Merge pull request #325 from SuanmoSuanyangTechnology/feature/workflow-file

feat(workflow, skill): add multimodal image support to workflows and skill prompt generation

api/app/controllers/prompt_optimizer_controller.py

@@ -120,7 +120,8 @@ async def get_prompt_opt(
                     session_id=session_id,
                     user_id=current_user.id,
                     current_prompt=data.current_prompt,
-                    user_require=data.message
+                    user_require=data.message,
+                    skill=data.skill
             ):
                 # chunk 是 prompt 的增量内容
                 yield f"event:message\ndata: {json.dumps(chunk)}\n\n"

api/app/core/workflow/executor.py

@@ -92,7 +92,7 @@ class WorkflowExecutor:
                 - "conversation_id": conversation identifier
         """
         user_message = input_data.get("message") or ""
-        user_file = input_data.get("file") or []
+        user_files = input_data.get("files") or []
 
         config_variables_list = self.workflow_config.get("variables") or []
         conv_vars = input_data.get("conv", {})
@@ -119,12 +119,12 @@ class WorkflowExecutor:
         input_variables = input_data.get("variables") or {}
         sys_vars = {
             "message": (user_message, VariableType.STRING),
-            "file": (user_file, VariableType.ARRAY_FILE),
             "conversation_id": (input_data.get("conversation_id"), VariableType.STRING),
             "execution_id": (self.execution_id, VariableType.STRING),
             "workspace_id": (self.workspace_id, VariableType.STRING),
             "user_id": (self.user_id, VariableType.STRING),
             "input_variables": (input_variables, VariableType.OBJECT),
+            "files": (user_files, VariableType.ARRAY_FILE)
         }
         for key, var_def in sys_vars.items():
             value = var_def[0]
@@ -564,6 +564,7 @@ class WorkflowExecutor:
                     "input": result.get("node_outputs", {}).get(node_name, {}).get("input"),
                     "output": result.get("node_outputs", {}).get(node_name, {}).get("output"),
                     "elapsed_time": result.get("node_outputs", {}).get(node_name, {}).get("elapsed_time"),
+                    "token_usage": result.get("node_outputs", {}).get(node_name, {}).get("token_usage")
                 }
             }
 

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

@@ -11,6 +11,7 @@ from app.core.config import settings
 from app.core.workflow.nodes.enums import BRANCH_NODES
 from app.core.workflow.variable.base_variable import VariableType
 from app.core.workflow.variable_pool import VariablePool
+from app.services.multimodal_service import PROVIDER_STRATEGIES
 
 logger = logging.getLogger(__name__)
 
@@ -651,3 +652,21 @@ class BaseNode(ABC):
             True if the variable exists in the pool, False otherwise.
         """
         return variable_pool.has(selector)
+
+    @staticmethod
+    async def process_message(provider, content, enable_file=False) -> dict | str | None:
+        if isinstance(content, str):
+            if enable_file:
+                return {"text": content}
+            return content
+        elif isinstance(content, dict):
+            trans_tool = PROVIDER_STRATEGIES[provider]()
+            result = await trans_tool.format_image(content["url"])
+            return result
+        raise TypeError('Unexpect input value type')
+
+    @staticmethod
+    def process_model_output(content) -> str:
+        if isinstance(content, dict):
+            return content.get("text")
+        return content

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

@@ -71,6 +71,16 @@ class LLMNodeConfig(BaseNodeConfig):
         description="对话上下文窗口"
     )
 
+    vision: bool = Field(
+        default=False,
+        description="是否启用视觉模型"
+    )
+
+    vision_input: str = Field(
+        default=None,
+        description="视觉输入"
+    )
+
     # 简单模式
     prompt: str | None = Field(
         default=None,

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

@@ -79,12 +79,12 @@ class LLMNode(BaseNode):
         context = f"<context>{self._render_template(self.typed_config.context, variable_pool)}</context>"
         return re.sub(r"{{context}}", context, message)
 
-    def _prepare_llm(
+    async def _prepare_llm(
             self,
             state: WorkflowState,
             variable_pool: VariablePool,
             stream: bool = False
-    ) -> tuple[RedBearLLM, list | str]:
+    ) -> RedBearLLM:
         """准备 LLM 实例（公共逻辑）
         
         Args:
@@ -93,42 +93,9 @@ class LLMNode(BaseNode):
         Returns:
             (llm, messages_or_prompt): LLM 实例和消息列表或 prompt 字符串
         """
-
-        # 1. 处理消息格式（优先使用 messages）
         self.typed_config = LLMNodeConfig(**self.config)
-        messages_config = self.typed_config.messages
 
