Merge #17 into develop from feature/20251219_myh

feat(workflow): add conditional branch (If-Else) node

* feature/20251219_myh: (10 commits)
  fix(workflow): fix run_workflow streaming issues
  fix(prompt-optimizer): switch to built-in system prompt
  feat(workflow): add conditional branch (If-Else) node
  perf(types): add Union type declaration for workflow nodes
  fix(expression-eval): fix variable extraction issue in Jinja2 templates
  docs(samples): add config example for If-Else node
  style(workflow): update condition edge comments for conditional nodes
  style(enums): correct enum class name spelling
  refactor(workflow): unify all enum classes in one file and restructure workflow...
  feat(workflow): add import for if-else node configuration

Signed-off-by: Eternity <1533512157@qq.com>
Commented-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/17

api/app/controllers/prompt_optimizer_controller.py

@@ -117,7 +117,7 @@ async def get_prompt_opt(
         session_id=session_id,
         user_id=current_user.id,
         current_prompt=data.current_prompt,
-        message=data.message
+        user_require=data.message
     )
     service.create_message(
         tenant_id=current_user.tenant_id,
@@ -136,35 +136,3 @@ async def get_prompt_opt(
     return success(data=result_schema)
 
 
-@router.put(
-    "/model",
-    summary="Create or update prompt model config",
-    response_model=ApiResponse
-)
-def set_system_prompt(
-        data: PromptOptModelSet = ...,
-        db: Session = Depends(get_db),
-        current_user=Depends(get_current_user),
-):
-    """
-    Create or update a system prompt model configuration for the tenant.
-
-    Args:
-        data (PromptOptModelSet): Model configuration data including model ID,
-                                   system prompt, and optional configuration ID
-        db (Session): Database session
-        current_user: Current user information
-
-    Returns:
-        UUID: The ID of the created or updated model configuration.
-    """
-    if data.id is None:
-        data.id = uuid.uuid4()
-
-    model_config = PromptOptimizerService(db).create_update_model_config(
-        current_user.tenant_id,
-        data.id,
-        data.system_prompt
-    )
-    return success(data=model_config.id)
-

api/app/controllers/workflow_controller.py

@@ -473,7 +473,7 @@ async def run_workflow(
             async def event_generator():
                 """生成 SSE 事件"""
                 try:
-                    async for event in service.run_workflow(
+                    async for event in await service.run_workflow(
                         app_id=app_id,
                         input_data=input_data,
                         triggered_by=current_user.id,

api/app/core/workflow/executor.py

@@ -4,16 +4,17 @@
 基于 LangGraph 的工作流执行引擎。
 """
 
-import logging
 import datetime
+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.nodes import WorkflowState, NodeFactory
 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__)
 
@@ -25,11 +26,11 @@ class WorkflowExecutor:
     """
 
     def __init__(
-        self,
-        workflow_config: dict[str, Any],
-        execution_id: str,
-        workspace_id: str,
-        user_id: str
+            self,
+            workflow_config: dict[str, Any],
+            execution_id: str,
+            workspace_id: str,
+            user_id: str
     ):
         """初始化执行器
 
@@ -90,8 +91,6 @@ class WorkflowExecutor:
             "error_node": None
         }
 
-
-
     def build_graph(self) -> CompiledStateGraph:
         """构建 LangGraph
 
@@ -112,19 +111,38 @@ class WorkflowExecutor:
             node_id = node.get("id")
 
             # 记录 start 和 end 节点 ID
-            if node_type == "start":
+            if node_type == NodeType.START:
                 start_node_id = node_id
-            elif node_type == "end":
+            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]:
+                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.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']}'"
+
             if node_instance:
                 # 包装节点的 run 方法
                 # 使用函数工厂避免闭包问题
                 def make_node_func(inst):
                     async def node_func(state: WorkflowState):
                         return await inst.run(state)
+
                     return node_func
 
                 workflow.add_node(node_id, make_node_func(node_instance))
@@ -165,14 +183,14 @@ class WorkflowExecutor:
                 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")
-                        }
+                            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
@@ -196,8 +214,8 @@ class WorkflowExecutor:
         return graph
 
     async def execute(
-        self,
-        input_data: dict[str, Any]
+            self,
+            input_data: dict[str, Any]
     ) -> dict[str, Any]:
         """执行工作流（非流式）
 
@@ -271,8 +289,8 @@ class WorkflowExecutor:
             }
 
     async def execute_stream(
-        self,
-        input_data: dict[str, Any]
+            self,
+            input_data: dict[str, Any]
     ):
         """执行工作流（流式）
 
@@ -305,7 +323,7 @@ class WorkflowExecutor:
         try:
             async for chunk in graph.astream(
                     initial_state,
-                    # subgraphs=True,  
+                    # subgraphs=True,
                     stream_mode="updates",
             ):
                 # print(chunk)
@@ -326,7 +344,6 @@ class WorkflowExecutor:
                 "token_usage": None
             }
 
