Initial commit for full stack chatbot

Co-authored-by: Mark Backman <m.backman@gmail.com>

.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

.gitignore

@@ -28,11 +28,4 @@ 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

@@ -5,163 +5,16 @@ 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.
+- `GroqLLMService` and `GrokLLMService` for Groq and Grok API integration, with
+  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`.
+  and Fireworks: `14f-function-calling-groq.py`, `14g-function-calling-grok.py`,
+  `14h-function-calling-azure.py`, and `14i-function-calling-fireworks.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
@@ -180,16 +33,8 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
 
 ### 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
+- All input frames (text, audio, image, etc.) 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
@@ -204,9 +49,6 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
 - 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
@@ -214,27 +56,6 @@ async def on_audio_data(processor, audio, sample_rate, num_channels):
 
 ### 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.
 

README.md

@@ -2,7 +2,7 @@
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
 </div></h1>
 
-[![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>
+[![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
 Pipecat is 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.
 
@@ -55,19 +55,19 @@ pip install "pipecat-ai[option,...]"
 
 Available options include:
 
-| 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]"`   |
+| Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | Install Command Example               |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------- |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/api-reference/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/api-reference/services/stt/azure), [Deepgram](https://docs.pipecat.ai/api-reference/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/api-reference/services/stt/gladia), [Whisper](https://docs.pipecat.ai/api-reference/services/stt/whisper)                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[deepgram]"`  |
+| LLMs                | [Anthropic](https://docs.pipecat.ai/api-reference/services/llm/anthropic), [Azure](https://docs.pipecat.ai/api-reference/services/llm/azure), [Fireworks AI](https://docs.pipecat.ai/api-reference/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/api-reference/services/llm/gemini), [Grok](https://docs.pipecat.ai/api-reference/services/llm/grok), [Groq](https://docs.pipecat.ai/api-reference/services/llm/groq) [Ollama](https://docs.pipecat.ai/api-reference/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/api-reference/services/llm/openai), [Together AI](https://docs.pipecat.ai/api-reference/services/llm/together)                                                                                                                            | `pip install "pipecat-ai[openai]"`    |
+| Text-to-Speech      | [AWS](https://docs.pipecat.ai/api-reference/services/tts/aws), [Azure](https://docs.pipecat.ai/api-reference/services/tts/azure), [Cartesia](https://docs.pipecat.ai/api-reference/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/api-reference/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/api-reference/services/tts/elevenlabs), [Google](https://docs.pipecat.ai/api-reference/services/tts/google), [LMNT](https://docs.pipecat.ai/api-reference/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/api-reference/services/tts/openai), [PlayHT](https://docs.pipecat.ai/api-reference/services/tts/playht), [Rime](https://docs.pipecat.ai/api-reference/services/tts/rime), [XTTS](https://docs.pipecat.ai/api-reference/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"`  |
+| Speech-to-Speech    | [OpenAI Realtime](https://docs.pipecat.ai/api-reference/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | `pip install "pipecat-ai[openai]"`    |
+| Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/api-reference/services/transport/daily), WebSocket, Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | `pip install "pipecat-ai[daily]"`     |
+| Video               | [Tavus](https://docs.pipecat.ai/api-reference/services/video/tavus)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | `pip install "pipecat-ai[tavus]"`     |
+| Vision & Image      | [Moondream](https://docs.pipecat.ai/api-reference/services/vision/moondream), [fal](https://docs.pipecat.ai/api-reference/services/image-generation/fal)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `pip install "pipecat-ai[moondream]"` |
+| Audio Processing    | [Silero VAD](https://docs.pipecat.ai/api-reference/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/api-reference/utilities/audio/krisp-filter), [Noisereduce](https://docs.pipecat.ai/api-reference/utilities/audio/noisereduce-filter)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[silero]"`    |
+| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/api-reference/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/api-reference/services/analytics/sentry)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | `pip install "pipecat-ai[canonical]"` |
 
-📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
+📚 [View full services documentation →](https://docs.pipecat.ai/api-reference/services/supported-services)
 
