Merge branch 'develop' into fix/memoryconfig-update

api/app/controllers/file_storage_controller.py

@@ -574,8 +574,12 @@ async def get_file_url(
             # For local storage, generate signed URL with expiration
             url = generate_signed_url(str(file_id), expires)
         else:
-            # For remote storage (OSS/S3), get presigned URL
-            url = await storage_service.get_file_url(file_key, expires=expires)
+            # For remote storage (OSS/S3), get presigned URL with forced download
+            url = await storage_service.get_file_url(
+                file_key,
+                expires=expires,
+                file_name=file_metadata.file_name,
+            )
             url = _match_scheme(request, url)
 
         api_logger.info(f"Generated file URL: file_id={file_id}")
@@ -786,7 +790,7 @@ async def permanent_download_file(
         # For remote storage, redirect to presigned URL with long expiration
         try:
             # Use a very long expiration (7 days max for most cloud providers)
-            presigned_url = await storage_service.get_file_url(file_key, expires=604800)
+            presigned_url = await storage_service.get_file_url(file_key, expires=604800, file_name=file_metadata.file_name)
             presigned_url = _match_scheme(request, presigned_url)
             return RedirectResponse(url=presigned_url, status_code=status.HTTP_302_FOUND)
         except Exception as e:

api/app/controllers/mcp_market_config_controller.py

@@ -91,9 +91,11 @@ async def get_mcp_servers(
 
     try:
         cookies = api.get_cookies(token)
+        headers=api.builder_headers(api.headers)
+        headers['Authorization'] = f'Bearer {token}'
         r = api.session.put(
             url=api.mcp_base_url,
-            headers=api.builder_headers(api.headers),
+            headers=headers,
             json=body,
             cookies=cookies)
         raise_for_http_status(r)
@@ -173,6 +175,7 @@ async def get_operational_mcp_servers(
 
     url = f'{api.mcp_base_url}/operational'
     headers = api.builder_headers(api.headers)
+    headers['Authorization'] = f'Bearer {token}'
 
     try:
         cookies = api.get_cookies(access_token=token, cookies_required=True)
@@ -260,7 +263,9 @@ async def create_mcp_market_config(
             api.login(create_data.token)
             body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
             cookies = api.get_cookies(create_data.token)
-            r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {create_data.token}'
+            r = api.session.put(url=api.mcp_base_url, headers=headers, json=body, cookies=cookies)
             raise_for_http_status(r)
         except Exception as e:
             api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")
@@ -290,9 +295,11 @@ async def create_mcp_market_config(
                 'search': ""
             }
             cookies = api.get_cookies(token)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {token}'
             r = api.session.put(
                 url=api.mcp_base_url,
-                headers=api.builder_headers(api.headers),
+                headers=headers,
                 json=body,
                 cookies=cookies)
             raise_for_http_status(r)
@@ -393,7 +400,9 @@ async def update_mcp_market_config(
             api.login(update_data.token)
             body = {'filter': {}, 'page_number': 1, 'page_size': 1, 'search': None}
             cookies = api.get_cookies(update_data.token)
-            r = api.session.put(url=api.mcp_base_url, headers=api.builder_headers(api.headers), json=body, cookies=cookies)
+            headers = api.builder_headers(api.headers)
+            headers['Authorization'] = f'Bearer {update_data.token}'
+            r = api.session.put(url=api.mcp_base_url, headers=headers, json=body, cookies=cookies)
             raise_for_http_status(r)
         except Exception as e:
             api_logger.warning(f"Token validation failed for ModelScope MCP market: {str(e)}")

api/app/controllers/public_share_controller.py

@@ -669,6 +669,7 @@ async def config_query(
         content = {
             "app_type": release.app.type,
             "variables": release.config.get("variables"),
+            "memory": release.config.get("memory", {}).get("enabled"),
             "features": release.config.get("features")
         }
     elif release.app.type == AppType.MULTI_AGENT:

api/app/core/memory/agent/langgraph_graph/routing/write_router.py

@@ -178,7 +178,7 @@ async def window_dialogue(end_user_id, langchain_messages, memory_config, scope)
         count_store.update_sessions_count(end_user_id, is_end_user_id, langchain_messages)
     elif int(is_end_user_id) == int(scope):
         logger.info('写入长期记忆NEO4J')
-        formatted_messages = (redis_messages)
+        formatted_messages = redis_messages
         # Get config_id (if memory_config is an object, extract config_id; otherwise use directly)
         if hasattr(memory_config, 'config_id'):
             config_id = memory_config.config_id

api/app/core/memory/llm_tools/openai_embedder.py

@@ -2,6 +2,7 @@
 OpenAI Embedder 客户端实现
 
 基于 LangChain 和 RedBearEmbeddings 的 OpenAI 嵌入模型客户端实现。
+自动支持火山引擎的多模态 Embedding。
 """
 
 from typing import List
@@ -13,6 +14,7 @@ from app.core.memory.llm_tools.embedder_client import (
 )
 from app.core.models.base import RedBearModelConfig
 from app.core.models.embedding import RedBearEmbeddings
+from app.models.models_model import ModelProvider
 
 logger = logging.getLogger(__name__)
 
@@ -25,6 +27,7 @@ class OpenAIEmbedderClient(EmbedderClient):
     - 批量文本嵌入
     - 自动重试机制
     - 错误处理
+    - 火山引擎多模态 Embedding（自动识别）
     """
 
     def __init__(self, model_config: RedBearModelConfig):
@@ -36,7 +39,7 @@ class OpenAIEmbedderClient(EmbedderClient):
         """
         super().__init__(model_config)
 
-        # 初始化 RedBearEmbeddings 模型
+        # 初始化 RedBearEmbeddings（自动支持火山引擎多模态）
         self.model = RedBearEmbeddings(
             RedBearModelConfig(
                 model_name=self.model_name,
@@ -47,8 +50,9 @@ class OpenAIEmbedderClient(EmbedderClient):
                 timeout=self.timeout,
             )
         )
+        self.is_multimodal = self.model.is_multimodal_supported()
 
-        logger.info("OpenAI Embedder 客户端初始化完成")
+        logger.info(f"OpenAI Embedder 客户端初始化完成 (provider={self.provider}, multimodal={self.is_multimodal})")
 
     async def response(
         self,
@@ -77,7 +81,14 @@ class OpenAIEmbedderClient(EmbedderClient):
                 return []
 
             # 生成嵌入向量
-            embeddings = await self.model.aembed_documents(texts)
+            if self.is_multimodal:
+                # 火山引擎多模态 Embedding
+                embeddings = await self.model.aembed_multimodal(
+                    [{"type": "text", "text": text} for text in texts]
+                )
+            else:
+                # 普通 Embedding
+                embeddings = await self.model.aembed_documents(texts)
 
             logger.debug(f"成功生成 {len(embeddings)} 个嵌入向量")
             return embeddings

api/app/core/memory/storage_services/extraction_engine/extraction_orchestrator.py

@@ -1099,7 +1099,6 @@ class ExtractionOrchestrator:
                     metadata=chunk.metadata,
                 )
                 chunk_nodes.append(chunk_node)
-                logger.error(f"chunk file: {chunk.files}")
 
                 for p, file_type in chunk.files:
 

api/app/core/models/__init__.py

@@ -2,6 +2,7 @@ from .base import  RedBearModelConfig, get_provider_llm_class, RedBearModelFacto
 from .llm import RedBearLLM
 from .embedding import RedBearEmbeddings
 from .rerank import RedBearRerank
+from .generation import RedBearImageGenerator, RedBearVideoGenerator
 
 __all__ = [
     "RedBearModelConfig",
@@ -9,5 +10,7 @@ __all__ = [
     "RedBearEmbeddings",
     "RedBearRerank",
     "RedBearModelFactory",
-    "get_provider_llm_class"
+    "get_provider_llm_class",
+    "RedBearImageGenerator",
+    "RedBearVideoGenerator"
 ]

api/app/core/models/base.py

@@ -67,7 +67,7 @@ class RedBearModelFactory:
                 **config.extra_params
             }
 
-        if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.OLLAMA]:
+        if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.OLLAMA, ModelProvider.VOLCANO]:
             # 使用 httpx.Timeout 对象来设置详细的超时配置
             # 这样可以分别控制连接超时和读取超时
             import httpx
@@ -160,11 +160,13 @@ def get_provider_llm_class(config: RedBearModelConfig, type: ModelType = ModelTy
     # dashscope 的 omni 模型使用 OpenAI 兼容模式
     if provider == ModelProvider.DASHSCOPE and config.is_omni:
         return ChatOpenAI
-    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
+    if provider in [ModelProvider.OPENAI, ModelProvider.XINFERENCE, ModelProvider.GPUSTACK, ModelProvider.VOLCANO]:
         if type == ModelType.LLM:
             return OpenAI
         elif type == ModelType.CHAT:
             return ChatOpenAI
+        else:
+            raise BusinessException(f"不支持的模型提供商及类型: {provider}-{type}", code=BizCode.PROVIDER_NOT_SUPPORTED)
     elif provider == ModelProvider.DASHSCOPE:
         return ChatTongyi
     elif provider == ModelProvider.OLLAMA:

api/app/core/models/embedding.py

@@ -1,23 +1,190 @@
 
-from typing import Any, Dict, List, Optional, TypeVar, Callable
+from typing import Any, Dict, List, Optional, Union
 from langchain_core.embeddings import Embeddings
 
-from app.core.models.base import RedBearModelConfig,get_provider_embedding_class,RedBearModelFactory
+from app.core.models.base import RedBearModelConfig, get_provider_embedding_class, RedBearModelFactory
+from app.models.models_model import ModelProvider
+
 
 class RedBearEmbeddings(Embeddings):
-    """Embedding → 完全符合 LangChain Embeddings"""
+    """统一的 Embedding 类，自动支持多模态（根据 provider 判断）"""
+    
     def __init__(self, config: RedBearModelConfig):
-        self._model = self._create_model(config)
         self._config = config
+        self._is_volcano = config.provider.lower() == ModelProvider.VOLCANO
+        
+        if self._is_volcano:
+            # 火山引擎使用 Ark SDK
+            self._client = self._create_volcano_client(config)
+            self._model = None
+        else:
+            # 其他 provider 使用 LangChain
+            self._model = self._create_model(config)
+            self._client = None
 
     def _create_model(self, config: RedBearModelConfig) -> Embeddings:
-        """根据配置创建模型"""
+        """根据配置创建 LangChain 模型"""
         embedding_class = get_provider_embedding_class(config.provider)
         model_params = RedBearModelFactory.get_model_params(config)
         return embedding_class(**model_params)
+    
+    def _create_volcano_client(self, config: RedBearModelConfig):
+        """创建火山引擎客户端"""
+        from volcenginesdkarkruntime import Ark
+        return Ark(api_key=config.api_key, base_url=config.base_url)
 
+    # ==================== LangChain 标准接口 ====================
+    
     def embed_documents(self, texts: list[str]) -> list[list[float]]:
-        return self._model.embed_documents(texts)
+        """批量文本向量化（LangChain 标准接口）"""
+        if self._is_volcano:
+            # 火山引擎多模态 Embedding
+            contents = [{"type": "text", "text": text} for text in texts]
+            response = self._client.multimodal_embeddings.create(
+                model=self._config.model_name,
+                input=contents,
+                encoding_format="float"
+            )
+            return [response.data.embedding]
+        else:
+            # 其他 provider
+            return self._model.embed_documents(texts)
 
     def embed_query(self, text: str) -> List[float]:
-        return self._model.embed_query(text)
+        """单个文本向量化（LangChain 标准接口）"""
+        if self._is_volcano:
+            # 火山引擎多模态 Embedding
+            result = self.embed_documents([text])
+            return result[0] if result else []
+        else:
+            # 其他 provider
+            return self._model.embed_query(text)
+    
+    # ==================== 多模态扩展方法 ====================
+    
+    def embed_multimodal(
+        self,
+        contents: List[Dict[str, Any]],
+        **kwargs
+    ) -> List[List[float]]:
+        """
+        多模态向量化（仅火山引擎支持）
+        
+        Args:
+            contents: 内容列表，格式：
+                - 文本: {"type": "text", "text": "..."}
+                - 图片: {"type": "image_url", "image_url": {"url": "..."}}
+                - 视频: {"type": "video_url", "video_url": {"url": "..."}}
+            **kwargs: 其他参数
+            
+        Returns:
+            向量列表
+        """
+        if not self._is_volcano:
+            raise NotImplementedError(
+                f"多模态 Embedding 仅支持火山引擎，当前 provider: {self._config.provider}"
+            )
+        
+        response = self._client.multimodal_embeddings.create(
+            model=self._config.model_name,
+            input=contents,
+            **kwargs
+        )
+        return [response.data.embedding]
+    
+    async def aembed_multimodal(
+        self,
+        contents: List[Dict[str, Any]],
+        **kwargs
+    ) -> List[List[float]]:
+        """异步多模态向量化"""
+        # 火山引擎 SDK 暂不支持异步，使用同步方法
+        return self.embed_multimodal(contents, **kwargs)
+    
+    def embed_text(self, text: str, **kwargs) -> List[float]:
+        """文本向量化（便捷方法）"""
+        if self._is_volcano:
+            result = self.embed_multimodal(
+                [{"type": "text", "text": text}],
+                **kwargs
+            )
+            return result[0] if result else []
+        else:
+            return self.embed_query(text)
+    
+    def embed_image(self, image_url: str, **kwargs) -> List[float]:
+        """图片向量化（仅火山引擎支持）"""
+        if not self._is_volcano:
+            raise NotImplementedError(
+                f"图片向量化仅支持火山引擎，当前 provider: {self._config.provider}"
+            )
+        
+        result = self.embed_multimodal(
+            [{"type": "image_url", "image_url": {"url": image_url}}],
+            **kwargs
+        )
+        return result[0] if result else []
+    
+    def embed_video(self, video_url: str, **kwargs) -> List[float]:
+        """视频向量化（仅火山引擎支持）"""
+        if not self._is_volcano:
+            raise NotImplementedError(
+                f"视频向量化仅支持火山引擎，当前 provider: {self._config.provider}"
+            )
+        
+        result = self.embed_multimodal(
+            [{"type": "video_url", "video_url": {"url": video_url}}],
+            **kwargs
+        )
+        return result[0] if result else []
+    
+    def embed_batch(
+        self,
+        items: List[Union[str, Dict[str, Any]]],
+        **kwargs
+    ) -> List[List[float]]:
+        """
+        批量向量化（支持混合类型）
+        
+        Args:
+            items: 可以是字符串列表或内容字典列表
+            **kwargs: 其他参数
+            
+        Returns:
+            向量列表
+        """
+        # 如果全是字符串，使用标准方法
+        if all(isinstance(item, str) for item in items):
+            return self.embed_documents(items)
+        
+        # 如果包含字典，需要多模态支持
+        if not self._is_volcano:
+            raise NotImplementedError(
+                f"混合类型批量向量化仅支持火山引擎，当前 provider: {self._config.provider}"
+            )
+        
+        # 标准化输入格式
+        contents = []
+        for item in items:
+            if isinstance(item, str):
+                contents.append({"type": "text", "text": item})
+            elif isinstance(item, dict):
+                contents.append(item)
+            else:
+                raise ValueError(f"不支持的输入类型: {type(item)}")
+        
+        return self.embed_multimodal(contents, **kwargs)
+    
+    # ==================== 工具方法 ====================
+    
+    def is_multimodal_supported(self) -> bool:
+        """检查是否支持多模态"""
+        return self._is_volcano
+    
+    def get_provider(self) -> str:
+        """获取 provider"""
+        return self._config.provider
+
+
+# 保留 RedBearMultimodalEmbeddings 作为别名，向后兼容
+RedBearMultimodalEmbeddings = RedBearEmbeddings

