Merge #104 into develop from feature/20251219_myh

feat(workflow): add support for question classifier in graph construction

* feature/20251219_myh: (11 commits)
  feat(workflow): support variable types(TODO)
  fix(workflow): fix passing of loop variable termination condition
  feat(workflow): add support for passing workspace ID
  feat(workflow): support retrieving variables wrapped in {{}} from variable pool
  feat(prompt_opt): support streaming output for prompt optimization API
  feat(workflow): update workflow conditional logic
  feat(workflow): enable front-end to cover pre-rendered non-variable values
  fix(workflow): ensure default values are properly retrieved in HTTP nodes
  refactor(workflow): refactor graph construction to support subgraph building
  Merge branch 'develop' into feature/20251219_myh
  feat(workflow): add support for question classifier in graph construction

Signed-off-by: Eternity <1533512157@qq.com>
Reviewed-by: zhuwenhui5566@163.com <zhuwenhui5566@163.com>
Merged-by: zhuwenhui5566@163.com <zhuwenhui5566@163.com>

CR-link: https://codeup.aliyun.com/redbearai/python/redbear-mem-open/change/104

api/app/controllers/app_controller.py

@@ -597,7 +597,8 @@ async def draft_run(
                 async for event in workflow_service.run_stream(
                         app_id=app_id,
                         payload=payload,
-                        config=config
+                        config=config,
+                        workspace_id=current_user.current_workspace_id
                 ):
                     # 提取事件类型和数据
                     event_type = event.get("event", "message")
@@ -627,7 +628,7 @@ async def draft_run(
             }
         )
 
-        result = await workflow_service.run(app_id, payload,config)
+        result = await workflow_service.run(app_id, payload, config, current_user.current_workspace_id)
 
         logger.debug(
             "工作流试运行返回结果",

api/app/controllers/prompt_optimizer_controller.py

@@ -1,7 +1,9 @@
 import uuid
+import json
 
 from fastapi import APIRouter, Depends, Path
 from sqlalchemy.orm import Session
+from starlette.responses import StreamingResponse
 
 from app.core.logging_config import get_api_logger
 from app.core.response_utils import success
@@ -70,12 +72,12 @@ def get_prompt_session(
         SessionMessage(role=role, content=content)
         for role, content in history
     ]
-    
+
     result = SessionHistoryResponse(
         session_id=session_id,
         messages=messages
     )
-    
+
     return success(data=result)
 
 
@@ -104,35 +106,25 @@ async def get_prompt_opt(
         ApiResponse: Contains the optimized prompt, description, and a list of variables.
     """
     service = PromptOptimizerService(db)
-    service.create_message(
-        tenant_id=current_user.tenant_id,
-        session_id=session_id,
-        user_id=current_user.id,
-        role=RoleType.USER,
-        content=data.message
-    )
-    opt_result = await service.optimize_prompt(
-        tenant_id=current_user.tenant_id,
-        model_id=data.model_id,
-        session_id=session_id,
-        user_id=current_user.id,
-        current_prompt=data.current_prompt,
-        user_require=data.message
-    )
-    service.create_message(
-        tenant_id=current_user.tenant_id,
-        session_id=session_id,
-        user_id=current_user.id,
-        role=RoleType.ASSISTANT,
-        content=opt_result.desc
-    )
-    variables = service.parser_prompt_variables(opt_result.prompt)
-    result = {
-        "prompt": opt_result.prompt,
-        "desc": opt_result.desc,
-        "variables": variables
-    }
-    result_schema = OptimizePromptResponse.model_validate(result)
-    return success(data=result_schema)
 
+    async def event_generator():
+        async for chunk in service.optimize_prompt(
+                tenant_id=current_user.tenant_id,
+                model_id=data.model_id,
+                session_id=session_id,
+                user_id=current_user.id,
+                current_prompt=data.current_prompt,
+                user_require=data.message
+        ):
+            # chunk 是 prompt 的增量内容
+            yield f"event:'message'\ndata: {json.dumps(chunk)}\n\n"
 
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no"
+        }
+    )

api/app/core/workflow/executor.py

@@ -10,11 +10,10 @@ import logging
 from typing import Any
 
 from langchain_core.messages import HumanMessage
-from langgraph.graph import StateGraph, START, END
 from langgraph.graph.state import CompiledStateGraph
 
-from app.core.workflow.expression_evaluator import evaluate_condition
-from app.core.workflow.nodes import WorkflowState, NodeFactory
+from app.core.workflow.graph_builder import GraphBuilder
+from app.core.workflow.nodes import WorkflowState
 from app.core.workflow.nodes.enums import NodeType
 
 # from app.core.tools.registry import ToolRegistry
@@ -191,155 +190,10 @@ class WorkflowExecutor:
             编译后的状态图
         """
         logger.info(f"开始构建工作流图: execution_id={self.execution_id}")
-
-        # 分析 End 节点的前缀配置和相邻且被引用的节点
-        end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if stream else ({}, set())
-
-        # 1. 创建状态图
-        workflow = StateGraph(WorkflowState)
-
-        # 2. 添加所有节点（包括 start 和 end）
-        start_node_id = None
-        end_node_ids = []
-
-        for node in self.nodes:
-            node_type = node.get("type")
-            node_id = node.get("id")
-            cycle_node = node.get("cycle")
-            if cycle_node:
-                # 处于循环子图中的节点由 CycleGraphNode 进行构建处理
-                continue
-
-            # 记录 start 和 end 节点 ID
-            if node_type == NodeType.START:
-                start_node_id = node_id
-            elif node_type == NodeType.END:
-                end_node_ids.append(node_id)
-
-            # 创建节点实例（现在 start 和 end 也会被创建）
-            node_instance = NodeFactory.create_node(node, self.workflow_config)
-
-            if node_type in [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER]:
-
-                # Find all edges whose source is the current node
-                related_edge = [edge for edge in self.edges if edge.get("source") == node_id]
-
-                # Iterate over each branch
-                for idx in range(len(related_edge)):
-                    # Generate a condition expression for each edge
-                    # Used later to determine which branch to take based on the node's output
-                    # Assumes node output `node.<node_id>.output` matches the edge's label
-                    # For example, if node.123.output == 'CASE1', take the branch labeled 'CASE1'
-                    related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
-
-            if node_instance:
-                # 如果是流式模式，且节点有 End 前缀配置，注入配置
-                if stream and node_id in end_prefixes:
-                    # 将 End 前缀配置注入到节点实例
-                    node_instance._end_node_prefix = end_prefixes[node_id]
-                    logger.info(f"为节点 {node_id} 注入 End 前缀配置")
-
-                # 如果是流式模式，标记节点是否与 End 相邻且被引用
-                if stream:
-                    node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced
-                    if node_id in adjacent_and_referenced:
-                        logger.info(f"节点 {node_id} 标记为与 End 相邻且被引用")
-
-                # 包装节点的 run 方法
-                # 使用函数工厂避免闭包问题
-                if stream:
-                    # 流式模式：创建 async generator 函数
-                    # LangGraph 会收集所有 yield 的值，最后一个 yield 的字典会被合并到 state
-                    def make_stream_func(inst):
-                        async def node_func(state: WorkflowState):
-                            # logger.debug(f"流式执行节点: {inst.node_id}, 支持流式: {inst.supports_streaming()}")
-                            async for item in inst.run_stream(state):
-                                yield item
-
-                        return node_func
-
-                    workflow.add_node(node_id, make_stream_func(node_instance))
-                else:
-                    # 非流式模式：创建 async function
-                    def make_func(inst):
-                        async def node_func(state: WorkflowState):
-                            return await inst.run(state)
-
-                        return node_func
-
-                    workflow.add_node(node_id, make_func(node_instance))
-
-                logger.debug(f"添加节点: {node_id} (type={node_type}, stream={stream})")
-
-        # 3. 添加边
-        # 从 START 连接到 start 节点
-        if start_node_id:
-            workflow.add_edge(START, start_node_id)
-            logger.debug(f"添加边: START -> {start_node_id}")
-
-        for edge in self.workflow_config.get("edges", []):
-            source = edge.get("source")
-            target = edge.get("target")
-            edge_type = edge.get("type")
-            condition = edge.get("condition")
-
-            # 跳过从 start 节点出发的边（因为已经从 START 连接到 start）
-            if source == start_node_id:
-                # 但要连接 start 到下一个节点
-                workflow.add_edge(source, target)
-                logger.debug(f"添加边: {source} -> {target}")
-                continue
-
-            # # 处理到 end 节点的边
-            # if target in end_node_ids:
-            #     # 连接到 end 节点
-            #     workflow.add_edge(source, target)
-            #     logger.debug(f"添加边: {source} -> {target}")
-            #     continue
-
-            # 跳过错误边（在节点内部处理）
-            if edge_type == "error":
-                continue
-
-            if condition:
-                # 条件边
-                def make_router(cond, tgt):
-                    """Dynamically generate a conditional router function to ensure each branch has a unique name."""
-
-
-                    def router_fn(state: WorkflowState):
-                        if evaluate_condition(
-                                cond,
-                                state.get("variables", {}),
-                                state.get("node_outputs", {}),
-                                {
-                                    "execution_id": state.get("execution_id"),
-                                    "workspace_id": state.get("workspace_id"),
-                                    "user_id": state.get("user_id")
-                                }
-                        ):
-                            return tgt
-                        return END
-
-                    # 动态修改函数名，避免重复
-                    router_fn.__name__ = f"router_{tgt}"
-                    return router_fn
-
-                router_fn = make_router(condition, target)
-                workflow.add_conditional_edges(source, router_fn)
-                logger.debug(f"添加条件边: {source} -> {target} (condition={condition})")
-            else:
-                # 普通边
-                workflow.add_edge(source, target)
-                logger.debug(f"添加边: {source} -> {target}")
-
-        # 从 end 节点连接到 END
-        for end_node_id in end_node_ids:
-            workflow.add_edge(end_node_id, END)
-            logger.debug(f"添加边: {end_node_id} -> END")
-
-        # 4. 编译图
-        graph = workflow.compile()
+        graph = GraphBuilder(
+            self.workflow_config,
+            stream=stream,
+        ).build()
         logger.info(f"工作流图构建完成: execution_id={self.execution_id}")
 