-
     def _extract_final_output(self, node_outputs: dict[str, Any]) -> str | None:
         """从节点输出中提取最终输出
 
@@ -386,11 +403,11 @@ class WorkflowExecutor:
 
 
 async def execute_workflow(
-    workflow_config: dict[str, Any],
-    input_data: dict[str, Any],
-    execution_id: str,
-    workspace_id: str,
-    user_id: str
+        workflow_config: dict[str, Any],
+        input_data: dict[str, Any],
+        execution_id: str,
+        workspace_id: str,
+        user_id: str
 ) -> dict[str, Any]:
     """执行工作流（便捷函数）
 
@@ -414,11 +431,11 @@ async def execute_workflow(
 
 
 async def execute_workflow_stream(
-    workflow_config: dict[str, Any],
-    input_data: dict[str, Any],
-    execution_id: str,
-    workspace_id: str,
-    user_id: str
+        workflow_config: dict[str, Any],
+        input_data: dict[str, Any],
+        execution_id: str,
+        workspace_id: str,
+        user_id: str
 ):
     """执行工作流（流式，便捷函数）
 

api/app/core/workflow/expression_evaluator.py

@@ -5,6 +5,7 @@
 """
 
 import logging
+import re
 from typing import Any
 
 from simpleeval import simple_eval, NameNotDefined, InvalidExpression
@@ -59,9 +60,10 @@ class ExpressionEvaluator:
         """
         # 移除 Jinja2 模板语法的花括号（如果存在）
         expression = expression.strip()
-        if expression.startswith("{{") and expression.endswith("}}"):
-            expression = expression[2:-2].strip()
-        
+        # "{{system.message}} == {{ user.messge }}" -> "system.message == user.message"
+        pattern = r"\{\{\s*(.*?)\s*\}\}"
+        expression = re.sub(pattern, r"\1", expression).strip()
+
         # 构建命名空间上下文
         context = {
             "var": variables,                    # 用户变量

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

@@ -4,13 +4,14 @@
 提供各种类型的节点实现，用于工作流执行。
 """
 
-from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
-from app.core.workflow.nodes.llm import LLMNode
 from app.core.workflow.nodes.agent import AgentNode
-from app.core.workflow.nodes.transform import TransformNode
-from app.core.workflow.nodes.start import StartNode
+from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.workflow.nodes.end import EndNode
-from app.core.workflow.nodes.node_factory import NodeFactory
+from app.core.workflow.nodes.if_else import IfElseNode
+from app.core.workflow.nodes.llm import LLMNode
+from app.core.workflow.nodes.node_factory import NodeFactory, WorkflowNode
+from app.core.workflow.nodes.start import StartNode
+from app.core.workflow.nodes.transform import TransformNode
 
 __all__ = [
     "BaseNode",
@@ -18,7 +19,9 @@ __all__ = [
     "LLMNode",
     "AgentNode",
     "TransformNode",
+    "IfElseNode",
     "StartNode",
     "EndNode",
     "NodeFactory",
+    "WorkflowNode"
 ]

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

@@ -13,6 +13,7 @@ from app.core.workflow.nodes.end.config import EndNodeConfig
 from app.core.workflow.nodes.llm.config import LLMNodeConfig, MessageConfig
 from app.core.workflow.nodes.agent.config import AgentNodeConfig
 from app.core.workflow.nodes.transform.config import TransformNodeConfig
+from app.core.workflow.nodes.if_else.config import IfElseNodeConfig
 
 __all__ = [
     # 基础类
@@ -26,4 +27,5 @@ __all__ = [
     "MessageConfig",
     "AgentNodeConfig",
     "TransformNodeConfig",
+    "IfElseNodeConfig",
 ]

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

@@ -1,5 +1,6 @@
 from enum import StrEnum
 
+
 class NodeType(StrEnum):
     START = "start"
     END = "end"
@@ -13,3 +14,23 @@ class NodeType(StrEnum):
     HTTP_REQUEST = "http-request"
     TOOL = "tool"
     AGENT = "agent"
+
+
+class ComparisonOperator(StrEnum):
+    EMPTY = "empty"
+    NOT_EMPTY = "not_empty"
+    CONTAINS = "contains"
+    NOT_CONTAINS = "not_contains"
+    START_WITH = "startwith"
+    END_WITH = "endwith"
+    EQ = "eq"
+    NE = "ne"
+    LT = "lt"
+    LE = "le"
+    GT = "gt"
+    GE = "ge"
+
+
+class LogicOperator(StrEnum):
+    AND = "and"
+    OR = "or"

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

@@ -0,0 +1,5 @@
+"""Condition Node"""
+from app.core.workflow.nodes.if_else.config import IfElseNodeConfig
+from app.core.workflow.nodes.if_else.node import IfElseNode
+
+__all__ = ["IfElseNode", "IfElseNodeConfig"]

