testing pushing a frame from function call start hook

In this commit we make all frame processors asynchronous, that is, they have an
internal queue and they push frames using a task from that queue.

.github/workflows/format.yaml

@@ -35,12 +35,7 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r dev-requirements.txt
       - name: Ruff formatter
-        id: ruff-format
+        id: ruff
         run: |
           source .venv/bin/activate
-          ruff format --diff
-      - name: Ruff import linter
-        id: ruff-check
-        run: |
-          source .venv/bin/activate
-          ruff check --select I
+          ruff format --config line-length=100 --diff --exclude "*_pb2.py"

.gitignore

@@ -4,7 +4,6 @@ __pycache__/
 *~
 venv
 .venv
-/.idea
 #*#
 
 # Distribution / packaging
@@ -28,11 +27,4 @@ share/python-wheels/
 MANIFEST
 .DS_Store
 .env
-fly.toml
-
-# Example files
-pipecat/examples/twilio-chatbot/templates/streams.xml
-
-# Documentation
-docs/api/_build/
-docs/api/api
+fly.toml

.readthedocs.yaml

@@ -1,36 +0,0 @@
-version: 2
-
-build:
-  os: ubuntu-22.04
-  tools:
-    python: '3.12'
-  apt_packages:
-    - portaudio19-dev
-    - python3-dev
-    - libasound2-dev
-  jobs:
-    pre_build:
-      - python -m pip install --upgrade pip
-      - pip install wheel setuptools
-    post_build:
-      - echo "Build completed"
-
-sphinx:
-  configuration: docs/api/conf.py
-  fail_on_warning: false
-
-python:
-  install:
-    - requirements: docs/api/requirements.txt
-    - method: pip
-      path: .
-
-search:
-  ranking:
-    api/*: 5
-    getting-started/*: 4
-    guides/*: 3
-
-submodules:
-  include: all
-  recursive: true

CHANGELOG.md

@@ -1,553 +1,20 @@
 # 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).
 
-## [0.0.52] - 2024-12-24
+## [Unreleased]
 
 ### Added
 
-- Constructor arguments for GoogleLLMService to directly set tools and tool_config.
-
-- Smart turn detection example (`22d-natural-conversation-gemini-audio.py`) that
-  leverages Gemini 2.0 capabilities ().
-  (see https://x.com/kwindla/status/1870974144831275410)
-
-- Added `DailyTransport.send_dtmf()` to send dial-out DTMF tones.
-
-- Added `DailyTransport.sip_call_transfer()` to forward SIP and PSTN calls to
-  another address or number. For example, transfer a SIP call to a different
-  SIP address or transfer a PSTN phone number to a different PSTN phone number.
-
-- Added `DailyTransport.sip_refer()` to transfer incoming SIP/PSTN calls from
-  outside Daily to another SIP/PSTN address.
-
-- Added an `auto_mode` input parameter to `ElevenLabsTTSService`. `auto_mode`
-  is set to `True` by default. Enabling this setting disables the chunk
-  schedule and all buffers, which reduces latency.
-
-- Added `KoalaFilter` which implement on device noise reduction using Koala
-  Noise Suppression.
-  (see https://picovoice.ai/platform/koala/)
-
-- Added `CerebrasLLMService` for Cerebras integration with an OpenAI-compatible
-  interface. Added foundational example `14k-function-calling-cerebras.py`.
-
-- Pipecat now supports Python 3.13. We had a dependency on the `audioop` package
-  which was deprecated and now removed on Python 3.13. We are now using
-  `audioop-lts` (https://github.com/AbstractUmbra/audioop) to provide the same
-  functionality.
-
-- Added timestamped conversation transcript support:
-
-  - New `TranscriptProcessor` factory provides access to user and assistant
-    transcript processors.
-  - `UserTranscriptProcessor` processes user speech with timestamps from
-    transcription.
-  - `AssistantTranscriptProcessor` processes assistant responses with LLM
-    context timestamps.
-  - Messages emitted with ISO 8601 timestamps indicating when they were spoken.
-  - Supports all LLM formats (OpenAI, Anthropic, Google) via standard message
-    format.
-  - New examples: `28a-transcription-processor-openai.py`,
-    `28b-transcription-processor-anthropic.py`, and
-    `28c-transcription-processor-gemini.py`.
-
-- Add support for more languages to ElevenLabs (Arabic, Croatian, Filipino,
-  Tamil) and PlayHT (Afrikans, Albanian, Amharic, Arabic, Bengali, Croatian,
-  Galician, Hebrew, Mandarin, Serbian, Tagalog, Urdu, Xhosa).
-
-### Changed
-
-- `PlayHTTTSService` uses the new v4 websocket API, which also fixes an issue
-  where text inputted to the TTS didn't return audio.
-
-- The default model for `ElevenLabsTTSService` is now `eleven_flash_v2_5`.
-
-- `OpenAIRealtimeBetaLLMService` now takes a `model` parameter in the
-  constructor.
-
-- Updated the default model for the `OpenAIRealtimeBetaLLMService`.
-
-- Room expiration (`exp`) in `DailyRoomProperties` is now optional (`None`) by
-  default instead of automatically setting a 5-minute expiration time. You must
-  explicitly set expiration time if desired.
-
-### Deprecated
-
-- `AWSTTSService` is now deprecated, use `PollyTTSService` instead.
-
-### Fixed
-
-- Fixed token counting in `GoogleLLMService`. Tokens were summed incorrectly
-  (double-counted in many cases).
-
-- Fixed an issue that could cause the bot to stop talking if there was a user
-  interruption before getting any audio from the TTS service.
-
-- Fixed an issue that would cause `ParallelPipeline` to handle `EndFrame`
-  incorrectly causing the main pipeline to not terminate or terminate too early.
-
-- Fixed an audio stuttering issue in `FastPitchTTSService`.
-
-- Fixed a `BaseOutputTransport` issue that was causing non-audio frames being
-  processed before the previous audio frames were played. This will allow, for
-  example, sending a frame `A` after a `TTSSpeakFrame` and the frame `A` will
-  only be pushed downstream after the audio generated from `TTSSpeakFrame` has
-  been spoken.
-
-- Fixed a `DeepgramSTTService` issue that was causing language to be passed as
-  an object instead of a string resulting in the connection to fail.
-
-## [0.0.51] - 2024-12-16
-
-### Fixed
-
-- Fixed an issue in websocket-based TTS services that was causing infinite
-  reconnections (Cartesia, ElevenLabs, PlayHT and LMNT).
-
-## [0.0.50] - 2024-12-11
-
-### Added
-
-- Added `GeminiMultimodalLiveLLMService`. This is an integration for Google's
-  Gemini Multimodal Live API, supporting:
-
-  - Real-time audio and video input processing
-  - Streaming text responses with TTS
-  - Audio transcription for both user and bot speech
-  - Function calling
-  - System instructions and context management
-  - Dynamic parameter updates (temperature, top_p, etc.)
-
-- Added `AudioTranscriber` utility class for handling audio transcription with
-  Gemini models.
-
-- Added new context classes for Gemini:
-
-  - `GeminiMultimodalLiveContext`
-  - `GeminiMultimodalLiveUserContextAggregator`
-  - `GeminiMultimodalLiveAssistantContextAggregator`
-  - `GeminiMultimodalLiveContextAggregatorPair`
-
-- Added new foundational examples for `GeminiMultimodalLiveLLMService`:
-
-  - `26-gemini-multimodal-live.py`
-  - `26a-gemini-multimodal-live-transcription.py`
-  - `26b-gemini-multimodal-live-video.py`
-  - `26c-gemini-multimodal-live-video.py`
-
-- Added `SimliVideoService`. This is an integration for Simli AI avatars.
-  (see https://www.simli.com)
-
-- Added NVIDIA Riva's `FastPitchTTSService` and `ParakeetSTTService`.
-  (see https://www.nvidia.com/en-us/ai-data-science/products/riva/)
-
-- Added `IdentityFilter`. This is the simplest frame filter that lets through
-  all incoming frames.
-
-- New `STTMuteStrategy` called `FUNCTION_CALL` which mutes the STT service
-  during LLM function calls.
-
-- `DeepgramSTTService` now exposes two event handlers `on_speech_started` and
-  `on_utterance_end` that could be used to implement interruptions. See new
-  example `examples/foundational/07c-interruptible-deepgram-vad.py`.
-
-- Added `GroqLLMService`, `GrokLLMService`, and `NimLLMService` for Groq, Grok,
-  and NVIDIA NIM API integration, with an OpenAI-compatible interface.
-
-- New examples demonstrating function calling with Groq, Grok, Azure OpenAI,
-  Fireworks, and NVIDIA NIM: `14f-function-calling-groq.py`,
-  `14g-function-calling-grok.py`, `14h-function-calling-azure.py`,
-  `14i-function-calling-fireworks.py`, and `14j-function-calling-nvidia.py`.
-
-- In order to obtain the audio stored by the `AudioBufferProcessor` you can now
-  also register an `on_audio_data` event handler. The `on_audio_data` handler
-  will be called every time `buffer_size` (a new constructor argument) is
-  reached. If `buffer_size` is 0 (default) you need to manually get the audio as
-  before using `AudioBufferProcessor.merge_audio_buffers()`.
-
-```
-@audiobuffer.event_handler("on_audio_data")
-async def on_audio_data(processor, audio, sample_rate, num_channels):
-    await save_audio(audio, sample_rate, num_channels)
-```
-
-- Added a new RTVI message called `disconnect-bot`, which when handled pushes
-  an `EndFrame` to trigger the pipeline to stop.
-
-### Changed
-
-- `STTMuteFilter` now supports multiple simultaneous muting strategies.
-
-- `XTTSService` language now defaults to `Language.EN`.
-
-- `SoundfileMixer` doesn't resample input files anymore to avoid startup
-  delays. The sample rate of the provided sound files now need to match the
-  sample rate of the output transport.
-
-- Input frames (audio, image and transport messages) are now system frames. This
-  means they are processed immediately by all processors instead of being queued
-  internally.
-
-- Expanded the transcriptions.language module to support a superset of
-  languages.
-
-- Updated STT and TTS services with language options that match the supported
-  languages for each service.
-
-- Updated the `AzureLLMService` to use the `OpenAILLMService`. Updated the
-  `api_version` to `2024-09-01-preview`.
-
-- Updated the `FireworksLLMService` to use the `OpenAILLMService`. Updated the
-  default model to `accounts/fireworks/models/firefunction-v2`.
-
-- Updated the `simple-chatbot` example to include a Javascript and React client
-  example, using RTVI JS and React.
-
-### Removed
-
-- Removed `AppFrame`. This was used as a special user custom frame, but there's
-  actually no use case for that.
-
-### Fixed
-
-- Fixed a `ParallelPipeline` issue that would cause system frames to be queued.
-
-- Fixed `FastAPIWebsocketTransport` so it can work with binary data (e.g. using
-  the protobuf serializer).
-
-- Fixed an issue in `CartesiaTTSService` that could cause previous audio to be
-  received after an interruption.
-
-- Fixed Cartesia, ElevenLabs, LMNT and PlayHT TTS websocket
-  reconnection. Before, if an error occurred no reconnection was happening.
-
-- Fixed a `BaseOutputTransport` issue that was causing audio to be discarded
-  after an `EndFrame` was received.
-
-- Fixed an issue in `WebsocketServerTransport` and `FastAPIWebsocketTransport`
-  that would cause a busy loop when using audio mixer.
-
-- Fixed a `DailyTransport` and `LiveKitTransport` issue where connections were
-  being closed in the input transport prematurely. This was causing frames
-  queued inside the pipeline being discarded.
-
-- Fixed an issue in `DailyTransport` that would cause some internal callbacks to
-  not be executed.
-
-- Fixed an issue where other frames were being processed while a `CancelFrame`
-  was being pushed down the pipeline.
-
-- `AudioBufferProcessor` now handles interruptions properly.
-
-- Fixed a `WebsocketServerTransport` issue that would prevent interruptions with
-  `TwilioSerializer` from working.
-
-- `DailyTransport.capture_participant_video` now allows capturing user's screen
-  share by simply passing `video_source="screenVideo"`.
-
-- Fixed Google Gemini message handling to properly convert appended messages to
-  Gemini's required format.
-
-- Fixed an issue with `FireworksLLMService` where chat completions were failing
-  by removing the `stream_options` from the chat completion options.
-
-## [0.0.49] - 2024-11-17
-
-### Added
-
-- Added RTVI `on_bot_started` event which is useful in a single turn
-  interaction.
-
-- Added `DailyTransport` events `dialin-connected`, `dialin-stopped`,
-  `dialin-error` and `dialin-warning`. Needs daily-python >= 0.13.0.
-
-- Added `RimeHttpTTSService` and the `07q-interruptible-rime.py` foundational
-  example.
-
-- Added `STTMuteFilter`, a general-purpose processor that combines STT
-  muting and interruption control. When active, it prevents both transcription
-  and interruptions during bot speech. The processor supports multiple
-  strategies: `FIRST_SPEECH` (mute only during bot's first
-  speech), `ALWAYS` (mute during all bot speech), or `CUSTOM` (using provided
-  callback).
-
-- Added `STTMuteFrame`, a control frame that enables/disables speech
-  transcription in STT services.
-
-## [0.0.48] - 2024-11-10 "Antonio release"
-
-### Added
-
-- There's now an input queue in each frame processor. When you call
-  `FrameProcessor.push_frame()` this will internally call
-  `FrameProcessor.queue_frame()` on the next processor (upstream or downstream)
-  and the frame will be internally queued (except system frames). Then, the
-  queued frames will get processed. With this input queue it is also possible
-  for FrameProcessors to block processing more frames by calling
-  `FrameProcessor.pause_processing_frames()`. The way to resume processing
-  frames is by calling `FrameProcessor.resume_processing_frames()`.
-
-- Added audio filter `NoisereduceFilter`.
-
-- Introduce input transport audio filters (`BaseAudioFilter`). Audio filters can
-  be used to remove background noises before audio is sent to VAD.
-
-- Introduce output transport audio mixers (`BaseAudioMixer`). Output transport
-  audio mixers can be used, for example, to add background sounds or any other
-  audio mixing functionality before the output audio is actually written to the
-  transport.
-
-- 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.
-
-- Added audio filter `KrispFilter`.
-
-### 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.
-
-- `AzureTTSService` now uses Azure websockets instead of HTTP requests.
-
-- The previous `AzureTTSService` HTTP implementation is now
-  `AzureHttpTTSService`.
-
-### Fixed
-
-- Websocket transports (FastAPI and Websocket) now synchronize with time before
-  sending data. This allows for interruptions to just work out of the box.
-
-- 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.
-
-- Fixed an issue where AzureTTSService wasn't initializing the specified
-  language.
-
-### Other
-
-- Add `23-bot-background-sound.py` foundational example.
-
-- Added a new foundational example `22-natural-conversation.py`. This example
-  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 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.
 
 ```
@@ -619,12 +86,8 @@ 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.
+- Updated individual update settings frame classes into a single UpdateSettingsFrame
+  class for STT, LLM, and TTS.
 
 - We now distinguish between input and output audio and image frames. We
   introduce `InputAudioRawFrame`, `OutputAudioRawFrame`, `InputImageRawFrame`
@@ -644,9 +107,9 @@ async def on_connected(processor):
   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.
+- `StartFrame` is back a system frame so we 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`.
 
@@ -670,11 +133,6 @@ 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.
@@ -688,10 +146,6 @@ 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

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

README.md

@@ -1,21 +1,14 @@
-<h1><div align="center">
+<div align="center">
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
-</div></h1>
+</div>
 
-[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Docs](https://img.shields.io/badge/Documentation-blue)](https://docs.pipecat.ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
+# Pipecat
 
-Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
-## What you can build
+`pipecat` is a framework for building voice (and multimodal) conversational agents. Things like personal coaches, meeting assistants, [story-telling toys for kids](https://storytelling-chatbot.fly.dev/), customer support bots, [intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0), and snarky social companions.
 
-- **Voice Assistants**: [Natural, real-time conversations with AI](https://demo.dailybots.ai/)
-- **Interactive Agents**: Personal coaches and meeting assistants
-- **Multimodal Apps**: Combine voice, video, images, and text
-- **Creative Tools**: [Story-telling experiences](https://storytelling-chatbot.fly.dev/) and social companions
-- **Business Solutions**: [Customer intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0) and support bots
-- **Complex conversational flows**: [Refer to Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) to learn more
-
-## See it in action
+Take a look at some example apps:
 
 <p float="left">
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/simple-chatbot/image.png" width="280" /></a>&nbsp;
@@ -25,61 +18,43 @@ Pipecat is an open source Python framework for building voice and multimodal con
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/moondream-chatbot/image.png" width="280" /></a>
 </p>
 
-## Key features
-
-- **Voice-first Design**: Built-in speech recognition, TTS, and conversation handling
-- **Flexible Integration**: Works with popular AI services (OpenAI, ElevenLabs, etc.)
-- **Pipeline Architecture**: Build complex apps from simple, reusable components
-- **Real-time Processing**: Frame-based pipeline architecture for fluid interactions
-- **Production Ready**: Enterprise-grade WebRTC and Websocket support
-
-💡 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
-## Getting started
+## Getting started with voice agents
 
 You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you’re ready. You can also add a 📞 telephone number, 🖼️ image output, 📺 video input, use different LLMs, and more.
 
