ai-video-fullstack/backend/services/pipecat/pipeline.py

"""管线核心:给定一个 transport + 配置,跑完整的语音闭环。

关键设计:**transport 由调用方传入**,管线本身不关心是 WebRTC 还是 WS。
这就是"同时支持多种输出"的落点——加输出方式不用动这里。

对话编排交给 Brain;本文件只保留共享媒体管线、输入输出和通话生命周期。
"""

import asyncio
import base64
from collections.abc import Callable
from io import BytesIO
from uuid import uuid4

from loguru import logger
from models import AssistantConfig
from openai import AsyncOpenAI
from PIL import Image
from services.brains import Brain, BrainRuntime, build_brain
from services.conversation_history import ConversationRecorder
from services.pipecat.call_lifecycle import (
    CallEndCoordinator,
    EndCallAfterSpeechProcessor,
)
from services.pipecat.service_factory import (
    create_realtime_service,
    create_stt,
    create_tts,
)

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.frames.frames import (
    EndFrame,
    InputTransportMessageFrame,
    InterruptionFrame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    LLMTextFrame,
    LLMMessagesAppendFrame,
    OutputTransportMessageUrgentFrame,
    TextFrame,
    TTSSpeakFrame,
    UserImageRawFrame,
    UserImageRequestFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMAssistantAggregator,
    LLMUserAggregator,
    LLMUserAggregatorParams,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.utils import (
    get_transport_client_id,
    maybe_capture_participant_camera,
)
from pipecat.services.llm_service import FunctionCallParams
from pipecat.turns.user_mute.base_user_mute_strategy import BaseUserMuteStrategy
from pipecat.turns.user_mute.function_call_user_mute_strategy import (
    FunctionCallUserMuteStrategy,
)
from services.pipecat.turn_config import (
    create_user_turn_strategies,
    create_vad_analyzer,
)
from pipecat.utils.time import time_now_iso8601
from pipecat.workers.runner import WorkerRunner


VISION_TOOL_NAME = "fetch_user_image"
VISION_SYSTEM_HINT = (
    "当前会话打开了视觉理解。用户询问当前画面、摄像头里有什么、人物/物品/"
    "环境状态或需要你看一眼时,调用 fetch_user_image 获取当前视频帧,再基于画面回答。"
)
VISION_ANALYSIS_SYSTEM_PROMPT = (
    "你是一个视觉理解模型。请只根据图片内容和用户问题给出准确、简洁的中文观察结果。"
    "如果画面不足以判断,请明确说明不确定。"
)


def _require(value: str, label: str) -> str:
    if value:
        return value
    raise ValueError(f"缺少模型资源配置: {label}")


def _vision_uses_main_llm(cfg: AssistantConfig) -> bool:
    """模型自己支持图片时,沿用 Pipecat 的同上下文视觉工具路径。"""
    return not cfg.vision_model_resource_id and cfg.llm_support_image_input


def _image_data_uri(frame: UserImageRawFrame) -> str:
    if not frame.format:
        raise ValueError("摄像头图片帧缺少 format,无法编码给视觉模型")
    buffer = BytesIO()
    Image.frombytes(frame.format, frame.size, frame.image).save(
        buffer,
        format="JPEG",
        quality=85,
    )
    encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
    return f"data:image/jpeg;base64,{encoded}"


async def _analyze_image_with_vision_model(
    cfg: AssistantConfig,
    frame: UserImageRawFrame,
    question: str,
) -> str:
    if cfg.vision_llm_interface_type not in {"openai-llm", "dashscope-llm"}:
        raise ValueError(f"不支持的视觉 LLM 接口类型: {cfg.vision_llm_interface_type}")

    data_uri = await asyncio.to_thread(_image_data_uri, frame)
    extra_body = cfg.vision_llm_values.get("extraBody")
    extra = {"extra_body": extra_body} if isinstance(extra_body, dict) else {}
    client = AsyncOpenAI(
        api_key=_require(cfg.vision_llm_api_key, "Vision LLM apiKey"),
        base_url=_require(cfg.vision_llm_base_url, "Vision LLM apiUrl"),
    )
    try:
        response = await client.chat.completions.create(
            model=_require(cfg.vision_model, "Vision LLM modelId"),
            messages=[
                {"role": "system", "content": VISION_ANALYSIS_SYSTEM_PROMPT},
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": question},
                        {"type": "image_url", "image_url": {"url": data_uri}},
                    ],
                },
            ],
            **extra,
        )
    finally:
        await client.close()

