working on a fastapi http transport

.github/workflows/lint.yaml

@@ -1,4 +1,4 @@
-name: format
+name: lint
 
 on:
   workflow_dispatch:
@@ -12,12 +12,12 @@ on:
       - "docs/**"
 
 concurrency:
-  group: build-format-${{ github.event.pull_request.number || github.ref }}
+  group: build-lint-${{ github.event.pull_request.number || github.ref }}
   cancel-in-progress: true
 
 jobs:
-  ruff-format:
-    name: "Formatting checker"
+  autopep8:
+    name: "Formatting lints"
     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
@@ -34,8 +34,11 @@ jobs:
           source .venv/bin/activate
           python -m pip install --upgrade pip
           pip install -r dev-requirements.txt
-      - name: Ruff formatter
-        id: ruff
+      - name: autopep8
+        id: autopep8
         run: |
           source .venv/bin/activate
-          ruff format --diff
+          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

.github/workflows/tests.yaml

@@ -20,24 +20,14 @@ jobs:
     name: "Unit and Integration Tests"
     runs-on: ubuntu-latest
     steps:
-      - name: Checkout repo
-        uses: actions/checkout@v4
+      - uses: actions/checkout@v4
       - name: Set up Python
         id: setup_python
         uses: actions/setup-python@v4
         with:
           python-version: "3.10"
-      - name: Cache virtual environment
-        uses: actions/cache@v3
-        with:
-          # 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
-        id: install_system_packages
-        run: |
-          sudo apt-get install -y portaudio19-dev
+        run: sudo apt-get install -y portaudio19-dev
       - name: Setup virtual environment
         run: |
           python -m venv .venv
@@ -45,8 +35,8 @@ jobs:
         run: |
           source .venv/bin/activate
           python -m pip install --upgrade pip
-          pip install -r dev-requirements.txt -r test-requirements.txt
+          pip install -r dev-requirements.txt
       - name: Test with pytest
         run: |
           source .venv/bin/activate
-          pytest --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
+          pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests

.gitignore

@@ -4,7 +4,6 @@ __pycache__/
 *~
 venv
 .venv
-/.idea
 #*#
 
 # Distribution / packaging

CHANGELOG.md

@@ -1,6 +1,6 @@
 # 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).
@@ -9,275 +9,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
-- Added `GatedOpenAILLMContextAggregator`. This aggregator keeps the last
-  received OpenAI LLM context frame and it doesn't let it through until the
-  notifier is notified.
-
-- Added `WakeNotifierFilter`. This processor expects a list of frame types and
-  will execute a given callback predicate when a frame of any of those type is
-  being processed. If the callback returns true the notifier will be notified.
-
-- Added `NullFilter`. A null filter doesn't push any frames upstream or
-  downstream. This is usually used to disable one of the pipelines in
-  `ParallelPipeline`.
-
-- Added `EventNotifier`. This can be used as a very simple synchronization
-  feature between processors.
-
-- Added `TavusVideoService`. This is an integration for Tavus digital twins.
-  (see https://www.tavus.io/)
-
-- Added `DailyTransport.update_subscriptions()`. This allows you to have fine
-  grained control of what media subscriptions you want for each participant in a
-  room.
-
-### Changed
-
-- The following `DailyTransport` functions are now `async` which means they need
-  to be awaited: `start_dialout`, `stop_dialout`, `start_recording`,
-  `stop_recording`, `capture_participant_transcription` and
-  `capture_participant_video`.
-
-- Changed default output sample rate to 24000. This changes all TTS service to
-  output to 24000 and also the default output transport sample rate. This
-  improves audio quality at the cost of some extra bandwidth.
-
-### Fixed
-
-- Improved bot speaking detection for all TTS services by using actual bot
-  audio.
-
-- Fixed an issue that was generating constant bot started/stopped speaking
-  frames for HTTP TTS services.
-
-- Fixed an issue that was causing stuttering with AWS TTS service.
-
-- Fixed an issue with PlayHTTTSService, where the TTFB metrics were reporting
-  very small time values.
-
-### Other
-
-- Added a new foundational example 22-natural-conversation.py. This examples
-  shows how to achieve a more natural conversation detecting when the user ends
-  statement.
-
-## [0.0.47] - 2024-10-22
-
-### 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`
-
-### Changed
-
-- Updated `GladiaSTTService` to use the V2 API.
-
-- Changed `DailyTransport` transcription model to `nova-2-general`.
-
-### Fixed
-
-- Fixed an issue that would cause an import error when importing
-  `SileroVADAnalyzer` from the old package `pipecat.vad.silero`.
-
-- 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
-
-- Changed `DeepgramSTTService` model to `nova-2-general`.
-
-- 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.
+- A clock can now be specified to `PipelineTask` (defaults to
+  `SystemClock`). This clock will be passed to each frame processor via the
+  `StartFrame`.
 
 - 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
@@ -285,12 +19,6 @@ async def on_connected(processor):
   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.
@@ -312,37 +40,6 @@ async def on_connected(processor):
 
 ### 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
@@ -363,15 +60,6 @@ async def on_connected(processor):
 
 ### 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.
@@ -381,10 +69,6 @@ async def on_connected(processor):
 - `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
@@ -609,7 +293,7 @@ async def on_connected(processor):
 - It is now possible to specify a Silero VAD version when using `SileroVADAnalyzer`
   or `SileroVAD`.
 
-- Added `AysncFrameProcessor` and `AsyncAIService`. Some services like
+- 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
@@ -626,7 +310,7 @@ async def on_connected(processor):
 
 - `WhisperSTTService` model can now also be a string.
 
-- Added missing \* keyword separators in services.
+- Added missing * keyword separators in services.
 
 ### Fixed
 
@@ -703,7 +387,7 @@ async def on_connected(processor):
 - 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.
@@ -943,7 +627,7 @@ async def on_connected(processor):
 - 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

@@ -38,7 +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`, `assemblyai`, `aws`, `azure`, `deepgram`, `gladia`, `google`, `fal`, `lmnt`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`, `xtts`
+- **AI services**: `anthropic`, `azure`, `deepgram`, `gladia`, `google`, `fal`, `lmnt`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`, `xtts`
 - **Transports**: `local`, `websocket`, `daily`
 
 ## Code examples
@@ -51,7 +51,10 @@ Your project may or may not need these, so they're made available as optional re
 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
@@ -61,43 +64,39 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 async def main():
-  # Use Daily as a real-time media transport (WebRTC)
-  transport = DailyTransport(
-    room_url=...,
-    token="", # leave empty. Note: token is _not_ your api key
-    bot_name="Bot Name",
-    params=DailyParams(audio_out_enabled=True))
+  async with aiohttp.ClientSession() as session:
+    # 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 Cartesia for Text-to-Speech
-  tts = CartesiaTTSService(
-    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_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}!"))
+    # 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 (Cartesia)
+      await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
 
-  # 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)
+    # Run the pipeline task
+    await runner.run(task)
 
 if __name__ == "__main__":
   asyncio.run(main())
@@ -111,6 +110,7 @@ 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))
@@ -129,6 +129,9 @@ Pipecat makes use of WebRTC VAD by default when using a WebRTC transport layer.
 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:_
@@ -162,29 +165,27 @@ pip install "path_to_this_repo[option,...]"
 From the root directory, run:
 
 ```shell
-pytest --doctest-modules --ignore-glob="*to_be_updated*" --ignore-glob=*pipeline_source* src tests
+pytest --doctest-modules --ignore-glob="*to_be_updated*" src tests
 ```
 
 ## Setting up your editor
 
-This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
+This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting.
 