api/app/core/models/generation.py

@@ -0,0 +1,344 @@
+"""
+图片和视频生成模型封装
+
+支持的 Provider:
+- Volcano (火山引擎): 使用 volcenginesdkarkruntime
+- OpenAI: 使用 openai SDK
+"""
+from typing import Any, Dict, Optional
+
+from volcenginesdkarkruntime import Ark
+from volcenginesdkarkruntime.types.images.images import (
+    SequentialImageGenerationOptions,
+    ContentGenerationTool,
+    OptimizePromptOptions
+)
+
+from app.core.models.base import RedBearModelConfig
+from app.core.exceptions import BusinessException
+from app.core.error_codes import BizCode
+from app.models.models_model import ModelProvider
+
+
+class RedBearImageGenerator:
+    """图片生成模型封装"""
+    
+    def __init__(self, config: RedBearModelConfig):
+        self._config = config
+        self._client = self._create_client(config)
+    
+    def _create_client(self, config: RedBearModelConfig):
+        """根据 provider 创建客户端"""
+        provider = config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            return Ark(api_key=config.api_key, base_url=config.base_url)
+        # elif provider == ModelProvider.OPENAI:
+        #     from openai import OpenAI
+        #     return OpenAI(api_key=config.api_key, base_url=config.base_url)
+        else:
+            raise BusinessException(
+                f"不支持的图片生成提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+    
+    def generate(
+        self,
+        prompt: str,
+        image: Optional[Any] = None,
+        size: Optional[str] = "2K",
+        output_format: str = "png",
+        response_format: str = "url",
+        watermark: bool = False,
+        sequential_image_generation: Optional[str] = None,
+        sequential_image_generation_options: Optional[Dict] = None,
+        tools: Optional[list] = None,
+        optimize_prompt_options: Optional[Dict] = None,
+        stream: bool = False,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        生成图片
+        
+        Args:
+            prompt: 提示词
+            image: 参考图片URL或URL列表（图文生图/多图融合）
+            size: 图片尺寸，支持 "2K", "2048x2048", "1920x1080" 等（至少3686400像素）
+            output_format: 输出格式，如 "png", "jpg"
+            response_format: 返回格式，"url" 或 "b64_json"
+            watermark: 是否添加水印
+            sequential_image_generation: 组图生成模式，"auto" 或 "disabled"
+            sequential_image_generation_options: 组图生成选项，如 {"max_images": 4}
+            tools: 工具列表，如 [{"type": "web_search"}] 用于联网搜索生图
+            optimize_prompt_options: 提示词优化选项，如 {"mode": "fast"}
+            stream: 是否使用流式生成
+            **kwargs: 其他参数
+            
+        Returns:
+            生成结果
+        """
+        provider = self._config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            params = {
+                "model": self._config.model_name,
+                "prompt": prompt,
+                "size": size,
+                "output_format": output_format,
+                "response_format": response_format,
+                "watermark": watermark,
+            }
+            
+            if image is not None:
+                params["image"] = image
+            
+            if sequential_image_generation:
+                params["sequential_image_generation"] = sequential_image_generation
+                if sequential_image_generation_options:
+                    params["sequential_image_generation_options"] = SequentialImageGenerationOptions(
+                        **sequential_image_generation_options
+                    )
+            
+            if tools:
+                params["tools"] = [ContentGenerationTool(**tool) if isinstance(tool, dict) else tool for tool in tools]
+            
+            if optimize_prompt_options:
+                params["optimize_prompt_options"] = OptimizePromptOptions(**optimize_prompt_options)
+            
+            if stream:
+                params["stream"] = True
+            
+            params.update(kwargs)
+            response = self._client.images.generate(**params)
+            
+        # elif provider == ModelProvider.OPENAI:
+        #     response = self._client.images.generate(
+        #         model=self._config.model_name,
+        #         prompt=prompt,
+        #         size=size,
+        #         n=n,
+        #         **kwargs
+        #     )
+        else:
+            raise BusinessException(
+                f"不支持的提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+        
+        return response.model_dump() if hasattr(response, 'model_dump') else response
+    
+    async def agenerate(
+        self,
+        prompt: str,
+        image: Optional[Any] = None,
+        size: Optional[str] = "2K",
+        output_format: str = "png",
+        response_format: str = "url",
+        watermark: bool = False,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """异步生成图片"""
+        return self.generate(prompt, image, size, output_format, response_format, watermark, **kwargs)
+
+
+class RedBearVideoGenerator:
+    """视频生成模型封装"""
+    
+    def __init__(self, config: RedBearModelConfig):
+        self._config = config
+        self._client = self._create_client(config)
+    
+    def _create_client(self, config: RedBearModelConfig):
+        """根据 provider 创建客户端"""
+        provider = config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            return Ark(api_key=config.api_key, base_url=config.base_url)
+        else:
+            raise BusinessException(
+                f"不支持的视频生成提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+    
+    def generate(
+        self,
+        prompt: str,
+        image_url: Optional[str] = None,
+        first_frame_url: Optional[str] = None,
+        last_frame_url: Optional[str] = None,
+        reference_images: Optional[list] = None,
+        draft_task_id: Optional[str] = None,
+        duration: Optional[int] = None,
+        frames: Optional[int] = None,
+        ratio: Optional[str] = None,
+        resolution: Optional[str] = None,
+        generate_audio: bool = False,
+        watermark: bool = False,
+        camera_fixed: bool = False,
+        seed: Optional[int] = None,
+        return_last_frame: bool = False,
+        service_tier: str = "default",
+        execution_expires_after: Optional[int] = None,
+        draft: bool = False,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        生成视频
+        
+        Args:
+            prompt: 提示词
+            image_url: 首帧图片URL（图生视频-基于首帧）
+            first_frame_url: 首帧图片URL（图生视频-基于首尾帧）
+            last_frame_url: 尾帧图片URL（图生视频-基于首尾帧）
+            reference_images: 参考图片URL列表（图生视频-基于参考图）
+            draft_task_id: Draft任务ID（基于Draft生成正式视频）
+            duration: 视频时长（秒），与frames二选一
+            frames: 视频帧数，与duration二选一
+            ratio: 视频比例，如 "16:9", "9:16", "adaptive"
+            resolution: 视频分辨率，如 "720p", "1080p"
+            generate_audio: 是否生成音频
+            watermark: 是否添加水印
+            camera_fixed: 是否固定镜头
+            seed: 随机种子
+            return_last_frame: 是否返回最后一帧
+            service_tier: 服务层级，"default" 或 "flex"（离线推理）
+            execution_expires_after: 任务过期时间（秒）
+            draft: 是否生成样片
+            **kwargs: 其他参数
+            
+        Returns:
+            生成结果（包含任务ID，需要轮询获取结果）
+        """
+        provider = self._config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            content = [{"type": "text", "text": prompt}]
+            
+            if draft_task_id:
+                content = [{"type": "draft_task", "draft_task": {"id": draft_task_id}}]
+            else:
+                if image_url:
+                    content.append({"type": "image_url", "image_url": {"url": image_url}})
+                
+                if first_frame_url:
+                    content.append({"type": "image_url", "image_url": {"url": first_frame_url}, "role": "first_frame"})
+                if last_frame_url:
+                    content.append({"type": "image_url", "image_url": {"url": last_frame_url}, "role": "last_frame"})
+                
+                if reference_images:
+                    for ref_url in reference_images:
+                        content.append({"type": "image_url", "image_url": {"url": ref_url}, "role": "reference_image"})
+            
+            params = {"model": self._config.model_name, "content": content, "watermark": watermark}
+            
+            if duration:
+                params["duration"] = duration
+            if frames:
+                params["frames"] = frames
+            if ratio:
+                params["ratio"] = ratio
+            if resolution:
+                params["resolution"] = resolution
+            if generate_audio:
+                params["generate_audio"] = generate_audio
+            if camera_fixed:
+                params["camera_fixed"] = camera_fixed
+            if seed is not None:
+                params["seed"] = seed
+            if return_last_frame:
+                params["return_last_frame"] = return_last_frame
+            if service_tier != "default":
+                params["service_tier"] = service_tier
+            if execution_expires_after:
+                params["execution_expires_after"] = execution_expires_after
+            if draft:
+                params["draft"] = draft
+            
+            params.update(kwargs)
+            response = self._client.content_generation.tasks.create(**params)
+        else:
+            raise BusinessException(
+                f"不支持的提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+        
+        return response.model_dump() if hasattr(response, 'model_dump') else response
+    
+    async def agenerate(
+        self,
+        prompt: str,
+        image_url: Optional[str] = None,
+        duration: Optional[int] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """异步生成视频"""
+        return self.generate(prompt, image_url=image_url, duration=duration, **kwargs)
+    
+    def get_task_status(self, task_id: str) -> Dict[str, Any]:
+        """
+        查询视频生成任务状态
+        
+        Args:
+            task_id: 任务ID
+            
+        Returns:
+            任务状态信息
+        """
+        provider = self._config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            response = self._client.content_generation.tasks.get(task_id=task_id)
+            return response.model_dump() if hasattr(response, 'model_dump') else response
+        else:
+            raise BusinessException(
+                f"不支持的提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+    
+    async def aget_task_status(self, task_id: str) -> Dict[str, Any]:
+        """异步查询任务状态"""
+        return self.get_task_status(task_id)
+    
+    def list_tasks(self, page_size: int = 10, status: Optional[str] = None, **kwargs) -> Dict[str, Any]:
+        """
+        查询视频生成任务列表
+        
+        Args:
+            page_size: 每页数量
+            status: 任务状态筛选，如 "succeeded", "failed", "pending"
+            **kwargs: 其他参数
+            
+        Returns:
+            任务列表
+        """
+        provider = self._config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            params = {"page_size": page_size}
+            if status:
+                params["status"] = status
+            params.update(kwargs)
+            response = self._client.content_generation.tasks.list(**params)
+            return response.model_dump() if hasattr(response, 'model_dump') else response
+        else:
+            raise BusinessException(
+                f"不支持的提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )
+    
+    def delete_task(self, task_id: str) -> None:
+        """
+        删除或取消视频生成任务
+        
+        Args:
+            task_id: 任务ID
+        """
+        provider = self._config.provider.lower()
+        
+        if provider == ModelProvider.VOLCANO:
+            self._client.content_generation.tasks.delete(task_id=task_id)
+        else:
+            raise BusinessException(
+                f"不支持的提供商: {provider}",
+                code=BizCode.PROVIDER_NOT_SUPPORTED
+            )

