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

api/app/controllers/knowledge_controller.py

@@ -1,26 +1,28 @@
-from typing import Optional
 import datetime
 import json
+from typing import Optional
 import uuid
+
 from fastapi import APIRouter, Depends, HTTPException, status, Query
 from sqlalchemy import or_
 from sqlalchemy.orm import Session
 
+from app.celery_app import celery_app
+from app.core.logging_config import get_api_logger
+from app.core.rag.common import settings
+from app.core.rag.llm.chat_model import Base
+from app.core.rag.nlp import rag_tokenizer, search
+from app.core.rag.prompts.generator import graph_entity_types
+from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
+from app.core.response_utils import success
 from app.db import get_db
 from app.dependencies import get_current_user
 from app.models.user_model import User
 from app.models import knowledge_model, document_model, file_model
 from app.schemas import knowledge_schema
 from app.schemas.response_schema import ApiResponse
-from app.core.response_utils import success
 from app.services import knowledge_service, document_service
-from app.core.rag.llm.chat_model import Base
+from app.services.model_service import ModelConfigService
-from app.core.rag.prompts.generator import graph_entity_types
-from app.core.rag.vdb.elasticsearch.elasticsearch_vector import ElasticSearchVectorFactory
-from app.core.logging_config import get_api_logger
-from app.core.rag.nlp import rag_tokenizer, search
-from app.core.rag.common import settings
-from app.celery_app import celery_app
 
 # Obtain a dedicated API logger
 api_logger = get_api_logger()
@@ -47,6 +49,45 @@ def get_parser_types():
     return success(msg="Successfully obtained the knowledge parser type", data=list(knowledge_model.ParserType))
 
 
+@router.get("/knowledge_graph_entity_types", response_model=ApiResponse)
+async def get_knowledge_graph_entity_types(
+        llm_id: uuid.UUID,
+        scenario: str,
+        db: Session = Depends(get_db),
+        current_user: User = Depends(get_current_user)
+):
+    """
+    get knowledge graph entity types based on llm_id
+    """
+    api_logger.info(f"Obtain details of the knowledge graph: llm_id={llm_id}, username: {current_user.username}")
+
+    try:
+        # 1. Check whether the model exists
+        api_logger.debug(f"Check whether the model exists: {llm_id}")
+        config = ModelConfigService.get_model_by_id(db=db, model_id=llm_id)
+
+        if not config:
+            api_logger.warning(
+                f"The model does not exist or you do not have permission to access it: llm_id={llm_id}")
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="The model does not exist or you do not have permission to access it"
+            )
+        # 2. Prepare to configure chat_mdl information
+        chat_model = Base(
+            key=config.api_keys[0].api_key,
+            model_name=config.api_keys[0].model_name,
+            base_url=config.api_keys[0].api_base
+        )
+        response = graph_entity_types(chat_model, scenario)
+        return success(data=response, msg="Successfully obtained knowledge graph entity types")
+    except HTTPException:
+        raise
+    except Exception as e:
+        api_logger.error(f"get knowledge graph entity types failed: llm_id={llm_id} - {str(e)}")
+        raise
+
+
 @router.get("/knowledges", response_model=ApiResponse)
 async def get_knowledges(
         parent_id: Optional[uuid.UUID] = Query(None, description="parent folder id"),
@@ -379,7 +420,7 @@ async def delete_knowledge_graph(
         current_user: User = Depends(get_current_user)
 ):
     """
-    Soft-delete knowledge graph
+    delete knowledge graph
     """
     api_logger.info(f"Request to delete knowledge graph: knowledge_id={knowledge_id}, username: {current_user.username}")
 
@@ -442,42 +483,3 @@ async def rebuild_knowledge_graph(
     except Exception as e:
         api_logger.error(f"Failed to rebuild knowledge graph: knowledge_id={knowledge_id} - {str(e)}")
         raise
-
-
-@router.get("/{knowledge_id}/knowledge_graph_entity_types", response_model=ApiResponse)
-async def get_knowledge_graph_entity_types(
-        knowledge_id: uuid.UUID,
-        scenario: str,
-        db: Session = Depends(get_db),
-        current_user: User = Depends(get_current_user)
-):
-    """
-    get knowledge graph entity types based on knowledge_id
-    """
-    api_logger.info(f"Obtain details of the knowledge graph: knowledge_id={knowledge_id}, username: {current_user.username}")
-
-    try:
-        # 1. Check whether the knowledge base exists
-        api_logger.debug(f"Check whether the knowledge base exists: {knowledge_id}")
-        db_knowledge = knowledge_service.get_knowledge_by_id(db, knowledge_id=knowledge_id, current_user=current_user)
-
-        if not db_knowledge:
-            api_logger.warning(
-                f"The knowledge base does not exist or you do not have permission to access it: knowledge_id={knowledge_id}")
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail="The knowledge base does not exist or you do not have permission to access it"
-            )
-        # 2. Prepare to configure chat_mdl 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
-        )
-        response = graph_entity_types(chat_model, scenario)
-        return success(data=response, msg="Successfully obtained knowledge graph entity types")
-    except HTTPException:
-        raise
-    except Exception as e:
-        api_logger.error(f"get knowledge graph entity types failed: knowledge_id={knowledge_id} - {str(e)}")
-        raise

