Adding some more doscstrings, cleanup

README.md

@@ -1,82 +1,21 @@
-# dailyai — an open source framework for real-time, multi-modal, conversational AI applications
+# Daily AI SDK
 
-Build things like this:
+Build conversational, multi-modal AI apps with real-time voice and video, like this:
 
-[![AI-powered voice patient intake for healthcare](https://img.youtube.com/vi/lDevgsp9vn0/0.jpg)](https://www.youtube.com/watch?v=lDevgsp9vn0)
+_Demo Video to come_
 
+With built-in support for many of the best AI platforms (or [add your own](/docs)):
 
+- Azure - DALL-E, ChatGPT, and Azure AI Text-to-Speech
+- Deepgram - Speech-to-text, and Aura text-to-speech
+- Eleven Labs text-to-speech
+- Fal.ai image generation
+- OpenAI DALL-E and ChatGPT
+- Whisper local speech-to-text
 
+## Step 1: Get Started
 
-**`dailyai` started as a toolkit for implementing generative AI voice bots.** Things like personal coaches, meeting assistants, story-telling toys for kids, customer support bots, and snarky social companions. 
-
-
-In 2023 a *lot* of us got excited about the possibility of having open-ended conversations with LLMs. It became clear pretty quickly that we were all solving the same [low-level problems](https://www.daily.co/blog/how-to-talk-to-an-llm-with-your-voice/):
-- low-latency, reliable audio transport
-- echo cancellation 
-- phrase endpointing (knowing when the bot should respond to human speech)
-- interruptibility
-- writing clean code to stream data through "pipelines" of speech-to-text, LLM inference, and text-to-speech models
-
-As our applications expanded to include additional things like image generation, function calling, and vision models, we started to think about what a complete framework for these kinds of apps could look like.
-
-Today, `dailyai` is:
-
-1. a set of code building blocks for interacting with generative AI services and creating low-latency, interruptible data pipelines that use multiple services
-2. transport services that moves audio, video, and events across the Internet
-3. implementations of specific generative AI services
-
-Currently implemented services:
-- Speech-to-text
-  - Deepgram
-  - Whisper
-- LLMs
-  - Azure
-  - OpenAI
-- Image generation
-  - Azure
-  - Fal
-  - OpenAI
-- Text-to-speech
-  - Azure
-  - Deepgram
-  - ElevenLabs
-- Transport
-  - Daily
-  - Local (in progress, intended as a quick start example service)
-
-If you'd like to [implement a service]((https://github.com/daily-co/daily-ai-sdk/tree/main/src/dailyai/services)), we welcome PRs! Our goal is to support lots of services in all of the above categories, plus new categories (like real-time video) as they emerge.
-
-## Step 1: Get started
-
-Today, the easiest way to get started with `dailyai` is to use [Daily](https://www.daily.co/) as your transport service. This toolkit started life as an internal SDK at Daily and millions of minutes of AI conversation have been served using it and its earlier prototype incarnations. (The [transport base class](https://github.com/daily-co/daily-ai-sdk/blob/main/src/dailyai/services/base_transport_service.py) is easy to extend, though, so feel free to submit PRs if you'd like to implement another transport service.)
-
-```
-# install the module
-pip install dailyai
-
-# set up an .env file with API keys
-# for example
-OPENAI_API_KEY=...
-ELEVENLABS_API_KEY=...
-ELEVENLABS_VOICE_ID=...
-DAILY_SAMPLE_ROOM_URL=https://...
-
-# sign up for a free Daily account, if you don't already have one, and
-# join the Daily room URL directly from a browser tab, then run one of the
-# samples
-python src/examples/foundational/02-llm-say-one-thing.py
-```
-
-## Code examples
-
-There are two directories of examples:
-
-- [foundational](https://github.com/daily-co/daily-ai-sdk/tree/main/src/examples/foundational) — demos that build on each other, introducing one or two concepts at a time
-- [starter apps](https://github.com/daily-co/daily-ai-sdk/tree/main/src/examples/starter-apps) — complete applications that you can use as starting points for development
-
-
-
-## Hacking on the framework itself
+## Build/Install
 
 _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:_
 
@@ -104,3 +43,117 @@ If you want to use this package from another directory, you can run:
 pip install path_to_this_repo
 ```
 
+## Running the samples
+
+Tou can run the simple sample like so:
+
+```
+python src/examples/theoretical-to-real/01-say-one-thing.py -u <url of your Daily meeting> -k <your Daily API Key>
+```
+## Overview
+
+The Daily AI SDK allows you to build applications that can participate in WebRTC sessions and interact with AI Services. Some examples of what you can build with this:
+
+- conversational bots that interact 1:1 with a user, using voice recognition and text-to-speech
+- assistant bots that aggregate transcriptions from multiple participants in a meeting and provide realtime summaries or other AI-generated output.
+- image-recognition bots
+- etc
+
+## Concepts
+
+### Transport Service
+
+The SDK provides one “transport service”, which is a wrapper around Daily’s `daily-python` client (tk add link). You can use this service to listen for events related to a WebRTC session, such as “a participant joined the meeting”.
+The transport service also exposes a send queue, and a receive queue. You can use the send queue to send audio and video to the WebRTC session, and you can listen to the receive queue to see audio, video and transcription data from the WebRTC session.
+
+### AI Services
+
+The AI Service classes provide wrappers around various AI providers, and allow you to query LLMs, convert text to speech and make images from text. The audio and images can then be placed on the transport service’s send queue, where they’ll be sent to the WebRTC session.
+
+### Queue Frames
+
+Communication between the transport service and AI services, and between various AI services, takes place in Queue Frames. These frames contain an indication of the type of data as well as the data itself.
+
+## Using Transports, AI Services and Frames
+
+AI Services all define a `.run` method. This method consumes and generates `QueueFrame` frames. The kind of frames that can be consumed and generated depend on the kind of service. For instance, an LLM AI Service consumes `LLM_MESSAGE` frames (which define a history of interaction with an LLM) and emit `TEXT` frames (the response from the LLM).
+
+The `.run` method is an `AsyncIterable`, and it takes an `iterable`, `AsyncIterable` or `asyncio.Queue` that produces QueueFrames as a parameter. This makes it easy to chain AI Services, and consume input from the Transport’s `receive_queue` .
+
+AI Services also have a `.run_to_queue` method. This method is not an AsyncIterable, but instead sends processed QueueFrames to a queue. This makes it easy to send the output of an AI Service to the Transport’s `send_queue`.
+
+AI Services also define convenience functions that let you bypass creating QueueFrames for some simple cases (eg. using the TTS service to convert a string to audio output and send that audio to the transport’s `send_queue`). See below for examples.
+
+## Examples
+
+### Say Something
+
+The base TTS AI service exposes a `.say` method. After creating a transport and TTS service, you can use this method like so:
+
+```
+transport = DailyTransportService(...)
+tts = AzureTTSService()
+await tts.say("hello world", transport.send_queue)
+```
+
+This will call the TTS service to render the text to audio frames, then put the audio frames on the transport’s send queue. The transport will then send those frames along to the WebRTC session.
+
+### Speak an LLM response
+
+Given a system prompt contained in a `messages` array, you can emit the LLM’s response as audio with a chain like this:
+
+```
+transport = DailyTransportService(...) # setup parameters omitted
+tts = AzureTTSService()
+llm = AzureLLMService()
+messages = [...] # system prompt omitted for brevity
+
+await tts.run_to_queue(
+  transport.send_queue,
+  llm.run([QueueFrame.LLM_MESSAGES, messages])
+)
+```
+
+In this code, the LLM service object sends the messages to Azure’s OpenAI implementation, which streams chunks back asynchronously. Those chunks are aggregated by the TTS Service to ensure the best audio response (TTS works best when it gets complete sentence, so it can inflect correctly), then sent to Azure’s TTS service, converted to audio frames, and sent to the WebRTC session via the Daily transport.
+
+### Pre-cache an LLM response
+
+Sometimes LLMs can be slower than we’d like for natural-feeling communication. Here’s an example where we take advantage of the time it takes to speak some pre-defined text to get a head start on the LLM response:
+
+(TK link to 04- sample)
+
+In this sample, we set up a buffer queue to receive the audio frames from the LLM response before while we are joining the call and start an asynchronous task to start filling this buffer:
+
+```
+    buffer_queue = asyncio.Queue()
+    llm_response_task = asyncio.create_task(
+        elevenlabs_tts.run_to_queue(
+            buffer_queue,
+            llm.run([QueueFrame(FrameType.LLM_MESSAGE, messages)]),
+            True,
+        )
+    )
+```
+
+Then, when we’ve joined the call, we speak the static text:
+
+```
+        await azure_tts.say("My friend...", transport.send_queue)
+```
+
+As that text is being spoken, the asynchronous LLM task continues in the background. When the text is done, we pull the frames off the buffer queue and put them in the transport’s `send_queue`:
+
+```
+        async def buffer_to_send_queue():
+            while True:
+                frame = await buffer_queue.get()
+                await transport.send_queue.put(frame)
+                buffer_queue.task_done()
+                if frame.frame_type == FrameType.END_STREAM:
+                    break
+
+        await asyncio.gather(llm_response_task, buffer_to_send_queue())
+
+```
+
+One thing to note here is the last parameter to `run_to_queue` in the first code clause above: this causes the `run_to_queue` method to send an `END_STREAM` frame when it’s done rendering. This lets us know when to stop our `buffer_to_send_queue` task above.

pyproject.toml

@@ -3,22 +3,9 @@ requires = ["setuptools"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "dailyai"
+name = "daily_ai"
 version = "0.0.1"
-description = "An open source framework for real-time, multi-modal, conversational AI applications"
-license = { text = "BSD 2-Clause License" }
-readme = "README.md"
-requires-python = ">=3.7"
-keywords = ["webrtc", "audio", "video", "ai"]
-classifiers = [
-    "Development Status :: 5 - Production/Stable",
-    "Intended Audience :: Developers",
-    "License :: OSI Approved :: BSD License",
-    "Topic :: Communications :: Conferencing",
-    "Topic :: Multimedia :: Sound/Audio",
-    "Topic :: Multimedia :: Video",
-    "Topic :: Scientific/Engineering :: Artificial Intelligence"
-]
+description = "Orchestrator for AI bots with Daily"
 dependencies = [
     "aiohttp",
     "azure-cognitiveservices-speech",
@@ -37,10 +24,6 @@ dependencies = [
     "typing-extensions"
 ]
 
-[project.urls]
-Source = "https://github.com/daily-co/daily-ai-sdk"
-Website = "https://daily.co"
-
 [tool.setuptools.packages.find]
 # All the following settings are optional:
 where = ["src"]

src/dailyai/pipeline/aggregators.py

@@ -5,7 +5,6 @@ from dailyai.pipeline.frame_processor import FrameProcessor
 
 from dailyai.pipeline.frames import (
     EndFrame,
-    AudioFrame,
     EndPipeFrame,
     Frame,
     ImageFrame,
@@ -15,28 +14,15 @@ from dailyai.pipeline.frames import (
     TextFrame,
     TranscriptionQueueFrame,
     UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
+    UserStoppedSpeakingFrame
 )
 from dailyai.pipeline.pipeline import Pipeline
 from dailyai.services.ai_services import AIService
 
-from typing import AsyncGenerator, Callable, Coroutine, List
-
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
+from typing import AsyncGenerator, Coroutine, List
 
 class ResponseAggregator(FrameProcessor):
-
-    def __init__(
-        self,
-        *,
-        messages: list[dict] | None,
-        role: str,
-        start_frame,
-        end_frame,
-        accumulator_frame,
-        pass_through=True,
-    ):
+    def __init__(self, *, messages: list[dict], role: str, start_frame, end_frame, accumulator_frame, pass_through=True):
         self.aggregation = ""
         self.aggregating = False
         self.messages = messages
@@ -46,21 +32,16 @@ class ResponseAggregator(FrameProcessor):
         self._accumulator_frame = accumulator_frame
         self._pass_through = pass_through
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if not self.messages:
-            return
-
+    async def process_frame(
+        self, frame: Frame
+    ) -> AsyncGenerator[Frame, None]:
         if isinstance(frame, self._start_frame):
             self.aggregating = True
         elif isinstance(frame, self._end_frame):
             self.aggregating = False
-            # Sometimes VAD triggers quickly on and off. If we don't get any transcription,
-            # it creates empty LLM message queue frames
-            if len(self.aggregation) > 0:
-                self.messages.append({"role": self._role, "content": self.aggregation})
-                self.aggregation = ""
-                yield self._end_frame()
-                yield LLMMessagesQueueFrame(self.messages)
+            self.messages.append({"role": self._role, "content": self.aggregation})
+            self.aggregation = ""
+            yield LLMMessagesQueueFrame(self.messages)
         elif isinstance(frame, self._accumulator_frame) and self.aggregating:
             self.aggregation += f" {frame.text}"
             if self._pass_through:
@@ -68,7 +49,6 @@ class ResponseAggregator(FrameProcessor):
         else:
             yield frame
 
-
 class LLMResponseAggregator(ResponseAggregator):
     def __init__(self, messages: list[dict]):
         super().__init__(
@@ -76,10 +56,9 @@ class LLMResponseAggregator(ResponseAggregator):
             role="assistant",
             start_frame=LLMResponseStartFrame,
             end_frame=LLMResponseEndFrame,
-            accumulator_frame=TextFrame,
+            accumulator_frame=TextFrame
         )
 
-
 class UserResponseAggregator(ResponseAggregator):
     def __init__(self, messages: list[dict]):
         super().__init__(
@@ -88,10 +67,9 @@ class UserResponseAggregator(ResponseAggregator):
             start_frame=UserStartedSpeakingFrame,
             end_frame=UserStoppedSpeakingFrame,
             accumulator_frame=TranscriptionQueueFrame,
-            pass_through=False,
+            pass_through=False
         )
 
-
 class LLMContextAggregator(AIService):
     def __init__(
         self,
@@ -109,7 +87,9 @@ class LLMContextAggregator(AIService):
         self.complete_sentences = complete_sentences
         self.pass_through = pass_through
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(
+        self, frame: Frame
+    ) -> AsyncGenerator[Frame, None]:
         # We don't do anything with non-text frames, pass it along to next in the pipeline.
         if not isinstance(frame, TextFrame):
             yield frame
@@ -141,7 +121,6 @@ class LLMContextAggregator(AIService):
             self.messages.append({"role": self.role, "content": frame.text})
             yield LLMMessagesQueueFrame(self.messages)
 
-
 class LLMUserContextAggregator(LLMContextAggregator):
     def __init__(
         self, messages: list[dict], bot_participant_id=None, complete_sentences=True
@@ -181,11 +160,12 @@ class SentenceAggregator(FrameProcessor):
     >>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
     Hello, world.
     """
-
     def __init__(self):
         self.aggregation = ""
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(
+        self, frame: Frame
+    ) -> AsyncGenerator[Frame, None]:
         if isinstance(frame, TextFrame):
             m = re.search("(.*[?.!])(.*)", frame.text)
             if m:
@@ -237,11 +217,12 @@ class LLMFullResponseAggregator(FrameProcessor):
     Hello, world. I am an LLM.
     LLMResponseEndFrame
     """
-
     def __init__(self):
         self.aggregation = ""
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(
+        self, frame: Frame
+    ) -> AsyncGenerator[Frame, None]:
         if isinstance(frame, TextFrame):
             self.aggregation += frame.text
         elif isinstance(frame, LLMResponseEndFrame):
@@ -277,9 +258,8 @@ class StatelessTextTransformer(FrameProcessor):
         else:
             yield frame
 
-
 class ParallelPipeline(FrameProcessor):
-    """Run multiple pipelines in parallel.