api/app/core/models/scripts/volcano_models.yaml

@@ -0,0 +1,334 @@
+provider: volcano
+models:
+# Doubao-Seed 2.0 系列
+- name: doubao-seed-2-0-pro-260215
+  type: chat
+  provider: volcano
+  description: 旗舰级全能通用模型，面向 Agent 时代的复杂推理与长链路任务执行场景。强调多模态理解、长上下文推理、结构化生成与工具增强执行。复杂指令与多约束执行能力突出，可稳定应对多步复杂规划、复杂图文推理、视频内容理解与高难度分析等场景。侧重长链路推理能力与复杂任务稳定性，适配真实业务中的复杂场景。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-2-0-lite-260215
+  type: chat
+  provider: volcano
+  description: 面向高频企业场景兼顾性能与成本的均衡型模型，综合能力超越上一代Doubao-Seed-1.8。胜任非结构化信息处理、内容创作、搜索推荐、数据分析等生产型工作，支持长上下文、多源信息融合、多步指令执行与高保真结构化输出。在保障稳定效果的同时显著优化成本。兼顾生成质量与响应速度，适合作为通用生产级模型。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-2-0-mini-260215
+  type: chat
+  provider: volcano
+  description: 面向低时延、高并发与成本敏感场景，提供极致的模型推理速度。模型效果与Doubao-Seed-1.6相当。支持256k上下文、4档思考长度和多模态理解，适合成本和速度优先的轻量级任务。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-2-0-code-preview-260215
+  type: chat
+  provider: volcano
+  description: 面向真实编程环境优化的 Coding 模型，能稳定调用 Claude Code 等常见 IDE 中的工具。模型特别优化了前端能力，在使用常见的前端框架时能有良好表现。模型支持使用 Skills，可以配合多种自定义技能使用。Seed 2.0 的编程加强版，更适合 Agentic Coding。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  - 代码模型
+  logo: volcano
+
+# Doubao-Seed 1.x 系列
+- name: doubao-seed-1-8-251228
+  type: chat
+  provider: volcano
+  description: Doubao-Seed-1.8 面向多模态 Agent 场景定向优化。Agent 能力上，Tool Use、复杂指令遵循等能力均大幅增强。多模态理解方面，视觉基础能力显著提升，可低帧率理解超长视频，视频运动理解、复杂空间理解及文档结构化解析能力也有所优化，还原生支持智能上下文管理，用户可配置上下文策略。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-1-6-251015
+  type: chat
+  provider: volcano
+  description: Doubao-Seed-1.6全新多模态深度思考模型，同时支持minimal/low/medium/high 四种reasoning effort。 更强模型效果，服务复杂任务和有挑战场景。支持 256k 上下文窗口，输出长度支持最大 32k tokens。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-1-6-lite-251015
+  type: chat
+  provider: volcano
+  description: 更高性价比，常见任务的最佳选择，支持minimal、low、medium、high 四种reasoning_effort思考深度
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-1-6-flash-250828
+  type: chat
+  provider: volcano
+  description: Doubao-Seed-1.6-flash推理速度极致的多模态深度思考模型，TPOT低至10ms； 同时支持文本和视觉理解，文本理解能力超过上一代lite，视觉理解比肩友商pro系列模型。支持 256k 上下文窗口，输出长度支持最大 16k tokens。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-seed-code-preview-251028
+  type: chat
+  provider: volcano
+  description: 面向Agentic编程任务进行了深度优化。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  - 代码模型
+  logo: volcano
+
+- name: doubao-seed-1-6-vision-250815
+  type: chat
+  provider: volcano
+  description: 全新Doubao-Seed-1.6系列视觉深度思考模型，视觉理解能力显著增强，并支持image_process视觉工具
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+    - video
+  is_omni: false
+  tags:
+  - 大语言模型
+  - 多模态模型
+  logo: volcano
+
+# Doubao 1.5 系列
+- name: doubao-1-5-vision-pro-32k-250115
+  type: chat
+  provider: volcano
+  description: 全新升级的多模态大模型，支持任意分辨率和极端长宽比图像识别，增强视觉推理、文档识别、细节信息理解和指令遵循能力。支持 32k 上下文窗口，输出长度支持最大 12k tokens。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 大语言模型
+  - 多模态模型
+  logo: volcano
+
+- name: doubao-1-5-pro-32k-250115
+  type: chat
+  provider: volcano
+  description: 全新一代主力模型，性能全面升级，在知识、代码、推理等方面表现卓越。最大支持 128k 上下文窗口，输出长度支持最大 12k tokens。
+  is_deprecated: false
+  is_official: true
+  capability: []
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+- name: doubao-1-5-lite-32k-250115
+  type: chat
+  provider: volcano
+  description: 全新一代轻量版模型，极致响应速度，效果与时延均达到全球一流水平。支持 32k 上下文窗口，输出长度支持最大 12k tokens。
+  is_deprecated: false
+  is_official: true
+  capability: []
+  is_omni: false
+  tags:
+  - 大语言模型
+  logo: volcano
+
+# Doubao-Seedance 视频生成系列
+- name: doubao-seedance-1-5-pro-251215
+  type: video
+  provider: volcano
+  description: 豆包视频生成模型Seedance 1.5 pro 作为全球领先的视频生成模型，可生成音画高精同步的视频内容。支持多人多语言对白，全面覆盖环境音、动作音、合成音、乐器音、背景音及人声，支持首尾帧，实现影视级叙事效果，满足影视、漫剧、电商及广告领域的高阶创作需求。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 视频生成
+  logo:  volcano
+
+- name: doubao-seedance-1-0-pro-250528
+  type: video
+  provider: volcano
+  description: 一款支持多镜头叙事的视频生成基础模型，在各维度表现出色。它在语义理解与指令遵循能力上取得突破，能生成运动流畅、细节丰富、风格多样且具备影视级美感的 1080P 高清视频
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 视频生成
+  logo: volcano
+
+- name: doubao-seedance-1-0-pro-fast-251015
+  type: video
+  provider: volcano
+  description: 一款价格触底、效能封顶的全面模型，在视频生成质量、速度、价格之间取得了卓越平衡。它继承了Seedance 1.0 pro 核心优势，同时生成速度提升、价格更具竞争力，为创作者带来效率与成本双重优化的体验。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 视频生成
+  logo: volcano
+
+- name: doubao-seedance-1-0-lite-i2v-250428
+  type: video
+  provider: volcano
+  description: 基于首帧图片、尾帧图片（可选）、参考图片（可选）和文本提示词（可选）相结合的方式生成视频
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 视频生成
+  - 图生视频
+  logo: volcano
+
+- name: doubao-seedance-1-0-lite-t2v-250428
+  type: video
+  provider: volcano
+  description: 基于文本提示词生成视频
+  is_deprecated: false
+  is_official: true
+  capability: []
+  is_omni: false
+  tags:
+  - 视频生成
+  - 文生视频
+  logo: volcano
+
+# Doubao-Seedream 图像生成系列
+- name: doubao-seedream-5-0-260128
+  type: image
+  provider: volcano
+  description: 字节跳动发布的最新图像创作模型。该模型首次搭载联网检索功能，能融合实时网络信息，提升生图时效性。同时，模型的聪明度进一步升级，能够精准解析复杂指令和视觉内容。此外，模型在世界知识广度、参考一致性及专业场景生成质量上均有增强，可更好地满足企业级视觉创作需求。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 图像生成
+  logo: volcano
+
+- name: doubao-seedream-4-5-251128
+  type: image
+  provider: volcano
+  description: 字节跳动最新推出的图像多模态模型，整合了文生图、图生图、组图输出等能力，融合常识和推理能力。相比前代4.0模型生成效果大幅提升，具备更好的编辑一致性和多图融合效果，能更精准的控制画面细节，小字、小人脸生成更自然，图片排版、色彩更和谐，美感提升。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 图像生成
+  logo: volcano
+
+- name: doubao-seedream-4-0-250828
+  type: image
+  provider: volcano
+  description: 基于领先架构的SOTA级多模态图像创作模型，其生成美感、指令遵循、结构完整度、主体保持一致性处于世界头部水平。模型采用同一套架构实现文生图与编辑能力的统一，原生支持文本 、单图和多图输入，并能通过对提示词的深度推理，自动适配最优的图像比例尺寸与生成数量，可一次性连续输出最多 15 张内容关联的图像，支持 4K 超高清输出。
+  is_deprecated: false
+  is_official: true
+  capability:
+    - vision
+  is_omni: false
+  tags:
+  - 图像生成
+  logo: volcano
+
+- name: doubao-seedream-3-0-t2i-250415
+  type: image
+  provider: volcano
+  description: 一款支持原生高分辨率的中英双语图像生成基础模型，综合能力媲美GPT-4o，处于世界第一梯队。支持原生 2K 分辨率输出；响应速度更快；小字生成更准确，文本排版效果增强；指令遵循能力强，美感&结构提升，保真度和细节表现较好。
+  is_deprecated: false
+  is_official: true
+  capability: []
+  is_omni: false
+  tags:
+  - 图像生成
+  - 文生图
+  logo: volcano
+
+# Doubao 翻译系列
+- name: doubao-seed-translation-250915
+  type: chat
+  provider: volcano
+  description: 通用多语言翻译模型，支持30余种语言互译，支持 4K 上下文窗口，输出长度支持最大 3K tokens
+  is_deprecated: false
+  is_official: true
+  capability: []
+  is_omni: false
+  tags:
+  - 翻译模型
+  logo: volcano
+
+# Doubao Embedding 系列
+- name: doubao-embedding-vision-251215
+  type: embedding
+  provider: volcano
+  description: 主要面向图文多模向量检索的使用场景，支持图片输入及中、英双语文本输入，最长 128K 上下文长度。
+  is_deprecated: false
+  is_official: true
+  capability:
+  - vision
+  - video
+  is_omni: false
+  tags:
+  - 向量模型
+  - 多模态模型
+  logo: volcano

api/app/core/rag/vdb/elasticsearch/elasticsearch_vector.py

@@ -61,24 +61,16 @@ class ElasticSearchConfig(BaseModel):
 class ElasticSearchVector(BaseVector):
     def __init__(self, index_name: str, config: ElasticSearchConfig, embedding_config: ModelApiKey, reranker_config: ModelApiKey):
         super().__init__(index_name.lower())
-        # self.embeddings = XinferenceEmbeddings(
-        #     server_url=os.getenv("XINFERENCE_URL", "http://127.0.0.1"),  # Default Xinference port
-        #     model_uid="bge-m3"  # replace model_uid with the model UID return from launching the model
-        # )
-        # Remove debug printing to avoid leaking sensitive information
-        # print("embedding:" + embedding_config.model_name + "|" + embedding_config.provider + "|" + embedding_config.api_key + "|" + embedding_config.api_base)
+        
+        # 初始化 Embedding 模型（自动支持火山引擎多模态）
         self.embeddings = RedBearEmbeddings(RedBearModelConfig(
             model_name=embedding_config.model_name,
             provider=embedding_config.provider,
             api_key=embedding_config.api_key,
             base_url=embedding_config.api_base
         ))
-        # self.reranker = XinferenceRerank(
-        #     server_url=os.getenv("XINFERENCE_URL", "http://127.0.0.1"),
-        #     model_uid="bge-reranker-large"
-        # )
-        # Remove debug printing to avoid leaking sensitive information
-        # print("reranker:"+ reranker_config.model_name + "|" + reranker_config.provider + "|" + reranker_config.api_key + "|" + reranker_config.api_base)
+        self.is_multimodal_embedding = self.embeddings.is_multimodal_supported()
+        
         self.reranker = RedBearRerank(RedBearModelConfig(
             model_name=reranker_config.model_name,
             provider=reranker_config.provider,
@@ -144,7 +136,11 @@ class ElasticSearchVector(BaseVector):
     def add_chunks(self, chunks: list[DocumentChunk], **kwargs):
         # 实现 Elasticsearch 保存向量
         texts = [chunk.page_content for chunk in chunks]
-        embeddings = self.embeddings.embed_documents(list(texts))
+        if self.is_multimodal_embedding:
+            # 火山引擎多模态 Embedding
+            embeddings = self.embeddings.embed_batch(texts)
+        else:
+            embeddings = self.embeddings.embed_documents(list(texts))
         self.create(chunks, embeddings, **kwargs)
 
     def create(self, chunks: list[DocumentChunk], embeddings: list[list[float]], **kwargs):
@@ -394,7 +390,11 @@ class ElasticSearchVector(BaseVector):
             updated count.
         """
         indices = kwargs.get("indices", self._collection_name)  # Default single index, multi-index available，etc "index1,index2,index3"
-        chunk.vector = self.embeddings.embed_query(chunk.page_content)
+        if self.is_multimodal_embedding:
+            # 火山引擎多模态 Embedding
+            chunk.vector = self.embeddings.embed_text(chunk.page_content)
+        else:
+            chunk.vector = self.embeddings.embed_query(chunk.page_content)
 
         body = {
             "script": {
@@ -454,7 +454,11 @@ class ElasticSearchVector(BaseVector):
 
     def search_by_vector(self, query: str, **kwargs: Any) -> list[DocumentChunk]:
         """Search the nearest neighbors to a vector."""
-        query_vector = self.embeddings.embed_query(query)
+        if self.is_multimodal_embedding:
+            # 火山引擎多模态 Embedding
+            query_vector = self.embeddings.embed_text(query)
+        else:
+            query_vector = self.embeddings.embed_query(query)
         top_k = kwargs.get("top_k", 1024)
         score_threshold = float(kwargs.get("score_threshold") or 0.3)
         indices = kwargs.get("indices", self._collection_name)  # Default single index, multi-index available，etc "index1,index2,index3"

api/app/core/storage/base.py

@@ -109,17 +109,13 @@ class StorageBackend(ABC):
         pass
 
     @abstractmethod
-    async def get_url(self, file_key: str, expires: int = 3600) -> str:
-        """
-        Get an access URL for the file.
-
-        Args:
-            file_key: Unique identifier for the file in the storage system.
-            expires: URL validity period in seconds (default: 1 hour).
-
-        Returns:
-            URL for accessing the file.
-        """
+    async def get_url(
+        self,
+        file_key: str,
+        expires: int = 3600,
+        file_name: Optional[str] = None
+    ) -> str:
+        """Get an access URL for the file."""
         pass
 
     async def get_permanent_url(self, file_key: str) -> Optional[str]:

api/app/core/storage/local.py

@@ -210,7 +210,12 @@ class LocalStorage(StorageBackend):
                 cause=e,
             )
 
-    async def get_url(self, file_key: str, expires: int = 3600) -> str:
+    async def get_url(
+        self,
+        file_key: str,
+        expires: int = 3600,
+        file_name: Optional[str] = None
+    ) -> str:
         """
         Get an access URL for the file.
 
@@ -220,6 +225,7 @@ class LocalStorage(StorageBackend):
         Args:
             file_key: Unique identifier for the file in the storage system.
             expires: URL validity period in seconds (not used for local storage).
+            file_name: If set, adds Content-Disposition: attachment to force download.
 
         Returns:
             A relative URL path for accessing the file.

api/app/core/storage/oss.py

@@ -7,6 +7,7 @@ Storage Service (OSS) using the oss2 SDK.
 
 import io
 import logging
+import urllib.parse
 from typing import AsyncIterator, Optional
 
 import oss2
@@ -242,24 +243,33 @@ class OSSStorage(StorageBackend):
             logger.error(f"Failed to check file existence in OSS {file_key}: {e}")
             return False
 
-    async def get_url(self, file_key: str, expires: int = 3600) -> str:
+    async def get_url(
+        self,
+        file_key: str,
+        expires: int = 3600,
+        file_name: Optional[str] = None,
+    ) -> str:
         """
         Get a presigned URL for accessing the file.
 
         Args:
             file_key: Unique identifier for the file in the storage system.
             expires: URL validity period in seconds (default: 1 hour).
+            file_name: If set, adds Content-Disposition: attachment to force download.
 
         Returns:
             A presigned URL for accessing the file.
         """
         try:
-            url = self.bucket.sign_url("GET", file_key, expires)
+            params = {}
+            if file_name:
+                filename_encoded = urllib.parse.quote(file_name.encode("utf-8"))
+                params["response-content-disposition"] = f"attachment; filename*=UTF-8''{filename_encoded}"
+            url = self.bucket.sign_url("GET", file_key, expires, params=params if params else None)
             logger.debug(f"Generated presigned URL for {file_key}, expires in {expires}s")
             return url
         except Exception as e:
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
-            # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.{self.endpoint.replace('https://', '').replace('http://', '')}/{file_key}"
 
     async def get_permanent_url(self, file_key: str) -> str:

api/app/core/storage/s3.py

@@ -6,6 +6,7 @@ using the boto3 SDK.
 """
 
 import io
+import urllib.parse
 import logging
 from typing import AsyncIterator, Optional
 
@@ -352,31 +353,37 @@ class S3Storage(StorageBackend):
             logger.error(f"Failed to check file existence in S3 {file_key}: {e}")
             return False
 
-    async def get_url(self, file_key: str, expires: int = 3600) -> str:
+    async def get_url(
+        self,
+        file_key: str,
+        expires: int = 3600,
+        file_name: Optional[str] = None,
+    ) -> str:
         """
         Get a presigned URL for accessing the file.
 
         Args:
             file_key: Unique identifier for the file in the storage system.
             expires: URL validity period in seconds (default: 1 hour).
+            file_name: If set, adds Content-Disposition: attachment to force download.
 
