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

"""Reusable frame processors shared by cascade and realtime pipelines."""

import asyncio
from collections.abc import Awaitable, Callable
from dataclasses import dataclass
from uuid import uuid4

from loguru import logger
from models import AssistantConfig
from services.brains import Brain
from services.brains.base import SessionVariableUpdate
from services.conversation_history import ConversationRecorder
from services.knowledge import search as search_knowledge
from db.session import SessionLocal

from pipecat.frames.frames import (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
    FunctionCallCancelFrame,
    FunctionCallResultFrame,
    FunctionCallsStartedFrame,
    InputAudioRawFrame,
    InputTransportMessageFrame,
    InterruptionFrame,
    LLMContextFrame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    LLMTextFrame,
    OutputTransportMessageUrgentFrame,
    TextFrame,
    UserImageRawFrame,
    UserImageRequestFrame,
)
from pipecat.processors.aggregators.llm_response_universal import (
    LLMAssistantAggregator,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.turns.user_mute.base_user_mute_strategy import BaseUserMuteStrategy
from pipecat.utils.time import time_now_iso8601


KNOWLEDGE_CONTEXT_MARKER = "<!-- knowledge-context -->"
MAX_SESSION_UPDATE_VARIABLES = 20


@dataclass(frozen=True)
class UserInput:
    """Validated application input submitted as one user turn."""

    input_id: str
    text: str
    has_camera_frame: bool
    image_asset_token: str | None
    run_immediately: bool
    interrupt: bool

    @property
    def has_image(self) -> bool:
        return self.has_camera_frame or bool(self.image_asset_token)

    @property
    def prompt_text(self) -> str:
        if self.text:
            return self.text
        return "请根据用户刚提交的图片进行回复。"

    @property
    def transcript_text(self) -> str:
        # Image turns are persisted as one structured media message, including
        # any optional text caption. Do not create a duplicate transcript row.
        return "" if self.has_image else self.text


class UserInputError(ValueError):
    def __init__(self, message: str, *, input_id: str = "") -> None:
        super().__init__(message)
        self.input_id = input_id


@dataclass(frozen=True)
class SessionUpdate:
    """Validated silent update to declared session variables."""

    update_id: str
    dynamic_variables: dict[str, str | int | float | bool]


class SessionUpdateError(ValueError):
    def __init__(self, message: str, *, update_id: str = "") -> None:
        super().__init__(message)
        self.update_id = update_id


def parse_session_update(message) -> SessionUpdate | None:
    """Parse the silent session-update wire format."""
    if not isinstance(message, dict) or message.get("type") != "session-update":
        return None

    update_id = str(message.get("update_id") or "").strip()
    if not update_id or len(update_id) > 128:
        raise SessionUpdateError(
            "session-update 缺少有效的 update_id",
            update_id=update_id,
        )
    if message.get("schema_version") != 1:
        raise SessionUpdateError(
            "不支持的 session-update schema_version",
            update_id=update_id,
        )

    raw_variables = message.get("dynamic_variables")
    if not isinstance(raw_variables, dict) or not raw_variables:
        raise SessionUpdateError(
            "session-update dynamic_variables 不能为空",
            update_id=update_id,
        )
    if len(raw_variables) > MAX_SESSION_UPDATE_VARIABLES:
        raise SessionUpdateError(
            f"session-update 一次最多更新 {MAX_SESSION_UPDATE_VARIABLES} 个变量",
            update_id=update_id,
        )

    dynamic_variables: dict[str, str | int | float | bool] = {}
    for raw_name, value in raw_variables.items():
        if not isinstance(raw_name, str):
            raise SessionUpdateError(
                "session-update 动态变量名必须是字符串",
                update_id=update_id,
            )
        if not isinstance(value, (str, int, float, bool)) or value is None:
            raise SessionUpdateError(
                f"动态变量 {raw_name} 仅支持字符串、数字或布尔值",
                update_id=update_id,
            )
        dynamic_variables[raw_name] = value