 ### Emacs
 
-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:
+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:
 
 ```elisp
-(use-package lazy-ruff
+(use-package py-autopep8
   :ensure t
-  :hook ((python-mode . lazy-ruff-mode))
+  :defer t
+  :hook ((python-mode . py-autopep8-mode))
   :config
-  (setq lazy-ruff-format-command "ruff format")
-  (setq lazy-ruff-only-format-block t)
-  (setq lazy-ruff-only-format-region t)
-  (setq lazy-ruff-only-format-buffer t))
+  (setq py-autopep8-options '("-a" "-a", "--max-line-length=100")))
 ```
 
-`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.
+`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.
 
 ```elisp
 (use-package pyvenv-auto
@@ -197,13 +198,18 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [e
 ### Visual Studio Code
 
 Install the
-[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, and enable formatting on save:
+[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:
 
 ```json
 "[python]": {
-    "editor.defaultFormatter": "charliermarsh.ruff",
+    "editor.defaultFormatter": "ms-python.autopep8",
     "editor.formatOnSave": true
-}
+},
+"autopep8.args": [
+    "-a",
+    "-a",
+    "--max-line-length=100"
+],
 ```
 
 ## Getting help

dev-requirements.txt

@@ -1,8 +1,8 @@
+autopep8~=2.3.1
 build~=1.2.1
 grpcio-tools~=1.62.2
 pip-tools~=7.4.1
 pyright~=1.1.376
 pytest~=8.3.2
-ruff~=0.6.7
 setuptools~=72.2.0
 setuptools_scm~=8.1.0

docs/CONTRIBUTING.md

@@ -1,165 +0,0 @@
-## Contributing to Pipecat
-
-We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
-
-1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
-
-2. **Clone the repository**: Clone your forked repository to your local machine.
-   ```bash
-   git clone https://github.com/your-username/pipecat
-   ```
-3. **Create a branch**: For your contribution, create a new branch.
-   ```bash
-   git checkout -b your-branch-name
-   ```
-4. **Make your changes**: Edit or add files as necessary.
-5. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
-6. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
-
-```bash
-git commit -m "Description of your changes"
-```
-
-7. **Push your changes**: Push your branch to your forked repository.
-
-```bash
-git push origin your-branch-name
-```
-
-9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo. 
-> Important: Describe the changes you've made clearly!
-
-Our maintainers will review your PR, and once everything is good, your contributions will be merged!
-
-
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official email address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at pipecat-ai@daily.co.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

docs/ISSUE_TEMPLATE.md

@@ -1,22 +0,0 @@
-# Description
-Is this reporting a bug or feature request?
-
-
-If reporting a bug, please fill out the following:
-
-### Environment
-- pipecat-ai version:
-- python version:
-- OS:
-
-### Issue description
-Provide a clear description of the issue.
-
-### Repro steps
-List the steps to reproduce the issue.
-
-### Expected behavior
-
-### Actual behavior
-
-### Logs

docs/PULL_REQUEST_TEMPLATE.md

@@ -1 +0,0 @@
-#### Please describe the changes in your PR. If it is addressing an issue, please reference that as well.

dot-env.template

@@ -1,11 +1,6 @@
 # Anthropic
 ANTHROPIC_API_KEY=...
 
-# AWS
-AWS_SECRET_ACCESS_KEY=...
-AWS_ACCESS_KEY_ID=...
-AWS_REGION=...
-
 # Azure
 AZURE_SPEECH_REGION=...
 AZURE_SPEECH_API_KEY=...
@@ -46,10 +41,5 @@ PLAY_HT_API_KEY=...
 # OpenAI
 OPENAI_API_KEY=...
 
-# OpenPipe
+#OpenPipe
 OPENPIPE_API_KEY=...
-
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
-TAVUS_PERSONA_ID=...

examples/canonical-metrics/.gitignore

@@ -1,161 +0,0 @@
-# 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

@@ -1,10 +0,0 @@
-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

@@ -1,66 +0,0 @@
-# Chatbot with canonical-metrics
-
-This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
-
-## Features
-
-- **Audio Input and Output**: Captures microphone input and plays back audio responses.
-- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
-- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
-- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
-- **Transcription Services**: Captures and transcribes participant speech for analytics.
-- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
-
-## Requirements
-
-- Python 3.10+
-- `python-dotenv`
-- Additional libraries from the `pipecat` package.
-
-## Setup
-
-1. Clone the repository.
-2. Install the required packages.
-3. Set up environment variables for API keys:
-   - `OPENAI_API_KEY`
-   - `ELEVENLABS_API_KEY`
-   - `CANONICAL_API_KEY`
-   - `CANONICAL_API_URL`
-4. Run the script.
-
-## Usage
-
-The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
-
-## Events
-
-- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
-
-
-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

@@ -1,146 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,6 +0,0 @@
-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

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

examples/canonical-metrics/runner.py

@@ -1,56 +0,0 @@
-#
-# 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

@@ -1,139 +0,0 @@
-#
-# 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

@@ -1,161 +0,0 @@
-# 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

@@ -1,15 +0,0 @@
-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

@@ -1,37 +0,0 @@
-# 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

@@ -1,141 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,4 +0,0 @@
-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

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

examples/chatbot-audio-recording/runner.py

@@ -1,56 +0,0 @@
-#
-# 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

@@ -1,139 +0,0 @@
-#
-# 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/README.md

@@ -34,6 +34,6 @@ Note: you can do this manually via the fly.io dashboard under the "secrets" sub-
 
 Send a post request to your running fly.io instance:
 
-`curl --location --request POST 'https://YOUR_FLY_APP_NAME/'`
+`curl --location --request POST 'https://YOUR_FLY_APP_NAME/start_bot'`
 
 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

@@ -3,20 +3,19 @@ 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.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 pipecat.vad.silero import SileroVADAnalyzer
 
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -40,7 +39,7 @@ async def main(room_url: str, token: str):
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
             transcription_enabled=True,
-        ),
+        )
     )
 
     tts = ElevenLabsTTSService(
@@ -48,7 +47,9 @@ async def main(room_url: str, token: str):
         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 = [
         {
@@ -57,25 +58,23 @@ async def main(room_url: str, token: str):
         },
     ]
 
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    tma_in = LLMUserResponseAggregator(messages)
+    tma_out = LLMAssistantResponseAggregator(messages)
 
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
+    pipeline = Pipeline([
+        transport.input(),
+        tma_in,
+        llm,
+        tts,
+        transport.output(),
+        tma_out,
+    ])
 
     task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
+        transport.capture_participant_transcription(participant["id"])
         await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")

examples/deployment/flyio-example/bot_runner.py

@@ -16,14 +16,9 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 
 from pipecat.transports.services.helpers.daily_rest import (
-    DailyRESTHelper,
-    DailyRoomObject,
-    DailyRoomProperties,
-    DailyRoomParams,
-)
+    DailyRESTHelper, DailyRoomObject, DailyRoomProperties, DailyRoomParams)
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 
@@ -31,37 +26,37 @@ load_dotenv(override=True)
 
 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",
-]
+    '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"}
+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,
+        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(
@@ -69,7 +64,7 @@ app.add_middleware(
     allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
-    allow_headers=["*"],
+    allow_headers=["*"]
 )
 
 # ----------------- Main ----------------- #
@@ -78,15 +73,13 @@ app.add_middleware(
 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:
+        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"]
+            image = data[0]['config']['image']
 
         # Machine configuration
         cmd = f"python3 bot.py -u {room_url} -t {token}"
@@ -95,28 +88,31 @@ async def spawn_fly_machine(room_url: str, token: str):
             "config": {
                 "image": image,
                 "auto_destroy": True,
-                "init": {"cmd": cmd},
-                "restart": {"policy": "no"},
-                "guest": {"cpu_kind": "shared", "cpus": 1, "memory_mb": 1024},
+                "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:
+        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"]
+            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:
+        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}")
@@ -124,7 +120,7 @@ async def spawn_fly_machine(room_url: str, token: str):
     print(f"Machine joined room: {room_url}")
 
 
-@app.post("/")
+@app.post("/start_bot")
 async def start_bot(request: Request) -> JSONResponse:
     try:
         data = await request.json()
@@ -138,23 +134,29 @@ async def start_bot(request: Request) -> JSONResponse:
     room_url = os.getenv("DAILY_SAMPLE_ROOM_URL", "")
 
     if not room_url:
-        params = DailyRoomParams(properties=DailyRoomProperties())
+        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}")
+            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}")
+            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}")
+        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)
@@ -165,26 +167,24 @@ async def start_bot(request: Request) -> JSONResponse:
                 [f"python3 -m bot -u {room.url} -t {token}"],
                 shell=True,
                 bufsize=1,
-                cwd=os.path.dirname(os.path.abspath(__file__)),
-            )
+                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}")
     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}")
+            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,
-        }
-    )
-
+    return JSONResponse({
+        "room_url": room.url,
+        "token": user_token,
+    })
 
 if __name__ == "__main__":
     # Check environment variables
@@ -193,19 +193,23 @@ 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=False, 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=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)
+        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_daily.py

@@ -3,20 +3,21 @@ 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.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
+from pipecat.frames.frames import (
+    LLMMessagesFrame,
+    EndFrame
+)
 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)
@@ -30,7 +31,10 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
     # 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 = DailyDialinSettings(
+        call_id=callId,
+        call_domain=callDomain
+    )
 
     transport = DailyTransport(
         room_url,
@@ -46,7 +50,7 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
             transcription_enabled=True,
-        ),
+        )
     )
 
     tts = ElevenLabsTTSService(
@@ -54,7 +58,10 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
         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 = [
         {
@@ -63,25 +70,23 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
         },
     ]
 
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    tma_in = LLMUserResponseAggregator(messages)
+    tma_out = LLMAssistantResponseAggregator(messages)
 
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
+    pipeline = Pipeline([
+        transport.input(),
+        tma_in,
+        llm,
+        tts,
+        transport.output(),
+        tma_out,
+    ])
 
     task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
+        transport.capture_participant_transcription(participant["id"])
         await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")

examples/dialin-chatbot/bot_runner.py

@@ -7,6 +7,7 @@ provisioning a room and starting a Pipecat bot in response.
 Refer to README for more information.
 """
 
+
 import aiohttp
 import os
 import argparse
@@ -24,18 +25,17 @@ from pipecat.transports.services.helpers.daily_rest import (