         return graph

api/app/core/workflow/graph_builder.py

@@ -0,0 +1,253 @@
+import logging
+import uuid
+from typing import Any
+
+from langgraph.graph.state import CompiledStateGraph, StateGraph
+from langgraph.graph import START, END
+
+from app.core.workflow.expression_evaluator import evaluate_condition
+from app.core.workflow.nodes import WorkflowState, NodeFactory
+from app.core.workflow.nodes.enums import NodeType
+
+logger = logging.getLogger(__name__)
+
+
+# TODO: 子图拆解支持
+class GraphBuilder:
+    def __init__(
+            self,
+            workflow_config: dict[str, Any],
+            stream: bool = False,
+            subgraph: bool = False,
+    ):
+        self.workflow_config = workflow_config
+
+        self.stream = stream
+        self.subgraph = subgraph
+
+        self.start_node_id = None
+        self.end_node_ids = []
+
+        self.graph: StateGraph | CompiledStateGraph | None = None
+
+    @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", [])
+
+    def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]:
+        """分析 End 节点的前缀配置
+
+        检查每个 End 节点的模板，找到直接上游节点的引用，
+        提取该引用之前的前缀部分。
+
+        Returns:
+            元组：({上游节点ID: End节点前缀}, {与End相邻且被引用的节点ID集合})
+        """
+        import re
+
+        prefixes = {}
+        adjacent_and_referenced = set()  # 记录与 End 节点相邻且被引用的节点
+
+        # 找到所有 End 节点
+        end_nodes = [node for node in self.nodes if node.get("type") == "end"]
+        logger.info(f"[前缀分析] 找到 {len(end_nodes)} 个 End 节点")
+
+        for end_node in end_nodes:
+            end_node_id = end_node.get("id")
+            output_template = end_node.get("config", {}).get("output")
+
+            logger.info(f"[前缀分析] End 节点 {end_node_id} 模板: {output_template}")
+
+            if not output_template:
+                continue
+
+            # 查找模板中引用了哪些节点
+            # 匹配 {{node_id.xxx}} 或 {{ node_id.xxx }} 格式（支持空格）
+            pattern = r'\{\{\s*([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\s*\}\}'
+            matches = list(re.finditer(pattern, output_template))
+
+            logger.info(f"[前缀分析] 模板中找到 {len(matches)} 个节点引用")
+
+            # 找到所有直接连接到 End 节点的上游节点
+            direct_upstream_nodes = []
+            for edge in self.edges:
+                if edge.get("target") == end_node_id:
+                    source_node_id = edge.get("source")
+                    direct_upstream_nodes.append(source_node_id)
+
+            logger.info(f"[前缀分析] End 节点的直接上游节点: {direct_upstream_nodes}")
+
+            # 找到第一个直接上游节点的引用
+            for match in matches:
+                referenced_node_id = match.group(1)
+                logger.info(f"[前缀分析] 检查引用: {referenced_node_id}")
+
+                if referenced_node_id in direct_upstream_nodes:
+                    # 这是直接上游节点的引用，提取前缀
+                    prefix = output_template[:match.start()]
+
+                    logger.info(f"[前缀分析] ✅ 找到直接上游节点 {referenced_node_id} 的引用，前缀: '{prefix}'")
+
+                    # 标记这个节点为"相邻且被引用"
+                    adjacent_and_referenced.add(referenced_node_id)
+
+                    if prefix:
+                        prefixes[referenced_node_id] = prefix
+                        logger.info(f"✅ [前缀分析] 为节点 {referenced_node_id} 配置前缀: '{prefix[:50]}...'")
+
+                    # 只处理第一个直接上游节点的引用
+                    break
+
+        logger.info(f"[前缀分析] 最终配置: {prefixes}")
+        logger.info(f"[前缀分析] 与 End 相邻且被引用的节点: {adjacent_and_referenced}")
+        return prefixes, adjacent_and_referenced
+
+    def add_nodes(self):
+        end_prefixes, adjacent_and_referenced = self._analyze_end_node_prefixes() if self.stream else ({}, set())
+
+        for node in self.nodes:
+            node_type = node.get("type")
+            node_id = node.get("id")
+            cycle_node = node.get("cycle")
+            if cycle_node:
+                # 处于循环子图中的节点由 CycleGraphNode 进行构建处理
+                if not self.subgraph:
+                    continue
+
+            # 记录 start 和 end 节点 ID
+            if node_type in [NodeType.START, NodeType.CYCLE_START]:
+                self.start_node_id = node_id
+            elif node_type == NodeType.END:
+                self.end_node_ids.append(node_id)
+
+            # 创建节点实例（现在 start 和 end 也会被创建）
+            # 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)
+
+            if node_type in [NodeType.IF_ELSE, NodeType.HTTP_REQUEST, NodeType.QUESTION_CLASSIFIER]:
+
+                # Find all edges whose source is the current node
+                related_edge = [edge for edge in self.edges if edge.get("source") == node_id]
+
+                # Iterate over each branch
+                for idx in range(len(related_edge)):
+                    # Generate a condition expression for each edge
+                    # Used later to determine which branch to take based on the node's output
+                    # Assumes node output `node.<node_id>.output` matches the edge's label
+                    # For example, if node.123.output == 'CASE1', take the branch labeled 'CASE1'
+                    related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
+
+            if node_instance:
+                # 如果是流式模式，且节点有 End 前缀配置，注入配置
+                if self.stream and node_id in end_prefixes:
+                    # 将 End 前缀配置注入到节点实例
+                    node_instance._end_node_prefix = end_prefixes[node_id]
+                    logger.info(f"为节点 {node_id} 注入 End 前缀配置")
+
+                # 如果是流式模式，标记节点是否与 End 相邻且被引用
+                if self.stream:
+                    node_instance._is_adjacent_to_end = node_id in adjacent_and_referenced
+                    if node_id in adjacent_and_referenced:
+                        logger.info(f"节点 {node_id} 标记为与 End 相邻且被引用")
+
+                # 包装节点的 run 方法
+                # 使用函数工厂避免闭包问题
+                if self.stream:
+                    # 流式模式：创建 async generator 函数
+                    # LangGraph 会收集所有 yield 的值，最后一个 yield 的字典会被合并到 state
+                    def make_stream_func(inst):
+                        async def node_func(state: WorkflowState):
+                            # logger.debug(f"流式执行节点: {inst.node_id}, 支持流式: {inst.supports_streaming()}")
+                            async for item in inst.run_stream(state):
+                                yield item
+
+                        return node_func
+
+                    self.graph.add_node(node_id, make_stream_func(node_instance))
+                else:
+                    # 非流式模式：创建 async function
+                    def make_func(inst):
+                        async def node_func(state: WorkflowState):
+                            return await inst.run(state)
+
+                        return node_func
+
+                    self.graph.add_node(node_id, make_func(node_instance))
+
+                logger.debug(f"添加节点: {node_id} (type={node_type}, stream={self.stream})")
+
+    def add_edges(self):
+        if self.start_node_id:
+            self.graph.add_edge(START, self.start_node_id)
+            logger.debug(f"添加边: START -> {self.start_node_id}")
+
+        for edge in self.edges:
+            source = edge.get("source")
+            target = edge.get("target")
+            edge_type = edge.get("type")
+            condition = edge.get("condition")
+
+            # 跳过从 start 节点出发的边（因为已经从 START 连接到 start）
+            if source == self.start_node_id:
+                # 但要连接 start 到下一个节点
+                self.graph.add_edge(source, target)
+                logger.debug(f"添加边: {source} -> {target}")
+                continue
+
+            # # 处理到 end 节点的边
+            # if target in end_node_ids:
+            #     # 连接到 end 节点
+            #     workflow.add_edge(source, target)
+            #     logger.debug(f"添加边: {source} -> {target}")
+            #     continue
+
+            # 跳过错误边（在节点内部处理）
+            if edge_type == "error":
+                continue
+
+            if condition:
+                # 条件边
+                def make_router(cond, tgt):
+                    """Dynamically generate a conditional router function to ensure each branch has a unique name."""
+
+                    def router_fn(state: WorkflowState):
+                        if evaluate_condition(
+                                cond,
+                                state.get("variables", {}),
+                                state.get("runtime_vars", {}),
+                                {
+                                    "execution_id": state.get("execution_id"),
+                                    "workspace_id": state.get("workspace_id"),
+                                    "user_id": state.get("user_id")
+                                }
+                        ):
+                            return tgt
+                        return END
+
+                    # 动态修改函数名，避免重复
+                    router_fn.__name__ = f"router_{uuid.uuid4().hex[:8]}_{tgt}"
+                    return router_fn
+
+                router_fn = make_router(condition, target)
+                self.graph.add_conditional_edges(source, router_fn)
+                logger.debug(f"添加条件边: {source} -> {target} (condition={condition})")
+            else:
+                # 普通边
+                self.graph.add_edge(source, target)
+                logger.debug(f"添加边: {source} -> {target}")
+
+        # 从 end 节点连接到 END
+        for end_node_id in self.end_node_ids:
+            self.graph.add_edge(end_node_id, END)
+            logger.debug(f"添加边: {end_node_id} -> END")
+        return
+
+    def build(self) -> CompiledStateGraph:
+        self.graph = StateGraph(WorkflowState)
+        self.add_nodes()
+        self.add_edges()  # 添加边必须在添加节点之后
+        return self.graph.compile()

