Initial commit for full stack chatbot

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

CHANGELOG.md

@@ -9,6 +9,119 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- `GroqLLMService` and `GrokLLMService` for Groq and Grok API integration, with
+  OpenAI-compatible interface.
+
+- New examples demonstrating function calling with Groq, Grok, Azure OpenAI,
+  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
+  will be called every time `buffer_size` (a new constructor argument) is
+  reached. If `buffer_size` is 0 (default) you need to manually get the audio as
+  before using `AudioBufferProcessor.merge_audio_buffers()`.
+
+```
+@audiobuffer.event_handler("on_audio_data")
+async def on_audio_data(processor, audio, sample_rate, num_channels):
+    await save_audio(audio, sample_rate, num_channels)
+```
+
+- Added a new RTVI message called `disconnect-bot`, which when handled pushes
+  an `EndFrame` to trigger the pipeline to stop.
+
+### Changed
+
+- 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
+  languages.
+
+- Updated STT and TTS services with language options that match the supported
+  languages for each service.
+
+- Updated the `AzureLLMService` to use the `OpenAILLMService`. Updated the
+  `api_version` to `2024-09-01-preview`.
+
+- Updated the `FireworksLLMService` to use the `OpenAILLMService`. Updated the
+  default model to `accounts/fireworks/models/firefunction-v2`.
+
+### Removed
+
+- Removed `AppFrame`. This was used as a special user custom frame, but there's
+  actually no use case for that.
+
+### Fixed
+
+- Fixed an issue in `DailyTransport` that would cause some internal callbacks to
+  not be executed.
+
+- Fixed an issue where other frames were being processed while a `CancelFrame`
+  was being pushed down the pipeline.
+
+- `AudioBufferProcessor` now handles interruptions properly.
+
+- Fixed a `WebsocketServerTransport` issue that would prevent interruptions with
+  `TwilioSerializer` from working.
+
+- `DailyTransport.capture_participant_video` now allows capturing user's screen
+  share by simply passing `video_source="screenVideo"`.
+
+- Fixed Google Gemini message handling to properly convert appended messages to
+  Gemini's required format.
+
+- Fixed an issue with `FireworksLLMService` where chat completions were failing
+  by removing the `stream_options` from the chat completion options.
+
+## [0.0.49] - 2024-11-17
+
+### Added
+
+- Added RTVI `on_bot_started` event which is useful in a single turn
+  interaction.
+
+- Added `DailyTransport` events `dialin-connected`, `dialin-stopped`,
+  `dialin-error` and `dialin-warning`. Needs daily-python >= 0.13.0.
+
+- Added `RimeHttpTTSService` and the `07q-interruptible-rime.py` foundational
+  example.
+
+- Added `STTMuteFilter`, a general-purpose processor that combines STT
+  muting and interruption control. When active, it prevents both transcription
+  and interruptions during bot speech. The processor supports multiple
+  strategies: `FIRST_SPEECH` (mute only during bot's first
+  speech), `ALWAYS` (mute during all bot speech), or `CUSTOM` (using provided
+  callback).
+
+- Added `STTMuteFrame`, a control frame that enables/disables speech
+  transcription in STT services.
+
+## [0.0.48] - 2024-11-10 "Antonio release"
+
+### Added
+
+- There's now an input queue in each frame processor. When you call
+  `FrameProcessor.push_frame()` this will internally call
+  `FrameProcessor.queue_frame()` on the next processor (upstream or downstream)
+  and the frame will be internally queued (except system frames). Then, the
+  queued frames will get processed. With this input queue it is also possible
+  for FrameProcessors to block processing more frames by calling
+  `FrameProcessor.pause_processing_frames()`. The way to resume processing
+  frames is by calling `FrameProcessor.resume_processing_frames()`.
+
+- Added audio filter `NoisereduceFilter`.
+
+- Introduce input transport audio filters (`BaseAudioFilter`). Audio filters can
+  be used to remove background noises before audio is sent to VAD.
+
+- Introduce output transport audio mixers (`BaseAudioMixer`). Output transport
+  audio mixers can be used, for example, to add background sounds or any other
+  audio mixing functionality before the output audio is actually written to the
+  transport.
+
 - Added `GatedOpenAILLMContextAggregator`. This aggregator keeps the last
   received OpenAI LLM context frame and it doesn't let it through until the
   notifier is notified.
@@ -31,6 +144,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   grained control of what media subscriptions you want for each participant in a
   room.
 
+- Added audio filter `KrispFilter`.
+
 ### Changed
 
 - The following `DailyTransport` functions are now `async` which means they need
@@ -42,8 +157,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   output to 24000 and also the default output transport sample rate. This
   improves audio quality at the cost of some extra bandwidth.
 
+- `AzureTTSService` now uses Azure websockets instead of HTTP requests.
+
+- The previous `AzureTTSService` HTTP implementation is now
+  `AzureHttpTTSService`.
+
 ### Fixed
 
+- Websocket transports (FastAPI and Websocket) now synchronize with time before
+  sending data. This allows for interruptions to just work out of the box.
+
 - Improved bot speaking detection for all TTS services by using actual bot
   audio.
 
@@ -55,9 +178,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed an issue with PlayHTTTSService, where the TTFB metrics were reporting
   very small time values.
 
+- Fixed an issue where AzureTTSService wasn't initializing the specified
+  language.
+
 ### Other
 
-- Added a new foundational example 22-natural-conversation.py. This examples
+- Add `23-bot-background-sound.py` foundational example.
+
+- Added a new foundational example `22-natural-conversation.py`. This example
   shows how to achieve a more natural conversation detecting when the user ends
   statement.
 

README.md

@@ -1,14 +1,21 @@
-<div align="center">
+<h1><div align="center">
  <img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
-</div>
-
-# Pipecat
+</div></h1>
 
 [![PyPI](https://img.shields.io/pypi/v/pipecat-ai)](https://pypi.org/project/pipecat-ai) [![Discord](https://img.shields.io/discord/1239284677165056021)](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
 
-`pipecat` is a framework for building voice (and multimodal) conversational agents. Things like personal coaches, meeting assistants, [story-telling toys for kids](https://storytelling-chatbot.fly.dev/), customer support bots, [intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0), and snarky social companions.
+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.
 
-Take a look at some example apps:
+## What you can build
+
+- **Voice Assistants**: [Natural, real-time conversations with AI](https://demo.dailybots.ai/)
+- **Interactive Agents**: Personal coaches and meeting assistants
+- **Multimodal Apps**: Combine voice, video, images, and text
+- **Creative Tools**: [Story-telling experiences](https://storytelling-chatbot.fly.dev/) and social companions
+- **Business Solutions**: [Customer intake flows](https://www.youtube.com/watch?v=lDevgsp9vn0) and support bots
+- **Complex conversational flows**: [Refer to Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) to learn more
+
+## See it in action
 
 <p float="left">
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/simple-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/simple-chatbot/image.png" width="280" /></a>&nbsp;
@@ -18,33 +25,54 @@ Take a look at some example apps:
     <a href="https://github.com/pipecat-ai/pipecat/tree/main/examples/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/examples/moondream-chatbot/image.png" width="280" /></a>
 </p>
 
-## Getting started with voice agents
+## Key features
+
+- **Voice-first Design**: Built-in speech recognition, TTS, and conversation handling
+- **Flexible Integration**: Works with popular AI services (OpenAI, ElevenLabs, etc.)
+- **Pipeline Architecture**: Build complex apps from simple, reusable components
+- **Real-time Processing**: Frame-based pipeline architecture for fluid interactions
+- **Production Ready**: Enterprise-grade WebRTC and Websocket support
+
+💡 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
+
+## Getting started
 
 You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you’re ready. You can also add a 📞 telephone number, 🖼️ image output, 📺 video input, use different LLMs, and more.
 
 ```shell
-# install the module
+# Install the module
 pip install pipecat-ai
 
-# set up an .env file with API keys
+# Set up your environment
 cp dot-env.template .env
 ```
 
-By default, in order to minimize dependencies, only the basic framework functionality is available. Some third-party AI services require additional dependencies that you can install with:
+To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:
 
 ```shell
 pip install "pipecat-ai[option,...]"
 ```
 
-Your project may or may not need these, so they're made available as optional requirements. Here is a list:
+Available options include:
 
-- **AI services**: `anthropic`, `assemblyai`, `aws`, `azure`, `deepgram`, `gladia`, `google`, `fal`, `lmnt`, `moondream`, `openai`, `openpipe`, `playht`, `silero`, `whisper`, `xtts`
-- **Transports**: `local`, `websocket`, `daily`
+| 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/api-reference/services/supported-services)
 
 ## Code examples
 
-- [foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
-- [example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
+- [Foundational](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational) — small snippets that build on each other, introducing one or two concepts at a time
+- [Example apps](https://github.com/pipecat-ai/pipecat/tree/main/examples/) — complete applications that you can use as starting points for development
 
 ## A simple voice agent running locally
 
@@ -109,7 +137,7 @@ Run it with:
 python app.py
 ```
 
-Daily provides a prebuilt WebRTC user interface. Whilst the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
+Daily provides a prebuilt WebRTC user interface. While the app is running, you can visit at `https://<yourdomain>.daily.co/<room_url>` and listen to the bot say hello!
 
 ## WebRTC for production use
 
@@ -119,16 +147,6 @@ One way to get up and running quickly with WebRTC is to sign up for a Daily deve
 
 Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://docs.daily.co/reference/rest-api/rooms) in the developer Dashboard.
 
-## What is VAD?
-
-Voice Activity Detection &mdash; very important for knowing when a user has finished speaking to your bot. If you are not using press-to-talk, and want Pipecat to detect when the user has finished talking, VAD is an essential component for a natural feeling conversation.
-
-Pipecat makes use of WebRTC VAD by default when using a WebRTC transport layer. Optionally, you can use Silero VAD for improved accuracy at the cost of higher CPU usage.
-
-```shell
-pip install pipecat-ai[silero]
-```
-
 ## Hacking on the framework itself
 
 _Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
@@ -206,8 +224,23 @@ Install the
 }
 ```
 
+## Contributing
+
+We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
+
+- **Found a bug?** Open an [issue](https://github.com/pipecat-ai/pipecat/issues)
+- **Have a feature idea?** Start a [discussion](https://discord.gg/pipecat)
+- **Want to contribute code?** Check our [CONTRIBUTING.md](CONTRIBUTING.md) guide
+- **Documentation improvements?** [Docs](https://github.com/pipecat-ai/docs) PRs are always welcome
+
+Before submitting a pull request, please check existing issues and PRs to avoid duplicates.
+
+We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.
+
 ## Getting help
 
 ➡️ [Join our Discord](https://discord.gg/pipecat)
 
+➡️ [Read the docs](https://docs.pipecat.ai)
+
 ➡️ [Reach us on X](https://x.com/pipecat_ai)

docs/frame.md

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

dot-env.template

@@ -52,4 +52,7 @@ OPENPIPE_API_KEY=...
 # Tavus
 TAVUS_API_KEY=...
 TAVUS_REPLICA_ID=...
-TAVUS_PERSONA_ID=...
+TAVUS_PERSONA_ID=...
+
+#Krisp
+KRISP_MODEL_PATH=...

examples/README.md

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

examples/canonical-metrics/bot.py

@@ -102,7 +102,6 @@ async def main():
             audio_buffer_processor=audio_buffer_processor,
             aiohttp_session=session,
             api_key=os.getenv("CANONICAL_API_KEY"),
-            api_url=os.getenv("CANONICAL_API_URL"),
             call_id=str(uuid.uuid4()),
             assistant="pipecat-chatbot",
             assistant_speaks_first=True,

examples/chatbot-audio-recording/bot.py

@@ -4,7 +4,9 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiofiles
 import asyncio
+import io
 import os
 import sys
 
@@ -32,15 +34,17 @@ logger.remove(0)
 logger.add(sys.stderr, level="DEBUG")
 
 
-async def save_audio(audiobuffer):
-    if audiobuffer.has_audio():
-        merged_audio = audiobuffer.merge_audio_buffers()
+async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
+    if len(audio) > 0:
         filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
-        with wave.open(filename, "wb") as wf:
-            wf.setnchannels(2)
-            wf.setsampwidth(2)
-            wf.setframerate(audiobuffer._sample_rate)
-            wf.writeframes(merged_audio)
+        with io.BytesIO() as buffer:
+            with wave.open(buffer, "wb") as wf:
+                wf.setsampwidth(2)
+                wf.setnchannels(num_channels)
+                wf.setframerate(sample_rate)
+                wf.writeframes(audio)
+            async with aiofiles.open(filename, "wb") as file:
+                await file.write(buffer.getvalue())
         print(f"Merged audio saved to {filename}")
     else:
         print("No audio data to save")
@@ -106,7 +110,9 @@ async def main():
         context = OpenAILLMContext(messages)
         context_aggregator = llm.create_context_aggregator(context)
 
-        audiobuffer = AudioBufferProcessor()
+        # Save audio every 10 seconds.
+        audiobuffer = AudioBufferProcessor(buffer_size=480000)
+
         pipeline = Pipeline(
             [
                 transport.input(),  # microphone
@@ -121,6 +127,10 @@ async def main():
 
         task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
 
+        @audiobuffer.event_handler("on_audio_data")
+        async def on_audio_data(buffer, audio, sample_rate, num_channels):
+            await save_audio(audio, sample_rate, num_channels)
+
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
@@ -130,7 +140,6 @@ async def main():
         async def on_participant_left(transport, participant, reason):
             print(f"Participant left: {participant}")
             await task.queue_frame(EndFrame())
-            await save_audio(audiobuffer)
 
         runner = PipelineRunner()
 

examples/chatbot-audio-recording/requirements.txt

@@ -1,3 +1,4 @@
+aiofiles
 python-dotenv
 fastapi[all]
 uvicorn

examples/deployment/modal-example/.gitignore

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

examples/deployment/modal-example/README.md

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

examples/deployment/modal-example/app.py

@@ -0,0 +1,75 @@
+import os
+
+import aiohttp
+import modal
+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")
+
+
+image = modal.Image.debian_slim(python_version="3.12").pip_install_from_requirements(
+    "requirements.txt"
+)
+
+
+@app.function(
+    image=image,
+    cpu=1.0,
+    secrets=[modal.Secret.from_dotenv()],
+    keep_warm=1,
+    enable_memory_snapshot=True,
+    max_inputs=1,  # Do not reuse instances across requests
+    retries=0,
+)
+def launch_bot_process(room_url: str, token: str):
+    _voice_bot_process(room_url, token)
+
+
+@app.function(
+    image=image,
+    secrets=[modal.Secret.from_dotenv()],
+)
+@modal.web_endpoint(method="POST")
+async def start():
+    from pipecat.transports.services.helpers.daily_rest import (
+        DailyRESTHelper,
+        DailyRoomParams,
+    )
+
+    logger.info("Request received")
+
+    async with aiohttp.ClientSession() as session:
+        daily_rest_helper = DailyRESTHelper(
+            daily_api_key=os.getenv("DAILY_API_KEY", ""),
+            daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
+            aiohttp_session=session,
+        )
+
+        # Create new Daily room
+        room = await daily_rest_helper.create_room(DailyRoomParams())
+        if not room.url:
+            raise HTTPException(
+                status_code=500,
+                detail="Unable to create room",
+            )
+        logger.info(f"Created room: {room.url}")
+
+        # Create bot token for room
+        token = await daily_rest_helper.get_token(room.url, MAX_SESSION_TIME)
+        if not token:
+            raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
+
+        logger.info(f"Bot token created: {token}")
+
+        # Spawn a new bot process
+        launch_bot_process.spawn(room_url=room.url, token=token)
+
+        # Return room URL to the user to join
+        # Note: in production, you would want to return a token to the user
+        return JSONResponse(content={"room_url": room.url, token: token})

examples/deployment/modal-example/bot.py

@@ -0,0 +1,90 @@
+import asyncio
+import os
+import sys
+
+from dotenv import load_dotenv
+from loguru import logger
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main(room_url: str, token: str):
+    from pipecat.audio.vad.silero import SileroVADAnalyzer
+    from pipecat.frames.frames import EndFrame, LLMMessagesFrame
+    from pipecat.pipeline.pipeline import Pipeline
+    from pipecat.pipeline.runner import PipelineRunner
+    from pipecat.pipeline.task import PipelineParams, PipelineTask
+    from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+    from pipecat.services.cartesia import CartesiaTTSService
+    from pipecat.services.openai import OpenAILLMService
+    from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+    transport = DailyTransport(
+        room_url,
+        token,
+        "bot",
+        DailyParams(
+            audio_out_enabled=True,
+            transcription_enabled=True,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        await transport.capture_participant_transcription(participant["id"])
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMMessagesFrame(messages)])
+
+    @transport.event_handler("on_participant_left")
+    async def on_participant_left(transport, participant, reason):
+        await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+def _voice_bot_process(room_url: str, token: str):
+    asyncio.run(main(room_url, token))

examples/deployment/modal-example/env.example

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

examples/deployment/modal-example/requirements.txt

@@ -0,0 +1,5 @@
+python-dotenv==1.0.1
+modal==0.65.48
+pipecat-ai[daily,silero,cartesia,openai]==0.0.48
+fastapi==0.115.4
+aiohttp==3.10.10

