Merge branch 'main' into cb/09-bots-arguing

.gitignore

@@ -2,6 +2,7 @@
 env/
 __pycache__/
 *~
+venv
 #*#
 
 # Distribution / packaging

README.md

@@ -53,3 +53,88 @@ If you have those environment variables stored in an .env file, you can quickly
 ```bash
 export $(grep -v '^#' .env | xargs)
 ```
+
+## 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.

src/dailyai/queue_aggregators.py

@@ -0,0 +1,47 @@
+import asyncio
+
+from dailyai.queue_frame import LLMMessagesQueueFrame, QueueFrame, TextQueueFrame
+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):
+        self.messages = messages
+        self.bot_participant_id = bot_participant_id
+        self.role = role
+        self.sentence = ""
+
+    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        content: str = ""
+
+        # TODO: split up transcription by participant
+        if isinstance(frame, TextQueueFrame):
+            content = frame.text
+
+        self.sentence += content
+        if self.sentence.endswith((".", "?", "!")):
+            self.messages.append({"role": self.role, "content": self.sentence})
+            self.sentence = ""
+            yield LLMMessagesQueueFrame(self.messages)
+
+        yield frame

src/dailyai/queue_frame.py

@@ -1,19 +1,38 @@
 from enum import Enum
 from dataclasses import dataclass
+from typing import Any
 
-class FrameType(Enum):
-    START_STREAM = 0
-    END_STREAM = 1
-    AUDIO = 2
-    IMAGE = 3
-    SENTENCE = 4
-    TEXT_CHUNK = 5
-    LLM_MESSAGE = 6
-    APP_MESSAGE = 7
-    IMAGE_DESCRIPTION = 8
-    TRANSCRIPTION = 9
-
-@dataclass(frozen=True)
 class QueueFrame:
-    frame_type: FrameType
+    pass
-    frame_data: str | dict | bytes | list | None
+
+class StartStreamQueueFrame(QueueFrame):
+    pass
+
+class EndStreamQueueFrame(QueueFrame):
+    pass
+
+@dataclass()
+class AudioQueueFrame(QueueFrame):
+    data: bytes
+
+@dataclass()
+class ImageQueueFrame(QueueFrame):
+    url: str | None
+    image: bytes
+
+@dataclass()
+class TextQueueFrame(QueueFrame):
+    text: str
+
+@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

@@ -1,17 +1,19 @@
 import asyncio
 import logging
-import re
 
-from httpx import request
+from dailyai.queue_frame import (
-
+    AudioQueueFrame,
-from dailyai.queue_frame import QueueFrame, FrameType
+    EndStreamQueueFrame,
+    ImageQueueFrame,
+    LLMMessagesQueueFrame,
+    QueueFrame,
+    TextQueueFrame,
+)
 
 from abc import abstractmethod
-from typing import AsyncGenerator, Iterable
+from typing import AsyncGenerator, AsyncIterable, Iterable
 from dataclasses import dataclass
-from typing import AsyncGenerator
 
-from collections.abc import Iterable, AsyncIterable
 
 class AIService:
 
@@ -21,95 +23,57 @@ class AIService:
     def stop(self):
         pass
 
-    def allowed_input_frame_types(self) -> set[FrameType]:
-        return set()
-
-    def possible_output_frame_types(self) -> set[FrameType]:
-        return set()
-
     async def run_to_queue(self, queue: asyncio.Queue, frames, add_end_of_stream=False) -> None:
         async for frame in self.run(frames):
             await queue.put(frame)
 
         if add_end_of_stream:
-            await queue.put(QueueFrame(FrameType.END_STREAM, None))
+            await queue.put(EndStreamQueueFrame())
 
