Merge branch 'refs/heads/develop' into feature/20251219_xjn

api/app/controllers/chunk_controller.py

@@ -18,6 +18,9 @@ from app.schemas.response_schema import ApiResponse
 from app.core.response_utils import success
 from app.services import knowledge_service, document_service, file_service, knowledgeshare_service
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
+from app.core.rag.common.settings import kg_retriever
+from app.core.rag.llm.chat_model import Base
+from app.core.rag.llm.embedding_model import OpenAIEmbed
 from app.core.logging_config import get_api_logger
 
 # Obtain a dedicated API logger
@@ -389,36 +392,41 @@ async def retrieve_chunks(
         knowledge_model.Knowledge.chunk_num > 0,
         knowledge_model.Knowledge.status == 1
     ]
-    existing_ids = knowledge_service.get_chunded_knowledgeids(
+    private_items = knowledge_service.get_chunded_knowledgeids(
         db=db,
         filters=filters,
         current_user=current_user
     )
+    private_kb_ids = [item[0] for item in private_items]
+    private_workspace_ids = [item[1] for item in private_items]
     filters = [
         knowledge_model.Knowledge.id.in_(retrieve_data.kb_ids),
         knowledge_model.Knowledge.permission_id == knowledge_model.PermissionType.Share,
         knowledge_model.Knowledge.chunk_num > 0,
         knowledge_model.Knowledge.status == 1
     ]
-    share_ids = knowledge_service.get_chunded_knowledgeids(
+    items = knowledge_service.get_chunded_knowledgeids(
         db=db,
         filters=filters,
         current_user=current_user
     )
-    if share_ids:
+    if items:
         filters = [
             knowledgeshare_model.KnowledgeShare.target_kb_id.in_(retrieve_data.kb_ids)
         ]
-        items = knowledgeshare_service.get_source_kb_ids_by_target_kb_id(
+        share_items = knowledgeshare_service.get_source_kb_ids_by_target_kb_id(
             db=db,
             filters=filters,
             current_user=current_user
         )
-        existing_ids.extend(items)
-    if not existing_ids:
+        share_kb_ids = [item[0] for item in share_items]
+        share_workspace_ids = [item[1] for item in share_items]
+        private_kb_ids.extend(share_kb_ids)
+        private_workspace_ids.extend(share_workspace_ids)
+    if not private_kb_ids:
         return success(data=[], msg="retrieval successful")
-    kb_id = existing_ids[0]
-    uuid_strs = [f"Vector_index_{kb_id}_Node".lower() for kb_id in existing_ids]
+    kb_id = private_kb_ids[0]
+    uuid_strs = [f"Vector_index_{kb_id}_Node".lower() for kb_id in private_kb_ids]
     indices = ",".join(uuid_strs)
     db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=kb_id, current_user=current_user)
     if not db_knowledge:
@@ -448,4 +456,21 @@ async def retrieve_chunks(
                     seen_ids.add(doc.metadata["doc_id"])
                     unique_rs.append(doc)
             rs = vector_service.rerank(query=retrieve_data.query, docs=unique_rs, top_k=retrieve_data.top_k)
+            if retrieve_data.retrieve_type == chunk_schema.RetrieveType.Graph:
+                kb_ids = [str(kb_id) for kb_id in private_kb_ids]
+                workspace_ids = [str(workspace_id) for workspace_id in private_workspace_ids]
+                # Prepare to configure chat_mdl、embedding_model、vision_model information
+                chat_model = Base(
+                    key=db_knowledge.llm.api_keys[0].api_key,
+                    model_name=db_knowledge.llm.api_keys[0].model_name,
+                    base_url=db_knowledge.llm.api_keys[0].api_base
+                )
+                embedding_model = OpenAIEmbed(
+                    key=db_knowledge.embedding.api_keys[0].api_key,
+                    model_name=db_knowledge.embedding.api_keys[0].model_name,
+                    base_url=db_knowledge.embedding.api_keys[0].api_base
+                )
+                doc = kg_retriever.retrieval(question=retrieve_data.query, workspace_ids=workspace_ids, kb_ids= kb_ids, emb_mdl=embedding_model, llm=chat_model)
+                if doc:
+                    rs.insert(0, doc)
             return success(data=rs, msg="retrieval successful")

api/app/core/memory/utils/prompt/prompts/evaluate.jinja2

@@ -86,5 +86,5 @@
 - **quality_assessment**:
     quality_assessment=true时输出评估对象，否则为null（注意：- summary输出的结果不允许含有（expired_at设为2024-01-01T00:00:00Z)等原数据字段以及涉及需要修改的字段以及内容）
 - **memory_verify**: memory_verify=true时输出隐私检测对象，否则为null
-    （注意：- summary输出的结果不允许含有（expired_at设为2024-01-01T00:00:00Z)等原数据字段以及涉及需要修改的字段以及内容）
+    （注意：- summary输出的结果不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true\memory_verify=false)等原数据字段以及涉及需要修改的字段以及内容）
 模式参考：{{ json_schema }}

api/app/core/models/rerank.py

@@ -1,4 +1,3 @@
-
 from typing import Any, Dict, List, Optional, Sequence, Type, Union
 from copy import deepcopy
 from urllib.parse import urlparse
@@ -8,8 +7,10 @@ from langchain_core.callbacks import Callbacks
 from app.core.models.base import RedBearModelConfig, get_provider_rerank_class, RedBearModelFactory
 from app.models import ModelProvider
 
+
 class RedBearRerank(BaseDocumentCompressor):
     """ Rerank → 作为 Runnable 插入任意 LCEL 链"""
+
     def __init__(self, config: RedBearModelConfig):
         self._model = self._create_model(config)
         self._config = config
@@ -22,10 +23,10 @@ class RedBearRerank(BaseDocumentCompressor):
         return model_class(**model_params)
 
     def compress_documents(
-        self,
-        documents: Sequence[Document],
-        query: str,
-        callbacks: Optional[Callbacks] = None,
+            self,
+            documents: Sequence[Document],
+            query: str,
+            callbacks: Optional[Callbacks] = None,
     ) -> Sequence[Document]:
         """
         Compress documents using Jina's Rerank API.
@@ -46,17 +47,17 @@ class RedBearRerank(BaseDocumentCompressor):
             compressed.append(doc_copy)
         return compressed
 
-
     def rerank(
-        self,
-        documents: Sequence[Union[str, Document, dict]],
-        query: str,
-        *,
-        top_n: Optional[int] = -1,
-    ) -> List[Dict[str, Any]]:   
-        provider = self._config.provider.lower()      
-        if provider in [ModelProvider.XINFERENCE, ModelProvider.GPUSTACK] :
+            self,
+            documents: Sequence[Union[str, Document, dict]],
+            query: str,
+            *,
+            top_n: Optional[int] = -1,
+    ) -> List[Dict[str, Any]]:
+        provider = self._config.provider.lower()
+        if provider in [ModelProvider.XINFERENCE, ModelProvider.GPUSTACK]:
             import langchain_community.document_compressors.jina_rerank as jina_mod
+
             # 规范化：如果不以 /v1/rerank 结尾，则补齐；若已以 /v1 结尾，则补 /rerank
             def _normalize_jina_base(base_url: Optional[str]) -> Optional[str]:
                 if not base_url:
@@ -73,8 +74,7 @@ class RedBearRerank(BaseDocumentCompressor):
                 # 设置完整的 rerank 端点，例如 http://host:port/v1/rerank
                 jina_mod.JINA_API_URL = jina_base
             from langchain_community.document_compressors import JinaRerank
-            model_instance : JinaRerank = self._model
-            return model_instance.rerank(documents = documents, query = query, top_n=top_n)     
+            model_instance: JinaRerank = self._model
+            return model_instance.rerank(documents=documents, query=query, top_n=top_n)
         else:
             raise ValueError(f"不支持的模型提供商: {provider}")
-        

api/app/core/rag/app/picture.py

@@ -51,7 +51,7 @@ def chunk(filename, binary, lang, callback=None, vision_model=None, **kwargs):
             img_binary = io.BytesIO()
             img.save(img_binary, format="JPEG")
             img_binary.seek(0)
-            ans = vision_model.describe(img_binary.read())
+            ans, ans_num_tokens = vision_model.describe(img_binary.read())
             callback(0.8, "CV LLM respond: %s ..." % ans[:32])
             txt += "\n" + ans
             tokenize(doc, txt, eng)

api/app/core/rag/deepdoc/parser/excel_parser.py

@@ -42,11 +42,14 @@ class RAGExcelParser:
                 file_like_object.seek(0)
                 try:
                     dfs = pd.read_excel(file_like_object, sheet_name=None)
+                    if isinstance(dfs, dict):
+                        dfs = next(iter(dfs.values()))
                     return RAGExcelParser._dataframe_to_workbook(dfs)
                 except Exception as ex:
                     logging.info(f"pandas with default engine load error: {ex}, try calamine instead")
                     file_like_object.seek(0)
                     df = pd.read_excel(file_like_object, engine="calamine")
+                    print(df)
                     return RAGExcelParser._dataframe_to_workbook(df)
             except Exception as e_pandas:
                 raise Exception(f"pandas.read_excel error: {e_pandas}, original openpyxl error: {e}")

api/app/core/rag/graphrag/search.py

@@ -4,6 +4,7 @@ from collections import defaultdict
 from copy import deepcopy
 import json_repair
 import pandas as pd
+import time
 import trio
 
 from app.core.rag.common.misc_utils import get_uuid
@@ -262,21 +263,21 @@ class KGSearch(Dealer):
             relas = ""
 
