Add conversation history management and API endpoints

- Introduce new database models for conversation sessions, messages, and artifacts to support conversation history tracking.
- Implement API routes for listing conversations and retrieving detailed conversation data, enhancing user interaction with historical records.
- Add a conversation recorder service to persist conversation messages in real-time without disrupting ongoing calls.
- Update the frontend to display conversation history, including filtering and sorting options, improving user experience.
- Enhance the pipeline to integrate conversation history recording seamlessly during interactions.

backend/services/pipecat/pipeline.py

@@ -17,6 +17,7 @@ from models import AssistantConfig
 from openai import AsyncOpenAI
 from PIL import Image
 from services.brains import build_brain
+from services.conversation_history import ConversationRecorder
 from services.pipecat.service_factory import (
     create_realtime_service,
     create_stt,
@@ -298,6 +299,20 @@ class RealtimeTextInputProcessor(FrameProcessor):
         )
 
 
+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。"""
 
@@ -363,6 +378,8 @@ async def run_pipeline(
     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。
 
@@ -388,7 +405,12 @@ async def run_pipeline(
     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)
+        await run_realtime_pipeline(
+            transport,
+            cfg,
+            assistant_id=assistant_id,
+            channel=channel,
+        )
         return
 
     stt = create_stt(cfg)
@@ -677,6 +699,12 @@ async def run_pipeline(
                 reason = str(call_end_state["reason"] or "completed")
                 await queue_call_end(reason)
 
+    recorder = await ConversationRecorder.start(
+        assistant_id=assistant_id,
+        assistant_name=cfg.name,
+        channel=channel,
+        runtime_mode=cfg.runtimeMode,
+    )
     pipeline = Pipeline(
         [
             transport.input(),
@@ -691,6 +719,7 @@ async def run_pipeline(
             assistant_aggregator,
             tts,
             EndCallAfterSpeech(),
+            ConversationHistoryProcessor(recorder),
             transport.output(),
         ]
     )
@@ -950,21 +979,42 @@ async def run_pipeline(
         await worker.queue_frame(EndFrame())
 
     runner = WorkerRunner(handle_sigint=False)
-    await runner.add_workers(worker)
-    await runner.run()
+    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) -> None:
+async def run_realtime_pipeline(
+    transport,
+    cfg: AssistantConfig,
+    *,
+    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)
     text_input = RealtimeTextInputProcessor()
 
+    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(),
         ]
     )
@@ -1017,6 +1067,14 @@ async def run_realtime_pipeline(transport, cfg: AssistantConfig) -> None:
         await worker.queue_frame(EndFrame())
 
     runner = WorkerRunner(handle_sigint=False)
-    await runner.add_workers(worker)
-    await runner.run()
+    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")