 ```shell
-# Install the module
+# install the module
 pip install pipecat-ai
 
-# Set up your environment
+# set up an .env file with API keys
 cp dot-env.template .env
 ```
 
-To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:
+By default, in order to minimize dependencies, only the basic framework functionality is available. Some third-party AI services require additional dependencies that you can install with:
 
 ```shell
 pip install "pipecat-ai[option,...]"
 ```
 
-Available options include:
+Your project may or may not need these, so they're made available as optional requirements. Here is a list:
 
-| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | Install Command Example                 |
-| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[deepgram]"`    |
-| LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                      | `pip install "pipecat-ai[openai]"`      |
-| Text-to-Speech      | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"`    |
-| Speech-to-Speech    | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | `pip install "pipecat-ai[openai]"`      |
-| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), WebSocket, Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | `pip install "pipecat-ai[daily]"`       |
-| Video               | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[tavus,simli]"` |
-| Vision & Image      | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[moondream]"`   |
-| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                | `pip install "pipecat-ai[silero]"`      |
-| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | `pip install "pipecat-ai[canonical]"`   |
-
-📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
+- **AI services**: `anthropic`, `aws`, `azure`, `deepgram`, `gladia`, `google`, `fal`, `lmnt`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`, `xtts`
+- **Transports**: `local`, `websocket`, `daily`
 
 ## Code examples
 
-- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
-- [Example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
+- [foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
+- [example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
 
 ## A simple voice agent running locally
 
 Here is a very basic Pipecat bot that greets a user when they join a real-time session. We'll use [Daily](https://daily.co) for real-time media transport, and [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
@@ -89,43 +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())
@@ -137,7 +108,7 @@ Run it with:
 python app.py
 ```
 
-Daily provides a prebuilt WebRTC user interface. While the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
+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
 
@@ -147,6 +118,18 @@ One way to get up and running quickly with WebRTC is to sign up for a Daily deve
 
 Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://docs.daily.co/reference/rest-api/rooms) in the developer Dashboard.
 