         return {
-                "chunk_id": get_uuid(),
-                "content_ltks": "",
-                "page_content": ents + relas + self._community_retrieval_([n for n, _ in ents_from_query], filters, kb_ids, idxnms,
-                                                        comm_topn, max_token),
+            "page_content": ents + relas + self._community_retrieval_([n for n, _ in ents_from_query], filters, kb_ids, idxnms, comm_topn, max_token),
+            "vector": None,
+            "metadata": {
+                "doc_id": get_uuid(),
+                "file_id": "",
+                "file_name": "Related content in Knowledge Graph",
+                "file_created_at": int(time.time() * 1000),
                 "document_id": "",
-                "docnm_kwd": "Related content in Knowledge Graph",
-                "kb_id": kb_ids,
-                "important_kwd": [],
-                "image_id": "",
-                "similarity": 1.,
-                "vector_similarity": 1.,
-                "term_similarity": 0,
-                "vector": [],
-                "positions": [],
-            }
+                "knowledge_id": kb_ids,
+                "sort_id": 0,
+                "status": 1,
+                "score": 1
+            },
+            "children": None
+        }
 
     def _community_retrieval_(self, entities, condition, kb_ids, idxnms, topn, max_token):
         ## Community retrieval

api/app/core/rag/nlp/search.py

@@ -26,6 +26,8 @@ from app.core.rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr
 from app.core.rag.common.string_utils import remove_redundant_spaces
 from app.core.rag.common.float_utils import get_float
 from app.core.rag.common.constants import PAGERANK_FLD, TAG_FLD
+from app.core.rag.llm.chat_model import Base
+from app.core.rag.llm.embedding_model import OpenAIEmbed
 
 
 def knowledge_retrieval(
@@ -48,6 +50,7 @@ def knowledge_retrieval(
             - merge_strategy: "weight" or other strategies
             - reranker_id: UUID of the reranker to use
             - reranker_top_k: int
+            - use_graph: bool, whether to use a graph
 
     Returns:
         Rearranged document block list (in descending order of relevance)
@@ -59,6 +62,7 @@ def knowledge_retrieval(
         merge_strategy = config.get("merge_strategy", "weight")
         reranker_id = config.get("reranker_id")
         reranker_top_k = config.get("reranker_top_k", 1024)
+        use_graph = config.get("use_graph", "false").lower() == "true"
 
         file_names_filter = []
         if user_ids:
@@ -67,6 +71,10 @@ def knowledge_retrieval(
         if not knowledge_bases:
             return []
 
+        kb_ids = []
+        workspace_ids = []
+        chat_model = None
+        embedding_model = None
         all_results = []
         # Search each knowledge base
         for kb_config in knowledge_bases:
@@ -87,6 +95,22 @@ def knowledge_retrieval(
                         else:
                             continue
 
+                    if str(db_knowledge.id) not in kb_ids:
+                        kb_ids.append(str(db_knowledge.id))
+                    if str(db_knowledge.workspace_id) not in workspace_ids:
+                        workspace_ids.append(str(db_knowledge.workspace_id))
+                    if not chat_model:
+                        chat_model = Base(
+                            key=db_knowledge.llm.api_keys[0].api_key,
+                            model_name=db_knowledge.llm.api_keys[0].model_name,
+                            base_url=db_knowledge.llm.api_keys[0].api_base
+                        )
+                    if not embedding_model:
+                        embedding_model = OpenAIEmbed(
+                            key=db_knowledge.embedding.api_keys[0].api_key,
+                            model_name=db_knowledge.embedding.api_keys[0].model_name,
+                            base_url=db_knowledge.embedding.api_keys[0].api_base
+                        )
                     vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
                     # Retrieve according to the configured retrieval type
                     match kb_config["retrieve_type"]:
@@ -136,6 +160,12 @@ def knowledge_retrieval(
         # Use the specified reranker for re-ranking
         if reranker_id:
             return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
+        # use graph
+        if use_graph:
+            from app.core.rag.common.settings import kg_retriever
+            doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
+            if doc:
+                all_results.insert(0, doc)
         return all_results
 
     except Exception as e:

api/app/core/rag/utils/es_conn.py

@@ -213,7 +213,7 @@ class ESConnection(DocStoreConnection):
                           m.topn * 2,
                           query_vector=list(m.embedding_data),
                           filter=bqry.to_dict(),
-                          similarity=similarity,
+                          # similarity=similarity
                           )
 
         if bqry and rank_feature:

api/app/core/workflow/executor.py

@@ -4,9 +4,9 @@
 基于 LangGraph 的工作流执行引擎。
 """
 
-import logging
 # import uuid
 import datetime
+import logging
 from typing import Any
 
 from langchain_core.messages import HumanMessage
@@ -107,7 +107,13 @@ class WorkflowExecutor:
             "user_id": self.user_id,
             "error": None,
             "error_node": None,
-            "streaming_buffer": {}  # 流式缓冲区
+            "streaming_buffer": {},  # 流式缓冲区
+            "cycle_nodes": [
+                node.get("id")
+                for node in self.workflow_config.get("nodes")
+                if node.get("type") in [NodeType.LOOP, NodeType.ITERATION]
+            ],  # loop, iteration node id
+            "looping": False  # loop runing flag, only use in loop node,not use in main loop
         }
 
     def _analyze_end_node_prefixes(self) -> tuple[dict[str, str], set[str]]:
@@ -199,6 +205,10 @@ class WorkflowExecutor:
         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:
@@ -271,7 +281,7 @@ class WorkflowExecutor:
             workflow.add_edge(START, start_node_id)
             logger.debug(f"添加边: START -> {start_node_id}")
 
-        for edge in self.edges:
+        for edge in self.workflow_config.get("edges", []):
             source = edge.get("source")
             target = edge.get("target")
             edge_type = edge.get("type")
@@ -284,12 +294,12 @@ class WorkflowExecutor:
                 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
+            # # 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":
@@ -297,22 +307,30 @@ class WorkflowExecutor:
 
             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
+                def make_router(cond, tgt):
+                    """Dynamically generate a conditional router function to ensure each branch has a unique name."""
 
-                workflow.add_conditional_edges(source, router)
+
+                    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

api/app/core/workflow/expression_evaluator.py

@@ -74,6 +74,7 @@ class ExpressionEvaluator:
         # 为了向后兼容，也支持直接访问（但会在日志中警告）
         context.update(variables)
         context["nodes"] = node_outputs
+        context.update(node_outputs)
         
         try:
             # simpleeval 只支持安全的操作：

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

@@ -6,7 +6,7 @@ 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
-from app.core.workflow.nodes.operators import AssignmentOperatorInstance
+from app.core.workflow.nodes.operators import AssignmentOperatorInstance, AssignmentOperatorResolver
 from app.core.workflow.variable_pool import VariablePool
 
 logger = logging.getLogger(__name__)
@@ -40,8 +40,8 @@ class AssignerNode(BaseNode):
                 variable_selector = expression.split('.')
 
             # Only conversation variables ('conv') are allowed
-            if variable_selector[0] != 'conv':  # TODO: Loop node variable support (Feature)
-                raise ValueError("Only conversation variables can be assigned.")
+            if variable_selector[0] != 'conv' and variable_selector[0] not in state["cycle_nodes"]:
+                raise ValueError("Only conversation or cycle variables can be assigned.")
 
             # Get the value or expression to assign
             value = assignment.value
@@ -55,7 +55,9 @@ class AssignerNode(BaseNode):
             )
 
             # Select the appropriate assignment operator instance based on the target variable type
-            operator: AssignmentOperatorInstance = AssignmentOperator.get_operator(pool.get(variable_selector))(
+            operator: AssignmentOperatorInstance = AssignmentOperatorResolver.resolve_by_value(
+                pool.get(variable_selector)
+            )(
                 pool, variable_selector, value
             )
 
@@ -81,3 +83,5 @@ class AssignerNode(BaseNode):
                     operator.remove_last()
                 case _:
                     raise ValueError(f"Invalid Operator: {assignment.operation}")
+            logger.info(f"Node {self.node_id}: execution completed")
+

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

@@ -14,9 +14,13 @@ class VariableType(StrEnum):
     STRING = "string"
     NUMBER = "number"
     BOOLEAN = "boolean"
-    ARRAY = "array"
     OBJECT = "object"
 
+    ARRAY_STRING = "array[string]"
+    ARRAY_NUMBER = "array[number]"
+    ARRAY_BOOLEAN = "array[boolean]"
+    ARRAY_OBJECT = "array[object]"
+
 
 class VariableDefinition(BaseModel):
     """变量定义

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

@@ -20,40 +20,44 @@ logger = logging.getLogger(__name__)
 
 
 class WorkflowState(TypedDict):
-    """工作流状态
-    
-    在节点间传递的状态对象，包含消息、变量、节点输出等信息。
+    """Workflow state
+
+    The state object passed between nodes in a workflow, containing messages, variables, node outputs, etc.
     """
-    # 消息列表（追加模式）
+    # List of messages (append mode)
     messages: Annotated[list[AnyMessage], add]
-    
-    # 输入变量（从配置的 variables 传入）
-    # 使用深度合并函数，支持嵌套字典的更新（如 conv.xxx）
+
+    # Set of loop node IDs, used for assigning values in loop nodes
+    cycle_nodes: list
+    looping: bool
+
+    # Input variables (passed from configured variables)
+    # Uses a deep merge function, supporting nested dict updates (e.g., conv.xxx)
     variables: Annotated[dict[str, Any], lambda x, y: {
         **x,
         **{k: {**x.get(k, {}), **v} if isinstance(v, dict) and isinstance(x.get(k), dict) else v 
            for k, v in y.items()}
     }]
-    
-    # 节点输出（存储每个节点的执行结果，用于变量引用）
-    # 使用自定义合并函数，将新的节点输出合并到现有字典中
+
+    # Node outputs (stores execution results of each node for variable references)
+    # Uses a custom merge function to combine new node outputs into the existing dictionary
     node_outputs: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
-    
-    # 运行时节点变量（简化版，只存储业务数据，供节点间快速访问）
-    # 格式：{node_id: business_result}
+
+    # Runtime node variables (simplified version, stores business data for fast access between nodes)
+    # Format: {node_id: business_result}
     runtime_vars: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
     