+    """ Run multiple pipelines in parallel.
 
     This class takes frames from its source queue and sends them to each
     sub-pipeline. Each sub-pipeline emits its frames into this class's
@@ -296,7 +276,6 @@ class ParallelPipeline(FrameProcessor):
     Since frame handlers pass through unhandled frames by convention, this
     class de-dupes frames in its sink before yielding them.
     """
-
     def __init__(self, pipeline_definitions: List[List[FrameProcessor]]):
         self.sources = [asyncio.Queue() for _ in pipeline_definitions]
         self.sink: asyncio.Queue[Frame] = asyncio.Queue()
@@ -332,7 +311,6 @@ class ParallelPipeline(FrameProcessor):
             if not isinstance(frame, EndPipeFrame):
                 yield frame
 
-
 class GatedAggregator(FrameProcessor):
     """Accumulate frames, with custom functions to start and stop accumulation.
     Yields gate-opening frame before any accumulated frames, then ensuing frames
@@ -358,7 +336,6 @@ class GatedAggregator(FrameProcessor):
     >>> asyncio.run(print_frames(aggregator, TextFrame("Goodbye.")))
     Goodbye.
     """
-
     def __init__(self, gate_open_fn, gate_close_fn, start_open):
         self.gate_open_fn = gate_open_fn
         self.gate_close_fn = gate_close_fn

src/dailyai/pipeline/frames.py

@@ -1,13 +1,9 @@
 from dataclasses import dataclass
-from typing import Any, List
-
-from dailyai.services.openai_llm_context import OpenAILLMContext
+from typing import Any
 
 
 class Frame:
-    def __str__(self):
-        return f"{self.__class__.__name__}"
-
+    pass
 
 class ControlFrame(Frame):
     # Control frames should contain no instance data, so
@@ -23,21 +19,10 @@ class StartFrame(ControlFrame):
 class EndFrame(ControlFrame):
     pass
 
-
 class EndPipeFrame(ControlFrame):
     pass
 
 
-class PipelineStartedFrame(ControlFrame):
-    """
-    Used by the transport to indicate that execution of a pipeline is starting
-    (or restarting). It should be the first frame your app receives when it
-    starts, or when an interruptible pipeline has been interrupted.
-    """
-
-    pass
-
-
 class LLMResponseStartFrame(ControlFrame):
     pass
 
@@ -50,34 +35,22 @@ class LLMResponseEndFrame(ControlFrame):
 class AudioFrame(Frame):
     data: bytes
 
-    def __str__(self):
-        return f"{self.__class__.__name__}, size: {len(self.data)} B"
-
 
 @dataclass()
 class ImageFrame(Frame):
     url: str | None
     image: bytes
 
-    def __str__(self):
-        return f"{self.__class__.__name__}, url: {self.url}, image size: {len(self.image)} B"
-
 
 @dataclass()
 class SpriteFrame(Frame):
     images: list[bytes]
 
-    def __str__(self):
-        return f"{self.__class__.name__}, list size: {len(self.images)}"
-
 
 @dataclass()
 class TextFrame(Frame):
     text: str
 
-    def __str__(self):
-        return f'{self.__class__.__name__}: "{self.text}"'
-
 
 @dataclass()
 class TranscriptionQueueFrame(TextFrame):
@@ -87,41 +60,20 @@ class TranscriptionQueueFrame(TextFrame):
 
 @dataclass()
 class LLMMessagesQueueFrame(Frame):
-    messages: List[dict]
-
-
-@dataclass()
-class OpenAILLMContextFrame(Frame):
-    context: OpenAILLMContext
+    messages: list[dict[str, str]]  # TODO: define this more concretely!
 
 
 class AppMessageQueueFrame(Frame):
     message: Any
     participantId: str
 
-
 class UserStartedSpeakingFrame(Frame):
     pass
 
-
 class UserStoppedSpeakingFrame(Frame):
     pass
 
-
-class BotStartedSpeakingFrame(Frame):
-    pass
-
-
-class BotStoppedSpeakingFrame(Frame):
-    pass
-
-
-@dataclass()
-class LLMFunctionStartFrame(Frame):
-    function_name: str
-
-
 @dataclass()
 class LLMFunctionCallFrame(Frame):
     function_name: str
-    arguments: str
+    arguments: str

src/dailyai/pipeline/opeanai_llm_aggregator.py

@@ -1,106 +0,0 @@
-from typing import Any, AsyncGenerator, Callable
-from dailyai.pipeline.frame_processor import FrameProcessor
-from dailyai.pipeline.frames import (
-    Frame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    OpenAILLMContextFrame,
-    TextFrame,
-    TranscriptionQueueFrame,
-    UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
-)
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-from openai.types.chat import ChatCompletionRole
-
-
-class OpenAIContextAggregator(FrameProcessor):
-
-    def __init__(
-        self,
-        context: OpenAILLMContext,
-        aggregator: Callable[[Frame, str | None], str | None],
-        role: ChatCompletionRole,
-        start_frame: type,
-        end_frame: type,
-        accumulator_frame: type,
-        pass_through=True,
-    ):
-        if not (
-            issubclass(start_frame, Frame)
-            and issubclass(end_frame, Frame)
-            and issubclass(accumulator_frame, Frame)
-        ):
-            raise TypeError(
-                "start_frame, end_frame and accumulator_frame must be instances of Frame"
-            )
-
-        self._context: OpenAILLMContext = context
-        self._aggregator: Callable[[Frame, str | None], None] = aggregator
-        self._role: ChatCompletionRole = role
-        self._start_frame = start_frame
-        self._end_frame = end_frame
-        self._accumulator_frame = accumulator_frame
-        self._pass_through = pass_through
-
-        self._aggregating = False
-        self._aggregation = None
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, self._start_frame):
-            self._aggregating = True
-        elif isinstance(frame, self._end_frame):
-            self._aggregating = False
-            if self._aggregation:
-                self._context.add_message(
-                    {
-                        "role": self._role,
-                        "content": self._aggregation,
-                        "name": self._role,
-                    }  # type: ignore
-                )
-            self._aggregation = None
-            yield OpenAILLMContextFrame(self._context)
-        elif isinstance(frame, self._accumulator_frame) and self._aggregating:
-            self._aggregation = self._aggregator(frame, self._aggregation)
-            if self._pass_through:
-                yield frame
-        else:
-            yield frame
-
-    def string_aggregator(self, frame: Frame, aggregation: str | None) -> str | None:
-        if not isinstance(frame, TextFrame):
-            raise TypeError(
-                "Frame must be a TextFrame instance to be aggregated by a string aggregator."
-            )
-        if not aggregation:
-            aggregation = ""
-        return " ".join([aggregation, frame.text])
-
-
-class OpenAIUserContextAggregator(OpenAIContextAggregator):
-    def __init__(self, context: OpenAILLMContext):
-        super().__init__(
-            context=context,
-            aggregator=self.string_aggregator,
-            role="user",
-            start_frame=UserStartedSpeakingFrame,
-            end_frame=UserStoppedSpeakingFrame,
-            accumulator_frame=TranscriptionQueueFrame,
-            pass_through=False,
-        )
-
-
-class OpenAIAssistantContextAggregator(OpenAIContextAggregator):
-
-    def __init__(self, context:OpenAILLMContext):
-        super().__init__(
-            context,
-            aggregator=self.string_aggregator,
-            role="assistant",
-            start_frame=LLMResponseStartFrame,
-            end_frame=LLMResponseEndFrame,
-            accumulator_frame=TextFrame,
-            pass_through=True,
-        )

src/dailyai/pipeline/pipeline.py

@@ -19,7 +19,7 @@ class Pipeline:
         source: asyncio.Queue | None = None,
         sink: asyncio.Queue[Frame] | None = None,
     ):
-        """Create a new pipeline. By default neither the source nor sink
+        """ Create a new pipeline. By default neither the source nor sink
         queues are set, so you'll need to pass them to this constructor or
         call set_source and set_sink before using the pipeline. Note that
         the transport's run_*_pipeline methods will set the source and sink
@@ -30,38 +30,26 @@ class Pipeline:
         self.sink: asyncio.Queue[Frame] | None = sink
 
     def set_source(self, source: asyncio.Queue[Frame]):
-        """Set the source queue for this pipeline. Frames from this queue
+        """ Set the source queue for this pipeline. Frames from this queue
         will be processed by each frame_processor in the pipeline, or order
-        from first to last."""
+        from first to last. """
         self.source = source
 
     def set_sink(self, sink: asyncio.Queue[Frame]):
-        """Set the sink queue for this pipeline. After the last frame_processor
+        """ Set the sink queue for this pipeline. After the last frame_processor
         has processed a frame, its output will be placed on this queue."""
         self.sink = sink
 
     async def get_next_source_frame(self) -> AsyncGenerator[Frame, None]:
-        """Convenience function to get the next frame from the source queue. This
+        """ Convenience function to get the next frame from the source queue. This
         lets us consistently have an AsyncGenerator yield frames, from either the
         source queue or a frame_processor."""
         if self.source is None:
             raise ValueError("Source queue not set")
         yield await self.source.get()
 
-    async def run_pipeline_recursively(
-        self, initial_frame: Frame, processors: List[FrameProcessor]
-    ) -> AsyncGenerator[Frame, None]:
-        if processors:
-            async for frame in processors[0].process_frame(initial_frame):
-                async for final_frame in self.run_pipeline_recursively(
-                    frame, processors[1:]
-                ):
-                    yield final_frame
-        else:
-            yield initial_frame
-
     async def run_pipeline(self):
-        """Run the pipeline. Take each frame from the source queue, pass it to
+        """ Run the pipeline. Take each frame from the source queue, pass it to
         the first frame_processor, pass the output of that frame_processor to the
         next in the list, etc. until the last frame_processor has processed the
         resulting frames, then place those frames in the sink queue.
@@ -70,35 +58,32 @@ class Pipeline:
 
         This method will exit when an EndStreamQueueFrame is placed on the sink queue.
         No more frames will be placed on the sink queue after an EndStreamQueueFrame, even
-        if it's not the last frame yielded by the last frame_processor in the pipeline..
-        """
+        if it's not the last frame yielded by the last frame_processor in the pipeline.."""
 
         if self.source is None or self.sink is None:
             raise ValueError("Source or sink queue not set")
 
         try:
             while True:
-                initial_frame = await self.source.get()
-                async for frame in self.run_pipeline_recursively(
-                    initial_frame, self.processors
-                ):
-                    await self.sink.put(frame)
+                frame_generators = [self.get_next_source_frame()]
+                for processor in self.processors:
+                    next_frame_generators = []
+                    for frame_generator in frame_generators:
+                        async for frame in frame_generator:
+                            next_frame_generators.append(processor.process_frame(frame))
+                    frame_generators = next_frame_generators
 
-                if isinstance(initial_frame, EndFrame) or isinstance(
-                    initial_frame, EndPipeFrame
-                ):
-                    break
+                for frame_generator in frame_generators:
+                    async for frame in frame_generator:
+                        await self.sink.put(frame)
+                        if isinstance(
+                            frame, EndFrame
+                        ) or isinstance(
+                            frame, EndPipeFrame
+                        ):
+                            return
         except asyncio.CancelledError:
             # this means there's been an interruption, do any cleanup necessary here.
             for processor in self.processors:
                 await processor.interrupted()
             pass
-
-    async def queue_frames(self, frames: Frame | List[Frame]):
-        """Insert frames directly into a pipeline. This is typically used inside a transport
-        participant_joined callback to prompt a bot to start a conversation, for example.
-        """
-        if not isinstance(frames, List):
-            frames = [frames]
-        for f in frames:
-            await self.source.put(f)

src/dailyai/requirements.txt

@@ -0,0 +1,3 @@
+Pillow==10.1.0
+typing_extensions==4.9.0
+faster-whisper==0.10.0

src/dailyai/services/ai_services.py

@@ -12,7 +12,6 @@ from dailyai.pipeline.frames import (
     LLMMessagesQueueFrame,
     LLMResponseEndFrame,
     LLMResponseStartFrame,
-    LLMFunctionStartFrame,
     LLMFunctionCallFrame,
     Frame,
     TextFrame,
@@ -22,8 +21,15 @@ from dailyai.pipeline.frames import (
 from abc import abstractmethod
 from typing import AsyncGenerator, AsyncIterable, BinaryIO, Iterable, List
 
-
 class AIService(FrameProcessor):
+    """ This is the base class for various AI services (LLM, TTS and Image)
+
+    This class adds some convenienence functions to run, effectively, a one-stage
+    pipeline where the incoming frames can come from an iterable or queue
+    and the processed frames go to a queue. Child classes extend those convenience
+    functions, eg. TTS's `say` method runs the TTS and emits the AudioFrames to a
+    queue.
+    """
 
     def __init__(self):
         self.logger = logging.getLogger("dailyai")
@@ -32,18 +38,33 @@ class AIService(FrameProcessor):
         pass
 
     async def run_to_queue(
-        self, queue: asyncio.Queue, frames, add_end_of_stream=False
+        self,
+        queue: asyncio.Queue,
+        frames: Iterable[Frame] | AsyncIterable[Frame] | asyncio.Queue[Frame]
     ) -> None:
+        """ Process the given frames (from an iterable or queue) and send them to
+        the given queue.
+        """
         async for frame in self.run(frames):
             await queue.put(frame)
 
-        if add_end_of_stream:
-            await queue.put(EndFrame())
 
     async def run(
         self,
-        frames: Iterable[Frame] | AsyncIterable[Frame] | asyncio.Queue[Frame],
+        frames: Iterable[Frame]
+        | AsyncIterable[Frame]
+        | asyncio.Queue[Frame],
     ) -> AsyncGenerator[Frame, None]:
+        """ Generates 0 or more frames from the given iterable or queue.
+
+        This is a convenience function to take a collection of frames, process
+        them, and yield processed frames.
+
+        The preferred way to use FrameProcessors is with a pipeline, but if you
+        have a very simple case (eg. a list of static text blocks you want to speak,
+        or a list of static image description you want to render) this function
+        will be helpful.
+        """
         try:
             if isinstance(frames, AsyncIterable):
                 async for frame in frames:
@@ -68,10 +89,42 @@ class AIService(FrameProcessor):
 
 
 class LLMService(AIService):
-    """This class is a no-op but serves as a base class for LLM services."""
