examples: add text chatbot example using small webrtc transport

examples: send small webrtc UI updates in RTVIServerMessageFrame

add explanation about RTVI server messages being specific to
small-webrtc-prebuilt UI

examples/foundational/41-text-only-webrtc.py

@@ -0,0 +1,167 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import asyncio
+import io
+import os
+import re
+import shutil
+import sys
+
+import aiohttp
+from dotenv import load_dotenv
+from loguru import logger
+from PIL import Image
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import (
+    Frame,
+    FunctionCallResultFrame,
+    LLMMessagesAppendFrame,
+    URLImageRawFrame,
+)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.processors.frameworks.rtvi import (
+    ActionResult,
+    RTVIAction,
+    RTVIActionArgument,
+    RTVIConfig,
+    RTVIObserver,
+    RTVIProcessor,
+    RTVIServerMessageFrame,
+)
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(),
+    )
+
+    # Create an HTTP session for API calls
+    async with aiohttp.ClientSession() as session:
+        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        context = OpenAILLMContext(messages)
+        context_aggregator = llm.create_context_aggregator(context)
+
+        async def action_llm_append_to_messages_handler(
+            rtvi: RTVIProcessor, service: str, arguments: dict[str, any]
+        ) -> ActionResult:
+            run_immediately = (
+                arguments["run_immediately"] if "run_immediately" in arguments else True
+            )
+
+            if run_immediately:
+                await rtvi.interrupt_bot()
+
+            # We just interrupted the bot so it should be fine to use the
+            # context directly instead of through frame.
+            if "messages" in arguments and arguments["messages"]:
+                mess = arguments["messages"]
+                frame = LLMMessagesAppendFrame(messages=arguments["messages"])
+                await rtvi.push_frame(frame)
+
+            if run_immediately:
+                frame = context_aggregator.user().get_context_frame()
+                await rtvi.push_frame(frame)
+
+            return True
+
+        action_llm_append_to_messages = RTVIAction(
+            service="llm",
+            action="append_to_messages",
+            result="bool",
+            arguments=[
+                RTVIActionArgument(name="messages", type="array"),
+                RTVIActionArgument(name="run_immediately", type="bool"),
+            ],
+            handler=action_llm_append_to_messages_handler,
+        )
+
+        rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
+        rtvi.register_action(action_llm_append_to_messages)
+
+        pipeline = Pipeline(
+            [
+                transport.input(),
+                rtvi,
+                context_aggregator.user(),
+                llm,
+                transport.output(),
+                context_aggregator.assistant(),
+            ]
+        )
+
+        task = PipelineTask(
+            pipeline,
+            params=PipelineParams(
+                allow_interruptions=True,
+                enable_metrics=True,
+            ),
+            observers=[RTVIObserver(rtvi)],
+        )
+
+        @rtvi.event_handler("on_client_ready")
+        async def on_client_ready(rtvi):
+            logger.info("Pipecat client ready.")
+            await rtvi.set_bot_ready()
+
+            # This block is frontend UI specific
+            # These messages are intended for small webrtc UI to only handle text
+            # https://github.com/pipecat-ai/small-webrtc-prebuilt
+            messages = {
+                "show_text_container": True,
+                "show_video_container": False,
+                "show_debug_container": False,
+            }
+            rtvi_frame = RTVIServerMessageFrame(data=messages)
+            await task.queue_frames([rtvi_frame])
+
+        @transport.event_handler("on_client_connected")
+        async def on_client_connected(transport, client):
+            logger.info(f"Client connected: {client}")
+            # Kick off the conversation.
+            await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+        @transport.event_handler("on_client_disconnected")
+        async def on_client_disconnected(transport, client):
+            logger.info(f"Client disconnected")
+
+        @transport.event_handler("on_client_closed")
+        async def on_client_closed(transport, client):
+            logger.info(f"Client closed connection")
+            await task.cancel()
+
+        runner = PipelineRunner(handle_sigint=False)
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()