 ## Code examples
 
@@ -197,7 +197,9 @@ You can use [use-package](https://github.com/jwiegley/use-package) to install [e
   :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-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.
@@ -207,6 +209,7 @@ 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
@@ -221,16 +224,6 @@ Install the
 }
 ```
 
-### 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:

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/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"

dot-env.template

@@ -54,9 +54,5 @@ TAVUS_API_KEY=...
 TAVUS_REPLICA_ID=...
 TAVUS_PERSONA_ID=...
 
-# Simli
-SIMLI_API_KEY=...
-SIMLI_FACE_ID=...
-
-# Krisp
-KRISP_MODEL_PATH=...
+#Krisp
+KRISP_MODEL_PATH=...

examples/chatbot-audio-recording/bot.py

@@ -4,15 +4,15 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiofiles
 import asyncio
-import datetime
 import io
 import os
 import sys
-import wave
 
-import aiofiles
 import aiohttp
+import datetime
+import wave
 from dotenv import load_dotenv
 from loguru import logger
 from runner import configure

examples/deployment/flyio-example/bot.py

@@ -1,21 +1,22 @@
-import argparse
 import asyncio
 import os
 import sys
-
-from dotenv import load_dotenv
-from loguru import logger
+import argparse
 
 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.frames.frames import LLMMessagesFrame, EndFrame
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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)
 
 

examples/deployment/modal-example/app.py

@@ -2,11 +2,12 @@ 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
 
+from bot import _voice_bot_process
+
 MAX_SESSION_TIME = 15 * 60  # 15 minutes
 
 app = modal.App("pipecat-modal")

examples/deployment/modal-example/requirements.txt

@@ -2,4 +2,4 @@ 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
+aiohttp==3.10.10

examples/dialin-chatbot/bot_daily.py

@@ -1,20 +1,21 @@
-import argparse
 import asyncio
 import os
 import sys
-
-from dotenv import load_dotenv
-from loguru import logger
+import argparse
 
 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.frames.frames import LLMMessagesFrame, EndFrame
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyDialinSettings
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 

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)
 
 

examples/dialin-chatbot/bot_twilio.py

@@ -1,22 +1,24 @@
-import argparse
 import asyncio
 import os
 import sys
-
-from dotenv import load_dotenv
-from loguru import logger
-from twilio.rest import Client
+import argparse
 
 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.frames.frames import LLMMessagesFrame, EndFrame
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from twilio.rest import Client
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/01-say-one-thing.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, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.runner import PipelineRunner
 from pipecat.services.cartesia import CartesiaTTSService
 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/01a-local-audio.py

@@ -5,12 +5,10 @@
 #
 
 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.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -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)

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

@@ -4,9 +4,6 @@ import os
 import sys
 
 import aiohttp
-from dotenv import load_dotenv
-from livekit import api
-from loguru import logger
 
 from pipecat.frames.frames import TextFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -15,6 +12,12 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.livekit import LiveKitParams, LiveKitTransport
 
+from livekit import api
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,14 +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.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -21,6 +17,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/03-still-frame.py

@@ -5,14 +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.frames.frames import EndFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -20,6 +16,12 @@ 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)

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

@@ -5,14 +5,11 @@
 #
 
 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 dataclasses import dataclass
 
 from pipecat.frames.frames import (
     DataFrame,
@@ -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)

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)

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

@@ -5,14 +5,11 @@
 #
 
 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
@@ -23,7 +20,14 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.services.cartesia import CartesiaHttpTTSService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.transports.services.daily import DailyTransport
+
+from pipecat.transports.services.daily import DailyParams
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 

examples/foundational/07-interruptible-vad.py

@@ -5,24 +5,26 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
 from pipecat.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.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/07-interruptible.py

@@ -5,14 +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 LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -23,6 +19,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -9,14 +9,6 @@ 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
@@ -31,6 +23,18 @@ from pipecat.processors.frameworks.langchain import LangchainProcessor
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+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)
 
 

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/07d-interruptible-elevenlabs.py

@@ -11,6 +11,7 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -18,7 +19,6 @@ 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.elevenlabs import ElevenLabsTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport

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

@@ -4,15 +4,11 @@
 # 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
@@ -22,6 +18,13 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -11,6 +11,7 @@ import sys
 import aiohttp
 from dotenv import load_dotenv
 from loguru import logger
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from runner import configure
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -18,7 +19,6 @@ 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.transports.services.daily import DailyParams, DailyTransport
 

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

@@ -5,14 +5,9 @@
 #
 
 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
@@ -24,6 +19,13 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openpipe import OpenPipeLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+import time
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,14 +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 LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -23,6 +19,12 @@ from pipecat.services.openai import OpenAILLMService
 from pipecat.services.xtts import XTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -48,6 +50,7 @@ async def main():
         tts = XTTSService(
             aiohttp_session=session,
             voice_id="Claribel Dervla",
+            language="en",
             base_url="http://localhost:8000",
         )
 

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

@@ -4,15 +4,11 @@
 # 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
@@ -23,6 +19,12 @@ from pipecat.services.lmnt import LmntTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -19,7 +19,7 @@ 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.aws import AWSTTSService
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -48,12 +48,12 @@ async def main():
 
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
 
-        tts = PollyTTSService(
+        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=PollyTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
+            params=AWSTTSService.InputParams(engine="neural", language="en-GB", rate="1.05"),
         )
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

examples/foundational/07p-interruptible-google-audio-in.py

@@ -4,37 +4,38 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
-from dataclasses import dataclass
 
-import aiohttp
 import google.ai.generativelanguage as glm
+
+from dataclasses import dataclass
 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.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.google import GoogleLLMService
+from pipecat.processors.frame_processor import FrameProcessor
+from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.frames.frames import (
-    Frame,
-    InputAudioRawFrame,
-    LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    LLMFullResponseEndFrame,
+    InputAudioRawFrame,
+    Frame,
     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)
 

examples/foundational/07p-interruptible-krisp.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
@@ -26,6 +25,7 @@ from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 from pipecat.vad.silero import SileroVADAnalyzer
+from pipecat.audio.filters.krisp_filter import KrispFilter
 
 load_dotenv(override=True)
 

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/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)
 

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)

examples/foundational/10-wake-phrase.py

@@ -5,14 +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.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -23,6 +19,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/11-sound-effects.py

@@ -4,35 +4,35 @@
 # 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.openai_llm_context import OpenAILLMContext
 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 runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -72,7 +72,7 @@ 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 +98,7 @@ 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
         )

examples/foundational/12-describe-video.py

@@ -5,14 +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
@@ -25,6 +21,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.moondream import MoondreamService
 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/12a-describe-video-gemini-flash.py

@@ -5,14 +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
@@ -25,6 +21,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.google import GoogleLLMService
 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/12b-describe-video-gpt-4o.py

@@ -5,14 +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
@@ -25,6 +21,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,14 +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
@@ -21,10 +17,16 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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, LiveOptions, Language
 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/14-function-calling.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 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
@@ -22,6 +17,14 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,23 +5,25 @@
 #
 
 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.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.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,23 +5,25 @@
 #
 
 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.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.services.cartesia import CartesiaTTSService
+from pipecat.services.anthropic import AnthropicLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/14d-function-calling-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 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