-
-    def __init__(self):
+    def __init__(self, messages=None, tools=None):
         super().__init__()
+        self._tools = tools
+        self._messages = messages
+
+    @abstractmethod
+    async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
+        yield ""
+
+    @abstractmethod
+    async def run_llm(self, messages) -> str:
+        pass
+
+    async def process_frame(self, frame: Frame, tool_choice: str | None = None) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, LLMMessagesQueueFrame):
+            function_name = ""
+            arguments = ""
+            if isinstance(frame, LLMMessagesQueueFrame):
+                yield LLMResponseStartFrame()
+                async for text_chunk in self.run_llm_async(frame.messages, tool_choice):
+                    if isinstance(text_chunk, str):
+                        yield TextFrame(text_chunk)
+                    elif text_chunk.function:
+                        if text_chunk.function.name:
+                            # function_name += text_chunk.function.name
+                            yield LLMFunctionCallFrame(function_name=text_chunk.function.name, arguments=None)
+                        if text_chunk.function.arguments:
+                            # arguments += text_chunk.function.arguments
+                            yield LLMFunctionCallFrame(function_name=None, arguments=text_chunk.function.arguments)
+
+                if (function_name and arguments):
+                    function_name = ""
+                    arguments = ""
+                yield LLMResponseEndFrame()
+        else:
+            yield frame
 
 
 class TTSService(AIService):
@@ -107,7 +160,7 @@ class TTSService(AIService):
             text = frame.text
         else:
             self.current_sentence += frame.text
-            if self.current_sentence.strip().endswith((".", "?", "!")):
+            if self.current_sentence.endswith((".", "?", "!")):
                 text = self.current_sentence
                 self.current_sentence = ""
 
@@ -120,9 +173,7 @@ class TTSService(AIService):
 
     # Convenience function to send the audio for a sentence to the given queue
     async def say(self, sentence, queue: asyncio.Queue):
-        await self.run_to_queue(
-            queue, [LLMResponseStartFrame(), TextFrame(sentence), LLMResponseEndFrame()]
-        )
+        await self.run_to_queue(queue, [LLMResponseStartFrame(), TextFrame(sentence), LLMResponseEndFrame()])
 
 
 class ImageGenService(AIService):
@@ -173,7 +224,7 @@ class STTService(AIService):
         ww.close()
         content.seek(0)
         text = await self.run_stt(content)
-        yield TranscriptionQueueFrame(text, "", str(time.time()))
+        yield TranscriptionQueueFrame(text, '', str(time.time()))
 
 
 class FrameLogger(AIService):

src/dailyai/services/azure_ai_services.py

@@ -14,14 +14,7 @@ from dailyai.services.ai_services import LLMService, TTSService, ImageGenService
 from PIL import Image
 
 # See .env.example for Azure configuration needed
-from azure.cognitiveservices.speech import (
-    SpeechSynthesizer,
-    SpeechConfig,
-    ResultReason,
-    CancellationReason,
-)
-
-from dailyai.services.openai_api_llm_service import BaseOpenAILLMService
+from azure.cognitiveservices.speech import SpeechSynthesizer, SpeechConfig, ResultReason, CancellationReason
 
 
 class AzureTTSService(TTSService):
@@ -30,21 +23,18 @@ class AzureTTSService(TTSService):
 
         self.speech_config = SpeechConfig(subscription=api_key, region=region)
         self.speech_synthesizer = SpeechSynthesizer(
-            speech_config=self.speech_config, audio_config=None
-        )
+            speech_config=self.speech_config, audio_config=None)
 
     async def run_tts(self, sentence) -> AsyncGenerator[bytes, None]:
         self.logger.info("Running azure tts")
-        ssml = (
-            "<speak version='1.0' xml:lang='en-US' xmlns='http://www.w3.org/2001/10/synthesis' "
-            "xmlns:mstts='http://www.w3.org/2001/mstts'>"
-            "<voice name='en-US-SaraNeural'>"
-            "<mstts:silence type='Sentenceboundary' value='20ms' />"
-            "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>"
-            "<prosody rate='1.05'>"
-            f"{sentence}"
+        ssml = "<speak version='1.0' xml:lang='en-US' xmlns='http://www.w3.org/2001/10/synthesis' " \
+            "xmlns:mstts='http://www.w3.org/2001/mstts'>" \
+            "<voice name='en-US-SaraNeural'>" \
+            "<mstts:silence type='Sentenceboundary' value='20ms' />" \
+            "<mstts:express-as style='lyrical' styledegree='2' role='SeniorFemale'>" \
+            "<prosody rate='1.05'>" \
+            f"{sentence}" \
             "</prosody></mstts:express-as></voice></speak> "
-        )
         result = await asyncio.to_thread(self.speech_synthesizer.speak_ssml, (ssml))
         self.logger.info("Got azure tts result")
         if result.reason == ResultReason.SynthesizingAudioCompleted:
@@ -53,39 +43,62 @@ class AzureTTSService(TTSService):
             yield result.audio_data[44:]
         elif result.reason == ResultReason.Canceled:
             cancellation_details = result.cancellation_details
-            self.logger.info(
-                "Speech synthesis canceled: {}".format(cancellation_details.reason)
-            )
+            self.logger.info("Speech synthesis canceled: {}".format(cancellation_details.reason))
             if cancellation_details.reason == CancellationReason.Error:
-                self.logger.info(
-                    "Error details: {}".format(cancellation_details.error_details)
-                )
+                self.logger.info("Error details: {}".format(cancellation_details.error_details))
 
 
-class AzureLLMService(BaseOpenAILLMService):
-    def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model):
-        super().__init__(model)
+class AzureLLMService(LLMService):
+    def __init__(self, *, api_key, endpoint, api_version="2023-12-01-preview", model, tools=None, messages=None):
+        super().__init__(tools=tools, messages=messages)
+        self._model: str = model
 
-        # This overrides the client created by the super class init
         self._client = AsyncAzureOpenAI(
             api_key=api_key,
             azure_endpoint=endpoint,
             api_version=api_version,
         )
 
+    async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via azure: {messages_for_log}")
+        if self._tools:
+            tools = self._tools
+        else:
+            tools = None
+        start_time = time.time()
+        chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages, tools=tools, tool_choice=tool_choice)
+        self.logger.info(f"=== Azure OpenAI LLM TTFB: {time.time() - start_time}")
+        async for chunk in chunks:
+            if len(chunk.choices) == 0:
+                continue
+            if chunk.choices[0].delta.tool_calls:
+                yield chunk.choices[0].delta.tool_calls[0]
+            elif chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+    async def run_llm(self, messages) -> str | None:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via azure: {messages_for_log}")
+
+        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
+        if response and len(response.choices) > 0:
+            return response.choices[0].message.content
+        else:
+            return None
+
 
 class AzureImageGenServiceREST(ImageGenService):
 
     def __init__(
-        self,
-        *,
-        api_version="2023-06-01-preview",
-        image_size: str,
-        aiohttp_session: aiohttp.ClientSession,
-        api_key,
-        endpoint,
-        model,
-    ):
+            self,
+            *,
+            api_version="2023-06-01-preview",
+            image_size: str,
+            aiohttp_session: aiohttp.ClientSession,
+            api_key,
+            endpoint,
+            model):
         super().__init__(image_size=image_size)
 
         self._api_key = api_key
@@ -108,7 +121,7 @@ class AzureImageGenServiceREST(ImageGenService):
         ) as submission:
             # We never get past this line, because this header isn't
             # defined on a 429 response, but something is eating our exceptions!
-            operation_location = submission.headers["operation-location"]
+            operation_location = submission.headers['operation-location']
             status = ""
             attempts_left = 120
             json_response = None
@@ -124,9 +137,7 @@ class AzureImageGenServiceREST(ImageGenService):
                 json_response = await response.json()
                 status = json_response["status"]
 
-            image_url = (
-                json_response["result"]["data"][0]["url"] if json_response else None
-            )
+            image_url = json_response["result"]["data"][0]["url"] if json_response else None
             if not image_url:
                 raise Exception("Image generation failed")
             # Load the image from the url

src/dailyai/services/base_transport_service.py

@@ -17,40 +17,43 @@ from dailyai.pipeline.frames import (
     EndFrame,
     ImageFrame,
     Frame,
-    PipelineStartedFrame,
     SpriteFrame,
     StartFrame,
     TranscriptionQueueFrame,
     UserStartedSpeakingFrame,
-    UserStoppedSpeakingFrame,
+    UserStoppedSpeakingFrame
 )
 from dailyai.pipeline.pipeline import Pipeline
 
 torch.set_num_threads(1)
 
-model, utils = torch.hub.load(
-    repo_or_dir="snakers4/silero-vad", model="silero_vad", force_reload=False
-)
+model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
+                              model='silero_vad',
+                              force_reload=False)
 
-(get_speech_timestamps, save_audio, read_audio, VADIterator, collect_chunks) = utils
+(get_speech_timestamps,
+ save_audio,
+ read_audio,
+ VADIterator,
+ collect_chunks) = utils
 
 # Taken from utils_vad.py
 
 
-def validate(model, inputs: torch.Tensor):
+def validate(model,
+             inputs: torch.Tensor):
     with torch.no_grad():
         outs = model(inputs)
     return outs
 
-
 # Provided by Alexander Veysov
 
 
 def int2float(sound):
     abs_max = np.abs(sound).max()
-    sound = sound.astype("float32")
+    sound = sound.astype('float32')
     if abs_max > 0:
-        sound *= 1 / 32768
+        sound *= 1/32768
     sound = sound.squeeze()  # depends on the use case
     return sound
 
@@ -70,7 +73,7 @@ class VADState(Enum):
     STOPPING = 4
 
 
-class BaseTransportService:
+class BaseTransportService():
 
     def __init__(
         self,
@@ -91,8 +94,7 @@ class BaseTransportService:
 
         if self._vad_enabled and self._speaker_enabled:
             raise Exception(
-                "Sorry, you can't use speaker_enabled and vad_enabled at the same time. Please set one to False."
-            )
+                "Sorry, you can't use speaker_enabled and vad_enabled at the same time. Please set one to False.")
 
         self._vad_samples = 1536
         vad_frame_s = self._vad_samples / SAMPLE_RATE
@@ -128,20 +130,20 @@ class BaseTransportService:
     async def run(self):
         self._prerun()
 
-        async_output_queue_marshal_task = asyncio.create_task(self._marshal_frames())
+        async_output_queue_marshal_task = asyncio.create_task(
+            self._marshal_frames())
 
-        self._camera_thread = threading.Thread(target=self._run_camera, daemon=True)
+        self._camera_thread = threading.Thread(
+            target=self._run_camera, daemon=True)
         self._camera_thread.start()
 
         self._frame_consumer_thread = threading.Thread(
-            target=self._frame_consumer, daemon=True
-        )
+            target=self._frame_consumer, daemon=True)
         self._frame_consumer_thread.start()
 
         if self._speaker_enabled:
             self._receive_audio_thread = threading.Thread(
-                target=self._receive_audio, daemon=True
-            )
+                target=self._receive_audio, daemon=True)
             self._receive_audio_thread.start()
 
         if self._vad_enabled:
@@ -149,7 +151,10 @@ class BaseTransportService:
             self._vad_thread.start()
 
         try:
-            while time.time() < self._expiration and not self._stop_threads.is_set():
+            while (
+                time.time() < self._expiration
+                and not self._stop_threads.is_set()
+            ):
                 await asyncio.sleep(1)
         except Exception as e:
             self._logger.error(f"Exception {e}")
@@ -273,7 +278,8 @@ class BaseTransportService:
             audio_chunk = self.read_audio_frames(self._vad_samples)
             audio_int16 = np.frombuffer(audio_chunk, np.int16)
             audio_float32 = int2float(audio_int16)
-            new_confidence = model(torch.from_numpy(audio_float32), 16000).item()
+            new_confidence = model(
+                torch.from_numpy(audio_float32), 16000).item()
             speaking = new_confidence > 0.5
 
             if speaking:
@@ -297,22 +303,18 @@ class BaseTransportService:
                     case VADState.STOPPING:
                         self._vad_stopping_count += 1
 
-            if (
-                self._vad_state == VADState.STARTING
-                and self._vad_starting_count >= self._vad_start_frames
-            ):
+            if self._vad_state == VADState.STARTING and self._vad_starting_count >= self._vad_start_frames:
                 asyncio.run_coroutine_threadsafe(
-                    self.receive_queue.put(UserStartedSpeakingFrame()), self._loop
+                    self.receive_queue.put(
+                        UserStartedSpeakingFrame()), self._loop
                 )
                 # self.interrupt()
                 self._vad_state = VADState.SPEAKING
                 self._vad_starting_count = 0
-            if (
-                self._vad_state == VADState.STOPPING
-                and self._vad_stopping_count >= self._vad_stop_frames
-            ):
+            if self._vad_state == VADState.STOPPING and self._vad_stopping_count >= self._vad_stop_frames:
                 asyncio.run_coroutine_threadsafe(
-                    self.receive_queue.put(UserStoppedSpeakingFrame()), self._loop
+                    self.receive_queue.put(
+                        UserStoppedSpeakingFrame()), self._loop
                 )
                 self._vad_state = VADState.QUIET
                 self._vad_stopping_count = 0
@@ -326,7 +328,7 @@ class BaseTransportService:
                 break
 
     def interrupt(self):
-        self._logger.debug("### Interrupting")
+        self._logger.debug("!!! Interrupting")
         self._is_interrupted.set()
 
     async def get_receive_frames(self) -> AsyncGenerator[Frame, None]:
@@ -351,7 +353,9 @@ class BaseTransportService:
                     self.receive_queue.put(frame), self._loop
                 )
 
-        asyncio.run_coroutine_threadsafe(self.receive_queue.put(EndFrame()), self._loop)
+        asyncio.run_coroutine_threadsafe(
+            self.receive_queue.put(EndFrame()), self._loop
+        )
 
     def _set_image(self, image: bytes):
         self._images = itertools.cycle([image])
@@ -376,19 +380,18 @@ class BaseTransportService:
         b = bytearray()
         smallest_write_size = 3200
         largest_write_size = 8000
+        all_audio_frames = bytearray()
         while True:
             try:
-                frames_or_frame: Frame | list[Frame] = self._threadsafe_send_queue.get()
-                if (
-                    isinstance(frames_or_frame, AudioFrame)
-                    and len(frames_or_frame.data) > largest_write_size
-                ):
+                frames_or_frame: Frame | list[Frame] = (
+                    self._threadsafe_send_queue.get()
+                )
+                if isinstance(frames_or_frame, AudioFrame) and len(frames_or_frame.data) > largest_write_size:
                     # subdivide large audio frames to enable interruption
                     frames = []
                     for i in range(0, len(frames_or_frame.data), largest_write_size):
-                        frames.append(
-                            AudioFrame(frames_or_frame.data[i : i + largest_write_size])
-                        )
+                        frames.append(AudioFrame(
+                            frames_or_frame.data[i: i+largest_write_size]))
                 elif isinstance(frames_or_frame, Frame):
                     frames: list[Frame] = [frames_or_frame]
                 elif isinstance(frames_or_frame, list):
@@ -411,13 +414,15 @@ class BaseTransportService:
                         if frame:
                             if isinstance(frame, AudioFrame):
                                 chunk = frame.data
+                                all_audio_frames.extend(chunk)
 
                                 b.extend(chunk)
                                 truncated_length: int = len(b) - (
                                     len(b) % smallest_write_size
                                 )
                                 if truncated_length:
-                                    self.write_frame_to_mic(bytes(b[:truncated_length]))
+                                    self.write_frame_to_mic(
+                                        bytes(b[:truncated_length]))
                                     b = b[truncated_length:]
                             elif isinstance(frame, ImageFrame):
                                 self._set_image(frame.image)
@@ -431,15 +436,12 @@ class BaseTransportService:
                         # can cause static in the audio stream.
                         if len(b):
                             truncated_length = len(b) - (len(b) % 160)
-                            self.write_frame_to_mic(bytes(b[:truncated_length]))
+                            self.write_frame_to_mic(
+                                bytes(b[:truncated_length]))
                             b = bytearray()
 
                         if isinstance(frame, StartFrame):
                             self._is_interrupted.clear()
-                            asyncio.run_coroutine_threadsafe(
-                                self.receive_queue.put(PipelineStartedFrame()),
-                                self._loop,
-                            )
 
                     if self._loop:
                         asyncio.run_coroutine_threadsafe(
@@ -453,5 +455,6 @@ class BaseTransportService:
 
                 b = bytearray()
             except Exception as e:
-                self._logger.error(f"Exception in frame_consumer: {e}, {len(b)}")
+                self._logger.error(
+                    f"Exception in frame_consumer: {e}, {len(b)}")
                 raise e

src/dailyai/services/daily_transport_service.py

@@ -81,10 +81,7 @@ class DailyTransportService(BaseTransportService, EventHandler):
             for handler in self._event_handlers[event_name]:
                 if inspect.iscoroutinefunction(handler):
                     if self._loop:
-                        future = asyncio.run_coroutine_threadsafe(handler(*args, **kwargs), self._loop)
-
-                        # wait for the coroutine to finish. This will also raise any exceptions raised by the coroutine.