     async def run(
         self,
         frames: Iterable[QueueFrame]
         | AsyncIterable[QueueFrame]
         | asyncio.Queue[QueueFrame],
-        requested_frame_types: set[FrameType] | None=None,
     ) -> AsyncGenerator[QueueFrame, None]:
-        if requested_frame_types and self.possible_output_frame_types().intersection(requested_frame_types) == set():
+        try:
-            raise Exception(f"Requested frame types {requested_frame_types} are not supported by this service.")
+            if isinstance(frames, AsyncIterable):
+                async for frame in frames:
+                    async for output_frame in self.process_frame(frame):
+                        yield output_frame
+            elif isinstance(frames, Iterable):
+                for frame in frames:
+                    async for output_frame in self.process_frame(frame):
+                        yield output_frame
+            elif isinstance(frames, asyncio.Queue):
+                while True:
+                    frame = await frames.get()
+                    async for output_frame in self.process_frame(frame):
+                        yield output_frame
+                    if isinstance(frame, EndStreamQueueFrame):
+                        break
+            else:
+                raise Exception("Frames must be an iterable or async iterable")
 
-        if not requested_frame_types:
+            async for output_frame in self.finalize():
-            requested_frame_types = self.possible_output_frame_types()
+                yield output_frame
-
+        except Exception as e:
-        if isinstance(frames, AsyncIterable):
+            self.logger.error("Exception occurred while running AI service", e)
-            async for frame in frames:
+            raise e
-                async for output_frame in self.process_frame(requested_frame_types, frame):
-                    yield output_frame
-        elif isinstance(frames, Iterable):
-            for frame in frames:
-                async for output_frame in self.process_frame(requested_frame_types, frame):
-                    yield output_frame
-        elif isinstance(frames, asyncio.Queue):
-            while True:
-                frame = await frames.get()
-                async for output_frame in self.process_frame(requested_frame_types, frame):
-                    yield output_frame
-                if frame.frame_type == FrameType.END_STREAM:
-                    break
-        else:
-            raise Exception("Frames must be an iterable or async iterable")
 
     @abstractmethod
-    async def process_frame(self, requested_frame_types:set[FrameType], frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        # Yield something so the linter can deduce what should happen here.
+        # This is a trick for the interpreter (and linter) to know that this is a generator.
-        yield QueueFrame(FrameType.END_STREAM, None)
+        if False:
-
+            yield QueueFrame()
-class SentenceAggregator(AIService):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.current_sentence = ""
-
-    def allowed_input_frame_types(self) -> set[FrameType]:
-        return set([FrameType.TEXT_CHUNK, FrameType.SENTENCE])
-
-    def possible_output_frame_types(self) -> set[FrameType]:
-        return set([FrameType.SENTENCE])
-
-    async def process_frame(self, requested_frame_types: set[FrameType], frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if not FrameType.SENTENCE in requested_frame_types:
-            return
-
-        if frame.frame_type == FrameType.TEXT_CHUNK:
-            if type(frame.frame_data) != str:
-                raise Exception(
-                    "Sentence aggregator requires a string for the data field"
-                )
-
-            self.current_sentence += frame.frame_data
-            if self.current_sentence.endswith((".", "?", "!")):
-                sentence = self.current_sentence
-                self.current_sentence = ""
-                yield QueueFrame(FrameType.SENTENCE, sentence)
-        elif frame.frame_type == FrameType.END_STREAM:
-            if self.current_sentence:
-                yield QueueFrame(FrameType.SENTENCE, self.current_sentence)
-        elif frame.frame_type == FrameType.SENTENCE:
-            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):
-    def allowed_input_frame_types(self) -> set[FrameType]:
-        return set([FrameType.LLM_MESSAGE, FrameType.SENTENCE, FrameType.TRANSCRIPTION])
-
-    def allowed_output_frame_types(self) -> set[FrameType]:
-        return set([FrameType.SENTENCE, FrameType.TEXT_CHUNK])
-
     @abstractmethod
     async def run_llm_async(self, messages) -> AsyncGenerator[str, None]:
         yield ""
@@ -118,52 +82,55 @@ class LLMService(AIService):
     async def run_llm(self, messages) -> str:
         pass
 
