Add vision model enhancements and image processing capabilities

- Update `requirements.txt` to include Pillow for image handling.
- Refactor vision model validation logic in `voice_webrtc.py` to improve error handling for unsupported image input.
- Introduce new functions in `pipeline.py` for image data processing and analysis using vision models.
- Implement `VisionCaptureProcessor` to manage video frame requests for auxiliary vision model analysis.
- Enhance the pipeline to support image input requests and integrate vision model responses into the processing flow.

backend/requirements.txt

@@ -3,6 +3,7 @@
 # silero  -> 本地 VAD(判断用户说话起止),语音必备
 # openai  -> OpenAI 兼容的 LLM/STT/TTS 客户端(DeepSeek、SenseVoice、CosyVoice 都走它)
 pipecat-ai[webrtc,websocket,silero,openai]==1.3.0
+Pillow>=11.1.0,<13
 
 # FastGPT 类型助手:本地 SDK(包 /api/v1/chat/completions 流式 + chatId 会话)
 fastgpt-client @ file:///Users/wangx/Code/AI-VideoAssistant-Project/fastgpt-python-sdk

backend/routes/voice_webrtc.py

@@ -74,16 +74,6 @@ async def _resolve_config(offer: SignalingOffer) -> AssistantConfig:
     raise ValueError("offer 缺少 assistant_id 或 inline_config")
 
 
-def _apply_vision_model(cfg: AssistantConfig) -> None:
-    cfg.model = cfg.vision_model
-    cfg.llm_interface_type = cfg.vision_llm_interface_type
-    cfg.llm_values = cfg.vision_llm_values
-    cfg.llm_secrets = cfg.vision_llm_secrets
-    cfg.llm_support_image_input = cfg.vision_llm_support_image_input
-    cfg.llm_api_key = cfg.vision_llm_api_key
-    cfg.llm_base_url = cfg.vision_llm_base_url
-
-
 async def _handle_offer(websocket, payload, peers):
     from pipecat.transports.smallwebrtc.connection import SmallWebRTCConnection
     from services.pipecat.pipeline import run_pipeline
@@ -99,11 +89,17 @@ async def _handle_offer(websocket, payload, peers):
         cfg = await _resolve_config(offer)  # 解析放在建连前,配置错就别建连
         vision_enabled = offer.vision_enabled or cfg.vision_enabled
         if vision_enabled:
-            if not cfg.vision_llm_support_image_input:
+            has_native_vision = (
+                not cfg.vision_model_resource_id and cfg.llm_support_image_input
+            )
+            has_aux_vision_model = (
+                bool(cfg.vision_model_resource_id)
+                and cfg.vision_llm_support_image_input
+            )
+            if not (has_native_vision or has_aux_vision_model):
                 raise ValueError(
-                    "当前视觉模型不支持图片输入,请在模型资源中选择支持图片输入的 LLM"
+                    "当前模型不支持图片输入,请在模型资源中选择支持图片输入的视觉模型"
                 )
-            _apply_vision_model(cfg)
         pc = SmallWebRTCConnection(ice_servers=aiortc_ice_servers())
         if pc_id:
             pc._pc_id = pc_id

backend/services/pipecat/pipeline.py

@@ -6,11 +6,16 @@
 对应 dograh 的 pipeline_builder.py + run_pipeline.py(已砍掉 workflow 引擎/DB/录音/指标)。
 """
 
+import asyncio
+import base64
+from io import BytesIO
 from uuid import uuid4
 
 import config
 from loguru import logger
 from models import AssistantConfig
+from openai import AsyncOpenAI
+from PIL import Image
 from services.brains import build_brain
 from services.pipecat.service_factory import (
     create_realtime_service,
@@ -35,6 +40,7 @@ from pipecat.frames.frames import (
     OutputTransportMessageUrgentFrame,
     TextFrame,
     TTSSpeakFrame,
+    UserImageRawFrame,
     UserImageRequestFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
@@ -65,6 +71,67 @@ 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 _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=cfg.vision_llm_api_key or config.LLM_API_KEY,
+        base_url=cfg.vision_llm_base_url or config.LLM_BASE_URL,
+    )
+    try:
+        response = await client.chat.completions.create(
+            model=cfg.vision_model or config.LLM_MODEL,
+            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:
@@ -130,6 +197,50 @@ class TextInputProcessor(FrameProcessor):
             await self._call_event_handler("on_text_append", text)
 
 
+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."""
 
@@ -314,6 +425,8 @@ async def run_pipeline(
     )
     assistant_aggregator = PassthroughLLMAssistantAggregator(context)
     text_input = TextInputProcessor()
+    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,
@@ -342,17 +455,51 @@ async def run_pipeline(
             )
             return
 
-        logger.debug(f"请求当前视频帧: user_id={user_id}, question={question}")
-        await params.llm.push_frame(
-            UserImageRequestFrame(
-                user_id=user_id,
-                text=question,
-                append_to_context=True,
-                function_name=params.function_name,
-                tool_call_id=params.tool_call_id,
-                result_callback=params.result_callback,
-            ),
-            FrameDirection.UPSTREAM,
+        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:
@@ -399,6 +546,7 @@ async def run_pipeline(
     pipeline = Pipeline(
         [
             transport.input(),
+            vision_capture,
             text_input,
             stt,
             user_aggregator,