api/app/core/memory/storage_services/reflection_engine/example/example.json

@@ -2,52 +2,39 @@
   "memory_verify": {
     "source_data": [
       {
-        "statement_name": "用户是2023年春天去北京工作的。",
+        "statement_name": "我是 2023 年春天去北京工作的，后来基本一直都在北京上班，也没怎么换过城市。不过后来公司调整，2024 年上半年我被调到上海待了差不多半年，那段时间每天都是在上海办公室打卡。当时入职资料用的还是我之前的身份信息，身份证号是 11010119950308123X，银行卡是 6222023847595898，这些一直没变。对了，其实我 从 2023 年开始就一直在北京生活，从来没有长期离开过北京，上海那段更多算是远程配合。"
-        "statement_id": "62beac695b1346f4871740a45db88782"
       },
       {
-        "statement_name": "用户后来基本一直都在北京上班。",
+        "statement_name": "用户后来基本一直都在北京上班。"
-        "statement_id": "4cba5ac08b674d7fb1e2ae634d2b8f0b"
       },
       {
-        "statement_name": "用户从2023年开始就一直在北京生活。",
+        "statement_name": "用户从2023年开始就一直在北京生活。"
-        "statement_id": "e612a44da4db483993c350df7c97a1a1"
       },
       {
-        "statement_name": "用户从来没有长期离开过北京。",
+        "statement_name": "用户从来没有长期离开过北京。"
-        "statement_id": "b3c787a2e33c49f7981accabbbb4538a"
       },
       {
-        "statement_name": "由于公司调整，用户在2024年上半年被调到上海待了差不多半年。",
+        "statement_name": "由于公司调整，用户在2024年上半年被调到上海待了差不多半年。"
-        "statement_id": "64cde4230cb24a4da726e7db9e7aa616"
       },
       {
-        "statement_name": "用户在被调到上海期间每天都是在上海办公室打卡。",
+        "statement_name": "用户在被调到上海期间每天都是在上海办公室打卡。"
-        "statement_id": "8b1b12e23b844b8088dfeb67da6ad669"
       },
       {
-        "statement_name": "用户在入职时使用的身份信息是之前的，身份证号为11010119950308123X。",
+        "statement_name": "用户在入职时使用的身份信息是之前的，身份证号为11010119950308123X。"
-        "statement_id": "030afd362e9b4110b139e68e5d3e7143"
       },
       {
-        "statement_name": "用户的银行卡号是6222023847595898。",
+        "statement_name": "用户的银行卡号是6222023847595898。"
-        "statement_id": "6c7567cd1f3c478bb42d1b65383e6f2f"
       },
       {
-        "statement_name": "用户的身份信息和银行卡信息一直没变。",
+        "statement_name": "用户的身份信息和银行卡信息一直没变。"
-        "statement_id": "b3ca618e1e204b83bebd70e75cf2073f"
       },
       {
-        "statement_name": "用户认为在上海的那段时间更多算是远程配合。",
+        "statement_name": "用户认为在上海的那段时间更多算是远程配合。"
-        "statement_id": "150af89d2c154e6eb41ff1a91e37f962"
       }
     ],
     "databasets": [
       {
         "entity1_name": "Person",
-        "description": "表示人类个体的通用类型",
-        "statement_id": "62beac695b1346f4871740a45db88782",
-        "entity2_name": "用户",
         "entity2": {
           "description": "叙述者，讲述个人工作与生活经历的个体",
           "name": "用户"
@@ -55,9 +42,6 @@
       },
       {
         "entity1_name": "用户",
-        "description": "叙述者，讲述个人工作与生活经历的个体",
-        "statement_id": "62beac695b1346f4871740a45db88782",
-        "entity2_name": "身份信息",
         "entity2": {
           "description": "用于个人身份识别的数据",
           "name": "身份信息"
@@ -65,9 +49,6 @@
       },
       {
         "entity1_name": "用户",
-        "description": "叙述者，讲述个人工作与生活经历的个体",
-        "statement_id": "62beac695b1346f4871740a45db88782",
-        "entity2_name": "6222023847595898",
         "entity2": {
           "description": "用户的银行卡号码",
           "name": "6222023847595898"
@@ -76,33 +57,24 @@
       {
         "entity1_name": "用户",
         "description": "叙述者，讲述个人工作与生活经历的个体",
-        "statement_id": "62beac695b1346f4871740a45db88782",
-        "entity2_name": "上海办公室",
         "entity2": {
           "entity_idx": 1,
           "aliases": ["上海办"],
-          "description": "位于上海的工作办公场所",
           "name": "上海办公室"
         }
       },
       {
         "entity1_name": "用户",
         "description": "叙述者，讲述个人工作与生活经历的个体",
-        "statement_id": "62beac695b1346f4871740a45db88782",
-        "entity2_name": "北京",
         "entity2": {
           "aliases": ["京", "京城", "北平"],
-          "description": "中国的首都城市，用户主要工作和生活所在地",
           "name": "北京"
         }
       },
       {
         "entity1_name": "11010119950308123X",
         "description": "具体的身份证号码值",
-        "statement_id": "030afd362e9b4110b139e68e5d3e7143",
-        "entity2_name": "身份证号",
         "entity2": {
-          "description": "中华人民共和国公民的身份号码",
           "name": "身份证号"
         }
       }