     DailyRoomObject,
     DailyRoomProperties,
     DailyRoomSipParams,
-    DailyRoomParams,
-)
+    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"]
+REQUIRED_ENV_VARS = ['OPENAI_API_KEY', 'DAILY_API_KEY',
+                     'ELEVENLABS_API_KEY', 'ELEVENLABS_VOICE_ID']
 
 daily_helpers = {}
 
@@ -47,13 +47,12 @@ 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,
+        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(
@@ -61,7 +60,7 @@ app.add_middleware(
     allow_origins=["*"],
     allow_credentials=True,
     allow_methods=["*"],
-    allow_headers=["*"],
+    allow_headers=["*"]
 )
 
 """
@@ -81,7 +80,10 @@ async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
             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
                 )
             )
         )
@@ -95,7 +97,8 @@ async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
             print(f"Joining existing room: {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}")
 
@@ -103,21 +106,28 @@ async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
     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
 
@@ -140,10 +150,11 @@ 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)
 
@@ -159,8 +170,7 @@ 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)
 
 
@@ -182,14 +192,18 @@ 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 = 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 ----------------- #
 
@@ -201,18 +215,24 @@ 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

@@ -3,30 +3,32 @@ 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.processors.aggregators.llm_response import LLMAssistantResponseAggregator, LLMUserResponseAggregator
+from pipecat.frames.frames import (
+    LLMMessagesFrame,
+    EndFrame
+)
 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", "")
@@ -49,7 +51,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
             vad_enabled=True,
             vad_analyzer=SileroVADAnalyzer(),
             transcription_enabled=True,
-        ),
+        )
     )
 
     tts = ElevenLabsTTSService(
@@ -57,7 +59,10 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
         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 = [
         {
@@ -66,25 +71,23 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
         },
     ]
 
-    context = OpenAILLMContext(messages)
-    context_aggregator = llm.create_context_aggregator(context)
+    tma_in = LLMUserResponseAggregator(messages)
+    tma_out = LLMAssistantResponseAggregator(messages)
 
-    pipeline = Pipeline(
-        [
-            transport.input(),
-            context_aggregator.user(),
-            llm,
-            tts,
-            transport.output(),
-            context_aggregator.assistant(),
-        ]
-    )
+    pipeline = Pipeline([
+        transport.input(),
+        tma_in,
+        llm,
+        tts,
+        transport.output(),
+        tma_out,
+    ])
 
     task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
     @transport.event_handler("on_first_participant_joined")
     async def on_first_participant_joined(transport, participant):
-        await transport.capture_participant_transcription(participant["id"])
+        transport.capture_participant_transcription(participant["id"])
         await task.queue_frames([LLMMessagesFrame(messages)])
 
     @transport.event_handler("on_participant_left")
@@ -100,7 +103,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
         try:
             # The TwiML is updated using Twilio's client library
             call = twilioclient.calls(callId).update(
-                twiml=f"<Response><Dial><Sip>{sipUri}</Sip></Dial></Response>"
+                twiml=f'<Response><Dial><Sip>{sipUri}</Sip></Dial></Response>'
             )
         except Exception as e:
             raise Exception(f"Failed to forward call: {str(e)}")

examples/dialin-chatbot/requirements.txt

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

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

@@ -9,11 +9,11 @@ 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.task import PipelineTask
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
@@ -21,7 +21,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -33,10 +32,9 @@ async def main():
         (room_url, _) = await configure(session)
 
         transport = DailyTransport(
-            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
-        )
+            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True))
 
-        tts = CartesiaHttpTTSService(
+        tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
@@ -47,18 +45,12 @@ async def main():
 
         # 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):
-            participant_name = participant.get("info", {}).get("userName", "")
+        @transport.event_handler("on_participant_joined")
+        async def on_new_participant_joined(transport, participant):
+            participant_name = participant["info"]["userName"] or ''
             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__":
     asyncio.run(main())

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

@@ -20,7 +20,6 @@ from pipecat.transports.local.audio import LocalAudioTransport
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -1,111 +0,0 @@
-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),
-        )
-
-        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

@@ -9,11 +9,11 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import EndFrame, LLMMessagesFrame
+from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -22,7 +22,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -34,22 +33,25 @@ async def main():
         (room_url, _) = await configure(session)
 
         transport = DailyTransport(
-            room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
-        )
+            room_url,
+            None,
+            "Say One Thing From an LLM",
+            DailyParams(audio_out_enabled=True))
 
-        tts = CartesiaHttpTTSService(
+        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 = [
             {
                 "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()
 
@@ -59,10 +61,6 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             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)
 
 

examples/foundational/03-still-frame.py

@@ -9,7 +9,7 @@ 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
@@ -21,7 +21,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -36,11 +35,17 @@ async def main():
             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"),
         )
@@ -51,11 +56,11 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            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())
+            # 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 runner.run(task)
 

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

@@ -22,7 +22,6 @@ from pipecat.transports.local.tk import TkLocalTransport
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -36,11 +35,15 @@ 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"),
         )

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

@@ -4,10 +4,6 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-#
-# This example broken on latest pipecat and needs updating.
-#
-
 import aiohttp
 import asyncio
 import os
@@ -28,7 +24,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -59,7 +54,8 @@ async def main():
             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
@@ -77,7 +73,8 @@ async def main():
             ]
         )
 
-        merge_pipeline = SequentialMergePipeline([simple_tts_pipeline, llm_pipeline])
+        merge_pipeline = SequentialMergePipeline(
+            [simple_tts_pipeline, llm_pipeline])
 
         await asyncio.gather(
             transport.run(merge_pipeline),

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

@@ -14,18 +14,21 @@ from dataclasses import dataclass
 from pipecat.frames.frames import (
     AppFrame,
     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
 
@@ -34,7 +37,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -82,46 +84,47 @@ async def main():
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_width=1024,
-                camera_out_height=1024,
-            ),
+                camera_out_height=1024
+            )
         )
 
-        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
+        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")
+
         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()
 
-        # 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
-            ]
-        )
+        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
+        ])
 
         frames = []
         for month in [

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

@@ -11,25 +11,18 @@ import sys
 
 import tkinter as tk
 
-from pipecat.frames.frames import (
-    Frame,
-    OutputAudioRawFrame,
-    TTSAudioRawFrame,
-    URLImageRawFrame,
-    LLMMessagesFrame,
-    TextFrame,
-)
+from pipecat.frames.frames import AudioRawFrame, Frame, URLImageRawFrame, LLMMessagesFrame, TextFrame
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 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.sentence import SentenceAggregator
+from pipecat.processors.aggregators.llm_response import LLMFullResponseAggregator
 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, TkOutputTransport
+from pipecat.transports.local.tk import TkLocalTransport
 
 from loguru import logger
 
@@ -49,12 +42,7 @@ 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):
@@ -72,17 +60,14 @@ 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, TTSAudioRawFrame):
+                    if isinstance(frame, AudioRawFrame):
                         self.audio.extend(frame.audio)
-                        self.frame = OutputAudioRawFrame(
-                            bytes(self.audio), frame.sample_rate, frame.num_channels
-                        )
-                    await self.push_frame(frame, direction)
+                        self.frame = AudioRawFrame(
+                            bytes(self.audio), frame.sample_rate, frame.num_channels)
 
             class ImageGrabber(FrameProcessor):
                 def __init__(self):
@@ -94,22 +79,23 @@ 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 = CartesiaHttpTTSService(
-                api_key=os.getenv("CARTESIA_API_KEY"),
-                voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-            )
+            tts = ElevenLabsTTSService(
+                api_key=os.getenv("ELEVENLABS_API_KEY"),
+                voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
 
             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"))
 
-            sentence_aggregator = SentenceAggregator()
+            aggregator = LLMFullResponseAggregator()
 
             description = ImageDescription()
 
@@ -117,27 +103,13 @@ async def main():
 
             image_grabber = ImageGrabber()
 
-            # 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
-                    ),
-                ]
-            )
+            pipeline = Pipeline([
+                llm,
+                aggregator,
+                description,
+                ParallelPipeline([tts, audio_grabber],
+                                 [imagegen, image_grabber])
+            ])
 
             task = PipelineTask(pipeline)
             await task.queue_frame(LLMMessagesFrame(messages))
@@ -158,9 +130,7 @@ 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()])
 

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

@@ -5,31 +5,29 @@
 #
 
 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 Frame, LLMMessagesFrame, MetricsFrame
-from pipecat.metrics.metrics import (
-    LLMUsageMetricsData,
-    ProcessingMetricsData,
-    TTFBMetricsData,
-    TTSUsageMetricsData,
-)
 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.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 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)
@@ -39,20 +37,8 @@ logger.add(sys.stderr, level="DEBUG")
 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}")
+            print(
+                f"!!! MetricsFrame: {frame}, ttfb: {frame.ttfb}, processing: {frame.processing}, tokens: {frame.tokens}, characters: {frame.characters}")
         await self.push_frame(frame, direction)
 
 
@@ -68,8 +54,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -77,7 +63,10 @@ async def main():
             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"
+        )
 
         ml = MetricsLogger()
 
@@ -87,32 +76,32 @@ async def main():
                 "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(),
+            tma_in,
+            llm,
