),
+            call_id=str(uuid.uuid4()),
+            assistant="pipecat-chatbot",
+            assistant_speaks_first=True,
+        )
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                audio_buffer_processor,  # captures audio into a buffer
+                canonical,  # uploads audio buffer to Canonical AI for metrics
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.queue_frame(EndFrame())
+
+        @transport.event_handler("on_call_state_updated")
+        async def on_call_state_updated(transport, state):
+            if state == "left":
+                await task.queue_frame(EndFrame())
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/canonical-metrics/env.example

@@ -0,0 +1,6 @@
+DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
+DAILY_API_KEY=7df...
+OPENAI_API_KEY=sk-PL...
+ELEVENLABS_API_KEY=aeb...
+CANONICAL_API_KEY=can...
+CANONICAL_API_URL=

examples/canonical-metrics/requirements.txt

@@ -0,0 +1,5 @@
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,openai,silero,elevenlabs,canonical]
+

examples/canonical-metrics/runner.py

@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    if not key:
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+
+    # Create a meeting token for the given room with an expiration 1 hour in
+    # the future.
+    expiry_time: float = 60 * 60
+
+    token = await daily_rest_helper.get_token(url, expiry_time)
+
+    return (url, token)
+    return (url, token)

examples/canonical-metrics/server.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+MAX_BOTS_PER_ROOM = 1
+
+# Bot sub-process dict for status reporting and concurrency control
+bot_procs = {}
+
+daily_helpers = {}
+
+load_dotenv(override=True)
+
+
+def cleanup():
+    # Clean up function, just to be extra safe
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def start_agent(request: Request):
+    print(f"!!! Creating room")
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    print(f"!!! Room URL: {room.url}")
+    # Ensure the room property is present
+    if not room.url:
+        raise HTTPException(
+            status_code=500,
+            detail="Missing 'room' property in request data. Cannot start agent without a target room!",
+        )
+
+    # Check if there is already an existing process running in this room
+    num_bots_in_room = sum(
+        1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
+    )
+    if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+        raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
+
+    # Get the token for the room
+    token = await daily_helpers["rest"].get_token(room.url)
+
+    if not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+    # Spawn a new agent, and join the user session
+    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
+    try:
+        proc = subprocess.Popen(
+            [f"python3 -m bot -u {room.url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room.url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    return RedirectResponse(room.url)
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+    # Look up the subprocess
+    proc = bot_procs.get(pid)
+
+    # If the subprocess doesn't exist, return an error
+    if not proc:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+    # Check the status of the subprocess
+    if proc[0].poll() is None:
+        status = "running"
+    else:
+        status = "finished"
+
+    return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

examples/chatbot-audio-recording/.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+runpod.toml

examples/chatbot-audio-recording/Dockerfile

@@ -0,0 +1,15 @@
+FROM python:3.10-bullseye
+
+RUN mkdir /app
+RUN mkdir /app/assets
+RUN mkdir /app/utils
+COPY *.py /app/
+COPY requirements.txt /app/
+
+
+WORKDIR /app
+RUN pip3 install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["python3", "server.py"]

examples/chatbot-audio-recording/README.md

@@ -0,0 +1,37 @@
+# Simple Chatbot
+
+<img src="image.png" width="420px">
+
+This app connects you to a chatbot powered by GPT-4, complete with animations generated by Stable Video Diffusion.
+
+See a video of it in action: https://x.com/kwindla/status/1778628911817183509
+
+And a quick video walkthrough of the code: https://www.loom.com/share/13df1967161f4d24ade054e7f8753416
+
+ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
+
+## Get started
+
+```python
+python3 -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+
+cp env.example .env # and add your credentials
+
+```
+
+## Run the server
+
+```bash
+python server.py
+```
+
+Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
+
+## Build and test the Docker image
+
+```
+docker build -t chatbot .
+docker run --env-file .env -p 7860:7860 chatbot
+```

examples/chatbot-audio-recording/bot.py

@@ -0,0 +1,141 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+import datetime
+import wave
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import EndFrame, LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def save_audio(audiobuffer):
+    if audiobuffer.has_audio():
+        merged_audio = audiobuffer.merge_audio_buffers()
+        filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
+        with wave.open(filename, "wb") as wf:
+            wf.setnchannels(2)
+            wf.setsampwidth(2)
+            wf.setframerate(audiobuffer._sample_rate)
+            wf.writeframes(merged_audio)
+        print(f"Merged audio saved to {filename}")
+    else:
+        print("No audio data to save")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Chatbot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_in_enabled=True,
+                camera_out_enabled=False,
+                vad_enabled=True,
+                vad_audio_passthrough=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                transcription_enabled=True,
+                #
+                # Spanish
+                #
+                # transcription_settings=DailyTranscriptionSettings(
+                #     language="es",
+                #     tier="nova",
+                #     model="2-general"
+                # )
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            #
+            # English
+            #
+            voice_id="cgSgspJ2msm6clMCkdW9",
+            aiohttp_session=session,
+            #
+            # Spanish
+            #
+            # model="eleven_multilingual_v2",
+            # voice_id="gD1IexrzCvsXPHUuT0s3",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                #
+                # English
+                #
+                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your response to 12 words or fewer.",
+                #
+                # Spanish
+                #
+                # "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        audiobuffer = AudioBufferProcessor()
+        pipeline = Pipeline(
+            [
+                transport.input(),  # microphone
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                audiobuffer,  # used to buffer the audio in the pipeline
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            print(f"Participant left: {participant}")
+            await task.queue_frame(EndFrame())
+            await save_audio(audiobuffer)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/chatbot-audio-recording/env.example

@@ -0,0 +1,4 @@
+DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
+DAILY_API_KEY=7df...
+OPENAI_API_KEY=sk-PL...
+ELEVENLABS_API_KEY=aeb...

examples/chatbot-audio-recording/requirements.txt

@@ -0,0 +1,4 @@
+python-dotenv
+fastapi[all]
+uvicorn
+pipecat-ai[daily,openai,silero,elevenlabs]

examples/chatbot-audio-recording/runner.py

@@ -0,0 +1,56 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+import aiohttp
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
+
+
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
+    parser.add_argument(
+        "-k",
+        "--apikey",
+        type=str,
+        required=False,
+        help="Daily API Key (needed to create an owner token for the room)",
+    )
+
+    args, unknown = parser.parse_known_args()
+
+    url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
+    key = args.apikey or os.getenv("DAILY_API_KEY")
+
+    if not url:
+        raise Exception(
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
+
+    if not key:
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+
+    # Create a meeting token for the given room with an expiration 1 hour in
+    # the future.
+    expiry_time: float = 60 * 60
+
+    token = await daily_rest_helper.get_token(url, expiry_time)
+
+    return (url, token)
+    return (url, token)

examples/chatbot-audio-recording/server.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+import subprocess
+from contextlib import asynccontextmanager
+
+import aiohttp
+from dotenv import load_dotenv
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
+
+MAX_BOTS_PER_ROOM = 1
+
+# Bot sub-process dict for status reporting and concurrency control
+bot_procs = {}
+
+daily_helpers = {}
+
+load_dotenv(override=True)
+
+
+def cleanup():
+    # Clean up function, just to be extra safe
+    for entry in bot_procs.values():
+        proc = entry[0]
+        proc.terminate()
+        proc.wait()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+    cleanup()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/")
+async def start_agent(request: Request):
+    print(f"!!! Creating room")
+    room = await daily_helpers["rest"].create_room(DailyRoomParams())
+    print(f"!!! Room URL: {room.url}")
+    # Ensure the room property is present
+    if not room.url:
+        raise HTTPException(
+            status_code=500,
+            detail="Missing 'room' property in request data. Cannot start agent without a target room!",
+        )
+
+    # Check if there is already an existing process running in this room
+    num_bots_in_room = sum(
+        1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
+    )
+    if num_bots_in_room >= MAX_BOTS_PER_ROOM:
+        raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
+
+    # Get the token for the room
+    token = await daily_helpers["rest"].get_token(room.url)
+
+    if not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+    # Spawn a new agent, and join the user session
+    # Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
+    try:
+        proc = subprocess.Popen(
+            [f"python3 -m bot -u {room.url} -t {token}"],
+            shell=True,
+            bufsize=1,
+            cwd=os.path.dirname(os.path.abspath(__file__)),
+        )
+        bot_procs[proc.pid] = (proc, room.url)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+
+    return RedirectResponse(room.url)
+
+
+@app.get("/status/{pid}")
+def get_status(pid: int):
+    # Look up the subprocess
+    proc = bot_procs.get(pid)
+
+    # If the subprocess doesn't exist, return an error
+    if not proc:
+        raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
+
+    # Check the status of the subprocess
+    if proc[0].poll() is None:
+        status = "running"
+    else:
+        status = "finished"
+
+    return JSONResponse({"bot_id": pid, "status": status})
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    default_host = os.getenv("HOST", "0.0.0.0")
+    default_port = int(os.getenv("FAST_API_PORT", "7860"))
+
+    parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
+    parser.add_argument("--host", type=str, default=default_host, help="Host address")
+    parser.add_argument("--port", type=int, default=default_port, help="Port number")
+    parser.add_argument("--reload", action="store_true", help="Reload code on change")
+
+    config = parser.parse_args()
+
+    uvicorn.run(
+        "server:app",
+        host=config.host,
+        port=config.port,
+        reload=config.reload,
+    )

examples/deployment/flyio-example/Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.11-bullseye
+
+# Open port 7860 for http service
+ENV FAST_API_PORT=7860
+EXPOSE 7860
+
+# Install Python dependencies
+COPY *.py .
+COPY ./requirements.txt requirements.txt
+RUN pip3 install --no-cache-dir --upgrade -r requirements.txt
+
+# Start the FastAPI server
+CMD python3 bot_runner.py --port ${FAST_API_PORT}

examples/deployment/flyio-example/README.md

@@ -0,0 +1,39 @@
+# Fly.io deployment example
+
+This project modifies the `bot_runner.py` server to launch a new machine for each user session. This is a recommended approach for production vs. running shell processess as your deployment will quickly run out of system resources under load.
+
+For this example, we are using Daily as a WebRTC transport and provisioning a new room and token for each session. You can use another transport, such as WebSockets, by modifying the `bot.py` and `bot_runner.py` files accordingly.
+
+## Setting up your fly.io deployment
+
+### Create your fly.toml file
+
+You can copy the `example-fly.toml` as a reference. Be sure to change the app name to something unique.
+
+### Create your .env file
+
+Copy the base `env.example` to `.env` and enter the necessary API keys.
+
+`FLY_APP_NAME` should match that in the `fly.toml` file.
+
+### Launch a new fly.io project
+
+`fly launch` or `fly launch --org your-org-name`
+
+### Set the necessary app secrets from your .env
+
+Note: you can do this manually via the fly.io dashboard under the "secrets" sub-section of your deployment (e.g. "https://fly.io/apps/fly-app-name/secrets") or run the following terminal command:
+
+`cat .env | tr '\n' ' ' | xargs flyctl secrets set`
+
+### Deploy your machine
+
+`fly deploy`
+
+## Connecting to your bot
+
+Send a post request to your running fly.io instance:
+
+`curl --location --request POST 'https://YOUR_FLY_APP_NAME/'`
+
+This request will wait until the machine enters into a `starting` state, before returning the a room URL and token to join.

examples/deployment/flyio-example/bot.py

@@ -0,0 +1,101 @@
+import asyncio
+import os
+import sys
+import argparse
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.frames.frames import LLMMessagesFrame, EndFrame
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+daily_api_key = os.getenv("DAILY_API_KEY", "")
+daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
+
+
+async def main(room_url: str, token: str):
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            api_url=daily_api_url,
+            api_key=daily_api_key,
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_enabled=False,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+        ),
+    )
+
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Chatbot, a friendly, helpful robot. Your output will be converted to audio so don't include special characters other than '!' or '?' in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying hello.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        transport.capture_participant_transcription(participant["id"])
+        await task.queue_frames([LLMMessagesFrame(messages)])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.queue_frame(EndFrame())
+
+    @transport.event_handler("on_call_state_updated")
+    async def on_call_state_updated(transport, state):
+        if state == "left":
+            await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Pipecat Bot")
+    parser.add_argument("-u", type=str, help="Room URL")
+    parser.add_argument("-t", type=str, help="Token")
+    config = parser.parse_args()
+
+    asyncio.run(main(config.u, config.t))