+## What is VAD?
+
+Voice Activity Detection &mdash; very important for knowing when a user has finished speaking to your bot. If you are not using press-to-talk, and want Pipecat to detect when the user has finished talking, VAD is an essential component for a natural feeling conversation.
+
+Pipecat makes use of WebRTC VAD by default when using a WebRTC transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.
+
+```shell
+pip install pipecat-ai[silero]
+```
+
+The first time your run your bot with Silero, startup may take a while whilst it downloads and caches the model in the background. You can check the progress of this in the console.
+
 ## Hacking on the framework itself
 
 _Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
@@ -196,8 +179,10 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [e
   :ensure t
   :hook ((python-mode . lazy-ruff-mode))
   :config
-  (setq lazy-ruff-format-command "ruff format")
-  (setq lazy-ruff-check-command "ruff check --select I"))
+  (setq lazy-ruff-format-command "ruff format --config line-length=100")
+  (setq lazy-ruff-only-format-block t)
+  (setq lazy-ruff-only-format-region t)
+  (setq lazy-ruff-only-format-buffer t))
 ```
 
 `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.
@@ -207,47 +192,24 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [e
   :ensure t
   :defer t
   :hook ((python-mode . pyvenv-auto-run)))
+
 ```
 
 ### 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:
+[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, enable formatting on save and configure `ruff` arguments:
 
 ```json
 "[python]": {
     "editor.defaultFormatter": "charliermarsh.ruff",
     "editor.formatOnSave": true
-}
+},
+"ruff.format.args": ["--config", "line-length=100"]
 ```
 
-### PyCharm
-
-`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
-
-1. **Name**: `Ruff formatter`
-2. **File type**: `Python`
-3. **Working directory**: `$ContentRoot$`
-4. **Arguments**: `format $FilePath$`
-5. **Program**: `$PyInterpreterDirectory$/ruff`
-
-## Contributing
-
-We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
-
-- **Found a bug?** Open an [issue](https://github.com/pipecat-ai/pipecat/issues)
-- **Have a feature idea?** Start a [discussion](https://discord.gg/pipecat)
-- **Want to contribute code?** Check our [CONTRIBUTING.md](CONTRIBUTING.md) guide
-- **Documentation improvements?** [Docs](https://github.com/pipecat-ai/docs) PRs are always welcome
-
-Before submitting a pull request, please check existing issues and PRs to avoid duplicates.
-
-We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.
-
 ## Getting help
 
 ➡️ [Join our Discord](https://discord.gg/pipecat)
 
-➡️ [Read the docs](https://docs.pipecat.ai)
-
 ➡️ [Reach us on X](https://x.com/pipecat_ai)

dev-requirements.txt

@@ -1,9 +1,8 @@
-build~=1.2.2
-grpcio-tools~=1.68.1
+build~=1.2.1
+grpcio-tools~=1.62.2
 pip-tools~=7.4.1
-pyright~=1.1.390
-pytest~=8.3.4
-ruff~=0.8.3
-setuptools~=75.6.0
+pyright~=1.1.376
+pytest~=8.3.2
+ruff~=0.6.7
+setuptools~=72.2.0
 setuptools_scm~=8.1.0
-python-dotenv~=1.0.1

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.

docs/api/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/api/README.md

@@ -1,109 +0,0 @@
-# Pipecat Documentation
-
-This directory contains the source files for auto-generating Pipecat's server API reference documentation.
-
-## Setup
-
-1. Install documentation dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-2. Make the build scripts executable:
-
-```bash
-chmod +x build-docs.sh rtd-test.py
-```
-
-## Building Documentation
-
-From this directory, you can build the documentation in several ways:
-
-### Local Build
-
-```bash
-# Using the build script (automatically opens docs when done)
-./build-docs.sh
-
-# Or directly with sphinx-build
-sphinx-build -b html . _build/html -W --keep-going
-```
-
-### ReadTheDocs Test Build
-
-To test the documentation build process exactly as it would run on ReadTheDocs:
-
-```bash
-./rtd-test.py
-```
-
-This script:
-
-- Creates a fresh virtual environment
-- Installs all dependencies as specified in requirements files
-- Handles conflicting dependencies (like grpcio versions for Riva and PlayHT)
-- Builds the documentation in an isolated environment
-- Provides detailed logging of the build process
-
-Use this script to verify your documentation will build correctly on ReadTheDocs before pushing changes.
-
-## Viewing Documentation
-
-The built documentation will be available at `_build/html/index.html`. To open:
-
-```bash
-# On MacOS
-open _build/html/index.html
-
-# On Linux
-xdg-open _build/html/index.html
-
-# On Windows
-start _build/html/index.html
-```
-
-## Directory Structure
-
-```
-.
-├── api/            # Auto-generated API documentation
-├── _build/         # Built documentation
-├── _static/        # Static files (images, css, etc.)
-├── conf.py         # Sphinx configuration
-├── index.rst       # Main documentation entry point
-├── requirements-base.txt    # Base documentation dependencies
-├── requirements-riva.txt    # Riva-specific dependencies
-├── requirements-playht.txt  # PlayHT-specific dependencies
-├── build-docs.sh   # Local build script
-└── rtd-test.py     # ReadTheDocs test build script
-```
-
-## Notes
-
-- Documentation is auto-generated from Python docstrings
-- Service modules are automatically detected and included
-- The build process matches our ReadTheDocs configuration
-- Warnings are treated as errors (-W flag) to maintain consistency
-- The --keep-going flag ensures all errors are reported
-- Dependencies are split into multiple requirements files to handle version conflicts
-
-## Troubleshooting
-
-If you encounter missing service modules:
-
-1. Verify the service is installed with its extras: `pip install pipecat-ai[service-name]`
-2. Check the build logs for import errors
-3. Ensure the service module is properly initialized in the package
-4. Run `./rtd-test.py` to test in an isolated environment matching ReadTheDocs
-
-For dependency conflicts:
-
-1. Check the requirements files for version specifications
-2. Use `rtd-test.py` to verify dependency resolution
-3. Consider adding service-specific requirements files if needed
-
-For more information:
-
-- [ReadTheDocs Configuration](.readthedocs.yaml)
-- [Sphinx Documentation](https://www.sphinx-doc.org/)

docs/api/build-docs.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-# Clean previous build
-rm -rf _build
-
-# Build docs matching ReadTheDocs configuration
-sphinx-build -b html -d _build/doctrees . _build/html -W --keep-going
-
-# Open docs (MacOS)
-open _build/html/index.html