+            tts,
+            ml,
+            transport.output(),
+            tma_out,
+        ])
 
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                ml,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(enable_metrics=True, enable_usage_metrics=True),
-        )
+        task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline, PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            report_only_initial_ttfb=False,
+        ))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -11,16 +11,19 @@ import sys
 
 from PIL import Image
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextFrame
+from pipecat.frames.frames import ImageRawFrame, Frame, SystemFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 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
@@ -28,7 +31,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -50,21 +52,9 @@ class ImageSyncAggregator(FrameProcessor):
         await super().process_frame(frame, direction)
 
         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(ImageRawFrame(image=self._speaking_image_bytes, size=(1024, 1024), format=self._speaking_image_format))
             await self.push_frame(frame)
-            await self.push_frame(
-                OutputImageRawFrame(
-                    image=self._waiting_image_bytes,
-                    size=(1024, 1024),
-                    format=self._waiting_image_format,
-                )
-            )
+            await self.push_frame(ImageRawFrame(image=self._waiting_image_bytes, size=(1024, 1024), format=self._waiting_image_format))
         else:
             await self.push_frame(frame)
 
@@ -85,15 +75,17 @@ async def main():
                 transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-            ),
+            )
         )
 
-        tts = CartesiaHttpTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        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 = [
             {
@@ -102,32 +94,30 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
         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,
-                context_aggregator.user(),
-                llm,
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
+        pipeline = Pipeline([
+            transport.input(),
+            image_sync_aggregator,
+            tma_in,
+            llm,
+            tts,
+            transport.output(),
+            tma_out
+        ])
 
         task = PipelineTask(pipeline)
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            participant_name = participant.get("info", {}).get("userName", "")
-            await transport.capture_participant_transcription(participant["id"])
+            participant_name = participant["info"]["userName"] or ''
+            transport.capture_participant_transcription(participant["id"])
             await task.queue_frames([TextFrame(f"Hi there {participant_name}!")])
 
         runner = PipelineRunner()

examples/foundational/07-interruptible-vad.py

@@ -1,103 +0,0 @@
-#
-# 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):
-            await 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

@@ -9,22 +9,22 @@ 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.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)
@@ -43,8 +43,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -52,7 +52,9 @@ async def main():
             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 = [
             {
@@ -61,35 +63,31 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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,
-                enable_metrics=True,
-                enable_usage_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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -5,24 +5,26 @@
 #
 
 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.openai_llm_context import OpenAILLMContext
-from pipecat.services.anthropic import AnthropicLLMService
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 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)
@@ -41,8 +43,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -51,8 +53,8 @@ async def main():
         )
 
         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
@@ -64,25 +66,23 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             await task.queue_frames([LLMMessagesFrame(messages)])
 

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

@@ -10,18 +10,16 @@ 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.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
@@ -34,7 +32,6 @@ from loguru import logger
 from runner import configure
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 
@@ -73,22 +70,19 @@ async def main():
 
         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()
@@ -96,12 +90,12 @@ async def main():
 
         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
             ]
         )
 
@@ -109,13 +103,17 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             lc.set_participant_id(participant["id"])
             # Kick off the conversation.
             # 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()

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

@@ -5,24 +5,26 @@
 #
 
 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.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 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)
@@ -41,15 +43,21 @@ async def main():
                 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(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+        tts = DeepgramTTSService(
+            aiohttp_session=session,
+            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 = [
             {
@@ -58,27 +66,27 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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
+        ])
 
         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()

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

@@ -5,24 +5,26 @@
 #
 
 import asyncio
+import aiohttp
 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.elevenlabs import ElevenLabsTTSService
 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)
@@ -41,8 +43,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = ElevenLabsTTSService(
@@ -50,7 +52,9 @@ async def main():
             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 = [
             {
@@ -59,35 +63,31 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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,
-                enable_metrics=True,
-                enable_usage_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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -4,26 +4,27 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 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.openai import OpenAILLMService
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 from pipecat.services.playht import PlayHTTTSService
-from pipecat.transcriptions.language import Language
+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)
@@ -40,20 +41,22 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
+                audio_out_sample_rate=16000,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                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",
-            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 = [
             {
@@ -62,35 +65,26 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -9,14 +9,15 @@ 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.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 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
@@ -24,7 +25,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -41,10 +41,11 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
+                audio_out_sample_rate=16000,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
-            ),
+            )
         )
 
         stt = AzureSTTService(
@@ -70,28 +71,27 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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
+        ])
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -4,24 +4,27 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 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.openai import OpenAILLMService, OpenAITTSService
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.services.openai import OpenAITTSService
+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)
@@ -41,13 +44,18 @@ async def main():
                 audio_out_sample_rate=24000,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                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 = [
             {
@@ -56,27 +64,26 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -9,15 +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.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 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
 
@@ -25,7 +28,6 @@ from loguru import logger
 import time
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -44,8 +46,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -58,7 +60,9 @@ async def main():
             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 = [
@@ -67,28 +71,26 @@ async def main():
                 "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
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
+        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, params=PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -9,22 +9,23 @@ 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.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.services.xtts import XTTSService
 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)
@@ -44,17 +45,19 @@ async def main():
                 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",
+            base_url="http://localhost:8000"
         )
 
-        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 = [
             {
@@ -63,27 +66,26 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -5,25 +5,27 @@
 #
 
 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 EndFrame, LLMMessagesFrame
+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.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 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 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)
@@ -43,7 +45,7 @@ async def main():
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
-            ),
+            )
         )
 
         stt = GladiaSTTService(
@@ -55,7 +57,9 @@ async def main():
             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 = [
             {
@@ -64,35 +68,29 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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
+        ])
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            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)])
 
-        # 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())
-
         runner = PipelineRunner()
 
         await runner.run(task)

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

@@ -9,22 +9,22 @@ 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.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 from pipecat.services.lmnt import LmntTTSService
 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)
@@ -44,13 +44,18 @@ async def main():
                 audio_out_sample_rate=24000,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
-        tts = LmntTTSService(api_key=os.getenv("LMNT_API_KEY"), voice_id="morgan")
+        tts = LmntTTSService(
+            api_key=os.getenv("LMNT_API_KEY"),
+            voice_id="morgan"
+        )
 
-        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 = [
             {
@@ -59,27 +64,26 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        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
-            ]
-        )
+        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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

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

@@ -1,109 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,98 +0,0 @@
-#
-# 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,
-                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):
-            await 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

@@ -1,96 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,97 +0,0 @@
-#
-# 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):
-            await 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

@@ -3,19 +3,18 @@ import aiohttp
 import asyncio
 import logging
 import os
-from pipecat.processors.aggregators import SentenceAggregator
+from pipecat.pipeline.aggregators import SentenceAggregator
 from pipecat.pipeline.pipeline import Pipeline
 