examples/deployment/flyio-example/bot_runner.py

@@ -0,0 +1,211 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
+import argparse
+import subprocess
+import os
+
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+
+from pipecat.transports.services.helpers.daily_rest import (
+    DailyRESTHelper,
+    DailyRoomObject,
+    DailyRoomProperties,
+    DailyRoomParams,
+)
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+
+# ------------ Configuration ------------ #
+
+MAX_SESSION_TIME = 5 * 60  # 5 minutes
+REQUIRED_ENV_VARS = [
+    "DAILY_API_KEY",
+    "OPENAI_API_KEY",
+    "ELEVENLABS_API_KEY",
+    "ELEVENLABS_VOICE_ID",
+    "FLY_API_KEY",
+    "FLY_APP_NAME",
+]
+
+FLY_API_HOST = os.getenv("FLY_API_HOST", "https://api.machines.dev/v1")
+FLY_APP_NAME = os.getenv("FLY_APP_NAME", "pipecat-fly-example")
+FLY_API_KEY = os.getenv("FLY_API_KEY", "")
+FLY_HEADERS = {"Authorization": f"Bearer {FLY_API_KEY}", "Content-Type": "application/json"}
+
+daily_helpers = {}
+
+
+# ----------------- API ----------------- #
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+
+
+app = FastAPI(lifespan=lifespan)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ----------------- Main ----------------- #
+
+
+async def spawn_fly_machine(room_url: str, token: str):
+    async with aiohttp.ClientSession() as session:
+        # Use the same image as the bot runner
+        async with session.get(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines", headers=FLY_HEADERS
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Unable to get machine info from Fly: {text}")
+
+            data = await r.json()
+            image = data[0]["config"]["image"]
+
+        # Machine configuration
+        cmd = f"python3 bot.py -u {room_url} -t {token}"
+        cmd = cmd.split()
+        worker_props = {
+            "config": {
+                "image": image,
+                "auto_destroy": True,
+                "init": {"cmd": cmd},
+                "restart": {"policy": "no"},
+                "guest": {"cpu_kind": "shared", "cpus": 1, "memory_mb": 1024},
+            },
+        }
+
+        # Spawn a new machine instance
+        async with session.post(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines", headers=FLY_HEADERS, json=worker_props
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Problem starting a bot worker: {text}")
+
+            data = await r.json()
+            # Wait for the machine to enter the started state
+            vm_id = data["id"]
+
+        async with session.get(
+            f"{FLY_API_HOST}/apps/{FLY_APP_NAME}/machines/{vm_id}/wait?state=started",
+            headers=FLY_HEADERS,
+        ) as r:
+            if r.status != 200:
+                text = await r.text()
+                raise Exception(f"Bot was unable to enter started state: {text}")
+
+    print(f"Machine joined room: {room_url}")
+
+
+@app.post("/")
+async def start_bot(request: Request) -> JSONResponse:
+    try:
+        data = await request.json()
+        # Is this a webhook creation request?
+        if "test" in data:
+            return JSONResponse({"test": True})
+    except Exception as e:
+        pass
+
+    # Use specified room URL, or create a new one if not specified
+    room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
+
+    if not room_url:
+        params = DailyRoomParams(properties=DailyRoomProperties())
+        try:
+            room: DailyRoomObject = await daily_helpers["rest"].create_room(params=params)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Unable to provision room {e}")
+    else:
+        # Check passed room URL exists, we should assume that it already has a sip set up
+        try:
+            room: DailyRoomObject = await daily_helpers["rest"].get_room_from_url(room_url)
+        except Exception:
+            raise HTTPException(status_code=500, detail=f"Room not found: {room_url}")
+
+    # Give the agent a token to join the session
+    token = await daily_helpers["rest"].get_token(room.url, MAX_SESSION_TIME)
+
+    if not room or not token:
+        raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room_url}")
+
+    # Launch a new fly.io machine, or run as a shell process (not recommended)
+    run_as_process = os.getenv("RUN_AS_PROCESS", False)
+
+    if run_as_process:
+        try:
+            subprocess.Popen(
+                [f"python3 -m bot -u {room.url} -t {token}"],
+                shell=True,
+                bufsize=1,
+                cwd=os.path.dirname(os.path.abspath(__file__)),
+            )
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
+    else:
+        try:
+            await spawn_fly_machine(room.url, token)
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=f"Failed to spawn VM: {e}")
+
+    # Grab a token for the user to join with
+    user_token = await daily_helpers["rest"].get_token(room.url, MAX_SESSION_TIME)
+
+    return JSONResponse(
+        {
+            "room_url": room.url,
+            "token": user_token,
+        }
+    )
+
+
+if __name__ == "__main__":
+    # Check environment variables
+    for env_var in REQUIRED_ENV_VARS:
+        if env_var not in os.environ:
+            raise Exception(f"Missing environment variable: {env_var}.")
+
+    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
+    parser.add_argument(
+        "--host", type=str, default=os.getenv("HOST", "0.0.0.0"), help="Host address"
+    )
+    parser.add_argument("--port", type=int, default=os.getenv("PORT", 7860), help="Port number")
+    parser.add_argument(
+        "--reload", action="store_true", default=False, help="Reload code on change"
+    )
+
+    config = parser.parse_args()
+
+    try:
+        import uvicorn
+
+        uvicorn.run("bot_runner:app", host=config.host, port=config.port, reload=config.reload)
+    except KeyboardInterrupt:
+        print("Pipecat runner shutting down...")

examples/deployment/flyio-example/env.example

@@ -0,0 +1,8 @@
+DAILY_API_KEY=
+DAILY_SAMPLE_ROOM_URL= # Enter a Daily room URL to use a set room URL each time (useful for local testing)
+OPENAI_API_KEY=
+ELEVENLABS_API_KEY=
+ELEVENLABS_VOICE_ID=
+FLY_API_KEY=
+FLY_APP_NAME=
+RUN_AS_PROCESS= # Spawn fly.io machine for each session or run as local process

examples/deployment/flyio-example/example-fly.toml

@@ -0,0 +1,25 @@
+# fly.toml app configuration file generated for pipecat-fly-example on 2024-07-01T15:04:53+01:00
+#
+# See https://fly.io/docs/reference/configuration/ for information about how to use this file.
+#
+
+app = 'pipecat-fly-example'
+primary_region = 'sjc'
+
+[build]
+
+[env]
+  FLY_APP_NAME = 'pipecat-fly-example'
+
+[http_service]
+  internal_port = 7860
+  force_https = true
+  auto_stop_machines = true
+  auto_start_machines = true
+  min_machines_running = 0
+  processes = ['app']
+
+[[vm]]
+  memory = 512
+  cpu_kind = 'shared'
+  cpus = 1

examples/deployment/flyio-example/requirements.txt

@@ -0,0 +1,5 @@
+pipecat-ai[daily,openai,silero]
+fastapi
+uvicorn
+python-dotenv
+loguru

examples/dialin-chatbot/bot_daily.py

@@ -1,24 +1,22 @@
 import asyncio
-import aiohttp
 import os
 import sys
 import argparse
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
-from pipecat.frames.frames import (
-    LLMMessagesFrame,
-    EndFrame
-)
+from pipecat.frames.frames import LLMMessagesFrame, EndFrame
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyDialinSettings
-from pipecat.vad.silero import SileroVADAnalyzer
+
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -29,75 +27,70 @@ daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
 
 
 async def main(room_url: str, token: str, callId: str, callDomain: str):
-    async with aiohttp.ClientSession() as session:
-        # diallin_settings are only needed if Daily's SIP URI is used
-        # If you are handling this via Twilio, Telnyx, set this to None
-        # and handle call-forwarding when on_dialin_ready fires.
-        diallin_settings = DailyDialinSettings(
-            call_id=callId,
-            call_domain=callDomain
-        )
+    # diallin_settings are only needed if Daily's SIP URI is used
+    # If you are handling this via Twilio, Telnyx, set this to None
+    # and handle call-forwarding when on_dialin_ready fires.
+    diallin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
 
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Chatbot",
-            DailyParams(
-                api_url=daily_api_url,
-                api_key=daily_api_key,
-                dialin_settings=diallin_settings,
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                camera_out_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
-            )
-        )
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            api_url=daily_api_url,
+            api_key=daily_api_key,
+            dialin_settings=diallin_settings,
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_enabled=False,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+        ),
+    )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        messages = [
-            {
-                "role": "system",
-                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
-            },
-        ]
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
+        },
+    ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
+    pipeline = Pipeline(
+        [
             transport.input(),
-            tma_in,
+            context_aggregator.user(),
             llm,
             tts,
             transport.output(),
-            tma_out,
-        ])
+            context_aggregator.assistant(),
+        ]
+    )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([LLMMessagesFrame(messages)])
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        transport.capture_participant_transcription(participant["id"])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.queue_frame(EndFrame())
 
-        runner = PipelineRunner()
+    runner = PipelineRunner()
 
-        await runner.run(task)
+    await runner.run(task)
 
 
 if __name__ == "__main__":

examples/dialin-chatbot/bot_runner.py

@@ -6,40 +6,62 @@ provisioning a room and starting a Pipecat bot in response.
 
 Refer to README for more information.
 """
+
+import aiohttp
 import os
 import argparse
 import subprocess
-from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomObject, DailyRoomProperties, DailyRoomSipParams, DailyRoomParams
+
+from contextlib import asynccontextmanager
+
 from fastapi import FastAPI, Request, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, PlainTextResponse
 from twilio.twiml.voice_response import VoiceResponse
 
+from pipecat.transports.services.helpers.daily_rest import (
+    DailyRESTHelper,
+    DailyRoomObject,
+    DailyRoomProperties,
+    DailyRoomSipParams,
+    DailyRoomParams,
+)
+
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 
 # ------------ Configuration ------------ #
 
 MAX_SESSION_TIME = 5 * 60  # 5 minutes
-REQUIRED_ENV_VARS = ['OPENAI_API_KEY', 'DAILY_API_KEY',
-                     'ELEVENLABS_API_KEY', 'ELEVENLABS_VOICE_ID']
-
-daily_rest_helper = DailyRESTHelper(
-    os.getenv("DAILY_API_KEY", ""),
-    os.getenv("DAILY_API_URL", 'https://api.daily.co/v1'))
+REQUIRED_ENV_VARS = ["OPENAI_API_KEY", "DAILY_API_KEY", "ELEVENLABS_API_KEY", "ELEVENLABS_VOICE_ID"]
 
+daily_helpers = {}
 
 # ----------------- API ----------------- #
 
-app = FastAPI()
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    aiohttp_session = aiohttp.ClientSession()
+    daily_helpers["rest"] = DailyRESTHelper(
+        daily_api_key=os.getenv("DAILY_API_KEY", ""),
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
+    yield
+    await aiohttp_session.close()
+
+
+app = FastAPI(lifespan=lifespan)
 
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
-    allow_headers=["*"]
+    allow_headers=["*"],
 )
 
 """
@@ -53,61 +75,49 @@ action using the Twilio Client library.
 """
 
 
-def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
+async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
     if not room_url:
         params = DailyRoomParams(
             properties=DailyRoomProperties(
                 # Note: these are the default values, except for the display name
                 sip=DailyRoomSipParams(
-                    display_name="dialin-user",
-                    video=False,
-                    sip_mode="dial-in",
-                    num_endpoints=1
+                    display_name="dialin-user", video=False, sip_mode="dial-in", num_endpoints=1
                 )
             )
         )
 
         print(f"Creating new room...")
-        room: DailyRoomObject = daily_rest_helper.create_room(params=params)
+        room: DailyRoomObject = await daily_helpers["rest"].create_room(params=params)
 
     else:
         # Check passed room URL exist (we assume that it already has a sip set up!)
         try:
             print(f"Joining existing room: {room_url}")
-            room: DailyRoomObject = daily_rest_helper.get_room_from_url(
-                room_url)
+            room: DailyRoomObject = await daily_helpers["rest"].get_room_from_url(room_url)
         except Exception:
-            raise HTTPException(
-                status_code=500, detail=f"Room not found: {room_url}")
+            raise HTTPException(status_code=500, detail=f"Room not found: {room_url}")
 
     print(f"Daily room: {room.url} {room.config.sip_endpoint}")
 
     # Give the agent a token to join the session
-    token = daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+    token = await daily_helpers["rest"].get_token(room.url, MAX_SESSION_TIME)
 
     if not room or not token:
-        raise HTTPException(
-            status_code=500, detail=f"Failed to get room or token token")
+        raise HTTPException(status_code=500, detail=f"Failed to get room or token token")
 
     # Spawn a new agent, and join the user session
     # Note: this is mostly for demonstration purposes (refer to 'deployment' in docs)
     if vendor == "daily":
-        bot_proc = f"python3 -m bot_daily -u {room.url} -t {token} -i {
-            callId} -d {callDomain}"
+        bot_proc = f"python3 -m bot_daily -u {room.url} -t {token} -i {callId} -d {callDomain}"
     else:
-        bot_proc = f"python3 -m bot_twilio -u {room.url} -t {
-            token} -i {callId} -s {room.config.sip_endpoint}"
+        bot_proc = f"python3 -m bot_twilio -u {room.url} -t {token} -i {callId} -s {room.config.sip_endpoint}"
 
     try:
         subprocess.Popen(
-            [bot_proc],
-            shell=True,
-            bufsize=1,
-            cwd=os.path.dirname(os.path.abspath(__file__))
+            [bot_proc], shell=True, bufsize=1, cwd=os.path.dirname(os.path.abspath(__file__))
         )
     except Exception as e:
-        raise HTTPException(
-            status_code=500, detail=f"Failed to start subprocess: {e}")
+        raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
 
     return room
 
@@ -130,18 +140,16 @@ async def twilio_start_bot(request: Request):
         pass
 
     room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", None)
-    callId = data.get('CallSid')
+    callId = data.get("CallSid")
 
     if not callId:
-        raise HTTPException(
-            status_code=500, detail="Missing 'CallSid' in request")
+        raise HTTPException(status_code=500, detail="Missing 'CallSid' in request")
 
     print("CallId: %s" % callId)
 
     # create room and tell the bot to join the created room
     # note: Twilio does not require a callDomain
-    room: DailyRoomObject = _create_daily_room(
-        room_url, callId, None, "twilio")
+    room: DailyRoomObject = await _create_daily_room(room_url, callId, None, "twilio")
 
     print(f"Put Twilio on hold...")
     # We have the room and the SIP URI,
@@ -151,7 +159,8 @@ async def twilio_start_bot(request: Request):
     # http://com.twilio.music.classical.s3.amazonaws.com/BusyStrings.mp3
     resp = VoiceResponse()
     resp.play(
-        url="http://com.twilio.sounds.music.s3.amazonaws.com/MARKOVICHAMP-Borghestral.mp3", loop=10)
+        url="http://com.twilio.sounds.music.s3.amazonaws.com/MARKOVICHAMP-Borghestral.mp3", loop=10
+    )
     return str(resp)
 
 
@@ -173,19 +182,14 @@ async def daily_start_bot(request: Request) -> JSONResponse:
         callId = data.get("callId", None)
         callDomain = data.get("callDomain", None)
     except Exception:
-        raise HTTPException(
-            status_code=500,
-            detail="Missing properties 'callId' or 'callDomain'")
+        raise HTTPException(status_code=500, detail="Missing properties 'callId' or 'callDomain'")
 
     print(f"CallId: {callId}, CallDomain: {callDomain}")
-    room: DailyRoomObject = _create_daily_room(
-        room_url, callId, callDomain, "daily")
+    room: DailyRoomObject = await _create_daily_room(room_url, callId, callDomain, "daily")
 
     # Grab a token for the user to join with
-    return JSONResponse({
-        "room_url": room.url,
-        "sipUri": room.config.sip_endpoint
-    })
+    return JSONResponse({"room_url": room.url, "sipUri": room.config.sip_endpoint})
+
 
 # ----------------- Main ----------------- #
 
@@ -197,24 +201,18 @@ if __name__ == "__main__":
             raise Exception(f"Missing environment variable: {env_var}.")
 
     parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
-    parser.add_argument("--host", type=str,
-                        default=os.getenv("HOST", "0.0.0.0"), help="Host address")
-    parser.add_argument("--port", type=int,
-                        default=os.getenv("PORT", 7860), help="Port number")
-    parser.add_argument("--reload", action="store_true",
-                        default=True, help="Reload code on change")
+    parser.add_argument(
+        "--host", type=str, default=os.getenv("HOST", "0.0.0.0"), help="Host address"
+    )
+    parser.add_argument("--port", type=int, default=os.getenv("PORT", 7860), help="Port number")
+    parser.add_argument("--reload", action="store_true", default=True, help="Reload code on change")
 
     config = parser.parse_args()
 
     try:
         import uvicorn
 
-        uvicorn.run(
-            "bot_runner:app",
-            host=config.host,
-            port=config.port,
-            reload=config.reload
-        )
+        uvicorn.run("bot_runner:app", host=config.host, port=config.port, reload=config.reload)
 
     except KeyboardInterrupt:
         print("Pipecat runner shutting down...")

examples/dialin-chatbot/bot_twilio.py

@@ -1,117 +1,112 @@
 import asyncio
-import aiohttp
 import os
 import sys
 import argparse
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
-from pipecat.frames.frames import (
-    LLMMessagesFrame,
-    EndFrame
-)
+from pipecat.frames.frames import LLMMessagesFrame, EndFrame
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from twilio.rest import Client
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-twilio_account_sid = os.getenv('TWILIO_ACCOUNT_SID')
-twilio_auth_token = os.getenv('TWILIO_AUTH_TOKEN')
+twilio_account_sid = os.getenv("TWILIO_ACCOUNT_SID")
+twilio_auth_token = os.getenv("TWILIO_AUTH_TOKEN")
 twilioclient = Client(twilio_account_sid, twilio_auth_token)
 
 daily_api_key = os.getenv("DAILY_API_KEY", "")
 
 
 async def main(room_url: str, token: str, callId: str, sipUri: str):
-    async with aiohttp.ClientSession() as session:
-        # diallin_settings are only needed if Daily's SIP URI is used
-        # If you are handling this via Twilio, Telnyx, set this to None
-        # and handle call-forwarding when on_dialin_ready fires.
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Chatbot",
-            DailyParams(
-                api_key=daily_api_key,
-                dialin_settings=None,  # Not required for Twilio
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-                camera_out_enabled=False,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
-            )
-        )
+    # dialin_settings are only needed if Daily's SIP URI is used
+    # If you are handling this via Twilio, Telnyx, set this to None
+    # and handle call-forwarding when on_dialin_ready fires.
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Chatbot",
+        DailyParams(
+            api_key=daily_api_key,
+            dialin_settings=None,  # Not required for Twilio
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_enabled=False,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            transcription_enabled=True,
+        ),
+    )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
-        )
+    tts = ElevenLabsTTSService(
+        api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+        voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+    )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        messages = [
-            {
-                "role": "system",
-                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Hello! Who dares dial me at this hour?!'.",
-            },
-        ]
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Hello! Who dares dial me at this hour?!'.",
+        },
+    ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
+    pipeline = Pipeline(
+        [
             transport.input(),
-            tma_in,
+            context_aggregator.user(),
             llm,
             tts,
             transport.output(),
-            tma_out,
-        ])
+            context_aggregator.assistant(),
+        ]
+    )
 
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([LLMMessagesFrame(messages)])
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        transport.capture_participant_transcription(participant["id"])
+        await task.queue_frames([LLMMessagesFrame(messages)])
 
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.queue_frame(EndFrame())
 
-        @transport.event_handler("on_dialin_ready")
-        async def on_dialin_ready(transport, cdata):
-            # For Twilio, Telnyx, etc. You need to update the state of the call
-            # and forward it to the sip_uri..
-            print(f"Forwarding call: {callId} {sipUri}")
+    @transport.event_handler("on_dialin_ready")
+    async def on_dialin_ready(transport, cdata):
+        # For Twilio, Telnyx, etc. You need to update the state of the call
+        # and forward it to the sip_uri..
+        print(f"Forwarding call: {callId} {sipUri}")
 
-            try:
-                # The TwiML is updated using Twilio's client library
-                call = twilioclient.calls(callId).update(
-                    twiml=f'<Response><Dial><Sip>{sipUri}</Sip></Dial></Response>'
-                )
-            except Exception as e:
-                raise Exception(f"Failed to forward call: {str(e)}")
+        try:
+            # The TwiML is updated using Twilio's client library
+            call = twilioclient.calls(callId).update(
+                twiml=f"<Response><Dial><Sip>{sipUri}</Sip></Dial></Response>"
+            )
+        except Exception as e:
+            raise Exception(f"Failed to forward call: {str(e)}")
 
-        runner = PipelineRunner()
-        await runner.run(task)
+    runner = PipelineRunner()
+    await runner.run(task)
 
 
 if __name__ == "__main__":

examples/dialin-chatbot/requirements.txt

@@ -1,7 +1,6 @@
-pipecat-ai[daily,openai,silero]
+pipecat-ai[daily,elevenlabs,openai,silero]
 fastapi
 uvicorn
-requests
 python-dotenv
-loguru
-twilio
+twilio
+python-multipart

examples/foundational/01-say-one-thing.py

@@ -13,7 +13,7 @@ from pipecat.frames.frames import EndFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
@@ -21,21 +21,24 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True))
+        (room_url, _) = await configure(session)
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        transport = DailyTransport(
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
+        )
+
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         runner = PipelineRunner()
@@ -44,13 +47,18 @@ async def main(room_url):
 
         # Register an event handler so we can play the audio when the
         # participant joins.
-        @transport.event_handler("on_participant_joined")
-        async def on_new_participant_joined(transport, participant):
-            participant_name = participant["info"]["userName"] or ''
-            await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            participant_name = participant.get("info", {}).get("userName", "")
+            await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))
+
+        # Register an event handler to exit the application when the user leaves.
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/01a-local-audio.py

@@ -9,17 +9,18 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.audio import LocalAudioTransport
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -30,10 +31,9 @@ async def main():
     async with aiohttp.ClientSession() as session:
         transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         pipeline = Pipeline([tts, transport.output()])
@@ -42,7 +42,7 @@ async def main():
 
         async def say_something():
             await asyncio.sleep(1)
-            await task.queue_frames([TextFrame("Hello there!"), EndFrame()])
+            await task.queue_frame(TextFrame("Hello there!"))
 
         runner = PipelineRunner()
 

examples/foundational/01b-livekit-audio.py

@@ -0,0 +1,111 @@
+import argparse
+import asyncio
+import os
+import sys
+
+import aiohttp
+
+from pipecat.frames.frames import TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
+
+from livekit import api
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+def generate_token(room_name: str, participant_name: str, api_key: str, api_secret: str) -> str:
+    token = api.AccessToken(api_key, api_secret)
+    token.with_identity(participant_name).with_name(participant_name).with_grants(
+        api.VideoGrants(
+            room_join=True,
+            room=room_name,
+        )
+    )
+
+    return token.to_jwt()
+
+
+async def configure_livekit():
+    parser = argparse.ArgumentParser(description="LiveKit AI SDK Bot Sample")
+    parser.add_argument(
+        "-r", "--room", type=str, required=False, help="Name of the LiveKit room to join"
+    )
+    parser.add_argument("-u", "--url", type=str, required=False, help="URL of the LiveKit server")
+
+    args, unknown = parser.parse_known_args()
+
+    room_name = args.room or os.getenv("LIVEKIT_ROOM_NAME")
+    url = args.url or os.getenv("LIVEKIT_URL")
+    api_key = os.getenv("LIVEKIT_API_KEY")
+    api_secret = os.getenv("LIVEKIT_API_SECRET")
+
+    if not room_name:
+        raise Exception(
+            "No LiveKit room specified. Use the -r/--room option from the command line, or set LIVEKIT_ROOM_NAME in your environment."
+        )
+
+    if not url:
+        raise Exception(
+            "No LiveKit server URL specified. Use the -u/--url option from the command line, or set LIVEKIT_URL in your environment."
+        )
+
+    if not api_key or not api_secret:
+        raise Exception(
+            "LIVEKIT_API_KEY and LIVEKIT_API_SECRET must be set in environment variables."
+        )
+
+    token = generate_token(room_name, "Say One Thing", api_key, api_secret)
+
+    user_token = generate_token(room_name, "User", api_key, api_secret)
+    logger.info(f"User token: {user_token}")
+
+    return (url, token, room_name)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (url, token, room_name) = await configure_livekit()
+
+        transport = LiveKitTransport(
+            url=url,
+            token=token,
+            room_name=room_name,
+            params=LiveKitParams(audio_out_enabled=True, audio_out_sample_rate=16000),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        runner = PipelineRunner()
+
+        task = PipelineTask(Pipeline([tts, transport.output()]))
+
+        # Register an event handler so we can play the audio when the
+        # participant joins.
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant_id):
+            await asyncio.sleep(1)
+            await task.queue_frame(
+                TextFrame(
+                    "Hello there! How are you doing today? Would you like to talk about the weather?"
+                )
+            )
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/02-llm-say-one-thing.py

@@ -13,7 +13,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -22,35 +22,34 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Say One Thing From an LLM",
-            DailyParams(audio_out_enabled=True))
+        (room_url, _) = await configure(session)
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        transport = DailyTransport(
+            room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
                 "role": "system",
                 "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-            }]
+            }
+        ]
 
         runner = PipelineRunner()
 
@@ -58,11 +57,14 @@ async def main(room_url):
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
+            await task.queue_frame(LLMMessagesFrame(messages))
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/03-still-frame.py

@@ -9,7 +9,7 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import TextFrame
+from pipecat.frames.frames import EndFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
@@ -21,29 +21,26 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             None,
             "Show a still frame image",
-            DailyParams(
-                camera_out_enabled=True,
-                camera_out_width=1024,
-                camera_out_height=1024
-            )
+            DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
         )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -54,15 +51,14 @@ async def main(room_url):
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            # Note that we do not put an EndFrame() item in the pipeline for this demo.
-            # This means that the bot will stay in the channel until it times out.
-            # An EndFrame() in the pipeline would cause the transport to shut
-            # down.
-            await task.queue_frames([TextFrame("a cat in the style of picasso")])
+            await task.queue_frame(TextFrame("a cat in the style of picasso"))
+
+        @transport.event_handler("on_participant_left")
+        async def on_participant_left(transport, participant, reason):
+            await task.queue_frame(EndFrame())
 
         await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/03a-local-still-frame.py

@@ -22,6 +22,7 @@ from pipecat.transports.local.tk import TkLocalTransport
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -35,15 +36,11 @@ async def main():
 
         transport = TkLocalTransport(
             tk_root,
-            TransportParams(
-                camera_out_enabled=True,
-                camera_out_width=1024,
-                camera_out_height=1024))
+            TransportParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
+        )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -56,7 +53,7 @@ async def main():
         runner = PipelineRunner()
 
         async def run_tk():
-            while runner.is_active():
+            while not task.has_finished():
                 tk_root.update()
                 tk_root.update_idletasks()
                 await asyncio.sleep(0.1)

examples/foundational/04-utterance-and-speech.py

@@ -4,6 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+#
+# This example broken on latest pipecat and needs updating.
+#
+
 import aiohttp
 import asyncio
 import os
@@ -24,14 +28,17 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(room_url, None, "Static And Dynamic Speech")
 
         meeting = TransportServiceOutput(transport, mic_enabled=True)
@@ -52,8 +59,7 @@ async def main(room_url: str):
             voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
         )
 
-        messages = [{"role": "system",
-                     "content": "tell the user a joke about llamas"}]
+        messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
 
         # Start a task to run the LLM to create a joke, and convert the LLM
         # output to audio frames. This task will run in parallel with generating
@@ -71,8 +77,7 @@ async def main(room_url: str):
             ]
         )
 
-        merge_pipeline = SequentialMergePipeline(
-            [simple_tts_pipeline, llm_pipeline])
+        merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
 
         await asyncio.gather(
             transport.run(merge_pipeline),
@@ -82,5 +87,4 @@ async def main(room_url: str):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/05-sync-speech-and-image.py

@@ -13,23 +13,19 @@ from dataclasses import dataclass
 
 from pipecat.frames.frames import (
     AppFrame,
-    EndFrame,
     Frame,
-    ImageRawFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    TextFrame
+    TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
-from pipecat.pipeline.parallel_task import ParallelTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.aggregators.gated import GatedAggregator
-from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
 from pipecat.processors.aggregators.sentence import SentenceAggregator
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.fal import FalImageGenService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -38,6 +34,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -73,8 +70,10 @@ class MonthPrepender(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def main(room_url):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             None,
@@ -83,48 +82,46 @@ async def main(room_url):
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
-                camera_out_height=1024
-            )
+                camera_out_height=1024,
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
         imagegen = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="square_hd"
-            ),
+            params=FalImageGenService.InputParams(image_size="square_hd"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
 
-        gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageRawFrame),
-            gate_close_fn=lambda frame: isinstance(frame, LLMFullResponseStartFrame),
-            start_open=False
-        )
-
         sentence_aggregator = SentenceAggregator()
         month_prepender = MonthPrepender()
-        llm_full_response_aggregator = LLMFullResponseAggregator()
 
-        pipeline = Pipeline([
-            llm,                     # LLM
-            sentence_aggregator,     # Aggregates LLM output into full sentences
-            ParallelTask(            # Run pipelines in parallel aggregating the result
-                [month_prepender, tts],                   # Create "Month: sentence" and output audio
-                [llm_full_response_aggregator, imagegen]  # Aggregate full LLM response
-            ),
-            gated_aggregator,        # Queues everything until an image is available
-            transport.output()       # Transport output
-        ])
+        # With `SyncParallelPipeline` we synchronize audio and images by pushing
+        # them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2 I3 A3). To do
+        # that, each pipeline runs concurrently and `SyncParallelPipeline` will
+        # wait for the input frame to be processed.
+        #
+        # Note that `SyncParallelPipeline` requires the last processor in each
+        # of the pipelines to be synchronous. In this case, we use
+        # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
+        # requests and wait for the response.
+        pipeline = Pipeline(
+            [
+                llm,  # LLM
+                sentence_aggregator,  # Aggregates LLM output into full sentences
+                SyncParallelPipeline(  # Run pipelines in parallel aggregating the result
+                    [month_prepender, tts],  # Create "Month: sentence" and output audio
+                    [imagegen],  # Generate image
+                ),
+                transport.output(),  # Transport output
+            ]
+        )
 
         frames = []
         for month in [
@@ -150,8 +147,6 @@ async def main(room_url):
             frames.append(MonthFrame(month=month))
             frames.append(LLMMessagesFrame(messages))
 