    content = response.choices[0].message.content if response.choices else ""
    if isinstance(content, str):
        return content.strip()
    return str(content or "").strip()


def _text_input(message) -> tuple[str, bool] | None:
    """解析现有 user-text 与 RTVI send-text 两种前端文字消息。"""
    if not isinstance(message, dict):
        return None
    if message.get("type") == "user-text":
        text = str(message.get("text") or "").strip()
        return (text, True) if text else None
    if message.get("type") == "send-text":
        data = message.get("data")
        if not isinstance(data, dict):
            return None
        text = str(data.get("content") or "").strip()
        options = data.get("options")
        run_immediately = not isinstance(options, dict) or options.get(
            "run_immediately", True
        )
        return (text, bool(run_immediately)) if text else None
    return None


class TextInputProcessor(FrameProcessor):
    """把 transport 文字消息转换成 LLM 可消费的帧。

    run_immediately(默认/打断):先通过 on_text_input 事件把用户文字交给
    run_pipeline 登记,再用 broadcast_interruption() 打断当前播报。新的 LLM
    回复由 assistant aggregator 确认处理完 interruption 后触发。
    run_immediately=False(RTVI send-text 静默追加):仅把文字写进上下文,
    不打断、不触发推理。
    """

    def __init__(self, should_ignore_input: Callable[[], bool] | None = None):
        super().__init__()
        self._should_ignore_input = should_ignore_input or (lambda: False)
        # 立即触发的文字(含打断语义)走 on_text_input;静默追加另走一条事件
        self._register_event_handler("on_text_input")
        self._register_event_handler("on_text_append")
        self._register_event_handler("on_client_ready")

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if not isinstance(frame, InputTransportMessageFrame):
            await self.push_frame(frame, direction)
            return

        if isinstance(frame.message, dict) and frame.message.get("type") == "client-ready":
            await self._call_event_handler("on_client_ready")
            return

        parsed = _text_input(frame.message)
        if not parsed:
            await self.push_frame(frame, direction)
            return

        if self._should_ignore_input():
            logger.debug("通话正在结束,忽略后续文字输入")
            return

        text, run_immediately = parsed
        if run_immediately:
            # 先登记文字再打断。下一轮 LLM 由 assistant aggregator 在真正处理完
            # InterruptionFrame 后触发,避免新回复被这次 interruption 一起取消。
            await self._call_event_handler("on_text_input", text)
            await self.broadcast_interruption()
        else:
            await self._call_event_handler("on_text_append", text)


class CallEndingUserMuteStrategy(BaseUserMuteStrategy):
    """Keep user media muted after an end-call tool starts terminating a call."""

    def __init__(self, is_call_ending: Callable[[], bool]):
        super().__init__()
        self._is_call_ending = is_call_ending

    async def process_frame(self, frame) -> bool:
        await super().process_frame(frame)
        return self._is_call_ending()


class VisionCaptureProcessor(FrameProcessor):
    """Capture one requested video frame for auxiliary vision-model analysis."""

    def __init__(self, timeout_s: float = 3.0):
        super().__init__()
        self._timeout_s = timeout_s
        self._pending: dict[str, asyncio.Future[UserImageRawFrame]] = {}

    async def request_image(
        self,
        requester: FrameProcessor,
        request: UserImageRequestFrame,
    ) -> UserImageRawFrame:
        key = request.tool_call_id or str(uuid4())
        request.tool_call_id = key
        request.append_to_context = False
        request.result_callback = None

        loop = asyncio.get_running_loop()
        future: asyncio.Future[UserImageRawFrame] = loop.create_future()
        self._pending[key] = future
        await requester.push_frame(request, FrameDirection.UPSTREAM)
        try:
            return await asyncio.wait_for(future, timeout=self._timeout_s)
        finally:
            self._pending.pop(key, None)