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

@@ -9,11 +9,11 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import EndFrame, TextFrame
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.task import PipelineTask
 from pipecat.pipeline.runner import PipelineRunner
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
 from runner import configure
@@ -36,7 +36,7 @@ async def main():
             room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
         )
 
-        tts = CartesiaHttpTTSService(
+        tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
@@ -50,12 +50,9 @@ async def main():
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
             participant_name = participant.get("info", {}).get("userName", "")
-            await task.queue_frame(TextFrame(f"Hello there, {participant_name}!"))
-
-        # Register an event handler to exit the application when the user leaves.
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames(
+                [TTSSpeakFrame(f"Hello there, {participant_name}!"), EndFrame()]
+            )
 
         await runner.run(task)
 

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

@@ -9,7 +9,7 @@ import aiohttp
 import os
 import sys
 
-from pipecat.frames.frames import TextFrame
+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
@@ -28,25 +28,24 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 async def main():
-    async with aiohttp.ClientSession() as session:
-        transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
+    transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
 
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+    )
 
-        pipeline = Pipeline([tts, transport.output()])
+    pipeline = Pipeline([tts, transport.output()])
 
-        task = PipelineTask(pipeline)
+    task = PipelineTask(pipeline)
 
-        async def say_something():
-            await asyncio.sleep(1)
-            await task.queue_frame(TextFrame("Hello there!"))
+    async def say_something():
+        await asyncio.sleep(1)
+        await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
 
-        runner = PipelineRunner()
+    runner = PipelineRunner()
 
-        await asyncio.gather(runner.run(task), say_something())
+    await asyncio.gather(runner.run(task), say_something())
 
 
 if __name__ == "__main__":

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

@@ -13,7 +13,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
-from pipecat.services.cartesia import CartesiaHttpTTSService
+from pipecat.services.cartesia import CartesiaTTSService
 from pipecat.services.openai import OpenAILLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
@@ -37,7 +37,7 @@ async def main():
             room_url, None, "Say One Thing From an LLM", DailyParams(audio_out_enabled=True)
         )
 
-        tts = CartesiaHttpTTSService(
+        tts = CartesiaTTSService(
             api_key=os.getenv("CARTESIA_API_KEY"),
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
@@ -57,11 +57,7 @@ async def main():
 
         @transport.event_handler("on_first_participant_joined")
         async def on_first_participant_joined(transport, participant):
-            await task.queue_frame(LLMMessagesFrame(messages))
-
-        @transport.event_handler("on_participant_left")
-        async def on_participant_left(transport, participant, reason):
-            await task.queue_frame(EndFrame())
+            await task.queue_frames([LLMMessagesFrame(messages), EndFrame()])
 
         await runner.run(task)
 

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

@@ -12,7 +12,7 @@ import sys
 from dataclasses import dataclass
 
 from pipecat.frames.frames import (
-    AppFrame,
+    DataFrame,
     Frame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
@@ -42,7 +42,7 @@ logger.add(sys.stderr, level="DEBUG")
 
 
 @dataclass
-class MonthFrame(AppFrame):
+class MonthFrame(DataFrame):
     month: str
 
     def __str__(self):

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

@@ -31,11 +31,11 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
+        (room_url, _) = await configure(session)
 
         transport = DailyTransport(
             room_url,
-            token,
+            None,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,

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

@@ -49,7 +49,7 @@ async def main():
         tts = PlayHTTTSService(
             user_id=os.getenv("PLAYHT_USER_ID"),
             api_key=os.getenv("PLAYHT_API_KEY"),
-            voice_url="s3://voice-cloning-zero-shot/801a663f-efd0-4254-98d0-5c175514c3e8/jennifer/manifest.json",
+            voice_url="s3://voice-cloning-zero-shot/d9ff78ba-d016-47f6-b0ef-dd630f59414e/female-cs/manifest.json",
             params=PlayHTTTSService.InputParams(language=Language.EN),
         )
 

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

@@ -32,11 +32,11 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
+        (room_url, _) = await configure(session)
 
         transport = DailyTransport(
             room_url,
-            token,
+            None,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,

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

@@ -32,11 +32,11 @@ logger.add(sys.stderr, level="DEBUG")
 
 async def main():
     async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
+        (room_url, _) = await configure(session)
 
         transport = DailyTransport(
             room_url,
-            token,
+            None,
             "Respond bot",
             DailyParams(
                 audio_out_enabled=True,

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

@@ -0,0 +1,278 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
+import asyncio
+import os
+import sys
+
+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 (
+    LLMFullResponseStartFrame,
+    LLMFullResponseEndFrame,
+    InputAudioRawFrame,
+    Frame,
+    StartInterruptionFrame,
+    TextFrame,
+    TranscriptionFrame,
+    UserStartedSpeakingFrame,
+    UserStoppedSpeakingFrame,
+)
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+marker = "|----|"
+system_message = f"""
+You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses.
+
+You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
+asked to transcribe what the user said, output an exact, word-for-word transcription.
+
+Your output will be converted to audio so don't include special characters in your answers.
+
+Each time you answer, you should respond in three parts.
+
+1. Transcribe exactly what the user said.
+2. Output the separator field '{marker}'.
+3. Respond to the user's input in a helpful, creative way using only simple text and punctuation.
+
+Example:
+
+User: How many ounces are in a pound?
+
+You: How many ounces are in a pound?
+{marker}
+There are 16 ounces in a pound.
+"""
+
+
+@dataclass
+class MagicDemoTranscriptionFrame(Frame):
+    text: str
+
+
+class UserAudioCollector(FrameProcessor):
+    def __init__(self, context, user_context_aggregator):
+        super().__init__()
+        self._context = context
+        self._user_context_aggregator = user_context_aggregator
+        self._audio_frames = []
+        self._start_secs = 0.2  # this should match VAD start_secs (hardcoding for now)
+        self._user_speaking = False
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TranscriptionFrame):
+            # We could gracefully handle both audio input and text/transcription input ...
+            # but let's leave that as an exercise to the reader. :-)
+            return
+        if isinstance(frame, UserStartedSpeakingFrame):
+            self._user_speaking = True
+        elif isinstance(frame, UserStoppedSpeakingFrame):
+            self._user_speaking = False
+            self._context.add_audio_frames_message(audio_frames=self._audio_frames)
+            await self._user_context_aggregator.push_frame(
+                self._user_context_aggregator.get_context_frame()
+            )
+        elif isinstance(frame, InputAudioRawFrame):
+            if self._user_speaking:
+                self._audio_frames.append(frame)
+            else:
+                # Append the audio frame to our buffer. Treat the buffer as a ring buffer, dropping the oldest
+                # frames as necessary. Assume all audio frames have the same duration.
+                self._audio_frames.append(frame)
+                frame_duration = len(frame.audio) / 16 * frame.num_channels / frame.sample_rate
+                buffer_duration = frame_duration * len(self._audio_frames)
+                while buffer_duration > self._start_secs:
+                    self._audio_frames.pop(0)
+                    buffer_duration -= frame_duration
+
+        await self.push_frame(frame, direction)
+
+
+class TranscriptExtractor(FrameProcessor):
+    def __init__(self, context):
+        super().__init__()
+        self._context = context
+        self._accumulator = ""
+        self._processing_llm_response = False
+        self._accumulating_transcript = False
+
+    def reset(self):
+        self._accumulator = ""
+        self._processing_llm_response = False
+        self._accumulating_transcript = False
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+        if isinstance(frame, LLMFullResponseStartFrame):
+            self._processing_llm_response = True
+            self._accumulating_transcript = True
+        elif isinstance(frame, TextFrame) and self._processing_llm_response:
+            if self._accumulating_transcript:
+                text = frame.text
+                split_index = text.find(marker)
+                if split_index < 0:
+                    self._accumulator += frame.text
+                    # do not push this frame
+                    return
+                else:
+                    self._accumulating_transcript = False
+                    self._accumulator += text[:split_index]
+                    frame.text = text[split_index + len(marker) :]
+            await self.push_frame(frame)
+            return
+        elif isinstance(frame, LLMFullResponseEndFrame):
+            await self.push_frame(MagicDemoTranscriptionFrame(text=self._accumulator.strip()))
+            self.reset()
+
+        await self.push_frame(frame, direction)
+
+
+class TanscriptionContextFixup(FrameProcessor):
+    def __init__(self, context):
+        super().__init__()
+        self._context = context
+        self._transcript = "THIS IS A TRANSCRIPT"
+
+    def swap_user_audio(self):
+        if not self._transcript:
+            return
+        message = self._context.messages[-2]
+        last_part = message.parts[-1]
+        if (
+            message.role == "user"
+            and last_part.inline_data
+            and last_part.inline_data.mime_type == "audio/wav"
+        ):
+            self._context.messages[-2] = glm.Content(
+                role="user", parts=[glm.Part(text=self._transcript)]
+            )
+
+    def add_transcript_back_to_inference_output(self):
+        if not self._transcript:
+            return
+        message = self._context.messages[-1]
+        last_part = message.parts[-1]
+        if message.role == "model" and last_part.text:
+            self._context.messages[-1].parts[-1].text += f"\n\n{marker}\n{self._transcript}\n"
+
+    async def process_frame(self, frame, direction):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, MagicDemoTranscriptionFrame):
+            self._transcript = frame.text
+        elif isinstance(frame, LLMFullResponseEndFrame) or isinstance(
+            frame, StartInterruptionFrame
+        ):
+            self.swap_user_audio()
+            self.add_transcript_back_to_inference_output()
+            self._transcript = ""
+
+        await self.push_frame(frame, direction)
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                # No transcription at all. just audio input to Gemini!
+                # transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = GoogleLLMService(
+            model="gemini-1.5-flash-latest",
+            # model="gemini-exp-1114",
+            api_key=os.getenv("GOOGLE_API_KEY"),
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": system_message,
+            },
+            {
+                "role": "user",
+                "content": "Start by saying hello.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+        audio_collector = UserAudioCollector(context, context_aggregator.user())
+        pull_transcript_out_of_llm_output = TranscriptExtractor(context)
+        fixup_context_messages = TanscriptionContextFixup(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                audio_collector,
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                pull_transcript_out_of_llm_output,
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+                fixup_context_messages,
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07p-interruptible-krisp.py

@@ -0,0 +1,95 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator,
+    LLMUserResponseAggregator,
+)
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+from pipecat.audio.filters.krisp_filter import KrispFilter
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+                audio_in_filter=KrispFilter(),
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                stt,  # STT
+                tma_in,  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                tma_out,  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/07q-interruptible-rime.py

@@ -0,0 +1,100 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.rime import RimeHttpTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = RimeHttpTTSService(
+            api_key=os.getenv("RIME_API_KEY", ""),
+            voice_id="rex",
+            params=RimeHttpTTSService.InputParams(reduce_latency=True),
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),  # Transport user input
+                context_aggregator.user(),  # User responses
+                llm,  # LLM
+                tts,  # TTS
+                transport.output(),  # Transport bot output
+                context_aggregator.assistant(),  # Assistant spoken responses
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+                report_only_initial_ttfb=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -67,7 +67,8 @@ async def main():
 
         llm = AnthropicLLMService(
             api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-5-sonnet-20240620",
+            # model="claude-3-5-sonnet-20240620",
+            model="claude-3-5-sonnet-latest",
             enable_prompt_caching_beta=True,
         )
         llm.register_function("get_weather", get_weather)

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

@@ -5,10 +5,15 @@
 #
 
 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
@@ -18,14 +23,6 @@ from pipecat.services.openai import OpenAILLMContext
 from pipecat.services.together import TogetherLLMService
 from pipecat.transports.services.daily import DailyParams, DailyTransport
 
-from openai.types.chat import ChatCompletionToolParam
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-
 load_dotenv(override=True)
 
 logger.remove(0)
@@ -125,7 +122,7 @@ async def main():
         async def on_first_participant_joined(transport, participant):
             await transport.capture_participant_transcription(participant["id"])
             # Kick off the conversation.
-            # await tts.say("Hi! Ask me about the weather in San Francisco.")
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
 
         runner = PipelineRunner()
 

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

@@ -64,7 +64,11 @@ async def main():
             voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
         )
 
-        llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
+        llm = GoogleLLMService(
+            model="gemini-1.5-flash-latest",
+            # model="gemini-exp-1114",
+            api_key=os.getenv("GOOGLE_API_KEY"),
+        )
         llm.register_function("get_weather", get_weather)
         llm.register_function("get_image", get_image)
 
@@ -151,7 +155,6 @@ indicate you should use the get_image tool are:
                 allow_interruptions=True,
                 enable_metrics=True,
                 enable_usage_metrics=True,
-                report_only_initial_ttfb=True,
             ),
         )
 

examples/foundational/14f-function-calling-groq.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.groq import GroqLLMService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = GroqLLMService(
+            api_key=os.getenv("GROQ_API_KEY"), model="llama3-groq-70b-8192-tool-use-preview"
+        )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "unit": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,137 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.grok import GrokLLMService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,141 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from openai.types.chat import ChatCompletionToolParam
+from runner import configure
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.services.azure import AzureLLMService
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.openai import OpenAILLMContext
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def start_fetch_weather(function_name, llm, context):
+    # note: we can't push a frame to the LLM here. the bot
+    # can interrupt itself and/or cause audio overlapping glitches.
+    # possible question for Aleix and Chad about what the right way
+    # to trigger speech is, now, with the new queues/async/sync refactors.
+    # await llm.push_frame(TextFrame("Let me check on that."))
+    logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
+
+
+async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback):
+    await result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/14i-function-calling-fireworks.py

@@ -0,0 +1,140 @@
+#
+# 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.fireworks import FireworksLLMService
+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 = FireworksLLMService(
+            api_key=os.getenv("FIREWORKS_API_KEY"),
+            model="accounts/fireworks/models/firefunction-v2",
+        )
+        # Register a function_name of None to get all functions
+        # sent to the same callback with an additional function_name parameter.
+        llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
+
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "get_current_weather",
+                    "description": "Get the current weather",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "location": {
+                                "type": "string",
+                                "description": "The city and state, e.g. San Francisco, CA",
+                            },
+                            "format": {
+                                "type": "string",
+                                "enum": ["celsius", "fahrenheit"],
+                                "description": "The temperature unit to use. Infer this from the users location.",
+                            },
+                        },
+                        "required": ["location", "format"],
+                    },
+                },
+            )
+        ]
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages, tools)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                context_aggregator.user(),
+                llm,
+                tts,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+                enable_usage_metrics=True,
+            ),
+        )
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            await transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -98,12 +98,13 @@ async def load_conversation(function_name, tool_call_id, args, llm, context, res
 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.",
+        "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 succinct, creative and helpful way. Prefer responses that are one sentence long unless you are asked for a longer or more detailed response.",
     },
-    {"role": "user", "content": ""},
-    {"role": "assistant", "content": []},
-    {"role": "user", "content": "Tell me"},
-    {"role": "user", "content": "a joke"},
+    {"role": "user", "content": "Start the call by saying the word 'hello'. Say only that word."},
+    # {"role": "user", "content": ""},
+    # {"role": "assistant", "content": []},
+    # {"role": "user", "content": "Tell me"},
+    # {"role": "user", "content": "a joke"},
 ]
 tools = [
     {
@@ -183,7 +184,7 @@ async def main():
         )
 
         llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
+            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-latest"
         )
 