-    async def process_frame(self, requested_frame_types: set[FrameType], frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if frame.frame_type == FrameType.LLM_MESSAGE:
+        if isinstance(frame, LLMMessagesQueueFrame):
-            if type(frame.frame_data) != list:
+            async for text_chunk in self.run_llm_async(frame.messages):
-                raise Exception("LLM service requires a dict for the data field")
+                yield TextQueueFrame(text_chunk)
-
-            messages: list[dict[str, str]] = frame.frame_data
-            if FrameType.SENTENCE in requested_frame_types:
-                yield QueueFrame(FrameType.SENTENCE, await self.run_llm(messages))
-            else:
-                async for text_chunk in self.run_llm_async(messages):
-                    yield QueueFrame(FrameType.TEXT_CHUNK, text_chunk)
-
-        # TODO: handle other frame types! Need to aggregate into messages
 
 
 class TTSService(AIService):
+    def __init__(self, aggregate_sentences=True):
+        super().__init__()
+        self.aggregate_sentences: bool = aggregate_sentences
+        self.current_sentence: str = ""
+
     # Some TTS services require a specific sample rate. We default to 16k
     def get_mic_sample_rate(self):
         return 16000
 
-    def allowed_input_frame_types(self) -> set[FrameType]:
+    # Converts the text to audio. Yields a list of audio frames that can
-        return set([FrameType.SENTENCE, FrameType.TRANSCRIPTION, FrameType.TEXT_CHUNK])
-
-    def possible_output_frame_types(self) -> set[FrameType]:
-        return set([FrameType.AUDIO])
-
-    # Converts the sentence to audio. Yields a list of audio frames that can
     # be sent to the microphone device
     @abstractmethod
-    async def run_tts(self, sentence) -> AsyncGenerator[bytes, None]:
+    async def run_tts(self, text) -> AsyncGenerator[bytes, None]:
         # yield empty bytes here, so linting can infer what this method does
         yield bytes()
 
-    async def process_frame(self, requested_frame_types: set[FrameType], frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if not FrameType.AUDIO in requested_frame_types:
+        if not isinstance(frame, TextQueueFrame):
+            yield frame
             return
 
-        if type(frame.frame_data) != str:
+        text: str | None = None
-            raise Exception("TTS service requires a string for the data field")
+        if not self.aggregate_sentences:
+            text = frame.text
+        else:
+            self.current_sentence += frame.text
+            if self.current_sentence.endswith((".", "?", "!")):
+                text = self.current_sentence
+                self.current_sentence = ""
 
-        async for audio_chunk in self.run_tts(frame.frame_data):
+        if text:
-            yield QueueFrame(FrameType.AUDIO, audio_chunk)
+            async for audio_chunk in self.run_tts(text):
+                yield AudioQueueFrame(audio_chunk)
+
+    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, [QueueFrame(FrameType.SENTENCE, sentence)])
+        await self.run_to_queue(queue, [TextQueueFrame(sentence)])
 
 
 class ImageGenService(AIService):
@@ -171,26 +138,17 @@ class ImageGenService(AIService):
         super().__init__(**kwargs)
         self.image_size = image_size
 
-    def allowed_input_frame_types(self) -> set[FrameType]:
-        return set([FrameType.SENTENCE, FrameType.TRANSCRIPTION, FrameType.TEXT_CHUNK, FrameType.IMAGE_DESCRIPTION])
-
-    def possible_output_frame_types(self) -> set[FrameType]:
-        return set([FrameType.IMAGE])
-
     # Renders the image. Returns an Image object.
     @abstractmethod
-    async def run_image_gen(self, sentence) -> tuple[str, bytes]:
+    async def run_image_gen(self, sentence:str) -> tuple[str, bytes]:
         pass
 
-    async def process_frame(self, requested_frame_types: set[FrameType], frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        if not FrameType.IMAGE in requested_frame_types:
+        if not isinstance(frame, TextQueueFrame):
             return
 