docs/api/conf.py

@@ -1,252 +0,0 @@
-import logging
-import sys
-from pathlib import Path
-
-# Configure logging
-logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("sphinx-build")
-
-# Add source directory to path
-docs_dir = Path(__file__).parent
-project_root = docs_dir.parent.parent
-sys.path.insert(0, str(project_root / "src"))
-
-# Project information
-project = "pipecat-ai"
-copyright = "2024, Daily"
-author = "Daily"
-
-# General configuration
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.viewcode",
-    "sphinx.ext.intersphinx",
-]
-
-# Napoleon settings
-napoleon_google_docstring = True
-napoleon_numpy_docstring = False
-napoleon_include_init_with_doc = True
-
-# AutoDoc settings
-autodoc_default_options = {
-    "members": True,
-    "member-order": "bysource",
-    "special-members": "__init__",
-    "undoc-members": True,
-    "exclude-members": "__weakref__",
-    "no-index": True,
-    "show-inheritance": True,
-}
-
-# Mock imports for optional dependencies
-autodoc_mock_imports = [
-    "riva",
-    "livekit",
-    "pyht",  # Base PlayHT package
-    "pyht.async_client",  # PlayHT specific imports
-    "pyht.client",
-    "pyht.protos",
-    "pyht.protos.api_pb2",
-    "pipecat_ai_playht",  # PlayHT wrapper
-    "anthropic",
-    "assemblyai",
-    "boto3",
-    "azure",
-    "cartesia",
-    "deepgram",
-    "elevenlabs",
-    "fal",
-    "gladia",
-    "google",
-    "krisp",
-    "langchain",
-    "lmnt",
-    "noisereduce",
-    "openai",
-    "openpipe",
-    "simli",
-    "soundfile",
-    # Existing mocks
-    "pipecat_ai_krisp",
-    "pyaudio",
-    "_tkinter",
-    "tkinter",
-    "daily",
-    "daily_python",
-    "pydantic.BaseModel",
-    "pydantic.Field",
-    "pydantic._internal._model_construction",
-    "pydantic._internal._fields",
-]
-
-# HTML output settings
-html_theme = "sphinx_rtd_theme"
-html_static_path = ["_static"]
-autodoc_typehints = "description"
-html_show_sphinx = False
-
-
-def verify_modules():
-    """Verify that required modules are available."""
-    required_modules = {
-        "services": [
-            "assemblyai",
-            "aws",
-            "cartesia",
-            "deepgram",
-            "google",
-            "lmnt",
-            "riva",
-            "simli",
-        ],
-        "serializers": ["livekit"],
-        "vad": ["silero", "vad_analyzer"],
-        "transports": {
-            "services": ["daily", "livekit"],
-            "local": ["audio", "tk"],
-            "network": ["fastapi_websocket", "websocket_server"],
-        },
-    }
-
-    missing = []
-    for category, modules in required_modules.items():
-        if isinstance(modules, dict):
-            # Handle nested structure
-            for subcategory, submodules in modules.items():
-                for module in submodules:
-                    try:
-                        __import__(f"pipecat.{category}.{subcategory}.{module}")
-                        logger.info(
-                            f"Successfully imported pipecat.{category}.{subcategory}.{module}"
-                        )
-                    except (ImportError, TypeError, NameError) as e:
-                        missing.append(f"pipecat.{category}.{subcategory}.{module}")
-                        logger.warning(
-                            f"Optional module not available: pipecat.{category}.{subcategory}.{module} - {str(e)}"
-                        )
-        else:
-            # Handle flat structure
-            for module in modules:
-                try:
-                    __import__(f"pipecat.{category}.{module}")
-                    logger.info(f"Successfully imported pipecat.{category}.{module}")
-                except (ImportError, TypeError, NameError) as e:
-                    missing.append(f"pipecat.{category}.{module}")
-                    logger.warning(
-                        f"Optional module not available: pipecat.{category}.{module} - {str(e)}"
-                    )
-
-    if missing:
-        logger.warning(f"Some optional modules are not available: {missing}")
-
-
-def clean_title(title: str) -> str:
-    """Automatically clean module titles."""
-    # Remove everything after space (like 'module', 'processor', etc.)
-    title = title.split(" ")[0]
-
-    # Get the last part of the dot-separated path
-    parts = title.split(".")
-    title = parts[-1]
-
-    # Special cases for service names and common acronyms
-    special_cases = {
-        "ai": "AI",
-        "aws": "AWS",
-        "api": "API",
-        "vad": "VAD",
-        "assemblyai": "AssemblyAI",
-        "deepgram": "Deepgram",
-        "elevenlabs": "ElevenLabs",
-        "openai": "OpenAI",
-        "openpipe": "OpenPipe",
-        "playht": "PlayHT",
-        "xtts": "XTTS",
-        "lmnt": "LMNT",
-    }
-
-    # Check if the entire title is a special case
-    if title.lower() in special_cases:
-        return special_cases[title.lower()]
-
-    # Otherwise, capitalize each word
-    words = title.split("_")
-    cleaned_words = []
-    for word in words:
-        if word.lower() in special_cases:
-            cleaned_words.append(special_cases[word.lower()])
-        else:
-            cleaned_words.append(word.capitalize())
-
-    return " ".join(cleaned_words)
-
-
-def setup(app):
-    """Generate API documentation during Sphinx build."""
-    from sphinx.ext.apidoc import main
-
-    docs_dir = Path(__file__).parent
-    project_root = docs_dir.parent.parent
-    output_dir = str(docs_dir / "api")
-    source_dir = str(project_root / "src" / "pipecat")
-
-    # Clean existing files
-    if Path(output_dir).exists():
-        import shutil
-
-        shutil.rmtree(output_dir)
-        logger.info(f"Cleaned existing documentation in {output_dir}")
-
-    logger.info(f"Generating API documentation...")
-    logger.info(f"Output directory: {output_dir}")
-    logger.info(f"Source directory: {source_dir}")
-
-    excludes = [
-        str(project_root / "src/pipecat/pipeline/to_be_updated"),
-        str(project_root / "src/pipecat/processors/gstreamer"),
-        str(project_root / "src/pipecat/services/to_be_updated"),
-        str(project_root / "src/pipecat/vad"),  # deprecated
-        "**/test_*.py",
-        "**/tests/*.py",
-    ]
-
-    try:
-        main(
-            [
-                "-f",  # Force overwriting
-                "-e",  # Don't generate empty files
-                "-M",  # Put module documentation before submodule documentation
-                "--no-toc",  # Don't create a table of contents file
-                "--separate",  # Put documentation for each module in its own page
-                "--module-first",  # Module documentation before submodule documentation
-                "--implicit-namespaces",  # Added: Handle implicit namespace packages
-                "-o",
-                output_dir,
-                source_dir,
-            ]
-            + excludes
-        )
-
-        logger.info("API documentation generated successfully!")
-
-        # Process generated RST files to update titles
-        for rst_file in Path(output_dir).glob("**/*.rst"):  # Changed to recursive glob
-            content = rst_file.read_text()
-            lines = content.split("\n")
-
-            # Find and clean up the title
-            if lines and "=" in lines[1]:  # Title is typically the first line
-                old_title = lines[0]
-                new_title = clean_title(old_title)
-                content = content.replace(old_title, new_title)
-                rst_file.write_text(content)
-                logger.info(f"Updated title: {old_title} -> {new_title}")
-
-    except Exception as e:
-        logger.error(f"Error generating API documentation: {e}", exc_info=True)
-
-
-# Run module verification
-verify_modules()

docs/api/index.rst

@@ -1,77 +0,0 @@
-Pipecat API Reference Docs
-==========================
-
-Welcome to Pipecat's API reference documentation!
-
-Pipecat is an open source framework for building voice and multimodal assistants.
-It provides a flexible pipeline architecture for connecting various AI services,
-audio processing, and transport layers.
-
-Quick Links
------------
-
-* `GitHub Repository <https://github.com/pipecat-ai/pipecat>`_
-* `Website <https://pipecat.ai>`_
-
-API Reference
--------------
-
-Core Components
-~~~~~~~~~~~~~~~
-
-* :mod:`Frames <pipecat.frames>`
-* :mod:`Processors <pipecat.processors>`
-* :mod:`Pipeline <pipecat.pipeline>`
-
-Audio Processing
-~~~~~~~~~~~~~~~~
-
-* :mod:`Audio <pipecat.audio>`
-
-Services
-~~~~~~~~
-
-* :mod:`Services <pipecat.services>`
-
-Transport & Serialization
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-* :mod:`Transports <pipecat.transports>`
-   * :mod:`Local <pipecat.transports.local>`
-   * :mod:`Network <pipecat.transports.network>`
-   * :mod:`Services <pipecat.transports.services>`
-* :mod:`Serializers <pipecat.serializers>`
-
-Utilities
-~~~~~~~~~
-
-* :mod:`Clocks <pipecat.clocks>`
-* :mod:`Metrics <pipecat.metrics>`
-* :mod:`Sync <pipecat.sync>`
-* :mod:`Transcriptions <pipecat.transcriptions>`
-* :mod:`Utils <pipecat.utils>`
-
-.. toctree::
-   :maxdepth: 3
-   :caption: API Reference
-   :hidden:
-
-   Audio <api/pipecat.audio>
-   Clocks <api/pipecat.clocks>
-   Frames <api/pipecat.frames>
-   Metrics <api/pipecat.metrics>
-   Pipeline <api/pipecat.pipeline>
-   Processors <api/pipecat.processors>
-   Serializers <api/pipecat.serializers>
-   Services <api/pipecat.services>
-   Sync <api/pipecat.sync>
-   Transcriptions <api/pipecat.transcriptions>
-   Transports <api/pipecat.transports>
-   Utils <api/pipecat.utils>
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`

docs/api/make.bat

@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=.
-set BUILDDIR=_build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd

docs/api/requirements.txt

@@ -1,40 +0,0 @@
-# Sphinx dependencies
-sphinx>=8.1.3
-sphinx-rtd-theme
-sphinx-markdown-builder
-sphinx-autodoc-typehints
-toml
-
-# Install all extras individually to ensure they're properly resolved
-pipecat-ai[anthropic]
-pipecat-ai[assemblyai]
-pipecat-ai[aws]
-pipecat-ai[azure]
-pipecat-ai[canonical]
-pipecat-ai[cartesia]
-pipecat-ai[daily]
-pipecat-ai[deepgram]
-pipecat-ai[elevenlabs]
-pipecat-ai[fal]
-pipecat-ai[fireworks]
-pipecat-ai[gladia]
-pipecat-ai[google]
-pipecat-ai[grok]
-pipecat-ai[groq]
-# pipecat-ai[krisp] # Mocked instead
-pipecat-ai[langchain]
-pipecat-ai[livekit]
-pipecat-ai[lmnt]
-pipecat-ai[local]
-pipecat-ai[moondream]
-pipecat-ai[nim]
-pipecat-ai[noisereduce]
-pipecat-ai[openai]
-# pipecat-ai[openpipe]
-# pipecat-ai[playht] # Mocked due to grpcio conflict with riva
-pipecat-ai[riva]
-pipecat-ai[silero]
-pipecat-ai[simli]
-pipecat-ai[soundfile]
-pipecat-ai[websocket]
-pipecat-ai[whisper]

docs/api/rtd-test.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-set -e
-
-# Configuration
-DOCS_DIR=$(pwd)
-PROJECT_ROOT=$(cd ../../ && pwd)
-TEST_DIR="/tmp/rtd-test-$(date +%Y%m%d_%H%M%S)"
-
-echo "Creating test directory: $TEST_DIR"
-mkdir -p "$TEST_DIR"
-cd "$TEST_DIR"
-
-# Create virtual environment
-python -m venv venv
-source venv/bin/activate
-
-echo "Installing build dependencies..."
-pip install --upgrade pip wheel setuptools
-
-echo "Installing documentation dependencies..."
-pip install -r "$DOCS_DIR/requirements.txt"
-
-echo "Building documentation..."
-cd "$DOCS_DIR"
-sphinx-build -b html . "_build/html"
-
-echo "Build complete. Check _build/html directory for output."
-
-# Print summary
-echo -e "\n=== Build Summary ==="
-echo "Documentation: $DOCS_DIR/_build/html"
-echo "Test environment: $TEST_DIR"
-echo -e "\nTo view the documentation:"
-echo "open $DOCS_DIR/_build/html/index.html"
-
-# Print installed packages for verification
-echo -e "\n=== Installed Packages ==="
-pip freeze | grep -E "sphinx|pipecat"

