pipecat/examples/foundational/04-transports-small-webrtc.py

#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import argparse
import asyncio
import os
from contextlib import asynccontextmanager
from typing import Dict

import uvicorn
from dotenv import load_dotenv
from fastapi import BackgroundTasks, FastAPI
from fastapi.responses import RedirectResponse
from loguru import logger
from pipecat_ai_small_webrtc_prebuilt.frontend import SmallWebRTCPrebuiltUI

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.smallwebrtc.connection import IceServer, SmallWebRTCConnection
from pipecat.transports.smallwebrtc.transport import SmallWebRTCTransport

load_dotenv(override=True)

app = FastAPI()

# Store connections by pc_id
pcs_map: Dict[str, SmallWebRTCConnection] = {}

ice_servers = [
    IceServer(
        urls="stun:stun.l.google.com:19302",
    )
]

# Mount the frontend at /
app.mount("/client", SmallWebRTCPrebuiltUI)


async def run_example(webrtc_connection: SmallWebRTCConnection):
    logger.info(f"Starting bot")

    # Create a transport using the WebRTC connection
    transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
        ),
    )

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        settings=CartesiaTTSService.Settings(
            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        ),
    )

    llm = OpenAILLMService(
        api_key=os.getenv("OPENAI_API_KEY"),
        settings=OpenAILLMService.Settings(
            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
        ),
    )

    context = LLMContext()
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,
            user_aggregator,  # User responses
            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
            assistant_aggregator,  # Assistant spoken responses
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation.
        context.add_message(
            {"role": "developer", "content": "Please introduce yourself to the user."}
        )
        await task.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)


@app.get("/", include_in_schema=False)
async def root_redirect():
    return RedirectResponse(url="/client/")


@app.post("/api/offer")
async def offer(request: dict, background_tasks: BackgroundTasks):
    pc_id = request.get("pc_id")

    if pc_id and pc_id in pcs_map:
        pipecat_connection = pcs_map[pc_id]
        logger.info(f"Reusing existing connection for pc_id: {pc_id}")
        await pipecat_connection.renegotiate(
            sdp=request["sdp"],
            type=request["type"],
            restart_pc=request.get("restart_pc", False),
        )
    else:
        pipecat_connection = SmallWebRTCConnection(ice_servers)
        await pipecat_connection.initialize(sdp=request["sdp"], type=request["type"])

        @pipecat_connection.event_handler("closed")
        async def handle_disconnected(webrtc_connection: SmallWebRTCConnection):
            logger.info(f"Discarding peer connection for pc_id: {webrtc_connection.pc_id}")
            pcs_map.pop(webrtc_connection.pc_id, None)

        # Run example function with SmallWebRTC transport arguments.
        background_tasks.add_task(run_example, pipecat_connection)

    answer = pipecat_connection.get_answer()
    # Updating the peer connection inside the map
    pcs_map[answer["pc_id"]] = pipecat_connection

    return answer


@asynccontextmanager
async def lifespan(app: FastAPI):
    yield  # Run app
    coros = [pc.disconnect() for pc in pcs_map.values()]
    await asyncio.gather(*coros)
    pcs_map.clear()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Pipecat Bot Runner")
    parser.add_argument(
        "--host", default="localhost", help="Host for HTTP server (default: localhost)"
    )
    parser.add_argument(
        "--port", type=int, default=7860, help="Port for HTTP server (default: 7860)"
    )
    args = parser.parse_args()

    uvicorn.run(app, host=args.host, port=args.port)