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

@@ -0,0 +1,97 @@
+"""Condition Configuration"""
+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
+
+
+class ConditionDetail(BaseModel):
+    comparison_operator: ComparisonOperator = Field(
+        ...,
+        description="Comparison operator used to evaluate the condition"
+    )
+
+    left: str = Field(
+        ...,
+        description="Value to compare against"
+    )
+
+    right: str = Field(
+        ...,
+        description="Value to compare with"
+    )
+
+
+class ConditionBranchConfig(BaseModel):
+    """Configuration for a conditional branch"""
+
+    logical_operator: LogicOperator = Field(
+        default=LogicOperator.AND.value,
+        description="Logical operator used to combine multiple condition expressions"
+    )
+
+    conditions: list[ConditionDetail] = Field(
+        ...,
+        description="List of condition expressions within this branch"
+    )
+
+
+class IfElseNodeConfig(BaseNodeConfig):
+    cases: list[ConditionBranchConfig] = Field(
+        ...,
+        description="List of branch conditions or expressions"
+    )
+
+    @field_validator("cases")
+    @classmethod
+    def validate_case_number(cls, v, info):
+        if len(v) < 1:
+            raise ValueError("At least one cases are required")
+        return v
+
+    class Config:
+        json_schema_extra = {
+            "examples": [
+                {
+                    "cases": [
+                        # CASE1 / IF Branch
+                        {
+                            "logical_operator": "and",
+                            "conditions": [
+                                [
+                                    {
+                                        "left": "node.userinput.message",
+                                        "comparison_operator": "eq",
+                                        "right": "'123'"
+                                    },
+                                    {
+                                        "left": "node.userinput.test",
+                                        "comparison_operator": "eq",
+                                        "right": "True"
+                                    }
+                                ]
+                            ]
+                        },
+                        # CASE1 / ELIF Branch
+                        {
+                            "logical_operator": "or",
+                            "conditions": [
+                                [
+                                    {
+                                        "left": "node.userinput.test",
+                                        "comparison_operator": "eq",
+                                        "right": "False"
+                                    },
+                                    {
+                                        "left": "node.userinput.message",
+                                        "comparison_operator": "contains",
+                                        "right": "'123'"
+                                    }
+                                ]
+                            ]
+                        }
+                        # CASE3 / ELSE Branch
+                    ]
+                }
+            ]
+        }

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

@@ -0,0 +1,167 @@
+import logging
+from typing import Any
+
+from app.core.workflow.nodes import BaseNode, WorkflowState
+from app.core.workflow.nodes.enums import ComparisonOperator
+from app.core.workflow.nodes.if_else import IfElseNodeConfig
+from app.core.workflow.nodes.if_else.config import ConditionDetail
+
+logger = logging.getLogger(__name__)
+
+
+class ConditionExpressionBuilder:
+    """
+    Build a Python boolean expression string based on a comparison operator.
+
+    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, left: str, operator: ComparisonOperator, right: str):
+        self.left = left
+        self.operator = operator
+        self.right = right
+
+    def _empty(self):
+        return f"{self.left} == ''"
+
+    def _not_empty(self):
+        return f"{self.left} != ''"
+
+    def _contains(self):
+        return f"{self.right} in {self.left}"
+
+    def _not_contains(self):
+        return f"{self.right} not in {self.left}"
+
+    def _startwith(self):
+        return f'{self.left}.startswith({self.right})'
+
+    def _endwith(self):
+        return f'{self.left}.endswith({self.right})'
+
+    def _eq(self):
+        return f"{self.left} == {self.right}"
+
+    def _ne(self):
+        return f"{self.left} != {self.right}"
+
+    def _lt(self):
+        return f"{self.left} < {self.right}"
+
+    def _le(self):
+        return f"{self.left} <= {self.right}"
+
+    def _gt(self):
+        return f"{self.left} > {self.right}"
+
+    def _ge(self):
+        return f"{self.left} >= {self.right}"
+
+    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._startwith()
+            case ComparisonOperator.END_WITH:
+                return self._endwith()
+            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}")
+
+
+class IfElseNode(BaseNode):
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
+        super().__init__(node_config, workflow_config)
+        self.typed_config = IfElseNodeConfig(**self.config)
+
+    @staticmethod
+    def _build_condition_expression(
+            condition: ConditionDetail,
+    ) -> str:
+        """
+        Build a single boolean condition expression string.
+
+        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]:
+        """
+        Build conditional edge expressions for the If-Else node.
+
+        This method does NOT evaluate any condition at runtime.
+        Instead, it converts each case branch into a Python boolean
+        expression string, which will later be attached to LangGraph
+        as conditional edges.
+
+        Each returned expression corresponds to one branch and is
+        evaluated in order. A fallback 'True' condition is appended
+        to ensure a default branch when no previous conditions match.
+
+        Returns:
+            list[str]: A list of Python boolean expression strings,
+            ordered by branch priority.
+        """
+        branch_index = 0
+        conditions = []
+
+        for case_branch in self.typed_config.cases:
+            branch_index += 1
+
+            branch_conditions = [
+                self._build_condition_expression(condition)
+                for condition in case_branch.conditions
+            ]
+            if len(branch_conditions) > 1:
+                combined_condition = f' {case_branch.logical_operator} '.join(branch_conditions)
+            else:
+                combined_condition = branch_conditions[0]
+            conditions.append(combined_condition)
+
+        # Default fallback branch
+        conditions.append("True")
+
+        return conditions
+
+    async def execute(self, state: WorkflowState) -> Any:
+        """
+        """
+        expressions = self.build_conditional_edge_expressions()
+        for i in range(len(expressions)):
+            logger.info(expressions[i])
+            if self._evaluate_condition(expressions[i], state):
+                return f'CASE{i+1}'
+        return f'CASE{len(expressions)}'

