some more changes

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

@@ -3,28 +3,16 @@ 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",
     "daily-python",
     "fal",
     "faster_whisper",
+    "groq",
     "google-cloud-texttospeech",
     "numpy",
     "openai",
@@ -37,10 +25,6 @@ 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/conversation_wrappers.py

@@ -0,0 +1,77 @@
+import asyncio
+import copy
+import functools
+from typing import AsyncGenerator, Awaitable, Callable
+from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator
+from dailyai.queue_frame import EndStreamQueueFrame, QueueFrame, TranscriptionQueueFrame
+
+
+class InterruptibleConversationWrapper:
+
+    def __init__(
+        self,
+        frame_generator: Callable[[], AsyncGenerator[QueueFrame, None]],
+        runner: Callable[
+            [str, LLMContextAggregator, LLMContextAggregator], Awaitable[None]
+        ],
+        interrupt: Callable[[], None],
+        my_participant_id: str | None,
+        llm_messages: list[dict[str, str]],
+        llm_context_aggregator_in=LLMUserContextAggregator,
+        llm_context_aggregator_out=LLMAssistantContextAggregator,
+        delay_before_speech_seconds: float = 1.0,
+    ):
+        self._frame_generator: Callable[[], AsyncGenerator[QueueFrame, None]] = frame_generator
+        self._runner: Callable[
+            [str, LLMContextAggregator, LLMContextAggregator], Awaitable[None]
+        ] = runner
+        self._interrupt: Callable[[], None] = interrupt
+        self._my_participant_id = my_participant_id
+        self._messages: list[dict[str, str]] = llm_messages
+        self._delay_before_speech_seconds = delay_before_speech_seconds
+        self._llm_context_aggregator_in = llm_context_aggregator_in
+        self._llm_context_aggregator_out = llm_context_aggregator_out
+
+        self._current_phrase = ""
+
+    def update_messages(self, new_messages: list[dict[str, str]], task: asyncio.Task | None):
+        if task:
+            if not task.cancelled():
+                self._current_phrase = ""
+                self._messages = new_messages
+
+    async def speak_after_delay(self, user_speech, messages):
+        await asyncio.sleep(self._delay_before_speech_seconds)
+        tma_in = self._llm_context_aggregator_in(
+            messages, self._my_participant_id, complete_sentences=False
+        )
+        tma_out = self._llm_context_aggregator_out(
+            messages, self._my_participant_id
+        )
+
+        await self._runner(user_speech, tma_in, tma_out)
+
+    async def run_conversation(self):
+        current_response_task = None
+
+        async for frame in self._frame_generator():
+            if isinstance(frame, EndStreamQueueFrame):
+                break
+            elif not isinstance(frame, TranscriptionQueueFrame):
+                continue
+
+            if frame.participantId == self._my_participant_id:
+                continue
+
+            if current_response_task:
+                current_response_task.cancel()
+                self._interrupt()
+
+            self._current_phrase += " " + frame.text
+            current_llm_messages = copy.deepcopy(self._messages)
+            current_response_task = asyncio.create_task(
+                self.speak_after_delay(self._current_phrase, current_llm_messages)
+            )
+            current_response_task.add_done_callback(
+                functools.partial(self.update_messages, current_llm_messages)
+            )

src/dailyai/pipeline/aggregators.py

@@ -1,376 +0,0 @@
-import asyncio
-import re
-
-from dailyai.pipeline.frame_processor import FrameProcessor
-
-from dailyai.pipeline.frames import (
-    EndFrame,
-    EndPipeFrame,
-    Frame,
-    ImageFrame,
-    LLMMessagesQueueFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    TextFrame,
-    TranscriptionQueueFrame,
-    UserStartedSpeakingFrame,
-    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
-
-class ResponseAggregator(FrameProcessor):
-
-    def __init__(
-        self,
-        *,
-        messages: list[dict] | None,
-        role: str,
-        start_frame,
-        end_frame,
-        accumulator_frame,
-        pass_through=True,
-    ):
-        self.aggregation = ""
-        self.aggregating = False
-        self.messages = messages
-        self._role = role
-        self._start_frame = start_frame
-        self._end_frame = end_frame
-        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
-
-        if isinstance(frame, self._start_frame):
-            self.aggregating = True
-        elif isinstance(frame, self._end_frame):
-            self.aggregating = False
-            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:
-                yield frame
-        else:
-            yield frame
-
-class LLMResponseAggregator(ResponseAggregator):
-    def __init__(self, messages: list[dict]):
-        super().__init__(
-            messages=messages,
-            role="assistant",
-            start_frame=LLMResponseStartFrame,
-            end_frame=LLMResponseEndFrame,
-            accumulator_frame=TextFrame
-        )
-
-class UserResponseAggregator(ResponseAggregator):
-    def __init__(self, messages: list[dict]):
-        super().__init__(
-            messages=messages,
-            role="user",
-            start_frame=UserStartedSpeakingFrame,
-            end_frame=UserStoppedSpeakingFrame,
-            accumulator_frame=TranscriptionQueueFrame,
-            pass_through=False
-        )
-
-
-class LLMContextAggregator(AIService):
-    def __init__(
-        self,
-        messages: list[dict],
-        role: str,
-        bot_participant_id=None,
-        complete_sentences=True,
-        pass_through=True,
-    ):
-        super().__init__()
-        self.messages = messages
-        self.bot_participant_id = bot_participant_id
-        self.role = role
-        self.sentence = ""
-        self.complete_sentences = complete_sentences
-        self.pass_through = pass_through
-
-    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
-            return
-
-        # Ignore transcription frames from the bot
-        if isinstance(frame, TranscriptionQueueFrame):
-            if frame.participantId == self.bot_participant_id:
-                return
-
-        # The common case for "pass through" is receiving frames from the LLM that we'll
-        # use to update the "assistant" LLM messages, but also passing the text frames
-        # along to a TTS service to be spoken to the user.
-        if self.pass_through:
-            yield frame
-
-        # TODO: split up transcription by participant
-        if self.complete_sentences:
-            # type: ignore -- the linter thinks this isn't a TextQueueFrame, even
-            # though we check it above
-            self.sentence += frame.text
-            if self.sentence.endswith((".", "?", "!")):
-                self.messages.append({"role": self.role, "content": self.sentence})
-                self.sentence = ""
-                yield LLMMessagesQueueFrame(self.messages)
-        else:
-            # type: ignore -- the linter thinks this isn't a TextQueueFrame, even
-            # though we check it above
-            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
-    ):
-        super().__init__(
-            messages, "user", bot_participant_id, complete_sentences, pass_through=False
-        )
-
-
-class LLMAssistantContextAggregator(LLMContextAggregator):
-    def __init__(
-        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
-    ):
-        super().__init__(
-            messages,
-            "assistant",
-            bot_participant_id,
-            complete_sentences,
-            pass_through=True,
-        )
-
-
-class SentenceAggregator(FrameProcessor):
-    """This frame processor aggregates text frames into complete sentences.
-
-    Frame input/output:
-        TextFrame("Hello,") -> None
-        TextFrame(" world.") -> TextFrame("Hello world.")
-
-    Doctest:
-    >>> async def print_frames(aggregator, frame):
-    ...     async for frame in aggregator.process_frame(frame):
-    ...         print(frame.text)
-
-    >>> aggregator = SentenceAggregator()
-    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello,")))
-    >>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
-    Hello, world.
-    """
-    def __init__(self):
-        self.aggregation = ""
-
-    async def process_frame(
-        self, frame: Frame
-    ) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, TextFrame):
-            m = re.search("(.*[?.!])(.*)", frame.text)
-            if m:
-                yield TextFrame(self.aggregation + m.group(1))
-                self.aggregation = m.group(2)
-            else:
-                self.aggregation += frame.text
-        elif isinstance(frame, EndFrame):
-            if self.aggregation:
-                yield TextFrame(self.aggregation)
-            yield frame
-        else:
-            yield frame
-
-
-class LLMFullResponseAggregator(FrameProcessor):
-    """This class aggregates Text frames until it receives a
-    LLMResponseEndFrame, then emits the concatenated text as
-    a single text frame.
-
-    given the following frames:
-
-        TextFrame("Hello,")
-        TextFrame(" world.")
-        TextFrame(" I am")
-        TextFrame(" an LLM.")
-        LLMResponseEndFrame()]
-
-    this processor will yield nothing for the first 4 frames, then
-
-        TextFrame("Hello, world. I am an LLM.")
-        LLMResponseEndFrame()
-
-    when passed the last frame.
-
-    >>> async def print_frames(aggregator, frame):
-    ...     async for frame in aggregator.process_frame(frame):
-    ...         if isinstance(frame, TextFrame):
-    ...             print(frame.text)
-    ...         else:
-    ...             print(frame.__class__.__name__)
-
-    >>> aggregator = LLMFullResponseAggregator()
-    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello,")))
-    >>> asyncio.run(print_frames(aggregator, TextFrame(" world.")))
-    >>> asyncio.run(print_frames(aggregator, TextFrame(" I am")))
-    >>> asyncio.run(print_frames(aggregator, TextFrame(" an LLM.")))
-    >>> asyncio.run(print_frames(aggregator, LLMResponseEndFrame()))
-    Hello, world. I am an LLM.
-    LLMResponseEndFrame
-    """
-    def __init__(self):
-        self.aggregation = ""
-
-    async def process_frame(
-        self, frame: Frame
-    ) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, TextFrame):
-            self.aggregation += frame.text
-        elif isinstance(frame, LLMResponseEndFrame):
-            yield TextFrame(self.aggregation)
-            yield frame
-            self.aggregation = ""
-        else:
-            yield frame
-
-
-class StatelessTextTransformer(FrameProcessor):
-    """This processor calls the given function on any text in a text frame.
-
-    >>> async def print_frames(aggregator, frame):
-    ...     async for frame in aggregator.process_frame(frame):
-    ...         print(frame.text)
-
-    >>> aggregator = StatelessTextTransformer(lambda x: x.upper())
-    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello")))
-    HELLO
-    """
-
-    def __init__(self, transform_fn):
-        self.transform_fn = transform_fn
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, TextFrame):
-            result = self.transform_fn(frame.text)
-            if isinstance(result, Coroutine):
-                result = await result
-
-            yield TextFrame(result)
-        else:
-            yield frame
-
-class ParallelPipeline(FrameProcessor):
-    """ 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
-    sink queue. No guarantees are made about the ordering of frames in
-    the sink queue (that is, no sub-pipeline has higher priority than
-    any other, frames are put on the sink in the order they're emitted
-    by the sub-pipelines).
-
-    After each frame is taken from this class's source queue and placed
-    in each sub-pipeline's source queue, an EndPipeFrame is put on each
-    sub-pipeline's source queue. This indicates to the sub-pipe runner
-    that it should exit.
-
-    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()
-        self.pipelines: list[Pipeline] = [
-            Pipeline(
-                pipeline_definition,
-                source,
-                self.sink,
-            )
-            for source, pipeline_definition in zip(self.sources, pipeline_definitions)
-        ]
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        for source in self.sources:
-            await source.put(frame)
-            await source.put(EndPipeFrame())
-
-        await asyncio.gather(*[pipeline.run_pipeline() for pipeline in self.pipelines])
-
-        seen_ids = set()
-        while not self.sink.empty():
-            frame = await self.sink.get()
-
-            # de-dup frames. Because the convention is to yield a frame that isn't processed,
-            # each pipeline will likely yield the same frame, so we will end up with _n_ copies
-            # of unprocessed frames where _n_ is the number of parallel pipes that don't
-            # process that frame.
-            if id(frame) in seen_ids:
-                continue
-            seen_ids.add(id(frame))
-
-            # Skip passing along EndParallelPipeQueueFrame, because we use them for our own flow control.
-            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
-    until and not including the gate-closed frame.
-
-    >>> async def print_frames(aggregator, frame):
-    ...     async for frame in aggregator.process_frame(frame):
-    ...         if isinstance(frame, TextFrame):
-    ...             print(frame.text)
-    ...         else:
-    ...             print(frame.__class__.__name__)
-
-    >>> aggregator = GatedAggregator(
-    ...     gate_close_fn=lambda x: isinstance(x, LLMResponseStartFrame),
-    ...     gate_open_fn=lambda x: isinstance(x, ImageFrame),
-    ...     start_open=False)
-    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello")))
-    >>> asyncio.run(print_frames(aggregator, TextFrame("Hello again.")))
-    >>> asyncio.run(print_frames(aggregator, ImageFrame(url='', image=bytes([]))))
-    ImageFrame
-    Hello
-    Hello again.
-    >>> 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
-        self.gate_open = start_open
-        self.accumulator: List[Frame] = []
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if self.gate_open:
-            if self.gate_close_fn(frame):
-                self.gate_open = False
-        else:
-            if self.gate_open_fn(frame):
-                self.gate_open = True
-
-        if self.gate_open:
-            yield frame
-            if self.accumulator:
-                for frame in self.accumulator:
-                    yield frame
-            self.accumulator = []
-        else:
-            self.accumulator.append(frame)