-        if type(frame.frame_data) != str:
+        (url, image_data) = await self.run_image_gen(frame.text)
-            raise Exception("Image service requires a string for the data field")
+        yield ImageQueueFrame(url, image_data)
-
-        (_, image_data) = await self.run_image_gen(frame.frame_data)
-        yield QueueFrame(FrameType.IMAGE, image_data)
 
 
 @dataclass

src/dailyai/services/daily_transport_service.py

@@ -1,15 +1,23 @@
 import asyncio
 import inspect
 import logging
+import threading
 import time
 import types
 
 from functools import partial
 from queue import Queue, Empty
 
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_frame import (
+    AudioQueueFrame,
+    EndStreamQueueFrame,
+    ImageQueueFrame,
+    QueueFrame,
+    StartStreamQueueFrame,
+    TranscriptionQueueFrame,
+)
 
-from threading import Thread, Event, Timer
+from threading import Thread, Event
 
 from daily import (
     EventHandler,
@@ -21,12 +29,15 @@ from daily import (
 )
 
 class DailyTransportService(EventHandler):
+    _daily_initialized = False
+    _lock = threading.Lock()
     def __init__(
         self,
         room_url: str,
         token: str | None,
         bot_name: str,
         duration: float = 10,
+        min_others_count: int = 1,
     ):
         super().__init__()
         self.bot_name: str = bot_name
@@ -34,6 +45,7 @@ class DailyTransportService(EventHandler):
         self.token: str | None = token
         self.duration: float = duration
         self.expiration = time.time() + duration * 60
+        self.min_others_count = min_others_count
 
         # This queue is used to marshal frames from the async send queue to the thread that emits audio & video.
         # We need this to maintain the asynchronous behavior of asyncio queues -- to give async functions
@@ -54,6 +66,7 @@ class DailyTransportService(EventHandler):
         self.receive_queue = asyncio.Queue()
 
         self.other_participant_has_joined = False
+        self.my_participant_id = None
 
         self.camera_thread = None
         self.frame_consumer_thread = None
@@ -114,7 +127,11 @@ class DailyTransportService(EventHandler):
         return decorator
 
     def configure_daily(self):
-        Daily.init()
+        # Only initialize Daily once
+        if not DailyTransportService._daily_initialized:
+            with DailyTransportService._lock:
+                Daily.init()
+                DailyTransportService._daily_initialized = True
         self.client = CallClient(event_handler=self)
 
         if self.mic_enabled:
@@ -143,6 +160,7 @@ class DailyTransportService(EventHandler):
 
         self.client.set_user_name(self.bot_name)
         self.client.join(self.room_url, self.token, completion=self.call_joined)
+        self.my_participant_id = self.client.participants()["local"]["id"]
 
         self.client.update_inputs(
             {
@@ -186,13 +204,11 @@ class DailyTransportService(EventHandler):
         if self.token:
             self.client.start_transcription(self.transcription_settings)
 
-        self.my_participant_id = self.client.participants()["local"]["id"]
-
     async def get_receive_frames(self):
         while True:
             frame = await self.receive_queue.get()
             yield frame
-            if frame.frame_type == FrameType.END_STREAM:
+            if isinstance(frame, EndStreamQueueFrame):
                 break
 
     def get_async_send_queue(self):
@@ -203,7 +219,7 @@ class DailyTransportService(EventHandler):
             frame: QueueFrame | list = await self.send_queue.get()
             self.threadsafe_send_queue.put(frame)
             self.send_queue.task_done()
-            if type(frame) == QueueFrame and frame.frame_type == FrameType.END_STREAM:
+            if isinstance(frame, EndStreamQueueFrame):
                 break
 
     async def wait_for_send_queue_to_empty(self):
@@ -217,14 +233,14 @@ class DailyTransportService(EventHandler):
     async def run(self) -> None:
         self.configure_daily()
 