         Returns:
             A presigned URL for accessing the file.
         """
         try:
+            params = {"Bucket": self.bucket_name, "Key": file_key}
+            if file_name:
+                filename_encoded = urllib.parse.quote(file_name.encode("utf-8"))
+                params["ResponseContentDisposition"] = f"attachment; filename*=UTF-8''{filename_encoded}"
             url = self.client.generate_presigned_url(
                 "get_object",
-                Params={
-                    "Bucket": self.bucket_name,
-                    "Key": file_key,
-                },
+                Params=params,
                 ExpiresIn=expires,
             )
             logger.debug(f"Generated presigned URL for {file_key}, expires in {expires}s")
             return url
         except Exception as e:
             logger.error(f"Failed to generate presigned URL for {file_key}: {e}")
-            # Return a basic URL format as fallback
             return f"https://{self.bucket_name}.s3.{self.region}.amazonaws.com/{file_key}"
 
     async def get_permanent_url(self, file_key: str) -> str:

api/app/core/workflow/adapters/base_adapter.py

@@ -9,7 +9,7 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from app.core.workflow.adapters.errors import ExceptionDefineition
+from app.core.workflow.adapters.errors import ExceptionDefinition
 from app.schemas.workflow_schema import (
     EdgeDefinition,
     NodeDefinition,
@@ -40,8 +40,8 @@ class WorkflowParserResult(BaseModel):
     edges: list[EdgeDefinition] = Field(default_factory=list)
     nodes: list[NodeDefinition] = Field(default_factory=list)
     variables: list[VariableDefinition] = Field(default_factory=list)
-    warnings: list[ExceptionDefineition] = Field(default_factory=list)
-    errors: list[ExceptionDefineition] = Field(default_factory=list)
+    warnings: list[ExceptionDefinition] = Field(default_factory=list)
+    errors: list[ExceptionDefinition] = Field(default_factory=list)
 
 
 class WorkflowImportResult(BaseModel):
@@ -51,8 +51,8 @@ class WorkflowImportResult(BaseModel):
     edges: list[EdgeDefinition] = Field(default_factory=list)
     nodes: list[NodeDefinition] = Field(default_factory=list)
     variables: list[VariableDefinition] = Field(default_factory=list)
-    warnings: list[ExceptionDefineition] = Field(default_factory=list)
-    errors: list[ExceptionDefineition] = Field(default_factory=list)
+    warnings: list[ExceptionDefinition] = Field(default_factory=list)
+    errors: list[ExceptionDefinition] = Field(default_factory=list)
 
 
 class BasePlatformAdapter(ABC):

api/app/core/workflow/adapters/dify/converter.py

@@ -9,9 +9,9 @@ from urllib.parse import quote
 
 from app.core.workflow.adapters.base_converter import BaseConverter
 from app.core.workflow.adapters.errors import (
-    UnsupportVariableType,
-    UnknowModelWarning,
-    ExceptionDefineition,
+    UnsupportedVariableType,
+    UnknownModelWarning,
+    ExceptionDefinition,
     ExceptionType
 )
 from app.core.workflow.nodes.assigner.config import AssignmentItem
@@ -54,7 +54,7 @@ from app.core.workflow.nodes.http_request.config import (
     HttpFormData,
     HttpTimeOutConfig,
     HttpRetryConfig,
-    HttpErrorDefaultTamplete,
+    HttpErrorDefaultTemplate,
     HttpErrorHandleConfig
 )
 from app.core.workflow.nodes.if_else.config import ConditionDetail, ConditionBranchConfig
@@ -108,7 +108,7 @@ class DifyConverter(BaseConverter):
         try:
             return config.model_validate(value)
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.CONFIG,
                 node_id=node_id,
                 node_name=node_name,
@@ -138,7 +138,7 @@ class DifyConverter(BaseConverter):
         var_selector = mapping.get(var_selector, var_selector)
         return var_selector
 
-    def _process_list_variable_litearl(self, variable_selector: list) -> str | None:
+    def _process_list_variable_literal(self, variable_selector: list) -> str | None:
         if not self.process_var_selector(".".join(variable_selector)):
             return None
         return "{{" + self.process_var_selector(".".join(variable_selector)) + "}}"
@@ -269,7 +269,7 @@ class DifyConverter(BaseConverter):
             var_type = self.variable_type_map(var["type"])
             if not var_type:
                 self.errors.append(
-                    UnsupportVariableType(
+                    UnsupportedVariableType(
                         scope=node["id"],
                         name=var["variable"],
                         var_type=var["type"],
@@ -281,7 +281,7 @@ class DifyConverter(BaseConverter):
 
             if var_type in ["file", "array[file]"]:
                 self.errors.append(
-                    ExceptionDefineition(
+                    ExceptionDefinition(
                         type=ExceptionType.VARIABLE,
                         node_id=node["id"],
                         node_name=node_data["title"],
@@ -311,7 +311,7 @@ class DifyConverter(BaseConverter):
     def convert_question_classifier_node_config(self, node: dict) -> dict:
         node_data = node["data"]
         self.warnings.append(
-            UnknowModelWarning(
+            UnknownModelWarning(
                 node_id=node["id"],
                 node_name=node_data["title"],
                 model_name=node_data["model"].get("name")
@@ -327,7 +327,7 @@ class DifyConverter(BaseConverter):
             )
 
         result = QuestionClassifierNodeConfig.model_construct(
-            input_variable=self._process_list_variable_litearl(node_data.get("query_variable_selector")),
+            input_variable=self._process_list_variable_literal(node_data.get("query_variable_selector")),
             user_supplement_prompt=self.trans_variable_format(node_data.get("instructions", "")),
             categories=categories,
         ).model_dump()
@@ -337,13 +337,13 @@ class DifyConverter(BaseConverter):
     def convert_llm_node_config(self, node: dict) -> dict:
         node_data = node["data"]
         self.warnings.append(
-            UnknowModelWarning(
+            UnknownModelWarning(
                 node_id=node["id"],
                 node_name=node_data["title"],
                 model_name=node_data["model"].get("name")
             )
         )
-        context = self._process_list_variable_litearl(node_data["context"]["variable_selector"])
+        context = self._process_list_variable_literal(node_data["context"]["variable_selector"])
         memory = MemoryWindowSetting(
             enable=bool(node_data.get("memory")),
             enable_window=bool(node_data.get("memory", {}).get("window", {}).get("enabled", False)),
@@ -367,7 +367,7 @@ class DifyConverter(BaseConverter):
                 )
             )
         vision = node_data["vision"]["enabled"]
-        vision_input = self._process_list_variable_litearl(
+        vision_input = self._process_list_variable_literal(
             node_data["vision"]["configs"]["variable_selector"]
         ) if vision else None
         result = LLMNodeConfig.model_construct(
@@ -433,7 +433,7 @@ class DifyConverter(BaseConverter):
             conditions.append(
                 LoopConditionDetail.model_construct(
                     operator=self.convert_compare_operator(condition["comparison_operator"]),
-                    left=self._process_list_variable_litearl(condition["variable_selector"]),
+                    left=self._process_list_variable_literal(condition["variable_selector"]),
                     right=self.trans_variable_format(
                         right_value
                     ) if isinstance(right_value, str) and self.is_variable(right_value) else self.convert_variable_type(
@@ -453,7 +453,7 @@ class DifyConverter(BaseConverter):
             right_input_type = variable["value_type"]
             right_value_type = self.variable_type_map(variable["var_type"])
             if right_input_type == ValueInputType.VARIABLE:
-                right_value = self._process_list_variable_litearl(variable.get("value", ""))
+                right_value = self._process_list_variable_literal(variable.get("value", ""))
             else:
                 right_value = self.convert_variable_type(right_value_type, variable.get("value", ""))
             loop_variables.append(
@@ -475,10 +475,10 @@ class DifyConverter(BaseConverter):
     def convert_iteration_node_config(self, node: dict) -> dict:
         node_data = node["data"]
         result = IterationNodeConfig.model_construct(
-            input=self._process_list_variable_litearl(node_data["iterator_selector"]),
+            input=self._process_list_variable_literal(node_data["iterator_selector"]),
             parallel=node_data["is_parallel"],
             parallel_count=node_data["parallel_nums"],
-            output=self._process_list_variable_litearl(node_data["output_selector"]),
+            output=self._process_list_variable_literal(node_data["output_selector"]),
             output_type=self.variable_type_map(node_data.get("output_type")),
             flatten=node_data["flatten_output"],
         ).model_dump()
@@ -494,8 +494,8 @@ class DifyConverter(BaseConverter):
                 continue
             assignments.append(
                 AssignmentItem(
-                    variable_selector=self._process_list_variable_litearl(assignment["variable_selector"]),
-                    value=self._process_list_variable_litearl(
+                    variable_selector=self._process_list_variable_literal(assignment["variable_selector"]),
+                    value=self._process_list_variable_literal(
                         assignment["value"]
                     ) if assignment["input_type"] == ValueInputType.VARIABLE else assignment["value"],
                     operation=self.convert_assignment_operator(assignment["operation"])
@@ -514,7 +514,7 @@ class DifyConverter(BaseConverter):
             input_variables.append(
                 InputVariable.model_construct(
                     name=input_variable["variable"],
-                    variable=self._process_list_variable_litearl(input_variable["value_selector"]),
+                    variable=self._process_list_variable_literal(input_variable["value_selector"]),
                 )
             )
 
@@ -570,7 +570,7 @@ class DifyConverter(BaseConverter):
         else:
             if node_data["body"]["data"]:
                 body_content = (node_data["body"]["data"][0].get("value") or
-                                self._process_list_variable_litearl(node_data["body"]["data"][0].get("file")))
+                                self._process_list_variable_literal(node_data["body"]["data"][0].get("file")))
             else:
                 body_content = ""
 
@@ -585,7 +585,7 @@ class DifyConverter(BaseConverter):
                     self.trans_variable_format(key_value[0])
                 ] = self.trans_variable_format(key_value[1])
             else:
-                self.warnings.append(ExceptionDefineition(
+                self.warnings.append(ExceptionDefinition(
                     type=ExceptionType.CONFIG,
                     node_id=node["id"],
                     node_name=node_data["title"],
@@ -603,7 +603,7 @@ class DifyConverter(BaseConverter):
                     self.trans_variable_format(key_value[0])
                 ] = self.trans_variable_format(key_value[1])
             else:
-                self.warnings.append(ExceptionDefineition(
+                self.warnings.append(ExceptionDefinition(
                     type=ExceptionType.CONFIG,
                     node_id=node["id"],
                     node_name=node_data["title"],
@@ -625,7 +625,7 @@ class DifyConverter(BaseConverter):
                     default_header = var["value"]
                 elif var["key"] == "status_code":
                     default_status_code = var["value"]
-            default_value = HttpErrorDefaultTamplete(
+            default_value = HttpErrorDefaultTemplate(
                 body=default_body,
                 headers=default_header,
                 status_code=default_status_code,
@@ -668,7 +668,7 @@ class DifyConverter(BaseConverter):
         for variable in node_data["variables"]:
             mapping.append(VariablesMappingConfig.model_construct(
                 name=variable["variable"],
-                value=self._process_list_variable_litearl(variable["value_selector"])
+                value=self._process_list_variable_literal(variable["value_selector"])
             ))
         result = JinjaRenderNodeConfig.model_construct(
             template=node_data["template"],
@@ -679,14 +679,14 @@ class DifyConverter(BaseConverter):
 
     def convert_knowledge_node_config(self, node: dict) -> dict:
         node_data = node["data"]
-        self.warnings.append(ExceptionDefineition(
+        self.warnings.append(ExceptionDefinition(
             node_id=node["id"],
             node_name=node_data["title"],
             type=ExceptionType.CONFIG,
             detail=f"Please reconfigure the Knowledge Retrieval node.",
         ))
         result = KnowledgeRetrievalNodeConfig.model_construct(
-            query=self._process_list_variable_litearl(node_data["query_variable_selector"]),
+            query=self._process_list_variable_literal(node_data["query_variable_selector"]),
         ).model_dump()
 
         self.config_validate(node["id"], node["data"]["title"], KnowledgeRetrievalNodeConfig, result)
@@ -695,7 +695,7 @@ class DifyConverter(BaseConverter):
     def convert_parameter_extractor_node_config(self, node: dict) -> dict:
         node_data = node["data"]
         self.warnings.append(
-            UnknowModelWarning(
+            UnknownModelWarning(
                 node_id=node["id"],
                 node_name=node_data["title"],
                 model_name=node_data["model"].get("name")
@@ -712,7 +712,7 @@ class DifyConverter(BaseConverter):
                 )
             )
         result = ParameterExtractorNodeConfig.model_construct(
-            text=self._process_list_variable_litearl(node_data["query"]),
+            text=self._process_list_variable_literal(node_data["query"]),
             params=params,
             prompt=node_data.get("instruction")
         ).model_dump()
@@ -727,14 +727,14 @@ class DifyConverter(BaseConverter):
         group_type = {}
         if not advanced_settings or not advanced_settings["group_enabled"]:
             group_variables = [
-                self._process_list_variable_litearl(variable)
+                self._process_list_variable_literal(variable)
                 for variable in node_data["variables"]
             ]
             group_type["output"] = node_data["output_type"]
         else:
             for group in advanced_settings["groups"]:
                 group_variables[group["group_name"]] = [
-                    self._process_list_variable_litearl(variable)
+                    self._process_list_variable_literal(variable)
                     for variable in group["variables"]
                 ]
                 group_type[group["group_name"]] = group["output_type"]
@@ -751,7 +751,7 @@ class DifyConverter(BaseConverter):
 
     def convert_tool_node_config(self, node: dict) -> dict:
         node_data = node["data"]
-        self.warnings.append(ExceptionDefineition(
+        self.warnings.append(ExceptionDefinition(
             node_id=node["id"],
             node_name=node_data["title"],
             type=ExceptionType.CONFIG,

api/app/core/workflow/adapters/dify/dify_adapter.py

@@ -12,7 +12,7 @@ from app.core.workflow.adapters.base_adapter import (
     WorkflowParserResult
 )
 from app.core.workflow.adapters.dify.converter import DifyConverter
-from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType
+from app.core.workflow.adapters.errors import ExceptionDefinition, ExceptionType
 from app.core.workflow.nodes.enums import NodeType
 from app.schemas.workflow_schema import (
     NodeDefinition,
@@ -85,7 +85,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
         if not all(field in self.config for field in require_fields):
             return False
         if self.config.get("app", {}).get("mode") == "workflow":
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.PLATFORM,
                 detail="workflow mode is not supported"
             ))
@@ -111,12 +111,12 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             edge = self._convert_edge(edge)
             if edge:
                 self.edges.append(edge)
-        #
+
         for variable in self.config.get("workflow").get("conversation_variables"):
             con_var = self._convert_variable(variable)
             if variable:
                 self.conv_variables.append(con_var)
-        #
+
         # for variables in config.get("workflow").get("environment_variables"):
         #     variable = self._convert_variable(variables)
         #     conv_variables.append(variable)
@@ -152,7 +152,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
                     "y": node["position"]["y"] + position["y"]
                 }
         self.errors.append(
-            ExceptionDefineition(
+            ExceptionDefinition(
                 type=ExceptionType.NODE,
                 node_id=node_id,
                 detail="parent cycle node not found"
@@ -189,7 +189,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
             node_data = node["data"]
             converter = self.get_node_convert(node_type)
             if node_type == NodeType.UNKNOWN:
-                self.errors.append(ExceptionDefineition(
+                self.errors.append(ExceptionDefinition(
                     type=ExceptionType.NODE,
                     node_id=node["id"],
                     node_name=node["data"]["title"],
@@ -197,7 +197,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
                 ))
             return converter(node)
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.NODE,
                 node_id=node["id"],
                 node_name=node["data"]["title"],
@@ -207,7 +207,6 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
 
     def _convert_edge(self, edge: dict[str, Any]) -> EdgeDefinition | None:
         try:
-
             source = edge["source"]
             target = edge["target"]
             label = None
@@ -230,7 +229,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
                 label=label,
             )
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.EDGE,
                 detail=f"convert edge error - {e}",
             ))
@@ -246,7 +245,7 @@ class DifyAdapter(BasePlatformAdapter, DifyConverter):
                 description=variable.get("description")
             )
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.VARIABLE,
                 name=variable.get("name"),
                 detail=f"convert variable error - {e}",

api/app/core/workflow/adapters/errors.py

@@ -18,7 +18,7 @@ class ExceptionType(StrEnum):
     UNKNOWN = "unknown"
 