-        frames.append(EndFrame())
-
         runner = PipelineRunner()
 
         task = PipelineTask(pipeline)
@@ -162,5 +157,4 @@ async def main(room_url):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/05a-local-sync-speech-and-image.py

@@ -11,18 +11,25 @@ import sys
 
 import tkinter as tk
 
-from pipecat.frames.frames import AudioRawFrame, Frame, URLImageRawFrame, LLMMessagesFrame, TextFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.frames.frames import (
+    Frame,
+    OutputAudioRawFrame,
+    TTSAudioRawFrame,
+    URLImageRawFrame,
+    LLMMessagesFrame,
+    TextFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
+from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.fal import FalImageGenService
 from pipecat.transports.base_transport import TransportParams
-from pipecat.transports.local.tk import TkLocalTransport
+from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
 
 from loguru import logger
 
@@ -42,7 +49,12 @@ async def main():
         runner = PipelineRunner()
 
         async def get_month_data(month):
-            messages = [{"role": "system", "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.", }]
+            messages = [
+                {
+                    "role": "system",
+                    "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
+                }
+            ]
 
             class ImageDescription(FrameProcessor):
                 def __init__(self):
@@ -60,14 +72,17 @@ async def main():
                 def __init__(self):
                     super().__init__()
                     self.audio = bytearray()
+                    self.frame = None
 
                 async def process_frame(self, frame: Frame, direction: FrameDirection):
                     await super().process_frame(frame, direction)
 
-                    if isinstance(frame, AudioRawFrame):
+                    if isinstance(frame, TTSAudioRawFrame):
                         self.audio.extend(frame.audio)
-                        self.frame = AudioRawFrame(
-                            bytes(self.audio), frame.sample_rate, frame.num_channels)
+                        self.frame = OutputAudioRawFrame(
+                            bytes(self.audio), frame.sample_rate, frame.num_channels
+                        )
+                    await self.push_frame(frame, direction)
 
             class ImageGrabber(FrameProcessor):
                 def __init__(self):
@@ -79,24 +94,22 @@ async def main():
 
                     if isinstance(frame, URLImageRawFrame):
                         self.frame = frame
+                    await self.push_frame(frame, direction)
 
-            llm = OpenAILLMService(
-                api_key=os.getenv("OPENAI_API_KEY"),
-                model="gpt-4o")
+            llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-            tts = ElevenLabsTTSService(
-                aiohttp_session=session,
-                api_key=os.getenv("ELEVENLABS_API_KEY"),
-                voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+            tts = CartesiaHttpTTSService(
+                api_key=os.getenv("CARTESIA_API_KEY"),
+                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            )
 
             imagegen = FalImageGenService(
-                params=FalImageGenService.InputParams(
-                    image_size="square_hd"
-                ),
+                params=FalImageGenService.InputParams(image_size="square_hd"),
                 aiohttp_session=session,
-                key=os.getenv("FAL_KEY"))
+                key=os.getenv("FAL_KEY"),
+            )
 
-            aggregator = LLMFullResponseAggregator()
+            sentence_aggregator = SentenceAggregator()
 
             description = ImageDescription()
 
@@ -104,13 +117,27 @@ async def main():
 
             image_grabber = ImageGrabber()
 
-            pipeline = Pipeline([
-                llm,
-                aggregator,
-                description,
-                ParallelPipeline([tts, audio_grabber],
-                                 [imagegen, image_grabber])
-            ])
+            # With `SyncParallelPipeline` we synchronize audio and images by
+            # pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
+            # I3 A3). To do that, each pipeline runs concurrently and
+            # `SyncParallelPipeline` will wait for the input frame to be
+            # processed.
+            #
+            # Note that `SyncParallelPipeline` requires the last processor in
+            # each of the pipelines to be synchronous. In this case, we use
+            # `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
+            # requests and wait for the response.
+            pipeline = Pipeline(
+                [
+                    llm,  # LLM
+                    sentence_aggregator,  # Aggregates LLM output into full sentences
+                    description,  # Store sentence
+                    SyncParallelPipeline(
+                        [tts, audio_grabber],  # Generate and store audio for the given sentence
+                        [imagegen, image_grabber],  # Generate and storeimage for the given sentence
+                    ),
+                ]
+            )
 
             task = PipelineTask(pipeline)
             await task.queue_frame(LLMMessagesFrame(messages))
@@ -131,20 +158,19 @@ async def main():
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
-                camera_out_height=1024))
+                camera_out_height=1024,
+            ),
+        )
 
         pipeline = Pipeline([transport.output()])
 
         task = PipelineTask(pipeline)
 
-        # We only specify 5 months as we create tasks all at once and we might
-        # get rate limited otherwise.
+        # We only specify a few months as we create tasks all at once and we
+        # might get rate limited otherwise.
         months: list[str] = [
             "January",
             "February",
-            # "March",
-            # "April",
-            # "May",
         ]
 
         # We create one task per month. This will be executed concurrently.

examples/foundational/06-listen-and-respond.py

@@ -9,33 +9,59 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import Frame, LLMMessagesFrame, MetricsFrame
+from pipecat.metrics.metrics import (
+    TTFBMetricsData,
+    ProcessingMetricsData,
+    LLMUsageMetricsData,
+    TTSUsageMetricsData,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from pipecat.processors.logger import FrameLogger
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+class MetricsLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        if isinstance(frame, MetricsFrame):
+            for d in frame.data:
+                if isinstance(d, TTFBMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, ttfb: {d.value}")
+                elif isinstance(d, ProcessingMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, processing: {d.value}")
+                elif isinstance(d, LLMUsageMetricsData):
+                    tokens = d.value
+                    print(
+                        f"!!! MetricsFrame: {frame}, tokens: {
+                            tokens.prompt_tokens}, characters: {
+                            tokens.completion_tokens}"
+                    )
+                elif isinstance(d, TTSUsageMetricsData):
+                    print(f"!!! MetricsFrame: {frame}, characters: {d.value}")
+        await self.push_frame(frame, direction)
+
+
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -44,23 +70,18 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        fl = FrameLogger("!!! after LLM", "red")
-        fltts = FrameLogger("@@@ out of tts", "green")
-        flend = FrameLogger("### out of the end", "magenta")
+        ml = MetricsLogger()
 
         messages = [
             {
@@ -68,20 +89,21 @@ async def main(room_url: str, token):
                 "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),
-            tma_in,
-            llm,
-            fl,
-            tts,
-            fltts,
-            transport.output(),
-            tma_out,
-            flend
-        ])
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                ml,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
@@ -89,8 +111,7 @@ async def main(room_url: str, token):
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -99,5 +120,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/06a-image-sync.py

@@ -11,19 +11,16 @@ import sys
 
 from PIL import Image
 
-from pipecat.frames.frames import ImageRawFrame, Frame, SystemFrame, TextFrame
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.transports.services.daily import DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from pipecat.transports.services.daily import DailyParams
 from runner import configure
@@ -31,6 +28,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -51,39 +49,51 @@ class ImageSyncAggregator(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if not isinstance(frame, SystemFrame):
-            await self.push_frame(ImageRawFrame(image=self._speaking_image_bytes, size=(1024, 1024), format=self._speaking_image_format))
+        if not isinstance(frame, SystemFrame) and direction == FrameDirection.DOWNSTREAM:
+            await self.push_frame(
+                OutputImageRawFrame(
+                    image=self._speaking_image_bytes,
+                    size=(1024, 1024),
+                    format=self._speaking_image_format,
+                )
+            )
             await self.push_frame(frame)
-            await self.push_frame(ImageRawFrame(image=self._waiting_image_bytes, size=(1024, 1024), format=self._waiting_image_format))
+            await self.push_frame(
+                OutputImageRawFrame(
+                    image=self._waiting_image_bytes,
+                    size=(1024, 1024),
+                    format=self._waiting_image_format,
+                )
+            )
         else:
             await self.push_frame(frame)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
+                camera_out_enabled=True,
                 camera_out_width=1024,
                 camera_out_height=1024,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -92,31 +102,33 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
         image_sync_aggregator = ImageSyncAggregator(
             os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
             os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
         )
 
-        pipeline = Pipeline([
-            transport.input(),
-            image_sync_aggregator,
-            tma_in,
-            llm,
-            tts,
-            transport.output(),
-            tma_out
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                image_sync_aggregator,
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            participant_name = participant["info"]["userName"] or ''
+            participant_name = participant.get("info", {}).get("userName", "")
             transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([TextFrame(f"Hi, this is {participant_name}.")])
+            await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
 
         runner = PipelineRunner()
 
@@ -124,5 +136,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07-interruptible-vad.py

@@ -0,0 +1,103 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.audio.vad.silero import SileroVAD
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                transcription_enabled=True,
+            ),
+        )
+
+        vad = SileroVAD()
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                vad,
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07-interruptible.py

@@ -9,30 +9,32 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -41,19 +43,16 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -62,30 +61,35 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-        task = PipelineTask(pipeline, PipelineParams(
-            allow_interruptions=True,
-            enable_metrics=True,
-            report_only_initial_ttfb=True,
-        ))
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -94,5 +98,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07a-interruptible-anthropic.py

@@ -5,34 +5,34 @@
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -41,19 +41,18 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-opus-20240229")
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-opus-20240229"
+        )
 
         # todo: think more about how to handle system prompts in a more general way. OpenAI,
         # Google, and Anthropic all have slightly different approaches to providing a system
@@ -65,17 +64,19 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
@@ -91,5 +92,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07b-interruptible-langchain.py

@@ -10,16 +10,18 @@ import sys
 
 import aiohttp
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 from pipecat.processors.frameworks.langchain import LangchainProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_community.chat_message_histories import ChatMessageHistory
@@ -32,6 +34,7 @@ from loguru import logger
 from runner import configure
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 
@@ -47,8 +50,10 @@ def get_session_history(session_id: str) -> BaseChatMessageHistory:
     return message_store[session_id]
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -61,27 +66,29 @@ async def main(room_url: str, token):
             ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         prompt = ChatPromptTemplate.from_messages(
             [
-                ("system",
-                 "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
-                 "Your response will be synthesized to voice and those characters will create unnatural sounds.",
-                 ),
+                (
+                    "system",
+                    "Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
+                    "Your response will be synthesized to voice and those characters will create unnatural sounds.",
+                ),
                 MessagesPlaceholder("chat_history"),
                 ("human", "{input}"),
-            ])
+            ]
+        )
         chain = prompt | ChatOpenAI(model="gpt-4o", temperature=0.7)
         history_chain = RunnableWithMessageHistory(
             chain,
             get_session_history,
             history_messages_key="chat_history",
-            input_messages_key="input")
+            input_messages_key="input",
+        )
         lc = LangchainProcessor(history_chain)
 
         tma_in = LLMUserResponseAggregator()
@@ -89,12 +96,12 @@ async def main(room_url: str, token):
 
         pipeline = Pipeline(
             [
-                transport.input(),      # Transport user input
-                tma_in,                 # User responses
-                lc,                     # Langchain
-                tts,                    # TTS
-                transport.output(),     # Transport bot output
-                tma_out,                # Assistant spoken responses
+                transport.input(),  # Transport user input
+                tma_in,  # User responses
+                lc,  # Langchain
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -108,11 +115,7 @@ async def main(room_url: str, token):
             # the `LLMMessagesFrame` will be picked up by the LangchainProcessor using
             # only the content of the last message to inject it in the prompt defined
             # above. So no role is required here.
-            messages = [(
-                {
-                    "content": "Please briefly introduce yourself to the user."
-                }
-            )]
+            messages = [({"content": "Please briefly introduce yourself to the user."})]
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -121,5 +124,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07c-interruptible-deepgram.py

@@ -5,34 +5,34 @@
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -41,21 +41,15 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True
-            )
+                vad_audio_passthrough=True,
+            ),
         )
 
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            voice="aura-helios-en"
-        )
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,27 +58,27 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            stt,                 # STT
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -93,5 +87,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07d-interruptible-cartesia.py

@@ -1,94 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Respond bot",
-        DailyParams(
-            audio_out_enabled=True,
-            audio_out_sample_rate=44100,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer()
-        )
-    )
-
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_name="British Lady",
-        output_format="pcm_44100"
-    )
-
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o")
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    tma_in = LLMUserResponseAggregator(messages)
-    tma_out = LLMAssistantResponseAggregator(messages)
-
-    pipeline = Pipeline([
-        transport.input(),   # Transport user input
-        tma_in,              # User responses
-        llm,                 # LLM
-        tts,                 # TTS
-        transport.output(),  # Transport bot output
-        tma_out              # Assistant spoken responses
-    ])
-
-    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

examples/foundational/07d-interruptible-elevenlabs.py

@@ -0,0 +1,99 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY", ""),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07e-interruptible-playht.py

@@ -8,86 +8,96 @@ import asyncio
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.playht import PlayHTTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.playht import PlayHTTTSService
+from pipecat.transcriptions.language import Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Respond bot",
-        DailyParams(
-            audio_out_enabled=True,
-            audio_out_sample_rate=16000,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer()
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=16000,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
-    )
 
-    tts = PlayHTTTSService(
-        user_id=os.getenv("PLAYHT_USER_ID"),
-        api_key=os.getenv("PLAYHT_API_KEY"),
-        voice_url="s3://voice-cloning-zero-shot/801a663f-efd0-4254-98d0-5c175514c3e8/jennifer/manifest.json",
-    )
+        tts = PlayHTTTSService(
+            user_id=os.getenv("PLAYHT_USER_ID"),
+            api_key=os.getenv("PLAYHT_API_KEY"),
+            voice_url="s3://voice-cloning-zero-shot/801a663f-efd0-4254-98d0-5c175514c3e8/jennifer/manifest.json",
+            params=PlayHTTTSService.InputParams(language=Language.EN),
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
 