src/dailyai/pipeline/frame_processor.py

@@ -1,33 +0,0 @@
-from abc import abstractmethod
-from typing import AsyncGenerator
-
-from dailyai.pipeline.frames import ControlFrame, Frame
-
-
-class FrameProcessor:
-    """This is the base class for all frame processors. Frame processors consume a frame
-    and yield 0 or more frames. Generally frame processors are used as part of a pipeline
-    where frames come from a source queue, are processed by a series of frame processors,
-    then placed on a sink queue.
-
-    By convention, FrameProcessors should immediately yield any frames they don't process.
-
-    Stateful FrameProcessors should watch for the EndStreamQueueFrame and finalize their
-    output, eg. yielding an unfinished sentence if they're aggregating LLM output to full
-    sentences. EndStreamQueueFrame is also a chance to clean up any services that need to
-    be closed, del'd, etc.
-    """
-
-    @abstractmethod
-    async def process_frame(
-        self, frame: Frame
-    ) -> AsyncGenerator[Frame, None]:
-        """Process a single frame and yield 0 or more frames."""
-        if isinstance(frame, ControlFrame):
-            yield frame
-        yield frame
-
-    @abstractmethod
-    async def interrupted(self) -> None:
-        """Handle any cleanup if the pipeline was interrupted."""
-        pass

src/dailyai/pipeline/frames.py

@@ -1,89 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, List
-
-from dailyai.services.openai_llm_context import OpenAILLMContext
-
-
-class Frame:
-    pass
-
-class ControlFrame(Frame):
-    # Control frames should contain no instance data, so
-    # equality is based solely on the class.
-    def __eq__(self, other):
-        return type(other) == self.__class__
-
-
-class StartFrame(ControlFrame):
-    pass
-
-
-class EndFrame(ControlFrame):
-    pass
-
-class EndPipeFrame(ControlFrame):
-    pass
-
-
-class LLMResponseStartFrame(ControlFrame):
-    pass
-
-
-class LLMResponseEndFrame(ControlFrame):
-    pass
-
-
-@dataclass()
-class AudioFrame(Frame):
-    data: bytes
-
-
-@dataclass()
-class ImageFrame(Frame):
-    url: str | None
-    image: bytes
-
-
-@dataclass()
-class SpriteFrame(Frame):
-    images: list[bytes]
-
-
-@dataclass()
-class TextFrame(Frame):
-    text: str
-
-
-@dataclass()
-class TranscriptionQueueFrame(TextFrame):
-    participantId: str
-    timestamp: str
-
-
-@dataclass()
-class LLMMessagesQueueFrame(Frame):
-    messages: List[dict]
-
-
-@dataclass()
-class OpenAILLMContextFrame(Frame):
-    context: OpenAILLMContext
-
-
-class AppMessageQueueFrame(Frame):
-    message: Any
-    participantId: str
-
-class UserStartedSpeakingFrame(Frame):
-    pass
-
-class UserStoppedSpeakingFrame(Frame):
-    pass
-
-@dataclass()
-class LLMFunctionStartFrame(Frame):
-    function_name: str
-@dataclass()
-class LLMFunctionCallFrame(Frame):
-    function_name: 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

@@ -1,88 +0,0 @@
-import asyncio
-from typing import AsyncGenerator, List
-from dailyai.pipeline.frame_processor import FrameProcessor
-
-from dailyai.pipeline.frames import EndPipeFrame, EndFrame, Frame
-
-
-class Pipeline:
-    """
-    This class manages a pipe of FrameProcessors, and runs them in sequence. The "source"
-    and "sink" queues are managed by the caller. You can use this class stand-alone to
-    perform specialized processing, or you can use the Transport's run_pipeline method to
-    instantiate and run a pipeline with the Transport's sink and source queues.
-    """
-
-    def __init__(
-        self,
-        processors: List[FrameProcessor],
-        source: asyncio.Queue | None = None,
-        sink: asyncio.Queue[Frame] | None = None,
-    ):
-        """ 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
-        queues on the pipeline for you.
-        """
-        self.processors = processors
-        self.source: asyncio.Queue[Frame] | None = source
-        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
-        will be processed by each frame_processor in the pipeline, or order
-        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
-        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
-        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
-        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.
-
-        The source and sink queues must be set before calling this method.
-
-        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 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)
-
-                if isinstance(initial_frame, EndFrame) or isinstance(initial_frame, EndPipeFrame):
-                    break
-        except asyncio.CancelledError:
-            # this means there's been an interruption, do any cleanup necessary here.
-            for processor in self.processors:
-                await processor.interrupted()
-            pass

src/dailyai/queue_aggregators.py

@@ -0,0 +1,98 @@
+import asyncio
+
+from dailyai.queue_frame import LLMMessagesQueueFrame, QueueFrame, TextQueueFrame, TranscriptionQueueFrame
+from dailyai.services.ai_services import AIService
+
+from typing import AsyncGenerator, List
+
+
+class QueueTee:
+    async def run_to_queue_and_generate(
+        self,
+        output_queue: asyncio.Queue,
+        generator: AsyncGenerator[QueueFrame, None]
+    ) -> AsyncGenerator[QueueFrame, None]:
+        async for frame in generator:
+            await output_queue.put(frame)
+            yield frame
+
+    async def run_to_queues(
+        self,
+        output_queues: List[asyncio.Queue],
+        generator: AsyncGenerator[QueueFrame, None]
+    ):
+        async for frame in generator:
+            for queue in output_queues:
+                await queue.put(frame)
+
+
+class LLMContextAggregator(AIService):
+    def __init__(
+            self,
+            messages: list[dict],
+            role: str,
+            bot_participant_id=None,
+            complete_sentences=True,
+            pass_through=True):
+        super().__init__()
+        self.messages = messages
+        self.bot_participant_id = bot_participant_id
+        self.role = role
+        self.sentence = ""
+        self.complete_sentences = complete_sentences
+        self.pass_through = pass_through
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        # We don't do anything with non-text frames, pass it along to next in the pipeline.
+        if not isinstance(frame, TextQueueFrame):
+            yield frame
+            return
+
+        # Ignore transcription frames from the bot
+        if isinstance(frame, TranscriptionQueueFrame):
+            if frame.participantId == self.bot_participant_id:
+                return
+
+        # The common case for "pass through" is receiving frames from the LLM that we'll
+        # use to update the "assistant" LLM messages, but also passing the text frames
+        # along to a TTS service to be spoken to the user.
+        if self.pass_through:
+            yield frame
+
+        # TODO: split up transcription by participant
+        if self.complete_sentences:
+            # type: ignore -- the linter thinks this isn't a TextQueueFrame, even
+            # though we check it above
+            self.sentence += frame.text
+            if self.sentence.endswith((".", "?", "!")):
+                self.messages.append({"role": self.role, "content": self.sentence})
+                self.sentence = ""
+                yield LLMMessagesQueueFrame(self.messages)
+        else:
+            # type: ignore -- the linter thinks this isn't a TextQueueFrame, even
+            # though we check it above
+            self.messages.append({"role": self.role, "content": frame.text})
+            yield LLMMessagesQueueFrame(self.messages)
+
+    async def finalize(self) -> AsyncGenerator[QueueFrame, None]:
+        # Send any dangling words that weren't finished with punctuation.
+        if self.complete_sentences and self.sentence:
+            self.messages.append({"role": self.role, "content": self.sentence})
+            yield LLMMessagesQueueFrame(self.messages)
+
+
+class LLMUserContextAggregator(LLMContextAggregator):
+    def __init__(self,
+                 messages: list[dict],
+                 bot_participant_id=None,
+                 complete_sentences=True):
+        super().__init__(messages, "user", bot_participant_id, complete_sentences, pass_through=False)
+
+
+class LLMAssistantContextAggregator(LLMContextAggregator):
+    def __init__(
+        self, messages: list[dict], bot_participant_id=None, complete_sentences=True
+    ):
+        super().__init__(
+            messages, "assistant", bot_participant_id, complete_sentences, pass_through=True
+        )

src/dailyai/queue_frame.py

@@ -0,0 +1,79 @@
+from enum import Enum
+from dataclasses import dataclass
+from typing import Any
+
+
+class QueueFrame:
+    pass
+
+
+class ControlQueueFrame(QueueFrame):
+    pass
+
+
+class StartStreamQueueFrame(ControlQueueFrame):
+    pass
+
+
+class EndStreamQueueFrame(ControlQueueFrame):
+    pass
+
+
+class LLMResponseEndQueueFrame(QueueFrame):
+    pass
+
+
+class UserStartedSpeakingFrame(QueueFrame):
+    pass
+
+
+class UserStoppedSpeakingFrame(QueueFrame):
+    pass
+
+
+@dataclass()
+class AudioQueueFrame(QueueFrame):
+    data: bytes
+
+
+@dataclass()
+class ImageQueueFrame(QueueFrame):
+    url: str | None
+    image: bytes
+
+
+@dataclass()
+class SpriteQueueFrame(QueueFrame):
+    images: list[bytes]
+
+
+@dataclass()
+class TextQueueFrame(QueueFrame):
+    text: str
+
+
+@dataclass()
+class TextQueueOutOfBandFrame(TextQueueFrame):
+    outOfBand: bool = True
+
+
+@dataclass()
+class TTSCompletedFrame(QueueFrame):
+    text: str
+    outOfBand: bool = False
+
+
+@dataclass()
+class TranscriptionQueueFrame(TextQueueFrame):
+    participantId: str
+    timestamp: str
+
+
+@dataclass()
+class LLMMessagesQueueFrame(QueueFrame):
+    messages: list[dict[str, str]]  # TODO: define this more concretely!
+
+
+class AppMessageQueueFrame(QueueFrame):
+    message: Any
+    participantId: str

src/dailyai/services/ai_services.py

@@ -2,27 +2,30 @@ import asyncio
 import io
 import logging
 import time
+import datetime
 import wave
-from dailyai.pipeline.frame_processor import FrameProcessor
 
-from dailyai.pipeline.frames import (
-    AudioFrame,
-    EndFrame,
-    ImageFrame,
+from dailyai.queue_frame import (
+    QueueFrame,
+    AudioQueueFrame,
+    ControlQueueFrame,
+    EndStreamQueueFrame,
+    ImageQueueFrame,
     LLMMessagesQueueFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    LLMFunctionStartFrame,
-    LLMFunctionCallFrame,
-    Frame,
-    TextFrame,
-    TranscriptionQueueFrame
+    LLMResponseEndQueueFrame,
+    QueueFrame,
+    TextQueueFrame,
+    TTSCompletedFrame,
+    TranscriptionQueueFrame,
+    UserStoppedSpeakingFrame
 )
 
 from abc import abstractmethod
-from typing import AsyncGenerator, AsyncIterable, BinaryIO, Iterable, List
+from typing import AsyncGenerator, AsyncIterable, BinaryIO, Iterable
+from dataclasses import dataclass
 
-class AIService(FrameProcessor):
+
+class AIService:
 
     def __init__(self):
         self.logger = logging.getLogger("dailyai")
@@ -35,14 +38,14 @@ class AIService(FrameProcessor):
             await queue.put(frame)
 
         if add_end_of_stream:
-            await queue.put(EndFrame())
+            await queue.put(EndStreamQueueFrame())
 
     async def run(
         self,
-        frames: Iterable[Frame]
-        | AsyncIterable[Frame]
-        | asyncio.Queue[Frame],
-    ) -> AsyncGenerator[Frame, None]:
+        frames: Iterable[QueueFrame]
+        | AsyncIterable[QueueFrame]
+        | asyncio.Queue[QueueFrame],
+    ) -> AsyncGenerator[QueueFrame, None]:
         try:
             if isinstance(frames, AsyncIterable):
                 async for frame in frames:
@@ -57,19 +60,61 @@ class AIService(FrameProcessor):
                     frame = await frames.get()
                     async for output_frame in self.process_frame(frame):
                         yield output_frame
-                    if isinstance(frame, EndFrame):
+                    if isinstance(frame, EndStreamQueueFrame):
                         break
             else:
                 raise Exception("Frames must be an iterable or async iterable")
+
+            async for output_frame in self.finalize():
+                yield output_frame
         except Exception as e:
             self.logger.error("Exception occurred while running AI service", e)
             raise e
 