         # you can either register a single function for all function calls, or specific functions

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

@@ -0,0 +1,339 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
+import asyncio
+import os
+import sys
+import time
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesFrame, TextFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import (
+    OpenAILLMContext,
+)
+from pipecat.services.cartesia import CartesiaTTSService
+from pipecat.services.deepgram import DeepgramSTTService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.sync.event_notifier import EventNotifier
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from 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)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+classifier_statement = "Determine if the user's statement ends with a complete thought and you should respond. The user text is transcribed speech. It may contain multiple fragments concatentated together. You are trying to determine only the completeness of the last user statement. The previous assistant statement is provided only for context. Categorize the text as either complete with the user now expecting a response, or incomplete. Return 'YES' if text is likely complete and the user is expecting a response. Return 'NO' if the text seems to be a partial expression or unfinished thought."
+
+
+class StatementJudgeContextFilter(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
+        super().__init__(**kwargs)
+        self._notifier = notifier
+
+    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
+
+        # Just treat an LLMMessagesFrame as complete, no matter what.
+        if isinstance(frame, LLMMessagesFrame):
+            await self._notifier.notify()
+            return
+
+        # Otherwise, we only want to handle OpenAILLMContextFrames, and only want to push a simple
+        # messages frame that contains a system prompt and the most recent user messages,
+        # concatenated.
+        if isinstance(frame, OpenAILLMContextFrame):
+            logger.debug(f"Context Frame: {frame}")
+            # Take text content from the most recent user messages.
+            messages = frame.context.messages
+            user_text_messages = []
+            last_assistant_message = None
+            for message in reversed(messages):
+                if message["role"] != "user":
+                    if message["role"] == "assistant":
+                        last_assistant_message = message
+                    break
+                if isinstance(message["content"], str):
+                    user_text_messages.append(message["content"])
+                elif isinstance(message["content"], list):
+                    for content in message["content"]:
+                        if content["type"] == "text":
+                            user_text_messages.insert(0, content["text"])
+            # If we have any user text content, push an LLMMessagesFrame
+            if user_text_messages:
+                logger.debug(f"User text messages: {user_text_messages}")
+                user_message = " ".join(reversed(user_text_messages))
+                logger.debug(f"User message: {user_message}")
+                messages = [
+                    {
+                        "role": "system",
+                        "content": classifier_statement,
+                    }
+                ]
+                if last_assistant_message:
+                    messages.append(last_assistant_message)
+                messages.append({"role": "user", "content": user_message})
+                await self.push_frame(LLMMessagesFrame(messages))
+
+
+class CompletenessCheck(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier):
+        super().__init__()
+        self._notifier = notifier
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+        if isinstance(frame, TextFrame) and frame.text == "YES":
+            logger.debug("Completeness check YES")
+            await self.push_frame(UserStoppedSpeakingFrame())
+            await self._notifier.notify()
+        elif isinstance(frame, TextFrame) and frame.text == "NO":
+            logger.debug("Completeness check NO")
+
+
+class OutputGate(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
+        super().__init__(**kwargs)
+        self._gate_open = False
+        self._frames_buffer = []
+        self._notifier = notifier
+
+    def close_gate(self):
+        self._gate_open = False
+
+    def open_gate(self):
+        self._gate_open = True
+
+    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):
+            if isinstance(frame, StartFrame):
+                await self._start()
+            if isinstance(frame, (EndFrame, CancelFrame)):
+                await self._stop()
+            if isinstance(frame, StartInterruptionFrame):
+                self._frames_buffer = []
+                self.close_gate()
+            await self.push_frame(frame, direction)
+            return
+
+        # Ignore frames that are not following the direction of this gate.
+        if direction != FrameDirection.DOWNSTREAM:
+            await self.push_frame(frame, direction)
+            return
+
+        if self._gate_open:
+            await self.push_frame(frame, direction)
+            return
+
+        self._frames_buffer.append((frame, direction))
+
+    async def _start(self):
+        self._frames_buffer = []
+        self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
+
+    async def _stop(self):
+        self._gate_task.cancel()
+        await self._gate_task
+
+    async def _gate_task_handler(self):
+        while True:
+            try:
+                await self._notifier.wait()
+                self.open_gate()
+                for frame, direction in self._frames_buffer:
+                    await self.push_frame(frame, direction)
+                self._frames_buffer = []
+            except asyncio.CancelledError:
+                break
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we might as well!
+        statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        # This is the regular LLM.
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
+        # predicate which will wake up the notifier.
+        async def wake_check_filter(frame):
+            logger.debug(f"Completeness check frame: {frame}")
+            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 = StatementJudgeContextFilter(notifier=notifier)
+
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(notifier=notifier)
+
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        bot_output_gate = OutputGate(notifier=notifier)
+
+        async def block_user_stopped_speaking(frame):
+            return not isinstance(frame, UserStoppedSpeakingFrame)
+
+        async def pass_only_llm_trigger_frames(frame):
+            return (
+                isinstance(frame, OpenAILLMContextFrame)
+                or isinstance(frame, LLMMessagesFrame)
+                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, StopInterruptionFrame)
+            )
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                stt,
+                context_aggregator.user(),
+                ParallelPipeline(
+                    [
+                        # Pass everything except UserStoppedSpeaking to the elements after
+                        # this ParallelPipeline
+                        FunctionFilter(filter=block_user_stopped_speaking),
+                    ],
+                    [
+                        # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+                        # LLMMessagesFrame to the statement classifier LLM. The only frame this
+                        # sub-pipeline will output is a UserStoppedSpeakingFrame.
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
+                    ],
+                    [
+                        # 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(
+            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)])
+
+        @transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            logger.debug(f"Received app message: {message} - {sender}")
+            if "message" not in message:
+                return
+
+            await task.queue_frames(
+                [
+                    UserStartedSpeakingFrame(),
+                    TranscriptionFrame(
+                        user_id=sender, timestamp=time.time(), text=message["message"]
+                    ),
+                    UserStoppedSpeakingFrame(),
+                ]
+            )
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -0,0 +1,551 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+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.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.filters.function_filter import FunctionFilter
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.user_idle_processor import UserIdleProcessor
+from pipecat.services.anthropic import AnthropicLLMService
+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
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+classifier_statement = """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.
+
+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
+[{"role": "assistant", "content": "I can help you learn."}, 
+ {"role": "user", "content": "What's the fastest way to learn Spanish"}]
+Output: YES
+
+# Complete Yes/No question despite STT error
+[{"role": "assistant", "content": "I know about planets."}, 
+ {"role": "user", "content": "Is is Jupiter the biggest planet"}]
+Output: YES
+
+2. Complete Commands:
+- Direct instructions
+- Clear requests
+- Action demands
+- Complete statements needing response
+
+Examples:
+# Direct instruction
+[{"role": "assistant", "content": "I can explain many topics."}, 
+ {"role": "user", "content": "Tell me about black holes"}]
+Output: YES
+
+# Action demand
+[{"role": "assistant", "content": "I can help with math."}, 
+ {"role": "user", "content": "Solve this equation x plus 5 equals 12"}]
+Output: YES
+
+3. Direct Responses:
+- Answers to specific questions
+- Option selections
+- Clear acknowledgments with completion
+
+Examples:
+# Specific answer
+[{"role": "assistant", "content": "What's your favorite color?"}, 
+ {"role": "user", "content": "I really like blue"}]
+Output: YES
+
+# Option selection
+[{"role": "assistant", "content": "Would you prefer morning or evening?"}, 
+ {"role": "user", "content": "Morning"}]
+Output: YES
+
+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
+[{"role": "assistant", "content": "What would you like to know?"}, 
+ {"role": "user", "content": "Tell me about... no wait, explain how rainbows form"}]
+Output: YES
+
+# Topic change with complete thought
+[{"role": "assistant", "content": "The weather is nice today."}, 
+ {"role": "user", "content": "Actually can you tell me who invented the telephone"}]
+Output: YES
+
+# Mid-sentence completion
+[{"role": "assistant", "content": "Hello I'm ready."}, 
+ {"role": "user", "content": "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
+[{"role": "assistant", "content": "Should we talk about history?"}, 
+ {"role": "user", "content": "Sure"}]
+Output: YES
+
+# Disagreement with completion
+[{"role": "assistant", "content": "Is that what you meant?"}, 
+ {"role": "user", "content": "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
+[{"role": "assistant", "content": "I can help with that."}, 
+ {"role": "user", "content": "What what is the time right now"}]
+Output: YES
+
+# Missing punctuation but complete
+[{"role": "assistant", "content": "I can explain that."}, 
+ {"role": "user", "content": "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
+[{"role": "assistant", "content": "What would you like to know?"}, 
+ {"role": "user", "content": "Um uh how do airplanes fly"}]
+Output: YES
+
+# Thinking pause but incomplete
+[{"role": "assistant", "content": "I can explain anything."}, 
+ {"role": "user", "content": "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
+[{"role": "assistant", "content": "What would you like to know about?"}, 
+ {"role": "user", "content": "Can you tell me about"}]
+Output: NO
+
+# Complete despite multiple artifacts
+[{"role": "assistant", "content": "I can help you learn."}, 
+ {"role": "user", "content": "How do you I mean what's the best way to learn programming"}]
+Output: YES
+
+# Trailing off incomplete
+[{"role": "assistant", "content": "I can explain anything."}, 
+ {"role": "user", "content": "I was wondering if you could tell me why"}]
+Output: 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.
+
+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 StatementJudgeContextFilter(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
+        super().__init__(**kwargs)
+        self._notifier = notifier
+
+    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
+
+        # Just treat an LLMMessagesFrame as complete, no matter what.
+        if isinstance(frame, LLMMessagesFrame):
+            await self._notifier.notify()
+            return
+
+        # Otherwise, we only want to handle OpenAILLMContextFrames, and only want to push a simple
+        # messages frame that contains a system prompt and the most recent user messages,
+        # concatenated.
+        if isinstance(frame, OpenAILLMContextFrame):
+            # Take text content from the most recent user messages.
+            messages = frame.context.messages
+            user_text_messages = []
+            last_assistant_message = None
+            for message in reversed(messages):
+                if message["role"] != "user":
+                    if message["role"] == "assistant":
+                        last_assistant_message = message
+                    break
+                if isinstance(message["content"], str):
+                    user_text_messages.append(message["content"])
+                elif isinstance(message["content"], list):
+                    for content in message["content"]:
+                        if content["type"] == "text":
+                            user_text_messages.insert(0, content["text"])
+            # If we have any user text content, push an LLMMessagesFrame
+            if user_text_messages:
+                user_message = " ".join(reversed(user_text_messages))
+                logger.debug(f"!!! {user_message}")
+                messages = [
+                    {
+                        "role": "system",
+                        "content": classifier_statement,
+                    }
+                ]
+                if last_assistant_message:
+                    messages.append(last_assistant_message)
+                messages.append({"role": "user", "content": user_message})
+                await self.push_frame(LLMMessagesFrame(messages))
+
+
+class CompletenessCheck(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier):
+        super().__init__()
+        self._notifier = notifier
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TextFrame) and frame.text == "YES":
+            logger.debug("!!! Completeness check YES")
+            await self.push_frame(UserStoppedSpeakingFrame())
+            await self._notifier.notify()
+        elif isinstance(frame, TextFrame) and frame.text == "NO":
+            logger.debug("!!! Completeness check NO")
+
+
+class OutputGate(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
+        super().__init__(**kwargs)
+        self._gate_open = False
+        self._frames_buffer = []
+        self._notifier = notifier
+
+    def close_gate(self):
+        self._gate_open = False
+
+    def open_gate(self):
+        self._gate_open = True
+
+    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):
+            if isinstance(frame, StartFrame):
+                await self._start()
+            if isinstance(frame, (EndFrame, CancelFrame)):
+                await self._stop()
+            if isinstance(frame, StartInterruptionFrame):
+                self._frames_buffer = []
+                self.close_gate()
+            await self.push_frame(frame, direction)
+            return
+
+        # Ignore frames that are not following the direction of this gate.
+        if direction != FrameDirection.DOWNSTREAM:
+            await self.push_frame(frame, direction)
+            return
+
+        if self._gate_open:
+            await self.push_frame(frame, direction)
+            return
+
+        self._frames_buffer.append((frame, direction))
+
+    async def _start(self):
+        self._frames_buffer = []
+        self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
+
+    async def _stop(self):
+        self._gate_task.cancel()
+        await self._gate_task
+
+    async def _gate_task_handler(self):
+        while True:
+            try:
+                await self._notifier.wait()
+                self.open_gate()
+                for frame, direction in self._frames_buffer:
+                    await self.push_frame(frame, direction)
+                self._frames_buffer = []
+            except asyncio.CancelledError:
+                break
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but we have the machinery to use an LLM, so we might as well!
+        statement_llm = AnthropicLLMService(
+            api_key=os.getenv("ANTHROPIC_API_KEY"),
+            model="claude-3-5-sonnet-20241022",
+        )
+
+        # This is the regular LLM.
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o",
+        )
+
+        messages = [
+            {
+                "role": "system",
+                "content": conversational_system_message,
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
+        # predicate which will wake up the notifier.
+        async def wake_check_filter(frame):
+            return frame.text == "YES"
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This turns the LLM context into an inference request to classify the user's speech
+        # as complete or incomplete.
+        statement_judge_context_filter = StatementJudgeContextFilter(notifier=notifier)
+
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(notifier=notifier)
+
+        # # Notify if the user hasn't said anything.
+        async def user_idle_notifier(frame):
+            await notifier.notify()
+
+        # Sometimes the LLM will fail detecting if a user has completed a
+        # sentence, this will wake up the notifier if that happens.
+        user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
+
+        bot_output_gate = OutputGate(notifier=notifier)
+
+        async def block_user_stopped_speaking(frame):
+            return not isinstance(frame, UserStoppedSpeakingFrame)
+
+        async def pass_only_llm_trigger_frames(frame):
+            return (
+                isinstance(frame, OpenAILLMContextFrame)
+                or isinstance(frame, LLMMessagesFrame)
+                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, StopInterruptionFrame)
+            )
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                stt,
+                context_aggregator.user(),
+                ParallelPipeline(
+                    [
+                        # Pass everything except UserStoppedSpeaking to the elements after
+                        # this ParallelPipeline
+                        FunctionFilter(filter=block_user_stopped_speaking),
+                    ],
+                    [
+                        # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
+                        # LLMMessagesFrame to the statement classifier LLM. The only frame this
+                        # sub-pipeline will output is a UserStoppedSpeakingFrame.
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
+                    ],
+                    [
+                        # 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(
+            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.
+            messages.append(
+                {
+                    "role": "user",
+                    "content": "Start by just saying \"Hello I'm ready.\" Don't say anything else.",
+                }
+            )
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        @transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            logger.debug(f"Received app message: {message} - {sender}")
+            if "message" not in message:
+                return
+
+            await task.queue_frames(
+                [
+                    UserStartedSpeakingFrame(),
+                    TranscriptionFrame(
+                        user_id=sender, timestamp=time.time(), text=message["message"]
+                    ),
+                    UserStoppedSpeakingFrame(),
+                ]
+            )
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,355 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import aiohttp
+import asyncio
+import os
+import sys
+import time
+
+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,
+    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)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+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.
+
+Categorize the input you receive as either:
+
+1.  a complete thought, statement, or question, or
+2.  an incomplete thought, statement, or question
+
+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'.
+"""
+
+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.
+
+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 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._user_speaking = False
+
+    async def reset(self):
+        self._audio_frames = []
+        self._user_speaking = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        # ignore context frame
+        if isinstance(frame, OpenAILLMContextFrame):
+            return
+
+        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
+            context = GoogleLLMContext()
+            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):
+            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 CompletenessCheck(FrameProcessor):
+    def __init__(
+        self, notifier: BaseNotifier, audio_accumulator: StatementJudgeAudioContextAccumulator
+    ):
+        super().__init__()
+        self._notifier = notifier
+        self._audio_accumulator = audio_accumulator
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, TextFrame) and frame.text.startswith("YES"):
+            logger.debug("Completeness check YES")
+            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}'")
+
+
+class OutputGate(FrameProcessor):
+    def __init__(self, notifier: BaseNotifier, **kwargs):
+        super().__init__(**kwargs)
+        self._gate_open = False
+        self._frames_buffer = []
+        self._notifier = notifier
+
+    def close_gate(self):
+        self._gate_open = False
+
+    def open_gate(self):
+        self._gate_open = True
+
+    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):
+            if isinstance(frame, StartFrame):
+                await self._start()
+            if isinstance(frame, (EndFrame, CancelFrame)):
+                await self._stop()
+            if isinstance(frame, StartInterruptionFrame):
+                self._frames_buffer = []
+                self.close_gate()
+            await self.push_frame(frame, direction)
+            return
+
+        # Ignore frames that are not following the direction of this gate.
+        if direction != FrameDirection.DOWNSTREAM:
+            await self.push_frame(frame, direction)
+            return
+
+        if self._gate_open:
+            await self.push_frame(frame, direction)
+            return
+
+        self._frames_buffer.append((frame, direction))
+
+    async def _start(self):
+        self._frames_buffer = []
+        self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
+
+    async def _stop(self):
+        self._gate_task.cancel()
+        await self._gate_task
+
+    async def _gate_task_handler(self):
+        while True:
+            try:
+                await self._notifier.wait()
+                self.open_gate()
+                for frame, direction in self._frames_buffer:
+                    await self.push_frame(frame, direction)
+                self._frames_buffer = []
+            except asyncio.CancelledError:
+                break
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        (room_url, _) = await configure(session)
+
+        transport = DailyTransport(
+            room_url,
+            None,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        # This is the LLM that will be used to detect if the user has finished a
+        # statement. This doesn't really need to be an LLM, we could use NLP
+        # libraries for that, but 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 regular LLM.
+        llm = GoogleLLMService(model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY"))
+
+        messages = [
+            {
+                "role": "system",
+                "content": conversational_system_message,
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        # We have instructed the LLM to return 'YES' if it thinks the user
+        # completed a sentence. So, if it's 'YES' we will return true in this
+        # predicate which will wake up the notifier.
+        async def wake_check_filter(frame):
+            return frame.text == "YES"
+
+        # This is a notifier that we use to synchronize the two LLMs.
+        notifier = EventNotifier()
+
+        # This 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)
+
+        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
+        completeness_check = CompletenessCheck(
+            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)
+
+        async def pass_only_llm_trigger_frames(frame):
+            return (
+                isinstance(frame, OpenAILLMContextFrame)
+                or isinstance(frame, LLMMessagesFrame)
+                or isinstance(frame, StartInterruptionFrame)
+                or isinstance(frame, StopInterruptionFrame)
+            )
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                ParallelPipeline(
+                    [
+                        # Pass everything except UserStoppedSpeaking to the elements after
+                        # this ParallelPipeline
+                        FunctionFilter(filter=block_user_stopped_speaking),
+                    ],
+                    [
+                        statement_judge_context_filter,
+                        statement_llm,
+                        completeness_check,
+                    ],
+                    [
+                        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(
+            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()])
+
+        @transport.event_handler("on_app_message")
+        async def on_app_message(transport, message, sender):
+            logger.debug(f"Received app message: {message} - {sender}")
+            if "message" not in message:
+                return
+
+            await task.queue_frames(
+                [
+                    UserStartedSpeakingFrame(),
+                    TranscriptionFrame(
+                        user_id=sender, timestamp=time.time(), text=message["message"]
+                    ),
+                    UserStoppedSpeakingFrame(),
+                ]
+            )
+
+        runner = PipelineRunner()
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