-    tma_in = LLMUserResponseAggregator(messages)
-    tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-    pipeline = Pipeline([
-        transport.input(),   # Transport user input
-        tma_in,              # User responses
-        llm,                 # LLM
-        tts,                 # TTS
-        transport.output(),  # Transport bot output
-        tma_out              # Assistant spoken responses
-    ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
 
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07f-interruptible-azure.py

@@ -4,19 +4,19 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 
 from runner import configure
@@ -24,77 +24,81 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Respond bot",
-        DailyParams(
-            audio_out_enabled=True,
-            audio_out_sample_rate=16000,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer(),
-            vad_audio_passthrough=True,
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=16000,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
         )
-    )
 
-    stt = AzureSTTService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"),
-    )
+        stt = AzureSTTService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
 
-    tts = AzureTTSService(
-        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-        region=os.getenv("AZURE_SPEECH_REGION"),
-    )
+        tts = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"),
+        )
 
-    llm = AzureLLMService(
-        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-        model=os.getenv("AZURE_CHATGPT_MODEL"),
-    )
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
 
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
 
-    tma_in = LLMUserResponseAggregator(messages)
-    tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-    pipeline = Pipeline([
-        transport.input(),   # Transport user input
-        stt,                 # STT
-        tma_in,              # User responses
-        llm,                 # LLM
-        tts,                 # TTS
-        transport.output(),  # Transport bot output
-        tma_out              # Assistant spoken responses
-    ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07g-interruptible-openai-tts.py

@@ -8,85 +8,81 @@ import asyncio
 import os
 import sys
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.openai import OpenAITTSService
-from pipecat.services.openai import OpenAILLMService
+from pipecat.services.openai import OpenAILLMService, OpenAITTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Respond bot",
-        DailyParams(
-            audio_out_enabled=True,
-            audio_out_sample_rate=24000,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer()
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
-    )
 
-    tts = OpenAITTSService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        voice="alloy"
-    )
+        tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
 
-    tma_in = LLMUserResponseAggregator(messages)
-    tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-    pipeline = Pipeline([
-        transport.input(),   # Transport user input
-        tma_in,              # User responses
-        llm,                 # LLM
-        tts,                 # TTS
-        transport.output(),  # Transport bot output
-        tma_out              # Assistant spoken responses
-    ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
-    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
-            {"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07h-interruptible-openpipe.py

@@ -9,18 +9,15 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator,
-    LLMUserResponseAggregator,
-)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openpipe import OpenPipeLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
@@ -28,14 +25,17 @@ from loguru import logger
 import time
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -44,14 +44,13 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         timestamp = int(time.time())
@@ -59,9 +58,7 @@ async def main(room_url: str, token):
             api_key=os.getenv("OPENAI_API_KEY"),
             openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
             model="gpt-4o",
-            tags={
-                "conversation_id": f"pipecat-{timestamp}"
-            }
+            tags={"conversation_id": f"pipecat-{timestamp}"},
         )
 
         messages = [
@@ -70,17 +67,20 @@ async def main(room_url: str, token):
                 "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
             },
         ]
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
 
@@ -88,8 +88,7 @@ async def main(room_url: str, token):
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -98,5 +97,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/07i-interruptible-xtts.py

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.xtts import XTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = XTTSService(
+            aiohttp_session=session,
+            voice_id="Claribel Dervla",
+            language="en",
+            base_url="http://localhost:8000",
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07j-interruptible-gladia.py

@@ -0,0 +1,99 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.gladia import GladiaSTTService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = GladiaSTTService(
+            api_key=os.getenv("GLADIA_API_KEY"),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07k-interruptible-lmnt.py

@@ -0,0 +1,91 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
+import asyncio
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.lmnt import LmntTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User respones
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07l-interruptible-together.py

@@ -0,0 +1,109 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.ai_services import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.together import TogetherLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = TogetherLLMService(
+            api_key=os.getenv("TOGETHER_API_KEY"),
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+            params=TogetherLLMService.InputParams(
+                temperature=1.0,
+                top_p=0.9,
+                top_k=40,
+                extra={
+                    "frequency_penalty": 2.0,
+                    "presence_penalty": 0.0,
+                },
+            ),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond in plain language. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+        user_aggregator = context_aggregator.user()
+        assistant_aggregator = context_aggregator.assistant()
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                user_aggregator,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                assistant_aggregator,  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True, enable_metrics=True, enable_usage_metrics=True
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07m-interruptible-aws.py

@@ -0,0 +1,99 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.aws import AWSTTSService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=16000,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = AWSTTSService(
+            api_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+            aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+            region=os.getenv("AWS_REGION"),
+            voice_id="Amy",
+            params=AWSTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07n-interruptible-google.py

@@ -0,0 +1,96 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.google import GoogleTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = GoogleTTSService(
+            voice_id="en-US-Neural2-J",
+            params=GoogleTTSService.InputParams(language="en-US", rate="1.05"),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User respones
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07o-interruptible-assemblyai.py

@@ -0,0 +1,97 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.assemblyai import AssemblyAISTTService
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = AssemblyAISTTService(
+            api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/08-bots-arguing.py

@@ -3,18 +3,19 @@ import aiohttp
 import asyncio
 import logging
 import os
-from pipecat.pipeline.aggregators import SentenceAggregator
+from pipecat.processors.aggregators import SentenceAggregator
 from pipecat.pipeline.pipeline import Pipeline
 
-from pipecat.transports.daily_transport import DailyTransport
-from pipecat.services.azure_ai_services import AzureLLMService, AzureTTSService
-from pipecat.services.elevenlabs_ai_services import ElevenLabsTTSService
-from pipecat.services.fal_ai_services import FalImageGenService
-from pipecat.pipeline.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
+from pipecat.transports.services.daily import DailyTransport
+from pipecat.services.azure import AzureLLMService, AzureTTSService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.fal import FalImageGenService
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
 
 from runner import configure
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
@@ -22,8 +23,10 @@ logger = logging.getLogger("pipecat")
 logger.setLevel(logging.DEBUG)
 
 
-async def main(room_url: str):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             None,
@@ -51,9 +54,7 @@ async def main(room_url: str):
             voice_id="jBpfuIE2acCO8z3wKNLl",
         )
         dalle = FalImageGenService(
-            params=FalImageGenService.InputParams(
-                image_size="1024x1024"
-            ),
+            params=FalImageGenService.InputParams(image_size="1024x1024"),
             aiohttp_session=session,
             key=os.getenv("FAL_KEY"),
         )
@@ -73,13 +74,11 @@ async def main(room_url: str):
 
         async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
             """This function streams text from the LLM and uses the TTS service to convert
-             that text to speech as it's received. """
+            that text to speech as it's received."""
             source_queue = asyncio.Queue()
             sink_queue = asyncio.Queue()
             sentence_aggregator = SentenceAggregator()
-            pipeline = Pipeline(
-                [llm, sentence_aggregator, tts1], source_queue, sink_queue
-            )
+            pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
 
             await source_queue.put(LLMMessagesFrame(messages))
             await source_queue.put(EndFrame())
@@ -144,5 +143,4 @@ async def main(room_url: str):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/09-mirror.py

@@ -4,12 +4,21 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import sys
 
+from pipecat.frames.frames import (
+    Frame,
+    InputAudioRawFrame,
+    InputImageRawFrame,
+    OutputAudioRawFrame,
+    OutputImageRawFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.services.daily import DailyTransport, DailyParams
 
 from runner import configure
@@ -17,38 +26,63 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url, token):
-    transport = DailyTransport(
-        room_url, token, "Test",
-        DailyParams(
-            audio_in_enabled=True,
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_is_live=True,
-            camera_out_width=1280,
-            camera_out_height=720
+class MirrorProcessor(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, InputAudioRawFrame):
+            await self.push_frame(
+                OutputAudioRawFrame(
+                    audio=frame.audio,
+                    sample_rate=frame.sample_rate,
+                    num_channels=frame.num_channels,
+                )
+            )
+        elif isinstance(frame, InputImageRawFrame):
+            await self.push_frame(
+                OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
+            )
+        else:
+            await self.push_frame(frame, direction)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Test",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_out_enabled=True,
+                camera_out_enabled=True,
+                camera_out_is_live=True,
+                camera_out_width=1280,
+                camera_out_height=720,
+            ),
         )
-    )
 
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_video(participant["id"])
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_video(participant["id"])
 
-    pipeline = Pipeline([transport.input(), transport.output()])
+        pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    await runner.run(task)
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/09a-local-mirror.py

@@ -4,14 +4,23 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import sys
 
 import tkinter as tk
 
+from pipecat.frames.frames import (
+    Frame,
+    InputAudioRawFrame,
+    InputImageRawFrame,
+    OutputAudioRawFrame,
+    OutputImageRawFrame,
+)
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -21,46 +30,73 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url, token):
-    tk_root = tk.Tk()
-    tk_root.title("Local Mirror")
+class MirrorProcessor(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
 
-    daily_transport = DailyTransport(room_url, token, "Test", DailyParams(audio_in_enabled=True))
+        if isinstance(frame, InputAudioRawFrame):
+            await self.push_frame(
+                OutputAudioRawFrame(
+                    audio=frame.audio,
+                    sample_rate=frame.sample_rate,
+                    num_channels=frame.num_channels,
+                )
+            )
+        elif isinstance(frame, InputImageRawFrame):
+            await self.push_frame(
+                OutputImageRawFrame(image=frame.image, size=frame.size, format=frame.format)
+            )
+        else:
+            await self.push_frame(frame, direction)
 
-    tk_transport = TkLocalTransport(
-        tk_root,
-        TransportParams(
-            audio_out_enabled=True,
-            camera_out_enabled=True,
-            camera_out_is_live=True,
-            camera_out_width=1280,
-            camera_out_height=720))
 
-    @daily_transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_video(participant["id"])
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    pipeline = Pipeline([daily_transport.input(), tk_transport.output()])
+        tk_root = tk.Tk()
+        tk_root.title("Local Mirror")
 
-    task = PipelineTask(pipeline)
+        daily_transport = DailyTransport(
+            room_url, token, "Test", DailyParams(audio_in_enabled=True)
+        )
 
-    async def run_tk():
-        while not task.has_finished():
-            tk_root.update()
-            tk_root.update_idletasks()
-            await asyncio.sleep(0.1)
+        tk_transport = TkLocalTransport(
+            tk_root,
+            TransportParams(
+                audio_out_enabled=True,
+                camera_out_enabled=True,
+                camera_out_is_live=True,
+                camera_out_width=1280,
+                camera_out_height=720,
+            ),
+        )
 
-    runner = PipelineRunner()
+        @daily_transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_video(participant["id"])
 
-    await asyncio.gather(runner.run(task), run_tk())
+        pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
+
+        task = PipelineTask(pipeline)
+
+        async def run_tk():
+            while not task.has_finished():
+                tk_root.update()
+                tk_root.update_idletasks()
+                await asyncio.sleep(0.1)
+
+        runner = PipelineRunner()
+
+        await asyncio.gather(runner.run(task), run_tk())
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/10-wake-phrase.py

@@ -9,31 +9,32 @@ import aiohttp
 import os
 import sys
 
-from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
-
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -42,19 +43,16 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         messages = [
             {
@@ -64,18 +62,21 @@ async def main(room_url: str, token):
         ]
 
         hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            hey_robot_filter,    # Filter out speech not directed at the robot
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                hey_robot_filter,  # Filter out speech not directed at the robot
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
@@ -90,5 +91,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/11-sound-effects.py

@@ -10,31 +10,29 @@ import os
 import sys
 import wave
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
     Frame,
-    AudioRawFrame,
     LLMFullResponseEndFrame,
     LLMMessagesFrame,
+    OutputAudioRawFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMUserResponseAggregator,
-    LLMAssistantResponseAggregator,
-)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -53,12 +51,12 @@ for file in sound_files:
     filename = os.path.splitext(os.path.basename(full_path))[0]
     # Open the image and convert it to bytes
     with wave.open(full_path) as audio_file:
-        sounds[file] = AudioRawFrame(audio_file.readframes(-1),
-                                     audio_file.getframerate(), audio_file.getnchannels())
+        sounds[file] = OutputAudioRawFrame(
+            audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
+        )
 
 
 class OutboundSoundEffectWrapper(FrameProcessor):
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -71,7 +69,6 @@ class OutboundSoundEffectWrapper(FrameProcessor):
 
 
 class InboundSoundEffectWrapper(FrameProcessor):
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -83,8 +80,10 @@ class InboundSoundEffectWrapper(FrameProcessor):
             await self.push_frame(frame, direction)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -93,18 +92,15 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="ErXwobaYiN019PkySvjV",
+        tts = CartesiaHttpTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         messages = [
@@ -114,25 +110,27 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
         out_sound = OutboundSoundEffectWrapper()
         in_sound = InboundSoundEffectWrapper()
         fl = FrameLogger("LLM Out")
         fl2 = FrameLogger("Transcription In")
 
-        pipeline = Pipeline([
-            transport.input(),
-            tma_in,
-            in_sound,
-            fl2,
-            llm,
-            fl,
-            tts,
-            out_sound,
-            transport.output(),
-            tma_out
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                in_sound,
+                fl2,
+                llm,
+                fl,
+                tts,
+                out_sound,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
@@ -148,5 +146,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/12-describe-video.py

@@ -9,6 +9,7 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -16,16 +17,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.moondream import MoondreamService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -33,7 +34,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,12 +45,16 @@ class UserImageRequester(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if self._participant_id and isinstance(frame, TextFrame):
-            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+            )
         await self.push_frame(frame, direction)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -59,14 +63,8 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
-        )
-
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         user_response = UserResponseAggregator()
@@ -78,10 +76,9 @@ async def main(room_url: str, token):
         # If you run into weird description, try with use_cpu=True
         moondream = MoondreamService()
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         @transport.event_handler("on_first_participant_joined")
@@ -91,15 +88,17 @@ async def main(room_url: str, token):
             transport.capture_participant_transcription(participant["id"])
             image_requester.set_participant_id(participant["id"])
 