-        self.participant_left = False
+        self.do_shutdown = False
 
         async_output_queue_marshal_task = asyncio.create_task(self.marshal_frames())
 
         try:
             participant_count: int = len(self.client.participants())
             self.logger.info(f"{participant_count} participants in room")
-            while time.time() < self.expiration and not self.participant_left and not self.stop_threads.is_set():
+            while time.time() < self.expiration and not self.do_shutdown and not self.stop_threads.is_set():
                 await asyncio.sleep(1)
         except Exception as e:
             self.logger.error(f"Exception {e}")
@@ -233,8 +249,8 @@ class DailyTransportService(EventHandler):
 
         self.stop_threads.set()
 
-        await self.receive_queue.put(QueueFrame(FrameType.END_STREAM, None))
+        await self.receive_queue.put(EndStreamQueueFrame())
-        await self.send_queue.put(QueueFrame(FrameType.END_STREAM, None))
+        await self.send_queue.put(EndStreamQueueFrame())
         await async_output_queue_marshal_task
 
         if self.camera_thread and self.camera_thread.is_alive():
@@ -263,8 +279,8 @@ class DailyTransportService(EventHandler):
             self.on_first_other_participant_joined()
 
     def on_participant_left(self, participant, reason):
-        if len(self.client.participants()) < 2:
+        if len(self.client.participants()) < self.min_others_count + 1:
-            self.participant_left = True
+            self.do_shutdown = True
         pass
 
     def on_app_message(self, message, sender):
@@ -272,7 +288,12 @@ class DailyTransportService(EventHandler):
 
     def on_transcription_message(self, message:dict):
         if self.loop:
-            frame = QueueFrame(FrameType.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"])
             asyncio.run_coroutine_threadsafe(self.receive_queue.put(frame), self.loop)
 
     def on_transcription_stopped(self, stopped_by, stopped_by_error):
@@ -305,15 +326,15 @@ class DailyTransportService(EventHandler):
         while True:
             try:
                 frames_or_frame: QueueFrame | list[QueueFrame] = self.threadsafe_send_queue.get()
-                if type(frames_or_frame) == QueueFrame:
+                if isinstance(frames_or_frame, QueueFrame):
                     frames: list[QueueFrame] = [frames_or_frame]
-                elif type(frames_or_frame) == list:
+                elif isinstance(frames_or_frame,  list):
                     frames: list[QueueFrame] = frames_or_frame
                 else:
                     raise Exception("Unknown type in output queue")
 
                 for frame in frames:
-                    if frame.frame_type == FrameType.END_STREAM:
+                    if isinstance(frame, EndStreamQueueFrame):
                         self.logger.info("Stopping frame consumer thread")
                         self.threadsafe_send_queue.task_done()
                         return
@@ -321,8 +342,8 @@ class DailyTransportService(EventHandler):
                     # if interrupted, we just pull frames off the queue and discard them
                     if not self.is_interrupted.is_set():
                         if frame:
-                            if frame.frame_type == FrameType.AUDIO:
+                            if isinstance(frame, AudioQueueFrame):
-                                chunk = frame.frame_data
+                                chunk = frame.data
 
                                 all_audio_frames.extend(chunk)
 
@@ -331,8 +352,8 @@ class DailyTransportService(EventHandler):
                                 if l:
                                     self.mic.write_frames(bytes(b[:l]))
                                     b = b[l:]
-                            elif frame.frame_type == FrameType.IMAGE:
+                            elif isinstance(frame, ImageQueueFrame):
-                                self.set_image(frame.frame_data)
+                                self.set_image(frame.image)
                         elif len(b):
                             self.mic.write_frames(bytes(b))
                             b = bytearray()
@@ -343,7 +364,7 @@ class DailyTransportService(EventHandler):
                             )
                             self.interrupt_time = None
 