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

@@ -5,18 +5,29 @@
 """
 
 import logging
-from typing import Any
+from typing import Any, Union
 
-from app.core.workflow.nodes.base_node import BaseNode
-from app.core.workflow.nodes.enums import NodeType
-from app.core.workflow.nodes.llm import LLMNode
 from app.core.workflow.nodes.agent import AgentNode
-from app.core.workflow.nodes.transform import TransformNode
-from app.core.workflow.nodes.start import StartNode
+from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.end import EndNode
+from app.core.workflow.nodes.enums import NodeType
+from app.core.workflow.nodes.if_else import IfElseNode
+from app.core.workflow.nodes.llm import LLMNode
+from app.core.workflow.nodes.start import StartNode
+from app.core.workflow.nodes.transform import TransformNode
 
 logger = logging.getLogger(__name__)
 
+WorkflowNode = Union[
+    BaseNode,
+    StartNode,
+    EndNode,
+    LLMNode,
+    IfElseNode,
+    AgentNode,
+    TransformNode,
+]
+
 
 class NodeFactory:
     """节点工厂
@@ -25,16 +36,17 @@ class NodeFactory:
     """
 
     # 节点类型注册表
-    _node_types: dict[str, type[BaseNode]] = {
+    _node_types: dict[str, type[WorkflowNode]] = {
         NodeType.START: StartNode,
         NodeType.END: EndNode,
         NodeType.LLM: LLMNode,
         NodeType.AGENT: AgentNode,
         NodeType.TRANSFORM: TransformNode,
+        NodeType.IF_ELSE: IfElseNode
     }
 
     @classmethod
-    def register_node_type(cls, node_type: str, node_class: type[BaseNode]):
+    def register_node_type(cls, node_type: str, node_class: type[WorkflowNode]):
         """注册新的节点类型
 
         Args:
@@ -52,10 +64,10 @@ class NodeFactory:
 
     @classmethod
     def create_node(
-        cls,
-        node_config: dict[str, Any],
-        workflow_config: dict[str, Any]
-    ) -> BaseNode | None:
+            cls,
+            node_config: dict[str, Any],
+            workflow_config: dict[str, Any]
+    ) -> WorkflowNode | None:
         """创建节点实例
 
         Args:

api/app/models/__init__.py

@@ -20,7 +20,7 @@ from .data_config_model import DataConfig
 from .multi_agent_model import MultiAgentConfig, AgentInvocation
 from .workflow_model import WorkflowConfig, WorkflowExecution, WorkflowNodeExecution
 from .retrieval_info import RetrievalInfo
-from .prompt_optimizer_model import PromptOptimizerModelConfig, PromptOptimizerSession, PromptOptimizerSessionHistory
+from .prompt_optimizer_model import PromptOptimizerSession, PromptOptimizerSessionHistory
 
 __all__ = [
     "Tenants",
@@ -56,7 +56,6 @@ __all__ = [
     "WorkflowExecution",
     "WorkflowNodeExecution",
     "RetrievalInfo",
-    "PromptOptimizerModelConfig",
     "PromptOptimizerSession",
     "PromptOptimizerSessionHistory"
 ]

api/app/models/prompt_optimizer_model.py

@@ -27,49 +27,6 @@ class RoleType(StrEnum):
     ASSISTANT = "assistant"
 