+    @abstractmethod
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, ControlQueueFrame):
+            yield frame
+
+    @abstractmethod
+    async def finalize(self) -> AsyncGenerator[QueueFrame, None]:
+        # This is a trick for the interpreter (and linter) to know that this is a generator.
+        if False:
+            yield QueueFrame()
+
 
 class LLMService(AIService):
-    """ This class is a no-op but serves as a base class for LLM services. """
-    def __init__(self):
+
+    def __init__(self, context):
         super().__init__()
+        self._context = context
+
+    @abstractmethod
+    async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
+        yield ""
+
+    @abstractmethod
+    async def run_llm(self, messages) -> str:
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        print(f"##### process frame got a frame, {type(frame)}")
+        if isinstance(frame, UserStoppedSpeakingFrame):
+            print(
+                f"### Got a user stopped speaking frame, context is {self._context}")
+            async for chunk in self.run_llm_async(self._context):
+                # if we get a string, wrap it in a frame
+                if isinstance(chunk, str):
+                    yield TextQueueFrame(chunk)
+                # if we get a frame, pass it through
+                elif isinstance(chunk, QueueFrame):
+                    print(f"### Got a frame chunk: {chunk}")
+                    yield chunk
+                else:
+                    print(f"### Got an unknown chunk: {chunk}")
+            yield LLMResponseEndQueueFrame()
+        else:
+            yield frame
 
 
 class TTSService(AIService):
@@ -89,14 +134,14 @@ class TTSService(AIService):
         # yield empty bytes here, so linting can infer what this method does
         yield bytes()
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, EndFrame):
-            if self.current_sentence:
-                async for audio_chunk in self.run_tts(self.current_sentence):
-                    yield AudioFrame(audio_chunk)
-                yield TextFrame(self.current_sentence)
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if not isinstance(frame, TextQueueFrame):
+            # We don't want transcription frames, which are a subclass
+            yield frame
+            return
 
-        if not isinstance(frame, TextFrame):
+        # TODO-CB: Clean this up
+        if isinstance(frame, TranscriptionQueueFrame):
             yield frame
             return
 
@@ -111,14 +156,20 @@ class TTSService(AIService):
 
         if text:
             async for audio_chunk in self.run_tts(text):
-                yield AudioFrame(audio_chunk)
+                size = 8000
+                for i in range(0, len(audio_chunk), size):
+                    yield AudioQueueFrame(audio_chunk[i: i+size])
+            print("### ABOUT TO YIELD TTS COMPLETED FRAME", frame)
+            yield TTSCompletedFrame(text, hasattr(frame, 'outOfBand') and frame.outOfBand)
 
-            # note we pass along the text frame *after* the audio, so the text frame is completed after the audio is processed.
-            yield TextFrame(text)
+    async def finalize(self):
+        if self.current_sentence:
+            async for audio_chunk in self.run_tts(self.current_sentence):
+                yield AudioQueueFrame(audio_chunk)
 
     # 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, [TextQueueFrame(sentence)])
 
 
 class ImageGenService(AIService):
@@ -131,13 +182,13 @@ class ImageGenService(AIService):
     async def run_image_gen(self, sentence: str) -> tuple[str, bytes]:
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if not isinstance(frame, TextFrame):
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if not isinstance(frame, TextQueueFrame):
             yield frame
             return
 
         (url, image_data) = await self.run_image_gen(frame.text)
-        yield ImageFrame(url, image_data)
+        yield ImageQueueFrame(url, image_data)
 
 
 class STTService(AIService):
@@ -154,9 +205,9 @@ class STTService(AIService):
         """Returns transcript as a string"""
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         """Processes a frame of audio data, either buffering or transcribing it."""
-        if not isinstance(frame, AudioFrame):
+        if not isinstance(frame, AudioQueueFrame):
             return
 
         data = frame.data
@@ -177,10 +228,11 @@ class FrameLogger(AIService):
         super().__init__(**kwargs)
         self.prefix = prefix
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, (AudioFrame, ImageFrame)):
-            self.logger.info(f"{self.prefix}: {type(frame)}")
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, (AudioQueueFrame, ImageQueueFrame)):
+            self.logger.info(
+                f"{datetime.datetime.utcnow().isoformat()} {self.prefix}: {type(frame)}")
         else:
-            print(f"{self.prefix}: {frame}")
+            print(f"{datetime.datetime.utcnow().isoformat()} {self.prefix}: {frame}")
 
         yield frame

src/dailyai/services/azure_ai_services.py

@@ -2,7 +2,6 @@ import aiohttp
 import asyncio
 import io
 import json
-import time
 from openai import AsyncAzureOpenAI
 
 import os
@@ -14,14 +13,7 @@ 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 +22,18 @@ 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 +42,58 @@ 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, context):
+        super().__init__(context)
+        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) -> AsyncGenerator[str, None]:
+        messages_for_log = json.dumps(messages)
+        self.logger.debug(f"Generating chat via azure: {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 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
@@ -96,7 +104,8 @@ class AzureImageGenServiceREST(ImageGenService):
 
     async def run_image_gen(self, sentence) -> tuple[str, bytes]:
         url = f"{self._azure_endpoint}openai/images/generations:submit?api-version={self._api_version}"
-        headers = {"api-key": self._api_key, "Content-Type": "application/json"}
+        headers = {"api-key": self._api_key,
+                   "Content-Type": "application/json"}
         body = {
             # Enter your prompt text here
             "prompt": sentence,
@@ -106,9 +115,11 @@ class AzureImageGenServiceREST(ImageGenService):
         async with self._aiohttp_session.post(
             url, headers=headers, json=body
         ) as submission:
+            print(f"submission: {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']
+            print(f"submission status: {submission.status}")
             status = ""
             attempts_left = 120
             json_response = None
@@ -124,13 +135,12 @@ 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
             async with self._aiohttp_session.get(image_url) as response:
                 image_stream = io.BytesIO(await response.content.read())
                 image = Image.open(image_stream)
+                print("i got an image file!")
                 return (image_url, image.tobytes())

src/dailyai/services/base_transport_service.py

@@ -1,29 +1,36 @@
 from abc import abstractmethod
 import asyncio
+import copy
+import functools
 import itertools
 import logging
-import numpy as np
-import pyaudio
-import torch
 import queue
 import threading
 import time
 from typing import AsyncGenerator
+import numpy as np
+import pyaudio
+import torch
+import torchaudio
 from enum import Enum
-from dailyai.pipeline.frame_processor import FrameProcessor
+import datetime
+import traceback
 
-from dailyai.pipeline.frames import (
-    AudioFrame,
-    EndFrame,
-    ImageFrame,
-    Frame,
-    SpriteFrame,
-    StartFrame,
+from typing import AsyncGenerator, AsyncIterable, BinaryIO, Iterable
+from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
+
+from dailyai.queue_frame import (
+    AudioQueueFrame,
+    EndStreamQueueFrame,
+    ImageQueueFrame,
+    QueueFrame,
+    SpriteQueueFrame,
+    StartStreamQueueFrame,
     TranscriptionQueueFrame,
+    TTSCompletedFrame,
     UserStartedSpeakingFrame,
     UserStoppedSpeakingFrame
 )
-from dailyai.pipeline.pipeline import Pipeline
 
 torch.set_num_threads(1)
 
@@ -88,13 +95,8 @@ class BaseTransportService():
         self._speaker_sample_rate = kwargs.get("speaker_sample_rate") or 16000
         self._fps = kwargs.get("fps") or 8
         self._vad_start_s = kwargs.get("vad_start_s") or 0.2
-        self._vad_stop_s = kwargs.get("vad_stop_s") or 0.8
+        self._vad_stop_s = kwargs.get("vad_stop_s") or 0.5
         self._context = kwargs.get("context") or []
-        self._vad_enabled = kwargs.get("vad_enabled") or False
-
-        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.")
 
         self._vad_samples = 1536
         vad_frame_s = self._vad_samples / SAMPLE_RATE
@@ -103,7 +105,6 @@ class BaseTransportService():
         self._vad_starting_count = 0
         self._vad_stopping_count = 0
         self._vad_state = VADState.QUIET
-        self._user_is_speaking = False
 
         duration_minutes = kwargs.get("duration_minutes") or 10
         self._expiration = time.time() + duration_minutes * 60
@@ -111,11 +112,11 @@ class BaseTransportService():
         self.send_queue = asyncio.Queue()
         self.receive_queue = asyncio.Queue()
 
-        self.completed_queue = asyncio.Queue()
-
         self._threadsafe_send_queue = queue.Queue()
 
         self._images = None
+        self._user_is_speaking = False
+        self._current_phrase = ""
 
         try:
             self._loop: asyncio.AbstractEventLoop | None = asyncio.get_running_loop()
@@ -127,6 +128,72 @@ class BaseTransportService():
 
         self._logger: logging.Logger = logging.getLogger()
 
+    def update_messages(self, new_context: list[dict[str, str]], task: asyncio.Task | None):
+        if task:
+            if not task.cancelled():
+                self._current_phrase = ""
+                self._context = new_context
+
+    def append_to_context(self, role, chunk_or_text):
+        print("IN APPEND", chunk_or_text)
+        # if we get a non-string, append it to the context without further error checking
+        # unless the outOfBand property is True
+        if not isinstance(chunk_or_text, str):
+
+            if not chunk_or_text.get("outOfBand") == True:
+                self._context.append(chunk_or_text)
+            return
+
+        text = chunk_or_text
+        last_context_item = self._context[-1]
+
+        print("TEXT", text)
+        print("LAST CONTEXT ITEM", last_context_item)
+        traceback.print_stack()
+
+        if last_context_item and last_context_item['role'] == role:
+            last_context_item['content'] += f" {text}"
+        else:
+            self._context.append({"role": role, "content": text})
+
+    async def run_pipeline(self, frame):
+        print(f"starting to speak_after_delay, {frame}")
+        # TODO-CB: This exception for missing class gets eaten!
+        await self._runner(frame)
+
+    async def run_conversation(self, runner: Iterable[QueueFrame]
+                               | AsyncIterable[QueueFrame]
+                               | asyncio.Queue[QueueFrame],
+                               ) -> AsyncGenerator[QueueFrame, None]:
+        current_response_task = None
+        self._runner = runner
+
+        async for frame in self.get_receive_frames():
+            print(f"got frame of type: {type(frame)}, {frame}")
+            if isinstance(frame, EndStreamQueueFrame):
+                break
+            # elif not isinstance(frame, TranscriptionQueueFrame):
+                # continue
+            # TODO-CB: Verify this is an accurate replacement
+            # if hasattr(frame, 'participantId') and frame.participantId == self._my_participant_id:
+            if not isinstance(frame, UserStoppedSpeakingFrame):
+                continue
+
+            if current_response_task:
+                # TODO-CB: Maybe not always interrupt? Are there frame types we can pass through?
+                current_response_task.cancel()
+                self.interrupt()
+
+            # self._current_phrase += " " + frame.text
+           # current_llm_context = copy.deepcopy(self._context)
+            current_response_task = asyncio.create_task(
+                self.run_pipeline(
+                    frame)
+            )
+            current_response_task.add_done_callback(
+                functools.partial(self.update_messages, self._context)
+            )
+
     async def run(self):
         self._prerun()
 
@@ -142,11 +209,11 @@ class BaseTransportService():
         self._frame_consumer_thread.start()
 
         if self._speaker_enabled:
-            self._receive_audio_thread = threading.Thread(
-                target=self._receive_audio, daemon=True)
-            self._receive_audio_thread.start()
+            # TODO-CB: This is interesting
+            # self._receive_audio_thread = threading.Thread(
+            #     target=self._receive_audio, daemon=True)
+            # self._receive_audio_thread.start()
 
-        if self._vad_enabled:
             self._vad_thread = threading.Thread(target=self._vad, daemon=True)
             self._vad_thread.start()
 
@@ -165,7 +232,7 @@ class BaseTransportService():
 
         self._stop_threads.set()
 
-        await self.send_queue.put(EndFrame())
+        await self.send_queue.put(EndStreamQueueFrame())
         await async_output_queue_marshal_task
         await self.send_queue.join()
         self._frame_consumer_thread.join()
@@ -173,70 +240,6 @@ class BaseTransportService():
         if self._speaker_enabled:
             self._receive_audio_thread.join()
 