-        pipeline = Pipeline([
-            transport.input(),
-            user_response,
-            image_requester,
-            vision_aggregator,
-            moondream,
-            tts,
-            transport.output()
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                user_response,
+                image_requester,
+                vision_aggregator,
+                moondream,
+                tts,
+                transport.output(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
@@ -107,6 +106,6 @@ async def main(room_url: str, token):
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/12a-describe-video-gemini-flash.py

@@ -9,6 +9,7 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -16,16 +17,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.google import GoogleLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -33,7 +34,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,12 +45,16 @@ class UserImageRequester(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if self._participant_id and isinstance(frame, TextFrame):
-            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+            )
         await self.push_frame(frame, direction)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -60,8 +64,8 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         user_response = UserResponseAggregator()
@@ -71,13 +75,12 @@ async def main(room_url: str, token):
         vision_aggregator = VisionImageFrameAggregator()
 
         google = GoogleLLMService(
-            model="gemini-1.5-flash-latest",
-            api_key=os.getenv("GOOGLE_API_KEY"))
+            model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")
+        )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         @transport.event_handler("on_first_participant_joined")
@@ -87,15 +90,17 @@ async def main(room_url: str, token):
             transport.capture_participant_transcription(participant["id"])
             image_requester.set_participant_id(participant["id"])
 
-        pipeline = Pipeline([
-            transport.input(),
-            user_response,
-            image_requester,
-            vision_aggregator,
-            google,
-            tts,
-            transport.output()
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                user_response,
+                image_requester,
+                vision_aggregator,
+                google,
+                tts,
+                transport.output(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
@@ -103,6 +108,6 @@ async def main(room_url: str, token):
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/12b-describe-video-gpt-4o.py

@@ -9,6 +9,7 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -16,16 +17,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -33,7 +34,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,12 +45,16 @@ class UserImageRequester(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if self._participant_id and isinstance(frame, TextFrame):
-            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+            )
         await self.push_frame(frame, direction)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -59,8 +63,8 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         user_response = UserResponseAggregator()
@@ -69,15 +73,11 @@ async def main(room_url: str, token):
 
         vision_aggregator = VisionImageFrameAggregator()
 
-        openai = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o"
-        )
+        openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         @transport.event_handler("on_first_participant_joined")
@@ -87,15 +87,17 @@ async def main(room_url: str, token):
             transport.capture_participant_transcription(participant["id"])
             image_requester.set_participant_id(participant["id"])
 
-        pipeline = Pipeline([
-            transport.input(),
-            user_response,
-            image_requester,
-            vision_aggregator,
-            openai,
-            tts,
-            transport.output()
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                user_response,
+                image_requester,
+                vision_aggregator,
+                openai,
+                tts,
+                transport.output(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
@@ -103,6 +105,6 @@ async def main(room_url: str, token):
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/12c-describe-video-anthropic.py

@@ -9,6 +9,7 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import Frame, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -16,16 +17,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -33,7 +34,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
-
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,12 +45,16 @@ class UserImageRequester(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if self._participant_id and isinstance(frame, TextFrame):
-            await self.push_frame(UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM)
+            await self.push_frame(
+                UserImageRequestFrame(self._participant_id), FrameDirection.UPSTREAM
+            )
         await self.push_frame(frame, direction)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -59,8 +63,8 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         user_response = UserResponseAggregator()
@@ -69,15 +73,14 @@ async def main(room_url: str, token):
 
         vision_aggregator = VisionImageFrameAggregator()
 
-        anthropic = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-sonnet-20240229"
-        )
+        anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            params=CartesiaTTSService.InputParams(
+                sample_rate=16000,
+            ),
         )
 
         @transport.event_handler("on_first_participant_joined")
@@ -87,15 +90,17 @@ async def main(room_url: str, token):
             transport.capture_participant_transcription(participant["id"])
             image_requester.set_participant_id(participant["id"])
 
-        pipeline = Pipeline([
-            transport.input(),
-            user_response,
-            image_requester,
-            vision_aggregator,
-            anthropic,
-            tts,
-            transport.output()
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                user_response,
+                image_requester,
+                vision_aggregator,
+                anthropic,
+                tts,
+                transport.output(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
 
@@ -103,6 +108,6 @@ async def main(room_url: str, token):
 
         await runner.run(task)
 
+
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/13-whisper-transcription.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import sys
 
@@ -20,6 +21,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -27,7 +29,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptionLogger(FrameProcessor):
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -35,23 +36,26 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def main(room_url: str):
-    transport = DailyTransport(room_url, None, "Transcription bot",
-                               DailyParams(audio_in_enabled=True))
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    stt = WhisperSTTService()
+        transport = DailyTransport(
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
+        )
 
-    tl = TranscriptionLogger()
+        stt = WhisperSTTService()
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        tl = TranscriptionLogger()
 
-    task = PipelineTask(pipeline)
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    runner = PipelineRunner()
+        task = PipelineTask(pipeline)
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/13a-whisper-local.py

@@ -19,6 +19,7 @@ from pipecat.transports.local.audio import LocalAudioTransport
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -26,7 +27,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptionLogger(FrameProcessor):
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 

examples/foundational/13b-deepgram-transcription.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
@@ -13,7 +14,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.deepgram import DeepgramSTTService, LiveOptions, Language
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
@@ -21,6 +22,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -28,7 +30,6 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptionLogger(FrameProcessor):
-
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -36,23 +37,29 @@ class TranscriptionLogger(FrameProcessor):
             print(f"Transcription: {frame.text}")
 
 
-async def main(room_url: str):
-    transport = DailyTransport(room_url, None, "Transcription bot",
-                               DailyParams(audio_in_enabled=True))
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
 
-    stt = DeepgramSTTService(os.getenv("DEEPGRAM_API_KEY"))
+        transport = DailyTransport(
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
+        )
 
-    tl = TranscriptionLogger()
+        stt = DeepgramSTTService(
+            api_key=os.getenv("DEEPGRAM_API_KEY"),
+            # live_options=LiveOptions(language=Language.FR),
+        )
 
-    pipeline = Pipeline([transport.input(), stt, tl])
+        tl = TranscriptionLogger()
 
-    task = PipelineTask(pipeline)
+        pipeline = Pipeline([transport.input(), stt, tl])
 
-    runner = PipelineRunner()
+        task = PipelineTask(pipeline)
 
-    await runner.run(task)
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url))
+    asyncio.run(main())

examples/foundational/13c-gladia-transcription.py

@@ -0,0 +1,63 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import Frame, TranscriptionFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.gladia import GladiaSTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class TranscriptionLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            print(f"Transcription: {frame.text}")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
+        )
+
+        stt = GladiaSTTService(
+            api_key=os.getenv("GLADIA_API_KEY"),
+            # live_options=LiveOptions(language=Language.FR),
+        )
+
+        tl = TranscriptionLogger()
+
+        pipeline = Pipeline([transport.input(), stt, tl])
+
+        task = PipelineTask(pipeline)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/13d-assemblyai-transcription.py

@@ -0,0 +1,62 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import Frame, TranscriptionFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.services.assemblyai import AssemblyAISTTService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+class TranscriptionLogger(FrameProcessor):
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            print(f"Transcription: {frame.text}")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url, None, "Transcription bot", DailyParams(audio_in_enabled=True)
+        )
+
+        stt = AssemblyAISTTService(
+            api_key=os.getenv("ASSEMBLYAI_API_KEY"),
+        )
+
+        tl = TranscriptionLogger()
+
+        pipeline = Pipeline([transport.input(), stt, tl])
+
+        task = PipelineTask(pipeline)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14-function-calling.py

@@ -9,19 +9,13 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import TextFrame
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantContextAggregator,
-    LLMUserContextAggregator,
-)
-from pipecat.processors.logger import FrameLogger
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from openai.types.chat import ChatCompletionToolParam
 
@@ -30,22 +24,30 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def start_fetch_weather(llm):
-    await llm.push_frame(TextFrame("Let me think."))
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
 
 
-async def fetch_weather_from_api(llm, args):
-    return {"conditions": "nice", "temperature": "75"}
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -54,26 +56,19 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
-        llm.register_function(
-            "get_current_weather",
-            fetch_weather_from_api,
-            start_callback=start_fetch_weather)
-
-        fl_in = FrameLogger("Inner")
-        fl_out = FrameLogger("Outer")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
 
         tools = [
             ChatCompletionToolParam(
@@ -90,17 +85,15 @@ async def main(room_url: str, token):
                             },
                             "format": {
                                 "type": "string",
-                                "enum": [
-                                    "celsius",
-                                    "fahrenheit"],
+                                "enum": ["celsius", "fahrenheit"],
                                 "description": "The temperature unit to use. Infer this from the users location.",
                             },
                         },
-                        "required": [
-                            "location",
-                            "format"],
+                        "required": ["location", "format"],
                     },
-                })]
+                },
+            )
+        ]
         messages = [
             {
                 "role": "system",
@@ -109,26 +102,34 @@ async def main(room_url: str, token):
         ]
 
         context = OpenAILLMContext(messages, tools)
-        tma_in = LLMUserContextAggregator(context)
-        tma_out = LLMAssistantContextAggregator(context)
-        pipeline = Pipeline([
-            fl_in,
-            transport.input(),
-            tma_in,
-            llm,
-            fl_out,
-            tts,
-            transport.output(),
-            tma_out
-        ])
+        context_aggregator = llm.create_context_aggregator(context)
 
-        task = PipelineTask(pipeline)
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
-        @ transport.event_handler("on_first_participant_joined")
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await tts.say("Hi! Ask me about the weather in San Francisco.")
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         runner = PipelineRunner()
 
@@ -136,5 +137,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/14a-function-calling-anthropic.py

@@ -0,0 +1,118 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+    location = arguments["location"]
+    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
+        )
+        llm.register_function("get_weather", get_weather)
+
+        tools = [
+            {
+                "name": "get_weather",
+                "description": "Get the current weather in a given location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        }
+                    },
+                    "required": ["location"],
+                },
+            }
+        ]
+
+        # todo: test with very short initial user message
+
+        # messages = [{"role": "system",
+        #              "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
+        #             {"role": "user",
+        #              "content": " Start the conversation by introducing yourself."}]
+
+        messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User spoken responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses and tool context
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14b-function-calling-anthropic-video.py

@@ -0,0 +1,173 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+video_participant_id = None
+
+
+async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+    location = arguments["location"]
+    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+    question = arguments["question"]
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)
+
+
+async def main():
+    global llm
+
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-sonnet-20240620",
+            enable_prompt_caching_beta=True,
+        )
+        llm.register_function("get_weather", get_weather)
+        llm.register_function("get_image", get_image)
+
+        tools = [
+            {
+                "name": "get_weather",
+                "description": "Get the current weather in a given location",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        }
+                    },
+                    "required": ["location"],
+                },
+            },
+            {
+                "name": "get_image",
+                "description": "Get an image from the video stream.",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "question": {
+                            "type": "string",
+                            "description": "The question that the user is asking about the image.",
+                        }
+                    },
+                    "required": ["question"],
+                },
+            },
+        ]
+
+        # todo: test with very short initial user message
+
+        system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+  - What do you see?
+  - What's in the video?
+  - Can you describe the video?
+  - Tell me about what you see.
+  - Tell me something interesting about what you see.
+  - What's happening in the video?
+
+If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
+        """
+
+        messages = [
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": system_prompt,
+                    }
+                ],
+            },
+            {"role": "user", "content": "Start the conversation by introducing yourself."},
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User speech to text
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses and tool context
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            global video_participant_id
+            video_participant_id = participant["id"]
+            transport.capture_participant_transcription(video_participant_id)
+            transport.capture_participant_video(video_participant_id, framerate=0)
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14c-function-calling-together.py

@@ -0,0 +1,136 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.services.together import TogetherLLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = TogetherLLMService(
+            api_key=os.getenv("TOGETHER_API_KEY"),
+            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+        )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline)
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            # await tts.say("Hi! Ask me about the weather in San Francisco.")
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14d-function-calling-video.py

@@ -0,0 +1,167 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+video_participant_id = None
+
+
+async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
+    location = arguments["location"]
+    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
+    logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}")
+    question = arguments["question"]
+    await llm.request_image_frame(user_id=video_participant_id, text_content=question)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+        llm.register_function("get_weather", get_weather)
+        llm.register_function("get_image", get_image)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            ),
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_image",
+                    "description": "Get an image from the video stream.",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "question": {
+                                "type": "string",
+                                "description": "The question to ask the AI to generate an image of",
+                            },
+                        },
+                        "required": ["question"],
+                    },
+                },
+            ),
+        ]
+
+        system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+  - What do you see?
+  - What's in the video?
+  - Can you describe the video?
+  - Tell me about what you see.
+  - Tell me something interesting about what you see.
+  - What's happening in the video?
+"""
+        messages = [
+            {"role": "system", "content": system_prompt},
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(pipeline)
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            global video_participant_id
+            video_participant_id = participant["id"]
+            transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(video_participant_id, framerate=0)
+            # Kick off the conversation.
+            await tts.say("Hi! Ask me about the weather in San Francisco.")
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/15-switch-voices.py

@@ -4,26 +4,22 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import asyncio
 import aiohttp
+import asyncio
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantContextAggregator,
-    LLMUserContextAggregator
-)
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from openai.types.chat import ChatCompletionToolParam
 
@@ -32,6 +28,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -40,10 +37,14 @@ logger.add(sys.stderr, level="DEBUG")
 current_voice = "News Lady"
 
