Merge pull request #527 from SuanmoSuanyangTechnology/feature/agent-tool_xjn

fix(app)

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

@@ -4,65 +4,145 @@
 # @Time : 2026/2/25 14:11
 from typing import Any
 
+from app.core.logging_config import get_logger
 from app.core.workflow.adapters.base_adapter import (
     PlatformMetadata,
     PlatformType,
     BasePlatformAdapter,
     WorkflowParserResult
 )
-from app.schemas.workflow_schema import ExecutionConfig
+from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType, UnsupportNodeType
+from app.core.workflow.adapters.memory_bear.memory_bear_converter import MemoryBearConverter
+from app.core.workflow.nodes.enums import NodeType
+from app.schemas.workflow_schema import ExecutionConfig, NodeDefinition, EdgeDefinition, VariableDefinition
+
+logger = get_logger()
+
+VALID_NODE_TYPES = frozenset(t.value for t in NodeType if t != NodeType.UNKNOWN)
 
 
-class MemoryBearAdapter(BasePlatformAdapter):
-    NODE_TYPE_MAPPING = {}
+class MemoryBearAdapter(BasePlatformAdapter, MemoryBearConverter):
+    NODE_TYPE_MAPPING = {t.value: t for t in NodeType}
+
+    def __init__(self, config: dict[str, Any]):
+        MemoryBearConverter.__init__(self)
+        BasePlatformAdapter.__init__(self, config)
 
     @property
     def origin_nodes(self):
-        return self.config.get("workflow").get("nodes")
+        return self.config.get("workflow").get("nodes") or []
 
     @property
     def origin_edges(self):
-        return self.config.get("workflow").get("edges")
+        return self.config.get("workflow").get("edges") or []
 
     @property
     def origin_variables(self):
-        return self.config.get("workflow").get("variables")
+        return self.config.get("workflow").get("variables") or []
 
     def get_metadata(self) -> PlatformMetadata:
         return PlatformMetadata(
             platform_name=PlatformType.MEMORY_BEAR,
             version="0.2.5",
-            support_node_types=list(self.NODE_TYPE_MAPPING.keys())
+            support_node_types=list(VALID_NODE_TYPES)
         )
 
-    def map_node_type(self, platform_node_type) -> str:
-        return platform_node_type
+    def map_node_type(self, platform_node_type: str) -> NodeType:
+        return self.NODE_TYPE_MAPPING.get(platform_node_type, NodeType.UNKNOWN)
 
     @staticmethod
-    def _valid_nodes(node: dict[str, Any]):
-        if "type" not in node["data"]:
-            return False
+    def _valid_node(node: dict[str, Any]) -> bool:
         if "id" not in node or "type" not in node:
             return False
+        if not isinstance(node.get("config"), dict):
+            return False
         return True
 
     def validate_config(self) -> bool:
         require_fields = frozenset({'app', 'workflow'})
         if not all(field in self.config for field in require_fields):
             return False
-
         for node in self.origin_nodes:
-            if not self._valid_nodes(node):
+            if not self._valid_node(node):
                 return False
         return True
 
+    def _convert_node(self, node: dict[str, Any]) -> NodeDefinition | None:
+        node_id = node.get("id")
+        node_name = node.get("name")
+        try:
+            node_type = self.map_node_type(node["type"])
+            if node_type == NodeType.UNKNOWN:
+                self.errors.append(UnsupportNodeType(
+                    node_id=node_id,
+                    node_type=node["type"]
+                ))
+                return None
+
+            config = node.get("config") or {}
+            converter = self.get_node_convert(node_type)
+            converter(node_id, node_name, config)  # validates and appends errors if invalid
+
+            return NodeDefinition(**node)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.NODE,
+                node_id=node_id,
+                node_name=node_name,
+                detail=f"convert node error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert node error - {e}", exc_info=True)
+            return None
+
+    def _convert_edge(self, edge: dict[str, Any], valid_node_ids: set) -> EdgeDefinition | None:
+        try:
+            if edge.get("source") not in valid_node_ids or edge.get("target") not in valid_node_ids:
+                self.warnings.append(ExceptionDefineition(
+                    type=ExceptionType.EDGE,
+                    detail=f"edge {edge.get('id')} skipped: source or target node not found"
+                ))
+                return None
+            return EdgeDefinition(**edge)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.EDGE,
+                detail=f"convert edge error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert edge error - {e}", exc_info=True)
+            return None
+
+    def _convert_variable(self, variable: dict[str, Any]) -> VariableDefinition | None:
+        try:
+            return VariableDefinition(**variable)
+        except Exception as e:
+            self.warnings.append(ExceptionDefineition(
+                type=ExceptionType.VARIABLE,
+                name=variable.get("name"),
+                detail=f"convert variable error - {e}"
+            ))
+            logger.debug(f"MemoryBear convert variable error - {e}", exc_info=True)
+            return None
+
     def parse_workflow(self) -> WorkflowParserResult:
-        self.nodes = self.origin_nodes
-        self.edges = self.origin_edges
-        self.conv_variables = self.origin_variables
+        for node in self.origin_nodes:
+            converted = self._convert_node(node)
+            if converted:
+                self.nodes.append(converted)
+
+        valid_node_ids = {n.id for n in self.nodes}
+
+        for edge in self.origin_edges:
+            converted = self._convert_edge(edge, valid_node_ids)
+            if converted:
+                self.edges.append(converted)
+
+        for variable in self.origin_variables:
+            converted = self._convert_variable(variable)
+            if converted:
+                self.conv_variables.append(converted)
 