-    # 执行上下文
+    # Execution context
     execution_id: str
     workspace_id: str
     user_id: str
     
-    # 错误信息（用于错误边）
+    # Error information (for error edges)
     error: str | None
     error_node: str | None
-    
-    # 流式缓冲区（存储节点的实时流式输出）
-    # 格式：{node_id: {"chunks": [...], "full_content": "..."}}
+
+    # Streaming buffer (stores real-time streaming output of nodes)
+    # Format: {node_id: {"chunks": [...], "full_content": "..."}}
     streaming_buffer: Annotated[dict[str, Any], lambda x, y: {**x, **y}]
 
 
@@ -74,6 +78,7 @@ class BaseNode(ABC):
         self.workflow_config = workflow_config
         self.node_id = node_config["id"]
         self.node_type = node_config["type"]
+        self.cycle = node_config.get("cycle")
         self.node_name = node_config.get("name", self.node_id)
         # 使用 or 运算符处理 None 值
         self.config = node_config.get("config") or {}
@@ -170,10 +175,10 @@ class BaseNode(ABC):
         import time
         
         start_time = time.time()
+
+        timeout = self.get_timeout()
         
         try:
-            timeout = self.get_timeout()
-            
             # 调用节点的业务逻辑
             business_result = await asyncio.wait_for(
                 self.execute(state),
@@ -200,7 +205,8 @@ class BaseNode(ABC):
                 **wrapped_output,
                 "runtime_vars": {
                     self.node_id: runtime_var
-                }
+                },
+                "looping": state["looping"]
             }
             
         except TimeoutError:
@@ -236,10 +242,10 @@ class BaseNode(ABC):
         import time
         
         start_time = time.time()
+
+        timeout = self.get_timeout()
         
         try:
-            timeout = self.get_timeout()
-            
             # Get LangGraph's stream writer for sending custom data
             writer = get_stream_writer()
             

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

@@ -0,0 +1,3 @@
+from app.core.workflow.nodes.breaker.node import BreakNode
+
+__all__ = ["BreakNode"]

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

@@ -0,0 +1,33 @@
+import logging
+from typing import Any
+
+from app.core.workflow.nodes import BaseNode, WorkflowState
+
+logger = logging.getLogger(__name__)
+
+
+class BreakNode(BaseNode):
+    """
+    Workflow node that immediately stops loop execution.
+
+    When executed, this node sets the 'looping' flag in the workflow state
+    to False, signaling the outer loop runtime to terminate further iterations.
+    """
+
+    async def execute(self, state: WorkflowState) -> Any:
+        """
+        Execute the break node.
+
+        Args:
+            state: Current workflow state, including loop control flags.
+
+        Effects:
+            - Sets 'looping' in the state to False to stop the loop.
+            - Logs the action for debugging purposes.
+
+        Returns:
+            Optional dictionary indicating the loop has been stopped.
+        """
+        state["looping"] = False
+        logger.info(f"Setting cycle node exit flag, cycle={self.cycle}, looping={state['looping']}")
+

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

@@ -22,6 +22,7 @@ from app.core.workflow.nodes.variable_aggregator.config import VariableAggregato
 from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig
 from app.core.workflow.nodes.question_classifier.config import QuestionClassifierNodeConfig
 
+from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig
 __all__ = [
     # 基础类
     "BaseNodeConfig",
@@ -41,5 +42,7 @@ __all__ = [
     "JinjaRenderNodeConfig",
     "VariableAggregatorNodeConfig",
     "ParameterExtractorNodeConfig",
+    "LoopNodeConfig",
+    "IterationNodeConfig",
     "QuestionClassifierNodeConfig"
 ]

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

@@ -0,0 +1,4 @@
+from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig
+from app.core.workflow.nodes.cycle_graph.node import CycleGraphNode
+
+__all__ = ['CycleGraphNode', 'LoopNodeConfig', 'IterationNodeConfig']

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

@@ -0,0 +1,96 @@
+from pydantic import Field, BaseModel
+
+from app.core.workflow.nodes.base_config import BaseNodeConfig, VariableType
+from app.core.workflow.nodes.enums import ComparisonOperator, LogicOperator
+
+
+class CycleVariable(BaseNodeConfig):
+    name: str = Field(
+        ...,
+        description="Name of the loop variable"
+    )
+    type: VariableType = Field(
+        ...,
+        description="Data type of the loop variable"
+    )
+    value: str = Field(
+        ...,
+        description="Initial or current value of the loop variable"
+    )
+
+
+class ConditionDetail(BaseModel):
+    comparison_operator: ComparisonOperator = Field(
+        ...,
+        description="Operator used to compare the left and right operands"
+    )
+
+    left: str = Field(
+        ...,
+        description="Left-hand operand of the comparison expression"
+    )
+
+    right: str = Field(
+        ...,
+        description="Right-hand operand of the comparison expression"
+    )
+
+
+class ConditionsConfig(BaseModel):
+    """Configuration for loop condition evaluation"""
+
+    logical_operator: LogicOperator = Field(
+        default=LogicOperator.AND.value,
+        description="Logical operator used to combine multiple condition expressions"
+    )
+
+    expressions: list[ConditionDetail] = Field(
+        ...,
+        description="Collection of condition expressions to be evaluated"
+    )
+
+
+class LoopNodeConfig(BaseNodeConfig):
+    condition: ConditionsConfig = Field(
+        default_factory=list,
+        description="Conditional configuration that controls loop execution"
+    )
+
+    cycle_vars: list[CycleVariable] = Field(
+        default_factory=list,
+        description="List of variables used and updated during the loop"
+    )
+
+    max_loop: int = Field(
+        default=10,
+        description="Maximum number of loop iterations"
+    )
+
+
+class IterationNodeConfig(BaseNodeConfig):
+    input: str = Field(
+        ...,
+        description="Input of the loop iteration"
+    )
+
+    parallel: bool = Field(
+        default=False,
+        description="Whether to execute loop iterations in parallel"
+    )
+
+    parallel_count: int = Field(
+        default=4,
+        description="Number of iterations to run in parallel"
+    )
+
+    flatten: bool = Field(
+        default=False,
+        description="Whether to flatten the output list from iterations"
+    )
+
+    output: str = Field(
+        ...,
+        description="Output of the loop iteration"
+    )
+
+

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

@@ -0,0 +1,154 @@
+import asyncio
+import copy
+import logging
+import re
+from typing import Any
+
+from langgraph.graph.state import CompiledStateGraph
+
+from app.core.workflow.nodes import WorkflowState
+from app.core.workflow.nodes.cycle_graph import IterationNodeConfig
+from app.core.workflow.variable_pool import VariablePool
+
+logger = logging.getLogger(__name__)
+
+
+class IterationRuntime:
+    """
+    Runtime executor for loop/iteration nodes in a workflow.
+
+    This class handles executing iterations over a list variable, supporting
+    optional parallel execution, flattening of output, and loop control via
+    the workflow state.
+    """
+    def __init__(
+            self,
+            graph: CompiledStateGraph,
+            node_id: str,
+            config: dict[str, Any],
+            state: WorkflowState,
+    ):
+        """
+        Initialize the iteration runtime.
+
+        Args:
+            graph: Compiled workflow graph capable of async invocation.
+            node_id: Unique identifier of the loop node.
+            config: Dictionary containing iteration node configuration.
+            state: Current workflow state at the point of iteration.
+        """
+        self.graph = graph
+        self.state = state
+        self.node_id = node_id
+        self.typed_config = IterationNodeConfig(**config)
+        self.looping = True
+
+        self.output_value = None
+        self.result: list = []
+
+    def _init_iteration_state(self, item, idx):
+        """
+        Initialize a per-iteration copy of the workflow state.
+
+        Args:
+            item: Current element from the input array for this iteration.
+            idx: Index of the element in the input array.
+
+        Returns:
+            A deep copy of the workflow state with iteration-specific variables set.
+        """
+        loopstate = WorkflowState(
+            **copy.deepcopy(self.state)
+        )
+        loopstate["runtime_vars"][self.node_id] = {
+            "item": item,
+            "index": idx,
+        }
+        loopstate["node_outputs"][self.node_id] = {
+            "item": item,
+            "index": idx,
+        }
+        loopstate["looping"] = True
+        return loopstate
+
+    async def run_task(self, item, idx):
+        """
+        Execute a single iteration asynchronously.
+
+        Args:
+            item: The input element for this iteration.
+            idx: The index of this iteration.
+        """
+        result = await self.graph.ainvoke(self._init_iteration_state(item, idx))
+        output = VariablePool(result).get(self.output_value)
+        if isinstance(output, list) and self.typed_config.flatten:
+            self.result.extend(output)
+        else:
+            self.result.append(output)
+        if not result["looping"]:
+            self.looping = False
+
+    def _create_iteration_tasks(self, array_obj, idx):
+        """
+        Create async tasks for a batch of iterations based on parallel count.
+
+        Args:
+            array_obj: The input array to iterate over.
+            idx: Starting index for this batch of iterations.
+
+        Returns:
+            List of coroutine tasks ready to be executed in parallel.
+        """
+        tasks = []
+        for i in range(self.typed_config.parallel_count):
+            if idx + i >= len(array_obj):
+                break
+            item = array_obj[idx + i]
+            tasks.append(self.run_task(item, idx + i))
+        return tasks
+
+    async def run(self):
+        """
+        Execute the loop over the input array according to configuration.
+
+        Returns:
+            A list of outputs from all iterations, optionally flattened.
+
+        Raises:
+            RuntimeError: If the input variable is not a list.
+        """
+        pattern = r"\{\{\s*(.*?)\s*\}\}"
+        input_expression = re.sub(pattern, r"\1", self.typed_config.input).strip()
+        self.output_value = re.sub(pattern, r"\1", self.typed_config.output).strip()
+
+        array_obj = VariablePool(self.state).get(input_expression)
+        if not isinstance(array_obj, list):
+            raise RuntimeError("Cannot iterate over a non-list variable")
+
+        idx = 0
+        if self.typed_config.parallel:
+            # Execute iterations in parallel batches
+            while idx < len(array_obj) and self.looping:
+                tasks = self._create_iteration_tasks(array_obj, idx)
+                logger.info(f"Iteration node {self.node_id}: running, concurrency {len(tasks)}")
+                idx += self.typed_config.parallel_count
+                await asyncio.gather(*tasks)
+            logger.info(f"Iteration node {self.node_id}: execution completed")
+            return self.result
+        else:
+            # Execute iterations sequentially
+            while idx < len(array_obj) and self.looping:
+                logger.info(f"Iteration node {self.node_id}: running")
+                item = array_obj[idx]
+                result = await self.graph.ainvoke(self._init_iteration_state(item, idx))
+                output = VariablePool(result).get(self.output_value)
+                if isinstance(output, list) and self.typed_config.flatten:
+                    self.result.extend(output)
+                else:
+                    self.result.append(output)
+                if not result["looping"]:
+                    self.looping = False
+                idx += 1
+
+            logger.info(f"Iteration node {self.node_id}: execution completed")
+            return self.result