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

@@ -1,3 +1,5 @@
+from typing import Any
+
 from pydantic import Field, BaseModel
 
 from app.core.workflow.nodes.base_config import BaseNodeConfig
@@ -19,7 +21,7 @@ class AssignmentItem(BaseModel):
         description="Assignment operator",
     )
 
-    value: str | list[str] = Field(
+    value: Any = Field(
         ...,
         description="Value(s) to assign to the variable(s)",
     )

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

@@ -2,7 +2,6 @@ import logging
 import re
 from typing import Any
 
-from app.core.workflow.expression_evaluator import ExpressionEvaluator
 from app.core.workflow.nodes.assigner.config import AssignerNodeConfig
 from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.workflow.nodes.enums import AssignmentOperator
@@ -29,6 +28,7 @@ class AssignerNode(BaseNode):
             None or the result of the assignment operation.
         """
         # Initialize a variable pool for accessing conversation, node, and system variables
+        logger.info(f"节点 {self.node_id} 开始执行")
         pool = VariablePool(state)
         for assignment in self.typed_config.assignments:
             # Get the target variable selector (e.g., "conv.test")
@@ -45,14 +45,13 @@ class AssignerNode(BaseNode):
 
             # Get the value or expression to assign
             value = assignment.value
-            if isinstance(value, list):
-                value = '.'.join(value)
-            value = ExpressionEvaluator.evaluate(
-                expression=value,
-                variables=pool.get_all_conversation_vars(),
-                node_outputs=pool.get_all_node_outputs(),
-                system_vars=pool.get_all_system_vars(),
-            )
+            pattern = r"\{\{\s*(.*?)\s*\}\}"
+            if isinstance(value, str):
+                expression = re.match(pattern, value)
+                if expression:
+                    expression = expression.group(1)
+                    expression = re.sub(pattern, r"\1", expression).strip()
+                    value = self.get_variable(expression, state)
 
             # Select the appropriate assignment operator instance based on the target variable type
             operator: AssignmentOperatorInstance = AssignmentOperatorResolver.resolve_by_value(
@@ -63,6 +62,8 @@ class AssignerNode(BaseNode):
 
             # Execute the configured assignment operation
             match assignment.operation:
+                case AssignmentOperator.COVER:
+                    operator.assign()
                 case AssignmentOperator.ASSIGN:
                     operator.assign()
                 case AssignmentOperator.CLEAR:

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

@@ -4,13 +4,16 @@
 """
 
 from enum import StrEnum
+from typing import Any
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ConfigDict
+
+VARIABLE_PATTERN = r"\{\{\s*(.*?)\s*\}\}"
 
 
 class VariableType(StrEnum):
     """变量类型枚举"""
-    
+
     STRING = "string"
     NUMBER = "number"
     BOOLEAN = "boolean"
@@ -22,43 +25,94 @@ class VariableType(StrEnum):
     ARRAY_OBJECT = "array[object]"
 
 
+class TypedVariable(BaseModel):
+    """
+    TODO: 强类型限制
+    Strongly typed variable that validates value on assignment.
+    """
+
+    value: Any = Field(..., description="Variable value")
+    type: VariableType = Field(..., description="Declared type of the variable")
+
+    model_config = ConfigDict(
+        validate_assignment=True
+    )
+
+    def __setattr__(self, name, value):
+        if name == "value":
+            self._validate_value(value)
+        if name == "type":
+            raise RuntimeError("Cannot modify variable type at runtime")
+        super().__setattr__(name, value)
+
+    def _validate_value(self, v: Any):
+        t = self.type
+        match t:
+            case VariableType.STRING:
+                if not isinstance(v, str):
+                    raise TypeError("Variable value does not match type STRING")
+            case VariableType.BOOLEAN:
+                if not isinstance(v, bool):
+                    raise TypeError("Variable value does not match type BOOLEAN")
+            case VariableType.NUMBER:
+                if not isinstance(v, (int, float)):
+                    raise TypeError("Variable value does not match type NUMBER")
+            case VariableType.OBJECT:
+                if not isinstance(v, dict):
+                    raise TypeError("Variable value does not match type OBJECT")
+            case VariableType.ARRAY_STRING:
+                if not isinstance(v, list) or not all(isinstance(i, str) for i in v):
+                    raise TypeError("Variable value does not match type ARRAY_STRING")
+            case VariableType.ARRAY_NUMBER:
+                if not isinstance(v, list) or not all(isinstance(i, (int, float)) for i in v):
+                    raise TypeError("Variable value does not match type ARRAY_NUMBER")
+            case VariableType.ARRAY_BOOLEAN:
+                if not isinstance(v, list) or not all(isinstance(i, bool) for i in v):
+                    raise TypeError("Variable value does not match type ARRAY_BOOLEAN")
+            case VariableType.ARRAY_OBJECT:
+                if not isinstance(v, list) or not all(isinstance(i, dict) for i in v):
+                    raise TypeError("Variable value does not match type ARRAY_OBJECT")
+            case _:
+                raise TypeError(f"Unknown variable type: {t}")
+
+
 class VariableDefinition(BaseModel):
     """变量定义
     
     定义工作流或节点的输入/输出变量。
     这是一个通用的数据结构，可以在多个地方使用。
     """
-    
+
     name: str = Field(
         ...,
         description="变量名称"
     )
-    
+
     type: VariableType = Field(
         default=VariableType.STRING,
         description="变量类型"
     )
-    
+
     required: bool = Field(
         default=False,
         description="是否必需"
     )
-    
+
     default: str | int | float | bool | list | dict | None = Field(
         default=None,
         description="默认值"
     )
-    
+
     description: str | None = Field(
         default=None,
         description="变量描述"
     )
-    
+
     max_length: int = Field(
         default=200,
         description="只对字符串类型生效"
     )
-    
+
     class Config:
         json_schema_extra = {
             "examples": [
@@ -96,22 +150,22 @@ class BaseNodeConfig(BaseModel):
     - description: 节点描述
     - tags: 节点标签（用于分类和搜索）
     """
-    
+
     name: str | None = Field(
         default=None,
         description="节点名称（显示名称），如果不设置则使用节点 ID"
     )
-    
+
     description: str | None = Field(
         default=None,
         description="节点描述，说明节点的作用"
     )
-    
+
     tags: list[str] = Field(
         default_factory=list,
         description="节点标签，用于分类和搜索"
     )
-    
+
     class Config:
         """Pydantic 配置"""
         # 允许额外字段（向后兼容）

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

@@ -356,7 +356,8 @@ class BaseNode(ABC):
                 **final_output,
                 "runtime_vars": {
                     self.node_id: runtime_var
-                }
+                },
+                "looping": state["looping"]
             }
             
             # Add streaming buffer for non-End nodes

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

@@ -1,7 +1,9 @@
+from typing import Any
+
 from pydantic import Field, BaseModel
 
 from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableType
-from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator
+from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator, ValueInputType
 
 
 class CycleVariable(BaseNodeConfig):
@@ -9,18 +11,25 @@ class CycleVariable(BaseNodeConfig):
         ...,
         description="Name of the loop variable"
     )
+
     type: VariableType = Field(
         ...,
         description="Data type of the loop variable"
     )
-    value: str = Field(
+
+    input_type: ValueInputType = Field(
+        ...,
+        description="Input type of the loop variable"
+    )
+
+    value: Any = Field(
         ...,
         description="Initial or current value of the loop variable"
     )
 
 
 class ConditionDetail(BaseModel):
-    comparison_operator: ComparisonOperator = Field(
+    operator: ComparisonOperator = Field(
         ...,
         description="Operator used to compare the left and right operands"
     )
@@ -30,11 +39,16 @@ class ConditionDetail(BaseModel):
         description="Left-hand operand of the comparison expression"
     )
 
-    right: str = Field(
+    right: Any = Field(
         ...,
         description="Right-hand operand of the comparison expression"
     )
 
+    input_type: ValueInputType = Field(
+        ...,
+        description="Input type of the loop variable"
+    )
+
 
 class ConditionsConfig(BaseModel):
     """Configuration for loop condition evaluation"""

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

@@ -3,10 +3,11 @@ from typing import Any
 
 from langgraph.graph.state import CompiledStateGraph
 
-from app.core.workflow.expression_evaluator import evaluate_condition, evaluate_expression
+from app.core.workflow.expression_evaluator import evaluate_expression
 from app.core.workflow.nodes import WorkflowState
 from app.core.workflow.nodes.cycle_graph import LoopNodeConfig
-from app.core.workflow.nodes.operators import ConditionExpressionBuilder
+from app.core.workflow.nodes.enums import ValueInputType, ComparisonOperator, LogicOperator
+from app.core.workflow.nodes.operators import TypeTransformer, ConditionExpressionResolver, CompareOperatorInstance
 from app.core.workflow.variable_pool import VariablePool
 
 logger = logging.getLogger(__name__)
@@ -14,11 +15,13 @@ logger = logging.getLogger(__name__)
 
 class LoopRuntime:
     """
-    Runtime executor for loop nodes in a workflow.
+    Runtime executor for a loop node in a workflow graph.
 