-        if messages_config:
+        model_id = self.typed_config.model_id
-            # 使用 LangChain 消息格式
-            messages = []
-            for msg_config in messages_config:
-                role = msg_config.role.lower()
-                content_template = msg_config.content
-                content_template = self._render_context(content_template, variable_pool)
-                content = self._render_template(content_template, variable_pool)
-
-                # 根据角色创建对应的消息对象
-                if role == "system":
-                    messages.append({"role": "system", "content": content})
-                elif role in ["user", "human"]:
-                    messages.append({"role": "user", "content": content})
-                elif role in ["ai", "assistant"]:
-                    messages.append({"role": "assistant", "content": content})
-                else:
-                    logger.warning(f"未知的消息角色: {role}，默认使用 user")
-                    messages.append({"role": "user", "content": content})
-
-            if self.typed_config.memory.enable:
-                # if self.typed_config.memory.enable_window:
-                messages = messages[:-1] + state["messages"][-self.typed_config.memory.window_size:] + messages[-1:]
-            prompt_or_messages = messages
-        else:
-            # 使用简单的 prompt 格式（向后兼容）
-            prompt_template = self.config.get("prompt", "")
-            prompt_or_messages = self._render_template(prompt_template, variable_pool)
-
-        # 2. 获取模型配置
-        model_id = self.config.get("model_id")
         if not model_id:
             raise ValueError(f"节点 {self.node_id} 缺少 model_id 配置")
 
@@ -167,7 +134,61 @@ class LLMNode(BaseNode):
 
         logger.debug(f"创建 LLM 实例: provider={provider}, model={model_name}, streaming={stream}")
 
-        return llm, prompt_or_messages
+        messages_config = self.typed_config.messages
+
+        if messages_config:
+            # 使用 LangChain 消息格式
+            messages = []
+            for msg_config in messages_config:
+                role = msg_config.role.lower()
+                content_template = msg_config.content
+                content_template = self._render_context(content_template, variable_pool)
+                content = self._render_template(content_template, variable_pool)
+
+                # 根据角色创建对应的消息对象
+                if role == "system":
+                    messages.append({
+                        "role": "system",
+                        "content": content
+                    })
+                elif role in ["user", "human"]:
+                    messages.append({
+                        "role": "user",
+                        "content": content
+                    })
+                elif role in ["ai", "assistant"]:
+                    messages.append({
+                        "role": "assistant",
+                        "content": content
+                    })
+                else:
+                    logger.warning(f"未知的消息角色: {role}，默认使用 user")
+                    messages.append({
+                        "role": "user",
+                        "content": content
+                    })
+
+            if self.typed_config.vision_input and self.typed_config.vision:
+                file_content = []
+                files = variable_pool.get_value(self.typed_config.vision_input)
+                for file in files:
+                    content = await self.process_message(provider, file, self.typed_config.vision)
+                    if content:
+                        file_content.append(content)
+                if messages and messages[-1]["role"] == 'user':
+                    messages[-1]['content'] = [messages[-1]["content"]] + file_content
+                else:
+                    messages.append({"role": "user", "content": file_content})
+
+            if self.typed_config.memory.enable:
+                messages = messages[:-1] + state["messages"][-self.typed_config.memory.window_size:] + messages[-1:]
+            self.messages = messages
+        else:
+            # 使用简单的 prompt 格式（向后兼容）
+            prompt_template = self.config.get("prompt", "")
+            self.messages = self._render_template(prompt_template, variable_pool)
+
+        return llm
 
     async def execute(self, state: WorkflowState, variable_pool: VariablePool) -> AIMessage:
         """非流式执行 LLM 调用
@@ -180,15 +201,15 @@ class LLMNode(BaseNode):
             LLM 响应消息
         """
         # self.typed_config = LLMNodeConfig(**self.config)
-        llm, prompt_or_messages = self._prepare_llm(state, variable_pool, False)
+        llm = await self._prepare_llm(state, variable_pool, False)
 
         logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（非流式）")
 
         # 调用 LLM（支持字符串或消息列表）
-        response = await llm.ainvoke(prompt_or_messages)
+        response = await llm.ainvoke(self.messages)
         # 提取内容
         if hasattr(response, 'content'):
-            content = response.content
+            content = self.process_model_output(response.content)
         else:
             content = str(response)
 
@@ -199,14 +220,13 @@ class LLMNode(BaseNode):
 
     def _extract_input(self, state: WorkflowState, variable_pool: VariablePool) -> dict[str, Any]:
         """提取输入数据（用于记录）"""
-        _, prompt_or_messages = self._prepare_llm(state, variable_pool)
 
         return {
-            "prompt": prompt_or_messages if isinstance(prompt_or_messages, str) else None,
+            "prompt": self.messages if isinstance(self.messages, str) else None,
             "messages": [
                 {"role": msg.get("role"), "content": msg.get("content", "")}
-                for msg in prompt_or_messages
+                for msg in self.messages
-            ] if isinstance(prompt_or_messages, list) else None,
+            ] if isinstance(self.messages, list) else None,
             "config": {
                 "model_id": self.config.get("model_id"),
                 "temperature": self.config.get("temperature"),
@@ -226,8 +246,8 @@ class LLMNode(BaseNode):
             usage = business_result.response_metadata.get('token_usage')
             if usage:
                 return {
-                    "prompt_tokens": usage.get('prompt_tokens', 0),
+                    "prompt_tokens": usage.get('input_tokens', 0),
-                    "completion_tokens": usage.get('completion_tokens', 0),
+                    "completion_tokens": usage.get('output_tokens', 0),
                     "total_tokens": usage.get('total_tokens', 0)
                 }
         return None
@@ -244,7 +264,7 @@ class LLMNode(BaseNode):
         """
         self.typed_config = LLMNodeConfig(**self.config)
 