@@ -22,6 +17,14 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMContext, OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/14e-function-calling-gemini.py

@@ -5,14 +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.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -22,6 +18,12 @@ from pipecat.services.google import GoogleLLMService
 from pipecat.services.openai import OpenAILLMContext
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/14j-function-calling-nim.py

@@ -1,140 +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.nim import NimLLMService
-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
-            # text_filter=MarkdownTextFilter(),
-        )
-
-        llm = NimLLMService(
-            api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct"
-        )
-        # 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": "Returns the current weather at a location, if one is specified, and defaults to the user's location.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "location": {
-                                "type": "string",
-                                "description": "The location to find the weather of, or if not provided, it's the default location.",
-                            },
-                            "format": {
-                                "type": "string",
-                                "enum": ["celsius", "fahrenheit"],
-                                "description": "Whether to use SI or USCS units (celsius or fahrenheit).",
-                            },
-                        },
-                        "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/14k-function-calling-cerebras.py

@@ -1,148 +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.cerebras import CerebrasLLMService
-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 = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b")
-        # 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 for a specific location. You MUST use this function whenever asked about 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. Use fahrenheit for US locations, celsius for others.",
-                            },
-                        },
-                        "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. 
-
-You have one functions available:
-
-1. get_current_weather is used to get current weather information.
-
-Infer whether to use Fahrenheit or Celsius automatically based on the location, unless the user specifies a preference.
-
-Start by asking me for my location. Then, use 'get_weather_current' to give me a forecast.
-
-        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,
-                report_only_initial_ttfb=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/15-switch-voices.py

@@ -4,20 +4,15 @@
 # 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 openai.types.chat import ChatCompletionToolParam
-from runner import configure
-
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
@@ -26,6 +21,14 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -5,29 +5,30 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
 
-import aiohttp
-from deepgram import LiveOptions
-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.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.frames.frames import LLMMessagesFrame, TTSUpdateSettingsFrame
 from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.filters.function_filter import FunctionFilter
 from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from openai.types.chat import ChatCompletionToolParam
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -60,16 +61,13 @@ async def main():
             "Pipecat",
             DailyParams(
                 audio_out_enabled=True,
+                transcription_enabled=True,
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
             ),
         )
 
-        stt = DeepgramSTTService(
-            api_key=os.getenv("DEEPGRAM_API_KEY"), live_options=LiveOptions(language="multi")
-        )
-
         english_tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
@@ -115,7 +113,6 @@ async def main():
         pipeline = Pipeline(
             [
                 transport.input(),  # Transport user input
-                stt,  # STT
                 context_aggregator.user(),  # User responses
                 llm,  # LLM
                 ParallelPipeline(  # TTS (bot will speak the chosen language)

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

@@ -5,14 +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 LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
@@ -24,6 +20,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/18-gstreamer-filesrc.py

@@ -4,14 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import argparse
 import asyncio
-import sys
-
 import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure_with_args
+import argparse
+import sys
 
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -19,6 +15,12 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure_with_args
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -51,7 +53,7 @@ async def main():
             out_params=GStreamerPipelineSource.OutputParams(
                 video_width=1280,
                 video_height=720,
-                audio_sample_rate=24000,
+                audio_sample_rate=16000,
                 audio_channels=1,
             ),
         )

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

@@ -5,12 +5,8 @@
 #
 
 import asyncio
-import sys
-
 import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
+import sys
 
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
@@ -18,6 +14,12 @@ from pipecat.pipeline.task import PipelineTask
 from pipecat.processors.gstreamer.pipeline_source import GStreamerPipelineSource
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

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

@@ -24,8 +24,9 @@ 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.services.cartesia import CartesiaTTSService
+
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

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

@@ -24,8 +24,9 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
 )
-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
 
 load_dotenv(override=True)

examples/foundational/20d-persistent-context-gemini.py

@@ -26,6 +26,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.google import GoogleLLMService
+
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 load_dotenv(override=True)

examples/foundational/21-tavus-layer.py

@@ -5,15 +5,12 @@
 #
 
 import asyncio
+import aiohttp
 import os
 import sys
+
 from typing import Any, Mapping
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-
-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
@@ -23,10 +20,15 @@ from pipecat.processors.aggregators.llm_response import (
     LLMUserResponseAggregator,
 )
 from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
+from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.tavus import TavusVideoService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 

examples/foundational/22-natural-conversation.py

@@ -5,18 +5,14 @@
 #
 
 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, TextFrame
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.gated_openai_llm_context import GatedOpenAILLMContextAggregator
@@ -30,6 +26,12 @@ from pipecat.services.openai import OpenAILLMService
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/22b-natural-conversation-proposal.py

@@ -4,48 +4,50 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 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 (
-    CancelFrame,
-    EndFrame,
-    Frame,
-    LLMMessagesFrame,
-    StartFrame,
-    StartInterruptionFrame,
-    StopInterruptionFrame,
-    SystemFrame,
-    TextFrame,
-    TranscriptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.frames.frames import LLMMessagesFrame, TextFrame
 from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.openai_llm_context import (
     OpenAILLMContext,
-    OpenAILLMContextFrame,
 )
-from pipecat.processors.filters.function_filter import FunctionFilter
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.user_idle_processor import UserIdleProcessor
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.deepgram import DeepgramSTTService
 from pipecat.services.openai import OpenAILLMService
-from pipecat.sync.base_notifier import BaseNotifier
 from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
+from pipecat.frames.frames import (
+    CancelFrame,
+    EndFrame,
+    Frame,
+    StartFrame,
+    StartInterruptionFrame,
+    StopInterruptionFrame,
+    SystemFrame,
+    TranscriptionFrame,
+    UserStartedSpeakingFrame,
+    UserStoppedSpeakingFrame,
+)
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
+from pipecat.sync.base_notifier import BaseNotifier
+from pipecat.processors.filters.function_filter import FunctionFilter
+from pipecat.processors.user_idle_processor import UserIdleProcessor
+
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -4,374 +4,90 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
 import time
 
-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 LLMMessagesFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.google import GoogleLLMService, GoogleLLMContext
+from pipecat.sync.event_notifier import EventNotifier
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
     Frame,
     InputAudioRawFrame,
-    LLMFullResponseEndFrame,
-    LLMFullResponseStartFrame,
-    LLMMessagesFrame,
     StartFrame,
     StartInterruptionFrame,
     StopInterruptionFrame,
     SystemFrame,
-    TextFrame,
     TranscriptionFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame,
 )
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import LLMResponseAggregator
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
-from pipecat.processors.filters.function_filter import FunctionFilter
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.user_idle_processor import UserIdleProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.services.google import GoogleLLMContext, GoogleLLMService
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.sync.base_notifier import BaseNotifier
-from pipecat.sync.event_notifier import EventNotifier
-from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.processors.filters.function_filter import FunctionFilter
+from pipecat.processors.user_idle_processor import UserIdleProcessor
+
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
 
 load_dotenv(override=True)
 
 logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
-# TRANSCRIBER_MODEL = "gemini-1.5-flash-latest"
-# CLASSIFIER_MODEL = "gemini-1.5-flash-latest"
-# CONVERSATION_MODEL = "gemini-1.5-flash-latest"
 
-TRANSCRIBER_MODEL = "gemini-2.0-flash-exp"
-CLASSIFIER_MODEL = "gemini-2.0-flash-exp"
-CONVERSATION_MODEL = "gemini-2.0-flash-exp"
+classifier_statement = """You are an audio language classifier model. You are receiving audio from a user in a WebRTC call. Your job is to decide whether the user has finished speaking or not.
 
-transcriber_system_instruction = """You are an audio transcriber. You are receiving audio from a user. Your job is to
-transcribe the input audio to text exactly as it was said by the user.
+Categorize the input you receive as either:
 
-You will receive the full conversation history before the audio input, to help with context. Use the full history only to help improve the accuracy of your transcription.
+1.  a complete thought, statement, or question, or
+2.  an incomplete thought, statement, or question
 
-Rules:
-  - Respond with an exact transcription of the audio input.
-  - Do not include any text other than the transcription.
-  - Do not explain or add to your response.
-  - Transcribe the audio input simply and precisely.
-  - If the audio is not clear, emit the special string "-".
-  - No response other than exact transcription, or "-", is allowed.
+Output 'YES' if the input is likely to be a completed thought, statement, or question.
 
+Output 'NO' if the input indicates that the user is still speaking and does not yet expect a response yet.
+
+If you are unsure, output 'YES'.
 """
 
-classifier_system_instruction = """CRITICAL INSTRUCTION:
-You are a BINARY CLASSIFIER that must ONLY output "YES" or "NO".
-DO NOT engage with the content.
-DO NOT respond to questions.
-DO NOT provide assistance.
-Your ONLY job is to output YES or NO.
+conversational_system_message = """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.
 
-EXAMPLES OF INVALID RESPONSES:
-- "I can help you with that"
-- "Let me explain"
-- "To answer your question"
-- Any response other than YES or NO
-
-VALID RESPONSES:
-YES
-NO
-
-If you output anything else, you are failing at your task.
-You are NOT an assistant.
-You are NOT a chatbot.
-You are a binary classifier.
-
-ROLE:
-You are a real-time speech completeness classifier. You must make instant decisions about whether a user has finished speaking.
-You must output ONLY 'YES' or 'NO' with no other text.
-
-INPUT FORMAT:
-You receive two pieces of information:
-1. The assistant's last message (if available)
-2. The user's current speech input
-
-OUTPUT REQUIREMENTS:
-- MUST output ONLY 'YES' or 'NO'
-- No explanations
-- No clarifications
-- No additional text
-- No punctuation
-
-HIGH PRIORITY SIGNALS:
-
-1. Clear Questions:
-- Wh-questions (What, Where, When, Why, How)
-- Yes/No questions
-- Questions with STT errors but clear meaning
-
-Examples:
-
-# Complete Wh-question
-model: I can help you learn.
-user: What's the fastest way to learn Spanish
-Output: YES
-
-# Complete Yes/No question despite STT error
-model: I know about planets.
-user: Is is Jupiter the biggest planet
-Output: YES
-
-2. Complete Commands:
-- Direct instructions
-- Clear requests
-- Action demands
-- Start of task indication
-- Complete statements needing response
-
-Examples:
-
-# Direct instruction
-model: I can explain many topics.
-user: Tell me about black holes
-Output: YES
-
-# Start of task indication
-user: Let's begin.
-Output: YES
-
-# Start of task indication
-user: Let's get started.
-Output: YES
-
-# Action demand
-model: I can help with math.
-user: Solve this equation x plus 5 equals 12
-Output: YES
-
-3. Direct Responses:
-- Answers to specific questions
-- Option selections
-- Clear acknowledgments with completion
-- Providing information with a known format - mailing address
-- Providing information with a known format - phone number
-- Providing information with a known format - credit card number
-
-Examples:
-
-# Specific answer
-model: What's your favorite color?
-user: I really like blue
-Output: YES
-
-# Option selection
-model: Would you prefer morning or evening?
-user: Morning
-Output: YES
-
-# Providing information with a known format - mailing address
-model: What's your address?
-user: 1234 Main Street
-Output: NO
-
-# Providing information with a known format - mailing address
-model: What's your address?
-user: 1234 Main Street Irving Texas 75063
-Output: Yes
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 41086753
-Output: NO
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 4108675309
-Output: Yes
-
-# Providing information with a known format - phone number
-model: What's your phone number?
-user: 220
-Output: No
-
-# Providing information with a known format - credit card number
-model: What's your credit card number?
-user: 5556
-Output: NO
-
-# Providing information with a known format - phone number
-model: What's your credit card number?
-user: 5556710454680800
-Output: Yes
-
-model: What's your credit card number?
-user: 414067
-Output: NO
-
-
-MEDIUM PRIORITY SIGNALS:
-
-1. Speech Pattern Completions:
-- Self-corrections reaching completion
-- False starts with clear ending
-- Topic changes with complete thought
-- Mid-sentence completions
-
-Examples:
-
-# Self-correction reaching completion
-model: What would you like to know?
-user: Tell me about... no wait, explain how rainbows form
-Output: YES
-
-# Topic change with complete thought
-model: The weather is nice today.
-user: Actually can you tell me who invented the telephone
-Output: YES
-
-# Mid-sentence completion
-model: Hello I'm ready.
-user: What's the capital of? France
-Output: YES
-
-2. Context-Dependent Brief Responses:
-- Acknowledgments (okay, sure, alright)
-- Agreements (yes, yeah)
-- Disagreements (no, nah)
-- Confirmations (correct, exactly)
-
-Examples:
-
-# Acknowledgment
-model: Should we talk about history?
-user: Sure
-Output: YES
-
-# Disagreement with completion
-model: Is that what you meant?
-user: No not really
-Output: YES
-
-LOW PRIORITY SIGNALS:
-
-1. STT Artifacts (Consider but don't over-weight):
-- Repeated words
-- Unusual punctuation
-- Capitalization errors
-- Word insertions/deletions
-
-Examples:
-
-# Word repetition but complete
-model: I can help with that.
-user: What what is the time right now
-Output: YES
-
-# Missing punctuation but complete
-model: I can explain that.
-user: Please tell me how computers work
-Output: YES
-
-2. Speech Features:
-- Filler words (um, uh, like)
-- Thinking pauses
-- Word repetitions
-- Brief hesitations
-
-Examples:
-
-# Filler words but complete
-model: What would you like to know?
-user: Um uh how do airplanes fly
-Output: YES
-
-# Thinking pause but incomplete
-model: I can explain anything.
-user: Well um I want to know about the
-Output: NO
-
-DECISION RULES:
-
-1. Return YES if:
-- ANY high priority signal shows clear completion
-- Medium priority signals combine to show completion
-- Meaning is clear despite low priority artifacts
-
-2. Return NO if:
-- No high priority signals present
-- Thought clearly trails off
-- Multiple incomplete indicators
-- User appears mid-formulation
-
-3. When uncertain:
-- If you can understand the intent → YES
-- If meaning is unclear → NO
-- Always make a binary decision
-- Never request clarification
-
-Examples:
-
-# Incomplete despite corrections
-model: What would you like to know about?
-user: Can you tell me about
-Output: NO
-
-# Complete despite multiple artifacts
-model: I can help you learn.
-user: How do you I mean what's the best way to learn programming
-Output: YES
-
-# Trailing off incomplete
-model: I can explain anything.
-user: I was wondering if you could tell me why
-Output: NO
-"""
-
-conversation_system_instruction = """You are a helpful assistant participating in a voice converation.
-
-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.
-
-If you know that a number string is a phone number from the context of the conversation, write it as a phone number. For example 210-333-4567.
-
-If you know that a number string is a credit card number, write it as a credit card number. For example 4111-1111-1111-1111.
-
-Please be very concise in your responses. Unless you are explicitly asked to do otherwise, give me shortest complete answer possible without unnecessary elaboration. Generally you should answer with a single sentence.
+Please be very concise in your responses. Unless you are explicitly asked to do otherwise, give me the shortest complete answer possible without unnecessary elaboration. Generally you should answer with a single sentence.
 """
 
 
-class AudioAccumulator(FrameProcessor):
-    """Buffers user audio until the user stops speaking.
-
-    Always pushes a fresh context with a single audio message.
-    """
-
-    def __init__(self, **kwargs):
+class StatementJudgeAudioContextAccumulator(FrameProcessor):
+    def __init__(self, *, notifier: BaseNotifier, **kwargs):
         super().__init__(**kwargs)
+        self._notifier = notifier
+        self._audio_frames = []
         self._audio_frames = []
         self._start_secs = 0.2  # this should match VAD start_secs (hardcoding for now)
-        self._max_buffer_size_secs = 30
-        self._user_speaking_vad_state = False
-        self._user_speaking_utterance_state = False
+        self._user_speaking = False
 
     async def reset(self):
         self._audio_frames = []
-        self._user_speaking_vad_state = False
-        self._user_speaking_utterance_state = False
+        self._user_speaking = False
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
@@ -385,33 +101,22 @@ class AudioAccumulator(FrameProcessor):
             # but let's leave that as an exercise to the reader. :-)
             return
         if isinstance(frame, UserStartedSpeakingFrame):
-            self._user_speaking_vad_state = True
-            self._user_speaking_utterance_state = True
-
+            self._user_speaking = True
         elif isinstance(frame, UserStoppedSpeakingFrame):
-            data = b"".join(frame.audio for frame in self._audio_frames)
-            logger.debug(
-                f"Processing audio buffer seconds: ({len(self._audio_frames)}) ({len(data)}) {len(data) / 2 / 16000}"
-            )
             self._user_speaking = False
             context = GoogleLLMContext()
-            context.add_audio_frames_message(text="Audio follows", audio_frames=self._audio_frames)
+            context.set_messages([{"role": "system", "content": classifier_statement}])
+            context.add_audio_frames_message(audio_frames=self._audio_frames)
             await self.push_frame(OpenAILLMContextFrame(context=context))
         elif isinstance(frame, InputAudioRawFrame):
-            # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
-            # frames as necessary.
-            # Use a small buffer size when an utterance is not in progress. Just big enough to backfill the start_secs.
-            # Use a larger buffer size when an utterance is in progress.
-            # Assume all audio frames have the same duration.
-            self._audio_frames.append(frame)
-            frame_duration = len(frame.audio) / 2 * frame.num_channels / frame.sample_rate
-            buffer_duration = frame_duration * len(self._audio_frames)
-            #  logger.debug(f"!!! Frame duration: {frame_duration}")
-            if self._user_speaking_utterance_state:
-                while buffer_duration > self._max_buffer_size_secs:
-                    self._audio_frames.pop(0)
-                    buffer_duration -= frame_duration
+            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
@@ -420,143 +125,32 @@ class AudioAccumulator(FrameProcessor):
 
 
 class CompletenessCheck(FrameProcessor):
-    """Checks the result of the classifier LLM to determine if the user has finished speaking.
-
-    Triggers the notifier if the user has finished speaking. Also triggers the notifier if an
-    idle timeout is reached.
-    """
-
-    wait_time = 5.0
-
-    def __init__(self, notifier: BaseNotifier, audio_accumulator: AudioAccumulator, **kwargs):
+    def __init__(
+        self, notifier: BaseNotifier, audio_accumulator: StatementJudgeAudioContextAccumulator
+    ):
         super().__init__()
         self._notifier = notifier
         self._audio_accumulator = audio_accumulator
-        self._idle_task = None
-        self._wakeup_time = 0
 
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, UserStartedSpeakingFrame):
-            if self._idle_task:
-                self._idle_task.cancel()
-        elif isinstance(frame, TextFrame) and frame.text.startswith("YES"):
+        if isinstance(frame, TextFrame) and frame.text.startswith("YES"):