-    Handles iterative execution of a loop node according to defined loop variables
-    and conditional expressions. Supports maximum loop count and loop control
-    through the workflow state.
+    This class is responsible for executing a loop node at runtime:
+    - Initializing loop-scoped variables
+    - Evaluating loop continuation conditions
+    - Repeatedly invoking a compiled sub-graph
+    - Enforcing maximum loop count and external stop signals
     """
 
     def __init__(
@@ -29,13 +32,13 @@ class LoopRuntime:
             state: WorkflowState,
     ):
         """
-        Initialize the loop runtime.
+        Initialize the loop runtime executor.
 
         Args:
-            graph: Compiled workflow graph capable of async invocation.
-            node_id: Unique identifier of the loop node.
-            config: Dictionary containing loop node configuration.
-            state: Current workflow state at the point of loop execution.
+            graph: A compiled LangGraph state graph representing the loop body.
+            node_id: The unique identifier of the loop node in the workflow.
+            config: Raw configuration dictionary for the loop node.
+            state: The current workflow state before entering the loop.
         """
         self.graph = graph
         self.state = state
@@ -46,12 +49,15 @@ class LoopRuntime:
         """
         Initialize workflow state for loop execution.
 
-        - Evaluates initial values of loop variables.
-        - Stores loop variables in runtime_vars and node_outputs.
-        - Marks the loop as active by setting 'looping' to True.
+        This method:
+        - Evaluates initial values of loop variables
+        - Stores loop variables into both `runtime_vars` and `node_outputs`
+          under the current loop node's scope
+        - Creates a shallow copy of the workflow state
+        - Marks the loop as active by setting `looping = True`
 
         Returns:
-            A copy of the workflow state prepared for the loop execution.
+            WorkflowState: A prepared workflow state used for loop execution.
         """
         pool = VariablePool(self.state)
         # 循环变量
@@ -61,7 +67,7 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            )
+            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         self.state["node_outputs"][self.node_id] = {
@@ -70,7 +76,7 @@ class LoopRuntime:
                 variables=pool.get_all_conversation_vars(),
                 node_outputs=pool.get_all_node_outputs(),
                 system_vars=pool.get_all_system_vars(),
-            )
+            ) if variable.input_type == ValueInputType.VARIABLE else TypeTransformer.transform(variable.value, variable.type)
             for variable in self.typed_config.cycle_vars
         }
         loopstate = WorkflowState(
@@ -79,49 +85,93 @@ class LoopRuntime:
         loopstate["looping"] = True
         return loopstate
 
-    def _get_loop_expression(self):
+    @staticmethod
+    def _evaluate(operator, instance: CompareOperatorInstance) -> Any:
         """
-        Build the Python boolean expression for evaluating the loop condition.
+        Dispatch and execute a comparison operator against a resolved
+        CompareOperatorInstance.
 
-        - Converts each condition in the loop configuration into a Python expression string.
-        - Combines multiple conditions with the configured logical operator (AND/OR).
+        Args:
+            operator: A ComparisonOperator enum value.
+            instance: A CompareOperatorInstance bound to concrete operands.
 
         Returns:
-            A string representing the combined loop condition expression.
+            Any: The evaluation result, typically a boolean.
         """
-        branch_conditions = [
-            ConditionExpressionBuilder(
-                left=condition.left,
-                operator=condition.comparison_operator,
-                right=condition.right
-            ).build()
-            for condition in self.typed_config.condition.expressions
-        ]
-        if len(branch_conditions) > 1:
-            combined_condition = f' {self.typed_config.condition.logical_operator} '.join(branch_conditions)
-        else:
-            combined_condition = branch_conditions[0]
+        match operator:
+            case ComparisonOperator.EMPTY:
+                return instance.empty()
+            case ComparisonOperator.NOT_EMPTY:
+                return instance.not_empty()
+            case ComparisonOperator.CONTAINS:
+                return instance.contains()
+            case ComparisonOperator.NOT_CONTAINS:
+                return instance.not_contains()
+            case ComparisonOperator.START_WITH:
+                return instance.startswith()
+            case ComparisonOperator.END_WITH:
+                return instance.endswith()
+            case ComparisonOperator.EQ:
+                return instance.eq()
+            case ComparisonOperator.NE:
+                return instance.ne()
+            case ComparisonOperator.LT:
+                return instance.lt()
+            case ComparisonOperator.LE:
+                return instance.le()
+            case ComparisonOperator.GT:
+                return instance.gt()
+            case ComparisonOperator.GE:
+                return instance.ge()
+            case _:
+                raise ValueError(f"Invalid condition: {operator}")
 
-        return combined_condition
+    def evaluate_conditional(self, state) -> bool:
+        """
+        Evaluate the loop continuation condition at runtime.
+
+        This method:
+        - Resolves all condition expressions against the current workflow state
+        - Evaluates each comparison expression immediately
+        - Combines results using the configured logical operator (AND / OR)
+
+        Args:
+            state: The current workflow state during loop execution.
+
+        Returns:
+            bool: True if the loop should continue, False otherwise.
+        """
+        conditions = []
+
+        for expression in self.typed_config.condition.expressions:
+            left_value = VariablePool(state).get(expression.left)
+            evaluator = ConditionExpressionResolver.resolve_by_value(left_value)(
+                VariablePool(state),
+                expression.left,
+                expression.right,
+                expression.input_type
+            )
+            conditions.append(self._evaluate(expression.operator, evaluator))
+        if self.typed_config.condition.logical_operator == LogicOperator.AND:
+            return all(conditions)
+        else:
+            return any(conditions)
 
     async def run(self):
         """
-        Execute the loop node until the condition is no longer met, the loop is
-        manually stopped, or the maximum loop count is reached.
+        Execute the loop node until termination conditions are met.
+
+        The loop terminates when any of the following occurs:
+        - The loop condition evaluates to False
+        - The `looping` flag in the workflow state is set to False
+        - The maximum loop count is reached
 
         Returns:
-            The final runtime variables of this loop node after completion.
+            dict[str, Any]: The final runtime variables of this loop node.
         """
         loopstate = self._init_loop_state()
-        expression = self._get_loop_expression()
-        loop_variable_pool = VariablePool(loopstate)
         loop_time = self.typed_config.max_loop
-        while evaluate_condition(
-                expression=expression,
-                variables=loop_variable_pool.get_all_conversation_vars(),
-                node_outputs=loop_variable_pool.get_all_node_outputs(),
-                system_vars=loop_variable_pool.get_all_system_vars(),
-        ) and loopstate["looping"] and loop_time > 0:
+        while self.evaluate_conditional(loopstate) and loopstate["looping"] and loop_time > 0:
             logger.info(f"loop node {self.node_id}: running")
             await self.graph.ainvoke(loopstate)
             loop_time -= 1

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

@@ -1,10 +1,9 @@
 import logging
 from typing import Any
 
-from langgraph.graph import StateGraph, START, END
+from langgraph.graph import StateGraph
 from langgraph.graph.state import CompiledStateGraph
 
-from app.core.workflow.expression_evaluator import evaluate_condition
 from app.core.workflow.nodes import WorkflowState
 from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig
@@ -17,12 +16,18 @@ logger = logging.getLogger(__name__)
 
 class CycleGraphNode(BaseNode):
     """
-    Node representing a cycle (loop) subgraph within the workflow.
+    Node representing a cyclic (loop or iteration) subgraph within the workflow.
 
-    This node manages internal loop/iteration nodes, builds a subgraph
-    for execution, handles conditional routing, and executes loop
-    or iteration logic based on node type.
+    A CycleGraphNode is a structural node that:
+    - Extracts a group of nodes marked as belonging to the same cycle
+    - Builds an isolated internal StateGraph (subgraph)
+    - Delegates runtime execution to LoopRuntime or IterationRuntime
+      depending on the node type
+
+    This node itself does NOT execute business logic directly.
+    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.typed_config: LoopNodeConfig | IterationNodeConfig | None = None
@@ -38,16 +43,23 @@ class CycleGraphNode(BaseNode):
 
     def pure_cycle_graph(self) -> tuple[list, list]:
         """
-        Extract cycle nodes and internal edges from the workflow configuration,
-        removing them from the global workflow.
+        Extract cycle-scoped nodes and internal edges from the workflow configuration.
 