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

@@ -0,0 +1,130 @@
+import logging
+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.nodes import WorkflowState
+from app.core.workflow.nodes.cycle_graph import LoopNodeConfig
+from app.core.workflow.nodes.operators import ConditionExpressionBuilder
+from app.core.workflow.variable_pool import VariablePool
+
+logger = logging.getLogger(__name__)
+
+
+class LoopRuntime:
+    """
+    Runtime executor for loop nodes in a workflow.
+
+    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.
+    """
+
+    def __init__(
+            self,
+            graph: CompiledStateGraph,
+            node_id: str,
+            config: dict[str, Any],
+            state: WorkflowState,
+    ):
+        """
+        Initialize the loop runtime.
+
+        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.
+        """
+        self.graph = graph
+        self.state = state
+        self.node_id = node_id
+        self.typed_config = LoopNodeConfig(**config)
+
+    def _init_loop_state(self):
+        """
+        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.
+
+        Returns:
+            A copy of the workflow state prepared for the loop execution.
+        """
+        pool = VariablePool(self.state)
+        # 循环变量
+        self.state["runtime_vars"][self.node_id] = {
+            variable.name: evaluate_expression(
+                expression=variable.value,
+                variables=pool.get_all_conversation_vars(),
+                node_outputs=pool.get_all_node_outputs(),
+                system_vars=pool.get_all_system_vars(),
+            )
+            for variable in self.typed_config.cycle_vars
+        }
+        self.state["node_outputs"][self.node_id] = {
+            variable.name: evaluate_expression(
+                expression=variable.value,
+                variables=pool.get_all_conversation_vars(),
+                node_outputs=pool.get_all_node_outputs(),
+                system_vars=pool.get_all_system_vars(),
+            )
+            for variable in self.typed_config.cycle_vars
+        }
+        loopstate = WorkflowState(
+            **self.state
+        )
+        loopstate["looping"] = True
+        return loopstate
+
+    def _get_loop_expression(self):
+        """
+        Build the Python boolean expression for evaluating the loop condition.
+
+        - Converts each condition in the loop configuration into a Python expression string.
+        - Combines multiple conditions with the configured logical operator (AND/OR).
+
+        Returns:
+            A string representing the combined loop condition expression.
+        """
+        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]
+
+        return combined_condition
+
+    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.
+
+        Returns:
+            The final runtime variables of this loop node after completion.
+        """
+        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:
+            logger.info(f"loop node {self.node_id}: running")
+            await self.graph.ainvoke(loopstate)
+            loop_time -= 1
+
+        logger.info(f"loop node {self.node_id}: execution completed")
+        return loopstate["runtime_vars"][self.node_id]

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

@@ -0,0 +1,226 @@
+import logging
+from typing import Any
+
+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
+from app.core.workflow.nodes.base_node import BaseNode
+from app.core.workflow.nodes.cycle_graph.config import LoopNodeConfig, IterationNodeConfig
+from app.core.workflow.nodes.cycle_graph.iteration import IterationRuntime
+from app.core.workflow.nodes.cycle_graph.loop import LoopRuntime
+from app.core.workflow.nodes.enums import NodeType
+
+logger = logging.getLogger(__name__)
+
+
+class CycleGraphNode(BaseNode):
+    """
+    Node representing a cycle (loop) 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.
+    """
+    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
+
+        self.cycle_nodes = list()  # Nodes belonging to this cycle
+        self.cycle_edges = list()  # Edges connecting nodes within the cycle
+        self.start_node_id = None  # ID of the start node within the cycle
+        self.end_node_ids = []  # IDs of end nodes within the cycle
+
+        self.graph: StateGraph | CompiledStateGraph | None = None
+        self.build_graph()
+        self.iteration_flag = True
+
+    def pure_cycle_graph(self) -> tuple[list, list]:
+        """
+        Extract cycle nodes and internal edges from the workflow configuration,
+        removing them from the global workflow.
+
+        Raises:
+            ValueError: If cycle nodes are connected to external nodes improperly.
+
+        Returns:
+            Tuple containing:
+            - cycle_nodes: List of removed nodes
+            - cycle_edges: List of removed edges
+        """
+        nodes = self.workflow_config.get("nodes", [])
+        edges = self.workflow_config.get("edges", [])
+
+        # Select all nodes that belong to the current cycle
+        cycle_nodes = [node for node in nodes if node.get("cycle") == self.node_id]
+        cycle_node_ids = {node.get("id") for node in cycle_nodes}
+
+        cycle_edges = []
+        remain_edges = []
+
+        for edge in edges:
+            source_in = edge.get("source") in cycle_node_ids
+            target_in = edge.get("target") in cycle_node_ids
+
+            # Raise error if cycle nodes are connected with external nodes
+            if source_in ^ target_in:
+                raise ValueError(
+                    f"Cycle node is connected to external node, "
+                    f"source: {edge.get('source')}, target: {edge.get('target')}"
+                )
+
+            if source_in and target_in:
+                cycle_edges.append(edge)
+            else:
+                remain_edges.append(edge)
+
+        # Update workflow_config by removing cycle nodes and internal edges
+        self.workflow_config["nodes"] = [
+            node for node in nodes if node.get("cycle") != self.node_id
+        ]
+        self.workflow_config["edges"] = remain_edges
+
+        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.
+
+        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.
+        """
+        self.graph = StateGraph(WorkflowState)
+        self.cycle_nodes, self.cycle_edges = self.pure_cycle_graph()
+        self.create_node()
+        self.create_edge()
+        self.graph = self.graph.compile()
+
+    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.
+
+        Args:
+            state: Current workflow state.
+
+        Returns:
+            Runtime result of the cycle, typically the final loop/iteration variables.
+
+        Raises:
+            RuntimeError: If node type is unrecognized.
+        """
+        if self.node_type == NodeType.LOOP:
+            return await LoopRuntime(
+                graph=self.graph,
+                node_id=self.node_id,
+                config=self.config,
+                state=state,
+            ).run()
+        if self.node_type == NodeType.ITERATION:
+            return await IterationRuntime(
+                graph=self.graph,
+                node_id=self.node_id,
+                config=self.config,
+                state=state,
+            ).run()
+        raise RuntimeError("Unknown cycle node type")

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

@@ -1,14 +1,5 @@
 from enum import StrEnum
 
-from app.core.workflow.nodes.operators import (
-    StringOperator,
-    NumberOperator,
-    AssignmentOperatorType,
-    BooleanOperator,
-    ArrayOperator,
-    ObjectOperator
-)
-
 
 class NodeType(StrEnum):
     START = "start"
@@ -27,6 +18,10 @@ class NodeType(StrEnum):
     JINJARENDER = "jinja-render"
     VAR_AGGREGATOR = "var-aggregator"
     PARAMETER_EXTRACTOR = "parameter-extractor"
+    LOOP = "loop"
+    ITERATION = "iteration"
+    CYCLE_START = "cycle-start"
+    BREAK = "break"
 
 
 class ComparisonOperator(StrEnum):
@@ -62,21 +57,6 @@ class AssignmentOperator(StrEnum):
     REMOVE_LAST = "remove_last"
     REMOVE_FIRST = "remove_first"
 
-    @classmethod
-    def get_operator(cls, obj) -> AssignmentOperatorType:
-        if isinstance(obj, str):
-            return StringOperator
-        elif isinstance(obj, bool):
-            return BooleanOperator
-        elif isinstance(obj, (int, float)):
-            return NumberOperator
-        elif isinstance(obj, list):
-            return ArrayOperator
-        elif isinstance(obj, dict):
-            return ObjectOperator
-
-        raise TypeError(f"Unsupported variable type ({type(obj)})")
-
 
 class HttpRequestMethod(StrEnum):
     GET = "GET"

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

@@ -215,6 +215,7 @@ class HttpRequestNode(BaseNode):
                         **self._build_content(state)
                     )
                     resp.raise_for_status()
+                    logger.info(f"Node {self.node_id}: HTTP request succeeded")
                     return HttpRequestNodeOutput(
                         body=resp.text,
                         status_code=resp.status_code,
@@ -228,12 +229,21 @@ class HttpRequestNode(BaseNode):
             else:
                 match self.typed_config.error_handle.method:
                     case HttpErrorHandle.NONE:
+                        logger.warning(
+                            f"Node {self.node_id}: HTTP request failed, returning error response"
+                        )
                         return HttpRequestNodeOutput(
                             body="",
                             status_code=resp.status_code,
                             headers=resp.headers,
                         ).model_dump()
                     case HttpErrorHandle.DEFAULT:
+                        logger.warning(
+                            f"Node {self.node_id}: HTTP request failed, returning default result"
+                        )
                         return self.typed_config.error_handle.default.model_dump()
                     case HttpErrorHandle.BRANCH:
+                        logger.warning(
+                            f"Node {self.node_id}: HTTP request failed, switching to error handling branch"
+                        )
                         return "ERROR"