             logger.debug("Completeness check YES")
-            if self._idle_task:
-                self._idle_task.cancel()
             await self.push_frame(UserStoppedSpeakingFrame())
             await self._audio_accumulator.reset()
             await self._notifier.notify()
         elif isinstance(frame, TextFrame):
             if frame.text.strip():
                 logger.debug(f"Completeness check NO - '{frame.text}'")
-                # start timer to wake up if necessary
-                if self._wakeup_time:
-                    self._wakeup_time = time.time() + self.wait_time
-                else:
-                    # logger.debug("!!! CompletenessCheck idle wait START")
-                    self._wakeup_time = time.time() + self.wait_time
-                    self._idle_task = self.get_event_loop().create_task(self._idle_task_handler())
-
-    async def _idle_task_handler(self):
-        try:
-            while time.time() < self._wakeup_time:
-                await asyncio.sleep(0.01)
-            # logger.debug(f"!!! CompletenessCheck idle wait OVER")
-            await self._audio_accumulator.reset()
-            await self._notifier.notify()
-        except asyncio.CancelledError:
-            # logger.debug(f"!!! CompletenessCheck idle wait CANCEL")
-            pass
-        except Exception as e:
-            logger.error(f"CompletenessCheck idle wait error: {e}")
-            raise e
-        finally:
-            # logger.debug(f"!!! CompletenessCheck idle wait FINALLY")
-            self._wakeup_time = 0
-            self._idle_task = None
-
-
-class UserAggregatorBuffer(LLMResponseAggregator):
-    """Buffers the output of the transcription LLM. Used by the bot output gate."""
-
-    def __init__(self, **kwargs):
-        super().__init__(
-            messages=None,
-            role=None,
-            start_frame=LLMFullResponseStartFrame,
-            end_frame=LLMFullResponseEndFrame,
-            accumulator_frame=TextFrame,
-            handle_interruptions=True,
-            expect_stripped_words=False,
-        )
-        self._transcription = ""
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        # parent method pushes frames
-        if isinstance(frame, UserStartedSpeakingFrame):
-            self._transcription = ""
-
-    async def _push_aggregation(self):
-        if self._aggregation:
-            self._transcription = self._aggregation
-            self._aggregation = ""
-
-            logger.debug(f"[Transcription] {self._transcription}")
-
-    async def wait_for_transcription(self):
-        while not self._transcription:
-            await asyncio.sleep(0.01)
-        tx = self._transcription
-        self._transcription = ""
-        return tx
-
-
-class ConversationAudioContextAssembler(FrameProcessor):
-    """Takes the single-message context generated by the AudioAccumulator and adds it to the conversation LLM's context."""
-
-    def __init__(self, context: OpenAILLMContext, **kwargs):
-        super().__init__(**kwargs)
-        self._context = context
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-
-        # We must not block system frames.
-        if isinstance(frame, SystemFrame):
-            await self.push_frame(frame, direction)
-            return
-
-        if isinstance(frame, OpenAILLMContextFrame):
-            GoogleLLMContext.upgrade_to_google(self._context)
-            last_message = frame.context.messages[-1]
-            self._context._messages.append(last_message)
-            await self.push_frame(OpenAILLMContextFrame(context=self._context))
 
 
 class OutputGate(FrameProcessor):
-    """Buffers output frames until the notifier is triggered.
-
-    When the notifier fires, waits until a transcription is ready, then:
-      1. Replaces the last user audio message with the transcription.
-      2. Flushes the frames buffer.
-    """
-
-    def __init__(
-        self,
-        notifier: BaseNotifier,
-        context: OpenAILLMContext,
-        user_transcription_buffer: "UserAggregatorBuffer",
-        **kwargs,
-    ):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
         super().__init__(**kwargs)
         self._gate_open = False
         self._frames_buffer = []
         self._notifier = notifier
-        self._context = context
-        self._transcription_buffer = user_transcription_buffer
 
     def close_gate(self):
         self._gate_open = False
@@ -584,13 +178,6 @@ class OutputGate(FrameProcessor):
             await self.push_frame(frame, direction)
             return
 
-        if isinstance(frame, LLMFullResponseStartFrame):
-            # Remove the audio message from the context. We will never need it again.
-            # If the completeness check fails, a new audio message will be appended to the context.
-            # If the completeness check succeeds, our notifier will fire and we will append the
-            # transcription to the context.
-            self._context._messages.pop()
-
         if self._gate_open:
             await self.push_frame(frame, direction)
             return
@@ -609,22 +196,12 @@ class OutputGate(FrameProcessor):
         while True:
             try:
                 await self._notifier.wait()
-
-                transcription = await self._transcription_buffer.wait_for_transcription() or "-"
-                self._context._messages.append(
-                    glm.Content(role="user", parts=[glm.Part(text=transcription)])
-                )
-
                 self.open_gate()
                 for frame, direction in self._frames_buffer:
                     await self.push_frame(frame, direction)
                 self._frames_buffer = []
             except asyncio.CancelledError:
                 break
-            except Exception as e:
-                logger.error(f"OutputGate error: {e}")
-                raise e
-                break
 
 
 async def main():
@@ -640,63 +217,64 @@ async def main():
                 vad_enabled=True,
                 vad_analyzer=SileroVADAnalyzer(),
                 vad_audio_passthrough=True,
-                audio_in_sample_rate=16000,
             ),
         )
 
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
         tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        # This is the LLM that will transcribe user speech.
-        tx_llm = GoogleLLMService(
-            name="Transcriber",
-            model=TRANSCRIBER_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            temperature=0.0,
-            system_instruction=transcriber_system_instruction,
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we might as well!
+        statement_llm = GoogleLLMService(
+            model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")
         )
 
-        # This is the LLM that will classify user speech as complete or incomplete.
-        classifier_llm = GoogleLLMService(
-            name="Classifier",
-            model=CLASSIFIER_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            temperature=0.0,
-            system_instruction=classifier_system_instruction,
-        )
+        # This is the regular LLM.
+        llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
 
-        # This is the regular LLM that responds conversationally.
-        conversation_llm = GoogleLLMService(
-            name="Conversation",
-            model=CONVERSATION_MODEL,
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            system_instruction=conversation_system_instruction,
-        )
+        messages = [
+            {
+                "role": "system",
+                "content": conversational_system_message,
+            },
+        ]
 
-        context = OpenAILLMContext()
-        context_aggregator = conversation_llm.create_context_aggregator(context)
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
 
-        # We have instructed the LLM to return 'True' if it thinks the user
-        # completed a sentence. So, if it's 'True' we will return true in this
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
         # predicate which will wake up the notifier.
         async def wake_check_filter(frame):
-            return frame.text == "True"
+            return frame.text == "YES"
 
         # This is a notifier that we use to synchronize the two LLMs.
         notifier = EventNotifier()
 
         # This turns the LLM context into an inference request to classify the user's speech
         # as complete or incomplete.
-        # statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
+        statement_judge_context_filter = StatementJudgeAudioContextAccumulator(notifier=notifier)
 
-        audio_accumulater = AudioAccumulator()
         # This sends a UserStoppedSpeakingFrame and triggers the notifier event
         completeness_check = CompletenessCheck(
-            notifier=notifier, audio_accumulator=audio_accumulater
+            notifier=notifier, audio_accumulator=statement_judge_context_filter
         )
 
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        bot_output_gate = OutputGate(notifier=notifier)
+
         async def block_user_stopped_speaking(frame):
             return not isinstance(frame, UserStoppedSpeakingFrame)
 
@@ -708,18 +286,9 @@ async def main():
                 or isinstance(frame, StopInterruptionFrame)
             )
 
-        conversation_audio_context_assembler = ConversationAudioContextAssembler(context=context)
-
-        user_aggregator_buffer = UserAggregatorBuffer()
-
-        bot_output_gate = OutputGate(
-            notifier=notifier, context=context, user_transcription_buffer=user_aggregator_buffer
-        )
-
         pipeline = Pipeline(
             [
                 transport.input(),
-                audio_accumulater,
                 ParallelPipeline(
                     [
                         # Pass everything except UserStoppedSpeaking to the elements after
@@ -727,28 +296,24 @@ async def main():
                         FunctionFilter(filter=block_user_stopped_speaking),
                     ],
                     [
-                        ParallelPipeline(
-                            [
-                                classifier_llm,
-                                completeness_check,
-                            ],
-                            [
-                                tx_llm,
-                                user_aggregator_buffer,
-                            ],
-                        )
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
                     ],
                     [
-                        conversation_audio_context_assembler,
-                        conversation_llm,
-                        bot_output_gate,  # buffer output until notified, then flush frames and update context
-                        # TempPrinter(),
+                        stt,
+                        context_aggregator.user(),
+                        # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
+                        FunctionFilter(filter=pass_only_llm_trigger_frames),
+                        llm,
+                        bot_output_gate,  # Buffer all llm/tts output until notified.
                     ],
                 ),
                 tts,
+                user_idle,
                 transport.output(),
                 context_aggregator.assistant(),
-            ],
+            ]
         )
 
         task = PipelineTask(

examples/foundational/23-bot-background-sound.py

@@ -6,17 +6,13 @@
 
 import argparse
 import asyncio
+import aiohttp
 import os
 import sys
 
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure_with_args
-