 
-class ExceptionDefineition(BaseModel):
+class ExceptionDefinition(BaseModel):
     type: ExceptionType
     detail: str
 
@@ -29,7 +29,7 @@ class ExceptionDefineition(BaseModel):
     name: str | None = None
 
 
-class UnknowModelWarning(ExceptionDefineition):
+class UnknownModelWarning(ExceptionDefinition):
     type: ExceptionType = ExceptionType.NODE
 
     def __init__(self, node_id, node_name, model_name):
@@ -40,36 +40,36 @@ class UnknowModelWarning(ExceptionDefineition):
         )
 
 
-class UnknowError(ExceptionDefineition):
+class UnknownError(ExceptionDefinition):
     type: ExceptionType = ExceptionType.UNKNOWN
 
     def __init__(self, detail: str, **kwargs):
         super().__init__(detail=detail, **kwargs)
 
 
-class UnsupportPlatform(ExceptionDefineition):
+class UnsupportedPlatform(ExceptionDefinition):
     type: ExceptionType = ExceptionType.PLATFORM
 
     def __init__(self, platform: str):
-        super().__init__(detail=f"Unsupport platform {platform}")
+        super().__init__(detail=f"Unsupported platform {platform}")
 
 
-class UnsupportVariableType(ExceptionDefineition):
+class UnsupportedVariableType(ExceptionDefinition):
     type: ExceptionType = ExceptionType.VARIABLE
 
     def __init__(self, scope, name, var_type: str, **kwargs):
-        super().__init__(scope=scope, name=name, detail=f"Unsupport variable type：[{var_type}]", **kwargs)
+        super().__init__(scope=scope, name=name, detail=f"Unsupported variable type: [{var_type}]", **kwargs)
 
 
-class InvalidConfiguration(ExceptionDefineition):
+class InvalidConfiguration(ExceptionDefinition):
     type: ExceptionType = ExceptionType.CONFIG
 
     def __init__(self):
         super().__init__(detail="Invalid workflow configuration format")
 
 
-class UnsupportNodeType(ExceptionDefineition):
+class UnsupportedNodeType(ExceptionDefinition):
     type: ExceptionType = ExceptionType.NODE
 
     def __init__(self, node_id: str, node_type: str):
-        super().__init__(node_id=node_id, detail=f"Unsupport node Type {node_type}")
+        super().__init__(node_id=node_id, detail=f"Unsupported node type {node_type}")

api/app/core/workflow/adapters/memory_bear/memory_bear_adapter.py

@@ -11,7 +11,7 @@ from app.core.workflow.adapters.base_adapter import (
     BasePlatformAdapter,
     WorkflowParserResult
 )
-from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType, UnsupportNodeType
+from app.core.workflow.adapters.errors import ExceptionDefinition, ExceptionType, UnsupportedNodeType
 from app.core.workflow.adapters.memory_bear.memory_bear_converter import MemoryBearConverter
 from app.core.workflow.nodes.enums import NodeType
 from app.schemas.workflow_schema import ExecutionConfig, NodeDefinition, EdgeDefinition, VariableDefinition
@@ -73,7 +73,7 @@ class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
         try:
             node_type = self.map_node_type(node["type"])
             if node_type == NodeType.UNKNOWN:
-                self.errors.append(UnsupportNodeType(
+                self.errors.append(UnsupportedNodeType(
                     node_id=node_id,
                     node_type=node["type"]
                 ))
@@ -85,7 +85,7 @@ class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
 
             return NodeDefinition(**node)
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.NODE,
                 node_id=node_id,
                 node_name=node_name,
@@ -97,14 +97,14 @@ class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
     def _convert_edge(self, edge: dict[str, Any], valid_node_ids: set) -> EdgeDefinition | None:
         try:
             if edge.get("source") not in valid_node_ids or edge.get("target") not in valid_node_ids:
-                self.warnings.append(ExceptionDefineition(
+                self.warnings.append(ExceptionDefinition(
                     type=ExceptionType.EDGE,
                     detail=f"edge {edge.get('id')} skipped: source or target node not found"
                 ))
                 return None
             return EdgeDefinition(**edge)
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.EDGE,
                 detail=f"convert edge error - {e}"
             ))
@@ -115,7 +115,7 @@ class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
         try:
             return VariableDefinition(**variable)
         except Exception as e:
-            self.warnings.append(ExceptionDefineition(
+            self.warnings.append(ExceptionDefinition(
                 type=ExceptionType.VARIABLE,
                 name=variable.get("name"),
                 detail=f"convert variable error - {e}"

api/app/core/workflow/adapters/memory_bear/memory_bear_converter.py

@@ -1,6 +1,6 @@
 # -*- coding: UTF-8 -*-
 from app.core.workflow.adapters.base_converter import BaseConverter
-from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType
+from app.core.workflow.adapters.errors import ExceptionDefinition, ExceptionType
 from app.core.workflow.nodes.base_config import BaseNodeConfig
 from app.core.workflow.nodes.configs import (
     StartNodeConfig,
@@ -65,7 +65,7 @@ class MemoryBearConverter(BaseConverter):
         try:
             return config_cls.model_validate(value)
         except Exception as e:
-            self.errors.append(ExceptionDefineition(
+            self.errors.append(ExceptionDefinition(
                 type=ExceptionType.CONFIG,
                 node_id=node_id,
                 node_name=node_name,

api/app/core/workflow/engine/graph_builder.py

@@ -7,7 +7,7 @@ import re
 import uuid
 from collections import defaultdict
 from functools import lru_cache
-from typing import Any, Iterable
+from typing import Any, Iterable, Callable
 
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import START, END
@@ -41,48 +41,31 @@ class GraphBuilder:
             self,
             workflow_config: dict[str, Any],
             stream: bool = False,
-            subgraph: bool = False,
+            cycle: str = '',
             variable_pool: VariablePool | None = None
     ):
         self.workflow_config = workflow_config
 
         self.stream = stream
-        self.subgraph = subgraph
+        self.cycle = cycle
 
         self.start_node_id: str | None = None
 
-        self.node_map = {node["id"]: node for node in self.nodes}
+        self.node_map: dict[str, dict] = {}
         self.end_node_map: dict[str, StreamOutputConfig] = {}
-        self._find_upstream_activation_dep = lru_cache(
-            maxsize=len(self.nodes) * 2
-        )(self._find_upstream_activation_dep)
+        self._find_upstream_activation_dep: Callable = self._find_upstream_activation_dep
         if variable_pool:
             self.variable_pool = variable_pool
         else:
             self.variable_pool = VariablePool()
 
-        self.graph = StateGraph(WorkflowState)
-        self.add_nodes()
-        self.reachable_nodes = WorkflowValidator.get_reachable_nodes(self.start_node_id, self.edges)
-        self.end_nodes = [
-            node
-            for node in self.nodes
-            if node.get("type") == "end" and node.get("id") in self.reachable_nodes
-        ]
-        self.add_edges()
-        # EDGES MUST BE ADDED AFTER NODES ARE ADDED.
-
+        self.graph: StateGraph | None = None
+        self.nodes: list = []
+        self.edges: list = []
+        self.reachable_nodes: set[str] | None = None
+        self.end_nodes: list[dict] = []
         self._reverse_adj: dict[str, list[dict]] = defaultdict(list)
-        self._build_reverse_adj()
-        self._analyze_end_node_output()
-
-    @property
-    def nodes(self) -> list[dict[str, Any]]:
-        return self.workflow_config.get("nodes", [])
-
-    @property
-    def edges(self) -> list[dict[str, Any]]:
-        return self.workflow_config.get("edges", [])
+        self._adj: dict[str, list[str]] = defaultdict(list)
 
     def get_node_type(self, node_id: str) -> str:
         """Retrieve the type of node given its ID.
@@ -108,13 +91,14 @@ class GraphBuilder:
             result[node[0]].append(node[1])
         return result
 
-    def _build_reverse_adj(self):
+    def _build_adj(self):
         for edge in self.edges:
             if edge["source"] not in self.reachable_nodes:
                 continue
             self._reverse_adj[edge.get("target")].append({
                 "id": edge["source"], "branch": edge.get("label")
             })
+            self._adj[edge.get("source")].append(edge["target"])
 
     def _find_upstream_activation_dep(
             self,
@@ -302,22 +286,13 @@ class GraphBuilder:
         """
         for node in self.nodes:
             node_type = node.get("type")
-            if node_type == NodeType.NOTES:
-                continue
             node_id = node.get("id")
-            cycle_node = node.get("cycle")
-            if cycle_node:
-                # Nodes within a loop subgraph are constructed by CycleGraphNode
-                if not self.subgraph:
-                    continue
-
-            # Record start and end node IDs
-            if node_type in [NodeType.START, NodeType.CYCLE_START]:
-                self.start_node_id = node_id
+            if node_id not in self.reachable_nodes:
+                continue
 
             # Create node instance (start and end nodes are also created)
             # NOTE:Loop node creation automatically removes the nodes and edges of the subgraph from the current graph
-            node_instance = NodeFactory.create_node(node, self.workflow_config)
+            node_instance = NodeFactory.create_node(node, self.workflow_config, self._adj[node_id])
 
             if node_type in BRANCH_NODES:
 
@@ -413,11 +388,12 @@ class GraphBuilder:
         # Add conditional edges
         for source_node, branches in conditional_edges.items():
             def make_router(src, branch_list):
-                """reate a router function for each source node that routes to a NOP node for later merging."""
+                """Create a router function for each source node that routes to a NOP node for later merging."""
 
                 def make_branch_node(node_name, targets):
                     def node(s):
-                        # NOTE: NOP NODE MUST NOT MODIFY STATE
+                        # NOTE: NOP NODE USED FOR ROUTING ONLY.
+                        # MUST NOT MUTATE STATE DIRECTLY; ONLY EMIT ACTIVATE SIGNALS.
                         return {
                             "activate": {
                                 node_id: s["activate"][node_name]
@@ -504,14 +480,52 @@ class GraphBuilder:
                 logger.debug(f"Added waiting edge: {sources} -> {target}")
 
         # Connect End nodes to the global END node
-        for end_node in self.end_nodes:
-            end_node_id = end_node.get("id")
-            if end_node_id:
-                self.graph.add_edge(end_node_id, END)
-                logger.debug(f"Added edge: {end_node_id} -> END")
+        for node in self.reachable_nodes:
+            if not self._adj[node]:
+                self.graph.add_edge(node, END)
         return
 
     def build(self) -> CompiledStateGraph:
+        nodes = self.workflow_config.get("nodes", [])
+        edges = self.workflow_config.get("edges", [])
+
+        for node in nodes:
+            if (node.get("cycle") or '') == self.cycle:
+                node_type = node.get("type")
+                if node_type in [NodeType.START, NodeType.CYCLE_START]:
+                    self.start_node_id = node.get("id")
+                elif node_type == NodeType.NOTES:
+                    continue
+                self.nodes.append(node)
+                self.node_map[node.get("id")] = node
+
+        for edge in edges:
+            source_in = edge.get("source") in self.node_map
+            target_in = edge.get("target") in self.node_map
+            if source_in ^ target_in:
+                raise ValueError(
+                    f"Cycle node is connected to external node, "
+                    f"source: {edge.get('source')}, target: {edge.get('target')}"
+                )
+
+            if source_in and target_in:
+                self.edges.append(edge)
+
+        self.reachable_nodes = WorkflowValidator.get_reachable_nodes(self.start_node_id, self.edges)
+        self.end_nodes = [
+            node
+            for node in self.nodes
+            if node.get("type") == "end" and node.get("id") in self.reachable_nodes
+        ]
+        self._build_adj()
+        self._find_upstream_activation_dep: Callable = lru_cache(
+            maxsize=len(self.nodes)*2
+        )(self._find_upstream_activation_dep)
+
+        self.graph = StateGraph(WorkflowState)
+        self.add_nodes()
+        self.add_edges()
+
+        self._analyze_end_node_output()
         checkpointer = InMemorySaver()
-        self.graph = self.graph.compile(checkpointer=checkpointer)
-        return self.graph
+        return self.graph.compile(checkpointer=checkpointer)

api/app/core/workflow/engine/result_builder.py

@@ -2,6 +2,7 @@
 # Author: Eternity
 # @Email: 1533512157@qq.com
 # @Time : 2026/2/10 13:33
+from app.core.workflow.engine.runtime_schema import ExecutionContext
 from app.core.workflow.engine.variable_pool import VariablePool
 