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

@@ -30,7 +30,7 @@ class ConditionBranchConfig(BaseModel):
         description="Logical operator used to combine multiple condition expressions"
     )
 
-    conditions: list[ConditionDetail] = Field(
+    expressions: list[ConditionDetail] = Field(
         ...,
         description="List of condition expressions within this branch"
     )
@@ -57,7 +57,7 @@ class IfElseNodeConfig(BaseNodeConfig):
                         # CASE1 / IF Branch
                         {
                             "logical_operator": "and",
-                            "conditions": [
+                            "expressions": [
                                 [
                                     {
                                         "left": "node.userinput.message",
@@ -75,7 +75,7 @@ class IfElseNodeConfig(BaseNodeConfig):
                         # CASE1 / ELIF Branch
                         {
                             "logical_operator": "or",
-                            "conditions": [
+                            "expressions": [
                                 [
                                     {
                                         "left": "node.userinput.test",

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

@@ -2,93 +2,13 @@ import logging
 from typing import Any
 
 from app.core.workflow.nodes.base_node 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
+from app.core.workflow.nodes.operators import ConditionExpressionBuilder
 
 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)
@@ -143,7 +63,7 @@ class IfElseNode(BaseNode):
 
             branch_conditions = [
                 self._build_condition_expression(condition)
-                for condition in case_branch.conditions
+                for condition in case_branch.expressions
             ]
             if len(branch_conditions) > 1:
                 combined_condition = f' {case_branch.logical_operator} '.join(branch_conditions)
@@ -174,5 +94,6 @@ class IfElseNode(BaseNode):
         for i in range(len(expressions)):
             logger.info(expressions[i])
             if self._evaluate_condition(expressions[i], state):
+                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

@@ -1,3 +1,4 @@
+import logging
 from typing import Any
 
 from app.core.workflow.nodes import WorkflowState
@@ -5,6 +6,7 @@ from app.core.workflow.nodes.base_node import BaseNode
 from app.core.workflow.nodes.jinja_render.config import JinjaRenderNodeConfig
 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]):
@@ -41,5 +43,5 @@ class JinjaRenderNode(BaseNode):
             res = render.env.from_string(self.typed_config.template).render(**context)
         except Exception as e:
             raise RuntimeError(f"JinjaRender Node {self.node_name} render failed: {e}") from e
-
+        logger.info(f"Node {self.node_id}: Jinja template rendering completed")
         return res

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

@@ -1,18 +1,13 @@
 from uuid import UUID
 
-from pydantic import Field
+from pydantic import Field, BaseModel
 
 from app.core.workflow.nodes.base_config import BaseNodeConfig
 from app.schemas.chunk_schema import RetrieveType
 
 
-class KnowledgeRetrievalNodeConfig(BaseNodeConfig):
-    query: str = Field(
-        ...,
-        description="Search query string"
-    )
-
-    kb_ids: list[UUID] = Field(
+class KnowledgeBaseConfig(BaseModel):
+    kb_id: UUID = Field(
         ...,
         description="Knowledge base IDs"
     )
@@ -37,18 +32,42 @@ class KnowledgeRetrievalNodeConfig(BaseNodeConfig):
         description="Retrieve type"
     )
 
+
+class KnowledgeRetrievalNodeConfig(BaseNodeConfig):
+    query: str = Field(
+        ...,
+        description="Search query string"
+    )
+
+    knowledge_bases: list[KnowledgeBaseConfig] = Field(
+        ...,
+        description="Knowledge base config"
+    )
+
+    reranker_id: UUID = Field(
+        ...,
+        description="Reranker top k"
+    )
+
+    reranker_top_k: int = Field(
+        default=4,
+        description="Knowledge base top k"
+    )
+
     class Config:
         json_schema_extra = {
             "examples": [
                 {
                     "query": "{{sys.message}}",
-                    "kb_ids": [
-                        "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
-                    ],
-                    "similarity_threshold": 0.2,
-                    "vector_similarity_weight": 0.3,
-                    "top_k": 1,
-                    "retrieve_type": "hybrid"
+                    "knowledge_bases": [{
+                        "kb_id": "xxxxxxxx-xxxx-xxxx-xxxxxxxxxxxxxxxxx",
+                        "similarity_threshold": 0.2,
+                        "vector_similarity_weight": 0.3,
+                        "top_k": 4,
+                        "retrieve_type": "hybrid"
+                    }],
+                    "reranker_top_k": 1,
+                    "reranker_id": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
                 }
             ]
         }

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

@@ -2,14 +2,18 @@ import logging
 import uuid
 from typing import Any
 
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
+from app.core.models import RedBearRerank, RedBearModelConfig
 from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
 from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
 from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNodeConfig
 from app.db import get_db_read
-from app.models import knowledge_model, knowledgeshare_model
+from app.models import knowledge_model, knowledgeshare_model, ModelType
 from app.repositories import knowledge_repository
 from app.schemas.chunk_schema import RetrieveType
 from app.services import knowledge_service, knowledgeshare_service
+from app.services.model_service import ModelConfigService
 
 logger = logging.getLogger(__name__)
 
@@ -108,6 +112,44 @@ class KnowledgeRetrievalNode(BaseNode):
             existing_ids.extend(items)
         return existing_ids
 
+    def get_reranker_model(self) -> RedBearRerank:
+        """
+        Retrieve and initialize a RedBear reranker model based on configuration.
+
+        Raises:
+            BusinessException: If configuration is missing or API keys are not set.
+            RuntimeError: If the configured model is not of type RERANK.
+        """
+        with get_db_read() as db:
+            config = ModelConfigService.get_model_by_id(db=db, model_id=self.typed_config.reranker_id)
+
+            if not config:
+                raise BusinessException("Configured model does not exist", BizCode.NOT_FOUND)
+
+            if not config.api_keys or len(config.api_keys) == 0:
+                raise BusinessException("Model configuration is missing API Key", BizCode.INVALID_PARAMETER)
+
+            # 在 Session 关闭前提取所有需要的数据
+            api_config = config.api_keys[0]
+            model_name = api_config.model_name
+            provider = api_config.provider
+            api_key = api_config.api_key
+            api_base = api_config.api_base
+            model_type = config.type
+
+        if model_type != ModelType.RERANK:
+            raise RuntimeError("Model is not a reranker")
+
+        reranker = RedBearRerank(
+            RedBearModelConfig(
+                model_name=model_name,
+                provider=provider,
+                api_key=api_key,
+                base_url=api_base,
+            )
+        )
+        return reranker
+
     async def execute(self, state: WorkflowState) -> Any:
         """
         Execute the knowledge retrieval workflow node.
@@ -131,38 +173,45 @@ class KnowledgeRetrievalNode(BaseNode):
         """
         query = self._render_template(self.typed_config.query, state)
         with get_db_read() as db:
-            existing_ids = self._get_existing_kb_ids(db, self.typed_config.kb_ids)
+            knowledge_bases = self.typed_config.knowledge_bases
+            existing_ids = self._get_existing_kb_ids(db, [kb.kb_id for kb in knowledge_bases])
 
             if not existing_ids:
                 raise RuntimeError("Knowledge base retrieval failed: the knowledge base does not exist.")
 
-            kb_id = existing_ids[0]
-            uuid_strs = [f"Vector_index_{kb_id}_Node".lower() for kb_id in existing_ids]
-            indices = ",".join(uuid_strs)
+            rs = []
+            for kb_config in knowledge_bases:
+                db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_config.kb_id)
+                if not db_knowledge:
+                    raise RuntimeError("The knowledge base does not exist or access is denied.")
 
-            db_knowledge = knowledge_repository.get_knowledge_by_id(db=db, knowledge_id=kb_id)
-            if not db_knowledge:
-                raise RuntimeError("The knowledge base does not exist or access is denied.")
-
-            vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
-
-            match self.typed_config.retrieve_type:
-                case RetrieveType.PARTICIPLE:
-                    rs = vector_service.search_by_full_text(query=query, top_k=self.typed_config.top_k,
-                                                            indices=indices,
-                                                            score_threshold=self.typed_config.similarity_threshold)
-                case RetrieveType.SEMANTIC:
-                    rs = vector_service.search_by_vector(query=query, top_k=self.typed_config.top_k,
-                                                         indices=indices,
-                                                         score_threshold=self.typed_config.vector_similarity_weight)
-                case _:
-                    rs1 = vector_service.search_by_vector(query=query, top_k=self.typed_config.top_k,
-                                                          indices=indices,
-                                                          score_threshold=self.typed_config.vector_similarity_weight)
-                    rs2 = vector_service.search_by_full_text(query=query, top_k=self.typed_config.top_k,
-                                                             indices=indices,
-                                                             score_threshold=self.typed_config.similarity_threshold)
-                    # Deduplicate hybrid retrieval results
-                    unique_rs = self._deduplicate_docs(rs1, rs2)
-                    rs = vector_service.rerank(query=query, docs=unique_rs, top_k=self.typed_config.top_k)
-            return [chunk.model_dump() for chunk in rs]
+                vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+                indices = f"Vector_index_{kb_config.kb_id}_Node".lower()
+                match kb_config.retrieve_type:
+                    case RetrieveType.PARTICIPLE:
+                        rs.extend(vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                                     indices=indices,
+                                                                     score_threshold=kb_config.similarity_threshold))
+                    case RetrieveType.SEMANTIC:
+                        rs.extend(vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                                  indices=indices,
+                                                                  score_threshold=kb_config.vector_similarity_weight))
+                    case RetrieveType.HYBRID:
+                        rs1 = vector_service.search_by_vector(query=query, top_k=kb_config.top_k,
+                                                              indices=indices,
+                                                              score_threshold=kb_config.vector_similarity_weight)
+                        rs2 = vector_service.search_by_full_text(query=query, top_k=kb_config.top_k,
+                                                                 indices=indices,
+                                                                 score_threshold=kb_config.similarity_threshold)
+                        # Deduplicate hybrid retrieval results
+                        unique_rs = self._deduplicate_docs(rs1, rs2)
+                        vector_service.reranker = self.get_reranker_model()
+                        rs.extend(vector_service.rerank(query=query, docs=unique_rs, top_k=kb_config.top_k))
+                    case _:
+                        raise RuntimeError("Unknown retrieval type")
+            vector_service.reranker = self.get_reranker_model()
+            final_rs = vector_service.rerank(query=query, docs=rs, top_k=self.typed_config.reranker_top_k)
+            logger.info(
+                f"Node {self.node_id}: knowledge base retrieval completed, results count: {len(final_rs)}"
+            )
+            return [chunk.model_dump() for chunk in final_rs]