-                        if frame.frame_type == FrameType.START_STREAM:
+                        if isinstance(frame, StartStreamQueueFrame):
                             self.is_interrupted.clear()
 
                 self.threadsafe_send_queue.task_done()

src/dailyai/tests/test_ai_services.py

@@ -3,33 +3,27 @@ import unittest
 
 from typing import AsyncGenerator, Generator
 
-from dailyai.services.ai_services import AIService, SentenceAggregator
+from dailyai.services.ai_services import AIService
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_frame import EndStreamQueueFrame, QueueFrame, TextQueueFrame
 
 class SimpleAIService(AIService):
-    def allowed_input_frame_types(self) -> set[FrameType]:
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
-        return set([FrameType.TEXT_CHUNK])
+        yield frame
-
-    def possible_output_frame_types(self) -> set[FrameType]:
-        return set([FrameType.TEXT_CHUNK])
-
-    async def process_frame(self, requested_frame_types: set[FrameType], frame: QueueFrame) -> QueueFrame | None:
-        return frame
 
 class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
     async def test_async_input(self):
         service = SimpleAIService()
 
         input_frames = [
-            QueueFrame(FrameType.TEXT_CHUNK, "hello"),
+            TextQueueFrame("hello"),
-            QueueFrame(FrameType.END_STREAM, None),
+            EndStreamQueueFrame()
         ]
         async def iterate_frames() -> AsyncGenerator[QueueFrame, None]:
             for frame in input_frames:
                 yield frame
 
         output_frames = []
-        async for frame in service.run(set([FrameType.TEXT_CHUNK]), iterate_frames()):
+        async for frame in service.run(iterate_frames()):
             output_frames.append(frame)
 
         self.assertEqual(input_frames, output_frames)
@@ -37,93 +31,18 @@ class TestBaseAIService(unittest.IsolatedAsyncioTestCase):
     async def test_nonasync_input(self):
         service = SimpleAIService()
 
-        input_frames = [
+        input_frames = [TextQueueFrame("hello"), EndStreamQueueFrame()]
-            QueueFrame(FrameType.TEXT_CHUNK, "hello"),
-            QueueFrame(FrameType.END_STREAM, None),
-        ]
 
         def iterate_frames() -> Generator[QueueFrame, None, None]:
             for frame in input_frames:
                 yield frame
 
         output_frames = []
-        async for frame in service.run(set([FrameType.TEXT_CHUNK]), iterate_frames()):
+        async for frame in service.run(iterate_frames()):
             output_frames.append(frame)
 
         self.assertEqual(input_frames, output_frames)
 
 
-class TestSentenceAggregator(unittest.IsolatedAsyncioTestCase):
-    async def test_clause(self) -> None:
-        input_frames = [
-            QueueFrame(FrameType.TEXT_CHUNK, "hello"),
-            QueueFrame(FrameType.END_STREAM, None),
-        ]
-
-        service = SentenceAggregator()
-        output_frames = []
-        async for frame in service.run(set([FrameType.SENTENCE]), input_frames):
-            output_frames.append(frame)
-
-        self.assertEqual(1, len(output_frames))
-        self.assertEqual(QueueFrame(FrameType.SENTENCE, "hello"), output_frames[0])
-
-    async def test_sentence(self) -> None:
-        input_frames = [
-            QueueFrame(FrameType.TEXT_CHUNK, "hello, "),
-            QueueFrame(FrameType.TEXT_CHUNK, "world."),
-            QueueFrame(FrameType.END_STREAM, None),
-        ]
-
-        service = SentenceAggregator()
-        output_frames = []
-        async for frame in service.run(set([FrameType.SENTENCE]), input_frames):
-            output_frames.append(frame)
-
-        self.assertEqual(1, len(output_frames))
-        self.assertEqual(QueueFrame(FrameType.SENTENCE, "hello, world."), output_frames[0])
-
-    async def test_sentence_and_clause(self) -> None:
-        input_frames = [
-            QueueFrame(FrameType.TEXT_CHUNK, "hello, "),
-            QueueFrame(FrameType.TEXT_CHUNK, "world."),
-            QueueFrame(FrameType.TEXT_CHUNK, " How are"),
-            QueueFrame(FrameType.END_STREAM, None),
-        ]
-
-        service = SentenceAggregator()
-        output_frames = []
-        async for frame in service.run(set([FrameType.SENTENCE]), input_frames):
-            output_frames.append(frame)
-
-        self.assertEqual(2, len(output_frames))
-        self.assertEqual(
-            QueueFrame(FrameType.SENTENCE, "hello, world."), output_frames[0]
-        )
-        self.assertEqual(
-            QueueFrame(FrameType.SENTENCE, " How are"), output_frames[1]
-        )
-
-    async def test_two_sentences(self) -> None:
-        input_frames = [
-            QueueFrame(FrameType.TEXT_CHUNK, "hello, "),
-            QueueFrame(FrameType.TEXT_CHUNK, "world."),
-            QueueFrame(FrameType.TEXT_CHUNK, " How are"),
-            QueueFrame(FrameType.TEXT_CHUNK, " you doing?"),
-            QueueFrame(FrameType.END_STREAM, None),
-        ]
-
-        service = SentenceAggregator()
-        output_frames = []
-        async for frame in service.run(set([FrameType.SENTENCE]), input_frames):
-            output_frames.append(frame)
-
-        self.assertEqual(2, len(output_frames))
-        self.assertEqual(
-            QueueFrame(FrameType.SENTENCE, "hello, world."), output_frames[0]
-        )
-        self.assertEqual(QueueFrame(FrameType.SENTENCE, " How are you doing?"), output_frames[1])
-
-
 if __name__ == "__main__":
     unittest.main()