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

@@ -0,0 +1,121 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.audio.mixers.soundfile_mixer import SoundfileMixer
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+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
+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_with_args
+
+from loguru import logger
+
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main():
+    async with aiohttp.ClientSession() as session:
+        parser = argparse.ArgumentParser(description="Bot Background Sound")
+        parser.add_argument("-i", "--input", type=str, required=True, help="Input audio file")
+
+        (room_url, token, args) = await configure_with_args(session, parser)
+
+        soundfile_mixer = SoundfileMixer(
+            sound_files={"office": args.input},
+            default_sound="office",
+            volume=2.0,
+        )
+
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                audio_out_mixer=soundfile_mixer,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            ),
+        )
+
+        tts = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
+
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        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"])
+            # Show how to use mixer control frames.
+            await asyncio.sleep(10.0)
+            await task.queue_frame(MixerUpdateSettingsFrame({"volume": 0.5}))
+            await asyncio.sleep(5.0)
+            await task.queue_frame(MixerEnableFrame(False))
+            await asyncio.sleep(5.0)
+            await task.queue_frame(MixerEnableFrame(True))
+            await asyncio.sleep(5.0)
+            # 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/24-stt-mute-filter.py

@@ -0,0 +1,98 @@
+#
+# 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.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
+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_out_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+                vad_audio_passthrough=True,
+            ),
+        )
+
+        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+        # Configure the mute processor to mute only during first speech
+        stt_mute_processor = STTMuteFilter(
+            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")
+
+        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_mute_processor,  # Add the mute processor before STT
+                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/99-anthropic-hackathon.py

@@ -1,298 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import base64
-import io
-import os
-import sys
-from collections import deque
-
-import aiohttp
-from dotenv import load_dotenv
-from loguru import logger
-from PIL import Image
-from runner import configure
-
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import (
-    BotInterruptionFrame,
-    Frame,
-    ImageRawFrame,
-    LLMFullResponseEndFrame,
-    LLMMessagesFrame,
-    TextFrame,
-    TranscriptionFrame,
-)
-from pipecat.pipeline.parallel_pipeline import ParallelPipeline
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.processors.frameworks.rtvi import (
-    RTVIBotTranscriptionProcessor,
-    RTVIUserTranscriptionProcessor,
-)
-from pipecat.services.anthropic import AnthropicLLMContext, AnthropicLLMService
-from pipecat.services.cartesia import CartesiaTTSService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-MAX_FRAMES = 5
-FRAMES_PER_SECOND = 0.2
-
-
-video_participant_id = None
-anthropic_context = None
-recent_image_frames = deque(maxlen=MAX_FRAMES)
-most_recent_image_summary = ""
-
-
-class ImageFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        global recent_image_frames
-
-        await super().process_frame(frame, direction)
-        if isinstance(frame, ImageRawFrame):
-            recent_image_frames.append(frame)
-        else:
-            await self.push_frame(frame, direction)
-
-
-class TranscriptFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, TranscriptionFrame):
-            logger.debug(
-                f"TranscriptLogger: {frame}, num frames: {len(recent_image_frames)}, anthropic context: {anthropic_context}"
-            )
-            if anthropic_context:
-                add_message_with_images(
-                    anthropic_context, frame.text, frames=list(recent_image_frames)
-                )
-        await self.push_frame(frame, direction)
-
-
-class MessageFrameCatcher(FrameProcessor):
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        await super().process_frame(frame, direction)
-        if isinstance(frame, OpenAILLMContextFrame):
-            last_message = frame.context.messages[-1]
-
-            system_message = """
-Give me a concise summary of the images supplied.
-            """
-            frame = LLMMessagesFrame(
-                messages=[
-                    {
-                        "role": "system",
-                        "content": system_message,
-                    },
-                    last_message,
-                ],
-            )
-            await self.push_frame(frame, direction)
-            return
-
-
-class MessageFrameCatcher2(FrameProcessor):
-    def __init__(self):
-        super().__init__()
-        self.text_blob = ""
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        global most_recent_image_summary
-        await super().process_frame(frame, direction)
-        if isinstance(frame, TextFrame):
-            self.text_blob += f" {frame.text}"
-
-        if isinstance(frame, LLMFullResponseEndFrame):
-            logger.debug(f"MessageFrameCatcher2: {self.text_blob}")
-            most_recent_image_summary = self.text_blob
-            self.text_blob = ""
-
-        await self.push_frame(frame, direction)
-
-
-async def main():
-    global llm
-    global anthropic_context
-
-    async with aiohttp.ClientSession() as session:
-        (room_url, token) = await configure(session)
-
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer(),
-            ),
-        )
-
-        tts = CartesiaTTSService(
-            api_key=os.getenv("CARTESIA_API_KEY"),
-            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-        )
-
-        llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-5-sonnet-20240620",
-            enable_prompt_caching_beta=True,
-        )
-
-        vision_llm = AnthropicLLMService(
-            api_key=os.getenv("ANTHROPIC_API_KEY"),
-            model="claude-3-5-sonnet-20240620",
-            enable_prompt_caching_beta=True,
-        )
-
-        # todo: test with very short initial user message
-
-        system_prompt = """\
-You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions. Keep
-your answers brief unless explicitly asked for more information.
-
-Your response will be turned into speech so use only simple words and punctuation.
-        """
-
-        messages = [
-            {
-                "role": "system",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": system_prompt,
-                    }
-                ],
-            },
-            {"role": "user", "content": "Start the conversation by saying 'hello'."},
-        ]
-
-        context = OpenAILLMContext(messages)
-        anthropic_context = AnthropicLLMContext.upgrade_to_anthropic(context)
-        context_aggregator = llm.create_context_aggregator(context)
-
-        rtvi_user_transcription = RTVIUserTranscriptionProcessor()
-        rtvi_bot_transcription = RTVIBotTranscriptionProcessor()
-
-        pipeline = Pipeline(
-            [
-                transport.input(),  # Transport user input
-                ImageFrameCatcher(),
-                TranscriptFrameCatcher(),
-                rtvi_user_transcription,
-                context_aggregator.user(),  # User speech to text
-                ParallelPipeline(
-                    [
-                        llm,  # LLM
-                        rtvi_bot_transcription,
-                        tts,  # TTS
-                        transport.output(),  # Transport bot output
-                        context_aggregator.assistant(),  # Assistant spoken responses and tool context
-                    ],
-                    [MessageFrameCatcher(), vision_llm, MessageFrameCatcher2()],
-                ),
-            ],
-        )
-
-        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
-
-        @transport.event_handler("on_first_participant_joined")
-        async def on_first_participant_joined(transport, participant):
-            global video_participant_id
-            video_participant_id = participant["id"]
-            await transport.capture_participant_transcription(video_participant_id)
-            await transport.capture_participant_video(
-                video_participant_id, framerate=FRAMES_PER_SECOND, video_source="screenVideo"
-            )
-            # Kick off the conversation.
-            await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        @transport.event_handler("on_app_message")
-        async def on_app_message(transport, message, sender):
-            logger.debug(f"Received app message: {message} - {context}")
-
-            if not recent_image_frames:
-                logger.debug("No image frames to send")
-                return
-
-            add_message_with_images(
-                anthropic_context, message["message"], frames=list(recent_image_frames)
-            )
-
-            interrupt_message = "STOP"
-
-            if interrupt_message == message["message"]:
-                logger.debug("Interrupting")
-                await task.queue_frames([BotInterruptionFrame()])
-            else:
-                await task.queue_frames([context_aggregator.user().get_context_frame()])
-
-        runner = PipelineRunner()
-        await runner.run(task)
-
-
-def add_message_with_images(c, message, frames=None):
-    if frames is None:
-        frames = list(recent_image_frames)
-
-    if not frames:
-        logger.debug("No image frames to send")
-        return
-
-    # Create content list starting with all images
-    content = []
-    for frame in frames:
-        buffer = io.BytesIO()
-        Image.frombytes(frame.format, frame.size, frame.image).save(buffer, format="JPEG")
-        encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
-
-        content.append(
-            {
-                "type": "image",
-                "source": {
-                    "type": "base64",
-                    "media_type": "image/jpeg",
-                    "data": encoded_image,
-                },
-            }
-        )
-
-    # Add text message at the end if provided
-    if message:
-        content.append({"type": "text", "text": message})
-
-    # Go through all messages and replace user messages containing images
-    if c.messages:
-        for i, msg in enumerate(c.messages):
-            if (
-                msg["role"] == "user"
-                and isinstance(msg["content"], list)
-                and len(msg["content"]) > 0
-            ):
-                if msg["content"][0].get("type") == "image":
-                    logger.debug(
-                        f"Replacing user message {i} containing images with summary: {most_recent_image_summary}"
-                    )
-                    c.messages[i] = {"role": "user", "content": most_recent_image_summary}
-
-    c.add_message({"role": "user", "content": content})
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

examples/patient-intake/README.md

@@ -62,3 +62,11 @@ Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
 docker build -t chatbot .
 docker run --env-file .env -p 7860:7860 chatbot
 ```
+## Cartesia best practices
+
+Since this example is using Cartesia, checkout the best practices given in Cartesia's docs. LLM prompts should be modified accordingly.
+<https://docs.cartesia.ai/build-with-sonic/formatting-text-for-sonic/best-practices>
+
+<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>

examples/patient-intake/bot.py

@@ -182,7 +182,7 @@ class IntakeProcessor:
             }
         )
         print(f"!!! about to await llm process frame in start prescrpitions")
-        await llm.process_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
         print(f"!!! past await process frame in start prescriptions")
 
     async def start_allergies(self, function_name, llm, context):
@@ -222,7 +222,7 @@ class IntakeProcessor:
                 "content": "Now ask the user if they have any medical conditions the doctor should know about. Once they've answered the question, call the list_conditions function.",
             }
         )
-        await llm.process_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
 
     async def start_conditions(self, function_name, llm, context):
         print("!!! doing start conditions")
@@ -261,7 +261,7 @@ class IntakeProcessor:
                 "content": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
             }
         )
-        await llm.process_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
 
     async def start_visit_reasons(self, function_name, llm, context):
         print("!!! doing start visit reasons")
@@ -270,7 +270,7 @@ class IntakeProcessor:
         context.add_message(
             {"role": "system", "content": "Now, thank the user and end the conversation."}
         )
-        await llm.process_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
+        await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM)
 
     async def save_data(self, function_name, tool_call_id, args, llm, context, result_callback):
         logger.info(f"!!! Saving data: {args}")

examples/simple-chatbot-fullstack/README.md

@@ -0,0 +1,60 @@
+# Simple Chatbot Full Stack
+
+A full-stack implementation of an AI chatbot with real-time audio/video interaction.
+
+## Structure
+
+- `server/` - Python-based bot server using FastAPI
+- `client/` - JavaScript client using RTVI and Daily.co for WebRTC
+
+## Setup
+
+### Server Setup
+
+1. Navigate to the server directory:
+   ```bash
+   cd server
+   ```
+2. Create and activate a virtual environment:
+   ```bash
+   python3 -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+3. Install requirements:
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. Copy env.example to .env and add your credentials
+5. Start the server:
+   ```bash
+   python server.py
+   ```
+
+### Client Setup
+
+1. Navigate to the client directory:
+   ```bash
+   cd client
+   ```
+2. Install dependencies:
+   ```bash
+   npm install
+   ```
+3. Start the development server:
+   ```bash
+   npm run dev
+   ```
+4. Open the URL shown in the terminal (usually http://localhost:5173)
+
+## Usage
+
+1. Start the server (it will run on port 7860)
+2. Start the client server (it will run on port 5173)
+3. Open http://localhost:5173 in your browser
+4. Click "Connect" to start a session with the bot
+
+## Requirements
+
+- Python 3.10+
+- Node.js 14+
+- Modern web browser with WebRTC support

examples/simple-chatbot-fullstack/examples/javascript/index.html

@@ -0,0 +1,40 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>AI Chatbot</title>
+</head>
+
+<body>
+  <div class="container">
+    <div class="status-bar">
+      <div class="status">
+        Status: <span id="connection-status">Disconnected</span>
+      </div>
+      <div class="controls">
+        <button id="connect-btn">Connect</button>
+        <button id="disconnect-btn" disabled>Disconnect</button>
+      </div>
+    </div>
+
+    <div class="main-content">
+      <div class="bot-container">
+        <div id="bot-video-container">
+        </div>
+        <audio id="bot-audio" autoplay></audio>
+      </div>
+    </div>
+
+    <div class="debug-panel">
+      <h3>Debug Info</h3>
+      <div id="debug-log"></div>
+    </div>
+  </div>
+
+  <script type="module" src="/src/app.js"></script>
+  <link rel="stylesheet" href="/src/style.css">
+</body>
+
+</html>

examples/simple-chatbot-fullstack/examples/javascript/node_modules/.bin/esbuild

@@ -0,0 +1 @@
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examples/simple-chatbot-fullstack/examples/javascript/node_modules/.bin/nanoid

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examples/simple-chatbot-fullstack/examples/javascript/node_modules/.bin/rollup

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examples/simple-chatbot-fullstack/examples/javascript/node_modules/.bin/uuid

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examples/simple-chatbot-fullstack/examples/javascript/node_modules/.bin/vite