-                        future.result()
+                        asyncio.run_coroutine_threadsafe(handler(*args, **kwargs), self._loop)
                     else:
                         raise Exception(
                             "No event loop to run coroutine. In order to use async event handlers, you must run the DailyTransportService in an asyncio event loop.")

src/dailyai/services/fal_ai_services.py

@@ -9,19 +9,17 @@ from dailyai.services.ai_services import ImageGenService
 
 
 from dailyai.services.ai_services import ImageGenService
-
 # Fal expects FAL_KEY_ID and FAL_KEY_SECRET to be set in the env
 
 
 class FalImageGenService(ImageGenService):
     def __init__(
-        self,
-        *,
-        image_size,
-        aiohttp_session: aiohttp.ClientSession,
-        key_id=None,
-        key_secret=None
-    ):
+            self,
+            *,
+            image_size,
+            aiohttp_session: aiohttp.ClientSession,
+            key_id=None,
+            key_secret=None):
         super().__init__(image_size)
         self._aiohttp_session = aiohttp_session
         if key_id:
@@ -33,8 +31,9 @@ class FalImageGenService(ImageGenService):
         def get_image_url(sentence, size):
             handler = fal.apps.submit(
                 "110602490-fast-sdxl",
-                #"fal-ai/fast-sdxl",
-                arguments={"prompt": sentence},
+                arguments={
+                    "prompt": sentence
+                },
             )
             for event in handler.iter_events():
                 if isinstance(event, fal.apps.InProgress):
@@ -47,7 +46,6 @@ class FalImageGenService(ImageGenService):
                 raise Exception("Image generation failed")
 
             return image_url
-
         image_url = await asyncio.to_thread(get_image_url, sentence, self.image_size)
         # Load the image from the url
         async with self._aiohttp_session.get(image_url) as response:

src/dailyai/services/ollama_ai_services.py

@@ -1,7 +1,44 @@
-from dailyai.services.openai_api_llm_service import BaseOpenAILLMService
+from openai import AsyncOpenAI
+
+import json
+from collections.abc import AsyncGenerator
+
+from dailyai.services.ai_services import LLMService
 
 
-class OLLamaLLMService(BaseOpenAILLMService):
+class OLLamaLLMService(LLMService):
+    def __init__(self, model="llama2", base_url='http://localhost:11434/v1'):
+        super().__init__()
+        self._model = model
+        self._client = AsyncOpenAI(api_key="ollama", base_url=base_url)
 
-    def __init__(self, model="llama2", base_url="http://localhost:11434/v1"):
-        super().__init__(model=model, base_url=base_url, api_key="ollama")
+    async def get_response(self, messages, stream):
+        return await self._client.chat.completions.create(
+            stream=stream,
+            messages=messages,
+            model=self._model
+        )
+
+    async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
+
+        chunks = await self._client.chat.completions.create(
+            model=self._model, stream=True, messages=messages
+        )
+        async for chunk in chunks:
+            if len(chunk.choices) == 0:
+                continue
+
+            if chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+    async def run_llm(self, messages) -> str | None:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via ollama: {messages_for_log}")
+
+        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
+        if response and len(response.choices) > 0:
+            return response.choices[0].message.content
+        else:
+            return None

src/dailyai/services/open_ai_services.py

@@ -8,13 +8,49 @@ import json
 from collections.abc import AsyncGenerator
 
 from dailyai.services.ai_services import LLMService, ImageGenService
-from dailyai.services.openai_api_llm_service import BaseOpenAILLMService
 
 
-class OpenAILLMService(BaseOpenAILLMService):
+class OpenAILLMService(LLMService):
+    def __init__(self, *, api_key, model="gpt-4", tools=None, messages=None):
+        super().__init__(tools=tools, messages=messages)
+        self._model = model
+        self._client = AsyncOpenAI(api_key=api_key)
 
-    def __init__(self, model="gpt-4", * args, **kwargs):
-        super().__init__(model, *args, **kwargs)
+    async def get_response(self, messages, stream):
+        return await self._client.chat.completions.create(
+            stream=stream,
+            messages=messages,
+            model=self._model,
+            tools=self._tools
+        )
+
+    async def run_llm_async(self, messages, tool_choice=None) -> AsyncGenerator[str, None]:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
+        if self._tools:
+            tools = self._tools
+        else:
+            tools = None
+        start_time = time.time()
+        chunks = await self._client.chat.completions.create(model=self._model, stream=True, messages=messages, tools=tools, tool_choice=tool_choice)
+        self.logger.info(f"=== OpenAI LLM TTFB: {time.time() - start_time}")
+        async for chunk in chunks:
+            if len(chunk.choices) == 0:
+                continue
+            if chunk.choices[0].delta.tool_calls:
+                yield chunk.choices[0].delta.tool_calls[0]
+            elif chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+    async def run_llm(self, messages) -> str | None:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
+
+        response = await self._client.chat.completions.create(model=self._model, stream=False, messages=messages)
+        if response and len(response.choices) > 0:
+            return response.choices[0].message.content
+        else:
+            return None
 
 
 class OpenAIImageGenService(ImageGenService):

src/dailyai/services/openai_api_llm_service.py

@@ -1,120 +0,0 @@
-import json
-import time
-from typing import AsyncGenerator, List
-from openai import AsyncOpenAI, AsyncStream
-from dailyai.pipeline.frames import (
-    Frame,
-    LLMFunctionCallFrame,
-    LLMFunctionStartFrame,
-    LLMMessagesQueueFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    OpenAILLMContextFrame,
-    TextFrame,
-)
-from dailyai.services.ai_services import LLMService
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-from openai.types.chat import (
-    ChatCompletion,
-    ChatCompletionChunk,
-    ChatCompletionMessageParam,
-)
-
-
-class BaseOpenAILLMService(LLMService):
-    """This is the base for all services that use the AsyncOpenAI client.
-
-    This service consumes OpenAILLMContextFrame frames, which contain a reference
-    to an OpenAILLMContext frame. The OpenAILLMContext object defines the context
-    sent to the LLM for a completion. This includes user, assistant and system messages
-    as well as tool choices and the tool, which is used if requesting function
-    calls from the LLM.
-    """
-
-    def __init__(self, model: str, api_key=None, base_url=None):
-        super().__init__()
-        self._model: str = model
-        self._client = AsyncOpenAI(api_key=api_key, base_url=base_url)
-
-    async def _stream_chat_completions(
-        self, context: OpenAILLMContext
-    ) -> AsyncStream[ChatCompletionChunk]:
-        messages: List[ChatCompletionMessageParam] = context.get_messages()
-        messages_for_log = json.dumps(messages)
-        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
-
-        start_time = time.time()
-        chunks: AsyncStream[ChatCompletionChunk] = (
-            await self._client.chat.completions.create(
-                model=self._model,
-                stream=True,
-                messages=messages,
-                tools=context.tools,
-                tool_choice=context.tool_choice,
-            )
-        )
-        self.logger.info(f"=== OpenAI LLM TTFB: {time.time() - start_time}")
-        return chunks
-
-    async def _chat_completions(self, messages) -> str | None:
-        messages_for_log = json.dumps(messages)
-        self.logger.debug(f"Generating chat via openai: {messages_for_log}")
-
-        response: ChatCompletion = await self._client.chat.completions.create(
-            model=self._model, stream=False, messages=messages
-        )
-        if response and len(response.choices) > 0:
-            return response.choices[0].message.content
-        else:
-            return None
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, OpenAILLMContextFrame):
-            context: OpenAILLMContext = frame.context
-        elif isinstance(frame, LLMMessagesQueueFrame):
-            context = OpenAILLMContext.from_messages(frame.messages)
-        else:
-            yield frame
-            return
-
-        function_name = ""
-        arguments = ""
-
-        yield LLMResponseStartFrame()
-        chunk_stream: AsyncStream[ChatCompletionChunk] = (
-            await self._stream_chat_completions(context)
-        )
-        async for chunk in chunk_stream:
-            if len(chunk.choices) == 0:
-                continue
-
-            if chunk.choices[0].delta.tool_calls:
-                # We're streaming the LLM response to enable the fastest response times.
-                # For text, we just yield each chunk as we receive it and count on consumers
-                # to do whatever coalescing they need (eg. to pass full sentences to TTS)
-                #
-                # If the LLM is a function call, we'll do some coalescing here.
-                # If the response contains a function name, we'll yield a frame to tell consumers
-                # that they can start preparing to call the function with that name.
-                # We accumulate all the arguments for the rest of the streamed response, then when
-                # the response is done, we package up all the arguments and the function name and
-                # yield a frame containing the function name and the arguments.
-
-                tool_call = chunk.choices[0].delta.tool_calls[0]
-                if tool_call.function and tool_call.function.name:
-                    function_name += tool_call.function.name
-                    yield LLMFunctionStartFrame(function_name=tool_call.function.name)
-                if tool_call.function and tool_call.function.arguments:
-                    # Keep iterating through the response to collect all the argument fragments and
-                    # yield a complete LLMFunctionCallFrame after run_llm_async completes
-                    arguments += tool_call.function.arguments
-            elif chunk.choices[0].delta.content:
-                yield TextFrame(chunk.choices[0].delta.content)
-
-        # if we got a function name and arguments, yield the frame with all the info so
-        # frame consumers can take action based on the function call.
-        if function_name and arguments:
-            yield LLMFunctionCallFrame(function_name=function_name, arguments=arguments)
-
-        yield LLMResponseEndFrame()

src/dailyai/services/openai_llm_context.py

@@ -1,52 +0,0 @@
-from typing import List
-from openai._types import NOT_GIVEN, NotGiven
-
-from openai.types.chat import (
-    ChatCompletionToolParam,
-    ChatCompletionToolChoiceOptionParam,
-    ChatCompletionMessageParam,
-)
-
-
-class OpenAILLMContext:
-
-    def __init__(
-        self,
-        messages: List[ChatCompletionMessageParam] | None = None,
-        tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
-        tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = NOT_GIVEN
-    ):
-        self.messages: List[ChatCompletionMessageParam] = messages if messages else []
-        self.tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = tool_choice
-        self.tools: List[ChatCompletionToolParam] | NotGiven = tools
-
-    @staticmethod
-    def from_messages(messages: List[dict]) -> "OpenAILLMContext":
-        context = OpenAILLMContext()
-        for message in messages:
-            context.add_message({
-                "content":message["content"],
-                "role":message["role"],
-                "name":message["name"] if "name" in message else message["role"]
-            })
-        return context
-
-    #def __deepcopy__(self, memo):
-
-    def add_message(self, message: ChatCompletionMessageParam):
-        self.messages.append(message)
-
-    def get_messages(self) -> List[ChatCompletionMessageParam]:
-        return self.messages
-
-    def set_tool_choice(
-        self, tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven
-    ):
-        self.tool_choice = tool_choice
-
-    def set_tools(self, tools:List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN):
-        if tools != NOT_GIVEN and len(tools) == 0:
-            tools = NOT_GIVEN
-
-        self.tools = tools
-

src/dailyai/tests/integration/integration_azure_llm.py

@@ -1,29 +0,0 @@
-import asyncio
-import os
-from dailyai.pipeline.frames import (
-    OpenAILLMContextFrame,
-)
-from dailyai.services.azure_ai_services import AzureLLMService
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-from openai.types.chat import (
-    ChatCompletionSystemMessageParam,
-)
-
-if __name__=="__main__":
-    async def test_chat():
-        llm = AzureLLMService(
-            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
-            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        )
-        context = OpenAILLMContext()
-        message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
-        )
-        context.add_message(message)
-        frame = OpenAILLMContextFrame(context)
-        async for s in llm.process_frame(frame):
-            print(s)
-
-    asyncio.run(test_chat())

src/dailyai/tests/integration/integration_ollama_llm.py

@@ -1,24 +0,0 @@
-import asyncio
-from dailyai.pipeline.frames import (
-    OpenAILLMContextFrame,
-)
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-from openai.types.chat import (
-    ChatCompletionSystemMessageParam,
-)
-from dailyai.services.ollama_ai_services import OLLamaLLMService
-
-if __name__=="__main__":
-    async def test_chat():
-        llm = OLLamaLLMService()
-        context = OpenAILLMContext()
-        message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
-        )
-        context.add_message(message)
-        frame = OpenAILLMContextFrame(context)
-        async for s in llm.process_frame(frame):
-            print(s)
-
-    asyncio.run(test_chat())

src/dailyai/tests/integration/integration_openai_llm.py

@@ -1,84 +0,0 @@
-import asyncio
-import os
-from dailyai.pipeline.frames import (
-    OpenAILLMContextFrame,
-)
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-from openai.types.chat import (
-    ChatCompletionSystemMessageParam,
-    ChatCompletionToolParam,
-    ChatCompletionUserMessageParam,
-)
-
-from dailyai.services.openai_api_llm_service import BaseOpenAILLMService
-
-if __name__ == "__main__":
-    async def test_functions():
-        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"],
-                    },
-                }
-            )
-        ]
-
-        api_key = os.getenv("OPENAI_API_KEY")
-
-        llm = BaseOpenAILLMService(
-            api_key=api_key or "",
-            model="gpt-4-1106-preview",
-        )
-        context = OpenAILLMContext(tools=tools)
-        system_message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Ask the user to ask for a weather report", name="system", role="system"
-        )
-        user_message: ChatCompletionUserMessageParam = ChatCompletionUserMessageParam(
-            content="Could you tell me the weather for Boulder, Colorado",
-            name="user",
-            role="user",
-        )
-        context.add_message(system_message)
-        context.add_message(user_message)
-        frame = OpenAILLMContextFrame(context)
-        async for s in llm.process_frame(frame):
-            print(s)
-
-    async def test_chat():
-        api_key = os.getenv("OPENAI_API_KEY")
-
-        llm = BaseOpenAILLMService(
-            api_key=api_key or "",
-            model="gpt-4-1106-preview",
-        )
-        context = OpenAILLMContext()
-        message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam(
-            content="Please tell the world hello.", name="system", role="system"
-        )
-        context.add_message(message)
-        frame = OpenAILLMContextFrame(context)
-        async for s in llm.process_frame(frame):
-            print(s)
-
-    async def run_tests():
-        await test_functions()
-        await test_chat()
-
-    asyncio.run(run_tests())

src/dailyai/tests/test_daily_transport_service.py

@@ -1,5 +1,4 @@
 import asyncio
-import threading
 import unittest
 
 from unittest.mock import MagicMock, patch
@@ -25,8 +24,6 @@ class TestDailyTransport(unittest.IsolatedAsyncioTestCase):
 
         self.assertTrue(was_called)
 
-    """
-    TODO: fix this test, it broke when I added the `.result` call in the patch.
     async def test_event_handler_async(self):
         from dailyai.services.daily_transport_service import DailyTransportService
 
@@ -37,19 +34,13 @@ class TestDailyTransport(unittest.IsolatedAsyncioTestCase):
         @transport.event_handler("on_first_other_participant_joined")
         async def test_event_handler(transport):
             nonlocal event
-            print("sleeping")
             await asyncio.sleep(0.1)
-            print("setting")
             event.set()
-            print("returning")
 
-        thread = threading.Thread(target=transport.on_first_other_participant_joined)
-        thread.start()
-        thread.join()
+        transport.on_first_other_participant_joined()
 
         await asyncio.wait_for(event.wait(), timeout=1)
         self.assertTrue(event.is_set())
-    """
 
     """
     @patch("dailyai.services.daily_transport_service.CallClient")

src/dailyai/tests/test_pipeline.py

@@ -1,4 +1,5 @@
 import asyncio
+from doctest import OutputChecker
 import unittest
 from dailyai.pipeline.aggregators import SentenceAggregator, StatelessTextTransformer
 from dailyai.pipeline.frames import EndFrame, TextFrame

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

@@ -1,59 +1,62 @@
 import asyncio
 import aiohttp
-import logging
 import os
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.playht_ai_service import PlayHTAIService
 
-from examples.support.runner import configure
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
+        # create a transport service object using environment variables for
+        # the transport service's API key, room url, and any other configuration.
