Enhance LLM text streaming and message handling in backend and frontend

- Introduce event handlers in PassthroughLLMAssistantAggregator for managing LLM text streaming, including start, delta, and end events.
- Implement a new method to finalize text streams, ensuring proper handling of interruptions.
- Update useVoicePreview to support new message types for LLM text streaming, allowing real-time updates to chat messages.
- Enhance message sorting logic to maintain order based on timestamps and sequence numbers, improving user experience during voice interactions.

backend/services/pipecat/pipeline.py

@@ -6,6 +6,8 @@
 对应 dograh 的 pipeline_builder.py + run_pipeline.py(已砍掉 workflow 引擎/DB/录音/指标)。
 """
 
+from uuid import uuid4
+
 from loguru import logger
 from models import AssistantConfig
 from services.pipecat.service_factory import create_services
@@ -17,6 +19,7 @@ from pipecat.frames.frames import (
     InterruptionFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
+    LLMTextFrame,
     LLMMessagesAppendFrame,
     OutputTransportMessageUrgentFrame,
     TextFrame,
@@ -109,10 +112,35 @@ class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
     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(
@@ -121,8 +149,22 @@ class PassthroughLLMAssistantAggregator(LLMAssistantAggregator):
         ):
             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) -> None:
     """在给定 transport 上构建并运行管线,直到连接结束。
@@ -206,10 +248,42 @@ async def run_pipeline(transport, cfg: AssistantConfig) -> None:
     async def on_user_turn_stopped(_aggregator, _strategy, message):
         await queue_transcript("user", message.content, message.timestamp)
 
-    @assistant_aggregator.event_handler("on_assistant_turn_stopped")
-    async def on_assistant_turn_stopped(_aggregator, message):
-        # 助手半句此刻已写入上下文,上报为 transcript
-        await queue_transcript("assistant", message.content, message.timestamp)
+    @assistant_aggregator.event_handler("on_assistant_text_start")
+    async def on_assistant_text_start(_aggregator, turn_id, timestamp):
+        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,
+                }
+            )
+        )
 
     @text_input.event_handler("on_text_input")
     async def on_text_input(_processor, text):