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

@@ -10,6 +10,7 @@ from typing import Any, Union
 from app.core.workflow.nodes.agent import AgentNode
 from app.core.workflow.nodes.assigner import AssignerNode
 from app.core.workflow.nodes.base_node import BaseNode
+from app.core.workflow.nodes.cycle_graph.node import CycleGraphNode
 from app.core.workflow.nodes.end import EndNode
 from app.core.workflow.nodes.enums import NodeType
 from app.core.workflow.nodes.http_request import HttpRequestNode
@@ -22,6 +23,7 @@ from app.core.workflow.nodes.start import StartNode
 from app.core.workflow.nodes.transform import TransformNode
 from app.core.workflow.nodes.variable_aggregator import VariableAggregatorNode
 from app.core.workflow.nodes.question_classifier import QuestionClassifierNode
+from app.core.workflow.nodes.breaker import BreakNode
 
 logger = logging.getLogger(__name__)
 
@@ -39,6 +41,9 @@ WorkflowNode = Union[
     JinjaRenderNode,
     VariableAggregatorNode,
     ParameterExtractorNode,
+    CycleGraphNode,
+    BreakNode,
+    ParameterExtractorNode,
     QuestionClassifierNode
 ]
 
@@ -64,6 +69,9 @@ class NodeFactory:
         NodeType.VAR_AGGREGATOR: VariableAggregatorNode,
         NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNode,
         NodeType.QUESTION_CLASSIFIER: QuestionClassifierNode,
+        NodeType.LOOP: CycleGraphNode,
+        NodeType.ITERATION: CycleGraphNode,
+        NodeType.BREAK: BreakNode,
     }
 
     @classmethod

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

@@ -1,6 +1,7 @@
 from abc import ABC
 from typing import Union, Type
 
+from app.core.workflow.nodes.enums import ComparisonOperator
 from app.core.workflow.variable_pool import VariablePool
 
 
@@ -136,6 +137,23 @@ class ObjectOperator(OperatorBase):
         self.pool.set(self.left_selector, dict())
 
 
+class AssignmentOperatorResolver:
+    @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)}")
+
+
 AssignmentOperatorInstance = Union[
     StringOperator,
     NumberOperator,
@@ -144,3 +162,83 @@ AssignmentOperatorInstance = Union[
     ObjectOperator
 ]
 AssignmentOperatorType = Type[AssignmentOperatorInstance]
+
+
+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 _startswith(self):
+        return f'{self.left}.startswith({self.right})'
+
+    def _endswith(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._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}")

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

@@ -36,6 +36,11 @@ class ParamsConfig(BaseModel):
         description="Description of the parameter"
     )
 
+    required: bool = Field(
+        ...,
+        description="Whether the parameter is required"
+    )
+
 
 class ParameterExtractorNodeConfig(BaseNodeConfig):
     model_id: uuid.UUID = Field(
@@ -52,3 +57,8 @@ class ParameterExtractorNodeConfig(BaseNodeConfig):
         ...,
         description="List of parameters"
     )
+
+    prompt: str = Field(
+        ...,
+        description="User-provided supplemental prompt"
+    )

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

@@ -1,4 +1,5 @@
 import os
+import logging
 
 import json_repair
 from typing import Any
@@ -15,6 +16,8 @@ from app.db import get_db_read
 from app.models import ModelType
 from app.services.model_service import ModelConfigService
 
+logger = logging.getLogger(__name__)
+
 
 class ParameterExtractorNode(BaseNode):
     def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
@@ -114,7 +117,7 @@ class ParameterExtractorNode(BaseNode):
         """
         field_type = {}
         for param in self.typed_config.params:
-            field_type[param.name] = param.type
+            field_type[param.name] = f'{param.type}, required:{str(param.required)}'
         return field_type
 
     async def execute(self, state: WorkflowState) -> Any:
@@ -154,12 +157,12 @@ class ParameterExtractorNode(BaseNode):
 
         messages = [
             ("system", system_prompt),
+            ("user", self._render_template(self.typed_config.prompt, state)),
             ("user", rendered_user_prompt),
         ]
 
         model_resp = await llm.ainvoke(messages)
-        result = json_repair.repair_json(model_resp.content)
+        result = json_repair.repair_json(model_resp.content, return_objects=True)
+        logger.info(f"node: {self.node_id} get params:{result}")
 
-        return {
-            "output": result,
-        }
+        return result

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

@@ -9,43 +9,27 @@ class VariableAggregatorNodeConfig(BaseNodeConfig):
         description="输出变量是否需要分组",
     )
 
-    group_names: list[str] = Field(
-        default_factory=lambda: ["output"],
-        description="各个分组的名称"
-    )
-
-    group_variables: list[str] | list[list[str]] = Field(
+    group_variables: list[str] | dict[str, list[str]] = Field(
         ...,
         description="需要被聚合的变量"
     )
 
-    @field_validator("group_names", mode="before")
-    @classmethod
-    def group_names_validator(cls, v, info):
-        group_status = info.data.get("group")
-        if not group_status or not v:
-            return ["output"]
-        return v
-
     @field_validator("group_variables")
     @classmethod
     def group_variables_validator(cls, v, info):
         group_status = info.data.get("group")
-        group_names = info.data.get("group_names")
-        if not isinstance(v, list):
-            raise ValueError("group_variables must be a list")
 
         if not group_status:
             for variable in v:
                 if not isinstance(variable, str):
                     raise ValueError("When group=False, group_variables must be a list of strings")
         else:
-            if len(group_names) != len(v):
-                raise ValueError("group_names and group_variables length mismatch")
-            for group in v:
-                if not isinstance(group, list):
+            if not isinstance(v, dict):
+                raise ValueError("When group=True, group_variables must be a dict")
+            for group_name, group_values in v.items():
+                if not isinstance(group_name, str):
                     raise ValueError("When group=True, each element of group_variables must be a list")
-                for variable in group:
+                for variable in group_values:
                     if not isinstance(variable, str):
                         raise ValueError("Each element inside group_variables lists must be a string")
         return v

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

@@ -50,6 +50,7 @@ class VariableAggregatorNode(BaseNode):
                     continue
 
                 if value is not None:
+                    logger.info(f"Node: {self.node_id} variable aggregation result: {value}")
                     return value
 
             logger.info("No variable found in non-group mode; returning empty string.")
@@ -59,7 +60,7 @@ class VariableAggregatorNode(BaseNode):
         # Group mode
         # --------------------------
         result = {}
-        for group_name, variables in zip(self.typed_config.group_names, self.typed_config.group_variables):
+        for group_name, variables in self.typed_config.group_variables.items():
             for variable in variables:
                 var_express = self._get_express(variable)
                 try:
@@ -74,5 +75,5 @@ class VariableAggregatorNode(BaseNode):
             else:
                 result[group_name] = ""
                 logger.info(f"No variable found for group '{group_name}'; set empty string.")
-
+        logger.info(f"Node: {self.node_id} variable aggregation result: {result}")
         return result

api/app/core/workflow/validator.py

@@ -7,14 +7,87 @@
 import logging
 from typing import Any, Union
 
+from app.core.workflow.nodes.enums import NodeType
+
 logger = logging.getLogger(__name__)
 
 
 class WorkflowValidator:
     """工作流配置验证器"""
-    
-    @staticmethod
-    def validate(workflow_config: Union[dict[str, Any], Any]) -> tuple[bool, list[str]]:
+
+    @classmethod
+    def pure_cycle_graph(cls, workflow_config: Union[dict[str, Any], Any], node_id) -> tuple[list, list]:
+        """
+        Extract cycle nodes and internal edges from the workflow configuration,
+        removing them from the global workflow.
+
+        Raises:
+            ValueError: If cycle nodes are connected to external nodes improperly.
+
+        Returns:
+            Tuple containing:
+            - cycle_nodes: List of removed nodes
+            - cycle_edges: List of removed edges
+        """
+        nodes = workflow_config.get("nodes", [])
+        edges = workflow_config.get("edges", [])
+
+        # Select all nodes that belong to the current cycle
+        cycle_nodes = [node for node in nodes if node.get("cycle") == node_id]
+        cycle_node_ids = {node.get("id") for node in cycle_nodes}
+
+        cycle_edges = []
+        remain_edges = []
+
+        for edge in edges:
+            source_in = edge.get("source") in cycle_node_ids
+            target_in = edge.get("target") in cycle_node_ids
+
+            # Raise error if cycle nodes are connected with external nodes
+            if source_in ^ target_in:
+                raise ValueError(
+                    f"Cycle node is connected to external node, "
+                    f"source: {edge.get('source')}, target: {edge.get('target')}"
+                )
+
+            if source_in and target_in:
+                cycle_edges.append(edge)
+            else:
+                remain_edges.append(edge)
+
+        # Update workflow_config by removing cycle nodes and internal edges
+        workflow_config["nodes"] = [
+            node for node in nodes if node.get("cycle") != node_id
+        ]
+        workflow_config["edges"] = remain_edges
+
+        return cycle_nodes, cycle_edges
+
+    @classmethod
+    def get_subgraph(cls, workflow_config: Union[dict[str, Any], Any]) -> list:
+        if not isinstance(workflow_config, dict):
+            workflow_config = {
+                "nodes": workflow_config.nodes,
+                "edges": workflow_config.edges,
+                "variables": workflow_config.variables,
+            }
+        cycle_nodes = [
+            node.get("id")
+            for node in workflow_config.get("nodes", [])
+            if node.get("type") in [NodeType.LOOP, NodeType.ITERATION]
+        ]
+        graphs = []
+        for cycle_node in cycle_nodes:
+            nodes, edges = cls.pure_cycle_graph(workflow_config, cycle_node)
+            graphs.append({
+                "nodes": nodes,
+                "edges": edges,
+            })
+        graphs.append(workflow_config)
+        return graphs
+
+    @classmethod
+    def validate(cls, workflow_config: Union[dict[str, Any], Any]) -> tuple[bool, list[str]]:
         """验证工作流配置
         