+        # services can all define and document the environment variables they use.
+        # services all also take an optional config object that is used instead of
+        # environment variables.
+        #
+        # the abstract transport service APIs presumably can map pretty closely
+        # to the daily-python basic API
+        meeting_duration_minutes = 5
         transport = DailyTransportService(
             room_url,
             None,
             "Say One Thing",
-            mic_enabled=True,
+            meeting_duration_minutes,
+            mic_enabled=True
         )
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+        """
+        tts = PlayHTAIService(
+            api_key=os.getenv("PLAY_HT_API_KEY"),
+            user_id=os.getenv("PLAY_HT_USER_ID"),
+            voice_url=os.getenv("PLAY_HT_VOICE_URL"),
         )
-
-        other_joined_event = asyncio.Event()
-        participant_name = ''
-
-        async def say_hello():
-            nonlocal tts
-            nonlocal participant_name
-
-            await other_joined_event.wait()
-            await tts.say(
-                "Hello there, " + participant_name + "!",
-                transport.send_queue,
-            )
-            await transport.stop_when_done()
+        """
 
         # Register an event handler so we can play the audio when the participant joins.
         @transport.event_handler("on_participant_joined")
         async def on_participant_joined(transport, participant):
+            nonlocal tts
             if participant["info"]["isLocal"]:
                 return
 
-            nonlocal participant_name
-            participant_name = participant["info"]["userName"] or ''
-            other_joined_event.set()
+            await tts.say(
+                "Hello there, " + participant["info"]["userName"] + "!",
+                transport.send_queue,
+            )
 
-        await asyncio.gather(transport.run(), say_hello())
-        del tts
+            # wait for the output queue to be empty, then leave the meeting
+            await transport.stop_when_done()
+
+        await transport.run()
+        del(tts)
 
 
 if __name__ == "__main__":

src/examples/foundational/01a-local-transport.py

@@ -1,20 +1,17 @@
 import asyncio
 import aiohttp
-import logging
 import os
 
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.local_transport_service import LocalTransportService
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
 
 async def main():
     async with aiohttp.ClientSession() as session:
         meeting_duration_minutes = 1
         transport = LocalTransportService(
-            duration_minutes=meeting_duration_minutes, mic_enabled=True
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=True
         )
         tts = ElevenLabsTTSService(
             aiohttp_session=session,

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

@@ -1,61 +1,57 @@
 import asyncio
 import os
-import logging
 
 import aiohttp
 
 from dailyai.pipeline.frames import LLMMessagesQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.deepgram_ai_services import DeepgramTTSService
 from dailyai.services.open_ai_services import OpenAILLMService
-
-from examples.support.runner import configure
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 1
         transport = DailyTransportService(
             room_url,
             None,
             "Say One Thing From an LLM",
-            mic_enabled=True,
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=True
         )
 
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
+        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv("DEEPGRAM_API_KEY"), voice=os.getenv("DEEPGRAM_VOICE"))
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world.",
-            }
-        ]
-
-        other_joined_event = asyncio.Event()
-        async def speak_from_llm():
-            await other_joined_event.wait()
-            await tts.run_to_queue(
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
+        # llm = OpenAILLMService(api_key=os.getenv("OPENAI_CHATGPT_API_KEY"))
+        messages = [{
+            "role": "system",
+            "content": "You are an LLM in a WebRTC session, and this is a 'hello world' demo. Say hello to the world."
+        }]
+        tts_task = asyncio.create_task(
+            tts.run_to_queue(
                 transport.send_queue,
-                llm.run([LLMMessagesQueueFrame(messages)])
+                llm.run([LLMMessagesQueueFrame(messages)]),
             )
-            await transport.stop_when_done()
+        )
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            other_joined_event.set()
+            await tts_task
+            await transport.stop_when_done()
 
-        await asyncio.gather(transport.run(), speak_from_llm())
+        await transport.run()
 
 
 if __name__ == "__main__":

src/examples/foundational/03-still-frame.py

@@ -1,51 +1,51 @@
 import asyncio
 import aiohttp
-import logging
 import os
 
 from dailyai.pipeline.frames import TextFrame
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.services.azure_ai_services import AzureImageGenServiceREST
 
-from examples.support.runner import configure
+from examples.foundational.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+local_joined = False
+participant_joined = False
 
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 1
         transport = DailyTransportService(
             room_url,
             None,
             "Show a still frame image",
+            duration_minutes=meeting_duration_minutes,
+            mic_enabled=False,
             camera_enabled=True,
             camera_width=1024,
-            camera_height=1024,
-            duration_minutes=1
+            camera_height=1024
         )
 
         imagegen = FalImageGenService(
-            image_size="square_hd",
+            image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"),
-        )
+            key_secret=os.getenv("FAL_KEY_SECRET"))
+        # imagegen = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
+        # imagegen = AzureImageGenServiceREST(image_size="1024x1024", aiohttp_session=session, api_key=os.getenv("AZURE_DALLE_API_KEY"), endpoint=os.getenv("AZURE_DALLE_ENDPOINT"), model=os.getenv("AZURE_DALLE_MODEL"))
 
-        other_joined_event = asyncio.Event()
-
-        async def show_image():
-            await other_joined_event.wait()
-            await imagegen.run_to_queue(
-                transport.send_queue, [TextFrame("a cat in the style of picasso")]
-            )
+        image_task = asyncio.create_task(
+            imagegen.run_to_queue(
+                transport.send_queue, [
+                    TextFrame("a cat in the style of picasso")]))
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            other_joined_event.set()
+            await image_task
 
-        await asyncio.gather(transport.run(), show_image())
+        await transport.run()
 
 
 if __name__ == "__main__":

src/examples/foundational/03a-image-local.py

@@ -1,6 +1,5 @@
 import asyncio
 import aiohttp
-import logging
 import os
 
 import tkinter as tk
@@ -9,10 +8,6 @@ from dailyai.pipeline.frames import TextFrame
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.local_transport_service import LocalTransportService
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
 local_joined = False
 participant_joined = False
 
@@ -51,6 +46,5 @@ async def main():
 
         await asyncio.gather(transport.run(), image_task, run_tk())
 
-
 if __name__ == "__main__":
     asyncio.run(main())

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

@@ -1,5 +1,4 @@
 import asyncio
-import logging
 import os
 
 import aiohttp
@@ -7,14 +6,10 @@ from dailyai.pipeline.pipeline import Pipeline
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.services.deepgram_ai_services import DeepgramTTSService
 from dailyai.pipeline.frames import EndFrame, LLMMessagesQueueFrame
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from examples.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url: str):
@@ -26,27 +21,20 @@ async def main(room_url: str):
             duration_minutes=1,
             mic_enabled=True,
             mic_sample_rate=16000,
+            camera_enabled=False
         )
 
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        )
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
         azure_tts = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"),
-        )
-
-        deepgram_tts = DeepgramTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("DEEPGRAM_API_KEY"),
-        )
+            region=os.getenv("AZURE_SPEECH_REGION"))
         elevenlabs_tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+            voice_id=os.getenv("ELEVENLABS_VOICE_ID"))
 
         messages = [{"role": "system", "content": "tell the user a joke about llamas"}]
 
@@ -55,35 +43,13 @@ async def main(room_url: str):
         # speak the LLM response.
         buffer_queue = asyncio.Queue()
         source_queue = asyncio.Queue()
-        pipeline = Pipeline(
-            source=source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts]
-        )
-        await source_queue.put(LLMMessagesQueueFrame(messages))
-        await source_queue.put(EndFrame())
+        pipeline = Pipeline(source = source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts])
+        source_queue.put_nowait(LLMMessagesQueueFrame(messages))
         pipeline_run_task = pipeline.run_pipeline()
 
-        other_participant_joined = asyncio.Event()
-
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            other_participant_joined.set()
-
-        async def say_something():
-            await other_participant_joined.wait()
-
-            await azure_tts.say(
-                "My friend the LLM is now going to tell a joke about llamas.",
-                transport.send_queue,
-            )
-
-            # khk: deepgram_tts.say() doesn't seem to put bytes in the transport
-            # queue. I get a debug log line that indicates we're set up okay, but
-            # no further log lines or audio bytes. debug this later:
-            #   20 2024-03-10 13:24:46,235 Running deepgram tts for My friend the LLM is now going to tell a joke about llamas.
-            # await deepgram_tts.say(
-            #    "My friend the LLM is now going to tell a joke about llamas.",
-            #    transport.send_queue,
-            # )
+            await azure_tts.say("My friend the LLM is now going to tell a joke about llamas.", transport.send_queue)
 
             async def buffer_to_send_queue():
                 while True:
@@ -95,7 +61,9 @@ async def main(room_url: str):
 
             await asyncio.gather(pipeline_run_task, buffer_to_send_queue())
 
-        await asyncio.gather(transport.run(), say_something())
+            await transport.stop_when_done()
+
+        await transport.run()
 
 
 if __name__ == "__main__":

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

@@ -2,116 +2,58 @@ import asyncio
 from re import S
 import aiohttp
 import os
-import logging
-
-from dataclasses import dataclass
-from typing import AsyncGenerator
-
-from dailyai.pipeline.aggregators import (
-    GatedAggregator,
-    LLMFullResponseAggregator,
-    ParallelPipeline,
-    SentenceAggregator,
-)
-from dailyai.pipeline.frames import (
-    Frame,
-    TextFrame,
-    EndFrame,
-    ImageFrame,
-    LLMMessagesQueueFrame,
-    LLMResponseStartFrame,
-)
-from dailyai.pipeline.frame_processor import FrameProcessor
+from dailyai.pipeline.aggregators import GatedAggregator, LLMFullResponseAggregator, ParallelPipeline, SentenceAggregator
 
+from dailyai.pipeline.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesQueueFrame, LLMResponseStartFrame
 from dailyai.pipeline.pipeline import Pipeline
-from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureImageGenServiceREST, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
+from dailyai.services.open_ai_services import OpenAIImageGenService
 
-from examples.support.runner import configure
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
-
-@dataclass
-class MonthFrame(Frame):
-    month: str
-
-
-class MonthPrepender(FrameProcessor):
-    def __init__(self):
-        self.most_recent_month = "Placeholder, month frame not yet received"
-        self.prepend_to_next_text_frame = False
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, MonthFrame):
-            self.most_recent_month = frame.month
-        elif self.prepend_to_next_text_frame and isinstance(frame, TextFrame):
-            yield TextFrame(f"{self.most_recent_month}: {frame.text}")
-            self.prepend_to_next_text_frame = False
-        elif isinstance(frame, LLMResponseStartFrame):
-            self.prepend_to_next_text_frame = True
-            yield frame
-        else:
-            yield frame
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
+        meeting_duration_minutes = 5
         transport = DailyTransportService(
             room_url,
             None,
             "Month Narration Bot",
+            duration_minutes=meeting_duration_minutes,
             mic_enabled=True,
             camera_enabled=True,
             mic_sample_rate=16000,
             camera_width=1024,
-            camera_height=1024,
+            camera_height=1024
         )
 
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+            voice_id="ErXwobaYiN019PkySvjV")
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
-        imagegen = FalImageGenService(
-            image_size="square_hd",
+        dalle = FalImageGenService(
+            image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"),
-        )
+            key_secret=os.getenv("FAL_KEY_SECRET"))
 
         source_queue = asyncio.Queue()
 
-        for month in [
-            "January",
-            "February",
-            "March",
-            "April",
-            "May",
-            "June",
-            "July",
-            "August",
-            "September",
-            "October",
-            "November",
-            "December",
-        ]:
+        for month in ["January", "February"]:
             messages = [
                 {
                     "role": "system",
                     "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
                 }
             ]
-            await source_queue.put(MonthFrame(month))
             await source_queue.put(LLMMessagesQueueFrame(messages))
 
         await source_queue.put(EndFrame())
@@ -123,7 +65,6 @@ async def main(room_url):
         )
 
         sentence_aggregator = SentenceAggregator()
-        month_prepender = MonthPrepender()
         llm_full_response_aggregator = LLMFullResponseAggregator()
 
         pipeline = Pipeline(
@@ -132,27 +73,20 @@ async def main(room_url):
             processors=[
                 llm,
                 sentence_aggregator,
-                ParallelPipeline(
-                    [[month_prepender, tts], [llm_full_response_aggregator, imagegen]]
-                ),
+                ParallelPipeline([[tts], [llm_full_response_aggregator, dalle]]),
                 gated_aggregator,
             ],
         )
         pipeline_task = pipeline.run_pipeline()
 
-        other_joined = asyncio.Event()
-
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            other_joined.set()
-
-        async def show_calendar():
-            await other_joined.wait()
             await pipeline_task
+
+            # wait for the output queue to be empty, then leave the meeting
             await transport.stop_when_done()
 
-        await asyncio.gather(transport.run(), show_calendar())
-
+        await transport.run()
 
 if __name__ == "__main__":
     (url, token) = configure()

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

@@ -1,20 +1,15 @@
 import aiohttp
 import argparse
 import asyncio
-import logging
 import tkinter as tk
 import os
 
 from dailyai.pipeline.frames import AudioFrame, ImageFrame
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.local_transport_service import LocalTransportService
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
 
 async def main(room_url):
     async with aiohttp.ClientSession() as session:
@@ -31,16 +26,16 @@ async def main(room_url):
             tk_root=tk_root,
         )
 
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"),
+        )
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
+            voice_id="ErXwobaYiN019PkySvjV",
         )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
         dalle = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
@@ -72,7 +67,9 @@ async def main(room_url):
             to_speak = f"{month}: {image_description}"
             audio_task = asyncio.create_task(get_all_audio(to_speak))
             image_task = asyncio.create_task(dalle.run_image_gen(image_description))
-            (audio, image_data) = await asyncio.gather(audio_task, image_task)
+            (audio, image_data) = await asyncio.gather(
+                audio_task, image_task
+            )
 
             return {
                 "month": month,
@@ -126,7 +123,6 @@ async def main(room_url):
 
         await asyncio.gather(transport.run(), show_images(), run_tk())
 
-
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
     parser.add_argument(

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

@@ -1,81 +1,65 @@
 import asyncio
-import aiohttp
-import logging
 import os
 from dailyai.pipeline.pipeline import Pipeline
 
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.ai_services import FrameLogger
-from dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMUserContextAggregator,
-)
-from examples.support.runner import configure
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransportService(
-            room_url,
-            token,
-            "Respond bot",
-            duration_minutes=5,
-            start_transcription=True,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=False,
-            vad_enabled=True,
+    transport = DailyTransportService(
+        room_url,
+        token,
+        "Respond bot",
+        duration_minutes=5,
+        start_transcription=True,
+        mic_enabled=True,
+        mic_sample_rate=16000,
