Aggregators for LLM messages

src/dailyai/queue_aggregators.py

@@ -0,0 +1,67 @@
+import asyncio
+
+from dailyai.queue_frame import QueueFrame, FrameType
+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 TranscriptionToLLMMessageAggregator(AIService):
+    def __init__(self, messages, bot_participant_id):
+        self.messages = messages
+        self.bot_participant_id = bot_participant_id
+        self.sentence = ""
+
+    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if frame.frame_type != FrameType.TRANSCRIPTION:
+            return
+
+        message = frame.frame_data
+        if not isinstance(message, dict):
+            return
+
+        if message["session_id"] == self.bot_participant_id:
+            return
+
+        print("transcription to message", frame)
+
+        # todo: we could differentiate between transcriptions from different participants
+        self.sentence += message["text"]
+        if self.sentence.endswith((".", "?", "!")):
+            self.messages.append({"role": "user", "content": self.sentence})
+            self.sentence = ""
+            yield QueueFrame(FrameType.LLM_MESSAGE, self.messages)
+
+
+class LLMResponseToLLMMessageAggregator(AIService):
+    def __init__(self, messages):
+        self.messages = messages
+        self.sentence = ""
+
+    async def process_frame(self, frame:QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if frame.frame_type == FrameType.TEXT and isinstance(frame.frame_data, str):
+            print("llmresponse to message", frame)
+            self.sentence += frame.frame_data
+            if self.sentence.endswith((".", "?", "!")):
+                self.messages.append({"role": "assistant", "content": self.sentence})
+                self.sentence = ""
+
+        yield frame