 
-async def switch_voice(llm, args):
+async def switch_voice(function_name, tool_call_id, args, llm, context, result_callback):
     global current_voice
     current_voice = args["voice"]
-    return {"voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."}
+    await result_callback(
+        {
+            "voice": f"You are now using your {current_voice} voice. Your responses should now be as if you were a {current_voice}."
+        }
+    )
 
 
 async def news_lady_filter(frame) -> bool:
@@ -58,42 +59,38 @@ async def barbershop_man_filter(frame) -> bool:
     return current_voice == "Barbershop Man"
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
             "Pipecat",
             DailyParams(
                 audio_out_enabled=True,
-                audio_out_sample_rate=44100,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         news_lady = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_name="Newslady",
-            output_format="pcm_44100"
+            voice_id="bf991597-6c13-47e4-8411-91ec2de5c466",  # Newslady
         )
 
         british_lady = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_name="British Lady",
-            output_format="pcm_44100"
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
         barbershop_man = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_name="Barbershop Man",
-            output_format="pcm_44100"
+            voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
         llm.register_function("switch_voice", switch_voice)
 
         tools = [
@@ -112,7 +109,9 @@ async def main(room_url: str, token):
                         },
                         "required": ["voice"],
                     },
-                })]
+                },
+            )
+        ]
         messages = [
             {
                 "role": "system",
@@ -121,21 +120,22 @@ async def main(room_url: str, token):
         ]
 
         context = OpenAILLMContext(messages, tools)
-        tma_in = LLMUserContextAggregator(context)
-        tma_out = LLMAssistantContextAggregator(context)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            ParallelPipeline(    # TTS (one of the following vocies)
-                [FunctionFilter(news_lady_filter), news_lady],            # News Lady voice
-                [FunctionFilter(british_lady_filter), british_lady],      # British Lady voice
-                [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
-            ),
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                ParallelPipeline(  # TTS (one of the following vocies)
+                    [FunctionFilter(news_lady_filter), news_lady],  # News Lady voice
+                    [FunctionFilter(british_lady_filter), british_lady],  # British Lady voice
+                    [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
+                ),
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
@@ -146,7 +146,9 @@ async def main(room_url: str, token):
             messages.append(
                 {
                     "role": "system",
-                    "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}."})
+                    "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}.",
+                }
+            )
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -155,5 +157,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/15a-switch-languages.py

@@ -9,22 +9,18 @@ import aiohttp
 import os
 import sys
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantContextAggregator,
-    LLMUserContextAggregator
-)
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.services.whisper import Model, WhisperSTTService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from openai.types.chat import ChatCompletionToolParam
 
@@ -33,6 +29,7 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -41,10 +38,10 @@ logger.add(sys.stderr, level="DEBUG")
 current_language = "English"
 
 
-async def switch_language(llm, args):
+async def switch_language(function_name, tool_call_id, args, llm, context, result_callback):
     global current_language
     current_language = args["language"]
-    return {"voice": f"Your answers from now on should be in {current_language}."}
+    await result_callback({"voice": f"Your answers from now on should be in {current_language}."})
 
 
 async def english_filter(frame) -> bool:
@@ -55,8 +52,10 @@ async def spanish_filter(frame) -> bool:
     return current_language == "Spanish"
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -66,28 +65,23 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True
-            )
+                vad_audio_passthrough=True,
+            ),
         )
 
         stt = WhisperSTTService(model=Model.LARGE)
 
-        english_tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="pNInz6obpgDQGcFmaJgB",
+        english_tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        spanish_tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            model="eleven_multilingual_v2",
-            voice_id="9F4C8ztpNUmXkdDDbz3J",
+        spanish_tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="846d6cb0-2301-48b6-9683-48f5618ea2f6",  # Spanish-speaking Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
         llm.register_function("switch_language", switch_language)
 
         tools = [
@@ -106,7 +100,9 @@ async def main(room_url: str, token):
                         },
                         "required": ["language"],
                     },
-                })]
+                },
+            )
+        ]
         messages = [
             {
                 "role": "system",
@@ -115,21 +111,22 @@ async def main(room_url: str, token):
         ]
 
         context = OpenAILLMContext(messages, tools)
-        tma_in = LLMUserContextAggregator(context)
-        tma_out = LLMAssistantContextAggregator(context)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            stt,                 # STT
-            tma_in,              # User responses
-            llm,                 # LLM
-            ParallelPipeline(    # TTS (bot will speak the chosen language)
-                [FunctionFilter(english_filter), english_tts],  # English
-                [FunctionFilter(spanish_filter), spanish_tts],  # Spanish
-            ),
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                ParallelPipeline(  # TTS (bot will speak the chosen language)
+                    [FunctionFilter(english_filter), english_tts],  # English
+                    [FunctionFilter(spanish_filter), spanish_tts],  # Spanish
+                ),
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
@@ -140,7 +137,9 @@ async def main(room_url: str, token):
             messages.append(
                 {
                     "role": "system",
-                    "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}."})
+                    "content": f"Please introduce yourself to the user and let them know the languages you speak. Your initial responses should be in {current_language}.",
+                }
+            )
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()
@@ -149,5 +148,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/16-gpu-container-local-bot.py

@@ -5,35 +5,38 @@
 #
 
 import asyncio
-import aiohttp
 import os
 import sys
-import json
 
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.deepgram import DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
-from pipecat.vad.silero import SileroVADAnalyzer
+from pipecat.transports.services.daily import (
+    DailyParams,
+    DailyTransport,
+    DailyTransportMessageFrame,
+)
 
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -42,15 +45,15 @@ async def main(room_url: str, token):
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
         tts = DeepgramTTSService(
             aiohttp_session=session,
             api_key=os.getenv("DEEPGRAM_API_KEY"),
             voice="aura-asteria-en",
-            base_url="http://0.0.0.0:8080/v1/speak"
+            base_url="http://0.0.0.0:8080/v1/speak",
         )
 
         llm = OpenAILLMService(
@@ -59,7 +62,7 @@ async def main(room_url: str, token):
             # model="gpt-4o"
             # Or, to use a local vLLM (or similar) api server
             model="meta-llama/Meta-Llama-3-8B-Instruct",
-            base_url="http://0.0.0.0:8000/v1"
+            base_url="http://0.0.0.0:8000/v1",
         )
 
         messages = [
@@ -69,17 +72,19 @@ async def main(room_url: str, token):
             },
         ]
 
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            transport.output(),  # Transport bot output
-            tma_out              # Assistant spoken responses
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),
+            ]
+        )
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
 
@@ -92,8 +97,7 @@ async def main(room_url: str, token):
         # When the first participant joins, the bot should introduce itself.
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            messages.append(
-                {"role": "system", "content": "Please introduce yourself to the user."})
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         # Handle "latency-ping" messages. The client will send app messages that look like
@@ -110,14 +114,18 @@ async def main(room_url: str, token):
                     logger.debug(f"Received latency ping app message: {message}")
                     ts = message["latency-ping"]["ts"]
                     # Send immediately
-                    transport.output().send_message(DailyTransportMessageFrame(
-                        message={"latency-pong-msg-handler": {"ts": ts}},
-                        participant_id=sender))
+                    transport.output().send_message(
+                        DailyTransportMessageFrame(
+                            message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
+                        )
+                    )
                     # And push to the pipeline for the Daily transport.output to send
-                    await tma_in.push_frame(
+                    await task.queue_frame(
                         DailyTransportMessageFrame(
                             message={"latency-pong-pipeline-delivery": {"ts": ts}},
-                            participant_id=sender))
+                            participant_id=sender,
+                        )
+                    )
             except Exception as e:
                 logger.debug(f"message handling error: {e} - {message}")
 
@@ -126,5 +134,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/foundational/17-detect-user-idle.py

@@ -0,0 +1,113 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.user_idle_processor import UserIdleProcessor
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        async def user_idle_callback(user_idle: UserIdleProcessor):
+            messages.append(
+                {
+                    "role": "system",
+                    "content": "Ask the user if they are still there and try to prompt for some input, but be short.",
+                }
+            )
+            await user_idle.push_frame(LLMMessagesFrame(messages))
+
+        user_idle = UserIdleProcessor(callback=user_idle_callback, timeout=5.0)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                user_idle,  # Idle user check-in
+                context_aggregator.user(),
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/18-gstreamer-filesrc.py

@@ -0,0 +1,76 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import argparse
+import sys
+
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure_with_args
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+        parser.add_argument("-i", "--input", type=str, required=True, help="Input video file")
+
+        (room_url, _, args) = await configure_with_args(session, parser)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "GStreamer",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_is_live=True,
+                camera_out_enabled=True,
+                camera_out_width=1280,
+                camera_out_height=720,
+                camera_out_is_live=True,
+            ),
+        )
+
+        gst = GStreamerPipelineSource(
+            pipeline=f"filesrc location={args.input}",
+            out_params=GStreamerPipelineSource.OutputParams(
+                video_width=1280,
+                video_height=720,
+                audio_sample_rate=16000,
+                audio_channels=1,
+            ),
+        )
+
+        pipeline = Pipeline(
+            [
+                gst,  # GStreamer file source
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(pipeline)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/18a-gstreamer-videotestsrc.py

@@ -0,0 +1,67 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import sys
+
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "GStreamer",
+            DailyParams(
+                camera_out_enabled=True,
+                camera_out_width=1280,
+                camera_out_height=720,
+                camera_out_is_live=True,
+            ),
+        )
+
+        gst = GStreamerPipelineSource(
+            pipeline='videotestsrc ! capsfilter caps="video/x-raw,width=1280,height=720,framerate=30/1"',
+            out_params=GStreamerPipelineSource.OutputParams(
+                video_width=1280, video_height=720, clock_sync=False
+            ),
+        )
+
+        pipeline = Pipeline(
+            [
+                gst,  # GStreamer file source
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(pipeline)
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/19-openai-realtime-beta.py

@@ -0,0 +1,179 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+from datetime import datetime
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai_realtime_beta import (
+    InputAudioTranscription,
+    OpenAIRealtimeBetaLLMService,
+    SessionProperties,
+    TurnDetection,
+)
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    temperature = 75 if args["format"] == "fahrenheit" else 24
+    await result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": args["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+tools = [
+    {
+        "type": "function",
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "format": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The temperature unit to use. Infer this from the users location.",
+                },
+            },
+            "required": ["location", "format"],
+        },
+    }
+]
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_in_sample_rate=24000,
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                transcription_enabled=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        session_properties = SessionProperties(
+            input_audio_transcription=InputAudioTranscription(),
+            # Set openai TurnDetection parameters. Not setting this at all will turn it
+            # on by default
+            turn_detection=TurnDetection(silence_duration_ms=1000),
+            # Or set to False to disable openai turn detection and use transport VAD
+            # turn_detection=False,
+            # tools=tools,
+            instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+-
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+Remember, your responses should be short. Just one or two sentences, usually.""",
+        )
+
+        llm = OpenAIRealtimeBetaLLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            session_properties=session_properties,
+            start_audio_paused=False,
+        )
+
+        # you can either register a single function for all function calls, or specific functions
+        # llm.register_function(None, fetch_weather_from_api)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
+
+        # Create a standard OpenAI LLM context object using the normal messages format. The
+        # OpenAIRealtimeBetaLLMService will convert this internally to messages that the
+        # openai WebSocket API can understand.
+        context = OpenAILLMContext(
+            [{"role": "user", "content": "Say hello!"}],
+            # [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
+            #     [
+            #         {
+            #             "role": "user",
+            #             "content": [
+            #                 {"type": "text", "text": "Say"},
+            #                 {"type": "text", "text": "yo what's up!"},
+            #             ],
+            #         }
+            #     ],
+            tools,
+        )
+
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),
+                llm,  # LLM
+                context_aggregator.assistant(),
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                # report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/20a-persistent-context-openai.py

@@ -0,0 +1,236 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import glob
+import json
+import os
+import sys
+from datetime import datetime
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.cartesia import CartesiaTTSService
+
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+BASE_FILENAME = "/tmp/pipecat_conversation_"
+tts = None
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    temperature = 75 if args["format"] == "fahrenheit" else 24
+    await result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": args["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def get_saved_conversation_filenames(
+    function_name, tool_call_id, args, llm, context, result_callback
+):
+    # Construct the full pattern including the BASE_FILENAME
+    full_pattern = f"{BASE_FILENAME}*.json"
+
+    # Use glob to find all matching files
+    matching_files = glob.glob(full_pattern)
+    logger.debug(f"matching files: {matching_files}")
+
+    await result_callback({"filenames": matching_files})
+
+
+async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+    filename = f"{BASE_FILENAME}{timestamp}.json"
+    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    try:
+        with open(filename, "w") as file:
+            messages = context.get_messages_for_persistent_storage()
+            # remove the last message, which is the instruction we just gave to save the conversation
+            messages.pop()
+            json.dump(messages, file, indent=2)
+        await result_callback({"success": True})
+    except Exception as e:
+        await result_callback({"success": False, "error": str(e)})
+
+
+async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    global tts
+    filename = args["filename"]
+    logger.debug(f"loading conversation from {filename}")
+    try:
+        with open(filename, "r") as file:
+            context.set_messages(json.load(file))
+            logger.debug(
+                f"loaded conversation from {filename}\n{json.dumps(context.messages, indent=4)}"
+            )
+        await tts.say("Ok, I've loaded that conversation.")
+    except Exception as e:
+        await result_callback({"success": False, "error": str(e)})
+
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+    },
+]
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_current_weather",
+            "description": "Get the current weather",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                    "format": {
+                        "type": "string",
+                        "enum": ["celsius", "fahrenheit"],
+                        "description": "The temperature unit to use. Infer this from the users location.",
+                    },
+                },
+                "required": ["location", "format"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "save_conversation",
+            "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
+            "parameters": {
+                "type": "object",
+                "properties": {},
+                "required": [],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "get_saved_conversation_filenames",
+            "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
+            "parameters": {
+                "type": "object",
+                "properties": {},
+                "required": [],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "load_conversation",
+            "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "filename": {
+                        "type": "string",
+                        "description": "The filename of the conversation history to load.",
+                    }
+                },
+                "required": ["filename"],
+            },
+        },
+    },
+]
+
+
+async def main():
+    global tts
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        # you can either register a single function for all function calls, or specific functions
+        # llm.register_function(None, fetch_weather_from_api)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
+        llm.register_function("save_conversation", save_conversation)
+        llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+        llm.register_function("load_conversation", load_conversation)
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),
+                llm,  # LLM
+                tts,
+                context_aggregator.assistant(),
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                # report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/20b-persistent-context-openai-realtime.py