-        Raises:
-            ValueError: If cycle nodes are connected to external nodes improperly.
+        This method:
+        - Identifies all nodes marked with `cycle == self.node_id`
+        - Collects edges that fully connect cycle nodes
+        - Removes extracted nodes and edges from the global workflow configuration
+
+        Safety check:
+        - Raises an error if a cycle node is connected to an external node
 
         Returns:
-            Tuple containing:
-            - cycle_nodes: List of removed nodes
-            - cycle_edges: List of removed edges
+            tuple[list, list]:
+                - cycle_nodes: Nodes belonging to this cycle
+                - cycle_edges: Edges connecting nodes within the cycle
+
+        Raises:
+            ValueError: If a cycle node is improperly connected to an external node.
         """
         nodes = self.workflow_config.get("nodes", [])
         edges = self.workflow_config.get("edges", [])
@@ -83,131 +95,41 @@ class CycleGraphNode(BaseNode):
 
         return cycle_nodes, cycle_edges
 
-    def create_node(self):
-        """
-        Instantiate node objects for each node in the cycle subgraph and add them to the graph.
-
-        Special handling is applied for conditional nodes to generate
-        edge conditions based on node outputs.
-        """
-        from app.core.workflow.nodes import NodeFactory
-        for node in self.cycle_nodes:
-            node_type = node.get("type")
-            node_id = node.get("id")
-
-            if node_type == NodeType.CYCLE_START:
-                self.start_node_id = node_id
-                continue
-            elif node_type == NodeType.END:
-                self.end_node_ids.append(node_id)
-
-            node_instance = NodeFactory.create_node(node, self.workflow_config)
-
-            if node_type in [NodeType.IF_ELSE, NodeType.HTTP_REQUEST]:
-                expressions = node_instance.build_conditional_edge_expressions()
-
-                # Number of branches, usually matches the number of conditional expressions
-                branch_number = len(expressions)
-
-                # Find all edges whose source is the current node
-                related_edge = [edge for edge in self.cycle_edges if edge.get("source") == node_id]
-
-                # Iterate over each branch
-                for idx in range(branch_number):
-                    # Generate a condition expression for each edge
-                    # Used later to determine which branch to take based on the node's output
-                    # Assumes node output `node.<node_id>.output` matches the edge's label
-                    # For example, if node.123.output == 'CASE1', take the branch labeled 'CASE1'
-                    related_edge[idx]['condition'] = f"node.{node_id}.output == '{related_edge[idx]['label']}'"
-
-            def make_func(inst):
-                async def node_func(state: WorkflowState):
-                    return await inst.run(state)
-
-                return node_func
-
-            self.graph.add_node(node_id, make_func(node_instance))
-
-    def create_edge(self):
-        """
-        Connect nodes within the cycle subgraph by adding edges to the internal graph.
-
-        Conditional edges are routed based on evaluated expressions.
-        Start and end nodes are connected to global START and END nodes.
-        """
-        for edge in self.cycle_edges:
-            source = edge.get("source")
-            target = edge.get("target")
-            edge_type = edge.get("type")
-            condition = edge.get("condition")
-
-            # 跳过从 start 节点出发的边（因为已经从 START 连接到 start）
-            if source == self.start_node_id:
-                # 但要连接 start 到下一个节点
-                self.graph.add_edge(START, target)
-                logger.debug(f"添加边: {source} -> {target}")
-                continue
-
-            if condition:
-                # 条件边
-                def router(state: WorkflowState, cond=condition, tgt=target):
-                    """条件路由函数"""
-                    if evaluate_condition(
-                            cond,
-                            state.get("variables", {}),
-                            state.get("node_outputs", {}),
-                            {
-                                "execution_id": state.get("execution_id"),
-                                "workspace_id": state.get("workspace_id"),
-                                "user_id": state.get("user_id")
-                            }
-                    ):
-                        return tgt
-                    return END  # 条件不满足，结束
-
-                self.graph.add_conditional_edges(source, router)
-                logger.debug(f"添加条件边: {source} -> {target} (condition={condition})")
-            else:
-                # 普通边
-                self.graph.add_edge(source, target)
-                logger.debug(f"添加边: {source} -> {target}")
-
-        # 从 end 节点连接到 END
-        for end_node_id in self.end_node_ids:
-            self.graph.add_edge(end_node_id, END)
-            logger.debug(f"添加边: {end_node_id} -> END")
-
     def build_graph(self):
         """
-        Build the internal subgraph for the cycle node.
+        Build and compile the internal subgraph for this cycle node.
 
         Steps:
-        1. Extract cycle nodes and edges.
-        2. Create node instances and add them to the graph.
-        3. Connect edges and conditional routes.
-        4. Compile the graph for execution.
+        1. Extract cycle nodes and internal edges from the workflow
+        2. Construct a StateGraph using GraphBuilder in subgraph mode
+        3. Compile the graph for runtime execution
         """
-        self.graph = StateGraph(WorkflowState)
+        from app.core.workflow.graph_builder import GraphBuilder
         self.cycle_nodes, self.cycle_edges = self.pure_cycle_graph()
-        self.create_node()
-        self.create_edge()
-        self.graph = self.graph.compile()
+        self.graph = GraphBuilder(
+            {
+                "nodes": self.cycle_nodes,
+                "edges": self.cycle_edges,
+            },
+            subgraph=True
+        ).build()
 
     async def execute(self, state: WorkflowState) -> Any:
         """
         Execute the cycle node at runtime.
 
-        Depending on the node type, runs either a loop (LoopRuntime)
-        or an iteration (IterationRuntime) over the internal subgraph.
+        Based on the node type:
+        - LOOP: Executes LoopRuntime, repeatedly invoking the subgraph
+        - ITERATION: Executes IterationRuntime, iterating over a collection
 
         Args:
-            state: Current workflow state.
+            state: The current workflow state when entering the cycle node.
 
         Returns:
-            Runtime result of the cycle, typically the final loop/iteration variables.
+            Any: The runtime result produced by the loop or iteration executor.
 
         Raises:
-            RuntimeError: If node type is unrecognized.
+            RuntimeError: If the node type is unsupported.
         """
         if self.node_type == NodeType.LOOP:
             return await LoopRuntime(

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

@@ -61,7 +61,7 @@ class EndNode(BaseNode):
             引用的节点 ID 列表
         """
         # 匹配 {{node_id.xxx}} 格式
-        pattern = r'\{\{([a-zA-Z0-9_-]+)\.[a-zA-Z0-9_]+\}\}'
+        pattern = r'\{\{([a-zA-Z0-9_]+)\.[a-zA-Z0-9_]+\}\}'
         matches = re.findall(pattern, template)
         return list(set(matches))  # 去重
 

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

@@ -45,7 +45,8 @@ class LogicOperator(StrEnum):
 
 
 class AssignmentOperator(StrEnum):
-    ASSIGN = "assign"
+    COVER = "cover"  # 覆盖
+    ASSIGN = "assign"  # 设置
     CLEAR = "clear"
 
     ADD = "add"  # +=
@@ -87,3 +88,8 @@ class HttpErrorHandle(StrEnum):
     NONE = "none"
     DEFAULT = "default"
     BRANCH = "branch"
+
+
+class ValueInputType(StrEnum):
+    VARIABLE = "Variable"
+    CONSTANT = "Constant"

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

@@ -63,7 +63,7 @@ class HttpContentTypeConfig(BaseModel):
     )
 
     data: list[HttpFormData] | dict | str = Field(
-        ...,
+        default="",
         description="Data of the HTTP request body; type depends on content_type",
     )
 
@@ -98,6 +98,10 @@ class HttpTimeOutConfig(BaseModel):
 
 
 class HttpRetryConfig(BaseModel):
+    enable: bool = Field(
+        ...,
+        description="Enable/disable retry logic",
+    )
     max_attempts: int = Field(
         default=1,
         description="Maximum number of retry attempts for failed requests",
@@ -124,6 +128,11 @@ class HttpErrorDefaultTamplete(BaseModel):
         description="Default HTTP headers returned on error",
     )
 
+    output: str = Field(
+        default="SUCCESS",
+        description="HTTP response body",
+    )
+
 
 class HttpErrorHandleConfig(BaseModel):
     method: HttpErrorHandle = Field(
@@ -131,8 +140,8 @@ class HttpErrorHandleConfig(BaseModel):
         description="Error handling strategy: 'none', 'default', or 'branch'",
     )
 
-    default: HttpErrorDefaultTamplete = Field(
-        ...,
+    default: HttpErrorDefaultTamplete | None = Field(
+        default=None,
         description="Default response template for error handling",
     )
 

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

@@ -165,24 +165,6 @@ class HttpRequestNode(BaseNode):
             case _:
                 raise RuntimeError(f"HttpRequest method not supported: {self.typed_config.method}")
 
-    def build_conditional_edge_expressions(self):
-        """
-        Build conditional edge expressions for workflow branching.
-
-        When the HTTP error handling strategy is set to `BRANCH`,
-        this node exposes a single conditional output labeled "ERROR".
-        The workflow engine uses this output to create an explicit
-        error-handling branch for downstream nodes.
-
-        Returns:
-            list[str]:
-                - ["ERROR"] if error handling strategy is BRANCH
-                - An empty list if no conditional branching is required
-        """
-        if self.typed_config.error_handle.method == HttpErrorHandle.BRANCH:
-            return ["ERROR"]
-        return []
-
     async def execute(self, state: WorkflowState) -> dict | str:
         """
         Execute the HTTP request node.