-        if self._vad_enabled:
-            self._vad_thread.join()
-
-    async def run_uninterruptible_pipeline(self, pipeline: Pipeline):
-        pipeline.set_sink(self.send_queue)
-        pipeline.set_source(self.receive_queue)
-        await pipeline.run_pipeline()
-
-    async def run_interruptible_pipeline(
-        self,
-        pipeline: Pipeline,
-        allow_interruptions=True,
-        pre_processor=None,
-        post_processor: FrameProcessor | None = None,
-    ):
-        pipeline.set_sink(self.send_queue)
-        source_queue = asyncio.Queue()
-        pipeline.set_source(source_queue)
-        pipeline.set_sink(self.send_queue)
-        pipeline_task = asyncio.create_task(pipeline.run_pipeline())
-
-        async def yield_frame(frame: Frame) -> AsyncGenerator[Frame, None]:
-            yield frame
-
-        async def post_process(post_processor: FrameProcessor):
-            while True:
-                frame = await self.completed_queue.get()
-
-                # We ignore the output of the post_processor's process frame;
-                # this is called to update the post-processor's state.
-                async for frame in post_processor.process_frame(frame):
-                    pass
-
-                if isinstance(frame, EndFrame):
-                    break
-
-        if post_processor:
-            post_process_task = asyncio.create_task(post_process(post_processor))
-
-        started = False
-
-        async for frame in self.get_receive_frames():
-            if isinstance(frame, UserStartedSpeakingFrame):
-                pipeline_task.cancel()
-                self.interrupt()
-                pipeline_task = asyncio.create_task(pipeline.run_pipeline())
-                started = False
-
-            if not started:
-                await self.send_queue.put(StartFrame())
-
-            if pre_processor:
-                frame_generator = pre_processor.process_frame(frame)
-            else:
-                frame_generator = yield_frame(frame)
-
-            async for frame in frame_generator:
-                await source_queue.put(frame)
-
-            if isinstance(frame, EndFrame):
-                break
-
-        await asyncio.gather(pipeline_task, post_process_task)
-
     def _post_run(self):
         # Note that this function must be idempotent! It can be called multiple times
         # if, for example, a keyboard interrupt occurs.
@@ -304,14 +307,19 @@ class BaseTransportService():
                         self._vad_stopping_count += 1
 
             if self._vad_state == VADState.STARTING and self._vad_starting_count >= self._vad_start_frames:
+                print(
+                    f'!!! {datetime.datetime.utcnow().isoformat()} queueing start frame')
                 asyncio.run_coroutine_threadsafe(
                     self.receive_queue.put(
                         UserStartedSpeakingFrame()), self._loop
                 )
-                # self.interrupt()
+                print(f"!!! VAD started, calling interrupt")
+                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:
+                print(
+                    f'!!! {datetime.datetime.utcnow().isoformat()} queueing stop frame')
                 asyncio.run_coroutine_threadsafe(
                     self.receive_queue.put(
                         UserStoppedSpeakingFrame()), self._loop
@@ -321,21 +329,21 @@ class BaseTransportService():
 
     async def _marshal_frames(self):
         while True:
-            frame: Frame | list = await self.send_queue.get()
+            frame: QueueFrame | list = await self.send_queue.get()
             self._threadsafe_send_queue.put(frame)
             self.send_queue.task_done()
-            if isinstance(frame, EndFrame):
+            if isinstance(frame, EndStreamQueueFrame):
                 break
 
     def interrupt(self):
-        self._logger.debug("### Interrupting")
+        print(f"!!! setting interrupt")
         self._is_interrupted.set()
 
-    async def get_receive_frames(self) -> AsyncGenerator[Frame, None]:
+    async def get_receive_frames(self) -> AsyncGenerator[QueueFrame, None]:
         while True:
             frame = await self.receive_queue.get()
             yield frame
-            if isinstance(frame, EndFrame):
+            if isinstance(frame, EndStreamQueueFrame):
                 break
 
     def _receive_audio(self):
@@ -348,13 +356,13 @@ class BaseTransportService():
         while not self._stop_threads.is_set():
             buffer = self.read_audio_frames(desired_frame_count)
             if len(buffer) > 0:
-                frame = AudioFrame(buffer)
+                frame = AudioQueueFrame(buffer)
                 asyncio.run_coroutine_threadsafe(
                     self.receive_queue.put(frame), self._loop
                 )
 
         asyncio.run_coroutine_threadsafe(
-            self.receive_queue.put(EndFrame()), self._loop
+            self.receive_queue.put(EndStreamQueueFrame()), self._loop
         )
 
     def _set_image(self, image: bytes):
@@ -379,40 +387,31 @@ class BaseTransportService():
         self._logger.info("🎬 Starting frame consumer thread")
         b = bytearray()
         smallest_write_size = 3200
-        largest_write_size = 8000
+        all_audio_frames = bytearray()
         while True:
             try:
-                frames_or_frame: Frame | list[Frame] = (
+                frames_or_frame: QueueFrame | list[QueueFrame] = (
                     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]))
-                elif isinstance(frames_or_frame, Frame):
-                    frames: list[Frame] = [frames_or_frame]
+                if isinstance(frames_or_frame, QueueFrame):
+                    frames: list[QueueFrame] = [frames_or_frame]
                 elif isinstance(frames_or_frame, list):
-                    frames: list[Frame] = frames_or_frame
+                    frames: list[QueueFrame] = frames_or_frame
                 else:
                     raise Exception("Unknown type in output queue")
 
                 for frame in frames:
-                    if isinstance(frame, EndFrame):
+                    if isinstance(frame, EndStreamQueueFrame):
                         self._logger.info("Stopping frame consumer thread")
                         self._threadsafe_send_queue.task_done()
-                        if self._loop:
-                            asyncio.run_coroutine_threadsafe(
-                                self.completed_queue.put(frame), self._loop
-                            )
                         return
 
                     # if interrupted, we just pull frames off the queue and discard them
                     if not self._is_interrupted.is_set():
                         if frame:
-                            if isinstance(frame, AudioFrame):
+                            if isinstance(frame, AudioQueueFrame):
                                 chunk = frame.data
+                                all_audio_frames.extend(chunk)
 
                                 b.extend(chunk)
                                 truncated_length: int = len(b) - (
@@ -422,30 +421,29 @@ class BaseTransportService():
                                     self.write_frame_to_mic(
                                         bytes(b[:truncated_length]))
                                     b = b[truncated_length:]
-                            elif isinstance(frame, ImageFrame):
+                            elif isinstance(frame, ImageQueueFrame):
                                 self._set_image(frame.image)
-                            elif isinstance(frame, SpriteFrame):
+                            elif isinstance(frame, SpriteQueueFrame):
                                 self._set_images(frame.images)
+                            elif isinstance(frame, TTSCompletedFrame) and not frame.outOfBand:
+                                self.append_to_context(
+                                    "assistant", frame.text)
                         elif len(b):
                             self.write_frame_to_mic(bytes(b))
                             b = bytearray()
                     else:
                         # if there are leftover audio bytes, write them now; failing to do so
                         # can cause static in the audio stream.
+                        print(f"!!! interrupted, flushing audio")
                         if len(b):
                             truncated_length = len(b) - (len(b) % 160)
                             self.write_frame_to_mic(
                                 bytes(b[:truncated_length]))
                             b = bytearray()
 
-                        if isinstance(frame, StartFrame):
+                        if isinstance(frame, StartStreamQueueFrame):
                             self._is_interrupted.clear()
 
-                    if self._loop:
-                        asyncio.run_coroutine_threadsafe(
-                            self.completed_queue.put(frame), self._loop
-                        )
-
                 self._threadsafe_send_queue.task_done()
             except queue.Empty:
                 if len(b):

src/dailyai/services/daily_transport_service.py

@@ -1,18 +1,4 @@
-import asyncio
-import inspect
-import logging
-import signal
-import threading
-import types
-
-from functools import partial
-
-from dailyai.pipeline.frames import (
-    TranscriptionQueueFrame,
-)
-
-from threading import Event
-
+from dailyai.services.base_transport_service import BaseTransportService
 from daily import (
     EventHandler,
     CallClient,
@@ -21,8 +7,61 @@ from daily import (
     VirtualMicrophoneDevice,
     VirtualSpeakerDevice,
 )
+from threading import Event
+from dailyai.queue_frame import (
+    TranscriptionQueueFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame
+)
+from functools import partial
+import types
+import pyaudio
+import torchaudio
+import asyncio
+import inspect
+import io
+import logging
+import numpy as np
+import signal
+import threading
+import torch
+torch.set_num_threads(1)
 
-from dailyai.services.base_transport_service import BaseTransportService
+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
+
+# Taken from utils_vad.py
+
+
+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')
+    if abs_max > 0:
+        sound *= 1/32768
+    sound = sound.squeeze()  # depends on the use case
+    return sound
+
+
+FORMAT = pyaudio.paInt16
+CHANNELS = 1
+SAMPLE_RATE = 16000
+CHUNK = int(SAMPLE_RATE / 10)
+
+audio = pyaudio.PyAudio()
 
 
 class DailyTransportService(BaseTransportService, EventHandler):
@@ -31,7 +70,6 @@ class DailyTransportService(BaseTransportService, EventHandler):
 
     _speaker_enabled: bool
     _speaker_sample_rate: int
-    _vad_enabled: bool
 
     # This is necessary to override EventHandler's __new__ method.
     def __new__(cls, *args, **kwargs):
@@ -46,7 +84,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
         start_transcription: bool = False,
         **kwargs,
     ):
-        super().__init__(**kwargs)  # This will call BaseTransportService.__init__ method, not EventHandler
+        # This will call BaseTransportService.__init__ method, not EventHandler
+        super().__init__(**kwargs)
 
         self._room_url: str = room_url
         self._bot_name: str = bot_name
@@ -81,10 +120,8 @@ 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.")
@@ -96,7 +133,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
 
     def add_event_handler(self, event_name: str, handler):
         if not event_name.startswith("on_"):
-            raise Exception(f"Event handler {event_name} must start with 'on_'")
+            raise Exception(
+                f"Event handler {event_name} must start with 'on_'")
 
         methods = inspect.getmembers(self, predicate=inspect.ismethod)
         if event_name not in [method[0] for method in methods]:
@@ -109,7 +147,8 @@ class DailyTransportService(BaseTransportService, EventHandler):
                     handler, self)]
             setattr(self, event_name, partial(self._patch_method, event_name))
         else:
-            self._event_handlers[event_name].append(types.MethodType(handler, self))
+            self._event_handlers[event_name].append(
+                types.MethodType(handler, self))
 
     def event_handler(self, event_name: str):
         def decorator(handler):
@@ -146,60 +185,55 @@ class DailyTransportService(BaseTransportService, EventHandler):
                 "camera", width=self._camera_width, height=self._camera_height, color_format="RGB"
             )
 
-        if self._speaker_enabled or self._vad_enabled:
+        if self._speaker_enabled:
             self._speaker: VirtualSpeakerDevice = Daily.create_speaker_device(
                 "speaker", sample_rate=self._speaker_sample_rate, channels=1
             )
             Daily.select_speaker_device("speaker")
 
         self.client.set_user_name(self._bot_name)
-        self.client.join(
-            self._room_url,
-            self._token,
-            completion=self.call_joined,
-            client_settings={
-                "inputs": {
-                    "camera": {
-                        "isEnabled": True,
-                        "settings": {
-                            "deviceId": "camera",
-                        },
-                    },
-                    "microphone": {
-                        "isEnabled": True,
-                        "settings": {
-                            "deviceId": "mic",
-                            "customConstraints": {
-                                "autoGainControl": {"exact": False},
-                                "echoCancellation": {"exact": False},
-                                "noiseSuppression": {"exact": False},
-                            },
-                        },
-                    },
-                },
-                "publishing": {
-                    "camera": {
-                        "sendSettings": {
-                            "maxQuality": "low",
-                            "encodings": {
-                                "low": {
-                                    "maxBitrate": 250000,
-                                    "scaleResolutionDownBy": 1.333,
-                                    "maxFramerate": 8,
-                                }
-                            },
-                        }
-                    }
-                },
-            },
-        )
+        self.client.join(self._room_url, self._token,
+                         completion=self.call_joined)
         self._my_participant_id = self.client.participants()["local"]["id"]
 
-        self.client.update_subscription_profiles({
-            "base": {
-                "camera": "unsubscribed",
+        self.client.update_inputs(
+            {
+                "camera": {
+                    "isEnabled": True,
+                    "settings": {
+                        "deviceId": "camera",
+                    },
+                },
+                "microphone": {
+                    "isEnabled": True,
+                    "settings": {
+                        "deviceId": "mic",
+                        "customConstraints": {
+                            "autoGainControl": {"exact": False},
+                            "echoCancellation": {"exact": False},
+                            "noiseSuppression": {"exact": False},
+                        },
+                    },
+                },
             }
-        })
+        )
+
+        self.client.update_publishing(
+            {
+                "camera": {
+                    "sendSettings": {
+                        "maxQuality": "low",
+                        "encodings": {
+                            "low": {
+                                "maxBitrate": 250000,
+                                "scaleResolutionDownBy": 1.333,
+                                "maxFramerate": 8,
+                            }
+                        },
+                    }
+                }
+            }
+        )
 
         if self._token and self._start_transcription:
             self.client.start_transcription(self.transcription_settings)
@@ -242,19 +276,41 @@ class DailyTransportService(BaseTransportService, EventHandler):
         if len(self.client.participants()) < self._min_others_count + 1:
             self._stop_threads.set()
 