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+        "npm": ">=8.0.0"
+      },
+      "optionalDependencies": {
+        "@rollup/rollup-android-arm-eabi": "4.28.0",
+        "@rollup/rollup-android-arm64": "4.28.0",
+        "@rollup/rollup-darwin-arm64": "4.28.0",
+        "@rollup/rollup-darwin-x64": "4.28.0",
+        "@rollup/rollup-freebsd-arm64": "4.28.0",
+        "@rollup/rollup-freebsd-x64": "4.28.0",
+        "@rollup/rollup-linux-arm-gnueabihf": "4.28.0",
+        "@rollup/rollup-linux-arm-musleabihf": "4.28.0",
+        "@rollup/rollup-linux-arm64-gnu": "4.28.0",
+        "@rollup/rollup-linux-arm64-musl": "4.28.0",
+        "@rollup/rollup-linux-powerpc64le-gnu": "4.28.0",
+        "@rollup/rollup-linux-riscv64-gnu": "4.28.0",
+        "@rollup/rollup-linux-s390x-gnu": "4.28.0",
+        "@rollup/rollup-linux-x64-gnu": "4.28.0",
+        "@rollup/rollup-linux-x64-musl": "4.28.0",
+        "@rollup/rollup-win32-arm64-msvc": "4.28.0",
+        "@rollup/rollup-win32-ia32-msvc": "4.28.0",
+        "@rollup/rollup-win32-x64-msvc": "4.28.0",
+        "fsevents": "~2.3.2"
+      }
+    },
+    "node_modules/rxjs": {
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+      "resolved": "https://registry.npmjs.org/rxjs/-/rxjs-7.8.1.tgz",
+      "integrity": "sha512-AA3TVj+0A2iuIoQkWEK/tqFjBq2j+6PO6Y0zJcvzLAFhEFIO3HL0vls9hWLncZbAAbK0mar7oZ4V079I/qPMxg==",
+      "license": "Apache-2.0",
+      "optional": true,
+      "dependencies": {
+        "tslib": "^2.1.0"
+      }
+    },
+    "node_modules/shallow-clone": {
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/shallow-clone/-/shallow-clone-3.0.1.tgz",
+      "integrity": "sha512-/6KqX+GVUdqPuPPd2LxDDxzX6CAbjJehAAOKlNpqqUpAqPM6HeL8f+o3a+JsyGjn2lv0WY8UsTgUJjU9Ok55NA==",
+      "license": "MIT",
+      "dependencies": {
+        "kind-of": "^6.0.2"
+      },
+      "engines": {
+        "node": ">=8"
+      }
+    },
+    "node_modules/source-map-js": {
+      "version": "1.2.1",
+      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
+      "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
+      "dev": true,
+      "license": "BSD-3-Clause",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tslib": {
+      "version": "2.8.1",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz",
+      "integrity": "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w==",
+      "license": "0BSD",
+      "optional": true
+    },
+    "node_modules/typed-emitter": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/typed-emitter/-/typed-emitter-2.1.0.tgz",
+      "integrity": "sha512-g/KzbYKbH5C2vPkaXGu8DJlHrGKHLsM25Zg9WuC9pMGfuvT+X25tZQWo5fK1BjBm8+UrVE9LDCvaY0CQk+fXDA==",
+      "license": "MIT",
+      "optionalDependencies": {
+        "rxjs": "*"
+      }
+    },
+    "node_modules/uuid": {
+      "version": "10.0.0",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-10.0.0.tgz",
+      "integrity": "sha512-8XkAphELsDnEGrDxUOHB3RGvXz6TeuYSGEZBOjtTtPm2lwhGBjLgOzLHB63IUWfBpNucQjND6d3AOudO+H3RWQ==",
+      "funding": [
+        "https://github.com/sponsors/broofa",
+        "https://github.com/sponsors/ctavan"
+      ],
+      "license": "MIT",
+      "bin": {
+        "uuid": "dist/bin/uuid"
+      }
+    },
+    "node_modules/vite": {
+      "version": "6.0.2",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-6.0.2.tgz",
+      "integrity": "sha512-XdQ+VsY2tJpBsKGs0wf3U/+azx8BBpYRHFAyKm5VeEZNOJZRB63q7Sc8Iup3k0TrN3KO6QgyzFf+opSbfY1y0g==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "esbuild": "^0.24.0",
+        "postcss": "^8.4.49",
+        "rollup": "^4.23.0"
+      },
+      "bin": {
+        "vite": "bin/vite.js"
+      },
+      "engines": {
+        "node": "^18.0.0 || ^20.0.0 || >=22.0.0"
+      },
+      "funding": {
+        "url": "https://github.com/vitejs/vite?sponsor=1"
+      },
+      "optionalDependencies": {
+        "fsevents": "~2.3.3"
+      },
+      "peerDependencies": {
+        "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0",
+        "jiti": ">=1.21.0",
+        "less": "*",
+        "lightningcss": "^1.21.0",
+        "sass": "*",
+        "sass-embedded": "*",
+        "stylus": "*",
+        "sugarss": "*",
+        "terser": "^5.16.0",
+        "tsx": "^4.8.1",
+        "yaml": "^2.4.2"
+      },
+      "peerDependenciesMeta": {
+        "@types/node": {
+          "optional": true
+        },
+        "jiti": {
+          "optional": true
+        },
+        "less": {
+          "optional": true
+        },
+        "lightningcss": {
+          "optional": true
+        },
+        "sass": {
+          "optional": true
+        },
+        "sass-embedded": {
+          "optional": true
+        },
+        "stylus": {
+          "optional": true
+        },
+        "sugarss": {
+          "optional": true
+        },
+        "terser": {
+          "optional": true
+        },
+        "tsx": {
+          "optional": true
+        },
+        "yaml": {
+          "optional": true
+        }
+      }
+    }
+  }
+}

examples/simple-chatbot-fullstack/examples/javascript/node_modules/.vite/deps/_metadata.json

@@ -0,0 +1,25 @@
+{
+  "hash": "5b0fc1aa",
+  "configHash": "b261e656",
+  "lockfileHash": "49cc3cc9",
+  "browserHash": "0db32c31",
+  "optimized": {
+    "@daily-co/realtime-ai-daily": {
+      "src": "../../@daily-co/realtime-ai-daily/dist/index.module.js",
+      "file": "@daily-co_realtime-ai-daily.js",
+      "fileHash": "1c7897e6",
+      "needsInterop": false
+    },
+    "realtime-ai": {
+      "src": "../../realtime-ai/dist/index.module.js",
+      "file": "realtime-ai.js",
+      "fileHash": "ba7c0239",
+      "needsInterop": false
+    }
+  },
+  "chunks": {
+    "chunk-MC2NFNB2": {
+      "file": "chunk-MC2NFNB2.js"
+    }
+  }
+}

examples/simple-chatbot-fullstack/examples/javascript/node_modules/.vite/deps/package.json

@@ -0,0 +1,3 @@
+{
+  "type": "module"
+}

examples/simple-chatbot-fullstack/examples/javascript/node_modules/.vite/deps/realtime-ai.js

@@ -0,0 +1,46 @@
+import {
+  $08bedc6ef0d1c66c$export$4eda4fd287fbbca5,
+  $08bedc6ef0d1c66c$export$59b4786f333aac02,
+  $08bedc6ef0d1c66c$export$885fb96b850e8fbb,
+  $08bedc6ef0d1c66c$export$975d7330b0c579b7,
+  $08bedc6ef0d1c66c$export$c67992fa684a81a6,
+  $08bedc6ef0d1c66c$export$e0624a511a2c4e9,
+  $08bedc6ef0d1c66c$export$e7544ab812238a61,
+  $2665d8e6d1596258$export$86495b081fef8e52,
+  $4a333e41af7a850f$export$3cf39a62d076dd5c,
+  $4a333e41af7a850f$export$441bcd2e10762760,
+  $505461462111ea0b$export$23bc637255b2a471,
+  $74c1449bc91bda44$export$fa42a01c1d60f4a1,
+  $bff4129f8f902365$export$28ad8d0d400d3e2d,
+  $bff4129f8f902365$export$3336fb47fe34a146,
+  $bff4129f8f902365$export$378529d7a8bead8b,
+  $bff4129f8f902365$export$38b3db05cbf0e240,
+  $bff4129f8f902365$export$69aa9ab0334b212,
+  $bff4129f8f902365$export$882b13c7fda338f5,
+  $bff4129f8f902365$export$e9a960646cc432aa,
+  $cbe8e0de0049ed6e$export$6b4624d233c61fcb,
+  $d881613f2029ce0c$export$8728b60ea57bf43e
+} from "./chunk-MC2NFNB2.js";
+export {
+  $08bedc6ef0d1c66c$export$885fb96b850e8fbb as BotNotReadyError,
+  $08bedc6ef0d1c66c$export$4eda4fd287fbbca5 as ConfigUpdateError,
+  $08bedc6ef0d1c66c$export$c67992fa684a81a6 as ConnectionTimeoutError,
+  $4a333e41af7a850f$export$3cf39a62d076dd5c as LLMHelper,
+  $4a333e41af7a850f$export$441bcd2e10762760 as LLMMessageType,
+  $bff4129f8f902365$export$e9a960646cc432aa as MessageDispatcher,
+  $bff4129f8f902365$export$378529d7a8bead8b as RTVIActionRequest,
+  $74c1449bc91bda44$export$fa42a01c1d60f4a1 as RTVIClient,
+  $505461462111ea0b$export$23bc637255b2a471 as RTVIClientHelper,
+  $08bedc6ef0d1c66c$export$59b4786f333aac02 as RTVIError,
+  $cbe8e0de0049ed6e$export$6b4624d233c61fcb as RTVIEvent,
+  $bff4129f8f902365$export$69aa9ab0334b212 as RTVIMessage,
+  $bff4129f8f902365$export$38b3db05cbf0e240 as RTVIMessageType,
+  $bff4129f8f902365$export$28ad8d0d400d3e2d as RTVI_ACTION_TYPE,
+  $bff4129f8f902365$export$882b13c7fda338f5 as RTVI_MESSAGE_LABEL,
+  $08bedc6ef0d1c66c$export$e7544ab812238a61 as StartBotError,
+  $2665d8e6d1596258$export$86495b081fef8e52 as Transport,
+  $08bedc6ef0d1c66c$export$e0624a511a2c4e9 as TransportStartError,
+  $08bedc6ef0d1c66c$export$975d7330b0c579b7 as VoiceError,
+  $bff4129f8f902365$export$3336fb47fe34a146 as VoiceMessage,
+  $d881613f2029ce0c$export$8728b60ea57bf43e as httpActionGenerator
+};

examples/simple-chatbot-fullstack/examples/javascript/node_modules/.vite/deps/realtime-ai.js.map

@@ -0,0 +1,7 @@
+{
+  "version": 3,
+  "sources": [],
+  "sourcesContent": [],
+  "mappings": "",
+  "names": []
+}

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/LICENSE

@@ -0,0 +1,22 @@
+MIT License
+
+Copyright (c) 2014-present Sebastian McKenzie and other contributors
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/README.md

@@ -0,0 +1,19 @@
+# @babel/runtime
+
+> babel's modular runtime helpers
+
+See our website [@babel/runtime](https://babeljs.io/docs/babel-runtime) for more information.
+
+## Install
+
+Using npm:
+
+```sh
+npm install --save @babel/runtime
+```
+
+or using yarn:
+
+```sh
+yarn add @babel/runtime
+```

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/AwaitValue.js

@@ -0,0 +1,4 @@
+function _AwaitValue(t) {
+  this.wrapped = t;
+}
+module.exports = _AwaitValue, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/OverloadYield.js

@@ -0,0 +1,4 @@
+function _OverloadYield(e, d) {
+  this.v = e, this.k = d;
+}
+module.exports = _OverloadYield, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecoratedDescriptor.js

@@ -0,0 +1,9 @@
+function _applyDecoratedDescriptor(i, e, r, n, l) {
+  var a = {};
+  return Object.keys(n).forEach(function (i) {
+    a[i] = n[i];
+  }), a.enumerable = !!a.enumerable, a.configurable = !!a.configurable, ("value" in a || a.initializer) && (a.writable = !0), a = r.slice().reverse().reduce(function (r, n) {
+    return n(i, e, r) || r;
+  }, a), l && void 0 !== a.initializer && (a.value = a.initializer ? a.initializer.call(l) : void 0, a.initializer = void 0), void 0 === a.initializer ? (Object.defineProperty(i, e, a), null) : a;
+}
+module.exports = _applyDecoratedDescriptor, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs.js

@@ -0,0 +1,236 @@
+var _typeof = require("./typeof.js")["default"];
+var setFunctionName = require("./setFunctionName.js");
+var toPropertyKey = require("./toPropertyKey.js");
+function old_createMetadataMethodsForProperty(e, t, a, r) {
+  return {
+    getMetadata: function getMetadata(o) {
+      old_assertNotFinished(r, "getMetadata"), old_assertMetadataKey(o);
+      var i = e[o];
+      if (void 0 !== i) if (1 === t) {
+        var n = i["public"];
+        if (void 0 !== n) return n[a];
+      } else if (2 === t) {
+        var l = i["private"];
+        if (void 0 !== l) return l.get(a);
+      } else if (Object.hasOwnProperty.call(i, "constructor")) return i.constructor;
+    },
+    setMetadata: function setMetadata(o, i) {
+      old_assertNotFinished(r, "setMetadata"), old_assertMetadataKey(o);
+      var n = e[o];
+      if (void 0 === n && (n = e[o] = {}), 1 === t) {
+        var l = n["public"];
+        void 0 === l && (l = n["public"] = {}), l[a] = i;
+      } else if (2 === t) {
+        var s = n.priv;
+        void 0 === s && (s = n["private"] = new Map()), s.set(a, i);
+      } else n.constructor = i;
+    }
+  };
+}
+function old_convertMetadataMapToFinal(e, t) {
+  var a = e[Symbol.metadata || Symbol["for"]("Symbol.metadata")],
+    r = Object.getOwnPropertySymbols(t);
+  if (0 !== r.length) {
+    for (var o = 0; o < r.length; o++) {
+      var i = r[o],
+        n = t[i],
+        l = a ? a[i] : null,
+        s = n["public"],
+        c = l ? l["public"] : null;
+      s && c && Object.setPrototypeOf(s, c);
+      var d = n["private"];
+      if (d) {
+        var u = Array.from(d.values()),
+          f = l ? l["private"] : null;
+        f && (u = u.concat(f)), n["private"] = u;
+      }
+      l && Object.setPrototypeOf(n, l);
+    }
+    a && Object.setPrototypeOf(t, a), e[Symbol.metadata || Symbol["for"]("Symbol.metadata")] = t;
+  }
+}
+function old_createAddInitializerMethod(e, t) {
+  return function (a) {
+    old_assertNotFinished(t, "addInitializer"), old_assertCallable(a, "An initializer"), e.push(a);
+  };
+}
+function old_memberDec(e, t, a, r, o, i, n, l, s) {
+  var c;
+  switch (i) {
+    case 1:
+      c = "accessor";
+      break;
+    case 2:
+      c = "method";
+      break;
+    case 3:
+      c = "getter";
+      break;
+    case 4:
+      c = "setter";
+      break;
+    default:
+      c = "field";
+  }
+  var d,
+    u,
+    f = {
+      kind: c,
+      name: l ? "#" + t : toPropertyKey(t),
+      isStatic: n,
+      isPrivate: l
+    },
+    p = {
+      v: !1
+    };
+  if (0 !== i && (f.addInitializer = old_createAddInitializerMethod(o, p)), l) {
+    d = 2, u = Symbol(t);
+    var v = {};
+    0 === i ? (v.get = a.get, v.set = a.set) : 2 === i ? v.get = function () {
+      return a.value;
+    } : (1 !== i && 3 !== i || (v.get = function () {
+      return a.get.call(this);
+    }), 1 !== i && 4 !== i || (v.set = function (e) {
+      a.set.call(this, e);
+    })), f.access = v;
+  } else d = 1, u = t;
+  try {
+    return e(s, Object.assign(f, old_createMetadataMethodsForProperty(r, d, u, p)));
+  } finally {
+    p.v = !0;
+  }
+}
+function old_assertNotFinished(e, t) {
+  if (e.v) throw Error("attempted to call " + t + " after decoration was finished");
+}
+function old_assertMetadataKey(e) {
+  if ("symbol" != _typeof(e)) throw new TypeError("Metadata keys must be symbols, received: " + e);
+}
+function old_assertCallable(e, t) {
+  if ("function" != typeof e) throw new TypeError(t + " must be a function");
+}
+function old_assertValidReturnValue(e, t) {
+  var a = _typeof(t);
+  if (1 === e) {
+    if ("object" !== a || null === t) throw new TypeError("accessor decorators must return an object with get, set, or init properties or void 0");
+    void 0 !== t.get && old_assertCallable(t.get, "accessor.get"), void 0 !== t.set && old_assertCallable(t.set, "accessor.set"), void 0 !== t.init && old_assertCallable(t.init, "accessor.init"), void 0 !== t.initializer && old_assertCallable(t.initializer, "accessor.initializer");
+  } else if ("function" !== a) throw new TypeError((0 === e ? "field" : 10 === e ? "class" : "method") + " decorators must return a function or void 0");
+}
+function old_getInit(e) {
+  var t;
+  return null == (t = e.init) && (t = e.initializer) && void 0 !== console && console.warn(".initializer has been renamed to .init as of March 2022"), t;
+}
+function old_applyMemberDec(e, t, a, r, o, i, n, l, s) {
+  var c,
+    d,
+    u,
+    f,
+    p,
+    v,
+    y,
+    h = a[0];
+  if (n ? (0 === o || 1 === o ? (c = {
+    get: a[3],
+    set: a[4]
+  }, u = "get") : 3 === o ? (c = {
+    get: a[3]
+  }, u = "get") : 4 === o ? (c = {
+    set: a[3]
+  }, u = "set") : c = {
+    value: a[3]
+  }, 0 !== o && (1 === o && setFunctionName(a[4], "#" + r, "set"), setFunctionName(a[3], "#" + r, u))) : 0 !== o && (c = Object.getOwnPropertyDescriptor(t, r)), 1 === o ? f = {
+    get: c.get,
+    set: c.set
+  } : 2 === o ? f = c.value : 3 === o ? f = c.get : 4 === o && (f = c.set), "function" == typeof h) void 0 !== (p = old_memberDec(h, r, c, l, s, o, i, n, f)) && (old_assertValidReturnValue(o, p), 0 === o ? d = p : 1 === o ? (d = old_getInit(p), v = p.get || f.get, y = p.set || f.set, f = {
+    get: v,
+    set: y
+  }) : f = p);else for (var m = h.length - 1; m >= 0; m--) {
+    var b;
+    void 0 !== (p = old_memberDec(h[m], r, c, l, s, o, i, n, f)) && (old_assertValidReturnValue(o, p), 0 === o ? b = p : 1 === o ? (b = old_getInit(p), v = p.get || f.get, y = p.set || f.set, f = {
+      get: v,
+      set: y
+    }) : f = p, void 0 !== b && (void 0 === d ? d = b : "function" == typeof d ? d = [d, b] : d.push(b)));
+  }
+  if (0 === o || 1 === o) {
+    if (void 0 === d) d = function d(e, t) {
+      return t;
+    };else if ("function" != typeof d) {
+      var g = d;
+      d = function d(e, t) {
+        for (var a = t, r = 0; r < g.length; r++) a = g[r].call(e, a);
+        return a;
+      };
+    } else {
+      var _ = d;
+      d = function d(e, t) {
+        return _.call(e, t);
+      };
+    }
+    e.push(d);
+  }
+  0 !== o && (1 === o ? (c.get = f.get, c.set = f.set) : 2 === o ? c.value = f : 3 === o ? c.get = f : 4 === o && (c.set = f), n ? 1 === o ? (e.push(function (e, t) {
+    return f.get.call(e, t);
+  }), e.push(function (e, t) {
+    return f.set.call(e, t);
+  })) : 2 === o ? e.push(f) : e.push(function (e, t) {
+    return f.call(e, t);
+  }) : Object.defineProperty(t, r, c));
+}
+function old_applyMemberDecs(e, t, a, r, o) {
+  for (var i, n, l = new Map(), s = new Map(), c = 0; c < o.length; c++) {
+    var d = o[c];
+    if (Array.isArray(d)) {
+      var u,
+        f,
+        p,
+        v = d[1],
+        y = d[2],
+        h = d.length > 3,
+        m = v >= 5;
+      if (m ? (u = t, f = r, 0 != (v -= 5) && (p = n = n || [])) : (u = t.prototype, f = a, 0 !== v && (p = i = i || [])), 0 !== v && !h) {
+        var b = m ? s : l,
+          g = b.get(y) || 0;
+        if (!0 === g || 3 === g && 4 !== v || 4 === g && 3 !== v) throw Error("Attempted to decorate a public method/accessor that has the same name as a previously decorated public method/accessor. This is not currently supported by the decorators plugin. Property name was: " + y);
+        !g && v > 2 ? b.set(y, v) : b.set(y, !0);
+      }
+      old_applyMemberDec(e, u, d, y, v, m, h, f, p);
+    }
+  }
+  old_pushInitializers(e, i), old_pushInitializers(e, n);
+}
+function old_pushInitializers(e, t) {
+  t && e.push(function (e) {
+    for (var a = 0; a < t.length; a++) t[a].call(e);
+    return e;
+  });
+}
+function old_applyClassDecs(e, t, a, r) {
+  if (r.length > 0) {
+    for (var o = [], i = t, n = t.name, l = r.length - 1; l >= 0; l--) {
+      var s = {
+        v: !1
+      };
+      try {
+        var c = Object.assign({
+            kind: "class",
+            name: n,
+            addInitializer: old_createAddInitializerMethod(o, s)
+          }, old_createMetadataMethodsForProperty(a, 0, n, s)),
+          d = r[l](i, c);
+      } finally {
+        s.v = !0;
+      }
+      void 0 !== d && (old_assertValidReturnValue(10, d), i = d);
+    }
+    e.push(i, function () {
+      for (var e = 0; e < o.length; e++) o[e].call(i);
+    });
+  }
+}
+function applyDecs(e, t, a) {
+  var r = [],
+    o = {},
+    i = {};
+  return old_applyMemberDecs(r, e, i, o, t), old_convertMetadataMapToFinal(e.prototype, i), old_applyClassDecs(r, e, o, a), old_convertMetadataMapToFinal(e, o), r;
+}
+module.exports = applyDecs, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs2203.js