    return SessionUpdate(
        update_id=update_id,
        dynamic_variables=dynamic_variables,
    )


def parse_user_input(message) -> UserInput | None:
    """Parse the sole public user-input wire format."""
    if not isinstance(message, dict):
        return None
    if message.get("type") != "user-input":
        return None

    input_id = str(message.get("input_id") or "").strip()
    if not input_id or len(input_id) > 128:
        raise UserInputError("user-input 缺少有效的 input_id", input_id=input_id)
    if message.get("schema_version") != 1:
        raise UserInputError("不支持的 user-input schema_version", input_id=input_id)

    parts = message.get("parts")
    if not isinstance(parts, list) or not parts:
        raise UserInputError("user-input parts 不能为空", input_id=input_id)

    text = ""
    has_camera_frame = False
    image_asset_token: str | None = None
    for part in parts:
        if not isinstance(part, dict):
            raise UserInputError("user-input part 格式不正确", input_id=input_id)
        part_type = part.get("type")
        if part_type == "input_text":
            if text:
                raise UserInputError("P0 只支持一个 input_text", input_id=input_id)
            text = str(part.get("text") or "").strip()
            if not text:
                raise UserInputError("input_text 不能为空", input_id=input_id)
        elif part_type == "input_image":
            if has_camera_frame or image_asset_token:
                raise UserInputError("P0 只支持一张图片", input_id=input_id)
            source = part.get("source")
            if not isinstance(source, dict):
                raise UserInputError("input_image source 格式不正确", input_id=input_id)
            if source.get("type") == "camera_frame" and source.get("frame") == "current":
                has_camera_frame = True
            elif source.get("type") == "uploaded_asset":
                token = str(source.get("asset_token") or "").strip()
                if not token or len(token) > 4096:
                    raise UserInputError(
                        "上传图片缺少有效的 asset_token",
                        input_id=input_id,
                    )
                image_asset_token = token
            else:
                raise UserInputError(
                    "仅支持当前摄像头画面或已上传图片",
                    input_id=input_id,
                )
        else:
            raise UserInputError(f"不支持的输入类型: {part_type}", input_id=input_id)

    if not text and not (has_camera_frame or image_asset_token):
        raise UserInputError("user-input 没有有效内容", input_id=input_id)

    options = message.get("options")
    options = options if isinstance(options, dict) else {}
    run_immediately = bool(options.get("run_immediately", True))
    interrupt = bool(options.get("interrupt", True))
    return UserInput(
        input_id=input_id,
        text=text,
        has_camera_frame=has_camera_frame,
        image_asset_token=image_asset_token,
        run_immediately=run_immediately,
        interrupt=interrupt,
    )


def message_text(message: dict) -> str:
    """Extract textual content from plain or multimodal LLM messages."""
    content = message.get("content")
    if isinstance(content, str):
        return content.strip()
    if not isinstance(content, list):
        return ""
    return "\n".join(
        str(part.get("text") or "").strip()
        for part in content
        if isinstance(part, dict)
        and part.get("type") in {"text", "input_text"}
        and str(part.get("text") or "").strip()
    ).strip()


class UserInputProcessor(FrameProcessor):
    """Validate app inputs and coordinate their interruption boundary."""

    def __init__(self, should_ignore_input: Callable[[], bool] | None = None):
        super().__init__()
        self._should_ignore_input = should_ignore_input or (lambda: False)
        self._register_event_handler("on_user_input")
        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

        try:
            user_input = parse_user_input(frame.message)
        except UserInputError as exc:
            await self._emit_result(exc.input_id, "error", str(exc))
            return
        if user_input is None:
            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")
            await self._emit_result(user_input.input_id, "error", "当前不能接收新的用户输入")
            return

        await self._call_event_handler("on_user_input", user_input)
        if user_input.run_immediately and user_input.interrupt:
            await self.broadcast_interruption()

    async def _emit_result(self, input_id: str, status: str, message: str = "") -> None:
        await self.push_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "user-input-result",
                    "input_id": input_id,
                    "status": status,
                    **({"message": message} if message else {}),
                }
            )
        )


class SessionUpdateProcessor(FrameProcessor):
    """Apply silent dynamic-variable updates without creating a user turn."""