 
-class PromptOptimizerModelConfig(Base):
-    """
-    Prompt Optimization Model Configuration.
-
-    This table stores system-level prompt configurations for each tenant.
-    The configuration defines the base system prompt used during prompt
-    optimization sessions and serves as a foundational instruction set
-    for the optimization process.
-
-    Each tenant may have one or more model configurations depending on
-    business requirements.
-
-    Table Name:
-        prompt_model_config
-
-    Columns:
-        id (UUID):
-            Primary key. Unique identifier for the prompt model configuration.
-        tenant_id (UUID):
-            Foreign key referencing `tenants.id`.
-            Identifies the tenant that owns this configuration.
-        system_prompt (Text):
-            The system-level prompt used to guide prompt optimization logic.
-        created_at (DateTime):
-            Timestamp indicating when the configuration was created.
-        updated_at (DateTime):
-            Timestamp indicating the last update time of the configuration.
-
-    Usage:
-        - Loaded when initializing a prompt optimization session
-        - Acts as the root system instruction for all subsequent prompts
-    """
-    __tablename__ = "prompt_model_config"
-
-    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4, index=True)
-    tenant_id = Column(UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=False, comment="Tenant ID")
-    # model_id = Column(UUID(as_uuid=True), nullable=False, comment="Model ID")
-    system_prompt = Column(Text, nullable=False, comment="System Prompt")
-
-    created_at = Column(DateTime, default=datetime.datetime.now, comment="Creation Time")
-    updated_at = Column(DateTime, default=datetime.datetime.now, onupdate=datetime.datetime.now, comment="Update Time")
-
-
 class PromptOptimizerSession(Base):
     """
     Prompt Optimization Session Registry.

api/app/repositories/prompt_optimizer_repository.py

@@ -1,120 +1,15 @@
 import uuid
-from typing import Optional
 
 from sqlalchemy.orm import Session
 
 from app.core.logging_config import get_db_logger
 from app.models.prompt_optimizer_model import (
-    PromptOptimizerModelConfig,
     PromptOptimizerSession, PromptOptimizerSessionHistory, RoleType
 )
 
 db_logger = get_db_logger()
 
 
-class PromptOptimizerModelConfigRepository:
-    """Repository for managing prompt optimizer model configurations."""
-
-    def __init__(self, db: Session):
-        self.db = db
-
-    def get_by_tenant_id(self, tenant_id: uuid.UUID) -> Optional[PromptOptimizerModelConfig]:
-        """
-        Retrieve the prompt optimizer model configuration for a specific tenant.
-
-        Args:
-           tenant_id (uuid.UUID): The unique identifier of the tenant.
-
-        Returns:
-           Optional[PromptOptimizerModelConfig]: The model configuration if found, else None.
-        """
-        db_logger.debug(f"Get prompt optimization model configuration: tenant_id={tenant_id}")
-
-        try:
-            config = self.db.query(PromptOptimizerModelConfig).filter(
-                PromptOptimizerModelConfig.tenant_id == tenant_id,
-                # PromptOptimizerModelConfig.model_id == model_id
-            ).first()
-            if config:
-                db_logger.debug(f"Prompt optimization model configuration found: (ID: {config.id})")
-            else:
-                db_logger.debug(f"Prompt optimization model configuration not found: tenant_id={tenant_id}")
-            return config
-        except Exception as e:
-            db_logger.error(
-                f"Error retrieving prompt optimization model configuration: tenant_id={tenant_id} - {str(e)}")
-            raise
-
-    def get_by_config_id(self, tenant_id: uuid.UUID, config_id: uuid.UUID) -> Optional[PromptOptimizerModelConfig]:
-        """
-        Retrieve a specific prompt optimizer model configuration by config ID and tenant ID.
-
-        Args:
-            tenant_id (uuid.UUID): The unique identifier of the tenant.
-            config_id (uuid.UUID): The unique identifier of the model configuration.
-
-        Returns:
-            Optional[PromptOptimizerModelConfig]: The model configuration if found, else None.
-        """
-        db_logger.debug(f"Get prompt optimization model configuration: config_id={config_id}, tenant_id={tenant_id}")
-        try:
-            model = self.db.query(PromptOptimizerModelConfig).filter(
-                PromptOptimizerModelConfig.tenant_id == tenant_id,
-                PromptOptimizerModelConfig.id == config_id
-            ).first()
-            if model:
-                db_logger.debug(f"Prompt optimization model configuration found: (ID: {model.id})")
-            else:
-                db_logger.debug(f"Prompt optimization model configuration not found: config_id={config_id}")
-            return model
-        except Exception as e:
-            db_logger.error(
-                f"Error retrieving prompt optimization model configuration: model_id={config_id} - {str(e)}")
-            raise
-
-    def create_or_update(
-            self,
-            config_id: uuid.UUID,
-            tenant_id: uuid.UUID,
-            system_prompt: str,
-    ) -> Optional[PromptOptimizerModelConfig]:
-        """
-        Create a new or update an existing prompt optimizer model configuration.
-
-        If a configuration with the given config_id exists, it updates its system_prompt.
-        Otherwise, it creates a new configuration record.
-
-        Args:
-            config_id (uuid.UUID): The unique identifier for the configuration.
-            tenant_id (uuid.UUID): The tenant's unique identifier.
-            system_prompt (str): The system prompt content for prompt optimization.
-
-        Returns:
-            Optional[PromptOptimizerModelConfig]: The created or updated model configuration.
-        """
-        db_logger.debug(f"Create/Update prompt optimization model configuration: tenant_id={tenant_id}")
-        existing_config = self.get_by_config_id(tenant_id, config_id)
-
-        if existing_config:
-            existing_config.system_prompt = system_prompt
-            self.db.commit()
-            self.db.refresh(existing_config)
-            db_logger.debug(f"Prompt optimization model configuration update: ID:{config_id}")
-            return existing_config
-        else:
-            config = PromptOptimizerModelConfig(
-                id=config_id,
-                # model_id=model_id,
-                tenant_id=tenant_id,
-                system_prompt=system_prompt
-            )
-            self.db.add(config)
-            self.db.commit()
-            self.db.refresh(config)
-            db_logger.debug(f"Prompt optimization model configuration created: ID:{config.id}")
-            return config
-
-
 class PromptOptimizerSessionRepository:
     """Repository for managing prompt optimization sessions and session history."""
 