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

@@ -1,10 +1,7 @@
 import argparse
 import asyncio
-from typing import AsyncGenerator
 
-from dailyai.queue_frame import QueueFrame, FrameType
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.azure_ai_services import AzureTTSService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
 async def main(room_url):

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

@@ -1,10 +1,8 @@
 import argparse
 import asyncio
-from typing import AsyncGenerator
 
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_frame import LLMMessagesQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.ai_services import SentenceAggregator
 from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
@@ -28,9 +26,7 @@ async def main(room_url):
     tts_task = asyncio.create_task(
         tts.run_to_queue(
             transport.send_queue,
-            SentenceAggregator().run(
+            llm.run([LLMMessagesQueueFrame(messages)]),
-                llm.run([QueueFrame(FrameType.LLM_MESSAGE, messages)])
-            )
         )
     )
 

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

@@ -1,7 +1,7 @@
 import argparse
 import asyncio
 
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_frame import TextQueueFrame
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.open_ai_services import OpenAIImageGenService
 
@@ -23,7 +23,7 @@ async def main(room_url):
 
     imagegen = OpenAIImageGenService(image_size="1024x1024")
     image_task = asyncio.create_task(
-        imagegen.run_to_queue(transport.send_queue, [QueueFrame(FrameType.IMAGE_DESCRIPTION, "a cat in the style of picasso")])
+        imagegen.run_to_queue(transport.send_queue, [TextQueueFrame("a cat in the style of picasso")])
     )
 
     @transport.event_handler("on_participant_joined")

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

@@ -2,10 +2,9 @@ import argparse
 import asyncio
 import re
 
-from dailyai.services.ai_services import SentenceAggregator
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_frame import EndStreamQueueFrame, LLMMessagesQueueFrame
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
 
 async def main(room_url:str):
@@ -36,9 +35,7 @@ async def main(room_url:str):
     llm_response_task = asyncio.create_task(
         elevenlabs_tts.run_to_queue(
             buffer_queue,
-            SentenceAggregator().run(
+            llm.run([LLMMessagesQueueFrame(messages)]),
-                llm.run([QueueFrame(FrameType.LLM_MESSAGE, messages)])
-            ),
             True,
         )
     )
@@ -48,17 +45,14 @@ async def main(room_url:str):
         if participant["id"] == transport.my_participant_id:
             return
 
-        await azure_tts.run_to_queue(
+        await azure_tts.say("My friend the LLM is now going to tell a joke about llamas.", transport.send_queue)
-            transport.send_queue,
-            [QueueFrame(FrameType.SENTENCE, "My friend the LLM is now going to tell a joke about llamas.")]
-        )
 