docs/frame.md

@@ -1,110 +0,0 @@
-# Understanding Different Frame Types in the Pipecat System
-
-In the Pipecat system, frames are used to represent different types of data and control signals that flow through the pipeline. Understanding these frame types is crucial for working with the system effectively. This tutorial will cover the main categories of frames and their specific uses.
-
-## 1. Base Frame Classes
-
-### Frame
-The `Frame` class is the base class for all frames. It includes:
-- `id`: A unique identifier
-- `name`: A descriptive name
-- `pts`: Presentation timestamp (optional)
-
-### DataFrame
-`DataFrame` is a subclass of `Frame` and serves as a base for most data-carrying frames.
-
-## 2. Audio Frames
-
-### AudioRawFrame
-Represents a chunk of audio with properties:
-- `audio`: Raw audio data
-- `sample_rate`: Audio sample rate
-- `num_channels`: Number of audio channels
-
-Subclasses include:
-- `InputAudioRawFrame`: For audio from input sources
-- `OutputAudioRawFrame`: For audio to be played by output devices
-- `TTSAudioRawFrame`: For audio generated by Text-to-Speech services
-
-## 3. Image Frames
-
-### ImageRawFrame
-Represents an image with properties:
-- `image`: Raw image data
-- `size`: Image dimensions
-- `format`: Image format (e.g., JPEG, PNG)
-
-Subclasses include:
-- `InputImageRawFrame`: For images from input sources
-- `OutputImageRawFrame`: For images to be displayed
-- `UserImageRawFrame`: For images associated with a specific user
-- `VisionImageRawFrame`: For images with associated text for description
-- `URLImageRawFrame`: For images with an associated URL
-
-### SpriteFrame
-Represents an animated sprite, containing a list of `ImageRawFrame` objects.
-
-## 4. Text and Transcription Frames
-
-### TextFrame
-Represents a chunk of text, used for various purposes in the pipeline.
-
-### TranscriptionFrame
-A specialized `TextFrame` for speech transcriptions, including:
-- `user_id`: ID of the speaking user
-- `timestamp`: When the transcription was generated
-- `language`: Detected language of the speech
-
-### InterimTranscriptionFrame
-Similar to `TranscriptionFrame`, but for interim (not final) transcriptions.
-
-## 5. LLM (Language Model) Frames
-
-### LLMMessagesFrame
-Contains a list of messages for an LLM service to process.
-
-### LLMMessagesAppendFrame and LLMMessagesUpdateFrame
-Used to modify the current context of LLM messages.
-
-### LLMSetToolsFrame
-Specifies tools (functions) available for the LLM to use.
-
-### LLMEnablePromptCachingFrame
-Controls prompt caching in certain LLMs.
-
-## 6. System and Control Frames
-
-### SystemFrame
-Base class for system-level frames.
-
-Important system frames include:
-- `StartFrame`: Initiates a pipeline
-- `CancelFrame`: Stops a pipeline immediately
-- `ErrorFrame`: Notifies of errors (with `FatalErrorFrame` for unrecoverable errors)
-- `EndTaskFrame` and `CancelTaskFrame`: Control pipeline tasks
-- `StartInterruptionFrame` and `StopInterruptionFrame`: Indicate user speech for interruptions
-
-### ControlFrame
-Base class for control-flow frames.
-
-Notable control frames:
-- `EndFrame`: Signals the end of a pipeline
-- `LLMFullResponseStartFrame` and `LLMFullResponseEndFrame`: Bracket LLM responses
-- `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame`: Indicate user speech activity
-- `BotStartedSpeakingFrame` and `BotStoppedSpeakingFrame`: Indicate bot speech activity
-- `TTSStartedFrame` and `TTSStoppedFrame`: Bracket Text-to-Speech responses
-
-## 7. Special Purpose Frames
-
-### MetricsFrame
-Contains performance metrics data.
-
-### FunctionCallInProgressFrame and FunctionCallResultFrame
-Used for handling LLM function (tool) calls.
-
-### ServiceUpdateSettingsFrame
-Base class for updating service settings, with specific subclasses for LLM, TTS, and STT services.
-
-## Conclusion
-
-Understanding these frame types is essential for working with the Pipecat system. Each frame type serves a specific purpose in the pipeline, whether it's carrying data (like audio or images), controlling the flow of the pipeline, or managing system-level operations. By using the appropriate frame types, you can effectively process and transmit various kinds of information through your pipeline.

dot-env.template

@@ -46,17 +46,5 @@ PLAY_HT_API_KEY=...
 # OpenAI
 OPENAI_API_KEY=...
 
-# OpenPipe
+#OpenPipe
 OPENPIPE_API_KEY=...
-
-# Tavus
-TAVUS_API_KEY=...
-TAVUS_REPLICA_ID=...
-TAVUS_PERSONA_ID=...
-
-# Simli
-SIMLI_API_KEY=...
-SIMLI_FACE_ID=...
-
-# Krisp
-KRISP_MODEL_PATH=...

examples/README.md

@@ -42,7 +42,6 @@ Next, follow the steps in the README for each demo.
 | [Dialin Chatbot](dialin-chatbot)             | A chatbot that connects to an incoming phone call from Daily or Twilio.                                                                    | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [Twilio Chatbot](twilio-chatbot)             | A chatbot that connects to an incoming phone call from Twilio.                                                                             | Deepgram, ElevenLabs, OpenAI, Daily, Twilio                       |
 | [studypal](studypal)                         | A chatbot to have a conversation about any article on the web                                                                              |                                                                   |
-| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities | `python-websockets`, `openai`, `deepgram`, `silero-tts`, `numpy` |
 
 > [!IMPORTANT]
 > These example projects use Daily as a WebRTC transport and can be joined using their hosted Prebuilt UI.

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

@@ -1,145 +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"),
-            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/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/requirements.txt

@@ -1,5 +0,0 @@
-aiofiles
-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/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

@@ -1,20 +1,24 @@
-import argparse
 import asyncio
 import os
 import sys
+import argparse
 
-from dotenv import load_dotenv
-from loguru import logger
-
-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.services.elevenlabs import ElevenLabsTTSService
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
+from pipecat.frames.frames import LLMMessagesFrame, EndFrame
 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)
 
@@ -56,17 +60,17 @@ 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(),
+            tma_in,
             llm,
             tts,
             transport.output(),
-            context_aggregator.assistant(),
+            tma_out,
         ]
     )
 
@@ -74,7 +78,7 @@ async def main(room_url: str, token: str):
 
     @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

@@ -4,24 +4,26 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import argparse
-import os
 import subprocess
+import os
+
 from contextlib import asynccontextmanager
 
-import aiohttp
-from dotenv import load_dotenv
-from fastapi import FastAPI, HTTPException, Request
+from fastapi import FastAPI, Request, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 
 from pipecat.transports.services.helpers.daily_rest import (
     DailyRESTHelper,
     DailyRoomObject,
-    DailyRoomParams,
     DailyRoomProperties,
+    DailyRoomParams,
 )
 
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 
@@ -122,7 +124,7 @@ 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()

examples/deployment/modal-example/.gitignore

@@ -1,91 +0,0 @@
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-*.so
-.Python
-build/
-dist/
-*.egg-info/
-*.egg
-.installed.cfg
-.eggs/
-downloads/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-share/python-wheels/
-MANIFEST
-
-# Virtual Environments
-venv/
-env/
-.env
-.venv/
-ENV/
-env.bak/
-venv.bak/
-
-# IDE
-.idea/
-.vscode/
-.spyderproject
-.spyproject
-.ropeproject
-
-# Testing and Coverage
-.coverage
-.coverage.*
-htmlcov/
-.pytest_cache/
-.tox/
-.nox/
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-cover/
-
-# Logs and Databases
-*.log
-*.db
-db.sqlite3
-db.sqlite3-journal
-pip-log.txt
-
-# System Files
-.DS_Store
-Thumbs.db
-desktop.ini
-*.swp
-*.swo
-*.bak
-*.tmp
-*~
-
-# Build and Documentation
-docs/_build/
-.pybuilder/
-target/
-instance/
-.webassets-cache
-.pdm.toml
-.pdm-python
-.pdm-build/
-__pypackages__/
-
-# Other
-*.mo
-*.pot
-*.sage.py
-.mypy_cache/
-.dmypy.json
-dmypy.json
-.pyre/
-.pytype/
-cython_debug/
-.ipynb_checkpoints

examples/deployment/modal-example/README.md

@@ -1,37 +0,0 @@
-# Deploying Pipecat to Modal.com
-
-Barebones deployment example for [modal.com](https://www.modal.com)
-
-1. Install dependencies
-
-```bash
-python -m venv venv
-source venv/bin/active # or OS equivalent
-pip install -r requirements.txt
-```
-
-2. Setup .env
-
-```bash
-cp env.example .env
-```
-
-Alternatively, you can configure your Modal app to use [secrets](https://modal.com/docs/guide/secrets)
-
-3. Test the app locally
-
-```bash
-modal serve app.py
-```
-
-4. Deploy to production
-
-```bash
-modal deploy app.py
-```
-
-## Configuration options
-
-This app sets some sensible defaults for reducing cold starts, such as `minkeep_warm=1`, which will keep at least 1 warm instance ready for your bot function.
-
-It has been configured to only allow a concurrency of 1 (`max_inputs=1`) as each user will require their own running function.