api/app/services/prompt_optimizer_service.py

@@ -1,4 +1,3 @@
-import json
 import re
 import uuid
 
@@ -12,13 +11,11 @@ from app.core.models import RedBearModelConfig
 from app.core.models.llm import RedBearLLM
 from app.models import ModelConfig, ModelApiKey, ModelType, PromptOptimizerSessionHistory
 from app.models.prompt_optimizer_model import (
-    PromptOptimizerModelConfig,
     PromptOptimizerSession,
     RoleType
 )
 from app.repositories.model_repository import ModelConfigRepository
 from app.repositories.prompt_optimizer_repository import (
-    PromptOptimizerModelConfigRepository,
     PromptOptimizerSessionRepository
 )
 from app.schemas.prompt_optimizer_schema import OptimizePromptResult
@@ -34,32 +31,24 @@ class PromptOptimizerService:
             self,
             tenant_id: uuid.UUID,
             model_id: uuid.UUID
-    ) -> tuple[PromptOptimizerModelConfig, ModelConfig]:
+    ) -> ModelConfig:
         """
-        Retrieve the prompt optimizer model configuration and model configuration.
+        Retrieve the model configuration for a specific tenant.
 
-        This method retrieves the prompt optimizer model configuration associated
-        with the specified model ID and tenant. It also fetches the corresponding
-        model configuration.
+        This method fetches the model configuration associated with the given
+        tenant_id and model_id. If no configuration is found, a BusinessException
+        is raised.
 
         Args:
             tenant_id (uuid.UUID): The unique identifier of the tenant.
-            model_id (uuid.UUID): The unique identifier of the prompt optimization model.
+            model_id (uuid.UUID): The unique identifier of the model.
 
         Returns:
-            tuple[PromptOptimzerModelConfig, ModelConfig]:
-                A tuple containing the prompt optimizer model configuration
-                and the corresponding model configuration.
+            ModelConfig: The corresponding model configuration object.
 
         Raises:
-            BusinessException: If the prompt optimizer model configuration does not exist.
             BusinessException: If the model configuration does not exist.
         """
-        prompt_config = PromptOptimizerModelConfigRepository(self.db).get_by_tenant_id(
-            tenant_id
-        )
-        if not prompt_config:
-            raise BusinessException("提示词模型配置不存在", BizCode.NOT_FOUND)
 
         model = ModelConfigRepository.get_by_id(
             self.db, model_id, tenant_id=tenant_id
@@ -67,35 +56,7 @@ class PromptOptimizerService:
         if not model:
             raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND)
 
-        return prompt_config, model
-
-    def create_update_model_config(
-            self,
-            tenant_id: uuid.UUID,
-            config_id: uuid.UUID,
-            system_prompt: str,
-    ) -> PromptOptimizerModelConfig:
-        """
-        Create or update a prompt optimizer model configuration.
-
-        This method creates a new prompt optimizer model configuration or updates
-        an existing one identified by the given configuration ID. The configuration
-        defines the system prompt used for prompt optimization.
-
-        Args:
-            tenant_id (uuid.UUID): The unique identifier of the tenant.
-            config_id (uuid.UUID): The unique identifier of the configuration to create or update.
-            system_prompt (str): The system prompt content used for prompt optimization.
-
-        Returns:
-            PromptOptimzerModelConfig: The created or updated prompt optimizer model configuration.
-        """
-        prompt_config = PromptOptimizerModelConfigRepository(self.db).create_or_update(
-            config_id=config_id,
-            tenant_id=tenant_id,
-            system_prompt=system_prompt,
-        )
-        return prompt_config
+        return model
 
     def create_session(
             self,
@@ -159,37 +120,46 @@ class PromptOptimizerService:
             session_id: uuid.UUID,
             user_id: uuid.UUID,
             current_prompt: str,
-            message: str
+            user_require: str
     ) -> OptimizePromptResult:
         """
-        Optimize a prompt using a prompt optimizer LLM.
+        Optimize a user-provided prompt using a configured prompt optimizer LLM.
 