-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 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 runner import configure
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
@@ -54,7 +53,9 @@ async def main():
             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"),
         )
@@ -74,11 +75,13 @@ async def main():
 
         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())

examples/foundational/09-mirror.py

@@ -8,17 +8,9 @@ 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
@@ -26,57 +18,33 @@ 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")
 
 
-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",
+            room_url, token, "Test",
             DailyParams(
                 audio_in_enabled=True,
-                audio_in_sample_rate=24000,
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_is_live=True,
                 camera_out_width=1280,
-                camera_out_height=720,
-            ),
+                camera_out_height=720
+            )
         )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_video(participant["id"])
+            transport.capture_participant_video(participant["id"])
 
-        pipeline = Pipeline([transport.input(), MirrorProcessor(), transport.output()])
+        pipeline = Pipeline([transport.input(), transport.output()])
 
         runner = PipelineRunner()
 

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

@@ -10,17 +10,9 @@ 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
@@ -30,33 +22,12 @@ 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")
 
 
-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)
@@ -65,8 +36,8 @@ async def main():
         tk_root.title("Local Mirror")
 
         daily_transport = DailyTransport(
-            room_url, token, "Test", DailyParams(audio_in_enabled=True, audio_in_sample_rate=24000)
-        )
+            room_url, token, "Test", DailyParams(
+                audio_in_enabled=True))
 
         tk_transport = TkLocalTransport(
             tk_root,
@@ -75,15 +46,13 @@ async def main():
                 camera_out_enabled=True,
                 camera_out_is_live=True,
                 camera_out_width=1280,
-                camera_out_height=720,
-            ),
-        )
+                camera_out_height=720))
 
         @daily_transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_video(participant["id"])
+            transport.capture_participant_video(participant["id"])
 
-        pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
+        pipeline = Pipeline([daily_transport.input(), tk_transport.output()])
 
         task = PipelineTask(pipeline)
 

examples/foundational/10-wake-phrase.py

@@ -9,22 +9,22 @@ import aiohttp
 import os
 import sys
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
 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.filters.wake_check_filter import WakeCheckFilter
+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)
@@ -43,8 +43,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -52,7 +52,9 @@ async def main():
             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 = [
             {
@@ -62,27 +64,24 @@ async def main():
         ]
 
         hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
+        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
-                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
-            ]
-        )
+        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
+        ])
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             await tts.say("Hi! If you want to talk to me, just say 'Hey Robot'.")
 
         runner = PipelineRunner()

examples/foundational/11-sound-effects.py

@@ -10,29 +10,31 @@ 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.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMUserResponseAggregator,
+    LLMAssistantResponseAggregator,
+)
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.processors.logger import FrameLogger
-from pipecat.services.cartesia import CartesiaHttpTTSService
+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 runner import configure
 
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -51,12 +53,12 @@ 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] = OutputAudioRawFrame(
-            audio_file.readframes(-1), audio_file.getframerate(), audio_file.getnchannels()
-        )
+        sounds[file] = AudioRawFrame(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)
 
@@ -69,6 +71,7 @@ class OutboundSoundEffectWrapper(FrameProcessor):
 
 
 class InboundSoundEffectWrapper(FrameProcessor):
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -92,15 +95,17 @@ async def main():
                 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 = CartesiaHttpTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        tts = ElevenLabsTTSService(
+            api_key=os.getenv("ELEVENLABS_API_KEY"),
+            voice_id="ErXwobaYiN019PkySvjV",
         )
 
         messages = [
@@ -110,31 +115,29 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
         out_sound = OutboundSoundEffectWrapper()
         in_sound = InboundSoundEffectWrapper()
         fl = FrameLogger("LLM Out")
         fl2 = FrameLogger("Transcription In")
 
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                in_sound,
-                fl2,
-                llm,
-                fl,
-                tts,
-                out_sound,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
+        pipeline = Pipeline([
+            transport.input(),
+            tma_in,
+            in_sound,
+            fl2,
+            llm,
+            fl,
+            tts,
+            out_sound,
+            transport.output(),
+            tma_out
+        ])
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             await tts.say("Hi, I'm listening!")
             await transport.send_audio(sounds["ding1.wav"])
 

examples/foundational/12-describe-video.py

@@ -9,7 +9,6 @@ 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
@@ -20,13 +19,13 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 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)
@@ -34,6 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
+
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,9 +45,7 @@ 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)
 
 
@@ -63,8 +61,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         user_response = UserResponseAggregator()
@@ -84,21 +82,19 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await tts.say("Hi there! Feel free to ask me what I see.")
-            await transport.capture_participant_video(participant["id"], framerate=0)
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(participant["id"], framerate=0)
+            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)
 
@@ -106,6 +102,5 @@ async def main():
 
         await runner.run(task)
 
-
 if __name__ == "__main__":
     asyncio.run(main())

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

@@ -9,7 +9,6 @@ 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
@@ -20,13 +19,13 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 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)
@@ -34,6 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
+
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,9 +45,7 @@ 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)
 
 
@@ -64,8 +62,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         user_response = UserResponseAggregator()
@@ -75,8 +73,8 @@ async def main():
         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 = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
@@ -86,21 +84,19 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await tts.say("Hi there! Feel free to ask me what I see.")
-            await transport.capture_participant_video(participant["id"], framerate=0)
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(participant["id"], framerate=0)
+            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)
 
@@ -108,6 +104,5 @@ async def main():
 
         await runner.run(task)
 
-
 if __name__ == "__main__":
     asyncio.run(main())

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

@@ -9,7 +9,6 @@ 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
@@ -20,13 +19,13 @@ 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)
@@ -34,6 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
+
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,9 +45,7 @@ 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)
 
 
@@ -63,8 +61,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         user_response = UserResponseAggregator()
@@ -73,7 +71,10 @@ async def main():
 
         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 = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
@@ -83,21 +84,19 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await tts.say("Hi there! Feel free to ask me what I see.")
-            await transport.capture_participant_video(participant["id"], framerate=0)
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(participant["id"], framerate=0)
+            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)
 
@@ -105,6 +104,5 @@ async def main():
 
         await runner.run(task)
 
-
 if __name__ == "__main__":
     asyncio.run(main())

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

@@ -9,7 +9,6 @@ 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
@@ -20,13 +19,13 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 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)
@@ -34,6 +33,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class UserImageRequester(FrameProcessor):
+
     def __init__(self, participant_id: str | None = None):
         super().__init__()
         self._participant_id = participant_id
@@ -45,9 +45,7 @@ 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)
 
 
@@ -63,8 +61,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         user_response = UserResponseAggregator()
@@ -73,31 +71,32 @@ async def main():
 
         vision_aggregator = VisionImageFrameAggregator()
 
-        anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
+        anthropic = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY")
+        )
 
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            sample_rate=16000,
         )
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await tts.say("Hi there! Feel free to ask me what I see.")
-            await transport.capture_participant_video(participant["id"], framerate=0)
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(participant["id"], framerate=0)
+            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)
 
@@ -105,6 +104,5 @@ async def main():
 
         await runner.run(task)
 
-
 if __name__ == "__main__":
     asyncio.run(main())

examples/foundational/13-whisper-transcription.py

@@ -21,7 +21,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -29,6 +28,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptionLogger(FrameProcessor):
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -40,9 +40,8 @@ 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)
-        )
+        transport = DailyTransport(room_url, None, "Transcription bot",
+                                   DailyParams(audio_in_enabled=True))
 
         stt = WhisperSTTService()
 

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

@@ -19,7 +19,6 @@ from pipecat.transports.local.audio import LocalAudioTransport
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -27,6 +26,7 @@ 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

@@ -14,7 +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, LiveOptions, Language
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
@@ -22,7 +22,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -30,6 +29,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 class TranscriptionLogger(FrameProcessor):
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
@@ -41,14 +41,10 @@ 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)
-        )
+        transport = DailyTransport(room_url, None, "Transcription bot",
+                                   DailyParams(audio_in_enabled=True))
 
-        stt = DeepgramSTTService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            # live_options=LiveOptions(language=Language.FR),
-        )
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
         tl = TranscriptionLogger()
 

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

@@ -1,63 +0,0 @@
-#
-# 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

@@ -1,62 +0,0 @@
-#
-# 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

@@ -9,13 +9,15 @@ import aiohttp
 import os
 import sys