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if (
            isinstance(frame, UserImageRawFrame)
            and frame.request
            and frame.request.tool_call_id
            and frame.request.tool_call_id in self._pending
        ):
            future = self._pending[frame.request.tool_call_id]
            if not future.done():
                future.set_result(frame)
            return

        await self.push_frame(frame, direction)


class RealtimeTextInputProcessor(FrameProcessor):
    """Route text input directly to a realtime service without cascade semantics."""

    def __init__(self):
        super().__init__()
        self._register_event_handler("on_text_input")
        self._register_event_handler("on_text_append")

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if not isinstance(frame, InputTransportMessageFrame):
            await self.push_frame(frame, direction)
            return

        parsed = _text_input(frame.message)
        if not parsed:
            await self.push_frame(frame, direction)
            return

        text, run_immediately = parsed
        await self._call_event_handler(
            "on_text_input" if run_immediately else "on_text_append",
            text,
        )


class ConversationHistoryProcessor(FrameProcessor):
    """从最终客户端事件旁路保存历史，不改变 Pipecat 的上下文与帧语义。"""

    def __init__(self, recorder: ConversationRecorder | None):
        super().__init__()
        self._recorder = recorder

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        await self.push_frame(frame, direction)
        if self._recorder and isinstance(frame, OutputTransportMessageUrgentFrame):
            await self._recorder.record_transport_message(frame.message)


class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
    """聚合 LLM 回复进上下文,同时继续把回复帧交给下游 TTS。"""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._register_event_handler("on_interruption_processed")
        self._register_event_handler("on_assistant_text_start")
        self._register_event_handler("on_assistant_text_delta")
        self._register_event_handler("on_assistant_text_end")
        self._stream_turn_id: str | None = None
        self._stream_timestamp = ""
        self._stream_text = ""

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if isinstance(frame, LLMFullResponseStartFrame):
            self._stream_turn_id = uuid4().hex
            self._stream_timestamp = time_now_iso8601()
            self._stream_text = ""
            await self._call_event_handler(
                "on_assistant_text_start",
                self._stream_turn_id,
                self._stream_timestamp,
            )
        elif isinstance(frame, LLMTextFrame) and self._stream_turn_id:
            self._stream_text += frame.text
            await self._call_event_handler(
                "on_assistant_text_delta",
                self._stream_turn_id,
                frame.text,
            )
        elif isinstance(frame, LLMFullResponseEndFrame):
            await self._finish_text_stream(interrupted=False)

        # LLMAssistantAggregator 默认会消费这些帧。放在 TTS 前用于中断时保存
        # 已生成前缀时,必须显式透传,否则 TTS 收不到任何 LLM 回复。
        if isinstance(
            frame,
            (LLMFullResponseStartFrame, LLMFullResponseEndFrame, TextFrame),
        ):
            await self.push_frame(frame, direction)
        elif isinstance(frame, InterruptionFrame):
            await self._finish_text_stream(interrupted=True)
            await self._call_event_handler("on_interruption_processed")

    async def _finish_text_stream(self, *, interrupted: bool):
        if not self._stream_turn_id:
            return
        await self._call_event_handler(
            "on_assistant_text_end",
            self._stream_turn_id,
            self._stream_text,
            interrupted,
        )
        self._stream_turn_id = None
        self._stream_timestamp = ""
        self._stream_text = ""


async def run_pipeline(
    transport,
    cfg: AssistantConfig,
    *,
    vision_enabled: bool = False,
    assistant_id: str | None = None,
    channel: str = "webrtc",
) -> None:
    """在给定 transport 上构建并运行管线,直到连接结束。

    Args:
        transport: 任意 pipecat transport(WebRTC / WS / 电话…),
                   只要有 .input() / .output() / event_handler 即可。
        cfg:       助手配置(随请求内联传入)。
    """
    logger.info(
        f"启动管线: assistant={cfg.name} type={cfg.type} "
        f"mode={cfg.runtimeMode} vision={vision_enabled}"
    )

    # 大脑:按类型决定 LLM 槽/开场白/上下文归属。每通电话一个实例(可持会话状态)。
    brain = build_brain(cfg)
    if (
        cfg.runtimeMode == "realtime"
        and "realtime" not in brain.spec.supported_runtime_modes
    ):
        raise ValueError(f"类型 {cfg.type} 不支持 realtime 运行模式")

    if cfg.runtimeMode == "realtime":
        if vision_enabled:
            logger.warning("Realtime 模式暂未接入视频帧工具,本次仅启用语音通话")
        await run_realtime_pipeline(
            transport,
            cfg,
            brain=brain,
            assistant_id=assistant_id,
            channel=channel,
        )
        return

    stt = create_stt(cfg)
    tts = create_tts(cfg)

    greeting = await brain.greeting(cfg)
    system_content = brain.system_prompt(cfg)

    worker_holder: dict = {}

    async def queue_call_end(reason: str) -> None:
        worker = worker_holder.get("worker")
        if worker is None:
            return
        logger.info(f"结束通话: reason={reason}")
        await worker.queue_frame(
            OutputTransportMessageUrgentFrame(
                message={"type": "call-ended", "reason": reason}
            )
        )
        await worker.queue_frame(EndFrame())

    call_end = CallEndCoordinator(queue_call_end)

    def with_vision_hint(text: str) -> str:
        if not vision_enabled:
            return text
        if not text:
            return VISION_SYSTEM_HINT
        return f"{text}\n\n{VISION_SYSTEM_HINT}"

    context = LLMContext(
        messages=[{"role": "system", "content": with_vision_hint(system_content)}]
    )
    # LLM 槽由大脑提供:本地模型或 Dify/FastGPT 外部托管适配器。
    llm = brain.build_llm(cfg, context)
    user_aggregator = LLMUserAggregator(
        context,
        params=LLMUserAggregatorParams(
            vad_analyzer=create_vad_analyzer(cfg.turnConfig),
            user_mute_strategies=[
                FunctionCallUserMuteStrategy(),
                CallEndingUserMuteStrategy(lambda: call_end.ending),
            ],
            user_turn_strategies=create_user_turn_strategies(
                cfg.turnConfig,
                enable_interruptions=cfg.enableInterrupt,
            ),
        ),
    )
    assistant_aggregator = PassthroughLLMAssistantAggregator(context)
    text_input = TextInputProcessor(should_ignore_input=lambda: call_end.ending)
    vision_capture = VisionCaptureProcessor()
    vision_native_mode = vision_enabled and _vision_uses_main_llm(cfg)
    vision_state: dict[str, str | None] = {"client_id": None}
    vision_schema = FunctionSchema(
        name=VISION_TOOL_NAME,
        description=(
            "获取用户当前摄像头画面。当用户询问当前画面、看到了什么、"
            "人/物品/环境状态或需要视觉判断时调用。"
        ),
        properties={
            "question": {
                "type": "string",
                "description": "用户关于当前视频画面的具体问题。",
            }
        },
        required=["question"],
    )

    async def fetch_user_image(params: FunctionCallParams):
        question = str(params.arguments.get("question") or "请描述当前画面。")
        user_id = vision_state.get("client_id")
        if not user_id:
            await params.result_callback(
                {
                    "status": "no_video_client",
                    "message": "当前还没有可用的摄像头视频流。",
                }
            )
            return

        request = UserImageRequestFrame(
            user_id=user_id,
            text=question,
            append_to_context=vision_native_mode,
            function_name=params.function_name,
            tool_call_id=params.tool_call_id,
            result_callback=params.result_callback if vision_native_mode else None,
        )
        if vision_native_mode:
            logger.debug(
                f"请求当前视频帧进入主 LLM 上下文: user_id={user_id}, question={question}"
            )
            await params.llm.push_frame(request, FrameDirection.UPSTREAM)
            return

        logger.debug(
            f"请求当前视频帧给单独视觉模型分析: user_id={user_id}, question={question}"
        )
        try:
            frame = await vision_capture.request_image(params.llm, request)
            observation = await _analyze_image_with_vision_model(cfg, frame, question)
        except asyncio.TimeoutError:
            await params.result_callback(
                {
                    "status": "timeout",
                    "message": "等待摄像头视频帧超时。",
                }
            )
            return
        except Exception as e:
            logger.exception(f"视觉模型分析失败: {e}")
            await params.result_callback(
                {
                    "status": "error",
                    "message": f"视觉理解失败: {type(e).__name__}",
                }
            )
            return

        await params.result_callback(
            {
                "status": "ok",
                "question": question,
                "observation": observation or "视觉模型没有返回有效观察结果。",
            }
        )

    if vision_enabled:
        llm.register_function(VISION_TOOL_NAME, fetch_user_image)