    def __init__(
        self,
        apply_update: Callable[
            [dict[str, str | int | float | bool]],
            Awaitable[SessionVariableUpdate],
        ],
        *,
        after_update: Callable[[], Awaitable[None]] | None = None,
        should_ignore_update: Callable[[], bool] | None = None,
    ):
        super().__init__()
        self._apply_update = apply_update
        self._after_update = after_update
        self._should_ignore_update = should_ignore_update or (lambda: False)

    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

        try:
            update = parse_session_update(frame.message)
        except SessionUpdateError as exc:
            await self._emit_result(exc.update_id, "error", message=str(exc))
            return
        if update is None:
            await self.push_frame(frame, direction)
            return
        if self._should_ignore_update():
            await self._emit_result(
                update.update_id,
                "error",
                message="当前不能更新会话状态",
            )
            return

        try:
            result = await self._apply_update(update.dynamic_variables)
            if result.changed and self._after_update is not None:
                await self._after_update()
        except Exception as exc:  # noqa: BLE001 - protocol failures need a reply
            logger.warning(f"会话状态更新失败: {exc}")
            await self._emit_result(
                update.update_id,
                "error",
                message=str(exc),
            )
            return

        await self._emit_result(
            update.update_id,
            "accepted",
            changed=result.changed,
            dynamic_variables=result.dynamic_variables,
        )

    async def _emit_result(
        self,
        update_id: str,
        status: str,
        *,
        message: str = "",
        changed: list[str] | None = None,
        dynamic_variables: dict[str, str | int | float | bool] | None = None,
    ) -> None:
        await self.push_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "session-update-result",
                    "update_id": update_id,
                    "status": status,
                    **({"message": message} if message else {}),
                    **({"changed": changed} if changed is not None else {}),
                    **(
                        {"dynamic_variables": dynamic_variables}
                        if dynamic_variables is not None
                        else {}
                    ),
                }
            )
        )


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()


@dataclass
class _BlockedToolCall:
    tool_finished: bool = False
    response_started: bool = False
    response_finished: bool = False


class ToolInterruptionUserMuteStrategy(BaseUserMuteStrategy):
    """Mute user media for selected tools and their associated bot response.

    Pipecat's ``cancel_on_interruption`` also controls whether a tool is
    synchronous or asynchronous.  This strategy keeps the independent product
    setting ("allow user interruptions") at the user-input boundary.
    """

    def __init__(
        self,
        blocked_tools: dict[str, str] | set[str],
    ):
        super().__init__()
        self._blocked_tools = (
            dict(blocked_tools)
            if isinstance(blocked_tools, dict)
            else {name: "immediate" for name in blocked_tools}
        )
        self._calls: dict[str, _BlockedToolCall] = {}
        self._bot_speaking = False

    @property
    def is_muted(self) -> bool:
        return bool(self._calls)

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

        if isinstance(frame, BotStartedSpeakingFrame):
            self._bot_speaking = True
            for call in self._calls.values():
                call.response_started = True
        elif isinstance(frame, BotStoppedSpeakingFrame):
            self._bot_speaking = False
            for call in self._calls.values():
                if call.response_started:
                    call.response_finished = True
            self._release_completed()
        elif isinstance(frame, FunctionCallsStartedFrame):
            for call in frame.function_calls:
                execution_mode = self._blocked_tools.get(call.function_name)
                if execution_mode:
                    self._calls[call.tool_call_id] = _BlockedToolCall(
                        # An async tool may continue within the current model
                        # turn. An immediate tool's current speech is a preamble;
                        # its protected follow-up starts only after the result.
                        response_started=(
                            self._bot_speaking and execution_mode == "async"
                        ),
                    )
        elif isinstance(frame, FunctionCallResultFrame):
            call = self._calls.get(frame.tool_call_id)
            if call is not None:
                call.tool_finished = True
                self._release_completed()
        elif isinstance(frame, FunctionCallCancelFrame):
            # A canceled call cannot reliably produce a follow-up response.
            self._calls.pop(frame.tool_call_id, None)

        return self.is_muted

    def _release_completed(self) -> None:
        completed = [
            call_id
            for call_id, state in self._calls.items()
            if state.tool_finished and state.response_finished
        ]
        for call_id in completed:
            self._calls.pop(call_id, None)