@@ -0,0 +1,184 @@
+var _typeof = require("./typeof.js")["default"];
+function applyDecs2203Factory() {
+  function createAddInitializerMethod(e, t) {
+    return function (r) {
+      !function (e, t) {
+        if (e.v) throw Error("attempted to call addInitializer after decoration was finished");
+      }(t), assertCallable(r, "An initializer"), e.push(r);
+    };
+  }
+  function memberDec(e, t, r, a, n, i, s, o) {
+    var c;
+    switch (n) {
+      case 1:
+        c = "accessor";
+        break;
+      case 2:
+        c = "method";
+        break;
+      case 3:
+        c = "getter";
+        break;
+      case 4:
+        c = "setter";
+        break;
+      default:
+        c = "field";
+    }
+    var l,
+      u,
+      f = {
+        kind: c,
+        name: s ? "#" + t : t,
+        "static": i,
+        "private": s
+      },
+      p = {
+        v: !1
+      };
+    0 !== n && (f.addInitializer = createAddInitializerMethod(a, p)), 0 === n ? s ? (l = r.get, u = r.set) : (l = function l() {
+      return this[t];
+    }, u = function u(e) {
+      this[t] = e;
+    }) : 2 === n ? l = function l() {
+      return r.value;
+    } : (1 !== n && 3 !== n || (l = function l() {
+      return r.get.call(this);
+    }), 1 !== n && 4 !== n || (u = function u(e) {
+      r.set.call(this, e);
+    })), f.access = l && u ? {
+      get: l,
+      set: u
+    } : l ? {
+      get: l
+    } : {
+      set: u
+    };
+    try {
+      return e(o, f);
+    } finally {
+      p.v = !0;
+    }
+  }
+  function assertCallable(e, t) {
+    if ("function" != typeof e) throw new TypeError(t + " must be a function");
+  }
+  function assertValidReturnValue(e, t) {
+    var r = _typeof(t);
+    if (1 === e) {
+      if ("object" !== r || null === t) throw new TypeError("accessor decorators must return an object with get, set, or init properties or void 0");
+      void 0 !== t.get && assertCallable(t.get, "accessor.get"), void 0 !== t.set && assertCallable(t.set, "accessor.set"), void 0 !== t.init && assertCallable(t.init, "accessor.init");
+    } else if ("function" !== r) throw new TypeError((0 === e ? "field" : 10 === e ? "class" : "method") + " decorators must return a function or void 0");
+  }
+  function applyMemberDec(e, t, r, a, n, i, s, o) {
+    var c,
+      l,
+      u,
+      f,
+      p,
+      d,
+      h = r[0];
+    if (s ? c = 0 === n || 1 === n ? {
+      get: r[3],
+      set: r[4]
+    } : 3 === n ? {
+      get: r[3]
+    } : 4 === n ? {
+      set: r[3]
+    } : {
+      value: r[3]
+    } : 0 !== n && (c = Object.getOwnPropertyDescriptor(t, a)), 1 === n ? u = {
+      get: c.get,
+      set: c.set
+    } : 2 === n ? u = c.value : 3 === n ? u = c.get : 4 === n && (u = c.set), "function" == typeof h) void 0 !== (f = memberDec(h, a, c, o, n, i, s, u)) && (assertValidReturnValue(n, f), 0 === n ? l = f : 1 === n ? (l = f.init, p = f.get || u.get, d = f.set || u.set, u = {
+      get: p,
+      set: d
+    }) : u = f);else for (var v = h.length - 1; v >= 0; v--) {
+      var g;
+      void 0 !== (f = memberDec(h[v], a, c, o, n, i, s, u)) && (assertValidReturnValue(n, f), 0 === n ? g = f : 1 === n ? (g = f.init, p = f.get || u.get, d = f.set || u.set, u = {
+        get: p,
+        set: d
+      }) : u = f, void 0 !== g && (void 0 === l ? l = g : "function" == typeof l ? l = [l, g] : l.push(g)));
+    }
+    if (0 === n || 1 === n) {
+      if (void 0 === l) l = function l(e, t) {
+        return t;
+      };else if ("function" != typeof l) {
+        var y = l;
+        l = function l(e, t) {
+          for (var r = t, a = 0; a < y.length; a++) r = y[a].call(e, r);
+          return r;
+        };
+      } else {
+        var m = l;
+        l = function l(e, t) {
+          return m.call(e, t);
+        };
+      }
+      e.push(l);
+    }
+    0 !== n && (1 === n ? (c.get = u.get, c.set = u.set) : 2 === n ? c.value = u : 3 === n ? c.get = u : 4 === n && (c.set = u), s ? 1 === n ? (e.push(function (e, t) {
+      return u.get.call(e, t);
+    }), e.push(function (e, t) {
+      return u.set.call(e, t);
+    })) : 2 === n ? e.push(u) : e.push(function (e, t) {
+      return u.call(e, t);
+    }) : Object.defineProperty(t, a, c));
+  }
+  function pushInitializers(e, t) {
+    t && e.push(function (e) {
+      for (var r = 0; r < t.length; r++) t[r].call(e);
+      return e;
+    });
+  }
+  return function (e, t, r) {
+    var a = [];
+    return function (e, t, r) {
+      for (var a, n, i = new Map(), s = new Map(), o = 0; o < r.length; o++) {
+        var c = r[o];
+        if (Array.isArray(c)) {
+          var l,
+            u,
+            f = c[1],
+            p = c[2],
+            d = c.length > 3,
+            h = f >= 5;
+          if (h ? (l = t, 0 != (f -= 5) && (u = n = n || [])) : (l = t.prototype, 0 !== f && (u = a = a || [])), 0 !== f && !d) {
+            var v = h ? s : i,
+              g = v.get(p) || 0;
+            if (!0 === g || 3 === g && 4 !== f || 4 === g && 3 !== f) throw Error("Attempted to decorate a public method/accessor that has the same name as a previously decorated public method/accessor. This is not currently supported by the decorators plugin. Property name was: " + p);
+            !g && f > 2 ? v.set(p, f) : v.set(p, !0);
+          }
+          applyMemberDec(e, l, c, p, f, h, d, u);
+        }
+      }
+      pushInitializers(e, a), pushInitializers(e, n);
+    }(a, e, t), function (e, t, r) {
+      if (r.length > 0) {
+        for (var a = [], n = t, i = t.name, s = r.length - 1; s >= 0; s--) {
+          var o = {
+            v: !1
+          };
+          try {
+            var c = r[s](n, {
+              kind: "class",
+              name: i,
+              addInitializer: createAddInitializerMethod(a, o)
+            });
+          } finally {
+            o.v = !0;
+          }
+          void 0 !== c && (assertValidReturnValue(10, c), n = c);
+        }
+        e.push(n, function () {
+          for (var e = 0; e < a.length; e++) a[e].call(n);
+        });
+      }
+    }(a, e, r), a;
+  };
+}
+var applyDecs2203Impl;
+function applyDecs2203(e, t, r) {
+  return (applyDecs2203Impl = applyDecs2203Impl || applyDecs2203Factory())(e, t, r);
+}
+module.exports = applyDecs2203, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs2203R.js

@@ -0,0 +1,191 @@
+var _typeof = require("./typeof.js")["default"];
+var setFunctionName = require("./setFunctionName.js");
+var toPropertyKey = require("./toPropertyKey.js");
+function applyDecs2203RFactory() {
+  function createAddInitializerMethod(e, t) {
+    return function (r) {
+      !function (e, t) {
+        if (e.v) throw Error("attempted to call addInitializer after decoration was finished");
+      }(t), assertCallable(r, "An initializer"), e.push(r);
+    };
+  }
+  function memberDec(e, t, r, n, a, i, o, s) {
+    var c;
+    switch (a) {
+      case 1:
+        c = "accessor";
+        break;
+      case 2:
+        c = "method";
+        break;
+      case 3:
+        c = "getter";
+        break;
+      case 4:
+        c = "setter";
+        break;
+      default:
+        c = "field";
+    }
+    var l,
+      u,
+      f = {
+        kind: c,
+        name: o ? "#" + t : toPropertyKey(t),
+        "static": i,
+        "private": o
+      },
+      p = {
+        v: !1
+      };
+    0 !== a && (f.addInitializer = createAddInitializerMethod(n, p)), 0 === a ? o ? (l = r.get, u = r.set) : (l = function l() {
+      return this[t];
+    }, u = function u(e) {
+      this[t] = e;
+    }) : 2 === a ? l = function l() {
+      return r.value;
+    } : (1 !== a && 3 !== a || (l = function l() {
+      return r.get.call(this);
+    }), 1 !== a && 4 !== a || (u = function u(e) {
+      r.set.call(this, e);
+    })), f.access = l && u ? {
+      get: l,
+      set: u
+    } : l ? {
+      get: l
+    } : {
+      set: u
+    };
+    try {
+      return e(s, f);
+    } finally {
+      p.v = !0;
+    }
+  }
+  function assertCallable(e, t) {
+    if ("function" != typeof e) throw new TypeError(t + " must be a function");
+  }
+  function assertValidReturnValue(e, t) {
+    var r = _typeof(t);
+    if (1 === e) {
+      if ("object" !== r || null === t) throw new TypeError("accessor decorators must return an object with get, set, or init properties or void 0");
+      void 0 !== t.get && assertCallable(t.get, "accessor.get"), void 0 !== t.set && assertCallable(t.set, "accessor.set"), void 0 !== t.init && assertCallable(t.init, "accessor.init");
+    } else if ("function" !== r) throw new TypeError((0 === e ? "field" : 10 === e ? "class" : "method") + " decorators must return a function or void 0");
+  }
+  function applyMemberDec(e, t, r, n, a, i, o, s) {
+    var c,
+      l,
+      u,
+      f,
+      p,
+      d,
+      h,
+      v = r[0];
+    if (o ? (0 === a || 1 === a ? (c = {
+      get: r[3],
+      set: r[4]
+    }, u = "get") : 3 === a ? (c = {
+      get: r[3]
+    }, u = "get") : 4 === a ? (c = {
+      set: r[3]
+    }, u = "set") : c = {
+      value: r[3]
+    }, 0 !== a && (1 === a && setFunctionName(r[4], "#" + n, "set"), setFunctionName(r[3], "#" + n, u))) : 0 !== a && (c = Object.getOwnPropertyDescriptor(t, n)), 1 === a ? f = {
+      get: c.get,
+      set: c.set
+    } : 2 === a ? f = c.value : 3 === a ? f = c.get : 4 === a && (f = c.set), "function" == typeof v) void 0 !== (p = memberDec(v, n, c, s, a, i, o, f)) && (assertValidReturnValue(a, p), 0 === a ? l = p : 1 === a ? (l = p.init, d = p.get || f.get, h = p.set || f.set, f = {
+      get: d,
+      set: h
+    }) : f = p);else for (var g = v.length - 1; g >= 0; g--) {
+      var y;
+      void 0 !== (p = memberDec(v[g], n, c, s, a, i, o, f)) && (assertValidReturnValue(a, p), 0 === a ? y = p : 1 === a ? (y = p.init, d = p.get || f.get, h = p.set || f.set, f = {
+        get: d,
+        set: h
+      }) : f = p, void 0 !== y && (void 0 === l ? l = y : "function" == typeof l ? l = [l, y] : l.push(y)));
+    }
+    if (0 === a || 1 === a) {
+      if (void 0 === l) l = function l(e, t) {
+        return t;
+      };else if ("function" != typeof l) {
+        var m = l;
+        l = function l(e, t) {
+          for (var r = t, n = 0; n < m.length; n++) r = m[n].call(e, r);
+          return r;
+        };
+      } else {
+        var b = l;
+        l = function l(e, t) {
+          return b.call(e, t);
+        };
+      }
+      e.push(l);
+    }
+    0 !== a && (1 === a ? (c.get = f.get, c.set = f.set) : 2 === a ? c.value = f : 3 === a ? c.get = f : 4 === a && (c.set = f), o ? 1 === a ? (e.push(function (e, t) {
+      return f.get.call(e, t);
+    }), e.push(function (e, t) {
+      return f.set.call(e, t);
+    })) : 2 === a ? e.push(f) : e.push(function (e, t) {
+      return f.call(e, t);
+    }) : Object.defineProperty(t, n, c));
+  }
+  function applyMemberDecs(e, t) {
+    for (var r, n, a = [], i = new Map(), o = new Map(), s = 0; s < t.length; s++) {
+      var c = t[s];
+      if (Array.isArray(c)) {
+        var l,
+          u,
+          f = c[1],
+          p = c[2],
+          d = c.length > 3,
+          h = f >= 5;
+        if (h ? (l = e, 0 != (f -= 5) && (u = n = n || [])) : (l = e.prototype, 0 !== f && (u = r = r || [])), 0 !== f && !d) {
+          var v = h ? o : i,
+            g = v.get(p) || 0;
+          if (!0 === g || 3 === g && 4 !== f || 4 === g && 3 !== f) throw Error("Attempted to decorate a public method/accessor that has the same name as a previously decorated public method/accessor. This is not currently supported by the decorators plugin. Property name was: " + p);
+          !g && f > 2 ? v.set(p, f) : v.set(p, !0);
+        }
+        applyMemberDec(a, l, c, p, f, h, d, u);
+      }
+    }
+    return pushInitializers(a, r), pushInitializers(a, n), a;
+  }
+  function pushInitializers(e, t) {
+    t && e.push(function (e) {
+      for (var r = 0; r < t.length; r++) t[r].call(e);
+      return e;
+    });
+  }
+  return function (e, t, r) {
+    return {
+      e: applyMemberDecs(e, t),
+      get c() {
+        return function (e, t) {
+          if (t.length > 0) {
+            for (var r = [], n = e, a = e.name, i = t.length - 1; i >= 0; i--) {
+              var o = {
+                v: !1
+              };
+              try {
+                var s = t[i](n, {
+                  kind: "class",
+                  name: a,
+                  addInitializer: createAddInitializerMethod(r, o)
+                });
+              } finally {
+                o.v = !0;
+              }
+              void 0 !== s && (assertValidReturnValue(10, s), n = s);
+            }
+            return [n, function () {
+              for (var e = 0; e < r.length; e++) r[e].call(n);
+            }];
+          }
+        }(e, r);
+      }
+    };
+  };
+}
+function applyDecs2203R(e, t, r) {
+  return (module.exports = applyDecs2203R = applyDecs2203RFactory(), module.exports.__esModule = true, module.exports["default"] = module.exports)(e, t, r);
+}
+module.exports = applyDecs2203R, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs2301.js