+    async def insert_speech(self, text, sender, date):
+        await self.receive_queue.put(UserStartedSpeakingFrame())
+        await asyncio.sleep(0.3)
+
+        # frame = TranscriptionQueueFrame(text, sender, date)
+        # await self.receive_queue.put(frame)
+        self.on_transcription_message({
+            "text": text,
+            "participantId":  "cb65b845-aac0-4fc8-987d-2e7ce3c7d8f0",
+            "timestamp": date
+        })
+
+        await asyncio.sleep(0.3)
+        await self.receive_queue.put(UserStoppedSpeakingFrame())
+
     def on_app_message(self, message, sender):
-        pass
+        if self._loop:
+            print("APP MESSAGE", message)
+            asyncio.run_coroutine_threadsafe(
+                self.insert_speech(message["message"], sender, message["date"]), self._loop)
 
     def on_transcription_message(self, message: dict):
         if self._loop:
+            print(f"transcription: {message}")
             participantId = ""
             if "participantId" in message:
                 participantId = message["participantId"]
             elif "session_id" in message:
                 participantId = message["session_id"]
+            frame = TranscriptionQueueFrame(
+                message["text"], participantId, message["timestamp"])
             if self._my_participant_id and participantId != self._my_participant_id:
-                frame = TranscriptionQueueFrame(message["text"], participantId, message["timestamp"])
-                asyncio.run_coroutine_threadsafe(self.receive_queue.put(frame), self._loop)
+                self.append_to_context("user", message["text"])
+            asyncio.run_coroutine_threadsafe(
+                self.receive_queue.put(frame), self._loop)
 
     def on_transcription_stopped(self, stopped_by, stopped_by_error):
         pass

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 +31,10 @@ 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,
+                    "seed": 23
+                },
             )
             for event in handler.iter_events():
                 if isinstance(event, fal.apps.InProgress):
@@ -47,7 +47,6 @@ 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/fireworks_ai_services.py

@@ -0,0 +1,122 @@
+import aiohttp
+from PIL import Image
+import io
+from openai import AsyncOpenAI
+
+import asyncio
+import json
+from collections.abc import AsyncGenerator
+
+from dailyai.services.ai_services import LLMService, ImageGenService
+
+from dailyai.queue_frame import (TextQueueFrame, TextQueueOutOfBandFrame)
+
+
+class FireworksLLMService(LLMService):
+    def __init__(self, *, api_key, model="", tools=[], context, change_appearance, transport=""):
+        super().__init__(context)
+        self._model = model
+        self._tools = tools
+        self._change_appearance = change_appearance
+        self._transport = transport
+        self._client = AsyncOpenAI(
+            api_key=api_key,
+            base_url="https://api.fireworks.ai/inference/v1"
+        )
+
+    async def get_response(self, messages, stream):
+        print("GET RESPONSE ... WHEN DO WE EXPECT THIS TO BE CALLED?")
+        return await self._client.chat.completions.create(
+            stream=stream,
+            messages=messages,
+            model=self._model,
+            temperature=0.1,
+            tools=self._tools
+        )
+
+    async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
+        print("IN ASYNC")
+        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,  # BLARGH
+            messages=messages,
+            temperature=0.1,
+            tools=self._tools
+        )
+
+        tool_call = {}
+
+        async for chunk in chunks:
+            print(f"CHUNK: {chunk}")
+            if len(chunk.choices) == 0:
+                continue
+
+            if chunk.choices[0].delta.content:
+                yield chunk.choices[0].delta.content
+
+            if chunk.choices[0].delta.tool_calls:
+                print(f"TOOL CALLS: {chunk.choices[0].delta.tool_calls[0]}")
+                if chunk.choices[0].delta.tool_calls[0].function.name:
+                    tool_call["id"] = chunk.choices[0].delta.tool_calls[0].id
+                    tool_call["name"] = chunk.choices[0].delta.tool_calls[0].function.name
+                    tool_call["arguments"] = ''
+                if chunk.choices[0].delta.tool_calls[0].function.arguments:
+                    tool_call["arguments"] += chunk.choices[0].delta.tool_calls[0].function.arguments
+
+            if chunk.choices[0].finish_reason:
+                print(f"TOOL CALLS ACCUM -- {tool_call}")
+                if tool_call.get("name"):
+                    # hard coding tool call action for now. we should assemble the tool call
+                    # from the streaming response, then yield it to the pipeline.
+                    # this approach works for the first few change appearance requests but
+                    # then the model starts refusing. need to read more about function
+                    # calling, try this with the OpenAI APIs, and talk to the Fireworks people.
+                    self._transport.append_to_context("assistant", {
+                        # pipeline will append the content to this context after it goes
+                        # through tts. we need to manually append the tool call, though
+                        "content": "",
+                        "role": "assistant",
+                        "tool_calls": [
+                            {
+                                "id": tool_call["id"],
+                                "type": "function",
+                                "index": 0,
+                                "function": {
+                                    "name": tool_call["name"],
+                                    "arguments": tool_call["arguments"]
+                                },
+                            }
+                        ],
+                    })
+                    self._transport.append_to_context("tool", {
+                        "content": "image generated by prompt arguments: " + tool_call["arguments"],
+                        "role": "tool",
+                        "tool_call_id": tool_call["id"]
+                    })
+                    self._transport.append_to_context("assistant", {
+                        "content": f"call to {tool_call['name']} function succeeded",
+                        "role": "assistant",
+                    })
+                    print("APPENDED TO CONTEXT")
+                    image_prompt = json.loads(
+                        tool_call["arguments"]).get("appearance")
+                    print("IMAGE PROMPT", image_prompt)
+                    asyncio.create_task(
+                        self._change_appearance(image_prompt))
+                    yield TextQueueOutOfBandFrame("Sure, let me work on that for you!")
+                    # yield {"content": "Sure, let me work on that for you!"}
+                    # yield "Sure, let me work on that for you!"
+
+    async def run_llm(self, messages) -> str | None:
+        print("--> IN SYNC ... WHEN DO WE EXPECT THIS TO BE CALLED?")
+        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

src/dailyai/services/groq_ai_services.py

@@ -0,0 +1,33 @@
+import os
+import groq
+from groq import AsyncGroq
+from dailyai.services.ai_services import LLMService
+from collections.abc import AsyncGenerator
+
+
+class GroqLLMService(LLMService):
+    def __init__(self, *, api_key, model="mixtral-8x7b-32768", context):
+        super().__init__(context)
+        self._model = model
+        # os.environ["GROQ_SECRET_ACCESS_KEY"] = api_key
+        
+        self._client = AsyncGroq()
+
+    async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
+        print(f"messages are {messages}")
+        try:
+            resp = await self._client.chat.completions.create(messages=messages, model=self._model)
+            print(f"got chunks from groq: {resp}")
+
+            if resp.choices[0].message.content:
+                yield resp.choices[0].message.content
+        except groq.APIConnectionError as e:
+            print("The server could not be reached")
+            print(e.__cause__)  # an underlying Exception, likely raised within httpx.
+        except groq.RateLimitError as e:
+            print("A 429 status code was received; we should back off a bit.")
+        except groq.APIStatusError as e:
+            print("Another non-200-range status code was received")
+            print(e.status_code)
+            print(e.response)
+    

src/dailyai/services/local_stt_service.py

@@ -4,7 +4,7 @@ import math
 import time
 from typing import AsyncGenerator
 import wave
-from dailyai.pipeline.frames import AudioFrame, Frame, TranscriptionQueueFrame
+from dailyai.queue_frame import AudioQueueFrame, QueueFrame, TranscriptionQueueFrame
 from dailyai.services.ai_services import STTService
 
 
@@ -39,9 +39,9 @@ class LocalSTTService(STTService):
         ww.setframerate(self._frame_rate)
         self._wave = ww
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         """Processes a frame of audio data, either buffering or transcribing it."""
-        if not isinstance(frame, AudioFrame):
+        if not isinstance(frame, AudioQueueFrame):
             return
 
         data = frame.data

src/dailyai/services/ollama_ai_services.py

@@ -1,7 +1,42 @@
-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) -> 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 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

src/dailyai/services/open_ai_services.py

@@ -1,20 +1,48 @@
 import aiohttp
 from PIL import Image
 import io
-import time
 from openai import AsyncOpenAI
 
 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-turbo-preview", context):
+        super().__init__(context)
+        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
+        )
+
+    async def run_llm_async(self, messages) -> 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 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):
@@ -29,6 +57,7 @@ class OpenAIImageGenService(ImageGenService):
     ):
         super().__init__(image_size=image_size)
         self._model = model
+        print(f"api key: {api_key}")
         self._client = AsyncOpenAI(api_key=api_key)
         self._aiohttp_session = aiohttp_session
 

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_aggregators.py

@@ -1,128 +0,0 @@
-import asyncio
-import doctest
-import functools
-import unittest
-
-from dailyai.pipeline.aggregators import (
-    GatedAggregator,
-    ParallelPipeline,
-    SentenceAggregator,
-    StatelessTextTransformer,
-)
-from dailyai.pipeline.frames import (
-    AudioFrame,
-    EndFrame,
-    ImageFrame,
-    LLMResponseEndFrame,
-    LLMResponseStartFrame,
-    Frame,
-    TextFrame,
-)
-
-from dailyai.pipeline.pipeline import Pipeline
-
-
-class TestDailyFrameAggregators(unittest.IsolatedAsyncioTestCase):
-    async def test_sentence_aggregator(self):
-        sentence = "Hello, world. How are you? I am fine"
-        expected_sentences = ["Hello, world.", " How are you?", " I am fine "]
-        aggregator = SentenceAggregator()
-        for word in sentence.split(" "):
-            async for sentence in aggregator.process_frame(TextFrame(word + " ")):
-                self.assertIsInstance(sentence, TextFrame)
-                if isinstance(sentence, TextFrame):
-                    self.assertEqual(sentence.text, expected_sentences.pop(0))
-
-        async for sentence in aggregator.process_frame(EndFrame()):
-            if len(expected_sentences):
-                self.assertIsInstance(sentence, TextFrame)
-                if isinstance(sentence, TextFrame):
-                    self.assertEqual(sentence.text, expected_sentences.pop(0))
-            else:
-                self.assertIsInstance(sentence, EndFrame)
-
-        self.assertEqual(expected_sentences, [])
-
-    async def test_gated_accumulator(self):
-        gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageFrame),
-            gate_close_fn=lambda frame: isinstance(frame, LLMResponseStartFrame),
-            start_open=False,
-        )
-
-        frames = [
-            LLMResponseStartFrame(),
-            TextFrame("Hello, "),
-            TextFrame("world."),
-            AudioFrame(b"hello"),
-            ImageFrame("image", b"image"),
-            AudioFrame(b"world"),
-            LLMResponseEndFrame(),
-        ]
-
-        expected_output_frames = [
-            ImageFrame("image", b"image"),
-            LLMResponseStartFrame(),
-            TextFrame("Hello, "),
-            TextFrame("world."),
-            AudioFrame(b"hello"),
-            AudioFrame(b"world"),
-            LLMResponseEndFrame(),
-        ]
-        for frame in frames:
-            async for out_frame in gated_aggregator.process_frame(frame):
-                self.assertEqual(out_frame, expected_output_frames.pop(0))
-        self.assertEqual(expected_output_frames, [])
-
-    async def test_parallel_pipeline(self):
-
-        async def slow_add(sleep_time:float, name:str, x: str):
-            await asyncio.sleep(sleep_time)
-            return ":".join([x, name])
-
-        pipe1_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.1, 'pipe1'))
-        pipe2_annotation = StatelessTextTransformer(functools.partial(slow_add, 0.2, 'pipe2'))
-        sentence_aggregator = SentenceAggregator()
-        add_dots = StatelessTextTransformer(lambda x: x + ".")
-
-        source = asyncio.Queue()
-        sink = asyncio.Queue()
-        pipeline = Pipeline(
-            [
-                ParallelPipeline(
-                    [[pipe1_annotation], [sentence_aggregator, pipe2_annotation]]
-                ),
-                add_dots,
-            ],
-            source,
-            sink,
-        )
-
-        frames = [
-            TextFrame("Hello, "),
-            TextFrame("world."),
-            EndFrame()
-        ]
-
-        expected_output_frames: list[Frame] = [
-            TextFrame(text='Hello, :pipe1.'),
-            TextFrame(text='world.:pipe1.'),
-            TextFrame(text='Hello, world.:pipe2.'),
-            EndFrame()
-        ]
-
-        for frame in frames:
-            await source.put(frame)
-
-        await pipeline.run_pipeline()
-
-        while not sink.empty():
-            frame = await sink.get()
-            self.assertEqual(frame, expected_output_frames.pop(0))
-
-
-def load_tests(loader, tests, ignore):
-    """ Run doctests on the aggregators module. """
-    from dailyai.pipeline import aggregators
-    tests.addTests(doctest.DocTestSuite(aggregators))
-    return tests

src/dailyai/tests/test_ai_services.py

@@ -3,11 +3,11 @@ import unittest
 from typing import AsyncGenerator, Generator
 
 from dailyai.services.ai_services import AIService
-from dailyai.pipeline.frames import EndFrame, Frame, TextFrame
+from dailyai.queue_frame import EndStreamQueueFrame, QueueFrame, TextQueueFrame
 
 
 class SimpleAIService(AIService):
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         yield frame
 