 from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesFrame, MixerEnableFrame, MixerUpdateSettingsFrame
+from pipecat.frames.frames import LLMMessagesFrame, MixerUpdateSettingsFrame, MixerEnableFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -25,6 +21,12 @@ from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
+from runner import configure_with_args
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
 load_dotenv(override=True)
 
 logger.remove(0)

examples/foundational/24-stt-mute-filter.py

@@ -11,11 +11,12 @@ 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.frames.frames import 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
@@ -31,18 +32,6 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def start_fetch_weather(function_name, llm, context):
-    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):
-    # Add a delay to test interruption during function calls
-    logger.info("Weather API call starting...")
-    await asyncio.sleep(5)  # 5-second delay
-    logger.info("Weather API call completed")
-    await result_callback({"conditions": "nice", "temperature": "75"})
-
-
 async def main():
     async with aiohttp.ClientSession() as session:
         (room_url, _) = await configure(session)
@@ -60,52 +49,23 @@ async def main():
         )
 
         stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-        # Configure the mute processor with both strategies
+        # Configure the mute processor to mute only during first speech
         stt_mute_processor = STTMuteFilter(
-            stt_service=stt,
-            config=STTMuteConfig(
-                strategies={STTMuteStrategy.FIRST_SPEECH, STTMuteStrategy.FUNCTION_CALL}
-            ),
+            stt_service=stt, config=STTMuteConfig(strategy=STTMuteStrategy.FIRST_SPEECH)
         )
 
         tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
 
         llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
-        llm.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 assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be converted to audio so use only simple words and punctuation.",
+                "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 = OpenAILLMContext(messages)
         context_aggregator = llm.create_context_aggregator(context)
 
         pipeline = Pipeline(
@@ -125,13 +85,8 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            # Kick off the conversation with a weather-related prompt
-            messages.append(
-                {
-                    "role": "system",
-                    "content": "Ask the user what city they'd like to know the weather for.",
-                }
-            )
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
             await task.queue_frames([LLMMessagesFrame(messages)])
 
         runner = PipelineRunner()

examples/foundational/25-google-audio-in.py

@@ -1,373 +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,
-    MetricsFrame,
-    SystemFrame,
-    TextFrame,
-    TranscriptionFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
-from pipecat.processors.frame_processor import FrameProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.google import GoogleLLMContext, GoogleLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-#
-# The system prompt for the main conversation.
-#
-conversation_system_message = """
-You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses. Respond with one or two sentences at most, unless you are asked to
-respond at more length. Your output will be converted to audio so don't include special characters in your answers.
-"""
-
-#
-# The system prompt for the LLM doing the audio transcription.
-#
-# Note that we could provide additional instructions per-conversation, here, if that's helpful
-# for our use case. For example, names of people so that the transcription gets the spelling
-# right.
-#
-# A possible future improvement would be to use structured output so that we can include a
-# language tag and perhaps other analytic information.
-#
-transcriber_system_message = """
-You are an audio transcriber. You are receiving audio from a user. Your job is to
-transcribe the input audio to text exactly as it was said by the user..
-
-You will receive the full conversation history before the audio input, to help with context. Use the full history only to help improve the accuracy of your transcription.
-
-Rules:
-  - Respond with an exact transcription of the audio input.
-  - Do not include any text other than the transcription.
-  - Do not explain or add to your response.
-  - Transcribe the audio input simply and precisely.
-  - If the audio is not clear, emit the special string "EMPTY".
-  - No response other than exact transcription, or "EMPTY", is allowed.
-"""
-
-
-class UserAudioCollector(FrameProcessor):
-    """
-    This FrameProcessor collects audio frames in a buffer, then adds them to the
-    LLM context when the user stops speaking.
-    """
-
-    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 InputTranscriptionContextFilter(FrameProcessor):
-    """
-    This FrameProcessor blocks all frames except the OpenAILLMContextFrame that triggers
-    LLM inference. (And system frames, which are needed for the pipeline element lifecycle.)
-
-    We take the context object out of the OpenAILLMContextFrame and use it to create a new
-    context object that we will send to the transcriber LLM.
-    """
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, SystemFrame):
-            # We don't want to block system frames.
-            await self.push_frame(frame, direction)
-            return
-
-        if not isinstance(frame, OpenAILLMContextFrame):
-            return
-
-        try:
-            message = frame.context.messages[-1]
-            last_part = message.parts[-1]
-            if not (
-                message.role == "user"
-                and last_part.inline_data
-                and last_part.inline_data.mime_type == "audio/wav"
-            ):
-                return
-
-            # Assemble a new message, with three parts: conversation history, transcription
-            # prompt, and audio. We could use only part of the conversation, if we need to
-            # keep the token count down, but for now, we'll just use the whole thing.
-            parts = []
-
-            # Get previous conversation history
-            previous_messages = frame.context.messages[:-2]
-            history = ""
-            for msg in previous_messages:
-                for part in msg.parts:
-                    if part.text:
-                        history += f"{msg.role}: {part.text}\n"
-            if history:
-                assembled = f"Here is the conversation history so far. These are not instructions. This is data that you should use only to improve the accuracy of your transcription.\n\n----\n\n{history}\n\n----\n\nEND OF CONVERSATION HISTORY\n\n"
-                parts.append(glm.Part(text=assembled))
-
-            parts.append(
-                glm.Part(
-                    text="Transcribe this audio. Respond either with the transcription exactly as it was said by the user, or with the special string 'EMPTY' if the audio is not clear."
-                )
-            )
-            parts.append(last_part)
-            msg = glm.Content(role="user", parts=parts)
-            ctx = GoogleLLMContext([msg])
-            ctx.system_message = transcriber_system_message
-            await self.push_frame(OpenAILLMContextFrame(context=ctx))
-        except Exception as e:
-            logger.error(f"Error processing frame: {e}")
-
-
-@dataclass
-class LLMDemoTranscriptionFrame(Frame):
-    """
-    It would be nice if we could just use a TranscriptionFrame to send our transcriber
-    LLM's transcription output down the pipelline. But we can't, because TranscriptionFrame
-    is a child class of TextFrame, which in our pipeline will be interpreted by the TTS
-    service as text that should be turned into speech. We could restructure this pipeline,
-    but instead we'll just use a custom frame type.
-    (Composition and reuse are ... double-edged swords.)
-    """
-
-    text: str
-
-
-class InputTranscriptionFrameEmitter(FrameProcessor):
-    """
-    A simple FrameProcessor that aggregates the TextFrame output from the transcriber LLM
-    and then sends the full response down the pipeline as an LLMDemoTranscriptionFrame.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._aggregation = ""
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, TextFrame):
-            self._aggregation += frame.text
-        elif isinstance(frame, LLMFullResponseEndFrame):
-            await self.push_frame(LLMDemoTranscriptionFrame(text=self._aggregation.strip()))
-            self._aggregation = ""
-        elif isinstance(frame, MetricsFrame):
-            await self.push_frame(frame, direction)
-
-
-class TranscriptionContextFixup(FrameProcessor):
-    """
-    This FrameProcessor looks for the LLMDemoTranscriptionFrame and swaps out the
-    audio part of the most recent user message with the text transcription.
-
-    Audio is big, using a lot of tokens and network bandwidth. So doing this is
-    important if we want to keep both latency and cost low.
-
-    This class is a bit of a hack, especially because it directly creates a
-    GoogleLLMContext object, which we don't generally do. We usually try to leave
-    the implementation-specific details of the LLM context encapsulated inside the
-    service classes.
-    """
-
-    def __init__(self, context):
-        super().__init__()
-        self._context = context
-        self._transcript = "THIS IS A TRANSCRIPT"
-
-    def is_user_audio_message(self, message):
-        last_part = message.parts[-1]
-        return (
-            message.role == "user"
-            and last_part.inline_data
-            and last_part.inline_data.mime_type == "audio/wav"
-        )
-
-    def swap_user_audio(self):
-        if not self._transcript:
-            return
-        message = self._context.messages[-2]
-        if not self.is_user_audio_message(message):
-            message = self._context.messages[-1]
-            if not self.is_user_audio_message(message):
-                return
-
-        audio_part = message.parts[-1]
-        audio_part.inline_data = None
-        audio_part.text = self._transcript
-
-    async def process_frame(self, frame, direction):
-        await super().process_frame(frame, direction)
-
-        if isinstance(frame, LLMDemoTranscriptionFrame):
-            logger.info(f"Transcription from Gemini: {frame.text}")
-            self._transcript = frame.text
-            self.swap_user_audio()
-            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
-        )
-
-        conversation_llm = GoogleLLMService(
-            name="Conversation",
-            model="gemini-1.5-flash-latest",
-            # model="gemini-exp-1121",
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            # we can give the GoogleLLMService a system instruction to use directly
-            # in the GenerativeModel constructor. Let's do that rather than put
-            # our system message in the messages list.
-            system_instruction=conversation_system_message,
-        )
-
-        input_transcription_llm = GoogleLLMService(
-            name="Transcription",
-            model="gemini-1.5-flash-latest",
-            # model="gemini-exp-1121",
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            system_instruction=transcriber_system_message,
-        )
-
-        messages = [
-            {
-                "role": "user",