@@ -0,0 +1,222 @@
+var _typeof = require("./typeof.js")["default"];
+var checkInRHS = require("./checkInRHS.js");
+var setFunctionName = require("./setFunctionName.js");
+var toPropertyKey = require("./toPropertyKey.js");
+function applyDecs2301Factory() {
+  function createAddInitializerMethod(e, t) {
+    return function (r) {
+      !function (e, t) {
+        if (e.v) throw Error("attempted to call addInitializer after decoration was finished");
+      }(t), assertCallable(r, "An initializer"), e.push(r);
+    };
+  }
+  function assertInstanceIfPrivate(e, t) {
+    if (!e(t)) throw new TypeError("Attempted to access private element on non-instance");
+  }
+  function memberDec(e, t, r, n, a, i, s, o, c) {
+    var u;
+    switch (a) {
+      case 1:
+        u = "accessor";
+        break;
+      case 2:
+        u = "method";
+        break;
+      case 3:
+        u = "getter";
+        break;
+      case 4:
+        u = "setter";
+        break;
+      default:
+        u = "field";
+    }
+    var l,
+      f,
+      p = {
+        kind: u,
+        name: s ? "#" + t : toPropertyKey(t),
+        "static": i,
+        "private": s
+      },
+      d = {
+        v: !1
+      };
+    if (0 !== a && (p.addInitializer = createAddInitializerMethod(n, d)), s || 0 !== a && 2 !== a) {
+      if (2 === a) l = function l(e) {
+        return assertInstanceIfPrivate(c, e), r.value;
+      };else {
+        var h = 0 === a || 1 === a;
+        (h || 3 === a) && (l = s ? function (e) {
+          return assertInstanceIfPrivate(c, e), r.get.call(e);
+        } : function (e) {
+          return r.get.call(e);
+        }), (h || 4 === a) && (f = s ? function (e, t) {
+          assertInstanceIfPrivate(c, e), r.set.call(e, t);
+        } : function (e, t) {
+          r.set.call(e, t);
+        });
+      }
+    } else l = function l(e) {
+      return e[t];
+    }, 0 === a && (f = function f(e, r) {
+      e[t] = r;
+    });
+    var v = s ? c.bind() : function (e) {
+      return t in e;
+    };
+    p.access = l && f ? {
+      get: l,
+      set: f,
+      has: v
+    } : l ? {
+      get: l,
+      has: v
+    } : {
+      set: f,
+      has: v
+    };
+    try {
+      return e(o, p);
+    } finally {
+      d.v = !0;
+    }
+  }
+  function assertCallable(e, t) {
+    if ("function" != typeof e) throw new TypeError(t + " must be a function");
+  }
+  function assertValidReturnValue(e, t) {
+    var r = _typeof(t);
+    if (1 === e) {
+      if ("object" !== r || null === t) throw new TypeError("accessor decorators must return an object with get, set, or init properties or void 0");
+      void 0 !== t.get && assertCallable(t.get, "accessor.get"), void 0 !== t.set && assertCallable(t.set, "accessor.set"), void 0 !== t.init && assertCallable(t.init, "accessor.init");
+    } else if ("function" !== r) throw new TypeError((0 === e ? "field" : 10 === e ? "class" : "method") + " decorators must return a function or void 0");
+  }
+  function curryThis2(e) {
+    return function (t) {
+      e(this, t);
+    };
+  }
+  function applyMemberDec(e, t, r, n, a, i, s, o, c) {
+    var u,
+      l,
+      f,
+      p,
+      d,
+      h,
+      v,
+      y,
+      g = r[0];
+    if (s ? (0 === a || 1 === a ? (u = {
+      get: (d = r[3], function () {
+        return d(this);
+      }),
+      set: curryThis2(r[4])
+    }, f = "get") : 3 === a ? (u = {
+      get: r[3]
+    }, f = "get") : 4 === a ? (u = {
+      set: r[3]
+    }, f = "set") : u = {
+      value: r[3]
+    }, 0 !== a && (1 === a && setFunctionName(u.set, "#" + n, "set"), setFunctionName(u[f || "value"], "#" + n, f))) : 0 !== a && (u = Object.getOwnPropertyDescriptor(t, n)), 1 === a ? p = {
+      get: u.get,
+      set: u.set
+    } : 2 === a ? p = u.value : 3 === a ? p = u.get : 4 === a && (p = u.set), "function" == typeof g) void 0 !== (h = memberDec(g, n, u, o, a, i, s, p, c)) && (assertValidReturnValue(a, h), 0 === a ? l = h : 1 === a ? (l = h.init, v = h.get || p.get, y = h.set || p.set, p = {
+      get: v,
+      set: y
+    }) : p = h);else for (var m = g.length - 1; m >= 0; m--) {
+      var b;
+      void 0 !== (h = memberDec(g[m], n, u, o, a, i, s, p, c)) && (assertValidReturnValue(a, h), 0 === a ? b = h : 1 === a ? (b = h.init, v = h.get || p.get, y = h.set || p.set, p = {
+        get: v,
+        set: y
+      }) : p = h, void 0 !== b && (void 0 === l ? l = b : "function" == typeof l ? l = [l, b] : l.push(b)));
+    }
+    if (0 === a || 1 === a) {
+      if (void 0 === l) l = function l(e, t) {
+        return t;
+      };else if ("function" != typeof l) {
+        var I = l;
+        l = function l(e, t) {
+          for (var r = t, n = 0; n < I.length; n++) r = I[n].call(e, r);
+          return r;
+        };
+      } else {
+        var w = l;
+        l = function l(e, t) {
+          return w.call(e, t);
+        };
+      }
+      e.push(l);
+    }
+    0 !== a && (1 === a ? (u.get = p.get, u.set = p.set) : 2 === a ? u.value = p : 3 === a ? u.get = p : 4 === a && (u.set = p), s ? 1 === a ? (e.push(function (e, t) {
+      return p.get.call(e, t);
+    }), e.push(function (e, t) {
+      return p.set.call(e, t);
+    })) : 2 === a ? e.push(p) : e.push(function (e, t) {
+      return p.call(e, t);
+    }) : Object.defineProperty(t, n, u));
+  }
+  function applyMemberDecs(e, t, r) {
+    for (var n, a, i, s = [], o = new Map(), c = new Map(), u = 0; u < t.length; u++) {
+      var l = t[u];
+      if (Array.isArray(l)) {
+        var f,
+          p,
+          d = l[1],
+          h = l[2],
+          v = l.length > 3,
+          y = d >= 5,
+          g = r;
+        if (y ? (f = e, 0 != (d -= 5) && (p = a = a || []), v && !i && (i = function i(t) {
+          return checkInRHS(t) === e;
+        }), g = i) : (f = e.prototype, 0 !== d && (p = n = n || [])), 0 !== d && !v) {
+          var m = y ? c : o,
+            b = m.get(h) || 0;
+          if (!0 === b || 3 === b && 4 !== d || 4 === b && 3 !== d) throw Error("Attempted to decorate a public method/accessor that has the same name as a previously decorated public method/accessor. This is not currently supported by the decorators plugin. Property name was: " + h);
+          !b && d > 2 ? m.set(h, d) : m.set(h, !0);
+        }
+        applyMemberDec(s, f, l, h, d, y, v, p, g);
+      }
+    }
+    return pushInitializers(s, n), pushInitializers(s, a), s;
+  }
+  function pushInitializers(e, t) {
+    t && e.push(function (e) {
+      for (var r = 0; r < t.length; r++) t[r].call(e);
+      return e;
+    });
+  }
+  return function (e, t, r, n) {
+    return {
+      e: applyMemberDecs(e, t, n),
+      get c() {
+        return function (e, t) {
+          if (t.length > 0) {
+            for (var r = [], n = e, a = e.name, i = t.length - 1; i >= 0; i--) {
+              var s = {
+                v: !1
+              };
+              try {
+                var o = t[i](n, {
+                  kind: "class",
+                  name: a,
+                  addInitializer: createAddInitializerMethod(r, s)
+                });
+              } finally {
+                s.v = !0;
+              }
+              void 0 !== o && (assertValidReturnValue(10, o), n = o);
+            }
+            return [n, function () {
+              for (var e = 0; e < r.length; e++) r[e].call(n);
+            }];
+          }
+        }(e, r);
+      }
+    };
+  };
+}
+function applyDecs2301(e, t, r, n) {
+  return (module.exports = applyDecs2301 = applyDecs2301Factory(), module.exports.__esModule = true, module.exports["default"] = module.exports)(e, t, r, n);
+}
+module.exports = applyDecs2301, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs2305.js

@@ -0,0 +1,133 @@
+var _typeof = require("./typeof.js")["default"];
+var checkInRHS = require("./checkInRHS.js");
+var setFunctionName = require("./setFunctionName.js");
+var toPropertyKey = require("./toPropertyKey.js");
+function applyDecs2305(e, t, r, n, o, a) {
+  function i(e, t, r) {
+    return function (n, o) {
+      return r && r(n), e[t].call(n, o);
+    };
+  }
+  function c(e, t) {
+    for (var r = 0; r < e.length; r++) e[r].call(t);
+    return t;
+  }
+  function s(e, t, r, n) {
+    if ("function" != typeof e && (n || void 0 !== e)) throw new TypeError(t + " must " + (r || "be") + " a function" + (n ? "" : " or undefined"));
+    return e;
+  }
+  function applyDec(e, t, r, n, o, a, c, u, l, f, p, d, h) {
+    function m(e) {
+      if (!h(e)) throw new TypeError("Attempted to access private element on non-instance");
+    }
+    var y,
+      v = t[0],
+      g = t[3],
+      b = !u;
+    if (!b) {
+      r || Array.isArray(v) || (v = [v]);
+      var w = {},
+        S = [],
+        A = 3 === o ? "get" : 4 === o || d ? "set" : "value";
+      f ? (p || d ? w = {
+        get: setFunctionName(function () {
+          return g(this);
+        }, n, "get"),
+        set: function set(e) {
+          t[4](this, e);
+        }
+      } : w[A] = g, p || setFunctionName(w[A], n, 2 === o ? "" : A)) : p || (w = Object.getOwnPropertyDescriptor(e, n));
+    }
+    for (var P = e, j = v.length - 1; j >= 0; j -= r ? 2 : 1) {
+      var D = v[j],
+        E = r ? v[j - 1] : void 0,
+        I = {},
+        O = {
+          kind: ["field", "accessor", "method", "getter", "setter", "class"][o],
+          name: n,
+          metadata: a,
+          addInitializer: function (e, t) {
+            if (e.v) throw Error("attempted to call addInitializer after decoration was finished");
+            s(t, "An initializer", "be", !0), c.push(t);
+          }.bind(null, I)
+        };
+      try {
+        if (b) (y = s(D.call(E, P, O), "class decorators", "return")) && (P = y);else {
+          var k, F;
+          O["static"] = l, O["private"] = f, f ? 2 === o ? k = function k(e) {
+            return m(e), w.value;
+          } : (o < 4 && (k = i(w, "get", m)), 3 !== o && (F = i(w, "set", m))) : (k = function k(e) {
+            return e[n];
+          }, (o < 2 || 4 === o) && (F = function F(e, t) {
+            e[n] = t;
+          }));
+          var N = O.access = {
+            has: f ? h.bind() : function (e) {
+              return n in e;
+            }
+          };
+          if (k && (N.get = k), F && (N.set = F), P = D.call(E, d ? {
+            get: w.get,
+            set: w.set
+          } : w[A], O), d) {
+            if ("object" == _typeof(P) && P) (y = s(P.get, "accessor.get")) && (w.get = y), (y = s(P.set, "accessor.set")) && (w.set = y), (y = s(P.init, "accessor.init")) && S.push(y);else if (void 0 !== P) throw new TypeError("accessor decorators must return an object with get, set, or init properties or void 0");
+          } else s(P, (p ? "field" : "method") + " decorators", "return") && (p ? S.push(P) : w[A] = P);
+        }
+      } finally {
+        I.v = !0;
+      }
+    }
+    return (p || d) && u.push(function (e, t) {
+      for (var r = S.length - 1; r >= 0; r--) t = S[r].call(e, t);
+      return t;
+    }), p || b || (f ? d ? u.push(i(w, "get"), i(w, "set")) : u.push(2 === o ? w[A] : i.call.bind(w[A])) : Object.defineProperty(e, n, w)), P;
+  }
+  function u(e, t) {
+    return Object.defineProperty(e, Symbol.metadata || Symbol["for"]("Symbol.metadata"), {
+      configurable: !0,
+      enumerable: !0,
+      value: t
+    });
+  }
+  if (arguments.length >= 6) var l = a[Symbol.metadata || Symbol["for"]("Symbol.metadata")];
+  var f = Object.create(null == l ? null : l),
+    p = function (e, t, r, n) {
+      var o,
+        a,
+        i = [],
+        s = function s(t) {
+          return checkInRHS(t) === e;
+        },
+        u = new Map();
+      function l(e) {
+        e && i.push(c.bind(null, e));
+      }
+      for (var f = 0; f < t.length; f++) {
+        var p = t[f];
+        if (Array.isArray(p)) {
+          var d = p[1],
+            h = p[2],
+            m = p.length > 3,
+            y = 16 & d,
+            v = !!(8 & d),
+            g = 0 == (d &= 7),
+            b = h + "/" + v;
+          if (!g && !m) {
+            var w = u.get(b);
+            if (!0 === w || 3 === w && 4 !== d || 4 === w && 3 !== d) throw Error("Attempted to decorate a public method/accessor that has the same name as a previously decorated public method/accessor. This is not currently supported by the decorators plugin. Property name was: " + h);
+            u.set(b, !(d > 2) || d);
+          }
+          applyDec(v ? e : e.prototype, p, y, m ? "#" + h : toPropertyKey(h), d, n, v ? a = a || [] : o = o || [], i, v, m, g, 1 === d, v && m ? s : r);
+        }
+      }
+      return l(o), l(a), i;
+    }(e, t, o, f);
+  return r.length || u(e, f), {
+    e: p,
+    get c() {
+      var t = [];
+      return r.length && [u(applyDec(e, [r], n, e.name, 5, f, t), f), c.bind(null, t, e)];
+    }
+  };
+}
+module.exports = applyDecs2305, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/applyDecs2311.js