-        This method uses a configured prompt optimizer model to refine an existing
-        prompt based on the user's requirements. The optimized prompt is generated
-        according to predefined system rules, including Jinja2 variable syntax and
-        a strict JSON output format.
+        This method refines the original prompt according to the user's requirements,
+        generating an optimized version that is directly usable by AI tools. The
+        optimization process follows strict rules, including:
+        - Wrapping user-inserted variables in double curly braces {{}}.
+        - Adhering to Jinja2 variable syntax if applicable.
+        - Ensuring a clear logic flow, explicit instructions, and strong executability.
+        - Producing output in a strict JSON format.
+
+        Steps performed:
+        1. Retrieve the model configuration for the given tenant and model.
+        2. Fetch the session message history for context.
+        3. Instantiate the LLM with the appropriate API key and model configuration.
+        4. Build system messages outlining optimization rules.
+        5. Format the user's original prompt and requirements as a user message.
+        6. Send messages to the LLM to generate the optimized prompt.
+        7. Generate a concise description summarizing the changes made during optimization.
 
         Args:
-            tenant_id (uuid.UUID): The unique identifier of the tenant.
-            model_id (uuid.UUID): The unique identifier of the prompt optimizer model.
-            session_id (uuid.UUID): The unique identifier of the prompt optimization session.
-            user_id (uuid.UUID): The unique identifier of the user associated with the session.
-            current_prompt (str): The original prompt to be optimized.
-            message (str): The user's requirements or modification instructions.
+            tenant_id (uuid.UUID): Tenant identifier.
+            model_id (uuid.UUID): Prompt optimizer model identifier.
+            session_id (uuid.UUID): Prompt optimization session identifier.
+            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.
 
         Returns:
-            dict: A dictionary containing the optimized prompt and the description
-            of changes, in the following format:
-            {
-                "prompt": "<optimized_prompt>",
-                "desc": "<change_description>"
-            }
+            OptimizePromptResult: An object containing:
+                - prompt: The optimized prompt string.
+                - desc: A short description summarizing the changes.
 
         Raises:
-            BusinessException: If the model response cannot be parsed as valid JSON
+            BusinessException: If the LLM response cannot be parsed as valid JSON
             or does not conform to the expected output format.
         """
-        prompt_config, model_config = self.get_model_config(tenant_id, model_id)
+        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)
 
         # Create LLM instance
@@ -204,36 +174,65 @@ class PromptOptimizerService:
         # build message
         messages = [
             # init system_prompt
-            (RoleType.SYSTEM.value, prompt_config.system_prompt),
+            (
+                RoleType.SYSTEM.value,
+                "Your task is to optimize the original prompt provided by the user so that it can be directly used by AI tools,"
+                "and the variables that the user needs to insert must be wrapped in {{}}. "
+                "The optimized prompt should align with the optimization direction specified by the user (if any) and ensure clear logic, explicit instructions, and strong executability. "
+                "Please follow these rules when optimizing: "
+                '1. Ensure variables are wrapped in {{}}, e.g., optimize "Please enter your question" to "Please enter your {{question}}"'
+                "2. Instructions must be specific and operable, avoiding vague expressions"
+                "3. If the original prompt lacks key elements (such as output format requirements), supplement them completely "
+                "4. Keep the language concise and avoid redundancy "
+                "5. If the user does not specify an optimization direction, the default optimization is to make the prompt structurally clear and with explicit instructions"
+                "Please directly output the optimized prompt without additional explanations. The optimized prompt should be directly usable with correct variable positions."
+            ),
 
             # base model limit
             (RoleType.SYSTEM.value,
              "Optimization Rules:\n"
              "1. Fully adjust the prompt content according to the user's requirements.\n"
-             "2. When the user requests the insertion of variables, you must use Jinja2 syntax {{variable_name}} "
-             "(the variable name should be determined based on the user's requirement).\n"
+             "When variables are required, use double curly braces {{variable_name}} as placeholders."
+             "Variable names must be derived from the user's requirements.\n"
              "3. Keep the prompt logic clear and instructions explicit.\n"
-             "4. Ensure that the modified prompt can be directly used.\n\n"
-             "Output Requirements:\n"
-             "Provide the result in JSON format, containing exactly two fields:\n"
-             "  - prompt: The modified prompt (string).\n"
-             "  - desc: A response addressing the user's optimization request (string).")
+             "4. Ensure that the modified prompt can be directly used.\n\n")
         ]
         messages.extend(session_history[:-1])  # last message is current message
         user_message_template = ChatPromptTemplate.from_messages([
-            (RoleType.USER.value, "[current_prompt]\n{current_prompt}\n[user_require]\n{message}")
+            (RoleType.USER.value, "[original_prompt]\n{current_prompt}\n[user_require]\n{user_require}")
         ])
-        formatted_user_message = user_message_template.format(current_prompt=current_prompt, message=message)
+        formatted_user_message = user_message_template.format(current_prompt=current_prompt, user_require=user_require)
         messages.extend([(RoleType.USER.value, formatted_user_message)])
         logger.info(f"Prompt optimization message: {messages}")
-        result = await llm.ainvoke(messages)
-        try:
-            data_dict = json.loads(result.content)
-            model_resp = OptimizePromptResult.model_validate(data_dict)
-        except Exception as e:
-            logger.error(f"Failed to parse model reponse to json - Error: {str(e)}", exc_info=True)
-            raise BusinessException("Failed to parse model response", BizCode.PARSER_NOT_SUPPORTED)
-        return model_resp
+        optim_prompt = await llm.ainvoke(messages)
+        optim_desc = [
+            (
+                RoleType.SYSTEM.value,
+                "You are a prompt optimization assistant.\n"
+                "Compare the original prompt, the user's requirements, "
+                "and the optimized prompt.\n"
+                "Summarize the changes made during optimization.\n\n"
+                "Rules:\n"
+                "1. Output must be a single short sentence.\n"
+                "2. Be concise and factual.\n"
+                "3. Do not explain the prompts themselves.\n"
+                "4. Do not include any extra text."
+            ),
+            (
+                "[Original Prompt]\n"
+                f"{current_prompt}\n\n"
+                "[User Requirements]\n"
+                f"{user_require}\n\n"
+                "[Optimized Prompt]\n"
+                f"{optim_prompt.content}"
+            )
+        ]
+        optim_desc = await llm.ainvoke(optim_desc)
+
+        return OptimizePromptResult(
+            prompt=optim_prompt.content,
+            desc=optim_desc.content
+        )
 