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.logger import FrameLogger
 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
 
@@ -24,7 +26,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -32,12 +33,7 @@ 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}")
+    await llm.push_frame(TextFrame("Let me check on that."))
 
 
 async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
@@ -56,8 +52,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -65,10 +61,18 @@ async def main():
             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")
         # 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)
+        llm.register_function(
+            None,
+            fetch_weather_from_api,
+            start_callback=start_fetch_weather)
+
+        fl_in = FrameLogger("Inner")
+        fl_out = FrameLogger("Outer")
 
         tools = [
             ChatCompletionToolParam(
@@ -85,15 +89,17 @@ async def main():
                             },
                             "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",
@@ -104,37 +110,28 @@ async def main():
         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(),
-            ]
-        )
+        pipeline = Pipeline([
+            fl_in,
+            transport.input(),
+            context_aggregator.user(),
+            llm,
+            fl_out,
+            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,
-            ),
-        )
+        task = PipelineTask(pipeline)
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
+            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/14c-function-calling-together.py

@@ -1,136 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,167 +0,0 @@
-#
-# 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"]
-            await transport.capture_participant_transcription(participant["id"])
-            await 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/14e-function-calling-gemini.py

@@ -1,173 +0,0 @@
-#
-# 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.services.cartesia import CartesiaTTSService
-from pipecat.services.google import GoogleLLMService
-from pipecat.services.openai import OpenAILLMContext
-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):
-    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 = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
-        llm.register_function("get_weather", get_weather)
-        llm.register_function("get_image", get_image)
-
-        tools = [
-            {
-                "function_declarations": [
-                    {
-                        "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"],
-                        },
-                    },
-                    {
-                        "name": "get_image",
-                        "description": "Get and image from the camera or video stream.",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "question": {
-                                    "type": "string",
-                                    "description": "The question to to use when running inference on the acquired image.",
-                                },
-                            },
-                            "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},
-            {"role": "user", "content": "Say hello."},
-        ]
-
-        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,
-            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):
-            global video_participant_id
-            video_participant_id = participant["id"]
-            await transport.capture_participant_transcription(participant["id"])
-            await 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/15-switch-voices.py

@@ -9,7 +9,6 @@ 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
@@ -20,6 +19,7 @@ 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
 
@@ -28,7 +28,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -40,11 +39,7 @@ current_voice = "News Lady"
 async def switch_voice(function_name, tool_call_id, args, llm, context, result_callback):
     global current_voice
     current_voice = args["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}."
-        }
-    )
+    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:
@@ -71,8 +66,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         news_lady = CartesiaTTSService(
@@ -90,7 +85,9 @@ async def main():
             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 = [
@@ -109,9 +106,7 @@ async def main():
                         },
                         "required": ["voice"],
                     },
-                },
-            )
-        ]
+                })]
         messages = [
             {
                 "role": "system",
@@ -122,33 +117,29 @@ async def main():
         context = OpenAILLMContext(messages, tools)
         context_aggregator = llm.create_context_aggregator(context)
 
-        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
-            ]
-        )
+        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))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             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()

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

@@ -9,8 +9,7 @@ import aiohttp
 import os
 import sys
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesFrame, TTSUpdateSettingsFrame
+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
@@ -19,7 +18,9 @@ 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.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
 
@@ -28,7 +29,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -60,14 +60,16 @@ async def main():
             token,
             "Pipecat",
             DailyParams(
+                audio_in_enabled=True,
                 audio_out_enabled=True,
-                transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
+                vad_audio_passthrough=True
+            )
         )
 
+        stt = WhisperSTTService(model=Model.LARGE)
+
         english_tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
@@ -78,7 +80,9 @@ async def main():
             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 = [
@@ -97,9 +101,7 @@ async def main():
                         },
                         "required": ["language"],
                     },
-                },
-            )
-        ]
+                })]
         messages = [
             {
                 "role": "system",
@@ -110,32 +112,29 @@ async def main():
         context = OpenAILLMContext(messages, tools)
         context_aggregator = llm.create_context_aggregator(context)
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                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
-            ]
-        )
+        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))
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             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()

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

@@ -5,28 +5,26 @@
 #
 
 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.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 from pipecat.services.deepgram import DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import (
-    DailyParams,
-    DailyTransport,
-    DailyTransportMessageFrame,
-)
+from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyTransportMessageFrame
+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)
@@ -45,15 +43,15 @@ async def main():
                 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(
@@ -62,7 +60,7 @@ async def main():
             # 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 = [
@@ -72,19 +70,17 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),
-            ]
-        )
+        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, enable_metrics=True))
 
@@ -92,12 +88,13 @@ async def main():
         # bot can "hear" and respond to them.
         @transport.event_handler("on_participant_joined")
         async def on_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
 
         # 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
@@ -114,18 +111,14 @@ async def main():
                     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 task.queue_frame(
+                    await tma_in.push_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}")
 

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

@@ -9,23 +9,23 @@ 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.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
 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 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)
@@ -44,8 +44,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -53,7 +53,9 @@ async def main():
             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 = [
             {
@@ -62,46 +64,38 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
         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))
+                {"role": "system", "content": "Ask the user if they are still there and try to prompt for some input, but be short."})
+            await user_idle.queue_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(),
-            ]
-        )
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            user_idle,           # Idle user check-in
+            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,
-                enable_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
+        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):
-            await transport.capture_participant_transcription(participant["id"])
+            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()

examples/foundational/18-gstreamer-filesrc.py

@@ -20,7 +20,6 @@ from runner import configure_with_args
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -30,7 +29,12 @@ 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")
+        parser.add_argument(
+            "-i",
+            "--input",
+            type=str,
+            required=True,
+            help="Input video file")
 
         (room_url, _, args) = await configure_with_args(session, parser)
 
@@ -45,7 +49,7 @@ async def main():
                 camera_out_width=1280,
                 camera_out_height=720,
                 camera_out_is_live=True,
-            ),
+            )
         )
 
         gst = GStreamerPipelineSource(
@@ -55,15 +59,13 @@ async def main():
                 video_height=720,
                 audio_sample_rate=16000,
                 audio_channels=1,
-            ),
+            )
         )
 
-        pipeline = Pipeline(
-            [
-                gst,  # GStreamer file source
-                transport.output(),  # Transport bot output
-            ]
-        )
+        pipeline = Pipeline([
+            gst,                 # GStreamer file source
+            transport.output(),  # Transport bot output
+        ])
 
         task = PipelineTask(pipeline)
 

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

@@ -19,7 +19,6 @@ from runner import configure
 from loguru import logger
 
 from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -39,22 +38,20 @@ async def main():
                 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"',
+            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
-            ),
-        )
+                video_width=1280,
+                video_height=720,
+                clock_sync=False))
 
-        pipeline = Pipeline(
-            [
-                gst,  # GStreamer file source
-                transport.output(),  # Transport bot output
-            ]
-        )
+        pipeline = Pipeline([
+            gst,                 # GStreamer file source
+            transport.output(),  # Transport bot output
+        ])
 
         task = PipelineTask(pipeline)
 

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

@@ -1,179 +0,0 @@
-#
-# 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):
-            await 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/19a-tools-anthropic.py

@@ -9,7 +9,6 @@ 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
@@ -17,13 +16,13 @@ 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 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)
@@ -47,8 +46,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -57,7 +56,8 @@ async def main():
         )
 
         llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-sonnet-20240620"
         )
         llm.register_function("get_weather", get_weather)
 
@@ -90,22 +90,20 @@ async def main():
         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
-            ]
-        )
+        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")