api/app/core/memory/storage_services/reflection_engine/self_reflexion.py

@@ -387,7 +387,7 @@ class ReflectionEngine:
         result_data['memory_verifies'] = memory_verifies
         result_data['quality_assessments'] = quality_assessments
         conflicts_found=''
-
+        REMOVE_KEYS = {"created_at", "expired_at","relationship","predicate","statement_id","id","statement_id","relationship_statement_id"}
         # Clearn conflict_data，And memory_verify和quality_assessment
         cleaned_conflict_data = []
         for item in conflict_data:
@@ -396,7 +396,23 @@ class ReflectionEngine:
                 'conflict': item['conflict']
             }
             cleaned_conflict_data.append(cleaned_item)
-
+        cleaned_conflict_data_=[]
+        for item in conflict_data:
+            cleaned_data = []
+            for row in item.get("data", []):
+                # 删除 created_at / expired_at
+                cleaned_row = {
+                    k: v
+                    for k, v in row.items()
+                    if k not in REMOVE_KEYS
+                }
+                cleaned_data.append(cleaned_row)
+            cleaned_item = {
+                "data": cleaned_data,
+                "conflict": item.get("conflict"),
+            }
+            cleaned_conflict_data_.append(cleaned_item)
+        print(cleaned_conflict_data_)
         # 3. 解决冲突
         solved_data = await self._resolve_conflicts(cleaned_conflict_data, source_data)
         if not solved_data:

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

@@ -9,9 +9,7 @@
 
 ## 任务目标
 作为数据冲突解决专家，分析冲突原因，按类型分组处理，为每种冲突生成独立解决方案。
-
 **数据关系**: statement_databasets中的statement_id对应data中的记录，statement_created_at为用户输入时间。
-
 **处理模式**:
 - memory_verify=false: 仅处理数据冲突
 - memory_verify=true: 处理数据冲突 + 隐私脱敏
@@ -111,7 +109,8 @@
 - 隐私保护优先: 所有输出记录必须完成隐私脱敏
 - 脱敏变更记录: 隐私脱敏变更也必须在change字段中记录{% endif %}
 - 不可修改数据: 数据被判定为正确时不可修改，无数据可输出时为空
-- 输出的结果reflexion字段中的reason字段和solution不允许含有（expired_at设为2024-01-01T00:00:00Z、memory_verify=true)等原数据字段以及涉及需要修改的字段以及内容
+- 输出的结果reflexion字段中的reason字段和solution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，
+    ，如果是FACT，只记录事实冲突相关的数据；如果是TIME，只记录时间冲突相关的数据；如果是HYBRID，则记录所有冲突相关的数据
 