-        llm, prompt_or_messages = self._prepare_llm(state, variable_pool, True)
+        llm = await self._prepare_llm(state, variable_pool, True)
 
         logger.info(f"节点 {self.node_id} 开始执行 LLM 调用（流式）")
         logger.debug(f"LLM 配置: streaming={getattr(llm._model, 'streaming', 'unknown')}")
@@ -255,10 +275,10 @@ class LLMNode(BaseNode):
 
         # 调用 LLM（流式，支持字符串或消息列表）
         last_meta_data = {}
-        async for chunk in llm.astream(prompt_or_messages, stream_usage=True):
+        async for chunk in llm.astream(self.messages, stream_usage=True):
             # 提取内容
             if hasattr(chunk, 'content'):
-                content = chunk.content
+                content = self.process_model_output(chunk.content)
             else:
                 content = str(chunk)
             if hasattr(chunk, 'response_metadata'):

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

@@ -2,6 +2,10 @@ from enum import StrEnum
 from abc import abstractmethod, ABC
 from typing import Any
 
+from pydantic import BaseModel
+
+from app.schemas import FileType
+
 
 class VariableType(StrEnum):
     """Enumeration of supported variable types in the workflow."""
@@ -42,7 +46,7 @@ class VariableType(StrEnum):
             return cls.NUMBER
         elif isinstance(var_type, bool):
             return cls.BOOLEAN
-        elif isinstance(var_type, FileObj):
+        elif isinstance(var_type, FileObject) or (isinstance(var, dict) and var.get('__file')):
             return cls.FILE
         elif isinstance(var_type, dict):
             return cls.OBJECT
@@ -103,8 +107,10 @@ def DEFAULT_VALUE(var_type: VariableType) -> Any:
             raise TypeError(f"Invalid type - {type}")
 
 
-class FileObj:
+class FileObject(BaseModel):
-    pass
+    type: FileType
+    url: str
+    __file: bool
 
 
 class BaseVariable(ABC):

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

@@ -1,66 +1,84 @@
 from typing import Any, TypeVar, Type, Generic
 
-from app.core.workflow.variable.base_variable import BaseVariable, VariableType
+from deprecated import deprecated
+
+from app.core.workflow.variable.base_variable import BaseVariable, VariableType, FileObject
 
 T = TypeVar("T", bound=BaseVariable)
 
 
-class StringObject(BaseVariable):
+class StringVariable(BaseVariable):
     type = 'str'
 
     def valid_value(self, value) -> str:
         if not isinstance(value, str):
-            raise TypeError("Value must be a string")
+            raise TypeError(f"Value must be a string - {type(value)}:{value}")
         return value
 
     def to_literal(self) -> str:
         return self.value
 
 
-class NumberObject(BaseVariable):
+class NumberVariable(BaseVariable):
     type = 'number'
 
     def valid_value(self, value) -> int | float:
         if not isinstance(value, (int, float)):
-            raise TypeError("Value must be a number")
+            raise TypeError(f"Value must be a number - {type(value)}:{value}")
         return value
 
     def to_literal(self) -> str:
         return str(self.value)
 
 
-class BooleanObject(BaseVariable):
+class BooleanVariable(BaseVariable):
     type = 'boolean'
 
     def valid_value(self, value) -> bool:
         if not isinstance(value, bool):
-            raise TypeError("Value must be a boolean")
+            raise TypeError(f"Value must be a boolean - {type(value)}:{value}")
         return value
 
     def to_literal(self) -> str:
         return str(self.value).lower()
 
 
-class DictObject(BaseVariable):
+class DictVariable(BaseVariable):
     type = 'object'
 
     def valid_value(self, value) -> dict:
         if not isinstance(value, dict):
-            raise TypeError("Value must be a dict")
+            raise TypeError(f"Value must be a dict  - {type(value)}:{value}")
         return value
 
     def to_literal(self) -> str:
         return str(self.value)
 
 
-class FileObject(BaseVariable):
+class FileVariable(BaseVariable):
     type = 'file'
 