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 RealtimeDynamicVariableProcessor(FrameProcessor):
    """Keep realtime system turn/history variables current between responses."""

    def __init__(self, brain: Brain, cfg: AssistantConfig, realtime):
        super().__init__()
        self._brain = brain
        self._cfg = cfg
        self._realtime = realtime

    async def _refresh_instructions(self) -> None:
        update = getattr(self._realtime, "update_instructions", None)
        if callable(update):
            await update(self._brain.system_prompt(self._cfg))

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, OutputTransportMessageUrgentFrame):
            message = frame.message
            if isinstance(message, dict):
                event_type = message.get("type")
                if event_type == "transcript" and message.get("role") == "user":
                    content = str(message.get("content") or "").strip()
                    if content:
                        self._brain.record_user_message(content)
                        await self._refresh_instructions()
                elif event_type == "assistant-text-end":
                    await self._brain.on_assistant_text_end(
                        str(message.get("turn_id") or ""),
                        str(message.get("content") or ""),
                        bool(message.get("interrupted", False)),
                    )
                    await self._refresh_instructions()
        await self.push_frame(frame, direction)


class RealtimeInputAudioGateProcessor(FrameProcessor):
    """Drop live microphone frames while a deterministic node owns the turn."""

    def __init__(self, is_enabled: Callable[[], bool]):
        super().__init__()
        self._is_enabled = is_enabled

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, InputAudioRawFrame) and not self._is_enabled():
            return
        await self.push_frame(frame, direction)


class RealtimeUserInputProcessor(FrameProcessor):
    """Route text-only user-input messages to a realtime service."""

    def __init__(self, should_ignore_input: Callable[[], bool] | None = None):
        super().__init__()
        self._should_ignore_input = should_ignore_input or (lambda: False)
        self._register_event_handler("on_user_input")

    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

        try:
            user_input = parse_user_input(frame.message)
        except UserInputError as exc:
            await self._emit_error(exc.input_id, str(exc))
            return
        if user_input is None:
            await self.push_frame(frame, direction)
            return
        if self._should_ignore_input():
            await self._emit_error(
                user_input.input_id,
                "当前工作流节点暂不接收用户输入",
            )
            return
        if user_input.has_image:
            await self._emit_error(
                user_input.input_id,
                "Realtime 模式暂不支持图片输入",
            )
            return
        await self._call_event_handler("on_user_input", user_input)

    async def _emit_error(self, input_id: str, message: str) -> None:
        await self.push_frame(
            OutputTransportMessageUrgentFrame(
                message={
                    "type": "user-input-result",
                    "input_id": input_id,
                    "status": "error",
                    "message": message,
                }
            )
        )


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 KnowledgeRetrievalProcessor(FrameProcessor):
    """Retrieve before local LLM inference without changing Pipecat internals."""

    def __init__(
        self,
        knowledge_base_id: str | None,
        top_n: int = 5,
        score_threshold: float = 0.0,
    ):
        super().__init__()
        self._knowledge_base_id = knowledge_base_id
        self._top_n = top_n
        self._score_threshold = score_threshold
        self._mode = "automatic" if knowledge_base_id else "disabled"
        self._last_signature = ""

    def set_scope(self, scope: dict) -> None:
        self._knowledge_base_id = scope.get("knowledge_base_id") or None
        self._mode = str(scope.get("mode") or "disabled")
        self._top_n = int(scope.get("top_n") or 5)
        self._score_threshold = float(scope.get("score_threshold") or 0.0)
        self._last_signature = ""