+        camera_enabled=False,
+        vad_enabled=True
+    )
+
+    llm = AzureLLMService(
+        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+        model=os.getenv("AZURE_CHATGPT_MODEL"))
+    tts = AzureTTSService(
+        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+        region=os.getenv("AZURE_SPEECH_REGION"))
+    fl = FrameLogger("Inner")
+    fl2 = FrameLogger("Outer")
+    @transport.event_handler("on_first_other_participant_joined")
+    async def on_first_other_participant_joined(transport):
+        await tts.say("Hi, I'm listening!", transport.send_queue)
+
+    async def handle_transcriptions():
+        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. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+        tma_out = LLMAssistantContextAggregator(messages, transport._my_participant_id)
+        pipeline = Pipeline(
+            processors=[
+                fl,
+                tma_in,
+                llm,
+                fl2,
+                tma_out,
+                tts
+            ],
         )
+        await transport.run_uninterruptible_pipeline(pipeline)
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-        fl = FrameLogger("Inner")
-        fl2 = FrameLogger("Outer")
-
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport):
-            await tts.say("Hi, I'm listening!", transport.send_queue)
-
-        async def have_conversation():
-            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. Respond to what the user said in a creative and helpful way.",
-                },
-            ]
-
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
-            tma_out = LLMAssistantContextAggregator(
-                messages, transport._my_participant_id
-            )
-            pipeline = Pipeline(
-                processors=[
-                    fl,
-                    tma_in,
-                    llm,
-                    fl2,
-                    tts,
-                    tma_out,
-                ],
-            )
-            await transport.run_uninterruptible_pipeline(pipeline)
-
-        transport.transcription_settings["extra"]["endpointing"] = True
-        transport.transcription_settings["extra"]["punctuate"] = True
-        await asyncio.gather(transport.run(), have_conversation())
+    transport.transcription_settings["extra"]["endpointing"] = True
+    transport.transcription_settings["extra"]["punctuate"] = True
+    await asyncio.gather(transport.run(), handle_transcriptions())
 
 
 if __name__ == "__main__":

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

@@ -1,29 +1,22 @@
 import argparse
 import asyncio
 import os
-import logging
 from typing import AsyncGenerator
 import aiohttp
 import requests
 import time
 import urllib.parse
+
 from PIL import Image
-
 from dailyai.pipeline.frames import ImageFrame, Frame
-from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.ai_services import AIService
-from dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMUserContextAggregator,
-)
-from dailyai.services.open_ai_services import OpenAILLMService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.services.fal_ai_services import FalImageGenService
-from examples.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.ai_services import AIService
+from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
+from dailyai.services.fal_ai_services import FalImageGenService
+
+from examples.foundational.support.runner import configure
 
 
 class ImageSyncAggregator(AIService):
@@ -54,22 +47,18 @@ async def main(room_url: str, token):
         transport._mic_enabled = True
         transport._mic_sample_rate = 16000
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"))
         img = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"),
-        )
+            key_secret=os.getenv("FAL_KEY_SECRET"))
 
         async def get_images():
             get_speaking_task = asyncio.create_task(
@@ -91,13 +80,12 @@ async def main(room_url: str, token):
 
         async def handle_transcriptions():
             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. Respond to what the user said in a creative and helpful way.",
-                },
+                {"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. Respond to what the user said in a creative and helpful way."},
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id
+            )
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
@@ -108,8 +96,14 @@ async def main(room_url: str, token):
             await tts.run_to_queue(
                 transport.send_queue,
                 image_sync_aggregator.run(
-                    tma_out.run(llm.run(tma_in.run(transport.get_receive_frames())))
-                ),
+                    tma_out.run(
+                        llm.run(
+                            tma_in.run(
+                                transport.get_receive_frames()
+                            )
+                        )
+                    )
+                )
             )
 
         transport.transcription_settings["extra"]["punctuate"] = True

src/examples/foundational/07-interruptible.py

@@ -1,24 +1,14 @@
 import asyncio
 import aiohttp
-import logging
 import os
-from dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMResponseAggregator,
-    LLMUserContextAggregator,
-    UserResponseAggregator,
-)
+from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMResponseAggregator, LLMUserContextAggregator, UserResponseAggregator
 
 from dailyai.pipeline.pipeline import Pipeline
 from dailyai.services.ai_services import FrameLogger
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from examples.support.runner import configure
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from support.runner import configure
 
 
 async def main(room_url: str, token):
@@ -35,15 +25,13 @@ async def main(room_url: str, token):
             vad_enabled=True,
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = AzureTTSService(
+            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+            region=os.getenv("AZURE_SPEECH_REGION"))
 
         pipeline = Pipeline([FrameLogger(), llm, FrameLogger(), tts])
 
@@ -53,16 +41,17 @@ async def main(room_url: str, token):
 
         async def run_conversation():
             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. Respond to what the user said in a creative and helpful way.",
-                },
+                {"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. Respond to what the user said in a creative and helpful way."},
             ]
 
             await transport.run_interruptible_pipeline(
                 pipeline,
-                post_processor=LLMResponseAggregator(messages),
-                pre_processor=UserResponseAggregator(messages),
+                post_processor=LLMResponseAggregator(
+                    messages
+                ),
+                pre_processor=UserResponseAggregator(
+                    messages
+                ),
             )
 
         transport.transcription_settings["extra"]["punctuate"] = False

src/examples/foundational/08-bots-arguing.py

@@ -1,21 +1,14 @@
-from typing import Tuple
 import aiohttp
 import asyncio
-import logging
 import os
-from dailyai.pipeline.aggregators import SentenceAggregator
-from dailyai.pipeline.pipeline import Pipeline
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 from dailyai.services.fal_ai_services import FalImageGenService
-from dailyai.pipeline.frames import AudioFrame, EndFrame, ImageFrame, LLMMessagesQueueFrame, TextFrame
-from examples.support.runner import configure
+from dailyai.pipeline.frames import AudioFrame, ImageFrame
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url: str):
@@ -29,83 +22,62 @@ async def main(room_url: str):
             mic_sample_rate=16000,
             camera_enabled=True,
             camera_width=1024,
-            camera_height=1024,
+            camera_height=1024
         )
 
         llm = AzureLLMService(
             api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
             endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
-            model=os.getenv("AZURE_CHATGPT_MODEL"),
-        )
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
         tts1 = AzureTTSService(
             api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"),
-        )
+            region=os.getenv("AZURE_SPEECH_REGION"))
         tts2 = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="jBpfuIE2acCO8z3wKNLl",
-        )
+            voice_id="jBpfuIE2acCO8z3wKNLl")
         dalle = FalImageGenService(
             image_size="1024x1024",
             aiohttp_session=session,
             key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"),
-        )
+            key_secret=os.getenv("FAL_KEY_SECRET"))
 
         bot1_messages = [
-            {
-                "role": "system",
-                "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
-            },
+            {"role": "system", "content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long."},
         ]
         bot2_messages = [
             {
                 "role": "system",
-                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
-            },
+                "content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich."},
         ]
 
-        async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
-            """This function streams text from the LLM and uses the TTS service to convert
-             that text to speech as it's received. """
-            source_queue = asyncio.Queue()
-            sink_queue = asyncio.Queue()
-            sentence_aggregator = SentenceAggregator()
-            pipeline = Pipeline(
-                [llm, sentence_aggregator, tts1], source_queue, sink_queue
-            )
-
-            await source_queue.put(LLMMessagesQueueFrame(messages))
-            await source_queue.put(EndFrame())
-            await pipeline.run_pipeline()
-
-            message = ""
-            all_audio = bytearray()
-            while sink_queue.qsize():
-                frame = sink_queue.get_nowait()
-                if isinstance(frame, TextFrame):
-                    message += frame.text
-                elif isinstance(frame, AudioFrame):
-                    all_audio.extend(frame.data)
-
-            return (message, all_audio)
-
         async def get_bot1_statement():
-            message, audio = await get_text_and_audio(bot1_messages)
+            # Run the LLMs synchronously for the back-and-forth
+            bot1_msg = await llm.run_llm(bot1_messages)
+            print(f"bot1_msg: {bot1_msg}")
+            if bot1_msg:
+                bot1_messages.append({"role": "assistant", "content": bot1_msg})
+                bot2_messages.append({"role": "user", "content": bot1_msg})
 
-            bot1_messages.append({"role": "assistant", "content": message})
-            bot2_messages.append({"role": "user", "content": message})
+            all_audio = bytearray()
+            async for audio in tts1.run_tts(bot1_msg):
+                all_audio.extend(audio)
 
-            return audio
+            return all_audio
 
         async def get_bot2_statement():
-            message, audio = await get_text_and_audio(bot2_messages)
+            # Run the LLMs synchronously for the back-and-forth
+            bot2_msg = await llm.run_llm(bot2_messages)
+            print(f"bot2_msg: {bot2_msg}")
+            if bot2_msg:
+                bot2_messages.append({"role": "assistant", "content": bot2_msg})
+                bot1_messages.append({"role": "user", "content": bot2_msg})
 
-            bot2_messages.append({"role": "assistant", "content": message})
-            bot1_messages.append({"role": "user", "content": message})
+            all_audio = bytearray()
+            async for audio in tts2.run_tts(bot2_msg):
+                all_audio.extend(audio)
 
-            return audio
+            return all_audio
 
         async def argue():
             for i in range(100):

src/examples/foundational/10-wake-word.py

@@ -1,18 +1,15 @@
 import aiohttp
 import asyncio
-import logging
 import os
 import random
 from typing import AsyncGenerator
+
 from PIL import Image
 
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.pipeline.aggregators import (
-    LLMUserContextAggregator,
-    LLMAssistantContextAggregator,
-)
+from dailyai.pipeline.aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.pipeline.frames import (
     Frame,
     TextFrame,
@@ -21,21 +18,19 @@ from dailyai.pipeline.frames import (
     TranscriptionQueueFrame,
 )
 from dailyai.services.ai_services import AIService
-from examples.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
+
 
 sprites = {}
 image_files = [
-    "sc-default.png",
-    "sc-talk.png",
-    "sc-listen-1.png",
-    "sc-think-1.png",
-    "sc-think-2.png",
-    "sc-think-3.png",
-    "sc-think-4.png",
+    'sc-default.png',
+    'sc-talk.png',
+    'sc-listen-1.png',
+    'sc-think-1.png',
+    'sc-think-2.png',
+    'sc-think-3.png',
+    'sc-think-4.png'
 ]
 
 script_dir = os.path.dirname(__file__)
@@ -52,17 +47,16 @@ for file in image_files:
 # When the bot isn't talking, show a static image of the cat listening
 quiet_frame = ImageFrame("", sprites["sc-listen-1.png"])
 # When the bot is talking, build an animation from two sprites
-talking_list = [sprites["sc-default.png"], sprites["sc-talk.png"]]
+talking_list = [sprites['sc-default.png'], sprites['sc-talk.png']]
 talking = [random.choice(talking_list) for x in range(30)]
 talking_frame = SpriteFrame(images=talking)
 
 # TODO: Support "thinking" as soon as we get a valid transcript, while LLM is processing
 thinking_list = [
-    sprites["sc-think-1.png"],
-    sprites["sc-think-2.png"],
-    sprites["sc-think-3.png"],
-    sprites["sc-think-4.png"],
-]
+    sprites['sc-think-1.png'],
+    sprites['sc-think-2.png'],
+    sprites['sc-think-3.png'],
+    sprites['sc-think-4.png']]
 thinking_frame = SpriteFrame(images=thinking_list)
 
 
@@ -121,7 +115,7 @@ async def main(room_url: str, token):
             mic_sample_rate=16000,
             camera_enabled=True,
             camera_width=720,
-            camera_height=1280,
+            camera_height=1280
         )
         transport._mic_enabled = True
         transport._mic_sample_rate = 16000
@@ -129,33 +123,28 @@ async def main(room_url: str, token):
         transport._camera_width = 720
         transport._camera_height = 1280
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="jBpfuIE2acCO8z3wKNLl",
-        )
+            voice_id="jBpfuIE2acCO8z3wKNLl")
         isa = ImageSyncAggregator()
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            await tts.say(
-                "Hi! If you want to talk to me, just say 'hey Santa Cat'.",
-                transport.send_queue,
-            )
+            await tts.say("Hi! If you want to talk to me, just say 'hey Santa Cat'.", transport.send_queue)
 
         async def handle_transcriptions():
             messages = [
-                {
-                    "role": "system",
-                    "content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while.  Your responses should only be a few sentences long.",
-                },
+                {"role": "system", "content": "You are Santa Cat, a cat that lives in Santa's workshop at the North Pole. You should be clever, and a bit sarcastic. You should also tell jokes every once in a while.  Your responses should only be a few sentences long."},
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id
+            )
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
@@ -166,10 +155,16 @@ async def main(room_url: str, token):
                 isa.run(
                     tma_out.run(
                         llm.run(
-                            tma_in.run(ncf.run(tf.run(transport.get_receive_frames())))
+                            tma_in.run(
+                                ncf.run(
+                                    tf.run(
+                                        transport.get_receive_frames()
+                                    )
+                                )
+                            )
                         )
                     )
-                ),
+                )
             )
 
         async def starting_image():

src/examples/foundational/11-sound-effects.py

@@ -5,30 +5,24 @@ import os
 import wave
 
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.pipeline.aggregators import (
-    LLMContextAggregator,
-    LLMUserContextAggregator,
-    LLMAssistantContextAggregator,
-)
+from dailyai.pipeline.aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.services.ai_services import AIService, FrameLogger
-from dailyai.pipeline.frames import (
-    Frame,
-    AudioFrame,
-    LLMResponseEndFrame,
-    LLMMessagesQueueFrame,
-)
+from dailyai.pipeline.frames import Frame, AudioFrame, LLMResponseEndFrame, LLMMessagesQueueFrame
 from typing import AsyncGenerator
 
-from examples.support.runner import configure
+from examples.foundational.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")  # or whatever
 logger = logging.getLogger("dailyai")
 logger.setLevel(logging.DEBUG)
 
 sounds = {}
-sound_files = ["ding1.wav", "ding2.wav"]
+sound_files = [
+    'ding1.wav',
+    'ding2.wav'
+]
 
 script_dir = os.path.dirname(__file__)
 
@@ -77,18 +71,17 @@ async def main(room_url: str, token):
             duration_minutes=5,
             mic_enabled=True,
             mic_sample_rate=16000,
-            camera_enabled=False,
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
+            camera_enabled=False
         )
 
+        llm = AzureLLMService(
+            api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+            endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+            model=os.getenv("AZURE_CHATGPT_MODEL"))
         tts = ElevenLabsTTSService(
             aiohttp_session=session,
             api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="ErXwobaYiN019PkySvjV",
-        )
+            voice_id="ErXwobaYiN019PkySvjV")
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
@@ -97,13 +90,12 @@ async def main(room_url: str, token):
 