-    def valid_value(self, value) -> Any:
+    def valid_value(self, value) -> FileObject:
-        pass
+
+        if isinstance(value, dict):
+            if not value.get("__file"):
+                raise TypeError(f"Value must be a FileObject  - {type(value)}:{value}")
+            return FileObject(
+                **{
+                    "type": str(value.get('type')),
+                    "url": value.get('url'),
+                    "__file": True
+                }
+            )
+        if isinstance(value, FileObject):
+            return value
+        raise TypeError(f"Value must be a FileObject - {type(value)}:{value}")
 
     def to_literal(self) -> str:
-        pass
+        return str(self.value.model_dump())
+
+    def get_value(self) -> Any:
+        return self.value.model_dump()
 
 
 class ArrayObject(BaseVariable, Generic[T]):
@@ -74,7 +92,7 @@ class ArrayObject(BaseVariable, Generic[T]):
 
     def valid_value(self, value: list[Any]) -> list[T]:
         if not isinstance(value, list):
-            raise TypeError("Value must be a list")
+            raise TypeError(f"Value must be a list - {type(value)}:{value}")
         final_value = []
         for v in value:
             try:
@@ -86,13 +104,16 @@ class ArrayObject(BaseVariable, Generic[T]):
     def to_literal(self) -> str:
         return "\n".join([v.to_literal() for v in self.value])
 
+    def get_value(self) -> Any:
+        return [v.get_value() for v in self.value]
+
 
 class NestedArrayObject(BaseVariable):
     type = 'array_nest'
 
     def valid_value(self, value: list[T]) -> list[T]:
         if not isinstance(value, list):
-            raise TypeError("Value must be a list")
+            raise TypeError(f"Value must be a list - {type(value)}:{value}")
         final_value = []
         for v in value:
             if not isinstance(v, ArrayObject):
@@ -107,6 +128,10 @@ class NestedArrayObject(BaseVariable):
         return [[item.get_value() for item in row] for row in self.value]
 
 
+@deprecated(
+    reason="Using arbitrary-type values may cause unexpected errors; please switch to strongly-typed values.",
+    category=RuntimeWarning
+)
 class AnyObject(BaseVariable):
     type = 'any'
 
@@ -126,23 +151,23 @@ def make_array(child_type: Type[T], value: list[Any]) -> ArrayObject[T]:
 def create_variable_instance(var_type: VariableType, value: Any) -> T:
     match var_type:
         case VariableType.STRING:
-            return StringObject(value)
+            return StringVariable(value)
         case VariableType.NUMBER:
-            return NumberObject(value)
+            return NumberVariable(value)
         case VariableType.BOOLEAN:
-            return BooleanObject(value)
+            return BooleanVariable(value)
         case VariableType.OBJECT:
-            return DictObject(value)
+            return DictVariable(value)
         case VariableType.ARRAY_STRING:
-            return make_array(StringObject, value)
+            return make_array(StringVariable, value)
         case VariableType.ARRAY_NUMBER:
-            return make_array(NumberObject, value)
+            return make_array(NumberVariable, value)
         case VariableType.ARRAY_BOOLEAN:
-            return make_array(BooleanObject, value)
+            return make_array(BooleanVariable, value)
         case VariableType.ARRAY_OBJECT:
-            return make_array(DictObject, value)
+            return make_array(DictVariable, value)
         case VariableType.ARRAY_FILE:
-            return make_array(FileObject, value)
+            return make_array(FileVariable, value)
         case VariableType.ANY:
             return AnyObject(value)
         case _:

api/app/core/workflow/variable_pool.py

@@ -78,8 +78,8 @@ class VariableStruct(BaseModel, Generic[T]):
             serialization, and workflow type checking.
         instance:
             The concrete variable object. The actual Python type is
-            represented by the generic parameter ``T`` (e.g. StringObject,
+            represented by the generic parameter ``T`` (e.g. StringVariable,
-            NumberObject, ArrayObject[StringObject]).
+            NumberVariable, ArrayObject[StringVariable]).
         mut:
             Whether the variable is mutable.
     """
@@ -286,7 +286,7 @@ class VariablePool:
             系统变量字典
         """
         sys_namespace = self.variables.get("sys", {})
-        return {k: v.instance.value for k, v in sys_namespace.items()}
+        return {k: v.instance.get_value() for k, v in sys_namespace.items()}
 
     def get_all_conversation_vars(self) -> dict[str, Any]:
         """获取所有会话变量
@@ -295,7 +295,7 @@ class VariablePool:
             会话变量字典
         """
         conv_namespace = self.variables.get("conv", {})
-        return {k: v.instance.value for k, v in conv_namespace.items()}
+        return {k: v.instance.get_value() for k, v in conv_namespace.items()}
 