+        @ transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             await task.queue_frames([context_aggregator.user().get_context_frame()])
 

examples/foundational/19b-tools-video-anthropic.py

@@ -9,7 +9,6 @@ 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
@@ -17,13 +16,13 @@ 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 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)
@@ -56,8 +55,8 @@ async def main():
                 audio_out_enabled=True,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -68,7 +67,7 @@ async def main():
         llm = AnthropicLLMService(
             api_key=os.getenv("ANTHROPIC_API_KEY"),
             model="claude-3-5-sonnet-20240620",
-            enable_prompt_caching_beta=True,
+            enable_prompt_caching_beta=True
         )
         llm.register_function("get_weather", get_weather)
         llm.register_function("get_image", get_image)
@@ -101,7 +100,7 @@ async def main():
                     },
                     "required": ["question"],
                 },
-            },
+            }
         ]
 
         # todo: test with very short initial user message
@@ -135,33 +134,33 @@ If you need to use a tool, simply use the tool. Do not tell the user the tool yo
                         "type": "text",
                         "text": system_prompt,
                     }
-                ],
+                ]
             },
-            {"role": "user", "content": "Start the conversation by introducing yourself."},
-        ]
+            {
+                "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
-            ]
-        )
+        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")
+        @ transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             global video_participant_id
             video_participant_id = participant["id"]
-            await transport.capture_participant_transcription(video_participant_id)
-            await transport.capture_participant_video(video_participant_id, framerate=0)
+            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()])
 

examples/foundational/19c-tools-togetherai.py

@@ -0,0 +1,138 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+import json
+
+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.together import TogetherLLMService
+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 get_current_weather(
+        function_name,
+        tool_call_id,
+        arguments,
+        llm,
+        context,
+        result_callback):
+    logger.debug("IN get_current_weather")
+    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 = TogetherLLMService(
+            api_key=os.getenv("TOGETHER_API_KEY"),
+            model=os.getenv("TOGETHER_MODEL"),
+        )
+        llm.register_function("get_current_weather", get_current_weather)
+
+        weatherTool = {
+            "name": "get_current_weather",
+            "description": "Get the current weather in a given location",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, CA",
+                    },
+                },
+                "required": ["location"],
+            },
+        }
+
+        system_prompt = f"""\
+You have access to the following functions:
+
+Use the function '{weatherTool["name"]}' to '{weatherTool["description"]}':
+{json.dumps(weatherTool)}
+
+If you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+<function=example_function_name>{{\"example_name\": \"example_value\"}}</function>
+
+Reminder:
+- Function calls MUST follow the specified format, start with <function= and end with </function>
+- Required parameters MUST be specified
+- Only call one function at a time
+- Put the entire function call reply on one line
+- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls
+
+"""
+
+        messages = [{"role": "system",
+                     "content": system_prompt},
+                    {"role": "user",
+                     "content": "Wait for the user to say something."}]
+
+        context = OpenAILLMContext(messages)
+        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):
+            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/20a-persistent-context-openai.py

@@ -1,236 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,262 +0,0 @@
-#
-# 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):
-            await 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

@@ -1,232 +0,0 @@
-#
-# 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):
-            await 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/20d-persistent-context-gemini.py

@@ -1,290 +0,0 @@
-#
-# 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.google import GoogleLLMService
-
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-video_participant_id = None
-
-
-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_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 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.get_messages_for_logging(), 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 (the instruction to save the context)
-            messages.pop()
-            json.dump(messages, file, indent=2)
-        await result_callback({"success": True})
-    except Exception as e:
-        logger.debug(f"error saving conversation: {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))
-        await result_callback(
-            {
-                "success": True,
-                "message": "The most recent conversation has been loaded. Awaiting further instructions.",
-            }
-        )
-    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.
-
-You have several tools you can use to help you.
-
-You can respond to questions about the weather using the get_weather tool.
-
-You can save the current conversation using the save_conversation tool. This tool allows you to save
-the current conversation to external storage. If the user asks you to save the conversation, use this
-save_conversation too.
-
-You can load a saved conversation using the load_conversation tool. This tool allows you to load a
-conversation from external storage. You can get a list of conversations that have been saved using the
-get_saved_conversation_filenames 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?
-        """,
-    },
-    # {"role": "user", "content": ""},
-    # {"role": "assistant", "content": []},
-    # {"role": "user", "content": "Tell me"},
-    # {"role": "user", "content": "a joke"},
-]
-tools = [
-    {
-        "function_declarations": [
-            {
-                "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"],
-                },
-            },
-            {
-                "name": "save_conversation",
-                "description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "user_request_text": {
-                            "type": "string",
-                            "description": "The text of the user's request to save the conversation.",
-                        }
-                    },
-                    "required": ["user_request_text"],
-                },
-            },
-            {
-                "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": None,
-            },
-            {
-                "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"],
-                },
-            },
-            {
-                "name": "get_image",
-                "description": "Get and image from the camera or video stream.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "question": {
-                            "type": "string",
-                            "description": "The question to to use when running inference on the acquired image.",
-                        },
-                    },
-                    "required": ["question"],
-                },
-            },
-        ]
-    },
-]
-
-
-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 = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
-
-        # 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)
-        llm.register_function("get_image", get_image)
-
-        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):
-            global video_participant_id
-            video_participant_id = participant["id"]
-            await transport.capture_participant_transcription(participant["id"])
-            await 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/21-tavus-layer.py

@@ -1,133 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import aiohttp
-import os
-import sys
-
-from typing import Any, Mapping
-
-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.services.deepgram import DeepgramSTTService
-from pipecat.services.tavus import TavusVideoService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-
-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:
-        tavus = TavusVideoService(
-            api_key=os.getenv("TAVUS_API_KEY"),
-            replica_id=os.getenv("TAVUS_REPLICA_ID"),
-            persona_id=os.getenv("TAVUS_PERSONA_ID", "pipecat0"),
-            session=session,
-        )
-
-        # get persona, look up persona_name, set this as the bot name to ignore
-        persona_name = await tavus.get_persona_name()
-        room_url = await tavus.initialize()
-
-        transport = DailyTransport(
-            room_url=room_url,
-            token=None,
-            bot_name="Pipecat bot",
-            params=DailyParams(
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
-        )
-
-        llm = OpenAILLMService(model="gpt-4o-mini")
-
-        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
-                stt,  # STT
-                tma_in,  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                tavus,  # Tavus output layer
-                transport.output(),  # Transport bot output
-                tma_out,  # 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_participant_joined")
-        async def on_participant_joined(
-            transport: DailyTransport, participant: Mapping[str, Any]
-        ) -> None:
-            # Ignore the Tavus replica's microphone
-            if participant.get("info", {}).get("userName", "") == persona_name:
-                logger.debug(f"Ignoring {participant['id']}'s microphone")
-                await transport.update_subscriptions(
-                    participant_settings={
-                        participant["id"]: {
-                            "media": {"microphone": "unsubscribed"},
-                        }
-                    }
-                )
-
-            if participant.get("info", {}).get("userName", "") != persona_name:
-                # 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/22-natural-conversation.py

@@ -1,168 +0,0 @@
-#
-# 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, TextFrame
-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.gated_openai_llm_context import GatedOpenAILLMContextAggregator
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.processors.filters.null_filter import NullFilter
-from pipecat.processors.filters.wake_notifier_filter import WakeNotifierFilter
-from pipecat.processors.user_idle_processor import UserIdleProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.sync.event_notifier import EventNotifier
-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,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        # This is the LLM that will be used to detect if the user has finished a
-        # statement. This doesn't really need to be an LLM, we could use NLP
-        # libraries for that, but it was easier as an example because we
-        # leverage the context aggregators.
-        statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        statement_messages = [
-            {
-                "role": "system",
-                "content": "Determine if the user's statement is a complete sentence or question, ending in a natural pause or punctuation. Return 'YES' if it is complete and 'NO' if it seems to leave a thought unfinished.",
-            },
-        ]
-
-        statement_context = OpenAILLMContext(statement_messages)
-        statement_context_aggregator = statement_llm.create_context_aggregator(statement_context)
-
-        # This is the regular LLM.
-        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)
-
-        # We have instructed the LLM to return 'YES' if it thinks the user
-        # completed a sentence. So, if it's 'YES' we will return true in this
-        # predicate which will wake up the notifier.
-        async def wake_check_filter(frame):
-            return frame.text == "YES"
-
-        # This is a notifier that we use to synchronize the two LLMs.
-        notifier = EventNotifier()
-