         return WorkflowParserResult(
-            success=True,
+            success=not self.errors and not self.warnings,
             platform=self.get_metadata(),
             execution_config=ExecutionConfig(),
             origin_config=self.config,
@@ -72,5 +152,4 @@ class MemoryBearAdapter(BasePlatformAdapter):
             variables=self.conv_variables,
             warnings=self.warnings,
             errors=self.errors,
-
         )

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

@@ -0,0 +1,85 @@
+# -*- coding: UTF-8 -*-
+from app.core.workflow.adapters.base_converter import BaseConverter
+from app.core.workflow.adapters.errors import ExceptionDefineition, ExceptionType
+from app.core.workflow.nodes.base_config import BaseNodeConfig
+from app.core.workflow.nodes.configs import (
+    StartNodeConfig,
+    EndNodeConfig,
+    LLMNodeConfig,
+    AgentNodeConfig,
+    IfElseNodeConfig,
+    KnowledgeRetrievalNodeConfig,
+    AssignerNodeConfig,
+    CodeNodeConfig,
+    HttpRequestNodeConfig,
+    JinjaRenderNodeConfig,
+    VariableAggregatorNodeConfig,
+    ParameterExtractorNodeConfig,
+    LoopNodeConfig,
+    IterationNodeConfig,
+    QuestionClassifierNodeConfig,
+    ToolNodeConfig,
+    MemoryReadNodeConfig,
+    MemoryWriteNodeConfig,
+    NoteNodeConfig,
+)
+from app.core.workflow.nodes.enums import NodeType
+
+
+class MemoryBearConverter(BaseConverter):
+    errors: list
+    warnings: list
+
+    CONFIG_CLASS_MAP: dict[NodeType, type[BaseNodeConfig]] = {
+        NodeType.START: StartNodeConfig,
+        NodeType.END: EndNodeConfig,
+        NodeType.ANSWER: EndNodeConfig,
+        NodeType.LLM: LLMNodeConfig,
+        NodeType.AGENT: AgentNodeConfig,
+        NodeType.IF_ELSE: IfElseNodeConfig,
+        NodeType.KNOWLEDGE_RETRIEVAL: KnowledgeRetrievalNodeConfig,
+        NodeType.ASSIGNER: AssignerNodeConfig,
+        NodeType.CODE: CodeNodeConfig,
+        NodeType.HTTP_REQUEST: HttpRequestNodeConfig,
+        NodeType.JINJARENDER: JinjaRenderNodeConfig,
+        NodeType.VAR_AGGREGATOR: VariableAggregatorNodeConfig,
+        NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNodeConfig,
+        NodeType.LOOP: LoopNodeConfig,
+        NodeType.ITERATION: IterationNodeConfig,
+        NodeType.QUESTION_CLASSIFIER: QuestionClassifierNodeConfig,
+        NodeType.TOOL: ToolNodeConfig,
+        NodeType.MEMORY_READ: MemoryReadNodeConfig,
+        NodeType.MEMORY_WRITE: MemoryWriteNodeConfig,
+        NodeType.NOTES: NoteNodeConfig,
+    }
+
+    @staticmethod
+    def _convert_file(var):
+        return None
+
+    @staticmethod
+    def _convert_array_file(var):
+        return []
+
+    def config_validate(self, node_id: str, node_name: str, config_cls: type[BaseNodeConfig], value: dict):
+        try:
+            return config_cls.model_validate(value)
+        except Exception as e:
+            self.errors.append(ExceptionDefineition(
+                type=ExceptionType.CONFIG,
+                node_id=node_id,
+                node_name=node_name,
+                detail=str(e)
+            ))
+            return None
+
+    def get_node_convert(self, node_type: NodeType):
+        config_cls = self.CONFIG_CLASS_MAP.get(node_type)
+        if not config_cls:
+            return lambda node_id, node_name, config: config
+
+        def validate(node_id: str, node_name: str, config: dict):
+            self.config_validate(node_id, node_name, config_cls, config)
+            return config
+
+        return validate