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

@@ -1,12 +1,13 @@
 """Condition Configuration"""
+from typing import Any
 from pydantic import Field, BaseModel, field_validator
 
 from app.core.workflow.nodes.base_config import BaseNodeConfig
-from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator
+from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator, ValueInputType
 
 
 class ConditionDetail(BaseModel):
-    comparison_operator: ComparisonOperator = Field(
+    operator: ComparisonOperator = Field(
         ...,
         description="Comparison operator used to evaluate the condition"
     )
@@ -16,17 +17,22 @@ class ConditionDetail(BaseModel):
         description="Value to compare against"
     )
 
-    right: str = Field(
+    right: Any = Field(
         ...,
         description="Value to compare with"
     )
 
+    input_type: ValueInputType = Field(
+        ...,
+        description="Value input type for comparison"
+    )
+
 
 class ConditionBranchConfig(BaseModel):
     """Configuration for a conditional branch"""
 
     logical_operator: LogicOperator = Field(
-        default=LogicOperator.AND.value,
+        default=LogicOperator.AND,
         description="Logical operator used to combine multiple condition expressions"
     )
 

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

@@ -1,10 +1,11 @@
 import logging
+import re
 from typing import Any
 
 from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
+from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator
 from app.core.workflow.nodes.if_else import IfElseNodeConfig
-from app.core.workflow.nodes.if_else.config import ConditionDetail
-from app.core.workflow.nodes.operators import ConditionExpressionBuilder
+from app.core.workflow.nodes.operators import ConditionExpressionResolver, CompareOperatorInstance
 
 logger = logging.getLogger(__name__)
 
@@ -15,30 +16,36 @@ class IfElseNode(BaseNode):
         self.typed_config = IfElseNodeConfig(**self.config)
 
     @staticmethod
-    def _build_condition_expression(
-            condition: ConditionDetail,
-    ) -> str:
-        """
-        Build a single boolean condition expression string.
+    def _evaluate(operator, instance: CompareOperatorInstance) -> Any:
+        match operator:
+            case ComparisonOperator.EMPTY:
+                return instance.empty()
+            case ComparisonOperator.NOT_EMPTY:
+                return instance.not_empty()
+            case ComparisonOperator.CONTAINS:
+                return instance.contains()
+            case ComparisonOperator.NOT_CONTAINS:
+                return instance.not_contains()
+            case ComparisonOperator.START_WITH:
+                return instance.startswith()
+            case ComparisonOperator.END_WITH:
+                return instance.endswith()
+            case ComparisonOperator.EQ:
+                return instance.eq()
+            case ComparisonOperator.NE:
+                return instance.ne()
+            case ComparisonOperator.LT:
+                return instance.lt()
+            case ComparisonOperator.LE:
+                return instance.le()
+            case ComparisonOperator.GT:
+                return instance.gt()
+            case ComparisonOperator.GE:
+                return instance.ge()
+            case _:
+                raise ValueError(f"Invalid condition: {operator}")
 
-        This method does NOT evaluate the condition.
-        It only generates a valid Python boolean expression string
-        (e.g. "x > 10", "'a' in name") that can later be used
-        in a conditional edge or evaluated by the workflow engine.
-
-        Args:
-            condition (ConditionDetail): Definition of a single comparison condition.
-
-        Returns:
-            str: A Python boolean expression string.
-        """
-        return ConditionExpressionBuilder(
-            left=condition.left,
-            operator=condition.comparison_operator,
-            right=condition.right
-        ).build()
-
-    def build_conditional_edge_expressions(self) -> list[str]:
+    def evaluate_conditional_edge_expressions(self, state) -> list[bool]:
         """
         Build conditional edge expressions for the If-Else node.
 
@@ -60,19 +67,28 @@ class IfElseNode(BaseNode):
 
         for case_branch in self.typed_config.cases:
             branch_index += 1
-
-            branch_conditions = [
-                self._build_condition_expression(condition)
-                for condition in case_branch.expressions
-            ]
-            if len(branch_conditions) > 1:
-                combined_condition = f' {case_branch.logical_operator} '.join(branch_conditions)
+            branch_result = []
+            for expression in case_branch.expressions:
+                pattern = r"\{\{\s*(.*?)\s*\}\}"
+                left_string = re.sub(pattern, r"\1", expression.left).strip()
+                left_value = self.get_variable(left_string, state)
+                evaluator = ConditionExpressionResolver.resolve_by_value(left_value)(
+                    self.get_variable_pool(state),
+                    expression.left,
+                    expression.right,
+                    expression.input_type
+                )
+                branch_result.append(self._evaluate(expression.operator, evaluator))
+            if case_branch.logical_operator == LogicOperator.AND:
+                conditions.append(all(branch_result))
             else:
-                combined_condition = branch_conditions[0]
-            conditions.append(combined_condition)
+                condition_res = any(branch_result)
+                conditions.append(condition_res)
+                if condition_res:
+                    return conditions
 
         # Default fallback branch
-        conditions.append("True")
+        conditions.append(True)
 
         return conditions
 
@@ -90,10 +106,10 @@ class IfElseNode(BaseNode):
         Returns:
             str: The matched branch identifier, e.g., 'CASE1', 'CASE2', ..., used for node transitions.
         """
-        expressions = self.build_conditional_edge_expressions()
+        expressions = self.evaluate_conditional_edge_expressions(state)
+        # TODO: 变量类型及文本类型解析
         for i in range(len(expressions)):
-            logger.info(expressions[i])
-            if self._evaluate_condition(expressions[i], state):
+            if expressions[i]:
                 logger.info(f"Node {self.node_id}: switched to branch CASE {i + 1}")
                 return f'CASE{i + 1}'
         return f'CASE{len(expressions)}'

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

@@ -8,6 +8,7 @@ from app.core.workflow.template_renderer import TemplateRenderer
 
 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)

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

@@ -74,6 +74,7 @@ class NodeFactory:
         NodeType.LOOP: CycleGraphNode,
         NodeType.ITERATION: CycleGraphNode,
         NodeType.BREAK: BreakNode,
+        NodeType.CYCLE_START: StartNode,
         NodeType.TOOL: ToolNode,
     }
 

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

@@ -1,10 +1,73 @@
+import json
+import re
 from abc import ABC
-from typing import Union, Type
+from typing import Union, Type, NoReturn
 
-from app.core.workflow.nodes.enums import ComparisonOperator
+from app.core.workflow.nodes.base_config import VariableType
+from app.core.workflow.nodes.enums import ValueInputType
 from app.core.workflow.variable_pool import VariablePool
 
 
+class TypeTransformer:
+    @classmethod
+    def _fail(cls, value, target) -> NoReturn:
+        raise TypeError(f"Cannot convert {value!r} to {target} type")
+
+    @classmethod
+    def _json_load(cls, value, target):
+        try:
+            return json.loads(value)
+        except Exception:
+            cls._fail(value, target)
+
+    @classmethod
+    def transform(cls, variable_literal: str | bool, target_type: VariableType):
+        match target_type:
+            case VariableType.STRING:
+                return str(variable_literal)
+
+            case VariableType.NUMBER:
+                for caster in (int, float):
+                    try:
+                        return caster(variable_literal)
+                    except Exception:
+                        pass
+                cls._fail(variable_literal, target_type)
+
+            case VariableType.BOOLEAN:
+                if isinstance(variable_literal, bool):
+                    return variable_literal
+                cls._fail(variable_literal, target_type)
+
+            case VariableType.OBJECT:
+                obj = cls._json_load(variable_literal, target_type)
+                if isinstance(obj, dict):
+                    return obj
+                cls._fail(variable_literal, target_type)
+
+            case VariableType.ARRAY_BOOLEAN:
+                return cls._parse_list(variable_literal, bool, target_type)
+
+            case VariableType.ARRAY_NUMBER:
+                return cls._parse_list(variable_literal, (int, float), target_type)
+
+            case VariableType.ARRAY_STRING:
+                return cls._parse_list(variable_literal, str, target_type)
+
+            case VariableType.ARRAY_OBJECT:
+                return cls._parse_list(variable_literal, dict, target_type)
+
+            case _:
+                raise TypeError("Invalid type")
+
+    @classmethod
+    def _parse_list(cls, value, item_type, target):
+        arr = cls._json_load(value, target)
+        if isinstance(arr, list) and all(isinstance(i, item_type) for i in arr):
+            return arr
+        cls._fail(value, target)
+
+
 class OperatorBase(ABC):
     def __init__(self, pool: VariablePool, left_selector, right):
         self.pool = pool
@@ -19,7 +82,9 @@ class OperatorBase(ABC):
             raise TypeError(f"The variable to be operated on must be of {self.type_limit} type")
 
         if not no_right and not isinstance(self.right, self.type_limit):
-            raise TypeError(f"The value assigned to the string variable must also be of {self.type_limit} type")
+            raise TypeError(
+                f"The value assigned must be of {self.type_limit} type"
+            )
 
 
 class StringOperator(OperatorBase):
@@ -126,7 +191,7 @@ class ArrayOperator(OperatorBase):
 class ObjectOperator(OperatorBase):
     def __init__(self, pool: VariablePool, left_selector, right):
         super().__init__(pool, left_selector, right)
-        self.type_limit = object
+        self.type_limit = dict
 
     def assign(self) -> None:
         self.check()
@@ -138,20 +203,21 @@ class ObjectOperator(OperatorBase):
 
 
 class AssignmentOperatorResolver:
+    OPERATOR_MAP = {
+        str: StringOperator,
+        bool: BooleanOperator,
+        int: NumberOperator,
+        float: NumberOperator,
+        list: ArrayOperator,
+        dict: ObjectOperator,
+    }
+
     @classmethod
     def resolve_by_value(cls, value):
-        if isinstance(value, str):
-            return StringOperator
-        elif isinstance(value, bool):
-            return BooleanOperator
-        elif isinstance(value, (int, float)):
-            return NumberOperator
-        elif isinstance(value, list):
-            return ArrayOperator
-        elif isinstance(value, dict):
-            return ObjectOperator
-        else:
-            raise TypeError(f"Unsupported variable type: {type(value)}")
+        for t, op in cls.OPERATOR_MAP.items():
+            if isinstance(value, t):
+                return op
+        raise TypeError(f"Unsupported variable type: {type(value)}")
 
 
 AssignmentOperatorInstance = Union[
@@ -164,81 +230,186 @@ AssignmentOperatorInstance = Union[
 AssignmentOperatorType = Type[AssignmentOperatorInstance]
 
 
-class ConditionExpressionBuilder:
-    """
-    Build a Python boolean expression string based on a comparison operator.
+class ConditionBase(ABC):
+    type_limit: type[str, int, dict, list] = None
 