     @staticmethod
     def parser_prompt_variables(prompt: str):
@@ -277,4 +276,3 @@ class PromptOptimizerService:
             content=content
         )
         return message
-

api/app/services/workflow_service.py

@@ -5,7 +5,7 @@ import json
 import logging
 import uuid
 import datetime
-from typing import Any, Annotated
+from typing import Any, Annotated, AsyncGenerator
 
 from sqlalchemy.orm import Session
 from fastapi import Depends
@@ -81,7 +81,7 @@ class WorkflowService:
             if not is_valid:
                 logger.warning(f"工作流配置验证失败: {errors}")
                 raise BusinessException(
-                    error_code=BizCode.INVALID_PARAMETER,
+                    code=BizCode.INVALID_PARAMETER,
                     message=f"工作流配置无效: {'; '.join(errors)}"
                 )
 
@@ -140,7 +140,7 @@ class WorkflowService:
         config = self.get_workflow_config(app_id)
         if not config:
             raise BusinessException(
-                error_code=BizCode.RESOURCE_NOT_FOUND,
+                code=BizCode.NOT_FOUND,
                 message=f"工作流配置不存在: app_id={app_id}"
             )
 
@@ -166,7 +166,7 @@ class WorkflowService:
             if not is_valid:
                 logger.warning(f"工作流配置验证失败: {errors}")
                 raise BusinessException(
-                    error_code=BizCode.INVALID_PARAMETER,
+                    code=BizCode.INVALID_PARAMETER,
                     message=f"工作流配置无效: {'; '.join(errors)}"
                 )
 
@@ -245,7 +245,7 @@ class WorkflowService:
         config = self.get_workflow_config(app_id)
         if not config:
             raise BusinessException(
-                error_code=BizCode.RESOURCE_NOT_FOUND,
+                code=BizCode.NOT_FOUND,
                 message=f"工作流配置不存在: app_id={app_id}"
             )
 
@@ -359,7 +359,7 @@ class WorkflowService:
         execution = self.get_execution(execution_id)
         if not execution:
             raise BusinessException(
-                error_code=BizCode.RESOURCE_NOT_FOUND,
+                code=BizCode.NOT_FOUND,
                 message=f"执行记录不存在: execution_id={execution_id}"
             )
 
@@ -640,7 +640,7 @@ class WorkflowService:
         triggered_by: uuid.UUID,
         conversation_id: uuid.UUID | None = None,
         stream: bool = False
-    ):
+    ) -> AsyncGenerator | dict:
         """运行工作流
 
         Args:
@@ -660,7 +660,7 @@ class WorkflowService:
         config = self.get_workflow_config(app_id)
         if not config:
             raise BusinessException(
-                error_code=BizCode.RESOURCE_NOT_FOUND,
+                code=BizCode.NOT_FOUND,
                 message=f"工作流配置不存在: app_id={app_id}"
             )
 
@@ -687,7 +687,7 @@ class WorkflowService:
         app = self.db.query(App).filter(App.id == app_id).first()
         if not app:
             raise BusinessException(
-                error_code=BizCode.RESOURCE_NOT_FOUND,
+                code=BizCode.NOT_FOUND,
                 message=f"应用不存在: app_id={app_id}"
             )
 
@@ -750,7 +750,7 @@ class WorkflowService:
                 error_message=str(e)
             )
             raise BusinessException(
-                error_code=BizCode.INTERNAL_ERROR,
+                code=BizCode.INTERNAL_ERROR,
                 message=f"工作流执行失败: {str(e)}"
             )