examples/deployment/modal-example/app.py

@@ -1,74 +0,0 @@
-import os
-
-import aiohttp
-import modal
-from bot import _voice_bot_process
-from fastapi import HTTPException
-from fastapi.responses import JSONResponse
-from loguru import logger
-
-MAX_SESSION_TIME = 15 * 60  # 15 minutes
-
-app = modal.App("pipecat-modal")
-
-
-image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
-    "requirements.txt"
-)
-
-
-@app.function(
-    image=image,
-    cpu=1.0,
-    secrets=[modal.Secret.from_dotenv()],
-    keep_warm=1,
-    enable_memory_snapshot=True,
-    max_inputs=1,  # Do not reuse instances across requests
-    retries=0,
-)
-def launch_bot_process(room_url: str, token: str):
-    _voice_bot_process(room_url, token)
-
-
-@app.function(
-    image=image,
-    secrets=[modal.Secret.from_dotenv()],
-)
-@modal.web_endpoint(method="POST")
-async def start():
-    from pipecat.transports.services.helpers.daily_rest import (
-        DailyRESTHelper,
-        DailyRoomParams,
-    )
-
-    logger.info("Request received")
-
-    async with aiohttp.ClientSession() as session:
-        daily_rest_helper = DailyRESTHelper(
-            daily_api_key=os.getenv("DAILY_API_KEY", ""),
-            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
-            aiohttp_session=session,
-        )
-
-        # Create new Daily room
-        room = await daily_rest_helper.create_room(DailyRoomParams())
-        if not room.url:
-            raise HTTPException(
-                status_code=500,
-                detail="Unable to create room",
-            )
-        logger.info(f"Created room: {room.url}")
-
-        # Create bot token for room
-        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
-        if not token:
-            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
-
-        logger.info(f"Bot token created: {token}")
-
-        # Spawn a new bot process
-        launch_bot_process.spawn(room_url=room.url, token=token)
-
-        # Return room URL to the user to join
-        # Note: in production, you would want to return a token to the user
-        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -1,90 +0,0 @@
-import asyncio
-import os
-import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token: str):
-    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.services.cartesia import CartesiaTTSService
-    from pipecat.services.openai import OpenAILLMService
-    from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-    transport = DailyTransport(
-        room_url,
-        token,
-        "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"
-    )
-
-    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(),
-            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"])
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
-
-    @transport.event_handler("on_participant_left")
-    async def on_participant_left(transport, participant, reason):
-        await task.queue_frame(EndFrame())
-
-    runner = PipelineRunner()
-
-    await runner.run(task)
-
-
-def _voice_bot_process(room_url: str, token: str):
-    asyncio.run(main(room_url, token))

examples/deployment/modal-example/env.example

@@ -1,3 +0,0 @@
-DAILY_API_KEY=
-OPENAI_API_KEY=
-CARTESIA_API_KEY=

examples/deployment/modal-example/requirements.txt

@@ -1,5 +0,0 @@
-python-dotenv==1.0.1
-modal==0.65.48
-pipecat-ai[daily,silero,cartesia,openai]==0.0.48
-fastapi==0.115.4
-aiohttp==3.11.9

examples/dialin-chatbot/bot_daily.py

@@ -1,20 +1,23 @@
-import argparse
 import asyncio
 import os
 import sys
+import argparse
 
-from dotenv import load_dotenv
-from loguru import logger
-
-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.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 DailyDialinSettings, DailyParams, DailyTransport
+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)
 
@@ -62,17 +65,17 @@ 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(),
+            tma_in,
             llm,
             tts,
             transport.output(),
-            context_aggregator.assistant(),
+            tma_out,
         ]
     )
 
@@ -80,7 +83,7 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
 
     @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

@@ -7,14 +7,14 @@ provisioning a room and starting a Pipecat bot in response.
 Refer to README for more information.
 """
 
-import argparse
+import aiohttp
 import os
+import argparse
 import subprocess
+
 from contextlib import asynccontextmanager
 
-import aiohttp
-from dotenv import load_dotenv
-from fastapi import FastAPI, HTTPException, Request
+from fastapi import FastAPI, Request, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, PlainTextResponse
 from twilio.twiml.voice_response import VoiceResponse
@@ -22,11 +22,13 @@ from twilio.twiml.voice_response import VoiceResponse
 from pipecat.transports.services.helpers.daily_rest import (
     DailyRESTHelper,
     DailyRoomObject,
-    DailyRoomParams,
     DailyRoomProperties,
     DailyRoomSipParams,
+    DailyRoomParams,
 )
 
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 
@@ -106,9 +108,11 @@ async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
     # 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(

examples/dialin-chatbot/bot_twilio.py

@@ -1,21 +1,26 @@
-import argparse
 import asyncio
 import os
 import sys
+import argparse
 
-from dotenv import load_dotenv
-from loguru import logger
-from twilio.rest import Client
-
-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.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)
 
@@ -64,17 +69,17 @@ 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(),
+            tma_in,
             llm,
             tts,
             transport.output(),
-            context_aggregator.assistant(),
+            tma_out,
         ]
     )
 
@@ -82,7 +87,7 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
 
     @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/foundational/01-say-one-thing.py

@@ -5,20 +5,22 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
+from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
 from runner import configure
 
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
-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.daily import DailyParams, DailyTransport
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -34,7 +36,7 @@ async def main():
             room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
         )
 
-        tts = CartesiaTTSService(
+        tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
@@ -45,12 +47,10 @@ 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", "")
-            await task.queue_frames(
-                [TTSSpeakFrame(f"Hello there, {participant_name}!"), EndFrame()]
-            )
+        @transport.event_handler("on_participant_joined")
+        async def on_new_participant_joined(transport, participant):
+            participant_name = participant["info"]["userName"] or ""
+            await task.queue_frames([TextFrame(f"Hello there, {participant_name}!"), EndFrame()])
 
         await runner.run(task)
 

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

@@ -5,13 +5,11 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
 
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
@@ -19,6 +17,10 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.audio import LocalAudioTransport
 
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -26,24 +28,25 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 async def main():
-    transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
+    async with aiohttp.ClientSession() as session:
+        transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
 
-    tts = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-    )
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    pipeline = Pipeline([tts, transport.output()])
+        pipeline = Pipeline([tts, transport.output()])
 
-    task = PipelineTask(pipeline)
+        task = PipelineTask(pipeline)
 
-    async def say_something():
-        await asyncio.sleep(1)
-        await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
+        async def say_something():
+            await asyncio.sleep(1)
+            await task.queue_frame(TextFrame("Hello there!"))
 
-    runner = PipelineRunner()
+        runner = PipelineRunner()
 
-    await asyncio.gather(runner.run(task), say_something())
+        await asyncio.gather(runner.run(task), say_something())
 
 
 if __name__ == "__main__":

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

@@ -5,7 +5,7 @@ import sys
 
 import aiohttp
 from dotenv import load_dotenv
-from livekit import api
+from livekit import api  # pip install livekit-api
 from loguru import logger
 
 from pipecat.frames.frames import TextFrame
@@ -78,7 +78,7 @@ async def main():
             url=url,
             token=token,
             room_name=room_name,
-            params=LiveKitParams(audio_out_enabled=True),
+            params=LiveKitParams(audio_out_enabled=True, audio_out_sample_rate=16000),
         )
 
         tts = CartesiaTTSService(

examples/foundational/01c-fastpitch.py

@@ -1,54 +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 EndFrame, TTSSpeakFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineTask
-from pipecat.services.riva import FastPitchTTSService
-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, _) = await configure(session)
-
-        transport = DailyTransport(
-            room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
-        )
-
-        tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-        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):
-            participant_name = participant.get("info", {}).get("userName", "")
-            await task.queue_frames([TTSSpeakFrame(f"Aloha, {participant_name}!"), EndFrame()])
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -5,22 +5,24 @@
 #
 
 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.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 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)
@@ -35,7 +37,7 @@ async def main():
             room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
         )
 
-        tts = CartesiaTTSService(
+        tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )

examples/foundational/03-still-frame.py

@@ -5,21 +5,23 @@
 #
 
 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.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.fal import FalImageGenService
 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)
@@ -49,11 +51,11 @@ 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

@@ -5,13 +5,11 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
-import tkinter as tk
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
+import tkinter as tk
 
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -21,6 +19,10 @@ from pipecat.services.fal import FalImageGenService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport
 
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -8,24 +8,27 @@
 # This example broken on latest pipecat and needs updating.
 #
 
+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.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
 from pipecat.pipeline.merge_pipeline import SequentialMergePipeline
 from pipecat.pipeline.pipeline import Pipeline
+
+from pipecat.frames.frames import EndPipeFrame, LLMMessagesFrame, TextFrame
 from pipecat.pipeline.task import PipelineTask
 from pipecat.services.azure import AzureLLMService, AzureTTSService
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.transport_services import TransportServiceOutput
 from pipecat.services.transports.daily_transport import DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,17 +5,14 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
+
 from dataclasses import dataclass
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
 from pipecat.frames.frames import (
-    DataFrame,
+    AppFrame,
     Frame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
@@ -25,13 +22,19 @@ 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.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.services.cartesia import CartesiaHttpTTSService
-from pipecat.services.fal import FalImageGenService
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.fal import FalImageGenService
 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)
@@ -39,7 +42,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 @dataclass
-class MonthFrame(DataFrame):
+class MonthFrame(AppFrame):
     month: str
 
     def __str__(self):

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

@@ -4,22 +4,20 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
-import tkinter as tk
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
+import tkinter as tk
 
 from pipecat.frames.frames import (
     Frame,
-    LLMMessagesFrame,
     OutputAudioRawFrame,
-    TextFrame,
     TTSAudioRawFrame,
     URLImageRawFrame,
+    LLMMessagesFrame,
+    TextFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -28,11 +26,15 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.sentence import SentenceAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia import CartesiaHttpTTSService
-from pipecat.services.fal import FalImageGenService
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.fal import FalImageGenService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
 
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -80,7 +82,6 @@ async def main():
                         self.frame = OutputAudioRawFrame(
                             bytes(self.audio), frame.sample_rate, frame.num_channels
                         )
-                    await self.push_frame(frame, direction)
 
             class ImageGrabber(FrameProcessor):
                 def __init__(self):
@@ -92,7 +93,6 @@ 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")
 

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

@@ -5,30 +5,35 @@
 #
 
 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,
+    ProcessingMetricsData,
+    LLMUsageMetricsData,
     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)
 
@@ -87,30 +92,26 @@ 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.",
             },
         ]
-
-        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(),
+                tma_in,
                 llm,
                 tts,
                 ml,
                 transport.output(),
-                context_aggregator.assistant(),
+                tma_out,
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(enable_metrics=True, enable_usage_metrics=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.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,25 +5,32 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
 
-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 Frame, OutputImageRawFrame, SystemFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.processors.aggregators.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.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from pipecat.transports.services.daily import DailyParams
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -98,8 +105,8 @@ 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"),
@@ -110,11 +117,11 @@ async def main():
             [
                 transport.input(),
                 image_sync_aggregator,
-                context_aggregator.user(),
+                tma_in,
                 llm,
                 tts,
                 transport.output(),
-                context_aggregator.assistant(),
+                tma_out,
             ]
         )
 