-    This class does not evaluate the expression.
-    It only generates a valid Python expression string
-    that can be evaluated later in a workflow context.
-    """
+    def __init__(
+            self,
+            pool: VariablePool,
+            left_selector,
+            right_selector: str,
+            input_type: ValueInputType
+    ):
+        self.pool = pool
+        self.left_selector = left_selector
+        self.right_selector = right_selector
+        self.input_type = input_type
 
-    def __init__(self, left: str, operator: ComparisonOperator, right: str):
-        self.left = left
-        self.operator = operator
-        self.right = right
+        self.left_value = self.pool.get(self.left_selector)
+        self.right_value = self.resolve_right_literal_value()
 
-    def _empty(self):
-        return f"{self.left} == ''"
+        self.type_limit = getattr(self, "type_limit", None)
 
-    def _not_empty(self):
-        return f"{self.left} != ''"
+    def resolve_right_literal_value(self):
+        if self.input_type == ValueInputType.VARIABLE:
+            pattern = r"\{\{\s*(.*?)\s*\}\}"
+            right_expression = re.sub(pattern, r"\1", self.right_selector).strip()
+            return self.pool.get(right_expression)
+        elif self.input_type == ValueInputType.CONSTANT:
+            return self.right_selector
+        raise RuntimeError("Unsupported variable type")
 
-    def _contains(self):
-        return f"{self.right} in {self.left}"
+    def check(self, no_right=False):
+        left = self.pool.get(self.left_selector.variable_selector)
+        if not isinstance(left, self.type_limit):
+            raise TypeError(f"The variable to be compared on must be of {self.type_limit} type")
+        if not no_right:
+            right = self.resolve_right_literal_value()
+            if not isinstance(right, self.type_limit):
+                raise TypeError(
+                    f"The compared variable must be of {self.type_limit} type"
+                )
 
-    def _not_contains(self):
-        return f"{self.right} not in {self.left}"
 
-    def _startswith(self):
-        return f'{self.left}.startswith({self.right})'
+class StringComparisonOperator(ConditionBase):
+    type_limit = str
 
-    def _endswith(self):
-        return f'{self.left}.endswith({self.right})'
+    def __init__(self, pool: VariablePool, left_selector, right_selector, input_type):
+        super().__init__(pool, left_selector, right_selector, input_type)
 
-    def _eq(self):
-        return f"{self.left} == {self.right}"
+    def empty(self):
+        self.check(no_right=True)
+        return self.left_value == ""
 
-    def _ne(self):
-        return f"{self.left} != {self.right}"
+    def not_empty(self):
+        return not self.empty()
 
-    def _lt(self):
-        return f"{self.left} < {self.right}"
+    def contains(self):
+        self.check()
+        return self.right_value in self.left_value
 
-    def _le(self):
-        return f"{self.left} <= {self.right}"
+    def not_contains(self):
+        return self.right_value not in self.left_value
 
-    def _gt(self):
-        return f"{self.left} > {self.right}"
+    def startswith(self):
+        self.check()
+        return self.left_value.startswith(self.right_value)
 
-    def _ge(self):
-        return f"{self.left} >= {self.right}"
+    def endswith(self):
+        return self.left_value.endswith(self.right_value)
 
-    def build(self):
-        match self.operator:
-            case ComparisonOperator.EMPTY:
-                return self._empty()
-            case ComparisonOperator.NOT_EMPTY:
-                return self._not_empty()
-            case ComparisonOperator.CONTAINS:
-                return self._contains()
-            case ComparisonOperator.NOT_CONTAINS:
-                return self._not_contains()
-            case ComparisonOperator.START_WITH:
-                return self._startswith()
-            case ComparisonOperator.END_WITH:
-                return self._endswith()
-            case ComparisonOperator.EQ:
-                return self._eq()
-            case ComparisonOperator.NE:
-                return self._ne()
-            case ComparisonOperator.LT:
-                return self._lt()
-            case ComparisonOperator.LE:
-                return self._le()
-            case ComparisonOperator.GT:
-                return self._gt()
-            case ComparisonOperator.GE:
-                return self._ge()
-            case _:
-                raise ValueError(f"Invalid condition: {self.operator}")
+    def eq(self):
+        return self.left_value == self.right_value
+
+    def ne(self):
+        return self.left_value != self.right_value
+
+
+class NumberComparisonOperator(ConditionBase):
+    type_limit = (int, float)
+
+    def __init__(self, pool: VariablePool, left_selector, right_selector, input_type):
+        super().__init__(pool, left_selector, right_selector, input_type)
+
+    def empty(self):
+        return self.left_value == 0
+
+    def not_empty(self):
+        return self.left_value != 0
+
+    def eq(self):
+        return self.left_value == self.right_value
+
+    def ne(self):
+        return self.left_value != self.right_value
+
+    def lt(self):
+        return self.left_value < self.right_value
+
+    def le(self):
+        return self.left_value <= self.right_value
+
+    def gt(self):
+        return self.left_value > self.right_value
+
+    def ge(self):
+        return self.left_value >= self.right_value
+
+
+class BooleanComparisonOperator(ConditionBase):
+    type_limit = bool
+
+    def __init__(self, pool: VariablePool, left_selector, right_selector, input_type):
+        super().__init__(pool, left_selector, right_selector, input_type)
+
+    def eq(self):
+        return self.left_value == self.right_value
+
+    def ne(self):
+        return self.left_value != self.right_value
+
+
+class ObjectComparisonOperator(ConditionBase):
+    type_limit = dict
+
+    def __init__(self, pool: VariablePool, left_selector, right_selector, input_type):
+        super().__init__(pool, left_selector, right_selector, input_type)
+
+    def eq(self):
+        return self.left_value == self.right_value
+
+    def ne(self):
+        return self.left_value != self.right_value
+
+    def empty(self):
+        return not self.left_value
+
+    def not_empty(self):
+        return bool(self.left_value)
+
+
+class ArrayComparisonOperator(ConditionBase):
+    type_limit = list
+
+    def __init__(self, pool: VariablePool, left_selector, right_selector, input_type):
+        super().__init__(pool, left_selector, right_selector, input_type)
+
+    def empty(self):
+        return not self.left_value
+
+    def not_empty(self):
+        return bool(self.left_value)
+
+    def contains(self):
+        return self.right_value in self.left_value
+
+    def not_contains(self):
+        return self.right_value not in self.left_value
+
+
+CompareOperatorInstance = Union[
+    StringComparisonOperator,
+    NumberComparisonOperator,
+    BooleanComparisonOperator,
+    ArrayComparisonOperator,
+    ObjectComparisonOperator
+]
+CompareOperatorType = Type[CompareOperatorInstance]
+
+
+class ConditionExpressionResolver:
+    CONDITION_OPERATOR_MAP = {
+        str: StringComparisonOperator,
+        bool: BooleanComparisonOperator,
+        int: NumberComparisonOperator,
+        float: NumberComparisonOperator,
+        list: ArrayComparisonOperator,
+        dict: ObjectComparisonOperator,
+    }
+
+    @classmethod
+    def resolve_by_value(cls, value) -> CompareOperatorType:
+        for t, op in cls.CONDITION_OPERATOR_MAP.items():
+            if isinstance(value, t):
+                return op
+        raise TypeError(f"Unsupported variable type: {type(value)}")

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

@@ -1,4 +1,4 @@
 from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig
 from app.core.workflow.nodes.variable_aggregator.node import VariableAggregatorNode
 
-__all__ = ["VariableAggregatorNode", "VariableAggregatorNodeConfig"]
+__all__ = ["VariableAggregatorNode", "VariableAggregatorNodeConfig"]

api/app/core/workflow/template_loader.py

@@ -4,11 +4,11 @@
 从文件系统加载预定义的工作流模板
 """
 
-import os
-import yaml
 from pathlib import Path
 from typing import Optional
 
+import yaml
+
 
 class TemplateLoader:
     """工作流模板加载器"""

api/app/core/workflow/variable_pool.py

@@ -10,6 +10,7 @@
 """
 
 import logging
+import re
 from typing import Any, TYPE_CHECKING
 
 if TYPE_CHECKING:
@@ -28,7 +29,7 @@ class VariableSelector:
         >>> selector = VariableSelector(["node_A", "output"])
         >>> selector = VariableSelector.from_string("sys.message")
     """
-    
+
     def __init__(self, path: list[str]):
         """初始化变量选择器
         
@@ -37,11 +38,11 @@ class VariableSelector:
         """
         if not path or len(path) < 1:
             raise ValueError("变量路径不能为空")
-        
+
         self.path = path
         self.namespace = path[0]  # sys, var, 或 node_id
         self.key = path[1] if len(path) > 1 else None
-    
+
     @classmethod
     def from_string(cls, selector_str: str) -> "VariableSelector":
         """从字符串创建选择器
@@ -58,10 +59,10 @@ class VariableSelector:
         """
         path = selector_str.split(".")
         return cls(path)
-    
+
     def __str__(self) -> str:
         return ".".join(self.path)
-    
+
     def __repr__(self) -> str:
         return f"VariableSelector({self.path})"
 
@@ -84,7 +85,7 @@ class VariablePool:
         "AI 的回答"
         >>> pool.set(["conv", "user_name"], "张三")
     """
-    
+
     def __init__(self, state: "WorkflowState"):
         """初始化变量池
         
@@ -92,7 +93,7 @@ class VariablePool:
             state: 工作流状态（LangGraph State）
         """
         self.state = state
-    
+
     def get(self, selector: list[str] | str, default: Any = None) -> Any:
         """获取变量值
         