-                "content": "Start by saying hello.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = conversation_llm.create_context_aggregator(context)
-        audio_collector = UserAudioCollector(context, context_aggregator.user())
-        input_transcription_context_filter = InputTranscriptionContextFilter()
-        transcription_frames_emitter = InputTranscriptionFrameEmitter()
-        fixup_context_messages = TranscriptionContextFixup(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                audio_collector,
-                context_aggregator.user(),
-                ParallelPipeline(
-                    [  # transcribe
-                        input_transcription_context_filter,
-                        input_transcription_llm,
-                        transcription_frames_emitter,
-                    ],
-                    [  # conversation inference
-                        conversation_llm,
-                    ],
-                ),
-                tts,
-                transport.output(),
-                context_aggregator.assistant(),
-                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):
-            # 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/26-gemini-multimodal-live.py

@@ -1,80 +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.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-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_sample_rate=16000,
-                audio_out_sample_rate=24000,
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_audio_passthrough=True,
-                # set stop_secs to something roughly similar to the internal setting
-                # of the Multimodal Live api, just to align events. This doesn't really
-                # matter because we can only use the Multimodal Live API's phrase
-                # endpointing, for now.
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-            ),
-        )
-
-        llm = GeminiMultimodalLiveLLMService(
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            # system_instruction="Talk like a pirate."
-        )
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                llm,
-                transport.output(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-                enable_usage_metrics=True,
-            ),
-        )
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/26a-gemini-multimodal-live-transcription.py

@@ -1,111 +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.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-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_sample_rate=16000,
-                audio_out_sample_rate=24000,
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_audio_passthrough=True,
-                # set stop_secs to something roughly similar to the internal setting
-                # of the Multimodal Live api, just to align events. This doesn't really
-                # matter because we can only use the Multimodal Live API's phrase
-                # endpointing, for now.
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-            ),
-        )
-
-        llm = GeminiMultimodalLiveLLMService(
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            voice_id="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
-            # system_instruction="Talk like a pirate."
-            transcribe_user_audio=True,
-            transcribe_model_audio=True,
-            # inference_on_context_initialization=False,
-        )
-
-        context = OpenAILLMContext(
-            [
-                {
-                    "role": "user",
-                    "content": "Say hello. Then ask if I want to hear a joke.",
-                },
-                #     {"role": "assistant", "content": "Hello! Why don't scientists trust atoms?"},
-                #     {
-                #         "role": "user",
-                #         "content": [
-                #             {
-                #                 "type": "text",
-                #                 "text": "Oh, I know this one: because they make up everything.",
-                #             }
-                #         ],
-                #     },
-            ],
-        )
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                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 task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/26b-gemini-multimodal-live-function-calling.py

@@ -1,142 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from datetime import datetime
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
-    temperature = 75 if args["format"] == "fahrenheit" else 24
-    await result_callback(
-        {
-            "conditions": "nice",
-            "temperature": temperature,
-            "format": args["format"],
-            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
-        }
-    )
-
-
-tools = [
-    {
-        "function_declarations": [
-            {
-                "name": "get_current_weather",
-                "description": "Get the current weather",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "location": {
-                            "type": "string",
-                            "description": "The city and state, e.g. San Francisco, CA",
-                        },
-                        "format": {
-                            "type": "string",
-                            "enum": ["celsius", "fahrenheit"],
-                            "description": "The temperature unit to use. Infer this from the users location.",
-                        },
-                    },
-                    "required": ["location", "format"],
-                },
-            },
-        ]
-    }
-]
-
-system_instruction = """
-You are a helpful assistant who can answer questions and use tools.
-
-You have a tool called "get_current_weather" that can be used to get the current weather. If the user asks
-for the weather, call this function.
-"""
-
-
-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_sample_rate=16000,
-                audio_out_sample_rate=24000,
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_audio_passthrough=True,
-                # set stop_secs to something roughly similar to the internal setting
-                # of the Multimodal Live api, just to align events. This doesn't really
-                # matter because we can only use the Multimodal Live API's phrase
-                # endpointing, for now.
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-            ),
-        )
-
-        llm = GeminiMultimodalLiveLLMService(
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            system_instruction=system_instruction,
-            tools=tools,
-        )
-
-        llm.register_function("get_current_weather", fetch_weather_from_api)
-
-        context = OpenAILLMContext(
-            [{"role": "user", "content": "Say hello."}],
-        )
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                context_aggregator.assistant(),
-                transport.output(),
-            ]
-        )
-
-        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 task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/26c-gemini-multimodal-live-video.py

@@ -1,115 +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.audio.vad.vad_analyzer import VADParams
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
-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_sample_rate=16000,
-                audio_out_sample_rate=24000,
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_audio_passthrough=True,
-                # set stop_secs to something roughly similar to the internal setting
-                # of the Multimodal Live api, just to align events. This doesn't really
-                # matter because we can only use the Multimodal Live API's phrase
-                # endpointing, for now.
-                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
-                start_audio_paused=True,
-                start_video_paused=True,
-            ),
-        )
-
-        llm = GeminiMultimodalLiveLLMService(
-            api_key=os.getenv("GOOGLE_API_KEY"),
-            voice_id="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
-            # system_instruction="Talk like a pirate."
-            transcribe_user_audio=True,
-            transcribe_model_audio=True,
-            # inference_on_context_initialization=False,
-        )
-
-        context = OpenAILLMContext(
-            [
-                {
-                    "role": "user",
-                    "content": "Say hello.",
-                },
-            ],
-        )
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                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):
-            # Enable both camera and screenshare. From the client side
-            # send just one.
-            await transport.capture_participant_video(
-                participant["id"], framerate=1, video_source="camera"
-            )
-            await transport.capture_participant_video(
-                participant["id"], framerate=1, video_source="screenVideo"
-            )
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-            await asyncio.sleep(3)
-            logger.debug("Unpausing audio and video")
-            llm.set_audio_input_paused(False)
-            llm.set_video_input_paused(False)
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/27-simli-layer.py

@@ -1,103 +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 simli import SimliConfig
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.services.simli import SimliVideoService
-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, token = await configure(session)
-        transport = DailyTransport(
-            room,
-            token,
-            "Simli",
-            DailyParams(
-                audio_out_enabled=True,
-                camera_out_enabled=True,
-                camera_out_width=512,
-                camera_out_height=512,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                transcription_enabled=True,
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
-        )
-
-        simli_ai = SimliVideoService(
-            SimliConfig(os.getenv("SIMLI_API_KEY"), os.getenv("SIMLI_FACE_ID"))
-        )
-
-        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),
-                context_aggregator.user(),
-                llm,
-                tts,
-                simli_ai,
-                transport.output(),
-                context_aggregator.assistant(),
-            ]
-        )
-
-        task = PipelineTask(
-            pipeline,
-            PipelineParams(
-                allow_interruptions=True,
-                enable_metrics=True,
-            ),
-        )
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            await transport.capture_participant_transcription(participant["id"])
-            await task.queue_frames([LLMMessagesFrame(messages)])
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/28a-transcription-processor-openai.py

@@ -1,137 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from typing import List
-
-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 TranscriptionMessage, TranscriptionUpdateFrame
-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.transcript_processor import TranscriptProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-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")
-
-
-class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
-
-    Maintains a list of conversation messages and logs them with timestamps.
-    """
-
-    def __init__(self):
-        self.messages: List[TranscriptionMessage] = []