         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:
+                if isinstance(frame, EndStreamQueueFrame):
                     break
 
         await asyncio.gather(llm_response_task, buffer_to_send_queue())

src/samples/foundational/05-sync-speech-and-text.py

@@ -1,14 +1,9 @@
 import argparse
 import asyncio
 
-from asyncio.queues import Queue
+from dailyai.queue_frame import AudioQueueFrame, ImageQueueFrame
-import re
-
-from dailyai.queue_frame import QueueFrame, FrameType
-from dailyai.services.ai_services import SentenceAggregator
 from dailyai.services.azure_ai_services import AzureLLMService
 from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
-from dailyai.services.open_ai_services import OpenAIImageGenService
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.fal_ai_services import FalImageGenService
 
@@ -50,14 +45,20 @@ async def main(room_url):
         ]
 
         image_description = await llm.run_llm(messages)
+        if not image_description:
+            return
+
         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(
-            get_all_audio(to_speak), dalle.run_image_gen(image_description)
+            audio_task, image_task
         )
 
         return {
             "month": month,
             "text": image_description,
+            "image_url": image_data[0],
             "image": image_data[1],
             "audio": audio,
         }
@@ -86,8 +87,8 @@ async def main(room_url):
             data = await month_data_task
             await transport.send_queue.put(
                 [
-                    QueueFrame(FrameType.IMAGE, data["image"]),
+                    ImageQueueFrame(data["image_url"], data["image"]),
-                    QueueFrame(FrameType.AUDIO, data["audio"]),
+                    AudioQueueFrame(data["audio"]),
                 ]
             )
 

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

@@ -6,7 +6,7 @@ import urllib.parse
 
 from dailyai.services.daily_transport_service import DailyTransportService
 from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
-from dailyai.queue_frame import QueueFrame, FrameType
+from dailyai.queue_aggregators import LLMContextAggregator
 
 async def main(room_url:str, token):
     global transport
@@ -17,7 +17,7 @@ async def main(room_url:str, token):
         room_url,
         token,
         "Respond bot",
-        1,
+        5,
     )
     transport.mic_enabled = True
     transport.mic_sample_rate = 16000
@@ -26,33 +26,31 @@ async def main(room_url:str, token):
     llm = AzureLLMService()
     tts = AzureTTSService()
 
+    @transport.event_handler("on_first_other_participant_joined")
+    async def on_first_other_participant_joined(transport):
+        await tts.say("Hi, I'm listening!", transport.send_queue)
+
     async def handle_transcriptions():
         messages = [
             {"role": "system", "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way."},
         ]
 
-        sentence = ""
+        tma_in = LLMContextAggregator(
-        async for frame in transport.get_receive_frames():
+            messages, "user", transport.my_participant_id
-            if frame.frame_type != FrameType.TRANSCRIPTION:
+        )
-                continue
+        tma_out = LLMContextAggregator(
-
+            messages, "assistant", transport.my_participant_id
-            message = frame.frame_data
+        )
-            if message["session_id"] == transport.my_participant_id:
+        await tts.run_to_queue(
-                continue
+            transport.send_queue,
-
+            tma_out.run(
-            # todo: we could differentiate between transcriptions from different participants
+                llm.run(
-            sentence += message["text"]
+                    tma_in.run(
-            if sentence.endswith((".", "?", "!")):
+                        transport.get_receive_frames()
-                messages.append({"role": "user", "content": sentence})
+                    )
-                sentence = ''
+                )
-
+            )
-                full_response = ""
+        )
-                async for response in llm.run_llm_async_sentences(messages):
-                    full_response += response
-                    async for audio in tts.run_tts(response):
-                        await transport.send_queue.put(QueueFrame(FrameType.AUDIO, audio))
-
-                messages.append({"role": "assistant", "content": full_response})
 
     transport.transcription_settings["extra"]["punctuate"] = True
     await asyncio.gather(transport.run(), handle_transcriptions())