     def get_all_node_outputs(self) -> dict[str, Any]:
         """获取所有节点输出（运行时变量）
@@ -305,7 +305,7 @@ class VariablePool:
         """
         runtime_vars = {
             namespace: {
-                k: v.instance.value
+                k: v.instance.get_value()
                 for k, v in vars_dict.items()
             }
             for namespace, vars_dict in self.variables.items()
@@ -326,7 +326,7 @@ class VariablePool:
         """
         node_namespace = self.variables.get(node_id)
         if node_namespace:
-            return {k: v.instance.value for k, v in node_namespace.items()}
+            return {k: v.instance.get_value() for k, v in node_namespace.items()}
         if strict:
             raise KeyError(f"node {node_id} output not exist")
         else:

api/app/schemas/app_schema.py

@@ -1,14 +1,14 @@
 import datetime
 import uuid
 from typing import Optional, Any, List, Dict, Union
-from enum import Enum
+from enum import Enum, StrEnum
 
 from pydantic import BaseModel, Field, ConfigDict, field_serializer, field_validator
 
 
 # ---------- Multimodal File Support ----------
 
-class FileType(str, Enum):
+class FileType(StrEnum):
     """文件类型枚举"""
     IMAGE = "image"
     DOCUMENT = "document"

api/app/schemas/prompt_optimizer_schema.py

@@ -21,6 +21,11 @@ class PromptOptMessage(BaseModel):
         description="currently optimized prompt"
     )
 
+    skill: bool = Field(
+        default=False,
+        description="Enable variable output"
+    )
+
 
 class PromptSaveRequest(BaseModel):
     session_id: UUID = Field(

api/app/services/draft_run_service.py

@@ -1089,7 +1089,7 @@ class DraftRunService:
 
         except Exception as e:
             # 对于多 Agent 应用，没有直接的 AgentConfig 是正常的
-            logger.debug("获取配置快照失败（可能是多 Agent 应用）", extra={"error": str(e)})
+            logger.debug("获取配置快照失败（可能是多 Agent 应用）", exc_info=True, extra={"error": str(e)})
             return {}
 
     def _replace_variables(

api/app/services/multimodal_service.py

@@ -23,7 +23,7 @@ logger = get_business_logger()
 
 class ImageFormatStrategy(Protocol):
     """图片格式策略接口"""
-    
+
     async def format_image(self, url: str) -> Dict[str, Any]:
         """将图片 URL 转换为特定 provider 的格式"""
         ...
@@ -31,7 +31,7 @@ class ImageFormatStrategy(Protocol):
 
 class DashScopeImageStrategy:
     """通义千问图片格式策略"""
-    
+
     async def format_image(self, url: str) -> Dict[str, Any]:
         """通义千问格式: {"type": "image", "image": "url"}"""
         return {
@@ -42,7 +42,7 @@ class DashScopeImageStrategy:
 
 class BedrockImageStrategy:
     """Bedrock/Anthropic 图片格式策略"""
-    
+
     async def format_image(self, url: str) -> Dict[str, Any]:
         """
         Bedrock/Anthropic 格式: base64 编码
@@ -51,17 +51,17 @@ class BedrockImageStrategy:
         import httpx
         import base64
         from mimetypes import guess_type
-        
+
         logger.info(f"下载并编码图片: {url}")
-        
+
         # 下载图片
         async with httpx.AsyncClient(timeout=30.0) as client:
             response = await client.get(url)
             response.raise_for_status()
-            
+
             # 获取图片数据
             image_data = response.content
-            
+
             # 确定 media type
             content_type = response.headers.get("content-type")
             if content_type and content_type.startswith("image/"):
@@ -69,12 +69,12 @@ class BedrockImageStrategy:
             else:
                 guessed_type, _ = guess_type(url)
                 media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg"
-            
+
             # 转换为 base64
             base64_data = base64.b64encode(image_data).decode("utf-8")
-            
+
             logger.info(f"图片编码完成: media_type={media_type}, size={len(base64_data)}")
-            
+
             return {
                 "type": "image",
                 "source": {
@@ -87,7 +87,7 @@ class BedrockImageStrategy:
 
 class OpenAIImageStrategy:
     """OpenAI 图片格式策略"""
-    
+
     async def format_image(self, url: str) -> Dict[str, Any]:
         """OpenAI 格式: {"type": "image_url", "image_url": {"url": "..."}}"""
         return {
@@ -109,7 +109,7 @@ PROVIDER_STRATEGIES = {
 