         Args:
@@ -38,84 +111,79 @@ class WorkflowValidator:
             True
         """
         errors = []
-        
-        # 支持字典和 Pydantic 模型
-        if isinstance(workflow_config, dict):
-            nodes = workflow_config.get("nodes", [])
-            edges = workflow_config.get("edges", [])
-            variables = workflow_config.get("variables", [])
-        else:
-            # Pydantic 模型
-            nodes = getattr(workflow_config, "nodes", [])
-            edges = getattr(workflow_config, "edges", [])
-            variables = getattr(workflow_config, "variables", [])
-        
-        # 1. 验证 start 节点（有且只有一个）
-        start_nodes = [n for n in nodes if n.get("type") == "start"]
-        if len(start_nodes) == 0:
-            errors.append("工作流必须有一个 start 节点")
-        elif len(start_nodes) > 1:
-            errors.append(f"工作流只能有一个 start 节点，当前有 {len(start_nodes)} 个")
-        
-        # 2. 验证 end 节点（至少一个）
-        end_nodes = [n for n in nodes if n.get("type") == "end"]
-        if len(end_nodes) == 0:
-            errors.append("工作流必须至少有一个 end 节点")
-        
-        # 3. 验证节点 ID 唯一性
-        node_ids = [n.get("id") for n in nodes]
-        if len(node_ids) != len(set(node_ids)):
-            duplicates = [nid for nid in node_ids if node_ids.count(nid) > 1]
-            errors.append(f"节点 ID 必须唯一，重复的 ID: {set(duplicates)}")
-        
-        # 4. 验证节点必须有 id 和 type
-        for i, node in enumerate(nodes):
-            if not node.get("id"):
-                errors.append(f"节点 #{i} 缺少 id 字段")
-            if not node.get("type"):
-                errors.append(f"节点 #{i} (id={node.get('id', 'unknown')}) 缺少 type 字段")
-        
-        # 5. 验证边的有效性
-        node_id_set = set(node_ids)
-        for i, edge in enumerate(edges):
-            source = edge.get("source")
-            target = edge.get("target")
-            
-            if not source:
-                errors.append(f"边 #{i} 缺少 source 字段")
-            elif source not in node_id_set:
-                errors.append(f"边 #{i} 的 source 节点不存在: {source}")
-            
-            if not target:
-                errors.append(f"边 #{i} 缺少 target 字段")
-            elif target not in node_id_set:
-                errors.append(f"边 #{i} 的 target 节点不存在: {target}")
-        
-        # 6. 验证所有节点可达（从 start 节点出发）
-        if start_nodes and not errors:  # 只有在前面验证通过时才检查可达性
-            reachable = WorkflowValidator._get_reachable_nodes(
-                start_nodes[0]["id"], 
-                edges
-            )
-            unreachable = node_id_set - reachable
-            if unreachable:
-                errors.append(f"以下节点无法从 start 节点到达: {unreachable}")
-        
-        # 7. 检测循环依赖（非 loop 节点）
-        if not errors:  # 只有在前面验证通过时才检查循环
-            has_cycle, cycle_path = WorkflowValidator._has_cycle(nodes, edges)
-            if has_cycle:
-                errors.append(
-                    f"工作流存在循环依赖（请使用 loop 节点实现循环）: {' -> '.join(cycle_path)}"
+
+        graphs = cls.get_subgraph(workflow_config)
+        logger.info(graphs)
+        for graph in graphs:
+            nodes = graph.get("nodes", [])
+            edges = graph.get("edges", [])
+            variables = graph.get("variables", [])
+            # 1. 验证 start 节点（有且只有一个）
+            start_nodes = [n for n in nodes if n.get("type") in [NodeType.START, NodeType.CYCLE_START]]
+            if len(start_nodes) == 0:
+                errors.append("工作流必须有一个 start 节点")
+            elif len(start_nodes) > 1:
+                errors.append(f"工作流只能有一个 start 节点，当前有 {len(start_nodes)} 个")
+
+            # 2. 验证 end 节点（至少一个）
+            end_nodes = [n for n in nodes if n.get("type") == NodeType.END]
+            if len(end_nodes) == 0:
+                errors.append("工作流必须至少有一个 end 节点")
+
+            # 3. 验证节点 ID 唯一性
+            node_ids = [n.get("id") for n in nodes]
+            if len(node_ids) != len(set(node_ids)):
+                duplicates = [nid for nid in node_ids if node_ids.count(nid) > 1]
+                errors.append(f"节点 ID 必须唯一，重复的 ID: {set(duplicates)}")
+
+            # 4. 验证节点必须有 id 和 type
+            for i, node in enumerate(nodes):
+                if not node.get("id"):
+                    errors.append(f"节点 #{i} 缺少 id 字段")
+                if not node.get("type"):
+                    errors.append(f"节点 #{i} (id={node.get('id', 'unknown')}) 缺少 type 字段")
+
+            # 5. 验证边的有效性
+            node_id_set = set(node_ids)
+            for i, edge in enumerate(edges):
+                source = edge.get("source")
+                target = edge.get("target")
+
+                if not source:
+                    errors.append(f"边 #{i} 缺少 source 字段")
+                elif source not in node_id_set:
+                    errors.append(f"边 #{i} 的 source 节点不存在: {source}")
+
+                if not target:
+                    errors.append(f"边 #{i} 缺少 target 字段")
+                elif target not in node_id_set:
+                    errors.append(f"边 #{i} 的 target 节点不存在: {target}")
+
+            # 6. 验证所有节点可达（从 start 节点出发）
+            if start_nodes and not errors:  # 只有在前面验证通过时才检查可达性
+                reachable = WorkflowValidator._get_reachable_nodes(
+                    start_nodes[0]["id"],
+                    edges
                 )
-        
-        # 8. 验证变量名
-        from app.core.workflow.expression_evaluator import ExpressionEvaluator
-        var_errors = ExpressionEvaluator.validate_variable_names(variables)
-        errors.extend(var_errors)
-        
+                unreachable = node_id_set - reachable
+                if unreachable:
+                    errors.append(f"以下节点无法从 start 节点到达: {unreachable}")
+
+            # 7. 检测循环依赖（非 loop 节点）
+            if not errors:  # 只有在前面验证通过时才检查循环
+                has_cycle, cycle_path = WorkflowValidator._has_cycle(nodes, edges)
+                if has_cycle:
+                    errors.append(
+                        f"工作流存在循环依赖（请使用 loop 节点实现循环）: {' -> '.join(cycle_path)}"
+                    )
+
+            # 8. 验证变量名
+            from app.core.workflow.expression_evaluator import ExpressionEvaluator
+            var_errors = ExpressionEvaluator.validate_variable_names(variables)
+            errors.extend(var_errors)
+
         return len(errors) == 0, errors
-    
+
     @staticmethod
     def _get_reachable_nodes(start_id: str, edges: list[dict]) -> set[str]:
         """获取从 start 节点可达的所有节点
@@ -129,7 +197,7 @@ class WorkflowValidator:
         """
         reachable = {start_id}
         queue = [start_id]
-        
+
         while queue:
             current = queue.pop(0)
             for edge in edges:
@@ -138,9 +206,9 @@ class WorkflowValidator:
                     if target and target not in reachable:
                         reachable.add(target)
                         queue.append(target)
-        
+
         return reachable
-    
+
     @staticmethod
     def _has_cycle(nodes: list[dict], edges: list[dict]) -> tuple[bool, list[str]]:
         """检测是否存在循环依赖（DFS）
@@ -154,39 +222,39 @@ class WorkflowValidator:
         """
         # 排除 loop 类型的节点
         loop_nodes = {n["id"] for n in nodes if n.get("type") == "loop"}
-        
+
         # 构建邻接表（排除 loop 节点的边和错误边）
         graph: dict[str, list[str]] = {}
         for edge in edges:
             source = edge.get("source")
             target = edge.get("target")
             edge_type = edge.get("type")
-            
+
             # 跳过错误边
             if edge_type == "error":
                 continue
-            
+
             # 如果涉及 loop 节点，跳过
             if source in loop_nodes or target in loop_nodes:
                 continue
-            
+
             if source and target:
                 if source not in graph:
                     graph[source] = []
                 graph[source].append(target)
-        
+
         # DFS 检测环
         visited = set()
         rec_stack = set()
         path = []
         cycle_path = []
-        
+
         def dfs(node: str) -> bool:
             """DFS 检测环，返回是否找到环"""
             visited.add(node)
             rec_stack.add(node)
             path.append(node)
-            
+
             for neighbor in graph.get(node, []):
                 if neighbor not in visited:
                     if dfs(neighbor):
@@ -196,19 +264,19 @@ class WorkflowValidator:
                     cycle_start = path.index(neighbor)
                     cycle_path.extend([*path[cycle_start:], neighbor])
                     return True
-            
+
             rec_stack.remove(node)
             path.pop()
             return False
-        
+
         # 检查所有节点
         for node_id in graph:
             if node_id not in visited:
                 if dfs(node_id):
                     return True, cycle_path
-        
+
         return False, []
-    
+
     @staticmethod
     def validate_for_publish(workflow_config: dict[str, Any]) -> tuple[bool, list[str]]:
         """验证工作流配置是否可以发布（更严格的验证）
@@ -221,30 +289,30 @@ class WorkflowValidator:
         """
         # 先执行基础验证
         is_valid, errors = WorkflowValidator.validate(workflow_config)
-        
+
         if not is_valid:
             return False, errors
-        
+
         # 额外的发布验证
         nodes = workflow_config.get("nodes", [])
-        
+
         # 1. 验证所有节点都有名称
         for node in nodes:
-            if node.get("type") not in ["start", "end"] and not node.get("name"):
+            if node.get("type") not in [NodeType.START, NodeType.CYCLE_START, NodeType.END] and not node.get("name"):
                 errors.append(
                     f"节点 {node.get('id')} 缺少名称（发布时必须提供）"
                 )
-        
+
         # 2. 验证所有非 start/end 节点都有配置
         for node in nodes:
             node_type = node.get("type")
-            if node_type not in ["start", "end"]:
+            if node_type not in [NodeType.START, NodeType.CYCLE_START, NodeType.END, NodeType.BREAK]:
                 config = node.get("config")
                 if not config or not isinstance(config, dict):
                     errors.append(
                         f"节点 {node.get('id')} 缺少配置（发布时必须提供）"
                     )
-        
+
         # 3. 验证必填变量
         variables = workflow_config.get("variables", [])
         required_vars = [v for v in variables if v.get("required")]
@@ -254,13 +322,13 @@ class WorkflowValidator:
                 f"工作流包含 {len(required_vars)} 个必填变量: "
                 f"{[v.get('name') for v in required_vars]}"
             )
-        
+
         return len(errors) == 0, errors
 