-        # This a filter that will wake up the notifier if the given predicate
-        # (wake_check_filter) returns true.
-        completness_check = WakeNotifierFilter(
-            notifier, types=(TextFrame,), filter=wake_check_filter
-        )
-
-        # This processor keeps the last context and will let it through once the
-        # notifier is woken up.
-        gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier)
-
-        # Notify if the user hasn't said anything.
-        async def user_idle_notifier(frame):
-            await notifier.notify()
-
-        # Sometimes the LLM will fail detecting if a user has completed a
-        # sentence, this will wake up the notifier if that happens.
-        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=3.0)
-
-        # The ParallePipeline input are the user transcripts. We have two
-        # contexts. The first one will be used to determine if the user finished
-        # a statement and if so the notifier will be woken up. The second
-        # context is simply the regular context but it's gated waiting for the
-        # notifier to be woken up.
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,
-                ParallelPipeline(
-                    [
-                        statement_context_aggregator.user(),
-                        statement_llm,
-                        completness_check,
-                        NullFilter(),
-                    ],
-                    [context_aggregator.user(), gated_context_aggregator, llm],
-                ),
-                user_idle,
-                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):
-            await 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/99-anthropic-hackathon.py

@@ -1,298 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import base64
-import io
-import os
-import sys
-from collections import deque
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    BotInterruptionFrame,
-    Frame,
-    ImageRawFrame,
-    LLMFullResponseEndFrame,
-    LLMMessagesFrame,
-    TextFrame,
-    TranscriptionFrame,
-)
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-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,
-    OpenAILLMContextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import (
-    RTVIBotTranscriptionProcessor,
-    RTVIUserTranscriptionProcessor,
-)
-from pipecat.services.anthropic import AnthropicLLMContext, AnthropicLLMService
-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")
-
-MAX_FRAMES = 5
-FRAMES_PER_SECOND = 0.2
-
-
-video_participant_id = None
-anthropic_context = None
-recent_image_frames = deque(maxlen=MAX_FRAMES)
-most_recent_image_summary = ""
-
-
-class ImageFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        global recent_image_frames
-
-        await super().process_frame(frame, direction)
-        if isinstance(frame, ImageRawFrame):
-            recent_image_frames.append(frame)
-        else:
-            await self.push_frame(frame, direction)
-
-
-class TranscriptFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, TranscriptionFrame):
-            logger.debug(
-                f"TranscriptLogger: {frame}, num frames: {len(recent_image_frames)}, anthropic context: {anthropic_context}"
-            )
-            if anthropic_context:
-                add_message_with_images(
-                    anthropic_context, frame.text, frames=list(recent_image_frames)
-                )
-        await self.push_frame(frame, direction)
-
-
-class MessageFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, OpenAILLMContextFrame):
-            last_message = frame.context.messages[-1]
-
-            system_message = """
-Give me a concise summary of the images supplied.
-            """
-            frame = LLMMessagesFrame(
-                messages=[
-                    {
-                        "role": "system",
-                        "content": system_message,
-                    },
-                    last_message,
-                ],
-            )
-            await self.push_frame(frame, direction)
-            return
-
-
-class MessageFrameCatcher2(FrameProcessor):
-    def __init__(self):
-        super().__init__()
-        self.text_blob = ""
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        global most_recent_image_summary
-        await super().process_frame(frame, direction)
-        if isinstance(frame, TextFrame):
-            self.text_blob += f" {frame.text}"
-
-        if isinstance(frame, LLMFullResponseEndFrame):
-            logger.debug(f"MessageFrameCatcher2: {self.text_blob}")
-            most_recent_image_summary = self.text_blob
-            self.text_blob = ""
-
-        await self.push_frame(frame, direction)
-
-
-async def main():
-    global llm
-    global anthropic_context
-
-    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,
-        )
-
-        vision_llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-5-sonnet-20240620",
-            enable_prompt_caching_beta=True,
-        )
-
-        # 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. Keep
-your answers brief unless explicitly asked for more information.
-
-Your response will be turned into speech so use only simple words and punctuation.
-        """
-
-        messages = [
-            {
-                "role": "system",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": system_prompt,
-                    }
-                ],
-            },
-            {"role": "user", "content": "Start the conversation by saying 'hello'."},
-        ]
-
-        context = OpenAILLMContext(messages)
-        anthropic_context = AnthropicLLMContext.upgrade_to_anthropic(context)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        rtvi_user_transcription = RTVIUserTranscriptionProcessor()
-        rtvi_bot_transcription = RTVIBotTranscriptionProcessor()
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                ImageFrameCatcher(),
-                TranscriptFrameCatcher(),
-                rtvi_user_transcription,
-                context_aggregator.user(),  # User speech to text
-                ParallelPipeline(
-                    [
-                        llm,  # LLM
-                        rtvi_bot_transcription,
-                        tts,  # TTS
-                        transport.output(),  # Transport bot output
-                        context_aggregator.assistant(),  # Assistant spoken responses and tool context
-                    ],
-                    [MessageFrameCatcher(), vision_llm, MessageFrameCatcher2()],
-                ),
-            ],
-        )
-
-        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"]
-            await transport.capture_participant_transcription(video_participant_id)
-            await transport.capture_participant_video(
-                video_participant_id, framerate=FRAMES_PER_SECOND, video_source="screenVideo"
-            )
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            logger.debug(f"Received app message: {message} - {context}")
-
-            if not recent_image_frames:
-                logger.debug("No image frames to send")
-                return
-
-            add_message_with_images(
-                anthropic_context, message["message"], frames=list(recent_image_frames)
-            )
-
-            interrupt_message = "STOP"
-
-            if interrupt_message == message["message"]:
-                logger.debug("Interrupting")
-                await task.queue_frames([BotInterruptionFrame()])
-            else:
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-def add_message_with_images(c, message, frames=None):
-    if frames is None:
-        frames = list(recent_image_frames)
-
-    if not frames:
-        logger.debug("No image frames to send")
-        return
-
-    # Create content list starting with all images
-    content = []
-    for frame in frames:
-        buffer = io.BytesIO()
-        Image.frombytes(frame.format, frame.size, frame.image).save(buffer, format="JPEG")
-        encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
-
-        content.append(
-            {
-                "type": "image",
-                "source": {
-                    "type": "base64",
-                    "media_type": "image/jpeg",
-                    "data": encoded_image,
-                },
-            }
-        )
-
-    # Add text message at the end if provided
-    if message:
-        content.append({"type": "text", "text": message})
-
-    # Go through all messages and replace user messages containing images
-    if c.messages:
-        for i, msg in enumerate(c.messages):
-            if (
-                msg["role"] == "user"
-                and isinstance(msg["content"], list)
-                and len(msg["content"]) > 0
-            ):
-                if msg["content"][0].get("type") == "image":
-                    logger.debug(
-                        f"Replacing user message {i} containing images with summary: {most_recent_image_summary}"
-                    )
-                    c.messages[i] = {"role": "user", "content": most_recent_image_summary}
-
-    c.add_message({"role": "user", "content": content})
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/runner.py

@@ -17,13 +17,16 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
 
 
 async def configure_with_args(
-    aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
-):
+        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",
@@ -39,19 +42,15 @@ async def configure_with_args(
 
     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,
-    )
+        aiohttp_session=aiohttp_session)
 
     # Create a meeting token for the given room with an expiration 1 hour in
     # the future.

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/` in your browser to start a chatbot
+Then, visit `http://localhost:7860/start` 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/` again.
+You can try to visit `http://localhost:7860/start` again.

examples/moondream-chatbot/bot.py

@@ -11,24 +11,23 @@ import sys
 
 from PIL import Image
 
-from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
     ImageRawFrame,
-    OutputImageRawFrame,
     SpriteFrame,
     Frame,
     LLMMessagesFrame,
-    TTSAudioRawFrame,
+    AudioRawFrame,
     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.openai_llm_context import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -36,13 +35,13 @@ 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)
@@ -60,7 +59,7 @@ 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(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
+        sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))
 
 flipped = sprites[::-1]
 sprites.extend(flipped)
@@ -83,7 +82,7 @@ class TalkingAnimation(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, TTSAudioRawFrame):
+        if isinstance(frame, AudioRawFrame):
             if not self._is_talking:
                 await self.push_frame(talking_frame)
                 self._is_talking = True
@@ -106,9 +105,7 @@ 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)
@@ -152,8 +149,8 @@ async def main():
                 camera_out_height=576,
                 transcription_enabled=True,
                 vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
 
         tts = CartesiaTTSService(
@@ -161,7 +158,9 @@ async def main():
             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()
 
@@ -182,29 +181,27 @@ async def main():
             },
         ]
 
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
+        ura = LLMUserResponseAggregator(messages)
 
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                ParallelPipeline([sa, ir, va, moondream], [tf, imgf]),
-                tts,
-                ta,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
+        pipeline = Pipeline([
+            transport.input(),
+            ura,
+            llm,
+            ParallelPipeline(
+                [sa, ir, va, moondream],
+                [tf, imgf]),
+            tts,
+            ta,
+            transport.output()
+        ])
 
         task = PipelineTask(pipeline)
         await task.queue_frame(quiet_frame)
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            await transport.capture_participant_video(participant["id"], framerate=0)
+            transport.capture_participant_transcription(participant["id"])
+            transport.capture_participant_video(participant["id"], framerate=0)
             ir.set_participant_id(participant["id"])
             await task.queue_frames([LLMMessagesFrame(messages)])
 

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...
-CARTESIA_API_KEY=your_cartesia_api_key_here
+ELEVENLABS_API_KEY=aeb...