 
@@ -16,11 +16,11 @@ class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
         service = SimpleAIService()
 
         input_frames = [
-            TextFrame("hello"),
-            EndFrame()
+            TextQueueFrame("hello"),
+            EndStreamQueueFrame()
         ]
 
-        async def iterate_frames() -> AsyncGenerator[Frame, None]:
+        async def iterate_frames() -> AsyncGenerator[QueueFrame, None]:
             for frame in input_frames:
                 yield frame
 
@@ -33,9 +33,9 @@ class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
     async def test_nonasync_input(self):
         service = SimpleAIService()
 
-        input_frames = [TextFrame("hello"), EndFrame()]
+        input_frames = [TextQueueFrame("hello"), EndStreamQueueFrame()]
 
-        def iterate_frames() -> Generator[Frame, None, None]:
+        def iterate_frames() -> Generator[QueueFrame, None, None]:
             for frame in input_frames:
                 yield frame
 

src/dailyai/tests/test_daily_transport_service.py

@@ -1,10 +1,9 @@
 import asyncio
-import threading
 import unittest
 
 from unittest.mock import MagicMock, patch
 
-from dailyai.pipeline.frames import AudioFrame, ImageFrame
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
 
 
 class TestDailyTransport(unittest.IsolatedAsyncioTestCase):
@@ -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
 
@@ -37,21 +34,14 @@ 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")
     @patch("dailyai.services.daily_transport_service.Daily")
     async def test_run_with_camera_and_mic(self, daily_mock, callclient_mock):
@@ -89,4 +79,3 @@ class TestDailyTransport(unittest.IsolatedAsyncioTestCase):
 
         camera.write_frame.assert_called_with(b"test")
         mic.write_frames.assert_called()
-    """

src/dailyai/tests/test_pipeline.py

@@ -1,59 +0,0 @@
-import asyncio
-import unittest
-from dailyai.pipeline.aggregators import SentenceAggregator, StatelessTextTransformer
-from dailyai.pipeline.frames import EndFrame, TextFrame
-
-from dailyai.pipeline.pipeline import Pipeline
-
-
-class TestDailyPipeline(unittest.IsolatedAsyncioTestCase):
-
-    async def test_pipeline_simple(self):
-        aggregator = SentenceAggregator()
-
-        outgoing_queue = asyncio.Queue()
-        incoming_queue = asyncio.Queue()
-        pipeline = Pipeline([aggregator], incoming_queue, outgoing_queue)
-
-        await incoming_queue.put(TextFrame("Hello, "))
-        await incoming_queue.put(TextFrame("world."))
-        await incoming_queue.put(EndFrame())
-
-        await pipeline.run_pipeline()
-
-        self.assertEqual(await outgoing_queue.get(), TextFrame("Hello, world."))
-        self.assertIsInstance(await outgoing_queue.get(), EndFrame)
-
-    async def test_pipeline_multiple_stages(self):
-        sentence_aggregator = SentenceAggregator()
-        to_upper = StatelessTextTransformer(lambda x: x.upper())
-        add_space = StatelessTextTransformer(lambda x: x + " ")
-
-        outgoing_queue = asyncio.Queue()
-        incoming_queue = asyncio.Queue()
-        pipeline = Pipeline(
-            [add_space, sentence_aggregator, to_upper],
-            incoming_queue,
-            outgoing_queue
-        )
-
-        sentence = "Hello, world. It's me, a pipeline."
-        for c in sentence:
-            await incoming_queue.put(TextFrame(c))
-        await incoming_queue.put(EndFrame())
-
-        await pipeline.run_pipeline()
-
-        self.assertEqual(
-            await outgoing_queue.get(), TextFrame("H E L L O ,   W O R L D .")
-        )
-        self.assertEqual(
-            await outgoing_queue.get(),
-            TextFrame("   I T ' S   M E ,   A   P I P E L I N E ."),
-        )
-        # leftover little bit because of the spacing
-        self.assertEqual(
-            await outgoing_queue.get(),
-            TextFrame(" "),
-        )
-        self.assertIsInstance(await outgoing_queue.get(), EndFrame)

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 +1,58 @@
 import asyncio
 import os
-import logging
 
 import aiohttp
 
-from dailyai.pipeline.frames import LLMMessagesQueueFrame
+from dailyai.queue_frame 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",
+            duration_minutes=meeting_duration_minutes,
             mic_enabled=True,
+            speaker_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.queue_frame import TextQueueFrame
 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, [
+                    TextQueueFrame("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

@@ -1,18 +1,13 @@
 import asyncio
 import aiohttp
-import logging
 import os
 
 import tkinter as tk
 
-from dailyai.pipeline.frames import TextFrame
+from dailyai.queue_frame import TextQueueFrame
 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
 
@@ -39,7 +34,7 @@ async def main():
         )
         image_task = asyncio.create_task(
             imagegen.run_to_queue(
-                transport.send_queue, [TextFrame("a cat in the style of picasso")]
+                transport.send_queue, [TextQueueFrame("a cat in the style of picasso")]
             )
         )
 
@@ -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

@@ -1,20 +1,14 @@
 import asyncio
-import logging
 import os
 
 import aiohttp
-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.queue_frame import EndStreamQueueFrame, 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 +20,20 @@ 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"}]
 
@@ -54,48 +41,31 @@ async def main(room_url: str):
         # will run in parallel with generating and speaking the audio for static text, so there's no delay to
         # speak the LLM response.
         buffer_queue = asyncio.Queue()
-        source_queue = asyncio.Queue()
-        pipeline = Pipeline(
-            source=source_queue, sink=buffer_queue, processors=[llm, elevenlabs_tts]
+        llm_response_task = asyncio.create_task(
+            elevenlabs_tts.run_to_queue(
+                buffer_queue,
+                llm.run([LLMMessagesQueueFrame(messages)]),
+                True,
+            )
         )
-        await source_queue.put(LLMMessagesQueueFrame(messages))
-        await source_queue.put(EndFrame())
-        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:
                     frame = await buffer_queue.get()
                     await transport.send_queue.put(frame)
                     buffer_queue.task_done()
-                    if isinstance(frame, EndFrame):
+                    if isinstance(frame, EndStreamQueueFrame):
                         break
 
-            await asyncio.gather(pipeline_run_task, buffer_to_send_queue())
+            await asyncio.gather(llm_response_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

@@ -1,158 +1,133 @@
 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.pipeline import Pipeline
-from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
+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")
+        # tts = AzureTTSService(api_key=os.getenv("AZURE_SPEECH_API_KEY"), region=os.getenv("AZURE_SPEECH_REGION"))
 
-        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"))
+        # dalle = OpenAIImageGenService(aiohttp_session=session, api_key=os.getenv("OPENAI_DALLE_API_KEY"), image_size="1024x1024")
+        # dalle = 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"))
 
-        source_queue = asyncio.Queue()
+        # Get a complete audio chunk from the given text. Splitting this into its own
+        # coroutine lets us ensure proper ordering of the audio chunks on the send queue.
+        async def get_all_audio(text):
+            all_audio = bytearray()
+            async for audio in tts.run_tts(text):
+                all_audio.extend(audio)
 
-        for month in [
-            "January",
-            "February",
-            "March",
-            "April",
-            "May",
-            "June",
-            "July",
-            "August",
-            "September",
-            "October",
-            "November",
-            "December",
-        ]:
+            return all_audio
+
+        async def get_month_data(month):
             messages = [
                 {
                     "role": "system",
                     "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
                 }
             ]
-            await source_queue.put(MonthFrame(month))
-            await source_queue.put(LLMMessagesQueueFrame(messages))
 
-        await source_queue.put(EndFrame())
+            image_description = await llm.run_llm(messages)
+            if not image_description:
+                return
 
-        gated_aggregator = GatedAggregator(
-            gate_open_fn=lambda frame: isinstance(frame, ImageFrame),
-            gate_close_fn=lambda frame: isinstance(frame, LLMResponseStartFrame),
-            start_open=False,
-        )
-
-        sentence_aggregator = SentenceAggregator()
-        month_prepender = MonthPrepender()
-        llm_full_response_aggregator = LLMFullResponseAggregator()
-
-        pipeline = Pipeline(
-            source=source_queue,
-            sink=transport.send_queue,
-            processors=[
-                llm,
-                sentence_aggregator,
-                ParallelPipeline(
-                    [[month_prepender, tts], [llm_full_response_aggregator, imagegen]]
-                ),
-                gated_aggregator,
-            ],
-        )
-        pipeline_task = pipeline.run_pipeline()
-
-        other_joined = asyncio.Event()
+            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))
+            print(f"about to gather tasks for {month}")
+            (audio, image_data) = await asyncio.gather(
+                audio_task, image_task
+            )
+            print(f"about to return from get_month_data for {month}")
+            return {
+                "month": month,
+                "text": image_description,
+                "image_url": image_data[0],
+                "image": image_data[1],
+                "audio": audio,
+            }
 
+        months: list[str] = [
+            "January",
+            "February",
+            "March",
+            "April",
+            "May",
+            "June"
+        ]
+        """
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+        """
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
-            other_joined.set()
+            # This will play the months in the order they're completed. The benefit
+            # is we'll have as little delay as possible before the first month, and
+            # likely no delay between months, but the months won't display in order.
+            for month_data_task in asyncio.as_completed(month_tasks):
+                print(f"month_data_task: {month_data_task}")
+                try:
+                    data = await month_data_task
+                except Exception:
+                    print("OMG EXCEPTION!!!!")
+                if data:
+                    await transport.send_queue.put(
+                        [
+                            ImageQueueFrame(data["image_url"], data["image"]),
+                            AudioQueueFrame(data["audio"]),
+                        ]
+                    )
 
-        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())
+        month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
 
+        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.queue_frame import AudioQueueFrame, ImageQueueFrame
+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,
@@ -106,8 +103,8 @@ async def main(room_url):
                 if data:
                     await transport.send_queue.put(
                         [
-                            ImageFrame(data["image_url"], data["image"]),
-                            AudioFrame(data["audio"]),
+                            ImageQueueFrame(data["image_url"], data["image"]),
+                            AudioQueueFrame(data["audio"]),
                         ]
                     )
 
@@ -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 +1,68 @@
 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.queue_aggregators import LLMAssistantContextAggregator, LLMContextAggregator, LLMUserContextAggregator
+from examples.foundational.support.runner import configure
 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)
 
 
 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,
-        )
+    context = [
+        {
+            "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.",
+        },
+    ]
+    transport = DailyTransportService(
+        room_url,
+        token,
+        "Respond bot",
+        duration_minutes=5,
+        start_transcription=True,
+        mic_enabled=True,
+        mic_sample_rate=16000,
+        camera_enabled=False,
+        speaker_enabled=True,
+        context=context
+    )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+    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("transport")
 
-        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)
 
-        @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():
 
-        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
+        tma_in = LLMUserContextAggregator(
+            context, transport._my_participant_id)
+        tma_out = LLMAssistantContextAggregator(
+            context, transport._my_participant_id)
+        await tts.run_to_queue(
+            transport.send_queue,
+            tma_out.run(
+                llm.run(
+                    tma_in.run(
+                        fl.run(
+                            transport.get_receive_frames()
+                        )
+                    )
+                )
             )
-            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"]["punctuate"] = True
+    transport.transcription_settings["extra"]["endpointing"] = 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.queue_frame import ImageQueueFrame, QueueFrame
 
-from dailyai.pipeline.frames import ImageFrame, Frame
 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.open_ai_services import OpenAILLMService
-from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.queue_aggregators import LLMAssistantContextAggregator, LLMUserContextAggregator
 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 examples.foundational.support.runner import configure
 
 
 class ImageSyncAggregator(AIService):
@@ -34,10 +27,10 @@ class ImageSyncAggregator(AIService):
         self._waiting_image = Image.open(waiting_path)
         self._waiting_image_bytes = self._waiting_image.tobytes()
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        yield ImageFrame(None, self._speaking_image_bytes)
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        yield ImageQueueFrame(None, self._speaking_image_bytes)
         yield frame
-        yield ImageFrame(None, self._waiting_image_bytes)
+        yield ImageQueueFrame(None, self._waiting_image_bytes)
 
 
 async def main(room_url: str, token):
@@ -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/06c-listen-respond-interruptible-refactor.py

@@ -0,0 +1,83 @@
+import asyncio
+import aiohttp
+import os
+from dailyai.conversation_wrappers import InterruptibleConversationWrapper
+
+from dailyai.queue_frame import StartStreamQueueFrame, TextQueueFrame
+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.open_ai_services import OpenAILLMService
+from dailyai.services.deepgram_ai_services import DeepgramTTSService
+from dailyai.services.ai_services import FrameLogger
+from dailyai.services.groq_ai_services import GroqLLMService
+
+from examples.foundational.support.runner import configure
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        context = [
+            {
+                "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.",
+            },
+        ]
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            duration_minutes=5,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False,
+            # TODO-CB: Should this be VAD enabled or something?
+            speaker_enabled=True,
+            context=context
+        )
+
+        # llm = AzureLLMService(
+        #     api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+        #     endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+        #     model=os.getenv("AZURE_CHATGPT_MODEL"),
+        #     context=context)
+        llm = OpenAILLMService(
+            context=context, api_key=os.getenv("OPENAI_CHATGPT_API_KEY"))
+        # llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), context=context)
+        # 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=os.getenv("ELEVENLABS_VOICE_ID"))
+        # tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv("DEEPGRAM_API_KEY"), voice=os.getenv("DEEPGRAM_VOICE"))
+        fl = FrameLogger("just outside the innermost layer")
+
+        async def run_response(in_frame):
+            await tts.run_to_queue(
+                transport.send_queue,
+                # tma_out.run(
+                llm.run(
+                    # tma_in.run(
+                    fl.run(
+                        [StartStreamQueueFrame(), in_frame]
+                    )
+                    # )
+                )
+                # ),
+            )
+
+        @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)
+
+        transport.transcription_settings["extra"]["endpointing"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await asyncio.gather(transport.run(), transport.run_conversation(run_response))
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

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.conversation_wrappers import InterruptibleConversationWrapper
 
-from dailyai.pipeline.pipeline import Pipeline
-from dailyai.services.ai_services import FrameLogger
+from dailyai.queue_frame import StartStreamQueueFrame, TextQueueFrame
 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 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, token):
@@ -32,20 +22,27 @@ async def main(room_url: str, token):
             mic_enabled=True,
             mic_sample_rate=16000,
             camera_enabled=False,
-            vad_enabled=True,
         )
 
-        tts = ElevenLabsTTSService(
-            aiohttp_session=session,
-            api_key=os.getenv("ELEVENLABS_API_KEY"),
-            voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
-        )
+        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"))
 
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_CHATGPT_API_KEY"), model="gpt-4-turbo-preview"
-        )
-
-        pipeline = Pipeline([FrameLogger(), llm, FrameLogger(), tts])
+        async def run_response(user_speech, tma_in, tma_out):
+            await tts.run_to_queue(
+                transport.send_queue,
+                tma_out.run(
+                    llm.run(
+                        tma_in.run(
+                            [StartStreamQueueFrame(), TextQueueFrame(user_speech)]
+                        )
+                    )
+                ),
+            )
 
         @transport.event_handler("on_first_other_participant_joined")
         async def on_first_other_participant_joined(transport):
@@ -53,17 +50,17 @@ 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),
+            conversation_wrapper = InterruptibleConversationWrapper(
+                frame_generator=transport.get_receive_frames,
+                runner=run_response,
+                interrupt=transport.interrupt,
+                my_participant_id=transport._my_participant_id,
+                llm_messages=messages,
             )
+            await conversation_wrapper.run_conversation()
 
         transport.transcription_settings["extra"]["punctuate"] = False
         await asyncio.gather(transport.run(), run_conversation())

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.queue_frame import AudioQueueFrame, ImageQueueFrame
 
-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):
@@ -118,8 +90,8 @@ async def main(room_url: str):
                 )
                 await transport.send_queue.put(
                     [
-                        ImageFrame(None, image_data1[1]),
-                        AudioFrame(audio1),
+                        ImageQueueFrame(None, image_data1[1]),
+                        AudioQueueFrame(audio1),
                     ]
                 )
 
@@ -130,8 +102,8 @@ async def main(room_url: str):
                 )
                 await transport.send_queue.put(
                     [
-                        ImageFrame(None, image_data2[1]),
-                        AudioFrame(audio2),
+                        ImageQueueFrame(None, image_data2[1]),
+                        AudioQueueFrame(audio2),
                     ]
                 )
 

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

@@ -1,41 +1,36 @@
 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.frames import (
-    Frame,
-    TextFrame,
-    ImageFrame,
-    SpriteFrame,
+from dailyai.queue_aggregators import LLMUserContextAggregator, LLMAssistantContextAggregator
+from dailyai.queue_frame import (
+    QueueFrame,
+    TextQueueFrame,
+    ImageQueueFrame,
+    SpriteQueueFrame,
     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__)
@@ -50,27 +45,26 @@ for file in image_files:
         sprites[file] = img.tobytes()
 
 # When the bot isn't talking, show a static image of the cat listening
-quiet_frame = ImageFrame("", sprites["sc-listen-1.png"])
+quiet_frame = ImageQueueFrame("", 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)
+talking_frame = SpriteQueueFrame(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"],
-]
-thinking_frame = SpriteFrame(images=thinking_list)
+    sprites['sc-think-1.png'],
+    sprites['sc-think-2.png'],
+    sprites['sc-think-3.png'],
+    sprites['sc-think-4.png']]
+thinking_frame = SpriteQueueFrame(images=thinking_list)
 
 
 class TranscriptFilter(AIService):
     def __init__(self, bot_participant_id=None):
         self.bot_participant_id = bot_participant_id
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         if isinstance(frame, TranscriptionQueueFrame):
             if frame.participantId != self.bot_participant_id:
                 yield frame
@@ -81,11 +75,11 @@ class NameCheckFilter(AIService):
         self.names = names
         self.sentence = ""
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         content: str = ""
 
         # TODO: split up transcription by participant
-        if isinstance(frame, TextFrame):
+        if isinstance(frame, TextQueueFrame):
             content = frame.text
 
         self.sentence += content
@@ -93,7 +87,7 @@ class NameCheckFilter(AIService):
             if any(name in self.sentence for name in self.names):
                 out = self.sentence
                 self.sentence = ""
-                yield TextFrame(out)
+                yield TextQueueFrame(out)
             else:
                 out = self.sentence
                 self.sentence = ""
@@ -103,7 +97,7 @@ class ImageSyncAggregator(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         yield talking_frame
         yield frame
         yield quiet_frame
@@ -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.queue_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
 from dailyai.services.ai_services import AIService, FrameLogger
-from dailyai.pipeline.frames import (
-    Frame,
-    AudioFrame,
-    LLMResponseEndFrame,
-    LLMMessagesQueueFrame,
-)
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, 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__)
 
@@ -46,9 +40,9 @@ class OutboundSoundEffectWrapper(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, LLMResponseEndFrame):
-            yield AudioFrame(sounds["ding1.wav"])
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
             # In case anything else up the stack needs it
             yield frame
         else:
@@ -59,9 +53,9 @@ class InboundSoundEffectWrapper(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         if isinstance(frame, LLMMessagesQueueFrame):
-            yield AudioFrame(sounds["ding2.wav"])
+            yield AudioQueueFrame(sounds["ding2.wav"])
             # In case anything else up the stack needs it
             yield frame
         else:
@@ -77,33 +71,31 @@ 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):
             await tts.say("Hi, I'm listening!", transport.send_queue)
-            await transport.send_queue.put(AudioFrame(sounds["ding1.wav"]))
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
 
         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.queue_frame import EndStreamQueueFrame, 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()
@@ -35,7 +30,7 @@ async def main(room_url: str):
             print("got item from queue", item)
             if isinstance(item, TranscriptionQueueFrame):
                 print(item.text)
-            elif isinstance(item, EndFrame):
+            elif isinstance(item, EndStreamQueueFrame):
                 break
         print("handle_transcription done")
 
@@ -43,7 +38,7 @@ async def main(room_url: str):
         await stt.run_to_queue(
             transcription_output_queue, transport.get_receive_frames()
         )
-        await transcription_output_queue.put(EndFrame())
+        await transcription_output_queue.put(EndStreamQueueFrame())
         print("handle speaker done.")
 
     async def run_until_done():

src/examples/image-gen.py

@@ -7,7 +7,7 @@ import random
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.pipeline.frames import Frame, FrameType
+from dailyai.queue_frame import QueueFrame, FrameType
 from dailyai.services.fal_ai_services import FalImageGenService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
@@ -45,7 +45,7 @@ async def main(room_url: str, token):
             print(f"finder: {finder}")
             if finder >= 0:
                 async for audio in tts.run_tts(f"Resetting."):
-                    transport.output_queue.put(Frame(FrameType.AUDIO_FRAME, audio))
+                    transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
                 sentence = ""
                 continue
             # todo: we could differentiate between transcriptions from different participants
@@ -54,12 +54,12 @@ async def main(room_url: str, token):
             # TODO: Cache this audio
             phrase = random.choice(["OK.", "Got it.", "Sure.", "You bet.", "Sure thing."])
             async for audio in tts.run_tts(phrase):
-                transport.output_queue.put(Frame(FrameType.AUDIO_FRAME, audio))
+                transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
             img_result = img.run_image_gen(sentence, "1024x1024")
             awaited_img = await asyncio.gather(img_result)
             transport.output_queue.put(
                 [
-                    Frame(FrameType.IMAGE_FRAME, awaited_img[0][1]),
+                    QueueFrame(FrameType.IMAGE_FRAME, awaited_img[0][1]),
                 ]
             )
 
@@ -72,7 +72,7 @@ async def main(room_url: str, token):
             audio_generator = tts.run_tts(
                 f"Hello, {participant['info']['userName']}! Describe an image and I'll create it. To start over, just say 'start over'.")
             async for audio in audio_generator:
-                transport.output_queue.put(Frame(FrameType.AUDIO_FRAME, audio))
+                transport.output_queue.put(QueueFrame(FrameType.AUDIO_FRAME, audio))
 
     transport.transcription_settings["extra"]["punctuate"] = False
     transport.transcription_settings["extra"]["endpointing"] = False

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

@@ -5,12 +5,12 @@ import wave
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.pipeline.aggregators import LLMContextAggregator
+from dailyai.queue_aggregators import LLMContextAggregator
 from dailyai.services.ai_services import AIService, FrameLogger
-from dailyai.pipeline.frames import Frame, AudioFrame, LLMResponseEndFrame, LLMMessagesQueueFrame
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
 from typing import AsyncGenerator
 
-from examples.support.runner import configure
+from examples.foundational.support.runner import configure
 
 sounds = {}
 sound_files = [
@@ -34,9 +34,9 @@ class OutboundSoundEffectWrapper(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        if isinstance(frame, LLMResponseEndFrame):
-            yield AudioFrame(sounds["ding1.wav"])
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
             # In case anything else up the stack needs it
             yield frame
         else:
@@ -47,9 +47,9 @@ class InboundSoundEffectWrapper(AIService):
     def __init__(self):
         pass
 