@@ -114,13 +115,15 @@ class VariablePool:
         """
         # 转换为 VariableSelector
         if isinstance(selector, str):
-            selector = VariableSelector.from_string(selector).path
-        
+            pattern = r"\{\{\s*(.*?)\s*\}\}"
+            variable_literal = re.sub(pattern, r"\1", selector).strip()
+            selector = VariableSelector.from_string(variable_literal).path
+
         if not selector or len(selector) < 1:
             raise ValueError("变量选择器不能为空")
-        
+
         namespace = selector[0]
-        
+
         try:
             # 系统变量
             if namespace == "sys":
@@ -128,30 +131,30 @@ class VariablePool:
                 if not key:
                     return self.state.get("variables", {}).get("sys", {})
                 return self.state.get("variables", {}).get("sys", {}).get(key, default)
-            
+
             # 会话变量
             elif namespace == "conv":
                 key = selector[1] if len(selector) > 1 else None
                 if not key:
                     return self.state.get("variables", {}).get("conv", {})
                 return self.state.get("variables", {}).get("conv", {}).get(key, default)
-            
+
             # 节点输出（从 runtime_vars 读取）
             else:
                 node_id = namespace
                 runtime_vars = self.state.get("runtime_vars", {})
-                
+
                 if node_id not in runtime_vars:
                     if default is not None:
                         return default
                     raise KeyError(f"节点 '{node_id}' 的输出不存在")
-                
+
                 node_var = runtime_vars[node_id]
-                
+
                 # 如果只有节点 ID，返回整个变量
                 if len(selector) == 1:
                     return node_var
-                
+
                 # 获取特定字段
                 # 支持嵌套访问，如 node_id.field.subfield
                 result = node_var
@@ -166,14 +169,14 @@ class VariablePool:
                         if default is not None:
                             return default
                         raise KeyError(f"无法访问 '{'.'.join(selector)}'")
-                
+
                 return result
-        
+
         except KeyError:
             if default is not None:
                 return default
             raise
-    
+
     def set(self, selector: list[str] | str, value: Any):
         """设置变量值
         
@@ -192,17 +195,17 @@ class VariablePool:
         # 转换为 VariableSelector
         if isinstance(selector, str):
             selector = VariableSelector.from_string(selector).path
-        
+
         if not selector or len(selector) < 2:
             raise ValueError("变量选择器必须包含命名空间和键名")
-        
+
         namespace = selector[0]
-        
+
         if namespace != "conv" and namespace not in self.state["cycle_nodes"]:
             raise ValueError("Only conversation or cycle variables can be assigned.")
-        
+
         key = selector[1]
-        
+
         # 确保 variables 结构存在
         if "variables" not in self.state:
             self.state["variables"] = {"sys": {}, "conv": {}}
@@ -214,9 +217,9 @@ class VariablePool:
             self.state["variables"]["conv"][key] = value
         elif namespace in self.state["cycle_nodes"]:
             self.state["runtime_vars"][namespace][key] = value
-        
+
         logger.debug(f"设置变量: {'.'.join(selector)} = {value}")
-    
+
     def has(self, selector: list[str] | str) -> bool:
         """检查变量是否存在
         
@@ -237,7 +240,7 @@ class VariablePool:
             return True
         except KeyError:
             return False
-    
+
     def get_all_system_vars(self) -> dict[str, Any]:
         """获取所有系统变量
         
@@ -245,7 +248,7 @@ class VariablePool:
             系统变量字典
         """
         return self.state.get("variables", {}).get("sys", {})
-    
+
     def get_all_conversation_vars(self) -> dict[str, Any]:
         """获取所有会话变量
         
@@ -253,7 +256,7 @@ class VariablePool:
             会话变量字典
         """
         return self.state.get("variables", {}).get("conv", {})
-    
+
     def get_all_node_outputs(self) -> dict[str, Any]:
         """获取所有节点输出（运行时变量）
         
@@ -261,7 +264,7 @@ class VariablePool:
             节点输出字典，键为节点 ID
         """
         return self.state.get("runtime_vars", {})
-    
+
     def get_node_output(self, node_id: str) -> dict[str, Any] | None:
         """获取指定节点的输出（运行时变量）
         
@@ -272,7 +275,7 @@ class VariablePool:
             节点输出或 None
         """
         return self.state.get("runtime_vars", {}).get(node_id)
-    
+
     def to_dict(self) -> dict[str, Any]:
         """导出为字典
         
@@ -284,12 +287,12 @@ class VariablePool:
             "conversation": self.get_all_conversation_vars(),
             "nodes": self.get_all_node_outputs()  # 从 runtime_vars 读取
         }
-    
+
     def __repr__(self) -> str:
         sys_vars = self.get_all_system_vars()
         conv_vars = self.get_all_conversation_vars()
         runtime_vars = self.get_all_node_outputs()
-        
+
         return (
             f"VariablePool(\n"
             f"  system_vars={len(sys_vars)},\n"

api/app/services/prompt_optimizer_service.py

@@ -1,5 +1,6 @@
 import re
 import uuid
+from typing import Any, AsyncGenerator
 
 import json_repair
 from langchain_core.prompts import ChatPromptTemplate
@@ -123,7 +124,7 @@ class PromptOptimizerService:
             user_id: uuid.UUID,
             current_prompt: str,
             user_require: str
-    ) -> OptimizePromptResult:
+    ) -> AsyncGenerator[dict[str, str | Any], Any]:
         """
         Optimize a user-provided prompt using a configured prompt optimizer LLM.
 
@@ -161,6 +162,7 @@ class PromptOptimizerService:
             BusinessException: If the LLM response cannot be parsed as valid JSON
             or does not conform to the expected output format.
         """
+        self.create_message(tenant_id, session_id, user_id, role=RoleType.USER, content=user_require)
         model_config = self.get_model_config(tenant_id, model_id)
         session_history = self.get_session_message_history(session_id=session_id, user_id=user_id)
 
@@ -202,17 +204,54 @@ class PromptOptimizerService:
         messages.extend(session_history[:-1])  # last message is current message
         messages.extend([(RoleType.USER.value, rendered_user_message)])
         logger.info(f"Prompt optimization message: {messages}")
-        optim_resp = await llm.ainvoke(messages)
-        logger.info(optim_resp.content)
-        optim_result = json_repair.repair_json(optim_resp.content, return_objects=True)
-        prompt = optim_result.get("prompt")
-        desc = optim_result.get("desc")
+        buffer = ""
+        prompt_started = False
+        prompt_finished = False
+        idx = 0
 
-        return OptimizePromptResult(
-            prompt=prompt,
-            desc=desc
+        async for chunk in llm.astream(messages):
+            content = getattr(chunk, "content", chunk)
+            if not content:
+                continue
+            buffer += content
+            cache = buffer[:-20]
+
+            # 尝试找到 "prompt": " 开始位置
+            if prompt_finished:
+                continue
+
+            if not prompt_started:
+                m = re.search(r'"prompt"\s*:\s*"', cache)
+                if m:
+                    prompt_started = True
+                    prompt_index = m.end()
+                    idx = prompt_index
+            else:
+                m = re.search(r'"\s*,\s*\\?n?\s*"desc"\s*:\s*"', buffer)
+                if m:
+                    prompt_index = m.start()
+                    prompt_finished = True
+                    yield {"type": "delta", "content": buffer[idx:prompt_index]}
+                else:
+                    yield {"type": "delta", "content": cache[idx:]}
+                    if len(cache) != 0:
+                        idx = len(cache)
+
+        # optim_resp = await llm.astream(messages)
+        logger.info(buffer)
+        optim_result = json_repair.repair_json(buffer, return_objects=True)
+        # prompt = optim_result.get("prompt")
+        desc = optim_result.get("desc")
+        self.create_message(
+            tenant_id=tenant_id,
+            session_id=session_id,
+            user_id=user_id,
+            role=RoleType.ASSISTANT,
+            content=desc
         )
 
+        yield {"type": "done", "desc": optim_result.get("desc")}
+
     @staticmethod
     def parser_prompt_variables(prompt: str):
         try:

api/app/services/workflow_service.py

@@ -410,7 +410,8 @@ class WorkflowService:
             self,
             app_id: uuid.UUID,
             payload: DraftRunRequest,
-            config: WorkflowConfig
+            config: WorkflowConfig,
+            workspace_id: uuid.UUID,
     ):
         """运行工作流
 
@@ -484,7 +485,7 @@ class WorkflowService:
                 workflow_config=workflow_config_dict,
                 input_data=input_data,
                 execution_id=execution.execution_id,
-                workspace_id="",
+                workspace_id=str(workspace_id),
                 user_id=payload.user_id
             )
 
@@ -530,7 +531,8 @@ class WorkflowService:
             self,
             app_id: uuid.UUID,
             payload: DraftRunRequest,
-            config: WorkflowConfig
+            config: WorkflowConfig,
+            workspace_id: uuid.UUID,
     ):
         """运行工作流（流式）
 
@@ -603,7 +605,7 @@ class WorkflowService:
                     workflow_config=workflow_config_dict,
                     input_data=input_data,
                     execution_id=execution.execution_id,
-                    workspace_id="",
+                    workspace_id=str(workspace_id),
                     user_id=payload.user_id
             ):
                 # 直接转发 executor 的事件（已经是正确的格式）

api/app/templates/prompt/prompt_optimizer_system.jinja2

@@ -25,7 +25,7 @@ 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 including [Role], [Task], [Requirements], [Input], [Output], [Constraints] labels.
+Structure Rule: Use a clear block structure, and the contents of each block are roles, tasks, requirements, inputs, outputs, and constraints
 Language Rule: All label languages must fully match the user input language.
 
 Behavior Guidelines