@@ -122,8 +129,8 @@ async def main():
 
         @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

@@ -1,101 +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 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.vad.silero import SileroVAD
-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_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

@@ -5,23 +5,28 @@
 #
 
 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.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)
 
@@ -59,17 +64,17 @@ 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(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -85,7 +90,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,23 +5,28 @@
 #
 
 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)
 
@@ -64,17 +69,17 @@ 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(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -82,7 +87,7 @@ 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"])
             # Kick off the conversation.
             await task.queue_frames([LLMMessagesFrame(messages)])
 

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

@@ -9,16 +9,7 @@ import os
 import sys
 
 import aiohttp
-from dotenv import load_dotenv
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_community.chat_message_histories import ChatMessageHistory
-from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.runnables.history import RunnableWithMessageHistory
-from langchain_openai import ChatOpenAI
-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
@@ -30,6 +21,19 @@ from pipecat.processors.aggregators.llm_response import (
 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
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain_openai import ChatOpenAI
+
+from loguru import logger
+
+from runner import configure
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -105,7 +109,7 @@ 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

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

@@ -1,105 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-
-import aiohttp
-from deepgram import LiveOptions
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.frames.frames import (
-    BotInterruptionFrame,
-    LLMMessagesFrame,
-    StopInterruptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-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, DeepgramTTSService
-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, _) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Respond bot",
-            DailyParams(
-                audio_in_enabled=True,
-                audio_out_enabled=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-            live_options=LiveOptions(vad_events=True, utterance_end_ms="1000"),
-        )
-
-        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        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))
-
-        @stt.event_handler("on_speech_started")
-        async def on_speech_started(stt, *args, **kwargs):
-            await task.queue_frames([BotInterruptionFrame(), UserStartedSpeakingFrame()])
-
-        @stt.event_handler("on_utterance_end")
-        async def on_utterance_end(stt, *args, **kwargs):
-            await task.queue_frames([StopInterruptionFrame(), UserStoppedSpeakingFrame()])
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            # 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/07c-interruptible-deepgram.py

@@ -13,15 +13,18 @@ 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
 
 load_dotenv(override=True)
 
@@ -31,11 +34,11 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        (room_url, _) = await configure(session)
+        (room_url, token) = await configure(session)
 
         transport = DailyTransport(
             room_url,
-            None,
+            token,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
@@ -58,18 +61,18 @@ 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
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -77,6 +80,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -13,15 +13,18 @@ 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.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
 
 load_dotenv(override=True)
 
@@ -59,17 +62,17 @@ 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(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -85,7 +88,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -4,25 +4,29 @@
 # 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)
 
@@ -40,6 +44,7 @@ async def main():
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
+                audio_out_sample_rate=16000,
                 transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
@@ -49,8 +54,7 @@ async def main():
         tts = PlayHTTTSService(
             user_id=os.getenv("PLAYHT_USER_ID"),
             api_key=os.getenv("PLAYHT_API_KEY"),
-            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
-            params=PlayHTTTSService.InputParams(language=Language.EN),
+            voice_url="s3://voice-cloning-zero-shot/801a663f-efd0-4254-98d0-5c175514c3e8/jennifer/manifest.json",
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
@@ -62,33 +66,25 @@ 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
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                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."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -4,23 +4,29 @@
 # 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.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
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -38,6 +44,7 @@ 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,
@@ -67,18 +74,18 @@ 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
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -86,7 +93,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -4,23 +4,29 @@
 # 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, 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)
 
@@ -56,17 +62,17 @@ 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(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -74,7 +80,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,24 +5,29 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
-import time
 
-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.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
+
+from loguru import logger
+import time
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -65,18 +70,17 @@ 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.",
             },
         ]
-
-        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
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -84,7 +88,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,23 +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 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)
 
@@ -48,6 +54,7 @@ async def main():
         tts = XTTSService(
             aiohttp_session=session,
             voice_id="Claribel Dervla",
+            language="en",
             base_url="http://localhost:8000",
         )
 
@@ -60,17 +67,17 @@ 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(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -78,7 +85,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,24 +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 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)
 
@@ -64,18 +69,18 @@ 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
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -83,16 +88,11 @@ 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"])
             # Kick off the conversation.
             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

@@ -4,24 +4,29 @@
 # 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.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)
 
@@ -57,17 +62,17 @@ 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(),  # User respones
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -75,7 +80,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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

@@ -5,23 +5,28 @@
 #
 
 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.services.ai_services import OpenAILLMContext
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
 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)
 
@@ -52,7 +57,7 @@ async def main():
 
         llm = TogetherLLMService(
             api_key=os.getenv("TOGETHER_API_KEY"),
-            model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
+            model=os.getenv("TOGETHER_MODEL"),
             params=TogetherLLMService.InputParams(
                 temperature=1.0,
                 top_p=0.9,
@@ -67,36 +72,29 @@ async def main():
         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.",
+                "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)
-        user_aggregator = context_aggregator.user()
-        assistant_aggregator = context_aggregator.assistant()
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
 
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
-                user_aggregator,  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                assistant_aggregator,  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True, enable_metrics=True, enable_usage_metrics=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.
             await task.queue_frames([LLMMessagesFrame(messages)])
 

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

@@ -13,7 +13,6 @@ from dotenv import load_dotenv
 from loguru import logger
 from runner import configure
 
-from pipecat.audio.filters.krisp_filter import KrispFilter
 from pipecat.frames.frames import LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -22,7 +21,8 @@ from pipecat.processors.aggregators.llm_response import (
     LLMAssistantResponseAggregator,
     LLMUserResponseAggregator,
 )
-from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
+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
 from pipecat.vad.silero import SileroVADAnalyzer
@@ -43,16 +43,22 @@ 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,
-                audio_in_filter=KrispFilter(),
             ),
         )
 
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+        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")
 
@@ -82,6 +88,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

examples/foundational/07m-interruptible-polly.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 PollyTTSService
-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, _) = 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 = PollyTTSService(
-            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=PollyTTSService.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

@@ -13,16 +13,19 @@ 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
 from pipecat.services.google import GoogleTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
 
 load_dotenv(override=True)
 
@@ -32,11 +35,11 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        (room_url, _) = await configure(session)
+        (room_url, token) = await configure(session)
 
         transport = DailyTransport(
             room_url,
-            None,
+            token,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,
@@ -50,6 +53,7 @@ async def main():
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
         tts = GoogleTTSService(
+            credentials=os.getenv("GOOGLE_CREDENTIALS"),
             voice_id="en-US-Neural2-J",
             params=GoogleTTSService.InputParams(language="en-US", rate="1.05"),
         )
@@ -63,18 +67,18 @@ 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 respones
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -82,7 +86,7 @@ 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"])
             # Kick off the conversation.
             messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])

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/07q-interruptible-rime.py

@@ -1,100 +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.openai import OpenAILLMService
-from pipecat.services.rime import RimeHttpTTSService
-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 = RimeHttpTTSService(
-            api_key=os.getenv("RIME_API_KEY", ""),
-            voice_id="rex",
-            params=RimeHttpTTSService.InputParams(reduce_latency=True),
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            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/07r-interruptible-riva-nim.py

@@ -1,92 +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.nim import NimLLMService
-from pipecat.services.riva import FastPitchTTSService, ParakeetSTTService
-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, _) = 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 = ParakeetSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-        llm = NimLLMService(
-            api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
-        )
-
-        tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
-
-        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):
-            # 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/07s-interruptible-google-audio-in.py