@@ -0,0 +1,262 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import glob
+import json
+import os
+import sys
+from datetime import datetime
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.services.openai_realtime_beta import (
+    InputAudioTranscription,
+    OpenAIRealtimeBetaLLMService,
+    SessionProperties,
+    TurnDetection,
+)
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+BASE_FILENAME = "/tmp/pipecat_conversation_"
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    temperature = 75 if args["format"] == "fahrenheit" else 24
+    await result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": args["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def get_saved_conversation_filenames(
+    function_name, tool_call_id, args, llm, context, result_callback
+):
+    # Construct the full pattern including the BASE_FILENAME
+    full_pattern = f"{BASE_FILENAME}*.json"
+
+    # Use glob to find all matching files
+    matching_files = glob.glob(full_pattern)
+    logger.debug(f"matching files: {matching_files}")
+
+    await result_callback({"filenames": matching_files})
+
+
+# async def get_saved_conversation_filenames(
+#     function_name, tool_call_id, args, llm, context, result_callback
+# ):
+#     pattern = re.compile(re.escape(BASE_FILENAME) + "\\d{8}_\\d{6}\\.json$")
+#     matching_files = []
+
+#     for filename in os.listdir("."):
+#         if pattern.match(filename):
+#             matching_files.append(filename)
+
+#     await result_callback({"filenames": matching_files})
+
+
+async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+    filename = f"{BASE_FILENAME}{timestamp}.json"
+    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    try:
+        with open(filename, "w") as file:
+            messages = context.get_messages_for_persistent_storage()
+            # remove the last message, which is the instruction we just gave to save the conversation
+            messages.pop()
+            json.dump(messages, file, indent=2)
+        await result_callback({"success": True})
+    except Exception as e:
+        await result_callback({"success": False, "error": str(e)})
+
+
+async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    async def _reset():
+        filename = args["filename"]
+        logger.debug(f"loading conversation from {filename}")
+        try:
+            with open(filename, "r") as file:
+                context.set_messages(json.load(file))
+                await llm.reset_conversation()
+                await llm._create_response()
+        except Exception as e:
+            await result_callback({"success": False, "error": str(e)})
+
+    asyncio.create_task(_reset())
+
+
+tools = [
+    {
+        "type": "function",
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "format": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The temperature unit to use. Infer this from the users location.",
+                },
+            },
+            "required": ["location", "format"],
+        },
+    },
+    {
+        "type": "function",
+        "name": "save_conversation",
+        "description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "get_saved_conversation_filenames",
+        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
+        "parameters": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "type": "function",
+        "name": "load_conversation",
+        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "The filename of the conversation history to load.",
+                }
+            },
+            "required": ["filename"],
+        },
+    },
+]
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_in_enabled=True,
+                audio_in_sample_rate=24000,
+                audio_out_enabled=True,
+                audio_out_sample_rate=24000,
+                transcription_enabled=False,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        session_properties = SessionProperties(
+            input_audio_transcription=InputAudioTranscription(),
+            # Set openai TurnDetection parameters. Not setting this at all will turn it
+            # on by default
+            turn_detection=TurnDetection(silence_duration_ms=1000),
+            # Or set to False to disable openai turn detection and use transport VAD
+            # turn_detection=False,
+            # tools=tools,
+            instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI.
+
+Act like a human, but remember that you aren't a human and that you can't do human
+things in the real world. Your voice and personality should be warm and engaging, with a lively and
+playful tone.
+
+If interacting in a non-English language, start by using the standard accent or dialect familiar to
+the user. Talk quickly. You should always call a function if you can. Do not refer to these rules,
+even if you're asked about them.
+-
+You are participating in a voice conversation. Keep your responses concise, short, and to the point
+unless specifically asked to elaborate on a topic.
+
+Remember, your responses should be short. Just one or two sentences, usually.""",
+        )
+
+        llm = OpenAIRealtimeBetaLLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            session_properties=session_properties,
+            start_audio_paused=False,
+        )
+
+        # you can either register a single function for all function calls, or specific functions
+        # llm.register_function(None, fetch_weather_from_api)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
+        llm.register_function("save_conversation", save_conversation)
+        llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+        llm.register_function("load_conversation", load_conversation)
+
+        context = OpenAILLMContext([], tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),
+                llm,  # LLM
+                context_aggregator.assistant(),
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                # report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/20c-persistent-context-anthropic.py

@@ -0,0 +1,232 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import glob
+import json
+import os
+import sys
+from datetime import datetime
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
+
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+BASE_FILENAME = "/tmp/pipecat_conversation_"
+tts = None
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    temperature = 75 if args["format"] == "fahrenheit" else 24
+    await result_callback(
+        {
+            "conditions": "nice",
+            "temperature": temperature,
+            "format": args["format"],
+            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
+        }
+    )
+
+
+async def get_saved_conversation_filenames(
+    function_name, tool_call_id, args, llm, context, result_callback
+):
+    # Construct the full pattern including the BASE_FILENAME
+    full_pattern = f"{BASE_FILENAME}*.json"
+
+    # Use glob to find all matching files
+    matching_files = glob.glob(full_pattern)
+    logger.debug(f"matching files: {matching_files}")
+
+    await result_callback({"filenames": matching_files})
+
+
+async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+    filename = f"{BASE_FILENAME}{timestamp}.json"
+    logger.debug(f"writing conversation to {filename}\n{json.dumps(context.messages, indent=4)}")
+    try:
+        with open(filename, "w") as file:
+            # todo: extract 'system' into the first message in the list
+            messages = context.get_messages_for_persistent_storage()
+            # remove the last message, which is the instruction we just gave to save the conversation
+            messages.pop()
+            json.dump(messages, file, indent=2)
+        await result_callback({"success": True})
+    except Exception as e:
+        await result_callback({"success": False, "error": str(e)})
+
+
+async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
+    global tts
+    filename = args["filename"]
+    logger.debug(f"loading conversation from {filename}")
+    try:
+        with open(filename, "r") as file:
+            context.set_messages(json.load(file))
+            logger.debug(
+                f"loaded conversation from {filename}\n{json.dumps(context.messages, indent=4)}"
+            )
+        await tts.say("Ok, I've loaded that conversation.")
+    except Exception as e:
+        await result_callback({"success": False, "error": str(e)})
+
+
+# Test message munging ...
+messages = [
+    {
+        "role": "system",
+        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+    },
+    {"role": "user", "content": ""},
+    {"role": "assistant", "content": []},
+    {"role": "user", "content": "Tell me"},
+    {"role": "user", "content": "a joke"},
+]
+tools = [
+    {
+        "name": "get_current_weather",
+        "description": "Get the current weather",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, CA",
+                },
+                "format": {
+                    "type": "string",
+                    "enum": ["celsius", "fahrenheit"],
+                    "description": "The temperature unit to use. Infer this from the users location.",
+                },
+            },
+            "required": ["location", "format"],
+        },
+    },
+    {
+        "name": "save_conversation",
+        "description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
+        "input_schema": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "name": "get_saved_conversation_filenames",
+        "description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
+        "input_schema": {
+            "type": "object",
+            "properties": {},
+            "required": [],
+        },
+    },
+    {
+        "name": "load_conversation",
+        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "filename": {
+                    "type": "string",
+                    "description": "The filename of the conversation history to load.",
+                }
+            },
+            "required": ["filename"],
+        },
+    },
+]
+
+
+async def main():
+    global tts
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
+        )
+
+        # you can either register a single function for all function calls, or specific functions
+        # llm.register_function(None, fetch_weather_from_api)
+        llm.register_function("get_current_weather", fetch_weather_from_api)
+        llm.register_function("save_conversation", save_conversation)
+        llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
+        llm.register_function("load_conversation", load_conversation)
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),
+                llm,  # LLM
+                tts,
+                context_aggregator.assistant(),
+                transport.output(),  # Transport bot output
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                # report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/runner.py

@@ -1,18 +1,29 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
 import argparse
 import os
-import time
-import urllib
-import requests
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
 
 
-def configure():
-    parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
+async def configure(aiohttp_session: aiohttp.ClientSession):
+    (url, token, _) = await configure_with_args(aiohttp_session)
+    return (url, token)
+
+
+async def configure_with_args(
+    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
+):
+    if not parser:
+        parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
     parser.add_argument(
-        "-u",
-        "--url",
-        type=str,
-        required=False,
-        help="URL of the Daily room to join")
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
     parser.add_argument(
         "-k",
         "--apikey",
@@ -28,31 +39,24 @@ def configure():
 
     if not url:
         raise Exception(
-            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL.")
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
 
     if not key:
-        raise Exception("No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
 
     # Create a meeting token for the given room with an expiration 1 hour in
     # the future.
-    room_name: str = urllib.parse.urlparse(url).path[1:]
-    expiration: float = time.time() + 60 * 60
+    expiry_time: float = 60 * 60
 
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={
-            "Authorization": f"Bearer {key}"},
-        json={
-            "properties": {
-                "room_name": room_name,
-                "is_owner": True,
-                "exp": expiration}},
-    )
+    token = await daily_rest_helper.get_token(url, expiry_time)
 
-    if res.status_code != 200:
-        raise Exception(
-            f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
-
-    return (url, token)
+    return (url, token, args)

examples/moondream-chatbot/README.md

@@ -24,7 +24,7 @@ cp env.example .env # and add your credentials
 python server.py
 ```
 
-Then, visit `http://localhost:7860/start` in your browser to start a chatbot
+Then, visit `http://localhost:7860/` in your browser to start a chatbot
 session.
 
 ## Build and test the Docker image
@@ -41,4 +41,4 @@ docker build -t moonbot -f Dockerfile.intel .
 docker run --env-file .env -p 7860:7860 --device /dev/dri moonbot
 ```
 
-You can try to visit `http://localhost:7860/start` again.
+You can try to visit `http://localhost:7860/` again.

examples/moondream-chatbot/bot.py

@@ -1,3 +1,9 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
 import asyncio
 import aiohttp
 import os
@@ -5,37 +11,38 @@ import sys
 
 from PIL import Image
 
+from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
     ImageRawFrame,
+    OutputImageRawFrame,
     SpriteFrame,
     Frame,
     LLMMessagesFrame,
-    AudioRawFrame,
+    TTSAudioRawFrame,
     TTSStoppedFrame,
     TextFrame,
     UserImageRawFrame,
     UserImageRequestFrame,
 )
-
 from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.elevenlabs import ElevenLabsTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.moondream import MoondreamService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
 
 from runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -53,7 +60,7 @@ for i in range(1, 26):
     # Get the filename without the extension to use as the dictionary key
     # Open the image and convert it to bytes
     with Image.open(full_path) as img:
-        sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+        sprites.append(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
 
 flipped = sprites[::-1]
 sprites.extend(flipped)
@@ -76,7 +83,7 @@ class TalkingAnimation(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, AudioRawFrame):
+        if isinstance(frame, TTSAudioRawFrame):
             if not self._is_talking:
                 await self.push_frame(talking_frame)
                 self._is_talking = True
@@ -99,7 +106,9 @@ class UserImageRequester(FrameProcessor):
 
         if self.participant_id and isinstance(frame, TextFrame):
             if frame.text == user_request_answer:
-                await self.push_frame(UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM)
+                await self.push_frame(
+                    UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM
+                )
                 await self.push_frame(TextFrame("Describe the image in a short sentence."))
         elif isinstance(frame, UserImageRawFrame):
             await self.push_frame(frame)
@@ -128,8 +137,10 @@ class ImageFilterProcessor(FrameProcessor):
             await self.push_frame(frame)
 
 
-async def main(room_url: str, token):
+async def main():
     async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
         transport = DailyTransport(
             room_url,
             token,
@@ -141,19 +152,16 @@ async def main(room_url: str, token):
                 camera_out_height=576,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="pNInz6obpgDQGcFmaJgB",
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
         ta = TalkingAnimation()
 
@@ -174,19 +182,21 @@ async def main(room_url: str, token):
             },
         ]
 
-        ura = LLMUserResponseAggregator(messages)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline([
-            transport.input(),
-            ura,
-            llm,
-            ParallelPipeline(
-                [sa, ir, va, moondream],
-                [tf, imgf]),
-            tts,
-            ta,
-            transport.output()
-        ])
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                ParallelPipeline([sa, ir, va, moondream], [tf, imgf]),
+                tts,
+                ta,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
 
         task = PipelineTask(pipeline)
         await task.queue_frame(quiet_frame)
@@ -204,5 +214,4 @@ async def main(room_url: str, token):
 
 
 if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))
+    asyncio.run(main())

examples/moondream-chatbot/env.example

@@ -1,4 +1,4 @@
 DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
 DAILY_API_KEY=7df...
 OPENAI_API_KEY=sk-PL...
-ELEVENLABS_API_KEY=aeb...
+CARTESIA_API_KEY=your_cartesia_api_key_here

examples/moondream-chatbot/requirements.txt

@@ -1,5 +1,4 @@
 python-dotenv
-requests
 fastapi[all]
 uvicorn
-pipecat-ai[daily,moondream,openai,silero]
+pipecat-ai[daily,cartesia,moondream,openai,silero]

examples/moondream-chatbot/runner.py

@@ -1,18 +1,21 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
 import argparse
 import os
-import time
-import urllib
-import requests
+
+from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
 
 
-def configure():
+async def configure(aiohttp_session: aiohttp.ClientSession):
     parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
     parser.add_argument(
-        "-u",
-        "--url",
-        type=str,
-        required=False,
-        help="URL of the Daily room to join")
+        "-u", "--url", type=str, required=False, help="URL of the Daily room to join"
+    )
     parser.add_argument(
         "-k",
         "--apikey",
@@ -28,31 +31,24 @@ def configure():
 
     if not url:
         raise Exception(
-            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL.")
+            "No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
+        )
 
     if not key:
-        raise Exception("No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers.")
+        raise Exception(
+            "No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
+        )
+
+    daily_rest_helper = DailyRESTHelper(
+        daily_api_key=key,
+        daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+        aiohttp_session=aiohttp_session,
+    )
 
     # Create a meeting token for the given room with an expiration 1 hour in
     # the future.
-    room_name: str = urllib.parse.urlparse(url).path[1:]
-    expiration: float = time.time() + 60 * 60
+    expiry_time: float = 60 * 60
 
-    res: requests.Response = requests.post(
-        f"https://api.daily.co/v1/meeting-tokens",
-        headers={
-            "Authorization": f"Bearer {key}"},
-        json={
-            "properties": {
-                "room_name": room_name,
-                "is_owner": True,
-                "exp": expiration}},
-    )
-
-    if res.status_code != 200:
-        raise Exception(
-            f"Failed to create meeting token: {res.status_code} {res.text}")
-
-    token: str = res.json()["token"]
+    token = await daily_rest_helper.get_token(url, expiry_time)
 
     return (url, token)