         async def handle_transcriptions():
             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. Respond to what the user said in a creative and helpful way.",
-                },
+                {"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. Respond to what the user said in a creative and helpful way."},
             ]
 
-            tma_in = LLMUserContextAggregator(messages, transport._my_participant_id)
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id
+            )
             tma_out = LLMAssistantContextAggregator(
                 messages, transport._my_participant_id
             )
@@ -119,13 +111,15 @@ async def main(room_url: str, token):
                             llm.run(
                                 fl2.run(
                                     in_sound.run(
-                                        tma_in.run(transport.get_receive_frames())
+                                        tma_in.run(
+                                            transport.get_receive_frames()
+                                        )
                                     )
                                 )
                             )
                         )
                     )
-                ),
+                )
             )
 
         transport.transcription_settings["extra"]["punctuate"] = True

src/examples/foundational/13-whisper-transcription.py

@@ -1,13 +1,9 @@
 import asyncio
-import logging
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.whisper_ai_services import WhisperSTTService
-from examples.support.runner import configure
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+from examples.foundational.support.runner import configure
 
 
 async def main(room_url: str):
@@ -18,7 +14,7 @@ async def main(room_url: str):
         start_transcription=True,
         mic_enabled=False,
         camera_enabled=False,
-        speaker_enabled=True,
+        speaker_enabled=True
     )
 
     stt = WhisperSTTService()
@@ -32,9 +28,9 @@ async def main(room_url: str):
 
     async def handle_speaker():
         await stt.run_to_queue(
-            transcription_output_queue, transport.get_receive_frames()
+            transcription_output_queue,
+            transport.get_receive_frames()
         )
-
     await asyncio.gather(transport.run(), handle_speaker(), handle_transcription())
 
 

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

@@ -1,16 +1,11 @@
 import argparse
 import asyncio
-import logging
 import wave
 from dailyai.pipeline.frames import EndFrame, TranscriptionQueueFrame
 
 from dailyai.services.local_transport_service import LocalTransportService
 from dailyai.services.whisper_ai_services import WhisperSTTService
 
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
 
 async def main(room_url: str):
     global transport
@@ -22,7 +17,7 @@ async def main(room_url: str):
         camera_enabled=False,
         speaker_enabled=True,
         duration_minutes=meeting_duration_minutes,
-        start_transcription=True,
+        start_transcription=True
     )
     stt = WhisperSTTService()
     transcription_output_queue = asyncio.Queue()

src/examples/foundational/14-patient-intake.py

@@ -1,62 +1,42 @@
-import copy
 import aiohttp
 import asyncio
 import json
 import random
-import logging
 import os
 import re
 import wave
-from typing import AsyncGenerator, List
+from typing import AsyncGenerator
 from PIL import Image
-from dailyai.pipeline.opeanai_llm_aggregator import (
-    OpenAIAssistantContextAggregator,
-    OpenAIUserContextAggregator,
-)
 
 from dailyai.pipeline.pipeline import Pipeline
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.openai_llm_context import OpenAILLMContext
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
 from dailyai.services.open_ai_services import OpenAILLMService
 from dailyai.services.deepgram_ai_services import DeepgramTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from examples.support.runner import configure
-from dailyai.pipeline.frames import (
-    OpenAILLMContextFrame,
-    TranscriptionQueueFrame,
-    Frame,
-    LLMFunctionCallFrame,
-    LLMFunctionStartFrame,
-    AudioFrame,
-)
+from dailyai.pipeline.aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator, UserResponseAggregator, LLMResponseAggregator
+from support.runner import configure
+from dailyai.pipeline.frames import LLMMessagesQueueFrame, TranscriptionQueueFrame, Frame, TextFrame, LLMFunctionCallFrame, LLMResponseEndFrame, StartFrame, AudioFrame, SpriteFrame, ImageFrame
 from dailyai.services.ai_services import FrameLogger, AIService
-from openai._types import NotGiven, NOT_GIVEN
 
-from openai.types.chat import (
-    ChatCompletionToolParam,
-)
-
-logging.basicConfig(format="%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
+import logging
+logging.basicConfig(level=logging.DEBUG)
 
 sounds = {}
 sound_files = [
-    "clack-short.wav",
-    "clack.wav",
-    "clack-short-quiet.wav",
-    "ding.wav",
-    "ding2.wav",
+    'clack-short.wav',
+    'clack.wav',
+    'clack-short-quiet.wav'
 ]
 
 script_dir = os.path.dirname(__file__)
 
 for file in sound_files:
-    # Build the full path to the sound file
+    # Build the full path to the image file
     full_path = os.path.join(script_dir, "assets", file)
     # Get the filename without the extension to use as the dictionary key
     filename = os.path.splitext(os.path.basename(full_path))[0]
-    # Open the sound and convert it to bytes
+    # Open the image and convert it to bytes
     with wave.open(full_path) as audio_file:
         sounds[file] = audio_file.readframes(-1)
 
@@ -65,11 +45,9 @@ steps = [
     {
         "prompt": "Start by introducing yourself. Then, ask the user to confirm their identity by telling you their birthday, including the year. When they answer with their birthday, call the verify_birthday function.",
         "run_async": False,
-        "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.",
-        "tools": [
-            {
-                "type": "function",
-                "function": {
+        "failed": "The user provided an incorrect birthday. Ask them for their birthday again. When they answer, call the verify_birthday function.", "tools": [{
+            "type": "function",
+            "function": {
                     "name": "verify_birthday",
                     "description": "Use this function to verify the user has provided their correct birthday.",
                     "parameters": {
@@ -77,21 +55,18 @@ steps = [
                         "properties": {
                             "birthday": {
                                 "type": "string",
-                                "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function.",
+                                "description": "The user's birthdate, including the year. The user can provide it in any format, but convert it to YYYY-MM-DD format to call this function."
                             }
-                        },
-                    },
-                },
+                        }
+                    }
             }
-        ],
-    },
+        }]},
     {
         "prompt": "Next, thank the user for confirming their identity, then ask the user to list their current prescriptions. Each prescription needs to have a medication name and a dosage. Do not call the list_prescriptions function with any unknown dosages.",
         "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
+        "tools": [{
+            "type": "function",
+            "function": {
                     "name": "list_prescriptions",
                     "description": "Once the user has provided a list of their prescription medications, call this function.",
                     "parameters": {
@@ -104,20 +79,19 @@ steps = [
                                     "properties": {
                                         "medication": {
                                             "type": "string",
-                                            "description": "The medication's name",
+                                            "description": "The medication's name"
                                         },
                                         "dosage": {
                                             "type": "string",
-                                            "description": "The prescription's dosage",
-                                        },
-                                    },
-                                },
+                                            "description": "The prescription's dosage"
+                                        }
+                                    }
+                                }
                             }
-                        },
-                    },
-                },
+                        }
+                    }
             }
-        ],
+        }]
     },
     {
         "prompt": "Next, ask the user if they have any allergies. Once they have listed their allergies or confirmed they don't have any, call the list_allergies function.",
@@ -138,16 +112,16 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "What the user is allergic to",
+                                            "description": "What the user is allergic to"
                                         }
-                                    },
-                                },
+                                    }
+                                }
                             }
-                        },
-                    },
-                },
+                        }
+                    }
+                }
             }
-        ],
+        ]
     },
     {
         "prompt": "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.",
@@ -168,14 +142,14 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "The user's medical condition",
+                                            "description": "The user's medical condition"
                                         }
-                                    },
-                                },
+                                    }
+                                }
                             }
-                        },
-                    },
-                },
+                        }
+                    }
+                }
             },
         ],
     },
@@ -198,61 +172,51 @@ steps = [
                                     "properties": {
                                         "name": {
                                             "type": "string",
-                                            "description": "The user's reason for visiting the doctor",
+                                            "description": "The user's reason for visiting the doctor"
                                         }
-                                    },
-                                },
+                                    }
+                                }
                             }
-                        },
-                    },
-                },
+                        }
+                    }
+                }
             }
-        ],
+        ]
     },
-    {
-        "prompt": "Now, thank the user and end the conversation.",
-        "run_async": True,
-        "tools": [],
-    },
-    {"prompt": "", "run_async": True, "tools": []},
+    {"prompt": "Now, thank the user and end the conversation.",
+        "run_async": True, "tools": []},
+    {"prompt": "", "run_async": True, "tools": []}
 ]
 current_step = 0
 
 
-class ChecklistProcessor(AIService):
+class TranscriptFilter(AIService):
+    def __init__(self, bot_participant_id=None):
+        super().__init__()
+        self.bot_participant_id = bot_participant_id
 
-    def __init__(
-        self,
-        context: OpenAILLMContext,
-        llm: AIService,
-        tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
-        *args,
-        **kwargs,
-    ):
+    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+        if isinstance(frame, TranscriptionQueueFrame):
+            if frame.participantId != self.bot_participant_id:
+                yield frame
+
+
+class ChecklistProcessor(AIService):
+    def __init__(self, messages, llm, tools, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self._context: OpenAILLMContext = context
+        self._messages = messages
         self._llm = llm
+        self._tools = tools
+        self._function_name = ""
+        self._arguments = ""
         self._id = "You are Jessica, an agent for a company called Tri-County Health Services. Your job is to collect important information from the user before their doctor visit. You're talking to Chad Bailey. You should address the user by their first name and be polite and professional. You're not a medical professional, so you shouldn't provide any advice. Keep your responses short. Your job is to collect information to give to a doctor. Don't make assumptions about what values to plug into functions. Ask for clarification if a user response is ambiguous."
         self._acks = ["One sec.", "Let me confirm that.", "Thanks.", "OK."]
 
-        # Create an allowlist of functions that the LLM can call
-        self._functions = [
-            "verify_birthday",
-            "list_prescriptions",
-            "list_allergies",
-            "list_conditions",
-            "list_visit_reasons",
-        ]
-
-        self._context.add_message(
-            {"role": "system", "content": f"{self._id} {steps[0]['prompt']}"}
-        )
-
-        if tools:
-            self._context.set_tools(tools)
+        messages.append(
+            {"role": "system", "content": f"{self._id} {steps[0]['prompt']}"})
 
     def verify_birthday(self, args):
-        return args["birthday"] == "1983-01-01"
+        return args['birthday'] == "1983-01-01"
 
     def list_prescriptions(self, args):
         # print(f"--- Prescriptions: {args['prescriptions']}\n")
@@ -273,69 +237,57 @@ class ChecklistProcessor(AIService):
     async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
         global current_step
         this_step = steps[current_step]
-        self._context.set_tools(this_step["tools"])
-        if isinstance(frame, LLMFunctionStartFrame):
+        # TODO-CB: forcing a global here :/
+        self._tools.clear()
+        self._tools.extend(this_step['tools'])
+        if isinstance(frame, LLMFunctionCallFrame) and frame.function_name:
             print(f"... Preparing function call: {frame.function_name}")
             self._function_name = frame.function_name
-            if this_step["run_async"]:
+            if this_step['run_async']:
                 # Get the LLM talking about the next step before getting the rest
                 # of the function call completion
                 current_step += 1
-                self._context.add_message(
-                    {"role": "system", "content": steps[current_step]["prompt"]}
-                )
-                yield OpenAILLMContextFrame(self._context)
-
-                local_context = copy.deepcopy(self._context)
-                local_context.set_tool_choice("none")
-                async for frame in llm.process_frame(
-                    OpenAILLMContextFrame(local_context)
-                ):
+                # yield TextFrame(f"We should move on to Step {current_step}.")
+                self._messages.append({
+                    "role": "system", "content": steps[current_step]['prompt']})
+                # yield LLMMessagesQueueFrame(self._messages)
+                yield LLMMessagesQueueFrame(self._messages)
+                async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
                     yield frame
             else:
                 # Insert a quick response while we run the function
-                yield AudioFrame(sounds["ding2.wav"])
+                # yield AudioFrame(sounds["clack-short-quiet.wav"])
                 pass
-        elif isinstance(frame, LLMFunctionCallFrame):
+        elif isinstance(frame, LLMFunctionCallFrame) and frame.arguments:
+            self._arguments += frame.arguments
+        elif isinstance(frame, LLMResponseEndFrame):
 
-            if frame.function_name and frame.arguments:
-                print(f"--> Calling function: {frame.function_name} with arguments:")
-                pretty_json = re.sub(
-                    "\n", "\n    ", json.dumps(json.loads(frame.arguments), indent=2)
-                )
+            if self._function_name and self._arguments:
+                print(
+                    f"--> Calling function: {self._function_name} with arguments:")
+                pretty_json = re.sub("\n", "\n    ", json.dumps(
+                    json.loads(self._arguments), indent=2))
                 print(f"--> {pretty_json}\n")
-                if frame.function_name not in self._functions:
-                    raise Exception(
-                        f"The LLM tried to call a function named {frame.function_name}, which isn't in the list of known functions. Please check your prompt and/or self._functions."
-                    )
-                fn = getattr(self, frame.function_name)
-                result = fn(json.loads(frame.arguments))
-
-                if not this_step["run_async"]:
+                fn = getattr(self, self._function_name)
+                result = fn(json.loads(self._arguments))
+                self._function_name = ""
+                self._arguments = ""
+                if not this_step['run_async']:
                     if result:
                         current_step += 1
-                        self._context.add_message(
-                            {"role": "system", "content": steps[current_step]["prompt"]}
-                        )
-                        yield OpenAILLMContextFrame(self._context)
-
-                        local_context = copy.deepcopy(self._context)
-                        local_context.set_tool_choice("none")
-                        async for frame in llm.process_frame(
-                            OpenAILLMContextFrame(local_context)
-                        ):
+                        # yield TextFrame(f"We should move on to Step {current_step}.")
