move src/examples to examples

examples/foundational/07-interruptible.py

@@ -0,0 +1,74 @@
+import asyncio
+import aiohttp
+import logging
+import os
+from dailyai.pipeline.aggregators import (
+    LLMAssistantContextAggregator,
+    LLMResponseAggregator,
+    LLMUserContextAggregator,
+    UserResponseAggregator,
+)
+
+from dailyai.pipeline.pipeline import Pipeline
+from dailyai.services.ai_services import FrameLogger
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.open_ai_services import OpenAILLMService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from examples.support.runner import configure
+
+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,
+        )
+
+        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")
+
+        pipeline = Pipeline([FrameLogger(), llm, FrameLogger(), tts])
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await transport.say("Hi, I'm listening!", tts)
+
+        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.",
+                },
+            ]
+
+            await transport.run_interruptible_pipeline(
+                pipeline,
+                post_processor=LLMResponseAggregator(messages),
+                pre_processor=UserResponseAggregator(messages),
+            )
+
+        transport.transcription_settings["extra"]["punctuate"] = False
+        await asyncio.gather(transport.run(), run_conversation())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))