 class MultimodalService:
     """多模态文件处理服务"""
-    
+
     def __init__(self, db: Session, provider: str = "dashscope"):
         """
         初始化多模态服务
@@ -120,10 +120,10 @@ class MultimodalService:
         """
         self.db = db
         self.provider = provider.lower()
-    
+
     async def process_files(
-        self, 
+            self,
-        files: Optional[List[FileInput]]
+            files: Optional[List[FileInput]]
     ) -> List[Dict[str, Any]]:
         """
         处理文件列表，返回 LLM 可用的格式
@@ -136,7 +136,7 @@ class MultimodalService:
         """
         if not files:
             return []
-        
+
         result = []
         for idx, file in enumerate(files):
             try:
@@ -168,10 +168,10 @@ class MultimodalService:
                     "type": "text",
                     "text": f"[文件处理失败: {str(e)}]"
                 })
-        
+
         logger.info(f"成功处理 {len(result)}/{len(files)} 个文件，provider={self.provider}")
         return result
-    
+
     async def _process_image(self, file: FileInput) -> Dict[str, Any]:
         """
         处理图片文件
@@ -184,14 +184,10 @@ class MultimodalService:
                 - Anthropic/Bedrock: {"type": "image", "source": {"type": "base64", "media_type": "...", "data": "..."}}
                 - 通义千问: {"type": "image", "image": "url"}
         """
-        if file.transfer_method == TransferMethod.REMOTE_URL:
+        url = await self.get_file_url(file)
-            url = file.url
+
-        else:
-            # 本地文件，获取访问 URL
-            url = await self._get_file_url(file.upload_file_id)
-        
         logger.debug(f"处理图片: {url}, provider={self.provider}")
-        
+
         # 根据 provider 返回不同格式
         if self.provider in ["bedrock", "anthropic"]:
             # Anthropic/Bedrock 只支持 base64 格式，需要下载并转换
@@ -223,7 +219,7 @@ class MultimodalService:
                 "type": "image",
                 "image": url
             }
-    
+
     async def _download_and_encode_image(self, url: str) -> tuple[str, str]:
         """
         下载图片并转换为 base64
@@ -237,15 +233,15 @@ class MultimodalService:
         import httpx
         import base64
         from mimetypes import guess_type
-        
+
         # 下载图片
         async with httpx.AsyncClient(timeout=30.0) as client:
             response = await client.get(url)
             response.raise_for_status()
-            
+
             # 获取图片数据
             image_data = response.content
-            
+
             # 确定 media type
             content_type = response.headers.get("content-type")
             if content_type and content_type.startswith("image/"):
@@ -254,14 +250,14 @@ class MultimodalService:
                 # 从 URL 推断
                 guessed_type, _ = guess_type(url)
                 media_type = guessed_type if guessed_type and guessed_type.startswith("image/") else "image/jpeg"
-            
+
             # 转换为 base64
             base64_data = base64.b64encode(image_data).decode("utf-8")
-            
+
             logger.debug(f"图片编码完成: media_type={media_type}, size={len(base64_data)}")
-            
+
             return base64_data, media_type
-    
+
     async def _process_document(self, file: FileInput) -> Dict[str, Any]:
         """
         处理文档文件（PDF、Word 等）
@@ -284,14 +280,14 @@ class MultimodalService:
             generic_file = self.db.query(GenericFile).filter(
                 GenericFile.id == file.upload_file_id
             ).first()
-            
+
             file_name = generic_file.file_name if generic_file else "unknown"
-            
+
             return {
                 "type": "text",
                 "text": f"<document name=\"{file_name}\">\n{text}\n</document>"
             }
-    
+
     async def _process_audio(self, file: FileInput) -> Dict[str, Any]:
         """
         处理音频文件
@@ -307,7 +303,7 @@ class MultimodalService:
             "type": "text",
             "text": "[音频文件，暂不支持处理]"
         }
-    
+
     async def _process_video(self, file: FileInput) -> Dict[str, Any]:
         """
         处理视频文件
@@ -323,13 +319,13 @@ class MultimodalService:
             "type": "text",
             "text": "[视频文件，暂不支持处理]"
         }
-    
+
-    async def _get_file_url(self, file_id: uuid.UUID) -> str:
+    async def get_file_url(self, file: FileInput) -> str:
         """
         获取文件的访问 URL
         