+                        self._messages.append({
+                            "role": "system", "content": steps[current_step]['prompt']})
+                        # yield LLMMessagesQueueFrame(self._messages)
+                        yield LLMMessagesQueueFrame(self._messages)
+                        async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
                             yield frame
                     else:
-                        self._context.add_message(
-                            {"role": "system", "content": this_step["failed"]}
-                        )
-                        yield OpenAILLMContextFrame(self._context)
-
-                        local_context = copy.deepcopy(self._context)
-                        local_context.set_tool_choice("none")
-                        async for frame in llm.process_frame(
-                            OpenAILLMContextFrame(local_context)
-                        ):
+                        self._messages.append({
+                            "role": "system", "content": this_step['failed']})
+                        # yield LLMMessagesQueueFrame(self._messages)
+                        yield LLMMessagesQueueFrame(self._messages)
+                        async for frame in llm.process_frame(LLMMessagesQueueFrame(self._messages), tool_choice="none"):
                             yield frame
                     print(f"<-- Verify result: {result}\n")
 
@@ -358,51 +310,64 @@ async def main(room_url: str, token):
             mic_sample_rate=16000,
             camera_enabled=False,
             start_transcription=True,
-            vad_enabled=True,
+            vad_enabled=True
         )
+        # TODO-CB: Go back to vad_enabled
 
         messages = []
+        tools = []
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-            model="gpt-4-1106-preview",
-        )
-        # tts = DeepgramTTSService(
-        #     aiohttp_session=session,
-        #     api_key=os.getenv("DEEPGRAM_API_KEY"),
-        #     voice="aura-asteria-en",
-        # )
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="XrExE9yKIg1WjnnlVkGX",
-        )
-        context = OpenAILLMContext(
-            messages=messages,
-        )
+        # llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv(
+        #     "AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        llm = OpenAILLMService(api_key=os.getenv(
+            "OPENAI_CHATGPT_API_KEY"), model="gpt-4-1106-preview", tools=tools)  # gpt-4-1106-preview
+        # tts = AzureTTSService(api_key=os.getenv(
+        #     "AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv(
+            "ELEVENLABS_API_KEY"), voice_id="XrExE9yKIg1WjnnlVkGX")  # matilda
+        # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv(
+        #     "DEEPGRAM_API_KEY"), voice="aura-asteria-en")
 
-        checklist = ChecklistProcessor(context, llm)
+        # lca = LLMContextAggregator(
+        #     messages=messages, bot_participant_id=transport._my_participant_id)
+        checklist = ChecklistProcessor(messages, llm, tools)
         fl = FrameLogger("FRAME LOGGER 1:")
         fl2 = FrameLogger("FRAME LOGGER 2:")
-        pipeline = Pipeline(processors=[fl, llm, fl2, checklist, tts])
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            await pipeline.queue_frames([OpenAILLMContextFrame(context)])
+            fl = FrameLogger("first other participant")
+            # TODO-CB: Make sure this message gets into the context somehow
+            await tts.run_to_queue(
+                transport.send_queue,
+                    llm.run([LLMMessagesQueueFrame(messages)]),
 
-        async def handle_intake():
-            await transport.run_interruptible_pipeline(
-                pipeline,
-                post_processor=OpenAIAssistantContextAggregator(context),
-                pre_processor=OpenAIUserContextAggregator(context),
             )
+        
+        async def handle_intake():
+            pipeline = Pipeline(
+                processors=[
+                    fl,
+                    llm,
+                    fl2,
+                    checklist,
+                    tts
+                ]
+            )
+            await transport.run_interruptible_pipeline(pipeline,
+                post_processor=LLMResponseAggregator(
+                    messages
+                ),
+                pre_processor=UserResponseAggregator(messages)
+                )
+
 
         transport.transcription_settings["extra"]["endpointing"] = True
         transport.transcription_settings["extra"]["punctuate"] = True
         try:
             await asyncio.gather(transport.run(), handle_intake())
         except (asyncio.CancelledError, KeyboardInterrupt):
-            print("whoops")
+            print('whoops')
             transport.stop()
 
 

src/examples/internal/11a-dial-out.py

@@ -10,7 +10,7 @@ from dailyai.services.ai_services import AIService, FrameLogger
 from dailyai.pipeline.frames import Frame, AudioFrame, LLMResponseEndFrame, LLMMessagesQueueFrame
 from typing import AsyncGenerator
 
-from examples.support.runner import configure
+from examples.foundational.support.runner import configure
 
 sounds = {}
 sound_files = [

src/examples/starter-apps/chatbot.py

@@ -1,150 +0,0 @@
-import asyncio
-import aiohttp
-import logging
-import os
-from PIL import Image
-from typing import AsyncGenerator
-
-from dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMResponseAggregator,
-    LLMUserContextAggregator,
-    UserResponseAggregator,
-)
-from dailyai.pipeline.frames import (
-    ImageFrame,
-    SpriteFrame,
-    Frame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    LLMMessagesQueueFrame,
-    UserStartedSpeakingFrame,
-    AudioFrame,
-    PipelineStartedFrame,
-)
-from dailyai.services.ai_services import AIService
-from dailyai.pipeline.pipeline import Pipeline
-from dailyai.services.ai_services import FrameLogger
-from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from examples.support.runner import configure
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
-sprites = []
-
-script_dir = os.path.dirname(__file__)
-
-for i in range(1, 26):
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
-    # Get the filename without the extension to use as the dictionary key
-    # Open the image and convert it to bytes
-    with Image.open(full_path) as img:
-        sprites.append(img.tobytes())
-
-flipped = sprites[::-1]
-sprites.extend(flipped)
-# When the bot isn't talking, show a static image of the cat listening
-quiet_frame = ImageFrame("", sprites[0])
-talking_frame = SpriteFrame(images=sprites)
-
-
-class TalkingAnimation(AIService):
-    """
-    This class starts a talking animation when it receives an first AudioFrame,
-    and then returns to a "quiet" sprite when it sees a LLMResponseEndFrame.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self._is_talking = False
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, AudioFrame):
-            if not self._is_talking:
-                yield talking_frame
-                yield frame
-                self._is_talking = True
-            else:
-                yield frame
-        elif isinstance(frame, LLMResponseEndFrame):
-            yield quiet_frame
-            yield frame
-            self._is_talking = False
-        else:
-            yield frame
-
-
-class AnimationInitializer(AIService):
-    def __init__(self):
-        super().__init__()
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, PipelineStartedFrame):
-            yield quiet_frame
-            yield frame
-        else:
-            yield frame
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransportService(
-            room_url,
-            token,
-            "Chatbot",
-            duration_minutes=5,
-            start_transcription=True,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=True,
-            camera_width=1024,
-            camera_height=576,
-            vad_enabled=True,
-        )
-
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="pNInz6obpgDQGcFmaJgB",
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
-        ta = TalkingAnimation()
-        ai = AnimationInitializer()
-        pipeline = Pipeline([ai, llm, tts, ta])
-        messages = [
-            {
-                "role": "system",
-                "content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself.",
-            },
-        ]
-
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport):
-            print(f"!!! in here, pipeline.source is {pipeline.source}")
-            await pipeline.queue_frames(LLMMessagesQueueFrame(messages))
-
-        async def run_conversation():
-
-            await transport.run_interruptible_pipeline(
-                pipeline,
-                post_processor=LLMResponseAggregator(messages),
-                pre_processor=UserResponseAggregator(messages),
-            )
-
-        transport.transcription_settings["extra"]["endpointing"] = True
-        transport.transcription_settings["extra"]["punctuate"] = True
-        await asyncio.gather(transport.run(), run_conversation())
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

src/examples/starter-apps/storybot.py

@@ -1,291 +0,0 @@
-import aiohttp
-import asyncio
-import json
-import random
-import logging
-import os
-import re
-import wave
-from typing import AsyncGenerator
-from PIL import Image
-
-from dailyai.pipeline.pipeline import Pipeline
-from dailyai.pipeline.frame_processor import FrameProcessor
-from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.services.fal_ai_services import FalImageGenService
-from dailyai.services.open_ai_services import OpenAILLMService
-from dailyai.services.deepgram_ai_services import DeepgramTTSService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMContextAggregator,
-    LLMUserContextAggregator,
-    ParallelPipeline,
-    UserResponseAggregator,
-    LLMResponseAggregator,
-)
-from examples.support.runner import configure
-from dailyai.pipeline.frames import (
-    LLMMessagesQueueFrame,
-    TranscriptionQueueFrame,
-    Frame,
-    TextFrame,
-    LLMFunctionCallFrame,
-    LLMFunctionStartFrame,
-    LLMResponseEndFrame,
-    StartFrame,
-    AudioFrame,
-    SpriteFrame,
-    ImageFrame,
-    UserStoppedSpeakingFrame,
-)
-from dailyai.services.ai_services import FrameLogger, AIService
-
-logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
-logger = logging.getLogger("dailyai")
-logger.setLevel(logging.DEBUG)
-
-sounds = {}
-images = {}
-sound_files = ["talking.wav", "listening.wav", "ding3.wav"]
-image_files = ["grandma-writing.png", "grandma-listening.png"]
-script_dir = os.path.dirname(__file__)
-
-for file in sound_files:
-    # Build the full path to the sound file
-    full_path = os.path.join(script_dir, "assets", file)
-    # Get the filename without the extension to use as the dictionary key
-    filename = os.path.splitext(os.path.basename(full_path))[0]
-    # Open the sound and convert it to bytes
-    with wave.open(full_path) as audio_file:
-        sounds[file] = audio_file.readframes(-1)
-
-for file in image_files:
-    # Build the full path to the image file
-    full_path = os.path.join(script_dir, "assets", file)
-    # Get the filename without the extension to use as the dictionary key
-    filename = os.path.splitext(os.path.basename(full_path))[0]
-    # Open the image and convert it to bytes
-    with Image.open(full_path) as img:
-        images[file] = img.tobytes()
-
-
-class StoryStartFrame(TextFrame):
-    pass
-
-
-class StoryPageFrame(TextFrame):
-    pass
-
-
-class StoryPromptFrame(TextFrame):
-    pass
-
-
-class StoryProcessor(FrameProcessor):
-    def __init__(self, messages, story):
-        self._messages = messages
-        self._text = ""
-        self._story = story
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        """
-        The response from the LLM service looks like:
-        A comment about the user's choice
-        [start] (when the cat starts telling parts of the story)
-        A sentence of the story
-        [break] (between each sentence/'page' of the story)
-        [prompt] (when the cat asks the user to make a decision)
-        Question about the next part of the story
-
-        1. Catch the frames that are generated by the LLM service
-        """
-        if isinstance(frame, UserStoppedSpeakingFrame):
-            yield ImageFrame(None, images["grandma-writing.png"])
-            yield AudioFrame(sounds["talking.wav"])
-
-        elif isinstance(frame, TextFrame):
-            self._text += frame.text
-
-            if re.findall(r".*\[[sS]tart\].*", self._text):
-                # Then we have the intro. Send it to speech ASAP
-                self._text = self._text.replace("[Start]", "")
-                self._text = self._text.replace("[start]", "")
-
-                self._text = self._text.replace("\n", " ")
-                if len(self._text) > 2:
-                    yield ImageFrame(None, images["grandma-writing.png"])
-                    yield StoryStartFrame(self._text)
-                    yield AudioFrame(sounds["ding3.wav"])
-                self._text = ""
-
-            elif re.findall(r".*\[[bB]reak\].*", self._text):
-                # Then it's a page of the story. Get an image too
-                self._text = self._text.replace("[Break]", "")
-                self._text = self._text.replace("[break]", "")
-                self._text = self._text.replace("\n", " ")
-                if len(self._text) > 2:
-                    self._story.append(self._text)
-                    yield StoryPageFrame(self._text)
-                    yield AudioFrame(sounds["ding3.wav"])
-
-                self._text = ""
-            elif re.findall(r".*\[[pP]rompt\].*", self._text):
-                # Then it's question time. Flush any
-                # text here as a story page, then set
-                # the var to get to prompt mode
-                # cb: trying scene now
-                # self.handle_chunk(self._text)
-                self._text = self._text.replace("[Prompt]", "")
-                self._text = self._text.replace("[prompt]", "")
-
-                self._text = self._text.replace("\n", " ")
-                if len(self._text) > 2:
-                    self._story.append(self._text)
-                    yield StoryPageFrame(self._text)
-            else:
-                # After the prompt thing, we'll catch an LLM end to get the last bit
-                pass
-        elif isinstance(frame, LLMResponseEndFrame):
-            yield ImageFrame(None, images["grandma-writing.png"])
-            yield StoryPromptFrame(self._text)
-            self._text = ""
-            yield frame
-            yield ImageFrame(None, images["grandma-listening.png"])
-            yield AudioFrame(sounds["listening.wav"])
-
-        else:
-            # pass through everything that's not a TextFrame
-            yield frame
-
-
-class StoryImageGenerator(FrameProcessor):
-    def __init__(self, story, llm, img):
-        self._story = story
-        self._llm = llm
-        self._img = img
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, StoryPageFrame):
-            if len(self._story) == 1:
-                prompt = f'You are an illustrator for a children\'s story book. Generate a prompt for DALL-E to create an illustration for the first page of the book, which reads: "{self._story[0]}"\n\n Your response should start with the phrase "Children\'s book illustration of".'
-            else:
-                prompt = f"You are an illustrator for a children's story book. Here is the story so far:\n\n\"{' '.join(self._story[:-1])}\"\n\nGenerate a prompt for DALL-E to create an illustration for the next page. Here's the sentence for the next page:\n\n\"{self._story[-1:][0]}\"\n\n Your response should start with the phrase \"Children's book illustration of\"."
-            msgs = [{"role": "system", "content": prompt}]
-            image_prompt = ""
-            async for f in self._llm.process_frame(LLMMessagesQueueFrame(msgs)):
-                if isinstance(f, TextFrame):
-                    image_prompt += f.text
-            async for f in self._img.process_frame(TextFrame(image_prompt)):
-                yield f
-            # Yield the original StoryPageFrame for basic image/audio sync
-            yield frame
-        else:
-            yield frame
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        global transport
-        global llm
-        global tts
-
-        messages = [
-            {
-                "role": "system",
-                "content": "You are a storytelling grandma who loves to make up fantastic, fun, and educational stories for children between the ages of 5 and 10 years old. Your stories are full of friendly, magical creatures. Your stories are never scary. Each sentence of your story will become a page in a storybook. Stop after 3-4 sentences and give the child a choice to make that will influence the next part of the story. Once the child responds, start by saying something nice about the choice they made, then include [start] in your response. Include [break] after each sentence of the story. Include [prompt] between the story and the prompt.",
-            }
-        ]
-
-        story = []
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"),
-            model="gpt-4-1106-preview",
-        )  # gpt-4-1106-preview
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id="Xb7hH8MSUJpSbSDYk0k2",
-        )  # matilda
-        img = FalImageGenService(
-            image_size="1024x1024",
-            aiohttp_session=session,
-            key_id=os.getenv("FAL_KEY_ID"),
-            key_secret=os.getenv("FAL_KEY_SECRET"),
-        )
-        lra = LLMResponseAggregator(messages)
-        ura = UserResponseAggregator(messages)
-        sp = StoryProcessor(messages, story)
-        sig = StoryImageGenerator(story, llm, img)
-
-        transport = DailyTransportService(
-            room_url,
-            token,
-            "Storybot",
-            5,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=True,
-            camera_width=1024,
-            camera_height=1024,
-            start_transcription=True,
-            vad_enabled=True,
-            vad_stop_s=1.5,
-        )
-
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport):
-            # We're being a bit tricky here by using a special system prompt to
-            # ask the user for a story topic. After their intial response, we'll
-            # use a different system prompt to create story pages.
-            intro_messages = [
-                {
-                    "role": "system",
-                    "content": "You are a storytelling grandma who loves to make up fantastic, fun, and educational stories for children between the ages of 5 and 10 years old. Your stories are full of friendly, magical creatures. Your stories are never scary. Begin by asking what a child wants you to tell a story about. Keep your reponse to only a few sentences.",
-                }
-            ]
-            lca = LLMAssistantContextAggregator(messages)
-            await tts.run_to_queue(
-                transport.send_queue,
-                lca.run(
-                    llm.run(
-                        [
-                            ImageFrame(None, images["grandma-listening.png"]),
-                            LLMMessagesQueueFrame(intro_messages),
-                            AudioFrame(sounds["listening.wav"]),
-                        ]
-                    ),
-                ),
-            )
-
-        async def storytime():
-            fl = FrameLogger("### After Image Generation")
-            pipeline = Pipeline(
-                processors=[
-                    ura,
-                    llm,
-                    sp,
-                    sig,
-                    fl,
-                    tts,
-                    lra,
-                ]
-            )
-            await transport.run_uninterruptible_pipeline(
-                pipeline,
-            )
-
-        transport.transcription_settings["extra"]["endpointing"] = True
-        transport.transcription_settings["extra"]["punctuate"] = True
-        try:
-            await asyncio.gather(transport.run(), storytime())
-        except (asyncio.CancelledError, KeyboardInterrupt):
-            print("whoops")
-            transport.stop()
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))