@@ -1,277 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from dataclasses import dataclass
-
-import aiohttp
-import google.ai.generativelanguage as glm
-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,
-    InputAudioRawFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    StartInterruptionFrame,
-    TextFrame,
-    TranscriptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-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.frame_processor import FrameProcessor
-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")
-
-marker = "|----|"
-system_message = f"""
-You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses.
-
-You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
-asked to transcribe what the user said, output an exact, word-for-word transcription.
-
-Your output will be converted to audio so don't include special characters in your answers.
-
-Each time you answer, you should respond in three parts.
-
-1. Transcribe exactly what the user said.
-2. Output the separator field '{marker}'.
-3. Respond to the user's input in a helpful, creative way using only simple text and punctuation.
-
-Example:
-
-User: How many ounces are in a pound?
-
-You: How many ounces are in a pound?
-{marker}
-There are 16 ounces in a pound.
-"""
-
-
-@dataclass
-class MagicDemoTranscriptionFrame(Frame):
-    text: str
-
-
-class UserAudioCollector(FrameProcessor):
-    def __init__(self, context, user_context_aggregator):
-        super().__init__()
-        self._context = context
-        self._user_context_aggregator = user_context_aggregator
-        self._audio_frames = []
-        self._start_secs = 0.2  # this should match VAD start_secs (hardcoding for now)
-        self._user_speaking = False
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, TranscriptionFrame):
-            # We could gracefully handle both audio input and text/transcription input ...
-            # but let's leave that as an exercise to the reader. :-)
-            return
-        if isinstance(frame, UserStartedSpeakingFrame):
-            self._user_speaking = True
-        elif isinstance(frame, UserStoppedSpeakingFrame):
-            self._user_speaking = False
-            self._context.add_audio_frames_message(audio_frames=self._audio_frames)
-            await self._user_context_aggregator.push_frame(
-                self._user_context_aggregator.get_context_frame()
-            )
-        elif isinstance(frame, InputAudioRawFrame):
-            if self._user_speaking:
-                self._audio_frames.append(frame)
-            else:
-                # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
-                # frames as necessary. Assume all audio frames have the same duration.
-                self._audio_frames.append(frame)
-                frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate
-                buffer_duration = frame_duration * len(self._audio_frames)
-                while buffer_duration > self._start_secs:
-                    self._audio_frames.pop(0)
-                    buffer_duration -= frame_duration
-
-        await self.push_frame(frame, direction)
-
-
-class TranscriptExtractor(FrameProcessor):
-    def __init__(self, context):
-        super().__init__()
-        self._context = context
-        self._accumulator = ""
-        self._processing_llm_response = False
-        self._accumulating_transcript = False
-
-    def reset(self):
-        self._accumulator = ""
-        self._processing_llm_response = False
-        self._accumulating_transcript = False
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, LLMFullResponseStartFrame):
-            self._processing_llm_response = True
-            self._accumulating_transcript = True
-        elif isinstance(frame, TextFrame) and self._processing_llm_response:
-            if self._accumulating_transcript:
-                text = frame.text
-                split_index = text.find(marker)
-                if split_index < 0:
-                    self._accumulator += frame.text
-                    # do not push this frame
-                    return
-                else:
-                    self._accumulating_transcript = False
-                    self._accumulator += text[:split_index]
-                    frame.text = text[split_index + len(marker) :]
-            await self.push_frame(frame)
-            return
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self.push_frame(MagicDemoTranscriptionFrame(text=self._accumulator.strip()))
-            self.reset()
-
-        await self.push_frame(frame, direction)
-
-
-class TanscriptionContextFixup(FrameProcessor):
-    def __init__(self, context):
-        super().__init__()
-        self._context = context
-        self._transcript = "THIS IS A TRANSCRIPT"
-
-    def swap_user_audio(self):
-        if not self._transcript:
-            return
-        message = self._context.messages[-2]
-        last_part = message.parts[-1]
-        if (
-            message.role == "user"
-            and last_part.inline_data
-            and last_part.inline_data.mime_type == "audio/wav"
-        ):
-            self._context.messages[-2] = glm.Content(
-                role="user", parts=[glm.Part(text=self._transcript)]
-            )
-
-    def add_transcript_back_to_inference_output(self):
-        if not self._transcript:
-            return
-        message = self._context.messages[-1]
-        last_part = message.parts[-1]
-        if message.role == "model" and last_part.text:
-            self._context.messages[-1].parts[-1].text += f"\n\n{marker}\n{self._transcript}\n"
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, MagicDemoTranscriptionFrame):
-            self._transcript = frame.text
-        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(
-            frame, StartInterruptionFrame
-        ):
-            self.swap_user_audio()
-            self.add_transcript_back_to_inference_output()
-            self._transcript = ""
-
-        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,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                # No transcription at all. just audio input to Gemini!
-                # transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        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",
-            # model="gemini-exp-1114",
-            api_key=os.getenv("GOOGLE_API_KEY"),
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": system_message,
-            },
-            {
-                "role": "user",
-                "content": "Start by saying hello.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-        audio_collector = UserAudioCollector(context, context_aggregator.user())
-        pull_transcript_out_of_llm_output = TranscriptExtractor(context)
-        fixup_context_messages = TanscriptionContextFixup(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                audio_collector,
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                pull_transcript_out_of_llm_output,
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-                fixup_context_messages,
-            ]
-        )
-
-        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([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/07t-interruptible-fish.py

@@ -1,99 +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.fish import FishAudioTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = FishAudioTTSService(
-            api_key=os.getenv("FISH_API_KEY"),
-            model="4ce7e917cedd4bc2bb2e6ff3a46acaa1",  # Barack Obama
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            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

@@ -1,19 +1,20 @@
+from typing import Tuple
+import aiohttp
 import asyncio
 import logging
 import os
-from typing import Tuple
-
-import aiohttp
-from dotenv import load_dotenv
-from runner import configure
-
-from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
-from pipecat.pipeline.pipeline import Pipeline
 from pipecat.processors.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.transports.services.daily import DailyTransport
+from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesFrame, TextFrame
+
+from runner import configure
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 

examples/foundational/09-mirror.py

@@ -4,14 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import sys
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
 from pipecat.frames.frames import (
     Frame,
     InputAudioRawFrame,
@@ -23,7 +19,13 @@ 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 DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyTransport, DailyParams
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -61,7 +63,6 @@ async def main():
             "Test",
             DailyParams(
                 audio_in_enabled=True,
-                audio_in_sample_rate=24000,
                 audio_out_enabled=True,
                 camera_out_enabled=True,
                 camera_out_is_live=True,
@@ -72,7 +73,7 @@ async def main():
 
         @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()])
 

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

@@ -4,14 +4,11 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import sys
-import tkinter as tk
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
+import tkinter as tk
 
 from pipecat.frames.frames import (
     Frame,
@@ -28,6 +25,12 @@ from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.tk import TkLocalTransport
 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)
@@ -62,7 +65,7 @@ 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(
@@ -78,7 +81,7 @@ async def main():
 
         @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()])
 

examples/foundational/10-wake-phrase.py

@@ -5,23 +5,28 @@
 #
 
 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.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)
 
@@ -60,19 +65,18 @@ async def main():
         ]
 
         hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
-
-        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
                 hey_robot_filter,  # Filter out speech not directed at the robot
-                context_aggregator.user(),  # User responses
+                tma_in,  # User responses
                 llm,  # LLM
                 tts,  # TTS
                 transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
+                tma_out,  # Assistant spoken responses
             ]
         )
 
@@ -80,7 +84,7 @@ 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"])
             await tts.say("Hi! If you want to talk to me, just say 'Hey Robot'.")
 
         runner = PipelineRunner()

examples/foundational/11-sound-effects.py

@@ -4,34 +4,37 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
 import wave
 
-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,
     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,
-    OpenAILLMContextFrame,
+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 CartesiaTTSService
+from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
@@ -72,7 +75,7 @@ class InboundSoundEffectWrapper(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, OpenAILLMContextFrame):
+        if isinstance(frame, LLMMessagesFrame):
             await self.push_frame(sounds["ding2.wav"])
             # In case anything else downstream needs it
             await self.push_frame(frame, direction)
@@ -98,7 +101,7 @@ async def main():
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
 
-        tts = CartesiaTTSService(
+        tts = CartesiaHttpTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
@@ -110,8 +113,8 @@ 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")
@@ -120,7 +123,7 @@ async def main():
         pipeline = Pipeline(
             [
                 transport.input(),
-                context_aggregator.user(),
+                tma_in,
                 in_sound,
                 fl2,
                 llm,
@@ -128,13 +131,13 @@ async def main():
                 tts,
                 out_sound,
                 transport.output(),
-                context_aggregator.assistant(),
+                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

@@ -5,15 +5,10 @@
 #
 
 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, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -24,6 +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)
 
@@ -82,8 +84,8 @@ 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(

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

@@ -5,15 +5,10 @@
 #
 
 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, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -24,6 +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)
 
@@ -84,8 +86,8 @@ 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(

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

@@ -5,15 +5,10 @@
 #
 
 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, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -24,6 +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)
 
@@ -81,8 +83,8 @@ 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(

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

@@ -5,15 +5,10 @@
 #
 
 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, TextFrame, UserImageRequestFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -21,9 +16,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.anthropic import AnthropicLLMService
 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)
 
@@ -76,13 +78,16 @@ async def main():
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+            params=CartesiaTTSService.InputParams(
+                sample_rate=16000,
+            ),
         )
 
         @transport.event_handler("on_first_participant_joined")
         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(

examples/foundational/13-whisper-transcription.py

@@ -4,14 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 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
@@ -20,6 +16,12 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.whisper import WhisperSTTService
 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)

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

@@ -7,9 +7,6 @@
 import asyncio
 import sys
 
-from dotenv import load_dotenv
-from loguru import logger
-
 from pipecat.frames.frames import Frame, TranscriptionFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -19,6 +16,10 @@ from pipecat.services.whisper import WhisperSTTService
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.local.audio import LocalAudioTransport
 
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -4,23 +4,25 @@
 # 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.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.deepgram import DeepgramSTTService, Language, LiveOptions
+from pipecat.services.deepgram import DeepgramSTTService
 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)
@@ -43,10 +45,7 @@ async def main():
             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

@@ -14,13 +14,15 @@ from loguru import logger
 from openai.types.chat import ChatCompletionToolParam
 from runner import configure
 
-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
 
 load_dotenv(override=True)
 
@@ -33,7 +35,7 @@ async def start_fetch_weather(function_name, llm, context):
     # 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."))
+    await llm.push_frame(TextFrame("Let me check on that.  "))
     logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
 
 
@@ -67,6 +69,9 @@ async def main():
         # sent to the same callback with an additional function_name parameter.
         llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
 
+        fl_in = FrameLogger("Inner")
+        fl_out = FrameLogger("Outer")
+
         tools = [
             ChatCompletionToolParam(
                 type="function",
@@ -103,30 +108,24 @@ async def main():
 
         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()
 

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

@@ -1,133 +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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-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
-
-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 task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

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

@@ -1,164 +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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-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
-
-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

@@ -1,174 +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.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
-
-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",
-            # model="gemini-exp-1114",
-            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,
-            ),
-        )
-
-        @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/14f-function-calling-groq.py

@@ -1,139 +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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-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.groq import GroqLLMService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-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 = GroqLLMService(
-            api_key=os.getenv("GROQ_API_KEY"), model="llama3-groq-70b-8192-tool-use-preview"
-        )
-        # 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",
-                            },
-                            "unit": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "The temperature unit to use. Infer this from the users location.",
-                            },
-                        },
-                        "required": ["location"],
-                    },
-                },
-            )
-        ]
-        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,
-            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([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/14g-function-calling-grok.py

@@ -1,137 +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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-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.grok import GrokLLMService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-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 = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
-        # 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,
-            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([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/14h-function-calling-azure.py

@@ -1,141 +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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
-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.azure import AzureLLMService
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMContext
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-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 = AzureLLMService(
-            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        )
-        # 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,
-            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([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())