@@ -0,0 +1,124 @@
+var _typeof = require("./typeof.js")["default"];
+var checkInRHS = require("./checkInRHS.js");
+var setFunctionName = require("./setFunctionName.js");
+var toPropertyKey = require("./toPropertyKey.js");
+function applyDecs2311(e, t, n, r, o, i) {
+  var a,
+    c,
+    u,
+    s,
+    f,
+    l,
+    p,
+    d = Symbol.metadata || Symbol["for"]("Symbol.metadata"),
+    m = Object.defineProperty,
+    h = Object.create,
+    y = [h(null), h(null)],
+    v = t.length;
+  function g(t, n, r) {
+    return function (o, i) {
+      n && (i = o, o = e);
+      for (var a = 0; a < t.length; a++) i = t[a].apply(o, r ? [i] : []);
+      return r ? i : o;
+    };
+  }
+  function b(e, t, n, r) {
+    if ("function" != typeof e && (r || void 0 !== e)) throw new TypeError(t + " must " + (n || "be") + " a function" + (r ? "" : " or undefined"));
+    return e;
+  }
+  function applyDec(e, t, n, r, o, i, u, s, f, l, p) {
+    function d(e) {
+      if (!p(e)) throw new TypeError("Attempted to access private element on non-instance");
+    }
+    var h = [].concat(t[0]),
+      v = t[3],
+      w = !u,
+      D = 1 === o,
+      S = 3 === o,
+      j = 4 === o,
+      E = 2 === o;
+    function I(t, n, r) {
+      return function (o, i) {
+        return n && (i = o, o = e), r && r(o), P[t].call(o, i);
+      };
+    }
+    if (!w) {
+      var P = {},
+        k = [],
+        F = S ? "get" : j || D ? "set" : "value";
+      if (f ? (l || D ? P = {
+        get: setFunctionName(function () {
+          return v(this);
+        }, r, "get"),
+        set: function set(e) {
+          t[4](this, e);
+        }
+      } : P[F] = v, l || setFunctionName(P[F], r, E ? "" : F)) : l || (P = Object.getOwnPropertyDescriptor(e, r)), !l && !f) {
+        if ((c = y[+s][r]) && 7 != (c ^ o)) throw Error("Decorating two elements with the same name (" + P[F].name + ") is not supported yet");
+        y[+s][r] = o < 3 ? 1 : o;
+      }
+    }
+    for (var N = e, O = h.length - 1; O >= 0; O -= n ? 2 : 1) {
+      var T = b(h[O], "A decorator", "be", !0),
+        z = n ? h[O - 1] : void 0,
+        A = {},
+        H = {
+          kind: ["field", "accessor", "method", "getter", "setter", "class"][o],
+          name: r,
+          metadata: a,
+          addInitializer: function (e, t) {
+            if (e.v) throw new TypeError("attempted to call addInitializer after decoration was finished");
+            b(t, "An initializer", "be", !0), i.push(t);
+          }.bind(null, A)
+        };
+      if (w) c = T.call(z, N, H), A.v = 1, b(c, "class decorators", "return") && (N = c);else if (H["static"] = s, H["private"] = f, c = H.access = {
+        has: f ? p.bind() : function (e) {
+          return r in e;
+        }
+      }, j || (c.get = f ? E ? function (e) {
+        return d(e), P.value;
+      } : I("get", 0, d) : function (e) {
+        return e[r];
+      }), E || S || (c.set = f ? I("set", 0, d) : function (e, t) {
+        e[r] = t;
+      }), N = T.call(z, D ? {
+        get: P.get,
+        set: P.set
+      } : P[F], H), A.v = 1, D) {
+        if ("object" == _typeof(N) && N) (c = b(N.get, "accessor.get")) && (P.get = c), (c = b(N.set, "accessor.set")) && (P.set = c), (c = b(N.init, "accessor.init")) && k.unshift(c);else if (void 0 !== N) throw new TypeError("accessor decorators must return an object with get, set, or init properties or undefined");
+      } else b(N, (l ? "field" : "method") + " decorators", "return") && (l ? k.unshift(N) : P[F] = N);
+    }
+    return o < 2 && u.push(g(k, s, 1), g(i, s, 0)), l || w || (f ? D ? u.splice(-1, 0, I("get", s), I("set", s)) : u.push(E ? P[F] : b.call.bind(P[F])) : m(e, r, P)), N;
+  }
+  function w(e) {
+    return m(e, d, {
+      configurable: !0,
+      enumerable: !0,
+      value: a
+    });
+  }
+  return void 0 !== i && (a = i[d]), a = h(null == a ? null : a), f = [], l = function l(e) {
+    e && f.push(g(e));
+  }, p = function p(t, r) {
+    for (var i = 0; i < n.length; i++) {
+      var a = n[i],
+        c = a[1],
+        l = 7 & c;
+      if ((8 & c) == t && !l == r) {
+        var p = a[2],
+          d = !!a[3],
+          m = 16 & c;
+        applyDec(t ? e : e.prototype, a, m, d ? "#" + p : toPropertyKey(p), l, l < 2 ? [] : t ? s = s || [] : u = u || [], f, !!t, d, r, t && d ? function (t) {
+          return checkInRHS(t) === e;
+        } : o);
+      }
+    }
+  }, p(8, 0), p(0, 0), p(8, 1), p(0, 1), l(u), l(s), c = f, v || w(e), {
+    e: c,
+    get c() {
+      var n = [];
+      return v && [w(e = applyDec(e, [t], r, e.name, 5, n)), g(n, 1)];
+    }
+  };
+}
+module.exports = applyDecs2311, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/arrayLikeToArray.js

@@ -0,0 +1,6 @@
+function _arrayLikeToArray(r, a) {
+  (null == a || a > r.length) && (a = r.length);
+  for (var e = 0, n = Array(a); e < a; e++) n[e] = r[e];
+  return n;
+}
+module.exports = _arrayLikeToArray, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/arrayWithHoles.js

@@ -0,0 +1,4 @@
+function _arrayWithHoles(r) {
+  if (Array.isArray(r)) return r;
+}
+module.exports = _arrayWithHoles, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/arrayWithoutHoles.js

@@ -0,0 +1,5 @@
+var arrayLikeToArray = require("./arrayLikeToArray.js");
+function _arrayWithoutHoles(r) {
+  if (Array.isArray(r)) return arrayLikeToArray(r);
+}
+module.exports = _arrayWithoutHoles, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/assertClassBrand.js

@@ -0,0 +1,5 @@
+function _assertClassBrand(e, t, n) {
+  if ("function" == typeof e ? e === t : e.has(t)) return arguments.length < 3 ? t : n;
+  throw new TypeError("Private element is not present on this object");
+}
+module.exports = _assertClassBrand, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/assertThisInitialized.js

@@ -0,0 +1,5 @@
+function _assertThisInitialized(e) {
+  if (void 0 === e) throw new ReferenceError("this hasn't been initialised - super() hasn't been called");
+  return e;
+}
+module.exports = _assertThisInitialized, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/asyncGeneratorDelegate.js

@@ -0,0 +1,24 @@
+var OverloadYield = require("./OverloadYield.js");
+function _asyncGeneratorDelegate(t) {
+  var e = {},
+    n = !1;
+  function pump(e, r) {
+    return n = !0, r = new Promise(function (n) {
+      n(t[e](r));
+    }), {
+      done: !1,
+      value: new OverloadYield(r, 1)
+    };
+  }
+  return e["undefined" != typeof Symbol && Symbol.iterator || "@@iterator"] = function () {
+    return this;
+  }, e.next = function (t) {
+    return n ? (n = !1, t) : pump("next", t);
+  }, "function" == typeof t["throw"] && (e["throw"] = function (t) {
+    if (n) throw n = !1, t;
+    return pump("throw", t);
+  }), "function" == typeof t["return"] && (e["return"] = function (t) {
+    return n ? (n = !1, t) : pump("return", t);
+  }), e;
+}
+module.exports = _asyncGeneratorDelegate, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/asyncIterator.js

@@ -0,0 +1,45 @@
+function _asyncIterator(r) {
+  var n,
+    t,
+    o,
+    e = 2;
+  for ("undefined" != typeof Symbol && (t = Symbol.asyncIterator, o = Symbol.iterator); e--;) {
+    if (t && null != (n = r[t])) return n.call(r);
+    if (o && null != (n = r[o])) return new AsyncFromSyncIterator(n.call(r));
+    t = "@@asyncIterator", o = "@@iterator";
+  }
+  throw new TypeError("Object is not async iterable");
+}
+function AsyncFromSyncIterator(r) {
+  function AsyncFromSyncIteratorContinuation(r) {
+    if (Object(r) !== r) return Promise.reject(new TypeError(r + " is not an object."));
+    var n = r.done;
+    return Promise.resolve(r.value).then(function (r) {
+      return {
+        value: r,
+        done: n
+      };
+    });
+  }
+  return AsyncFromSyncIterator = function AsyncFromSyncIterator(r) {
+    this.s = r, this.n = r.next;
+  }, AsyncFromSyncIterator.prototype = {
+    s: null,
+    n: null,
+    next: function next() {
+      return AsyncFromSyncIteratorContinuation(this.n.apply(this.s, arguments));
+    },
+    "return": function _return(r) {
+      var n = this.s["return"];
+      return void 0 === n ? Promise.resolve({
+        value: r,
+        done: !0
+      }) : AsyncFromSyncIteratorContinuation(n.apply(this.s, arguments));
+    },
+    "throw": function _throw(r) {
+      var n = this.s["return"];
+      return void 0 === n ? Promise.reject(r) : AsyncFromSyncIteratorContinuation(n.apply(this.s, arguments));
+    }
+  }, new AsyncFromSyncIterator(r);
+}
+module.exports = _asyncIterator, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/asyncToGenerator.js

@@ -0,0 +1,26 @@
+function asyncGeneratorStep(n, t, e, r, o, a, c) {
+  try {
+    var i = n[a](c),
+      u = i.value;
+  } catch (n) {
+    return void e(n);
+  }
+  i.done ? t(u) : Promise.resolve(u).then(r, o);
+}
+function _asyncToGenerator(n) {
+  return function () {
+    var t = this,
+      e = arguments;
+    return new Promise(function (r, o) {
+      var a = n.apply(t, e);
+      function _next(n) {
+        asyncGeneratorStep(a, r, o, _next, _throw, "next", n);
+      }
+      function _throw(n) {
+        asyncGeneratorStep(a, r, o, _next, _throw, "throw", n);
+      }
+      _next(void 0);
+    });
+  };
+}
+module.exports = _asyncToGenerator, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/awaitAsyncGenerator.js

@@ -0,0 +1,5 @@
+var OverloadYield = require("./OverloadYield.js");
+function _awaitAsyncGenerator(e) {
+  return new OverloadYield(e, 0);
+}
+module.exports = _awaitAsyncGenerator, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/callSuper.js

@@ -0,0 +1,7 @@
+var getPrototypeOf = require("./getPrototypeOf.js");
+var isNativeReflectConstruct = require("./isNativeReflectConstruct.js");
+var possibleConstructorReturn = require("./possibleConstructorReturn.js");
+function _callSuper(t, o, e) {
+  return o = getPrototypeOf(o), possibleConstructorReturn(t, isNativeReflectConstruct() ? Reflect.construct(o, e || [], getPrototypeOf(t).constructor) : o.apply(t, e));
+}
+module.exports = _callSuper, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/checkInRHS.js

@@ -0,0 +1,6 @@
+var _typeof = require("./typeof.js")["default"];
+function _checkInRHS(e) {
+  if (Object(e) !== e) throw TypeError("right-hand side of 'in' should be an object, got " + (null !== e ? _typeof(e) : "null"));
+  return e;
+}
+module.exports = _checkInRHS, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/checkPrivateRedeclaration.js

@@ -0,0 +1,4 @@
+function _checkPrivateRedeclaration(e, t) {
+  if (t.has(e)) throw new TypeError("Cannot initialize the same private elements twice on an object");
+}
+module.exports = _checkPrivateRedeclaration, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classApplyDescriptorDestructureSet.js

@@ -0,0 +1,10 @@
+function _classApplyDescriptorDestructureSet(e, t) {
+  if (t.set) return "__destrObj" in t || (t.__destrObj = {
+    set value(r) {
+      t.set.call(e, r);
+    }
+  }), t.__destrObj;
+  if (!t.writable) throw new TypeError("attempted to set read only private field");
+  return t;
+}
+module.exports = _classApplyDescriptorDestructureSet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classApplyDescriptorGet.js

@@ -0,0 +1,4 @@
+function _classApplyDescriptorGet(e, t) {
+  return t.get ? t.get.call(e) : t.value;
+}
+module.exports = _classApplyDescriptorGet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classApplyDescriptorSet.js

@@ -0,0 +1,7 @@
+function _classApplyDescriptorSet(e, t, l) {
+  if (t.set) t.set.call(e, l);else {
+    if (!t.writable) throw new TypeError("attempted to set read only private field");
+    t.value = l;
+  }
+}
+module.exports = _classApplyDescriptorSet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classCallCheck.js

@@ -0,0 +1,4 @@
+function _classCallCheck(a, n) {
+  if (!(a instanceof n)) throw new TypeError("Cannot call a class as a function");
+}
+module.exports = _classCallCheck, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classCheckPrivateStaticAccess.js

@@ -0,0 +1,5 @@
+var assertClassBrand = require("./assertClassBrand.js");
+function _classCheckPrivateStaticAccess(s, a, r) {
+  return assertClassBrand(a, s, r);
+}
+module.exports = _classCheckPrivateStaticAccess, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classCheckPrivateStaticFieldDescriptor.js

@@ -0,0 +1,4 @@
+function _classCheckPrivateStaticFieldDescriptor(t, e) {
+  if (void 0 === t) throw new TypeError("attempted to " + e + " private static field before its declaration");
+}
+module.exports = _classCheckPrivateStaticFieldDescriptor, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classExtractFieldDescriptor.js

@@ -0,0 +1,5 @@
+var classPrivateFieldGet2 = require("./classPrivateFieldGet2.js");
+function _classExtractFieldDescriptor(e, t) {
+  return classPrivateFieldGet2(t, e);
+}
+module.exports = _classExtractFieldDescriptor, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classNameTDZError.js

@@ -0,0 +1,4 @@
+function _classNameTDZError(e) {
+  throw new ReferenceError('Class "' + e + '" cannot be referenced in computed property keys.');
+}
+module.exports = _classNameTDZError, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldDestructureSet.js

@@ -0,0 +1,7 @@
+var classApplyDescriptorDestructureSet = require("./classApplyDescriptorDestructureSet.js");
+var classPrivateFieldGet2 = require("./classPrivateFieldGet2.js");
+function _classPrivateFieldDestructureSet(e, t) {
+  var r = classPrivateFieldGet2(t, e);
+  return classApplyDescriptorDestructureSet(e, r);
+}
+module.exports = _classPrivateFieldDestructureSet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldGet.js

@@ -0,0 +1,7 @@
+var classApplyDescriptorGet = require("./classApplyDescriptorGet.js");
+var classPrivateFieldGet2 = require("./classPrivateFieldGet2.js");
+function _classPrivateFieldGet(e, t) {
+  var r = classPrivateFieldGet2(t, e);
+  return classApplyDescriptorGet(e, r);
+}
+module.exports = _classPrivateFieldGet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldGet2.js

@@ -0,0 +1,5 @@
+var assertClassBrand = require("./assertClassBrand.js");
+function _classPrivateFieldGet2(s, a) {
+  return s.get(assertClassBrand(s, a));
+}
+module.exports = _classPrivateFieldGet2, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldInitSpec.js

@@ -0,0 +1,5 @@
+var checkPrivateRedeclaration = require("./checkPrivateRedeclaration.js");
+function _classPrivateFieldInitSpec(e, t, a) {
+  checkPrivateRedeclaration(e, t), t.set(e, a);
+}
+module.exports = _classPrivateFieldInitSpec, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldLooseBase.js

@@ -0,0 +1,5 @@
+function _classPrivateFieldBase(e, t) {
+  if (!{}.hasOwnProperty.call(e, t)) throw new TypeError("attempted to use private field on non-instance");
+  return e;
+}
+module.exports = _classPrivateFieldBase, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldLooseKey.js

@@ -0,0 +1,5 @@
+var id = 0;
+function _classPrivateFieldKey(e) {
+  return "__private_" + id++ + "_" + e;
+}
+module.exports = _classPrivateFieldKey, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldSet.js

@@ -0,0 +1,7 @@
+var classApplyDescriptorSet = require("./classApplyDescriptorSet.js");
+var classPrivateFieldGet2 = require("./classPrivateFieldGet2.js");
+function _classPrivateFieldSet(e, t, r) {
+  var s = classPrivateFieldGet2(t, e);
+  return classApplyDescriptorSet(e, s, r), r;
+}
+module.exports = _classPrivateFieldSet, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateFieldSet2.js

@@ -0,0 +1,5 @@
+var assertClassBrand = require("./assertClassBrand.js");
+function _classPrivateFieldSet2(s, a, r) {
+  return s.set(assertClassBrand(s, a), r), r;
+}
+module.exports = _classPrivateFieldSet2, module.exports.__esModule = true, module.exports["default"] = module.exports;

examples/simple-chatbot-fullstack/examples/javascript/node_modules/@babel/runtime/helpers/classPrivateGetter.js

@@ -0,0 +1,5 @@
+var assertClassBrand = require("./assertClassBrand.js");
+function _classPrivateGetter(s, r, a) {
+  return a(assertClassBrand(s, r));
+}
+module.exports = _classPrivateGetter, module.exports.__esModule = true, module.exports["default"] = module.exports;