    def set_visible_tools(schemas: list[FunctionSchema] | None = None) -> None:
        tools = list(schemas or [])
        if vision_enabled:
            tools.append(vision_schema)
        if tools:
            context.set_tools(ToolsSchema(standard_tools=tools))
        else:
            context.set_tools()

    recorder = await ConversationRecorder.start(
        assistant_id=assistant_id,
        assistant_name=cfg.name,
        channel=channel,
        runtime_mode=cfg.runtimeMode,
    )
    pipeline = Pipeline(
        [
            transport.input(),
            vision_capture,
            text_input,
            stt,
            user_aggregator,
            llm,
            # Aggregate the streamed LLM text before TTS. On interruption,
            # Pipecat commits the generated prefix immediately instead of
            # waiting for a TTS provider to emit spoken-text/timestamp frames.
            assistant_aggregator,
            tts,
            EndCallAfterSpeechProcessor(call_end),
            ConversationHistoryProcessor(recorder),
            transport.output(),
        ]
    )

    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=False,
        ),
        enable_rtvi=False,
    )
    worker_holder["worker"] = worker

    async def queue_transcript(role: str, content: str, timestamp: str) -> None:
        if content:
            await worker.queue_frame(
                OutputTransportMessageUrgentFrame(
                    message={
                        "type": "transcript",
                        "role": role,
                        "content": content,
                        "timestamp": timestamp,
                    },
                )
            )

    greeting_transcript_sent = False
    pending_text_inputs: list[str] = []

    def set_system_prompt(text: str) -> None:
        """替换上下文里的系统提示(节点切换时整体替换,而非追加)。"""
        messages = context.get_messages()
        content = with_vision_hint(text)
        if messages and messages[0].get("role") == "system":
            messages[0] = {"role": "system", "content": content}
        else:
            messages.insert(0, {"role": "system", "content": content})

    set_visible_tools([])
    await brain.setup(
        cfg,
        BrainRuntime(
            context=context,
            llm=llm,
            queue_frame=worker.queue_frame,
            set_system_prompt=set_system_prompt,
            set_tools=set_visible_tools,
            call_end=call_end,
        ),
    )

    async def append_user_text_to_context(text: str, *, run_llm: bool) -> None:
        await worker.queue_frame(
            LLMMessagesAppendFrame(
                messages=[{"role": "user", "content": text}],
                run_llm=run_llm,
            )
        )

    @user_aggregator.event_handler("on_user_turn_stopped")
    async def on_user_turn_stopped(_aggregator, _strategy, message):
        if message.content:
            brain.record_user_message(message.content)
        await queue_transcript("user", message.content, message.timestamp)

    @assistant_aggregator.event_handler("on_assistant_text_start")
    async def on_assistant_text_start(_aggregator, turn_id, timestamp):
        await brain.on_assistant_text_start(turn_id)
        await worker.queue_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "assistant-text-start",
                    "turn_id": turn_id,
                    "timestamp": timestamp,
                }
            )
        )

    @assistant_aggregator.event_handler("on_assistant_text_delta")
    async def on_assistant_text_delta(_aggregator, turn_id, delta):
        await worker.queue_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "assistant-text-delta",
                    "turn_id": turn_id,
                    "delta": delta,
                }
            )
        )

    @assistant_aggregator.event_handler("on_assistant_text_end")
    async def on_assistant_text_end(_aggregator, turn_id, content, interrupted):
        await worker.queue_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "assistant-text-end",
                    "turn_id": turn_id,
                    "content": content,
                    "interrupted": interrupted,
                }
            )
        )
        await brain.on_assistant_text_end(turn_id, content, interrupted)

    @text_input.event_handler("on_text_input")
    async def on_text_input(_processor, text):
        pending_text_inputs.append(text)
        brain.record_user_message(text)
        # 前端显示不依赖 interruption 后续事件,必须在打断前先排入发送队列。
        await queue_transcript("user", text, time_now_iso8601())

    @assistant_aggregator.event_handler("on_interruption_processed")
    async def on_interruption_processed(_aggregator):
        if not pending_text_inputs:
            return
        text = pending_text_inputs.pop(0)
        # assistant aggregator 已处理完 interruption,现在再启动下一轮 LLM。
        await append_user_text_to_context(text, run_llm=True)

    @text_input.event_handler("on_text_append")
    async def on_text_append(_processor, text):
        # 静默追加:写进上下文但不打断、不触发推理;transcript 照常上报
        brain.record_user_message(text)
        await queue_transcript("user", text, time_now_iso8601())
        await append_user_text_to_context(text, run_llm=False)

    @text_input.event_handler("on_client_ready")
    async def on_client_ready(_processor):
        nonlocal greeting_transcript_sent
        if greeting and not greeting_transcript_sent:
            greeting_transcript_sent = True
            await queue_transcript("assistant", greeting, time_now_iso8601())

    @transport.event_handler("on_client_connected")
    async def on_client_connected(_transport, _client):
        if vision_enabled:
            try:
                vision_state["client_id"] = get_transport_client_id(
                    _transport,
                    _client,
                )
                await maybe_capture_participant_camera(_transport, _client)
                logger.info(f"视觉理解已接入视频客户端: {vision_state['client_id']}")
            except Exception as e:
                logger.warning(f"视觉理解摄像头捕获初始化失败: {e}")
        if greeting:
            # 外部托管类型的上下文由对方服务端维护,开场白不写入本地 context
            if brain.spec.owns_context:
                context.add_message({"role": "assistant", "content": greeting})
            await worker.queue_frame(TTSSpeakFrame(greeting, append_to_context=False))
        await brain.on_connected()

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(_transport, _client):
        logger.info("对端断开,结束管线")
        await worker.queue_frame(EndFrame())

    runner = WorkerRunner(handle_sigint=False)
    run_status = "completed"
    try:
        await runner.add_workers(worker)
        await runner.run()
    except Exception:
        run_status = "failed"
        raise
    finally:
        if recorder:
            await recorder.finish(status=run_status)
    logger.info("管线已结束")


async def run_realtime_pipeline(
    transport,
    cfg: AssistantConfig,
    *,
    brain: Brain,
    assistant_id: str | None = None,
    channel: str = "webrtc",
) -> None:
    """Run a speech-to-speech model that owns ASR, reasoning, and synthesis."""
    realtime = create_realtime_service(
        cfg,
        instructions=brain.system_prompt(cfg),
    )
    text_input = RealtimeTextInputProcessor()
    greeting = await brain.greeting(cfg)

    recorder = await ConversationRecorder.start(
        assistant_id=assistant_id,
        assistant_name=cfg.name,
        channel=channel,
        runtime_mode=cfg.runtimeMode,
    )
    pipeline = Pipeline(
        [
            transport.input(),
            text_input,
            realtime,
            ConversationHistoryProcessor(recorder),
            transport.output(),
        ]
    )
    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=False,
            audio_in_sample_rate=int(
                cfg.realtime_values.get("inputSampleRate") or 24000
            ),
            audio_out_sample_rate=int(
                cfg.realtime_values.get("outputSampleRate") or 24000
            ),
        ),
        enable_rtvi=False,
    )

    async def queue_transcript(role: str, content: str) -> None:
        if content:
            await worker.queue_frame(
                OutputTransportMessageUrgentFrame(
                    message={
                        "type": "transcript",
                        "role": role,
                        "content": content,
                        "timestamp": time_now_iso8601(),
                    },
                )
            )

    @text_input.event_handler("on_text_input")
    async def on_text_input(_processor, text):
        await queue_transcript("user", text)
        await realtime.interrupt()
        await realtime.send_text(text, run_immediately=True)

    @text_input.event_handler("on_text_append")
    async def on_text_append(_processor, text):
        await queue_transcript("user", text)
        await realtime.send_text(text, run_immediately=False)

    @transport.event_handler("on_client_connected")
    async def on_client_connected(_transport, _client):
        if greeting:
            await realtime.speak(greeting)

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(_transport, _client):
        logger.info("Realtime 对端断开,结束管线")
        await worker.queue_frame(EndFrame())

    runner = WorkerRunner(handle_sigint=False)
    run_status = "completed"
    try:
        await runner.add_workers(worker)
        await runner.run()
    except Exception:
        run_status = "failed"
        raise
    finally:
        if recorder:
            await recorder.finish(status=run_status)
    logger.info("Realtime 管线已结束")