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
         if isinstance(frame, LLMMessagesQueueFrame):
-            yield AudioFrame(sounds["ding2.wav"])
+            yield AudioQueueFrame(sounds["ding2.wav"])
             # In case anything else up the stack needs it
             yield frame
         else:
@@ -79,7 +79,7 @@ async def main(room_url: str, token, phone):
         @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)
-            await transport.send_queue.put(AudioFrame(sounds["ding1.wav"]))
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
 
         async def handle_transcriptions():
             messages = [

src/examples/starter-apps/patient-intake.py

@@ -1,415 +0,0 @@
-import copy
-import aiohttp
-import asyncio
-import json
-import random
-import logging
-import os
-import re
-import wave
-from typing import AsyncGenerator, List
-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.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.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)
-
-sounds = {}
-sound_files = [
-    "clack-short.wav",
-    "clack.wav",
-    "clack-short-quiet.wav",
-    "ding.wav",
-    "ding2.wav",
-]
-
-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)
-
-
-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": {
-                    "name": "verify_birthday",
-                    "description": "Use this function to verify the user has provided their correct birthday.",
-                    "parameters": {
-                        "type": "object",
-                        "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.",
-                            }
-                        },
-                    },
-                },
-            }
-        ],
-    },
-    {
-        "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": {
-                    "name": "list_prescriptions",
-                    "description": "Once the user has provided a list of their prescription medications, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "prescriptions": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "medication": {
-                                            "type": "string",
-                                            "description": "The medication's name",
-                                        },
-                                        "dosage": {
-                                            "type": "string",
-                                            "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.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_allergies",
-                    "description": "Once the user has provided a list of their allergies, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "allergies": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "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.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_conditions",
-                    "description": "Once the user has provided a list of their medical conditions, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "conditions": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "description": "The user's medical condition",
-                                        }
-                                    },
-                                },
-                            }
-                        },
-                    },
-                },
-            },
-        ],
-    },
-    {
-        "prompt": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_visit_reasons",
-                    "description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "visit_reasons": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "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": []},
-]
-current_step = 0
-
-
-class ChecklistProcessor(AIService):
-
-    def __init__(
-        self,
-        context: OpenAILLMContext,
-        llm: AIService,
-        tools: List[ChatCompletionToolParam] | NotGiven = NOT_GIVEN,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self._context: OpenAILLMContext = context
-        self._llm = llm
-        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)
-
-    def verify_birthday(self, args):
-        return args["birthday"] == "1983-01-01"
-
-    def list_prescriptions(self, args):
-        # print(f"--- Prescriptions: {args['prescriptions']}\n")
-        pass
-
-    def list_allergies(self, args):
-        # print(f"--- Allergies: {args['allergies']}\n")
-        pass
-
-    def list_conditions(self, args):
-        # print(f"--- Medical Conditions: {args['conditions']}")
-        pass
-
-    def list_visit_reasons(self, args):
-        # print(f"Visit Reasons: {args['visit_reasons']}")
-        pass
-
-    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):
-            print(f"... Preparing function call: {frame.function_name}")
-            self._function_name = frame.function_name
-            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 frame
-            else:
-                # Insert a quick response while we run the function
-                yield AudioFrame(sounds["ding2.wav"])
-                pass
-        elif isinstance(frame, LLMFunctionCallFrame):
-
-            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)
-                )
-                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"]:
-                    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 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)
-                        ):
-                            yield frame
-                    print(f"<-- Verify result: {result}\n")
-
-        else:
-            yield frame
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        global transport
-        global llm
-        global tts
-
-        transport = DailyTransportService(
-            room_url,
-            token,
-            "Intake Bot",
-            5,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=False,
-            start_transcription=True,
-            vad_enabled=True,
-        )
-
-        messages = []
-
-        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,
-        )
-
-        checklist = ChecklistProcessor(context, llm)
-        fl = FrameLogger("FRAME LOGGER 1:")
-        fl2 = FrameLogger("FRAME LOGGER 2:")
-
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport):
-            fl = FrameLogger("first other participant")
-            await tts.run_to_queue(
-                transport.send_queue,
-                llm.run([OpenAILLMContextFrame(context)]),
-            )
-
-        async def handle_intake():
-            pipeline = Pipeline(processors=[fl, llm, fl2, checklist, tts])
-            await transport.run_interruptible_pipeline(
-                pipeline,
-                post_processor=OpenAIAssistantContextAggregator(context),
-                pre_processor=OpenAIUserContextAggregator(context),
-            )
-
-        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")
-            transport.stop()
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

src/examples/starter-apps/storycat.py

@@ -1,409 +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.services.daily_transport_service import DailyTransportService
-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 dailyai.pipeline.aggregators import (
-    LLMAssistantContextAggregator,
-    LLMContextAggregator,
-    LLMUserContextAggregator,
-    UserResponseAggregator,
-    LLMResponseAggregator,
-)
-from examples.support.runner import configure
-from dailyai.pipeline.frames import (
-    LLMMessagesQueueFrame,
-    TranscriptionQueueFrame,
-    Frame,
-    TextFrame,
-    LLMFunctionCallFrame,
-    LLMFunctionStartFrame,
-    LLMResponseEndFrame,
-    StartFrame,
-    AudioFrame,
-    SpriteFrame,
-    ImageFrame,
-)
-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 = {}
-sound_files = ["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 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 wave.open(full_path) as audio_file:
-        sounds[file] = audio_file.readframes(-1)
-
-
-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": {
-                    "name": "verify_birthday",
-                    "description": "Use this function to verify the user has provided their correct birthday.",
-                    "parameters": {
-                        "type": "object",
-                        "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.",
-                            }
-                        },
-                    },
-                },
-            }
-        ],
-    },
-    {
-        "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": {
-                    "name": "list_prescriptions",
-                    "description": "Once the user has provided a list of their prescription medications, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "prescriptions": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "medication": {
-                                            "type": "string",
-                                            "description": "The medication's name",
-                                        },
-                                        "dosage": {
-                                            "type": "string",
-                                            "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.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_allergies",
-                    "description": "Once the user has provided a list of their allergies, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "allergies": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "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.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_conditions",
-                    "description": "Once the user has provided a list of their medical conditions, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "conditions": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "description": "The user's medical condition",
-                                        }
-                                    },
-                                },
-                            }
-                        },
-                    },
-                },
-            },
-        ],
-    },
-    {
-        "prompt": "Finally, ask the user the reason for their doctor visit today. Once they answer, call the list_visit_reasons function.",
-        "run_async": True,
-        "tools": [
-            {
-                "type": "function",
-                "function": {
-                    "name": "list_visit_reasons",
-                    "description": "Once the user has provided a list of the reasons they are visiting a doctor today, call this function.",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {
-                            "visit_reasons": {
-                                "type": "array",
-                                "items": {
-                                    "type": "object",
-                                    "properties": {
-                                        "name": {
-                                            "type": "string",
-                                            "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": []},
-]
-current_step = 0
-
-
-class TranscriptFilter(AIService):
-    def __init__(self, bot_participant_id=None):
-        super().__init__()
-        self.bot_participant_id = bot_participant_id
-
-    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._messages = messages
-        self._llm = llm
-        self._tools = tools
-        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",
-        ]
-
-        messages.append(
-            {"role": "system", "content": f"{self._id} {steps[0]['prompt']}"}
-        )
-
-    def verify_birthday(self, args):
-        return args["birthday"] == "1983-01-01"
-
-    def list_prescriptions(self, args):
-        # print(f"--- Prescriptions: {args['prescriptions']}\n")
-        pass
-
-    def list_allergies(self, args):
-        # print(f"--- Allergies: {args['allergies']}\n")
-        pass
-
-    def list_conditions(self, args):
-        # print(f"--- Medical Conditions: {args['conditions']}")
-        pass
-
-    def list_visit_reasons(self, args):
-        # print(f"Visit Reasons: {args['visit_reasons']}")
-        pass
-
-    async def process_frame(self, frame: Frame) -> AsyncGenerator[Frame, None]:
-        global current_step
-        this_step = steps[current_step]
-        # TODO-CB: forcing a global here :/
-        self._tools.clear()
-        self._tools.extend(this_step["tools"])
-        if isinstance(frame, LLMFunctionStartFrame):
-            print(f"... Preparing function call: {frame.function_name}")
-            self._function_name = frame.function_name
-            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._messages.append(
-                    {"role": "system", "content": steps[current_step]["prompt"]}
-                )
-                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["clack-short-quiet.wav"])
-                pass
-        elif isinstance(frame, LLMFunctionCallFrame):
-
-            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)
-                )
-                print(f"--> {pretty_json}\n")
-                if not frame.function_name 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"]:
-                    if result:
-                        current_step += 1
-                        self._messages.append(
-                            {"role": "system", "content": steps[current_step]["prompt"]}
-                        )
-                        yield LLMMessagesQueueFrame(self._messages)
-                        async for frame in llm.process_frame(
-                            LLMMessagesQueueFrame(self._messages), tool_choice="none"
-                        ):
-                            yield frame
-                    else:
-                        self._messages.append(
-                            {"role": "system", "content": this_step["failed"]}
-                        )
-                        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")
-
-        else:
-            yield frame
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        global transport
-        global llm
-        global tts
-
-        transport = DailyTransportService(
-            room_url,
-            token,
-            "Story Cat",
-            5,
-            mic_enabled=True,
-            mic_sample_rate=16000,
-            camera_enabled=False,
-            start_transcription=True,
-            vad_enabled=True,
-        )
-        # TODO-CB: Go back to vad_enabled
-
-        messages = []
-        tools = []
-
-        # 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")
-
-        # 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:")
-
-        @transport.event_handler("on_first_other_participant_joined")
-        async def on_first_other_participant_joined(transport):
-            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():
-            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")
-            transport.stop()
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

src/khk-hackathon/06d-listen.py

@@ -0,0 +1,160 @@
+from datetime import datetime
+import asyncio
+import aiohttp
+import os
+import sys
+from dailyai.conversation_wrappers import InterruptibleConversationWrapper
+
+from dailyai.queue_frame import StartStreamQueueFrame, TranscriptionQueueFrame, TextQueueFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.fireworks_ai_services import FireworksLLMService
+from dailyai.services.deepgram_ai_services import DeepgramTTSService
+from dailyai.services.ai_services import FrameLogger
+
+from dailyai.services.fal_ai_services import FalImageGenService
+
+from examples.foundational.support.runner import configure
+
+
+command_line_prompt = ' '.join(sys.argv[1:])
+
+system_prompt = """
+You are a friendly robot character with a cartoon body with head, torso, arms, feet,
+and legs.
+
+You can change your appearance using the `change_appearance` function call.
+You can add or remove items from your body, change
+your color, and more. You can use function calling to change your appearance.
+
+When changing your appearance, please create a prompt as an argument to the function.
+The prompt will help the image generation model
+create a new appearance for you. Include as much detail as possible. Include the
+keywords "robot", "friendly", "cartoon", "smiling", "happy", "animated". 
+The initial image prompt you are adding to or changing is
+"A friendly cartoon robot, smiling and happy, animated."
+
+Do not include the image model prompt in your response. The prompt must be passed to the function
+as a parameter. 
+"""
+
+change_appearance_function = {
+    "name": "change_appearance",
+    "description": "Call this function when the users want you to change your appearance.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "appearance": {
+                "type": "string",
+                "description": "The new appearance for the robot, in the form of a prompt for an generative AI diffusion model."
+            }
+        }
+    }
+}
+
+tools = [
+    {
+        "type": "function",
+        "function": change_appearance_function
+    }
+]
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        context = [
+            {
+                "role": "system",
+                "content": system_prompt,
+            },
+        ]
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            duration_minutes=30,
+            start_transcription=True,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=True,
+            camera_width=1024,
+            camera_height=1024,
+            # TODO-CB: Should this be VAD enabled or something?
+            speaker_enabled=True,
+            context=context
+        )
+
+        imagegen = FalImageGenService(
+            image_size="512x512",
+            aiohttp_session=session,
+            key_id=os.getenv("FAL_KEY_ID"),
+            key_secret=os.getenv("FAL_KEY_SECRET"))
+
+        async def change_appearance(appearance):
+            await asyncio.create_task(
+                imagegen.run_to_queue(
+                    transport.send_queue, [
+                        TextQueueFrame(appearance)]))
+
+        llm = FireworksLLMService(
+            context=context,
+            api_key=os.getenv("FIREWORKS_API_KEY"),
+            model="accounts/fireworks/models/firefunction-v1",
+            # TODO - how can we modify tools list on the fly?
+            tools=tools,
+            change_appearance=change_appearance,
+            transport=transport
+        )
+        tts = DeepgramTTSService(aiohttp_session=session, api_key=os.getenv(
+            "DEEPGRAM_API_KEY"), voice=os.getenv("DEEPGRAM_VOICE"))
+        fl = FrameLogger("just outside the innermost layer")
+
+        async def run_response(in_frame):
+            await tts.run_to_queue(
+                transport.send_queue,
+                # tma_out.run(
+                llm.run(
+                    # tma_in.run(
+                    fl.run(
+                        [StartStreamQueueFrame(), in_frame]
+                    )
+                    # )
+                )
+                # ),
+            )
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await change_appearance("A friendly cartoon robot, smiling and happy, animated.")
+            return
+
+            await tts.say("Hi, I'm listening!", transport.send_queue)
+            await asyncio.sleep(1)
+
+            await transport.receive_queue.put(UserStartedSpeakingFrame())
+            await asyncio.sleep(0.1)
+
+            transport.on_transcription_message({
+                "text": command_line_prompt,
+                "participantId":  "cb65b845-aac0-4fc8-987d-2e7ce3c7d8f0",
+                "timestamp": datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
+            })
+# putting the frame into the queue directly doesn't seem to work
+#            await transport.receive_queue.put(
+#                TranscriptionQueueFrame(
+#                    "tell me a joke.",
+#                    "cb65b845-aac0-4fc8-987d-2e7ce3c7d8f0",
+#                    datetime.utcnow().strftime(
+#                        '%Y-%m-%dT%H:%M:%S.%f')[:-3] + 'Z'
+#                ))
+            await asyncio.sleep(0.1)
+            await transport.receive_queue.put(UserStoppedSpeakingFrame())
+
+        transport.transcription_settings["extra"]["endpointing"] = True
+        transport.transcription_settings["extra"]["punctuate"] = True
+
+        await asyncio.gather(transport.run(), transport.run_conversation(run_response))
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))