 
 def validate_workflow_config(
-    workflow_config: dict[str, Any],
-    for_publish: bool = False
+        workflow_config: dict[str, Any],
+        for_publish: bool = False
 ) -> tuple[bool, list[str]]:
     """验证工作流配置（便捷函数）
     

api/app/core/workflow/variable_pool.py

@@ -198,19 +198,22 @@ class VariablePool:
         
         namespace = selector[0]
         
-        if namespace != "conv":
-            raise ValueError("只能设置会话变量 (conv.*)")
+        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": {}}
-        if "conv" not in self.state["variables"]:
-            self.state["variables"]["conv"] = {}
-        
-        # 设置值
-        self.state["variables"]["conv"][key] = value
+        if namespace == "conv":
+            if "conv" not in self.state["variables"]:
+                self.state["variables"]["conv"] = {}
+
+            # 设置值
+            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}")
     

api/app/models/document_model.py

@@ -26,6 +26,7 @@ class Document(Base):
                                "html4excel": False,
                                "graphrag": {
                                     "use_graphrag": False,
+                                    "scene_name": "",
                                     "entity_types": [
                                         "organization",
                                         "person",
@@ -33,7 +34,9 @@ class Document(Base):
                                         "event",
                                         "category"
                                     ],
-                                    "method": "general"
+                                    "method": "general",
+                                    "resolution": True,
+                                    "community": True
                                 }
                            }, comment="default parser config")
     chunk_num = Column(Integer, default=0, comment="chunk num")

api/app/models/knowledge_model.py

@@ -65,6 +65,7 @@ class Knowledge(Base):
                                "html4excel": False,
                                "graphrag": {
                                     "use_graphrag": False,
+                                    "scene_name": "",
                                     "entity_types": [
                                         "organization",
                                         "person",
@@ -72,7 +73,9 @@ class Knowledge(Base):
                                         "event",
                                         "category"
                                     ],
-                                    "method": "general"
+                                    "method": "general",
+                                    "resolution": True,
+                                    "community": True
                                 }
                            },
                            comment="default parser config")

api/app/repositories/knowledge_repository.py

@@ -58,13 +58,13 @@ def get_chunked_knowledgeids(
 ) -> list:
     """
     Query the list of vectorized knowledge base IDs
-    Return: list[UUID] - List of knowledge base IDs
+    Return: list[(id,workspace_id)] - List of knowledge base id and workspace_id
     """
     db_logger.debug(f"Query the list of vectorized knowledge base IDs: filters_count={len(filters)}")
 
     try:
         # Only query the id field
-        query = db.query(Knowledge.id)
+        query = db.query(Knowledge.id, Knowledge.workspace_id)
 
         # Apply filter conditions
         for filter_cond in filters:
@@ -74,8 +74,8 @@ def get_chunked_knowledgeids(
         items = query.all()
         db_logger.info(f"Querying the vectorized knowledge base id list succeeded: count={len(items)}")
 
-        # Return the list of IDs directly. Since only the ID field is queried, the returned data is a single column
-        return [item[0] for item in items]
+        # Return the list of ID and workspace_id directly. Since only the ID and workspace_id field is queried
+        return items
     except Exception as e:
         db_logger.error(f"Querying the vectorized knowledge base id list failed: {str(e)}")
         raise

api/app/repositories/knowledgeshare_repository.py

@@ -61,14 +61,14 @@ def get_source_kb_ids_by_target_kb_id(
 ) -> list:
     """
     Query the original knowledge base ID list by sharing the knowledge base
-    Return: list[UUID] - List of knowledge base IDs
+    Return: list[(source_kb_id,source_workspace_id)] - List of knowledge base source_kb_id and source_workspace_id
     """
     db_logger.debug(
         f"Query the original knowledge base id list by sharing the knowledge base: filters_count={len(filters)}")
 
     try:
         # Only query the id field
-        query = db.query(KnowledgeShare.source_kb_id)
+        query = db.query(KnowledgeShare.source_kb_id, KnowledgeShare.source_workspace_id)
 
         # Apply filter conditions
         for filter_cond in filters:
@@ -78,8 +78,8 @@ def get_source_kb_ids_by_target_kb_id(
         items = query.all()
         db_logger.info(f"Successfully queried the original knowledge base ID list by sharing the knowledge base: count={len(items)}")
 
-        # Return the list of IDs directly. Since only the ID field is queried, the returned data is a single column
-        return [item[0] for item in items]
+        # Return the list of source_kb_id and source_workspace_id directly. Since only the source_kb_id and source_workspace_id field is queried
+        return items
     except Exception as e:
         db_logger.error(f"Failed to query the original knowledge base ID list through knowledge base sharing: {str(e)}")
         raise

api/app/schemas/app_schema.py

@@ -32,6 +32,7 @@ class KnowledgeRetrievalConfig(BaseModel):
     )
     reranker_id: Optional[str] = Field(default=None, description="多知识库结果融合的模型ID")
     reranker_top_k: int = Field(default=10, ge=0, le=1024, description="多知识库结果融合的模型参数")
+    use_graph: bool = Field(default=False, description="是否使用图搜索")
 
 
 class ToolConfig(BaseModel):

api/app/schemas/chunk_schema.py

@@ -10,6 +10,7 @@ class RetrieveType(StrEnum):
     PARTICIPLE = "participle"
     SEMANTIC = "semantic"
     HYBRID = "hybrid"
+    Graph = "graph"
 
 
 class ChunkCreate(BaseModel):

api/app/schemas/memory_reflection_schemas.py

@@ -12,8 +12,8 @@ class Memory_Reflection(BaseModel):
     config_id: Optional[int] = None
     reflection_enabled: bool
     reflection_period_in_hours: str
-    reflexion_range: str
-    baseline: str
+    reflexion_range: Optional[str] = "partial"
+    baseline: Optional[str] = "TIME"
     reflection_model_id: str
     memory_verify: bool
     quality_assessment: bool

api/app/schemas/workflow_schema.py

@@ -20,6 +20,7 @@ class NodeDefinition(BaseModel):
     id: str = Field(..., description="节点唯一标识")
     type: str = Field(..., description="节点类型: start, end, llm, agent, tool, condition, loop, transform, human, code")
     name: str | None = Field(None, description="节点名称")
+    cycle: str | None = Field(None, description="父循环节点id")
     description: str | None = Field(None, description="节点描述")
     config: dict[str, Any] = Field(default_factory=dict, description="节点配置")
     position: dict[str, float] | None = Field(None, description="节点位置 {x, y}")

api/app/templates/workflows/simple_qa/template.yml

@@ -42,7 +42,7 @@ nodes:
             - 适当使用格式化（如列表、段落）提高可读性
         
         - role: user
-          content: "{{ sys.message }}"
+          content: "{{sys.message}}"
       
       model_id: null
       temperature: 0.7
@@ -55,7 +55,7 @@ nodes:
     type: end
     name: 结束
     config:
-      output: "{{ llm_qa.output }}"
+      output: "{{llm_qa.output}}"
     position:
       x: 900
       y: 100

api/pyproject.toml

@@ -135,6 +135,8 @@ dependencies = [
     "graspologic==3.4.5.dev2",
     "markdown-to-json==2.1.1",
     "valkey==6.0.2",
+    "python-calamine>=0.4.0",
+    "xlrd==2.0.2"
 ]
 
 [tool.pytest.ini_options]

api/requirements.txt

@@ -129,3 +129,5 @@ editdistance==0.8.1
 graspologic==3.4.5.dev2
 markdown-to-json==2.1.1
 valkey==6.0.2
+python-calamine>=0.4.0
+xlrd==2.0.2