         Args:
-            file_id: 文件ID
+            file: File Input Struct
             
         Returns:
             str: 文件访问 URL
@@ -337,26 +333,31 @@ class MultimodalService:
         Raises:
             BusinessException: 文件不存在
         """
-        generic_file = self.db.query(GenericFile).filter(
+        if file.transfer_method == TransferMethod.REMOTE_URL:
-            GenericFile.id == file_id,
+            return file.url
-            GenericFile.status == "active"
+        else:
-        ).first()
+            # 本地文件，获取访问 URL
-        
+            file_id = file.upload_file_id
-        if not generic_file:
+            generic_file = self.db.query(GenericFile).filter(
-            raise BusinessException(
+                GenericFile.id == file.upload_file_id,
-                f"文件不存在或已删除: {file_id}",
+                GenericFile.status == "active"
-                BizCode.NOT_FOUND
+            ).first()
-            )
+
-        
+            if not generic_file:
-        # 如果有 access_url，直接返回
+                raise BusinessException(
-        if generic_file.access_url:
+                    f"文件不存在或已删除: {file.upload_file_id}",
-            return generic_file.access_url
+                    BizCode.NOT_FOUND
-        
+                )
-        # 否则，根据 storage_path 生成 URL
+
-        # TODO: 根据实际存储方式生成 URL（本地存储、OSS 等）
+            # 如果有 access_url，直接返回
-        # 这里暂时返回一个占位 URL
+            if generic_file.access_url:
-        return f"/api/files/{file_id}/download"
+                return generic_file.access_url
-    
+
+            # 否则，根据 storage_path 生成 URL
+            # TODO: 根据实际存储方式生成 URL（本地存储、OSS 等）
+            # 这里暂时返回一个占位 URL
+            return f"/api/files/{file_id}/download"
+
     async def _extract_document_text(self, file_id: uuid.UUID) -> str:
         """
         提取文档文本内容
@@ -371,20 +372,20 @@ class MultimodalService:
             GenericFile.id == file_id,
             GenericFile.status == "active"
         ).first()
-        
+
         if not generic_file:
             raise BusinessException(
                 f"文件不存在或已删除: {file_id}",
                 BizCode.NOT_FOUND
             )
-        
+
         # TODO: 根据文件类型提取文本
         # - PDF: 使用 PyPDF2 或 pdfplumber
         # - Word: 使用 python-docx
         # - TXT/MD: 直接读取
-        
+
         file_ext = generic_file.file_ext.lower()
-        
+
         if file_ext in ['.txt', '.md', '.markdown']:
             return await self._read_text_file(generic_file.storage_path)
         elif file_ext == '.pdf':
@@ -393,7 +394,7 @@ class MultimodalService:
             return await self._extract_word_text(generic_file.storage_path)
         else:
             return f"[不支持的文档格式: {file_ext}]"
-    
+
     async def _read_text_file(self, storage_path: str) -> str:
         """读取纯文本文件"""
         try:
@@ -402,7 +403,7 @@ class MultimodalService:
         except Exception as e:
             logger.error(f"读取文本文件失败: {e}")
             return f"[文件读取失败: {str(e)}]"
-    
+
     async def _extract_pdf_text(self, storage_path: str) -> str:
         """提取 PDF 文本"""
         try:
@@ -412,7 +413,7 @@ class MultimodalService:
         except Exception as e:
             logger.error(f"提取 PDF 文本失败: {e}")
             return f"[PDF 提取失败: {str(e)}]"
-    
+
     async def _extract_word_text(self, storage_path: str) -> str:
         """提取 Word 文档文本"""
         try:

api/app/services/prompt/prompt_optimizer_system.jinja2

@@ -1,4 +1,3 @@
-{% raw %}
 Role: AI Prompt Optimization Expert
 
 Profile
@@ -12,11 +11,11 @@ Skills
 Core Optimization Skills
 Requirement Analysis: Accurately understand the relationship between the user’s current needs and the original prompt.
 Structural Reconstruction: Transform vague requirements into clear, block-structured instructions.
-Variable Handling: Identify and standardize dynamic variables in prompts.
+{% if skill != true %}Variable Handling: Identify and standardize dynamic variables in prompts.{% endif %}
 Conflict Resolution: Prioritize current requirements when historical requirements conflict with current needs.
 
 Auxiliary Generation Skills
-Completeness Check: Ensure all necessary elements (input, output, constraints, etc.) are explicitly defined.
+{% if skill != true %}Completeness Check: Ensure all necessary elements (input, output, constraints, etc.) are explicitly defined.{% endif %}
 Language Consistency: Maintain consistency between label language and user input language.
 Executability Verification: Ensure optimized prompts can be directly used in AI tools.
 Format Standardization: Strictly adhere to specified output format requirements.
@@ -25,30 +24,30 @@ Rules
 Basic Principles
 Priority Rule: When historical requirements conflict with current requirements, unconditionally prioritize current requirements.
 Completeness Rule: If the original prompt is empty, generate a complete prompt based on the current requirements.
-Structure Rule: Use a clear block structure, and the contents of each block are roles, tasks, requirements, inputs, outputs, and constraints
+{% if skill != true %}Structure Rule: Use a clear block structure, and the contents of each block are roles, tasks, requirements, inputs, outputs, and constraints{% endif %}
 Language Rule: All label languages must fully match the user input language.
 