    def _clear_context(self, messages: list[dict]) -> None:
        # Remove the legacy Workflow knowledge message so an in-flight context
        # created before this compatibility fix cannot keep sending that role.
        messages[:] = [
            message
            for message in messages
            if not (
                message.get("role") == "developer"
                and KNOWLEDGE_CONTEXT_MARKER in str(message.get("content") or "")
            )
        ]
        system_message = next(
            (message for message in messages if message.get("role") == "system"),
            None,
        )
        if system_message is not None:
            content = str(system_message.get("content") or "")
            system_message["content"] = content.split(KNOWLEDGE_CONTEXT_MARKER, 1)[0].rstrip()

    def _set_context(self, messages: list[dict], block: str) -> None:
        """Store retrieved knowledge in a provider-compatible system message."""
        self._clear_context(messages)
        system_message = next(
            (message for message in messages if message.get("role") == "system"),
            None,
        )
        if system_message is None:
            messages.insert(0, {"role": "system", "content": block})
            return
        content = str(system_message.get("content") or "").rstrip()
        system_message["content"] = f"{content}\n\n{block}" if content else block

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if not isinstance(frame, LLMContextFrame):
            await self.push_frame(frame, direction)
            return

        messages = frame.context.get_messages()
        if self._mode != "automatic" or not self._knowledge_base_id:
            self._clear_context(messages)
            await self.push_frame(frame, direction)
            return
        user_messages = [message for message in messages if message.get("role") == "user"]
        if not user_messages:
            await self.push_frame(frame, direction)
            return
        query = message_text(user_messages[-1])
        signature = f"{len(user_messages)}:{query}"
        if not query or signature == self._last_signature:
            await self.push_frame(frame, direction)
            return
        self._last_signature = signature

        try:
            async with SessionLocal() as session:
                results = await search_knowledge(
                    session,
                    self._knowledge_base_id,
                    query,
                    top_k=self._top_n,
                    score_threshold=self._score_threshold,
                )
        except Exception as exc:
            logger.warning(f"自动知识库检索失败: {exc}")
            results = []

        sources = "\n\n".join(
            f"[{index + 1}] 来源：{item['document']}（相关度 {item['score']}）\n{item['content']}"
            for index, item in enumerate(results)
        ) or "未检索到相关资料。"
        block = f"{KNOWLEDGE_CONTEXT_MARKER}\n当前问题的知识库检索结果：\n{sources}"
        self._set_context(messages, block)
        await self.push_frame(frame, direction)


class UserTurnRoutingProcessor(FrameProcessor):
    """Give a brain first right of refusal before a new user turn reaches the LLM."""

    def __init__(self, brain: Brain):
        super().__init__()
        self._brain = brain
        self._last_user_message: dict | None = None

    async def process_frame(self, frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if direction != FrameDirection.DOWNSTREAM or not isinstance(
            frame, LLMContextFrame
        ):
            await self.push_frame(frame, direction)
            return

        user_message = next(
            (
                message
                for message in reversed(frame.context.get_messages())
                if message.get("role") == "user"
                and message_text(message)
            ),
            None,
        )
        if user_message is None:
            await self.push_frame(frame, direction)
            return

        if user_message is self._last_user_message:
            # Programmatic LLMRunFrame after a node transition reuses the same
            # user message. It is a response run, not another routing event.
            await self.push_frame(frame, direction)
            return
        self._last_user_message = user_message

        content = message_text(user_message)
        handled = await self._brain.on_user_turn_end(
            content,
            user_message=user_message,
        )
        if not handled:
            await self.push_frame(frame, direction)


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 = ""


class WorkflowAggregatorPair:
    """Small public-shape adapter required by Pipecat FlowManager."""

    def __init__(self, user_aggregator, assistant_aggregator):
        self._user = user_aggregator
        self._assistant = assistant_aggregator

    def user(self):
        return self._user

    def assistant(self):
        return self._assistant