 
@@ -9,6 +10,7 @@ class WorkflowResultBuilder:
     def build_final_output(
             self,
             result: dict,
+            execution_context: ExecutionContext,
             variable_pool: VariablePool,
             elapsed_time: float,
             final_output: str,
@@ -26,6 +28,8 @@ class WorkflowResultBuilder:
                     - "node_outputs" (dict): Outputs of executed nodes.
                     - "messages" (list): Conversation messages exchanged during execution.
                     - "error" (str, optional): Error message if any node failed.
+            execution_context (ExecutionContext): The execution context containing metadata like
+                execution ID, workspace ID, and user ID.)
             variable_pool (VariablePool): Variable Pool
             elapsed_time (float): Total execution time in seconds.
             final_output (Any): The aggregated or final output content of the workflow
@@ -48,18 +52,23 @@ class WorkflowResultBuilder:
         """
         node_outputs = result.get("node_outputs", {})
         token_usage = self.aggregate_token_usage(node_outputs)
-        conversation_id = variable_pool.get_value("sys.conversation_id")
+        conversation_vars = {}
+        sys_vars = {}
+
+        if variable_pool:
+            conversation_vars = variable_pool.get_all_conversation_vars()
+            sys_vars = variable_pool.get_all_system_vars()
 
         return {
             "status": "completed" if success else "failed",
             "output": final_output,
             "variables": {
-                "conv": variable_pool.get_all_conversation_vars(),
-                "sys": variable_pool.get_all_system_vars()
+                "conv": conversation_vars,
+                "sys": sys_vars
             },
             "node_outputs": node_outputs,
             "messages": result.get("messages", []),
-            "conversation_id": conversation_id,
+            "conversation_id": execution_context.conversation_id,
             "elapsed_time": elapsed_time,
             "token_usage": token_usage,
             "error": result.get("error"),

api/app/core/workflow/engine/runtime_schema.py

@@ -12,6 +12,7 @@ class ExecutionContext(BaseModel):
     execution_id: str
     workspace_id: str
     user_id: str
+    conversation_id: str
     memory_storage_type: str
     user_rag_memory_id: str
     checkpoint_config: RunnableConfig
@@ -22,6 +23,7 @@ class ExecutionContext(BaseModel):
             execution_id: str,
             workspace_id: str,
             user_id: str,
+            conversation_id: str,
             memory_storage_type: str,
             user_rag_memory_id: str
     ):
@@ -29,6 +31,7 @@ class ExecutionContext(BaseModel):
             execution_id=execution_id,
             workspace_id=workspace_id,
             user_id=user_id,
+            conversation_id=conversation_id,
             memory_storage_type=memory_storage_type,
             user_rag_memory_id=user_rag_memory_id,
 

api/app/core/workflow/executor.py

@@ -3,6 +3,7 @@
 # @Email: 1533512157@qq.com
 # @Time : 2026/2/9 13:51
 import datetime
+import time
 import logging
 from typing import Any
 
@@ -82,13 +83,15 @@ class WorkflowExecutor:
             CompiledStateGraph: The compiled and ready-to-run state graph.
         """
         logger.info(f"Starting workflow graph build: execution_id={self.execution_context.execution_id}")
+        start_time = time.time()
         builder = GraphBuilder(
             self.workflow_config,
             stream=stream,
         )
+
+        self.graph = builder.build()
         self.start_node_id = builder.start_node_id
         self.variable_pool = builder.variable_pool
-        self.graph = builder.build()
 
         self.stream_coordinator.initialize_end_outputs(builder.end_node_map)
         self.event_handler = EventStreamHandler(
@@ -96,7 +99,8 @@ class WorkflowExecutor:
             variable_pool=self.variable_pool,
             execution_id=self.execution_context.execution_id
         )
-        logger.info(f"Workflow graph build completed: execution_id={self.execution_context.execution_id}")
+        logger.info(f"Workflow graph build completed: execution_id={self.execution_context.execution_id}, "
+                    f"cost: {time.time() - start_time:.4f}s")
 
         return self.graph
 
@@ -134,94 +138,12 @@ class WorkflowExecutor:
                 return event.get("data")
         return self.result_builder.build_final_output(
             {"error": "Workflow execution did not end as expected"},
+            self.execution_context,
             self.variable_pool,
             (datetime.datetime.now() - start).total_seconds(),
             "",
             success=False
         )
-        # logger.info(f"Starting workflow execution: execution_id={self.execution_context.execution_id}")
-        #
-        # start_time = datetime.datetime.now()
-        #
-        # # Execute the workflow
-        # try:
-        #     # Build the workflow graph
-        #     graph = self.build_graph()
-        #
-        #     # Initialize the variable pool with input data
-        #     await self.variable_initializer.initialize(
-        #         variable_pool=self.variable_pool,
-        #         input_data=input_data,
-        #         execution_context=self.execution_context
-        #     )
-        #     initial_state = self.state_manager.create_initial_state(
-        #         workflow_config=self.workflow_config,
-        #         input_data=input_data,
-        #         execution_context=self.execution_context,
-        #         start_node_id=self.start_node_id
-        #     )
-        #
-        #     result = await graph.ainvoke(initial_state, config=self.execution_context.checkpoint_config)
-        #
-        #     # Aggregate output from all End nodes
-        #     full_content = ''
-        #     for end_id in self.stream_coordinator.end_outputs.keys():
-        #         full_content += self.variable_pool.get_value(f"{end_id}.output", default="", strict=False)
-        #
-        #     # Append messages for user and assistant
-        #     if input_data.get("files"):
-        #         result["messages"].extend(
-        #             [
-        #                 {
-        #                     "role": "user",
-        #                     "content": input_data.get("message", '')
-        #                 },
-        #                 {
-        #                     "role": "user",
-        #                     "content": input_data.get("files")
-        #                 },
-        #                 {
-        #                     "role": "assistant",
-        #                     "content": full_content
-        #                 }
-        #             ]
-        #         )
-        #     else:
-        #         result["messages"].extend(
-        #             [
-        #                 {
-        #                     "role": "user",
-        #                     "content": input_data.get("message", '')
-        #                 },
-        #                 {
-        #                     "role": "assistant",
-        #                     "content": full_content
-        #                 }
-        #             ]
-        #         )
-        #     # Calculate elapsed time
-        #     end_time = datetime.datetime.now()
-        #     elapsed_time = (end_time - start_time).total_seconds()
-        #
-        #     logger.info(
-        #         f"Workflow execution completed: execution_id={self.execution_context.execution_id}, elapsed_time={elapsed_time:.2f}ms")
-        #
-        #     return self.result_builder.build_final_output(result, self.variable_pool, elapsed_time, full_content)
-        #
-        # except Exception as e:
-        #     end_time = datetime.datetime.now()
-        #     elapsed_time = (end_time - start_time).total_seconds()
-        #
-        #     logger.error(f"Workflow execution failed: execution_id={self.execution_context.execution_id}, error={e}",
-        #                  exc_info=True)
-        #     return {
-        #         "status": "failed",
-        #         "error": str(e),
-        #         "output": None,
-        #         "node_outputs": {},
-        #         "elapsed_time": elapsed_time,
-        #         "token_usage": None
-        #     }
 
     async def execute_stream(
             self,
@@ -255,7 +177,7 @@ class WorkflowExecutor:
             "data": {
                 "execution_id": self.execution_context.execution_id,
                 "workspace_id": self.execution_context.workspace_id,
-                "conversation_id": input_data.get("conversation_id"),
+                "conversation_id": self.execution_context.conversation_id,
                 "timestamp": int(start_time.timestamp() * 1000)
             }
         }
@@ -376,6 +298,7 @@ class WorkflowExecutor:
                 "event": "workflow_end",
                 "data": self.result_builder.build_final_output(
                     result,
+                    self.execution_context,
                     self.variable_pool,
                     elapsed_time,
                     full_content,
@@ -396,6 +319,7 @@ class WorkflowExecutor:
                 "event": "workflow_end",
                 "data": self.result_builder.build_final_output(
                     result,
+                    self.execution_context,
                     self.variable_pool,
                     elapsed_time,
                     full_content,
@@ -432,6 +356,7 @@ async def execute_workflow(
         execution_id=execution_id,
         workspace_id=workspace_id,
         user_id=user_id,
+        conversation_id=input_data.get("conversation_id"),
         memory_storage_type=memory_storage_type,
         user_rag_memory_id=user_rag_memory_id
     )
@@ -471,6 +396,7 @@ async def execute_workflow_stream(
         workspace_id=workspace_id,
         user_id=user_id,
         memory_storage_type=memory_storage_type,
+        conversation_id=input_data.get("conversation_id"),
         user_rag_memory_id=user_rag_memory_id
     )
     executor = WorkflowExecutor(

api/app/core/workflow/nodes/agent/node.py

@@ -64,9 +64,7 @@ class AgentNode(BaseNode):
         
         if not release:
             raise ValueError(f"Agent 不存在: {agent_id}")
-        
 
-        
         return release, message
     
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:

api/app/core/workflow/nodes/assigner/node.py

@@ -14,8 +14,8 @@ logger = logging.getLogger(__name__)
 
 
 class AssignerNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.variable_updater = True
         self.typed_config: AssignerNodeConfig | None = None
 

api/app/core/workflow/nodes/base_node.py

@@ -28,7 +28,7 @@ class BaseNode(ABC):
     All node types should inherit from this class and implement the `execute` method.
     """
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
         """Initialize the node.
 
         Args:
@@ -41,6 +41,7 @@ class BaseNode(ABC):
         self.node_type = node_config["type"]
         self.cycle = node_config.get("cycle")
         self.node_name = node_config.get("name", self.node_id)
+        self.down_stream_nodes = down_stream_nodes
         # 使用 or 运算符处理 None 值
         self.config = node_config.get("config") or {}
         self.error_handling = node_config.get("error_handling") or {}
@@ -93,18 +94,16 @@ class BaseNode(ABC):
             dict: A dict with a single key 'activate', mapping node IDs to
                   their activation status (True/False).
         """
-        edges = self.workflow_config.get("edges")
-        under_stream_nodes = [
-            edge.get("target")
-            for edge in edges
-            if edge.get("source") == self.node_id and self.node_type not in BRANCH_NODES
-        ]
-        return {
-            "activate": {
-                            node_id: self.check_activate(state)
-                            for node_id in under_stream_nodes
-                        } | {self.node_id: self.check_activate(state)}
-        }
+        activate_flag = self.check_activate(state)
+
+        if self.node_type not in BRANCH_NODES:
+            activate = {node_id: activate_flag for node_id in self.down_stream_nodes}
+        else:
+            activate = {}
+
+        activate[self.node_id] = activate_flag
+
+        return {"activate": activate}
 
     @abstractmethod
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
@@ -315,8 +314,8 @@ class BaseNode(ABC):
 
             elapsed_time = (time.time() - start_time) * 1000
 
-            logger.info(f"Node {self.node_id} streaming execution finished, "
-                        f"time elapsed: {elapsed_time:.2f}ms, chunks: {chunk_count}")
+            logger.debug(f"Node {self.node_id} streaming execution finished, "
+                         f"time elapsed: {elapsed_time:.2f}ms, chunks: {chunk_count}")
 
             # Extract processed output (call subclass's _extract_output)
             extracted_output = self._extract_output(final_result)
@@ -428,8 +427,8 @@ class BaseNode(ABC):
             when an error edge exists. If no error edge exists, this method
             raises an exception to stop the workflow.
         """
-        # Check if the node has an error edge defined
-        error_edge = self._find_error_edge()
+        # # Check if the node has an error edge defined
+        # error_edge = self._find_error_edge()
 
         # Extract input data (for logging or audit purposes)
         input_data = self._extract_input(state, variable_pool)
@@ -447,27 +446,26 @@ class BaseNode(ABC):
             "error": error_message
         }
 
-        if error_edge:
-            # If an error edge exists, log a warning and continue to error node
-            logger.warning(
-                f"Node {self.node_id} execution failed, redirecting to error node: {error_edge['target']}"
-            )
-            return {
-                "node_outputs": {
-                    self.node_id: node_output
-                },
-                "error": error_message,
-                "error_node": self.node_id
-            }
-        else:
-            # If no error edge, send the error via stream writer and stop the workflow
-            writer = get_stream_writer()
-            writer({
-                "type": "node_error",
-                **node_output
-            })
-            logger.error(f"Node {self.node_id} execution failed, stopping workflow: {error_message}")
-            raise Exception(f"Node {self.node_id} execution failed: {error_message}")
+        # if error_edge:
+        #     # If an error edge exists, log a warning and continue to error node
+        #     logger.warning(
+        #         f"Node {self.node_id} execution failed, redirecting to error node: {error_edge['target']}"
+        #     )
+        #     return {
+        #         "node_outputs": {
+        #             self.node_id: node_output
+        #         },
+        #         "error": error_message,
+        #         "error_node": self.node_id
+        #     }
+        # else:
+        writer = get_stream_writer()
+        writer({
+            "type": "node_error",
+            **node_output
+        })
+        logger.error(f"Node {self.node_id} execution failed, stopping workflow: {error_message}")
+        raise Exception(f"Node {self.node_id} execution failed: {error_message}")
 
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         """Extracts the input data for this node (used for logging or audit).
@@ -644,7 +642,7 @@ class BaseNode(ABC):
             if content.content_cache.get(f"{provider}_{ModelInfo.is_omni}"):
                 return content.content_cache[f"{provider}_{ModelInfo.is_omni}"]
             with get_db_read() as db:
-                multimodel_service = MultimodalService(db, api_config=api_config)
+                multimodal_service = MultimodalService(db, api_config=api_config)
                 file_obj = FileInput(
                     type=content.type,
                     url=content.url,
@@ -653,7 +651,7 @@ class BaseNode(ABC):
                     upload_file_id=uuid.UUID(content.file_id) if content.file_id else None,
                 )
                 file_obj.set_content(content.get_content())
-                message = await multimodel_service.process_files(
+                message = await multimodal_service.process_files(
                     [file_obj],
                 )
                 content.set_content(file_obj.get_content())
@@ -661,7 +659,7 @@ class BaseNode(ABC):
                     content.content_cache[f"{provider}_{ModelInfo.is_omni}"] = message
                     return message
                 return None
-        raise TypeError(f'Unexpect input value type - {type(content)}')
+        raise TypeError(f'Unexpected input value type - {type(content)}')
 
     @staticmethod
     def process_model_output(content) -> str:

api/app/core/workflow/nodes/code/node.py

@@ -51,8 +51,8 @@ console.log(result)
 
 
 class CodeNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: CodeNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/cycle_graph/node.py

@@ -30,17 +30,13 @@ class CycleGraphNode(BaseNode):
     It acts as a container and execution controller for a subgraph.
     """
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
-
-        self.cycle_nodes = list()  # Nodes belonging to this cycle
-        self.cycle_edges = list()  # Edges connecting nodes within the cycle
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
+        self.cycle_nodes, self.cycle_edges = self.pure_cycle_graph()
         self.start_node_id = None  # ID of the start node within the cycle
 
         self.graph: StateGraph | CompiledStateGraph | None = None
         self.child_variable_pool: VariablePool | None = None
-        self.build_graph()
-        self.iteration_flag = True
 
     def _output_types(self) -> dict[str, VariableType]:
         outputs = {"__child_state": VariableType.ARRAY_OBJECT}
@@ -119,11 +115,11 @@ class CycleGraphNode(BaseNode):
             else:
                 remain_edges.append(edge)
 
-        # Update workflow_config by removing cycle nodes and internal edges
-        self.workflow_config["nodes"] = [
-            node for node in nodes if node.get("cycle") != self.node_id
-        ]
-        self.workflow_config["edges"] = remain_edges
+        # # Update workflow_config by removing cycle nodes and internal edges
+        # self.workflow_config["nodes"] = [
+        #     node for node in nodes if node.get("cycle") != self.node_id
+        # ]
+        # self.workflow_config["edges"] = remain_edges
 
         return cycle_nodes, cycle_edges
 
@@ -137,18 +133,18 @@ class CycleGraphNode(BaseNode):
         3. Compile the graph for runtime execution
         """
         from app.core.workflow.engine.graph_builder import GraphBuilder
-        self.cycle_nodes, self.cycle_edges = self.pure_cycle_graph()
+
         self.child_variable_pool = VariablePool()
         builder = GraphBuilder(
             {
                 "nodes": self.cycle_nodes,
                 "edges": self.cycle_edges,
             },
-            subgraph=True,
-            variable_pool=self.child_variable_pool
+            variable_pool=self.child_variable_pool,
+            cycle=self.node_id
         )
-        self.start_node_id = builder.start_node_id
         self.graph = builder.build()
+        self.start_node_id = builder.start_node_id
         self.child_variable_pool = builder.variable_pool
 
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> Any:
@@ -169,6 +165,7 @@ class CycleGraphNode(BaseNode):
         Raises:
             RuntimeError: If the node type is unsupported.
         """
+        self.build_graph()
         if self.node_type == NodeType.LOOP:
             return await LoopRuntime(
                 start_id=self.start_node_id,
@@ -194,6 +191,7 @@ class CycleGraphNode(BaseNode):
         raise RuntimeError("Unknown cycle node type")
 
     async def execute_stream(self, state: WorkflowState, variable_pool: VariablePool):
+        self.build_graph()
         if self.node_type == NodeType.LOOP:
             yield {
                 "__final__": True,

api/app/core/workflow/nodes/end/config.py

@@ -1,9 +1,7 @@
 """End 节点配置"""
-
 from pydantic import Field
 
-from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableDefinition
-from app.core.workflow.variable.base_variable import VariableType
+from app.core.workflow.nodes.base_config import BaseNodeConfig
 
 
 class EndNodeConfig(BaseNodeConfig):

api/app/core/workflow/nodes/end/node.py

@@ -36,8 +36,6 @@ class EndNode(BaseNode):
         Returns:
             最终输出字符串
         """
-        logger.info(f"节点 {self.node_id} (End) 开始执行")
-
         # 获取配置的输出模板
         output_template = self.config.get("output")
 
@@ -46,11 +44,4 @@ class EndNode(BaseNode):
             output = self._render_template(output_template, variable_pool, strict=False)
         else:
             output = ""
-
-        # 统计信息（用于日志）
-        node_outputs = state.get("node_outputs", {})
-        total_nodes = len(node_outputs)
-
-        logger.info(f"节点 {self.node_id} (End) 执行完成，共执行 {total_nodes} 个节点")
-
         return output

api/app/core/workflow/nodes/enums.py

@@ -28,7 +28,7 @@ class NodeType(StrEnum):
     NOTES = "notes"
 
 
-BRANCH_NODES = [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER]
+BRANCH_NODES = frozenset({NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER})
 
 
 class ComparisonOperator(StrEnum):

api/app/core/workflow/nodes/http_request/config.py

@@ -115,7 +115,7 @@ class HttpRetryConfig(BaseModel):
     )
 
 
-class HttpErrorDefaultTamplete(BaseModel):
+class HttpErrorDefaultTemplate(BaseModel):
     body: str = Field(
         default="",
         description="Default body returned on HTTP error",
@@ -143,7 +143,7 @@ class HttpErrorHandleConfig(BaseModel):
         description="Error handling strategy: 'none', 'default', or 'branch'",
     )
 
-    default: HttpErrorDefaultTamplete | None = Field(
+    default: HttpErrorDefaultTemplate | None = Field(
         default=None,
         description="Default response template for error handling",
     )

api/app/core/workflow/nodes/http_request/node.py

@@ -16,7 +16,7 @@ from app.core.workflow.engine.variable_pool import VariablePool
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.enums import HttpRequestMethod, HttpErrorHandle, HttpAuthType, HttpContentType
 from app.core.workflow.nodes.http_request.config import HttpRequestNodeConfig, HttpRequestNodeOutput
-from app.core.workflow.utils.file_processer import mime_to_file_type
+from app.core.workflow.utils.file_processor import mime_to_file_type
 from app.core.workflow.variable.base_variable import VariableType, FileObject
 from app.core.workflow.variable.variable_objects import FileVariable, ArrayVariable
 from app.schemas import FileType, TransferMethod
@@ -157,8 +157,8 @@ class HttpRequestNode(BaseNode):
     or a branch identifier string when error branching is enabled.
     """
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: HttpRequestNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/if_else/node.py

@@ -14,8 +14,8 @@ logger = logging.getLogger(__name__)
 
 
 class IfElseNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: IfElseNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/jinja_render/node.py

@@ -12,8 +12,8 @@ logger = logging.getLogger(__name__)
 
 
 class JinjaRenderNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: JinjaRenderNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/knowledge/node.py

@@ -21,8 +21,8 @@ logger = logging.getLogger(__name__)
 
 
 class KnowledgeRetrievalNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: KnowledgeRetrievalNodeConfig | None = None
         self.vector_service: ElasticSearchVector | None = None
 

api/app/core/workflow/nodes/llm/node.py

@@ -70,8 +70,8 @@ class LLMNode(BaseNode):
     - ai/assistant: AI 消息（AIMessage）
     """
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: LLMNodeConfig | None = None
         self.messages = []
 

api/app/core/workflow/nodes/memory/node.py

@@ -14,8 +14,8 @@ from app.tasks import write_message_task
 
 
 class MemoryReadNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: MemoryReadNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:
@@ -45,8 +45,8 @@ class MemoryReadNode(BaseNode):
 
 
 class MemoryWriteNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: MemoryWriteNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/node_factory.py

@@ -104,13 +104,15 @@ class NodeFactory:
     def create_node(
             cls,
             node_config: dict[str, Any],
-            workflow_config: dict[str, Any]
+            workflow_config: dict[str, Any],
+            down_stream_nodes: list[str]
     ) -> WorkflowNode | None:
         """创建节点实例
 
         Args:
             node_config: 节点配置
             workflow_config: 工作流配置
+            down_stream_nodes: 下游节点
 
         Returns:
             节点实例或 None（对于不支持的节点类型）
@@ -127,7 +129,7 @@ class NodeFactory:
 
         # 创建节点实例
         logger.debug(f"create node instance: {node_config.get('id')} (type={node_type})")
-        return node_class(node_config, workflow_config)
+        return node_class(node_config, workflow_config, down_stream_nodes)
 
     @classmethod
     def get_supported_types(cls) -> list[str]:

api/app/core/workflow/nodes/parameter_extractor/node.py

@@ -21,8 +21,8 @@ logger = logging.getLogger(__name__)
 
 
 class ParameterExtractorNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: ParameterExtractorNodeConfig | None = None
         self.response_metadata = {}
 

api/app/core/workflow/nodes/question_classifier/node.py

@@ -22,8 +22,8 @@ DEFAULT_EMPTY_QUESTION_CASE = f"{DEFAULT_CASE_PREFIX}1"
 class QuestionClassifierNode(BaseNode):
     """问题分类器节点"""
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: QuestionClassifierNodeConfig | None = None
         self.category_to_case_map = {}
         self.response_metadata = {}

api/app/core/workflow/nodes/start/node.py

@@ -27,14 +27,8 @@ class StartNode(BaseNode):
     注意：变量的验证和默认值处理由 Executor 在初始化时完成。
     """
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        """初始化 Start 节点
-        
-        Args:
-            node_config: 节点配置
-            workflow_config: 工作流配置
-        """
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
 
         # 解析并验证配置
         self.typed_config: StartNodeConfig | None = None
@@ -62,7 +56,6 @@ class StartNode(BaseNode):
             包含系统参数、会话变量和自定义变量的字典
         """
         self.typed_config = StartNodeConfig(**self.config)
-        logger.info(f"节点 {self.node_id} (Start) 开始执行")
 
         # 处理自定义变量（传入 pool 避免重复创建）
         custom_vars = self._process_custom_variables(variable_pool)
@@ -77,9 +70,9 @@ class StartNode(BaseNode):
             **custom_vars  # 自定义变量作为节点输出的一部分
         }
 
-        logger.info(
-            f"节点 {self.node_id} (Start) 执行完成，"
-            f"输出了 {len(custom_vars)} 个自定义变量"
+        logger.debug(
+            f"Node {self.node_id} (Start) execution completed, "
+            f"outputting {len(custom_vars)} custom variables"
         )
 
         return result

api/app/core/workflow/nodes/tool/node.py

@@ -20,8 +20,8 @@ TEMPLATE_PATTERN = re.compile(r"\{\{.*?}}")
 class ToolNode(BaseNode):
     """工具节点"""
 
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: ToolNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/nodes/variable_aggregator/node.py

@@ -12,8 +12,8 @@ logger = logging.getLogger(__name__)
 
 
 class VariableAggregatorNode(BaseNode):
-    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
-        super().__init__(node_config, workflow_config)
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any], down_stream_nodes: list[str]):
+        super().__init__(node_config, workflow_config, down_stream_nodes)
         self.typed_config: VariableAggregatorNodeConfig | None = None
 
     def _output_types(self) -> dict[str, VariableType]:

api/app/core/workflow/utils/template_renderer.py

@@ -153,7 +153,8 @@ class TemplateRenderer:
 
 
 # 全局渲染器实例（严格模式）
-_default_renderer = TemplateRenderer(strict=True)
+_strict_renderer = TemplateRenderer(strict=True)
+_lenient_renderer = TemplateRenderer(strict=False)
 
 
 def render_template(
@@ -184,7 +185,7 @@ def render_template(
         ... )
         '请分析: 这是一段文本'
     """
-    renderer = TemplateRenderer(strict=strict)
+    renderer = _strict_renderer if strict else _lenient_renderer
     return renderer.render(template, conv_vars, node_outputs, system_vars)
 
 
@@ -197,4 +198,4 @@ def validate_template(template: str) -> list[str]:
     Returns:
         错误列表
     """
-    return _default_renderer.validate(template)
+    return _strict_renderer.validate(template)

api/app/core/workflow/validator.py

@@ -6,6 +6,7 @@
 
 import copy
 import logging
+from collections import defaultdict, deque
 from typing import Any, Union, TYPE_CHECKING
 
 from app.core.workflow.nodes.enums import NodeType
@@ -119,7 +120,6 @@ class WorkflowValidator:
         errors = []
 
         graphs = cls.get_subgraph(workflow_config)
-        logger.info(graphs)
         for index, graph in enumerate(graphs):
             nodes = graph.get("nodes", [])
             edges = graph.get("edges", [])
@@ -183,7 +183,7 @@ class WorkflowValidator:
                 has_cycle, cycle_path = WorkflowValidator._has_cycle(nodes, edges)
                 if has_cycle:
                     errors.append(
-                        f"工作流存在循环依赖（请使用 loop 节点实现循环）: {' -> '.join(cycle_path)}"
+                        f"工作流存在循环依赖（请使用 loop/iteration 节点实现循环）: {' -> '.join(cycle_path)}"
                     )
 
             # 8. 验证变量名
@@ -204,18 +204,18 @@ class WorkflowValidator:
         Returns:
             可达节点 ID 集合
         """
+        adj = defaultdict(list)
+        for edge in edges:
+            adj[edge["source"]].append(edge["target"])
+
         reachable = {start_id}
-        queue = [start_id]
-
+        queue = deque([start_id])
         while queue:
-            current = queue.pop(0)
-            for edge in edges:
-                if edge.get("source") == current:
-                    target = edge.get("target")
-                    if target and target not in reachable:
-                        reachable.add(target)
-                        queue.append(target)
-
+            current = queue.popleft()
+            for target in adj[current]:
+                if target not in reachable:
+                    reachable.add(target)
+                    queue.append(target)
         return reachable
 
     @staticmethod
@@ -229,10 +229,6 @@ class WorkflowValidator:
         Returns:
             (has_cycle, cycle_path): 是否有循环和循环路径
         """
-        # 排除 loop 类型的节点
-        loop_nodes = {n["id"] for n in nodes if n.get("type") == "loop"}
-
-        # 构建邻接表（排除 loop 节点的边和错误边）
         graph: dict[str, list[str]] = {}
         for edge in edges:
             source = edge.get("source")
@@ -243,10 +239,6 @@ class WorkflowValidator:
             if edge_type == "error":
                 continue
 
-            # 如果涉及 loop 节点，跳过
-            if source in loop_nodes or target in loop_nodes:
-                continue
-
             if source and target:
                 if source not in graph:
                     graph[source] = []

api/app/core/workflow/variable/variable_objects.py

@@ -54,7 +54,7 @@ class DictVariable(BaseVariable):
 
     def valid_value(self, value) -> dict:
         if not isinstance(value, dict):
-            raise TypeError(f"Value must be a dict  - {type(value)}:{value}")
+            raise TypeError(f"Value must be a dict - {type(value)}:{value}")
         return value
 
     def to_literal(self) -> str:

api/app/models/models_model.py

@@ -27,9 +27,9 @@ class ModelType(StrEnum):
     RERANK = "rerank"
     # TTS = "tts"
     # SPEECH2TEXT = "speech2text"
-    # IMAGE = "image"
+    IMAGE = "image"
     # AUDIO = "audio"
-    # VISION = "vision"
+    VIDEO = "video"
 
 
 class ModelProvider(StrEnum):
@@ -46,6 +46,7 @@ class ModelProvider(StrEnum):
     XINFERENCE = "xinference"
     GPUSTACK = "gpustack"
     BEDROCK = "bedrock"
+    VOLCANO = "volcano"
     COMPOSITE = "composite"
 
 

api/app/models/tenant_model.py

@@ -23,6 +23,17 @@ class Tenants(Base):
     # 国际化语言配置字段
     default_language = Column(String(10), nullable=False, default='zh', server_default='zh', index=True)  # 租户默认语言
     supported_languages = Column(ARRAY(String(10)), nullable=False, default=lambda: ['zh', 'en'], server_default=text("'{zh,en}'"))  # 租户支持的语言列表
+
+    # 租户联系信息
+    contact_name = Column(String(100), nullable=True)   # 联系人姓名
+    contact_email = Column(String(255), nullable=True)  # 联系人邮箱
+    contact_phone = Column(String(50), nullable=True)   # 联系人电话
+
+    # 租户套餐信息
+    plan = Column(String(50), nullable=True)                        # 套餐类型
+    plan_expired_at = Column(DateTime, nullable=True)               # 套餐到期时间
+    api_ops_rate_limit = Column(String(100), nullable=True)         # API 调用频率限制
+    status = Column(String(50), nullable=True, default='active')    # 租户状态
     
     # Relationship to users - one tenant has many users
     users = relationship("User", back_populates="tenant")

api/app/repositories/model_repository.py

@@ -439,7 +439,6 @@ class ModelConfigRepository:
                         ModelConfig.is_public
                     ),
                     ModelConfig.provider == provider,
-                    ModelConfig.is_active,
                     ~ModelConfig.is_composite
                 )
             ).all()

api/app/services/file_storage_service.py

@@ -325,27 +325,30 @@ class FileStorageService:
             )
             raise
 
-    async def get_file_url(self, file_key: str, expires: int = 3600) -> str:
+    async def get_file_url(
+        self,
+        file_key: str,
+        expires: int = 3600,
+        file_name: Optional[str] = None,
+    ) -> str:
         """
         Get an access URL for a file.
 