-
-    async def on_transcript_update(
-        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
-    ):
-        """Handle new transcript messages.
-
-        Args:
-            processor: The TranscriptProcessor that emitted the update
-            frame: TranscriptionUpdateFrame containing new messages
-        """
-        self.messages.extend(frame.messages)
-
-        # Log the new messages
-        logger.info("New transcript messages:")
-        for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        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, helpful, and brief way. Say hello.",
-            },
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        # Create transcript processor and handler
-        transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
-
-        # Register event handler for transcript updates
-        @transcript.event_handler("on_transcript_update")
-        async def on_transcript_update(processor, frame):
-            await transcript_handler.on_transcript_update(processor, frame)
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                transcript.user(),  # User transcripts
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-                transcript.assistant(),  # Assistant transcripts
-            ]
-        )
-
-        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.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/28b-transcript-processor-anthropic.py

@@ -1,137 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from typing import List
-
-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 TranscriptionMessage, TranscriptionUpdateFrame
-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.transcript_processor import TranscriptProcessor
-from pipecat.services.anthropic import AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
-
-    Maintains a list of conversation messages and logs them with timestamps.
-    """
-
-    def __init__(self):
-        self.messages: List[TranscriptionMessage] = []
-
-    async def on_transcript_update(
-        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
-    ):
-        """Handle new transcript messages.
-
-        Args:
-            processor: The TranscriptProcessor that emitted the update
-            frame: TranscriptionUpdateFrame containing new messages
-        """
-        self.messages.extend(frame.messages)
-
-        # Log the new messages
-        logger.info("New transcript messages:")
-        for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022"
-        )
-
-        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, helpful, and brief way.",
-            },
-            {"role": "user", "content": "Say hello."},
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        # Create transcript processor and handler
-        transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                transcript.user(),  # User transcripts
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-                transcript.assistant(),  # Assistant transcripts
-            ]
-        )
-
-        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.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        # Register event handler for transcript updates
-        @transcript.event_handler("on_transcript_update")
-        async def on_transcript_update(processor, frame):
-            await transcript_handler.on_transcript_update(processor, frame)
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/28c-transcription-processor-gemini.py

@@ -1,147 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import os
-import sys
-from typing import List
-
-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 TranscriptionMessage, TranscriptionUpdateFrame
-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.transcript_processor import TranscriptProcessor
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.services.deepgram import DeepgramSTTService
-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")
-
-
-class TranscriptHandler:
-    """Simple handler to demonstrate transcript processing.
-
-    Maintains a list of conversation messages and logs them with timestamps.
-    """
-
-    def __init__(self):
-        self.messages: List[TranscriptionMessage] = []
-
-    async def on_transcript_update(
-        self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
-    ):
-        """Handle new transcript messages.
-
-        Args:
-            processor: The TranscriptProcessor that emitted the update
-            frame: TranscriptionUpdateFrame containing new messages
-        """
-        self.messages.extend(frame.messages)
-
-        # Log the new messages
-        logger.info("New transcript messages:")
-        for msg in frame.messages:
-            timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
-            logger.info(f"{timestamp}{msg.role}: {msg.content}")
-
-
-async def main():
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            None,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-                vad_audio_passthrough=True,
-            ),
-        )
-
-        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        llm = GoogleLLMService(
-            model="models/gemini-2.0-flash-exp",
-            # model="gemini-exp-1114",
-            api_key=os.getenv("GOOGLE_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, helpful, and brief way.",
-            },
-            {"role": "user", "content": "Say hello."},
-        ]
-
-        context = OpenAILLMContext(messages)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        # Create transcript processor and handler
-        transcript = TranscriptProcessor()
-        transcript_handler = TranscriptHandler()
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                stt,  # STT
-                transcript.user(),  # User transcripts
-                context_aggregator.user(),  # User responses
-                llm,  # LLM
-                tts,  # TTS
-                transport.output(),  # Transport bot output
-                context_aggregator.assistant(),  # Assistant spoken responses
-                transcript.assistant(),  # Assistant transcripts
-            ]
-        )
-
-        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()])
-
-        # Register event handler for transcript updates
-        @transcript.event_handler("on_transcript_update")
-        async def on_transcript_update(processor, frame):
-            await transcript_handler.on_transcript_update(processor, frame)
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/foundational/runner.py

@@ -4,11 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import argparse
 import os
 
-import aiohttp
-
 from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
 
 

examples/moondream-chatbot/bot.py

@@ -5,24 +5,21 @@
 #
 
 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 (
-    BotStartedSpeakingFrame,
-    BotStoppedSpeakingFrame,
-    Frame,
     ImageRawFrame,
-    LLMMessagesFrame,
     OutputImageRawFrame,
     SpriteFrame,
+    Frame,
+    LLMMessagesFrame,
+    TTSAudioRawFrame,
+    TTSStoppedFrame,
     TextFrame,
     UserImageRawFrame,
     UserImageRequestFrame,
@@ -40,6 +37,12 @@ from pipecat.services.moondream import MoondreamService
 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)
@@ -80,15 +83,14 @@ class TalkingAnimation(FrameProcessor):
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         await super().process_frame(frame, direction)
 
-        if isinstance(frame, BotStartedSpeakingFrame):
+        if isinstance(frame, TTSAudioRawFrame):
             if not self._is_talking:
                 await self.push_frame(talking_frame)
                 self._is_talking = True
-        elif isinstance(frame, BotStoppedSpeakingFrame):
+        elif isinstance(frame, TTSStoppedFrame):
             await self.push_frame(quiet_frame)
             self._is_talking = False
-
-        await self.push_frame(frame, direction)
+        await self.push_frame(frame)
 
 
 class UserImageRequester(FrameProcessor):
@@ -124,7 +126,7 @@ class TextFilterProcessor(FrameProcessor):
             if frame.text != self.text:
                 await self.push_frame(frame)
         else:
-            await self.push_frame(frame, direction)
+            await self.push_frame(frame)
 
 
 class ImageFilterProcessor(FrameProcessor):
@@ -132,7 +134,7 @@ class ImageFilterProcessor(FrameProcessor):
         await super().process_frame(frame, direction)
 
         if not isinstance(frame, ImageRawFrame):
-            await self.push_frame(frame, direction)
+            await self.push_frame(frame)
 
 
 async def main():

examples/moondream-chatbot/runner.py

@@ -4,11 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import argparse
 import os
 
-import aiohttp
-
 from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
 
 

examples/moondream-chatbot/server.py

@@ -4,13 +4,14 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-import argparse
+import aiohttp
 import os
+import argparse
 import subprocess
+
 from contextlib import asynccontextmanager
 
-import aiohttp
-from fastapi import FastAPI, HTTPException, Request
+from fastapi import FastAPI, Request, HTTPException
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, RedirectResponse
 

examples/patient-intake/README.md

@@ -4,8 +4,6 @@
 
 This project implements an AI-powered chatbot designed to streamline the medical intake process for Tri-County Health Services. The chatbot, named Jessica, interacts with patients to collect essential information before their doctor's visit, enhancing efficiency and improving the patient experience.
 
-💡 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
-
 ## Features
 
 Identity Verification: Confirms patient identity by verifying their date of birth.
@@ -72,24 +70,3 @@ Since this example is using Cartesia, checkout the best practices given in Carte
 <https://docs.cartesia.ai/build-with-sonic/formatting-text-for-sonic/inserting-breaks-pauses>
 
 <https://docs.cartesia.ai/build-with-sonic/formatting-text-for-sonic/spelling-out-input-text>
-### Example
-```python
-messages = [
-    {
-        "role": "system",
-        "content": '''You are a helpful AI assistant. Format all responses following these guidelines:
-
-1. Use proper punctuation and end each response with appropriate punctuation
-2. Format dates as MM/DD/YYYY
-3. Insert pauses using - or <break time='1s' /> for longer pauses
-4. Use ?? for emphasized questions
-5. Avoid quotation marks unless citing
-6. Add spaces between URLs/emails and punctuation marks
-7. For domain-specific terms or proper nouns, provide pronunciation guidance in [brackets]
-8. Keep responses clear and concise
-9. Use appropriate voice/language pairs for multilingual content
-
-Your goal is to demonstrate these capabilities in a succinct way. Your output will be converted to audio, so maintain natural communication flow. Respond creatively and helpfully, but keep responses brief. Start by introducing yourself.'''
-    }
-]
-```

examples/patient-intake/bot.py

@@ -5,26 +5,28 @@
 #
 
 import asyncio
+import aiohttp
 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 OutputAudioRawFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.frame_processor import FrameDirection
 from pipecat.processors.logger import FrameLogger
+from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, 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)

examples/patient-intake/runner.py

@@ -4,11 +4,10 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import argparse
 import os
 
-import aiohttp
-
 from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper