Initial commit: AI Video Assistant fullstack platform.

Add pipecat-based backend with WebRTC/WS voice routes, Next.js frontend, and Docker Compose orchestration.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/services/pipecat/pipeline.py

@@ -0,0 +1,67 @@
+"""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。
+
+对应 dograh 的 pipeline_builder.py + run_pipeline.py(已砍掉 workflow 引擎/DB/录音/指标)。
+"""
+
+from loguru import logger
+from models import AssistantConfig
+from services.pipecat.service_factory import create_services
+
+from pipecat.frames.frames import EndFrame, TTSSpeakFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+
+
+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。
+
+    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} mode={cfg.runtimeMode}")
+
+    stt, llm, tts = create_services(cfg)
+
+    context = OpenAILLMContext(messages=[{"role": "system", "content": cfg.prompt}])
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=cfg.enableInterrupt,
+            enable_metrics=False,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(_transport, _client):
+        if cfg.greeting:
+            await task.queue_frame(TTSSpeakFrame(cfg.greeting))
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(_transport, _client):
+        logger.info("对端断开,结束管线")
+        await task.queue_frame(EndFrame())
+
+    runner = PipelineRunner(handle_sigint=False)
+    await runner.run(task)
+    logger.info("管线已结束")