         Args:
             file_key: The file key.
             expires: URL validity period in seconds (default: 1 hour).
+            file_name: If set, adds Content-Disposition: attachment to force download.
 
         Returns:
             URL for accessing the file.
         """
         logger.debug(f"Getting file URL: file_key={file_key}, expires={expires}s")
-
         try:
-            url = await self.storage.get_url(file_key, expires)
+            url = await self.storage.get_url(file_key, expires, file_name=file_name)
             logger.debug(f"File URL generated: file_key={file_key}")
             return url
         except Exception as e:
-            logger.error(
-                f"Error getting file URL: file_key={file_key}, error={str(e)}"
-            )
+            logger.error(f"Error getting file URL: file_key={file_key}, error={str(e)}")
             raise
 
 

api/app/services/generation_service.py

@@ -0,0 +1,162 @@
+"""
+图片和视频生成服务
+
+提供统一的生成接口，支持多种 Provider
+"""
+from typing import Dict, Any, Optional
+from sqlalchemy.orm import Session
+import uuid
+
+from app.core.models import RedBearModelConfig, RedBearImageGenerator, RedBearVideoGenerator
+from app.core.exceptions import BusinessException
+from app.core.error_codes import BizCode
+from app.models.models_model import ModelType
+from app.repositories.model_repository import ModelConfigRepository, ModelApiKeyRepository
+from app.services.model_service import ModelApiKeyService
+
+
+class GenerationService:
+    """生成服务"""
+    
+    def __init__(self, db: Session):
+        self.db = db
+    
+    async def generate_image(
+        self,
+        model_config_id: str,
+        prompt: str,
+        size: Optional[str] = "2k",
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        生成图片
+        
+        Args:
+            model_config_id: 模型配置ID
+            prompt: 提示词
+            size: 图片尺寸
+            **kwargs: 其他参数
+            
+        Returns:
+            生成结果
+        """
+        # 获取模型配置
+        model_config = ModelConfigRepository.get_by_id(self.db, uuid.UUID(model_config_id))
+        if not model_config:
+            raise BusinessException("模型配置不存在", code=BizCode.NOT_FOUND)
+        
+        if model_config.type != ModelType.IMAGE:
+            raise BusinessException(
+                f"模型类型错误，期望 {ModelType.IMAGE}，实际 {model_config.type}",
+                code=BizCode.INVALID_PARAMETER
+            )
+        
+        # 获取 API Key
+        api_key_info = ModelApiKeyService.get_available_api_key(self.db, uuid.UUID(model_config_id))
+        if not api_key_info:
+            raise BusinessException("没有可用的 API Key", code=BizCode.NOT_FOUND)
+        
+        # 创建配置
+        config = RedBearModelConfig(
+            model_name=api_key_info.model_name,
+            provider=api_key_info.provider,
+            api_key=api_key_info.api_key,
+            base_url=api_key_info.api_base,
+            extra_params=api_key_info.config or {}
+        )
+        
+        # 生成图片
+        generator = RedBearImageGenerator(config)
+        result = await generator.agenerate(prompt, size, **kwargs)
+        
+        return result
+    
+    async def generate_video(
+        self,
+        model_config_id: str,
+        prompt: str,
+        duration: Optional[int] = None,
+        **kwargs
+    ) -> Dict[str, Any]:
+        """
+        生成视频
+        
+        Args:
+            model_config_id: 模型配置ID
+            prompt: 提示词
+            duration: 视频时长（秒）
+            **kwargs: 其他参数
+            
+        Returns:
+            生成结果（包含任务ID）
+        """
+        # 获取模型配置
+        model_config = ModelConfigRepository.get_by_id(self.db, uuid.UUID(model_config_id))
+        if not model_config:
+            raise BusinessException("模型配置不存在", code=BizCode.NOT_FOUND)
+        
+        if model_config.type != ModelType.VIDEO:
+            raise BusinessException(
+                f"模型类型错误，期望 {ModelType.VIDEO}，实际 {model_config.type}",
+                code=BizCode.INVALID_PARAMETER
+            )
+        
+        # 获取 API Key
+        api_key_info = ModelApiKeyService.get_available_api_key(self.db, uuid.UUID(model_config_id))
+        if not api_key_info:
+            raise BusinessException("没有可用的 API Key", code=BizCode.NOT_FOUND)
+        
+        # 创建配置
+        config = RedBearModelConfig(
+            model_name=api_key_info.model_name,
+            provider=api_key_info.provider,
+            api_key=api_key_info.api_key,
+            base_url=api_key_info.api_base,
+            extra_params=api_key_info.config or {}
+        )
+        
+        # 生成视频
+        generator = RedBearVideoGenerator(config)
+        result = await generator.agenerate(prompt, duration, **kwargs)
+        
+        return result
+    
+    async def get_video_task_status(
+        self,
+        model_config_id: str,
+        task_id: str
+    ) -> Dict[str, Any]:
+        """
+        查询视频生成任务状态
+        
+        Args:
+            model_config_id: 模型配置ID
+            task_id: 任务ID
+            
+        Returns:
+            任务状态信息
+        """
+        # 获取模型配置
+        model_config = ModelConfigRepository.get_by_id(self.db, uuid.UUID(model_config_id))
+        if not model_config:
+            raise BusinessException("模型配置不存在", code=BizCode.NOT_FOUND)
+        
+        # 获取 API Key
+        api_key_info = ModelApiKeyService.get_available_api_key(self.db, uuid.UUID(model_config_id))
+        if not api_key_info:
+            raise BusinessException("没有可用的 API Key", code=BizCode.NOT_FOUND)
+        
+        # 创建配置
+        config = RedBearModelConfig(
+            model_name=api_key_info.model_name,
+            provider=api_key_info.provider,
+            api_key=api_key_info.api_key,
+            base_url=api_key_info.api_base,
+            extra_params=api_key_info.config or {}
+        )
+        
+        # 查询任务状态
+        generator = RedBearVideoGenerator(config)
+        result = await generator.aget_task_status(task_id)
+        
+        return result

api/app/services/memory_agent_service.py

@@ -357,6 +357,7 @@ class MemoryAgentService:
                 if file_object is None:
                     continue
                 message["file_content"].append((file_object, file["type"]))
+        logger.info(messages)
 
         message_text = "\n".join([f"{msg['role']}: {msg['content']}" for msg in messages])
         try:
@@ -606,7 +607,7 @@ class MemoryAgentService:
                                     retrieved_content.append({query: statements})
 
                     # 如果 retrieved_content 为空，设置为空字符串
-                    if retrieved_content == []:
+                    if not retrieved_content:
                         retrieved_content = ''
 
                     # 只有当回答不是"信息不足"且不是快速检索时才保存

api/app/services/model_service.py

@@ -154,10 +154,17 @@ class ModelConfigService:
                 }
 
             elif model_type_lower == "embedding":
-                # Embedding 模型验证（在线程中运行同步方法）
+                # Embedding 模型验证
+                # 统一使用 RedBearEmbeddings（自动支持火山引擎多模态）
                 embedding = RedBearEmbeddings(model_config)
                 test_texts = [test_message, "测试文本"]
-                vectors = await asyncio.to_thread(embedding.embed_documents, test_texts)
+                
+                # 火山引擎使用 embed_batch，其他使用 embed_documents
+                if provider.lower() == "volcano":
+                    vectors = await asyncio.to_thread(embedding.embed_batch, test_texts)
+                else:
+                    vectors = await asyncio.to_thread(embedding.embed_documents, test_texts)
+                
                 elapsed_time = time.time() - start_time
 
                 return {
@@ -193,6 +200,56 @@ class ModelConfigService:
                     },
                     "error": None
                 }
+            
+            elif model_type_lower == "image":
+                # 图片生成模型验证
+                from app.core.models.generation import RedBearImageGenerator
+                
+                generator = RedBearImageGenerator(model_config)
+                result = await generator.agenerate(
+                    prompt="a cute panda",
+                    size="2K"
+                )
+                elapsed_time = time.time() - start_time
+                logger.info(f"成功生成图片，结果: {result}")
+                
+                return {
+                    "valid": True,
+                    "message": "图片生成模型配置验证成功",
+                    "response": f"成功生成图片，结果: {result}",
+                    "elapsed_time": elapsed_time,
+                    "usage": {
+                        "prompt_length": len("a cute panda"),
+                        "image_count": 1
+                    },
+                    "error": None
+                }
+            
+            elif model_type_lower == "video":
+                # 视频生成模型验证
+                from app.core.models.generation import RedBearVideoGenerator
+                
+                generator = RedBearVideoGenerator(model_config)
+                result = await generator.agenerate(
+                    prompt="a cute panda playing in bamboo forest",
+                    duration=5
+                )
+                elapsed_time = time.time() - start_time
+                
+                # 视频生成是异步任务，返回任务ID
+                task_id = result.get("task_id") if isinstance(result, dict) else None
+                
+                return {
+                    "valid": True,
+                    "message": "视频生成模型配置验证成功",
+                    "response": f"成功创建视频生成任务，任务ID: {task_id}",
+                    "elapsed_time": elapsed_time,
+                    "usage": {
+                        "prompt_length": len("a cute panda playing in bamboo forest"),
+                        "task_id": task_id
+                    },
+                    "error": None
+                }
 
             else:
                 return {

api/app/services/multimodal_service.py

@@ -294,6 +294,7 @@ PROVIDER_STRATEGIES = {
     "bedrock": BedrockFormatStrategy,
     "anthropic": BedrockFormatStrategy,
     "openai": OpenAIFormatStrategy,
+    "volcano": OpenAIFormatStrategy,
 }
 
 

api/app/services/user_service.py

@@ -250,6 +250,20 @@ def deactivate_user(db: Session, user_id_to_deactivate: uuid.UUID, current_user:
                     }
                 )
 
+        # 检查是否为租户联系人
+        from app.models.tenant_model import Tenants
+        tenant = db.query(Tenants).filter(Tenants.id == db_user.tenant_id).first()
+        if tenant and tenant.contact_email and tenant.contact_email == db_user.email:
+            business_logger.warning(f"尝试停用租户联系人: {db_user.email}, tenant_id={db_user.tenant_id}")
+            raise BusinessException(
+                "该管理员是租户联系人，请先在租户信息中更换联系邮箱，再禁用此管理员",
+                code=BizCode.FORBIDDEN,
+                context={
+                    "user_id": str(user_id_to_deactivate),
+                    "tenant_id": str(db_user.tenant_id)
+                }
+            )
+
         # 停用用户
         business_logger.debug(f"执行用户停用: {db_user.username} (ID: {user_id_to_deactivate})")
         db_user.is_active = False

api/app/services/workflow_import_service.py

@@ -12,7 +12,7 @@ from app.aioRedis import aio_redis_set, aio_redis_get
 from app.core.config import settings
 from app.core.exceptions import BusinessException
 from app.core.workflow.adapters.base_adapter import WorkflowImportResult, WorkflowParserResult
-from app.core.workflow.adapters.errors import UnsupportPlatform, InvalidConfiguration
+from app.core.workflow.adapters.errors import UnsupportedPlatform, InvalidConfiguration
 from app.core.workflow.adapters.registry import PlatformAdapterRegistry
 from app.schemas import AppCreate
 from app.schemas.workflow_schema import WorkflowConfigCreate
@@ -46,7 +46,7 @@ class WorkflowImportService:
                 success=False,
                 temp_id=None,
                 workflow_id=None,
-                errors=[UnsupportPlatform(platform=platform)]
+                errors=[UnsupportedPlatform(platform=platform)]
             )
 
         adapter = self.registry.get_adapter(platform, config)

api/migrations/versions/1ea8fe97b5b7_202603252115.py

@@ -0,0 +1,42 @@
+"""202603252115
+
+Revision ID: 1ea8fe97b5b7
+Revises: e28bcc212da5
+Create Date: 2026-03-25 21:14:41.825048
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '1ea8fe97b5b7'
+down_revision: Union[str, None] = 'e28bcc212da5'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('tenants', sa.Column('contact_name', sa.String(length=100), nullable=True))
+    op.add_column('tenants', sa.Column('contact_email', sa.String(length=255), nullable=True))
+    op.add_column('tenants', sa.Column('contact_phone', sa.String(length=50), nullable=True))
+    op.add_column('tenants', sa.Column('plan', sa.String(length=50), nullable=True))
+    op.add_column('tenants', sa.Column('plan_expired_at', sa.DateTime(), nullable=True))
+    op.add_column('tenants', sa.Column('api_ops_rate_limit', sa.String(length=100), nullable=True))
+    op.add_column('tenants', sa.Column('status', sa.String(length=50), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('tenants', 'status')
+    op.drop_column('tenants', 'api_ops_rate_limit')
+    op.drop_column('tenants', 'plan_expired_at')
+    op.drop_column('tenants', 'plan')
+    op.drop_column('tenants', 'contact_phone')
+    op.drop_column('tenants', 'contact_email')
+    op.drop_column('tenants', 'contact_name')
+    # ### end Alembic commands ###

api/pyproject.toml

@@ -147,6 +147,7 @@ dependencies = [
     "modelscope>=1.34.0",
     "python-magic>=0.4.14; sys_platform == 'linux' or sys_platform == 'darwin'",
     "python-magic-bin>=0.4.14; sys_platform=='win32'",
+    "volcengine-python-sdk[ark]==5.0.19"
 ]
 
 [tool.pytest.ini_options]