 Behavior Guidelines
 Precision Guideline: All instructions must be precise and directly executable, avoiding ambiguity.
 Readability Guideline: Ensure optimized prompts have good readability and logical flow.
-Variable Handling Guideline: Use lowercase English variable names wrapped in {{}} when variables are needed.
+{% if skill != true %}{% raw %}Variable Handling Guideline: Use lowercase English variable names wrapped in {{}} when variables are needed.
-Constraint Handling Guideline: Do not mention variable-related limitations under the [Constraints] label.
+Constraint Handling Guideline: Do not mention variable-related limitations under the [Constraints] label.{% endraw %}{% endif %}
 
 Constraints
 Output Constraint: Must output in JSON format including the fields "prompt" and "desc".
 Content Constraint: Must not include any explanations, analyses, or additional comments.
 Language Constraint: Must use clear and concise language.
-Completeness Constraint: Must fully define all missing elements (input details, output format, constraints, etc.).
+{% if skill != true %}Completeness Constraint: Must fully define all missing elements (input details, output format, constraints, etc.).{% endif %}
 
 Workflows
 Goal: Optimize or generate AI prompts that can be directly used according to user requirements.
 Step 1: Receive the user’s current requirement description {{user_require}} and the original prompt {{original_prompt}}.
 Step 2: Analyze requirements, identify conflicts, and prioritize current requirements.
-Step 3: Optimize or generate the prompt in a block-structured format, ensuring all elements are fully defined.
+{% if skill != true %}Step 3: Optimize or generate the prompt in a block-structured format, ensuring all elements are fully defined.
 Step 4: Generate a JSON output containing the optimized prompt and its description.
+{% else %}Step 3: Generate a JSON output containing the optimized prompt and its description.{% endif %}
 
 Expected Outcome: Obtain a clear, directly executable AI prompt accompanied by an optimization description.
 
 Initialization
-As an AI Prompt Optimization Expert, you must follow the above Rules and execute tasks according to the Workflows.
+As an AI Prompt Optimization Expert, you must follow the above Rules and execute tasks according to the Workflows.
-{% endraw %}

api/app/services/prompt_optimizer_service.py

@@ -128,7 +128,8 @@ class PromptOptimizerService:
             session_id: uuid.UUID,
             user_id: uuid.UUID,
             current_prompt: str,
-            user_require: str
+            user_require: str,
+            skill: bool = False
     ) -> AsyncGenerator[dict[str, str | Any], Any]:
         """
         Optimize a user-provided prompt using a configured prompt optimizer LLM.
@@ -157,6 +158,7 @@ class PromptOptimizerService:
             user_id (uuid.UUID): Identifier of the user associated with the session.
             current_prompt (str): Original prompt to optimize.
             user_require (str): User's requirements or instructions for optimization.
+            skill(bool): Is skill required
 
         Returns:
             OptimizePromptResult: An object containing:
@@ -186,7 +188,7 @@ class PromptOptimizerService:
             prompt_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'prompt')
             with open(os.path.join(prompt_path, 'prompt_optimizer_system.jinja2'), 'r', encoding='utf-8') as f:
                 opt_system_prompt = f.read()
-            rendered_system_message = Template(opt_system_prompt).render()
+            rendered_system_message = Template(opt_system_prompt).render(skill=skill)
 
             with open(os.path.join(prompt_path, 'prompt_optimizer_user.jinja2'), 'r', encoding='utf-8') as f:
                 opt_user_prompt = f.read()

api/app/services/workflow_service.py

@@ -22,6 +22,7 @@ from app.repositories.workflow_repository import (
 from app.schemas import DraftRunRequest
 from app.services.conversation_service import ConversationService
 from app.services.multi_agent_service import convert_uuids_to_str
+from app.services.multimodal_service import MultimodalService
 
 logger = logging.getLogger(__name__)
 
@@ -35,6 +36,7 @@ class WorkflowService:
         self.execution_repo = WorkflowExecutionRepository(db)
         self.node_execution_repo = WorkflowNodeExecutionRepository(db)
         self.conversation_service = ConversationService(db)
+        self.multimodal_service = MultimodalService(db)
 
     # ==================== 配置管理 ====================
 
@@ -444,8 +446,19 @@ class WorkflowService:
                 code=BizCode.CONFIG_MISSING,
                 message=f"工作流配置不存在: app_id={app_id}"
             )
+        files = []
+        if payload.files:
+            for file in payload.files:
+                files.append(
+                    {
+                        "type": file.type,
+                        "url": await self.multimodal_service.get_file_url(file),
+                        "__file": True
+                    }
+                )
+
         input_data = {"message": payload.message, "variables": payload.variables,
-                      "conversation_id": payload.conversation_id}
+                      "conversation_id": payload.conversation_id, "files": files}
 
         # 转换 user_id 为 UUID
         triggered_by_uuid = None
@@ -633,8 +646,20 @@ class WorkflowService:
                 code=BizCode.CONFIG_MISSING,
                 message=f"工作流配置不存在: app_id={app_id}"
             )
+
+        files = []
+        if payload.files:
+            for file in payload.files:
+                files.append(
+                    {
+                        "type": file.type,
+                        "url": await self.multimodal_service.get_file_url(file),
+                        "__file": True
+                    }
+                )
+
         input_data = {"message": payload.message, "variables": payload.variables,
-                      "conversation_id": payload.conversation_id}
+                      "conversation_id": payload.conversation_id, "files": files}
 
         # 转换 user_id 为 UUID
         triggered_by_uuid = None