api/app/services/app_dsl_service.py

@@ -17,6 +17,7 @@ from app.models.models_model import ModelConfig
 from app.models.tool_model import ToolConfig as ToolConfigModel
 from app.models.workflow_model import WorkflowConfig
 from app.services.workflow_service import WorkflowService
+from app.core.workflow.adapters.memory_bear.memory_bear_adapter import MemoryBearAdapter
 
 
 class AppDslService:
@@ -243,7 +244,7 @@ class AppDslService:
                 model_parameters=cfg.get("model_parameters"),
                 default_model_config_id=self._resolve_model(cfg.get("default_model_config_ref"), tenant_id, warnings),
                 knowledge_retrieval=self._resolve_knowledge_retrieval(cfg.get("knowledge_retrieval"), workspace_id, warnings),
-                memory=cfg.get("memory"),
+                memory=self._resolve_memory(cfg.get("memory"), workspace_id, warnings),
                 variables=cfg.get("variables", []),
                 tools=self._resolve_tools(cfg.get("tools", []), tenant_id, warnings),
                 skills=cfg.get("skills", {}),
@@ -272,12 +273,20 @@ class AppDslService:
             ))
 
         elif app_type == AppType.WORKFLOW:
+            adapter = MemoryBearAdapter(dsl)
+            if not adapter.validate_config():
+                raise BusinessException("工作流配置格式无效", BizCode.BAD_REQUEST)
+            result = adapter.parse_workflow()
+            for e in result.errors:
+                warnings.append(f"[节点错误] {e.node_name or e.node_id}: {e.detail}")
+            for w in result.warnings:
+                warnings.append(f"[节点警告] {w.node_name or w.node_id}: {w.detail}")
             wf = dsl.get("workflow") or {}
             WorkflowService(self.db).create_workflow_config(
                 app_id=new_app.id,
-                nodes=wf.get("nodes", []),
-                edges=wf.get("edges", []),
-                variables=wf.get("variables", []),
+                nodes=[n.model_dump() for n in result.nodes],
+                edges=[e.model_dump() for e in result.edges],
+                variables=[v.model_dump() for v in result.variables],
                 execution_config=wf.get("execution_config", {}),
                 triggers=wf.get("triggers", []),
                 validate=False,
@@ -376,15 +385,37 @@ class AppDslService:
         for kb in kr.get("knowledge_bases", []):
             ref = kb.get("_ref") or ({"name": kb.get("kb_id")} if kb.get("kb_id") else None)
             entry = {k: v for k, v in kb.items() if k != "_ref"}
-            entry["kb_id"] = self._resolve_kb(ref, workspace_id, warnings)
+            resolved_id = self._resolve_kb(ref, workspace_id, warnings)
+            if resolved_id is None:
+                continue
+            entry["kb_id"] = resolved_id
             resolved_kbs.append(entry)
         return {k: v for k, v in kr.items() if k != "knowledge_bases"} | {"knowledge_bases": resolved_kbs}
 
+    def _resolve_memory(self, memory: Optional[dict], workspace_id: uuid.UUID, warnings: list) -> Optional[dict]:
+        if not memory:
+            return memory
+        config_id = memory.get("memory_config_id") or memory.get("memory_content")
+        if not config_id:
+            return memory
+        from app.models.memory_config_model import MemoryConfig as MemoryConfigModel
+        exists = self.db.query(MemoryConfigModel).filter(
+            MemoryConfigModel.config_id == config_id,
+            MemoryConfigModel.workspace_id == workspace_id
+        ).first()
+        if not exists:
+            warnings.append(f"记忆配置 '{config_id}' 未匹配，已置空，请导入后手动配置")
+            return {**memory, "memory_config_id": None, "enabled": False}
+        return memory
+
     def _resolve_tools(self, tools: list, tenant_id: uuid.UUID, warnings: list) -> list:
         result = []
         for t in (tools or []):
             ref = t.get("_ref") or ({"name": t.get("tool_id")} if t.get("tool_id") else None)
             entry = {k: v for k, v in t.items() if k != "_ref"}
-            entry["tool_id"] = self._resolve_tool(ref, tenant_id, warnings)
+            resolved_id = self._resolve_tool(ref, tenant_id, warnings)
+            if resolved_id is None:
+                continue
+            entry["tool_id"] = resolved_id
             result.append(entry)
         return result

api/env.example

@@ -75,7 +75,7 @@ REFRESH_TOKEN_EXPIRE_DAYS=7
 ENABLE_SINGLE_SESSION=
 
 # File Upload
-MAX_FILE_SIZE=52428800  # 50MB:10 * 1024 * 1024
+MAX_FILE_SIZE=52428800  # 50MB:50 * 1024 * 1024
 FILE_PATH=/files
 
 FILE_LOCAL_SERVER_URL="http://localhost:8000/api"