 **变更记录格式**:
 ```json
@@ -158,8 +157,9 @@
         "conflict": true
       },
       "reflexion": {
-        "reason": "该冲突类型的原因分析",
+        "reason": "该冲突类型的原因分析，如果是FACT就是存在事实冲突，分析该冲突原因，如果是TIME就是存在时间冲突，分析该冲突原因，如果是HYBRID，可以输出存在时间与事实的混合冲突再添加上原因分析，
-        "solution": "该冲突类型的解决方案"
+        不可以随意分配冲突类型以及原因，不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种",
+        "solution": "该冲突类型的解决方案（不允许输出字段比如（statement、description、entity1_name、entity2_name、name、memory_verify、expired_at、conflict）等类似这种）"
       },
       "resolved": {
         "original_memory_id": "被设为失效的记忆id",
@@ -182,4 +182,5 @@
 - **resolved.change**: 包含详细变更信息
 - 无需修改的冲突类型resolved为null
 - 与baseline不匹配的冲突类型不包含在results中
-模式参考: {{ json_schema }}
+模式参考: {{ json_schema }}
+

api/app/core/rag/app/naive.py

@@ -672,6 +672,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         excel_parser = ExcelParser()
         if parser_config.get("html4excel"):
             sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
+            parser_config["chunk_token_num"] = 0
         else:
             sections = [(_, "") for _ in excel_parser(binary) if _]
         parser_config["chunk_token_num"] = 12800

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

@@ -5,6 +5,7 @@ from io import BytesIO
 
 import pandas as pd
 from openpyxl import Workbook, load_workbook
+from PIL import Image
 
 from app.core.rag.nlp import find_codec
 
@@ -28,7 +29,7 @@ class RAGExcelParser:
 
             try:
                 file_like_object.seek(0)
-                df = pd.read_csv(file_like_object)
+                df = pd.read_csv(file_like_object, on_bad_lines='skip')
                 return RAGExcelParser._dataframe_to_workbook(df)
 
             except Exception as e_csv:
@@ -42,14 +43,11 @@ 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}")
@@ -68,7 +66,6 @@ class RAGExcelParser:
         # if contains multiple sheets use _dataframes_to_workbook
         if isinstance(df, dict) and len(df) > 1:
             return RAGExcelParser._dataframes_to_workbook(df)
-
         df = RAGExcelParser._clean_dataframe(df)
         wb = Workbook()
         ws = wb.active
@@ -80,15 +77,14 @@ class RAGExcelParser:
         for row_num, row in enumerate(df.values, 2):
             for col_num, value in enumerate(row, 1):
                 ws.cell(row=row_num, column=col_num, value=value)
-
         return wb
-    
+
     @staticmethod
     def _dataframes_to_workbook(dfs: dict):
         wb = Workbook()
         default_sheet = wb.active
         wb.remove(default_sheet)
-        
+
         for sheet_name, df in dfs.items():
             df = RAGExcelParser._clean_dataframe(df)
             ws = wb.create_sheet(title=sheet_name)
@@ -99,6 +95,52 @@ class RAGExcelParser:
                     ws.cell(row=row_num, column=col_num, value=value)
         return wb
 
+    @staticmethod
+    def _extract_images_from_worksheet(ws, sheetname=None):
+        """
+        Extract images from a worksheet and enrich them with vision-based descriptions.
+
+        Returns: List[dict]
+        """
+        images = getattr(ws, "_images", [])
+        if not images:
+            return []
+
+        raw_items = []
+
+        for img in images:
+            try:
+                img_bytes = img._data()
+                pil_img = Image.open(BytesIO(img_bytes)).convert("RGB")
+
+                anchor = img.anchor
+                if hasattr(anchor, "_from") and hasattr(anchor, "_to"):
+                    r1, c1 = anchor._from.row + 1, anchor._from.col + 1
+                    r2, c2 = anchor._to.row + 1, anchor._to.col + 1
+                    if r1 == r2 and c1 == c2:
+                        span = "single_cell"
+                    else:
+                        span = "multi_cell"
+                else:
+                    r1, c1 = anchor._from.row + 1, anchor._from.col + 1
+                    r2, c2 = r1, c1
+                    span = "single_cell"
+
+                item = {
+                    "sheet": sheetname or ws.title,
+                    "image": pil_img,
+                    "image_description": "",
+                    "row_from": r1,
+                    "col_from": c1,
+                    "row_to": r2,
+                    "col_to": c2,
+                    "span_type": span,
+                }
+                raw_items.append(item)
+            except Exception:
+                continue
+        return raw_items
+
     def html(self, fnm, chunk_rows=256):
         from html import escape
 
@@ -131,7 +173,7 @@ class RAGExcelParser:
                 tb = ""
                 tb += f"<table><caption>{sheetname}</caption>"
                 tb += tb_rows_0
-                for r in list(rows[1 + chunk_i * chunk_rows : min(1 + (chunk_i + 1) * chunk_rows, len(rows))]):
+                for r in list(rows[1 + chunk_i * chunk_rows: min(1 + (chunk_i + 1) * chunk_rows, len(rows))]):
                     tb += "<tr>"
                     for i, c in enumerate(r):
                         if c.value is None:
@@ -154,7 +196,7 @@ class RAGExcelParser:
         except Exception as e:
             logging.warning(f"Parse spreadsheet error: {e}, trying to interpret as CSV file")
             file_like_object.seek(0)
-            df = pd.read_csv(file_like_object)
+            df = pd.read_csv(file_like_object, on_bad_lines='skip')
         df = df.replace(r"^\s*$", "", regex=True)
         return df.to_markdown(index=False)
 
@@ -172,19 +214,35 @@ class RAGExcelParser:
                 continue
             if not rows:
                 continue
+            # 获取表头
             ti = list(rows[0])
-            for r in list(rows[1:]):
+            header_fields = []
-                fields = []
+            for cell in ti:
-                for i, c in enumerate(r):
+                if cell.value:  # 只添加有值的表头
-                    if not c.value:
+                    header_fields.append(str(cell.value))
-                        continue
+
-                    t = str(ti[i].value) if i < len(ti) else ""
+            # 如果有数据行，处理数据行；否则只处理表头
-                    t += ("：" if t else "") + str(c.value)
+            data_rows = rows[1:]
-                    fields.append(t)
+            if data_rows:
-                line = "; ".join(fields)
+                for r in data_rows:
-                if sheetname.lower().find("sheet") < 0:
+                    fields = []
-                    line += " ——" + sheetname
+                    for i, c in enumerate(r):
-                res.append(line)
+                        if not c.value:
+                            continue
+                        t = str(ti[i].value) if i < len(ti) else ""
+                        t += ("：" if t else "") + str(c.value)
+                        fields.append(t)
+                    line = "; ".join(fields)
+                    if sheetname.lower().find("sheet") < 0:
+                        line += " ——" + sheetname
+                    res.append(line)
+            else:
+                # 只有表头的情况
+                if header_fields:
+                    line = "; ".join(header_fields)
+                    if sheetname.lower().find("sheet") < 0:
+                        line += " ——" + sheetname
+                    res.append(line)
         return res
 
     @staticmethod
@@ -192,14 +250,14 @@ class RAGExcelParser:
         if fnm.split(".")[-1].lower().find("xls") >= 0:
             wb = RAGExcelParser._load_excel_to_workbook(BytesIO(binary))
             total = 0
-            
+
             for sheetname in wb.sheetnames:
-               try:
+                try:
-                   ws = wb[sheetname]
+                    ws = wb[sheetname]
-                   total += len(list(ws.rows))
+                    total += len(list(ws.rows))
-               except Exception as e:
+                except Exception as e:
-                   logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
+                    logging.warning(f"Skip sheet '{sheetname}' due to rows access error: {e}")
-                   continue
+                    continue
             return total
 
         if fnm.split(".")[-1].lower() in ["csv", "txt"]:

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

@@ -196,7 +196,7 @@ class EntityResolution(Extractor):
         ans_list = []
         records = [r.strip() for r in results.split(record_delimiter)]
         for record in records:
-            pattern_int = f"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}"
+            pattern_int = fr"{re.escape(entity_index_delimiter)}(\d+){re.escape(entity_index_delimiter)}"
             match_int = re.search(pattern_int, record)
             res_int = int(str(match_int.group(1) if match_int else '0'))
             if res_int > records_length:

api/app/core/validators/memory_config_validators.py

@@ -197,6 +197,10 @@ def validate_embedding_model(
         embedding_id, "embedding", db, tenant_id, required=True,
         config_id=config_id, workspace_id=workspace_id
     )
+    print(100*'-')
+    print(embedding_uuid)
+    print(_)
+    print(100*'-')
     
     if embedding_uuid is None:
         raise InvalidConfigError(

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

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

api/app/models/data_config_model.py

@@ -51,8 +51,8 @@ class DataConfig(Base):
     # 自我反思配置
     enable_self_reflexion = Column(Boolean, default=False, comment="是否启用自我反思")
     iteration_period = Column(String, default="3", comment="反思迭代周期")
-    reflexion_range = Column(String, default="retrieval", comment="反思范围：部分/全部")
+    reflexion_range = Column(String, default="partial", comment="反思范围：部分/全部")
-    baseline = Column(String, default="time", comment="基线：时间/事实/时间和事实")
+    baseline = Column(String, default="TIME", comment="基线：时间/事实/时间和事实")
     reflection_model_id = Column(String, nullable=True, comment="反思模型ID")
     memory_verify = Column(Boolean, default=True, comment="记忆验证")
     quality_assessment = Column(Boolean, default=True, comment="质量评估")

api/app/services/master_agent_router.py

@@ -361,14 +361,14 @@ class MasterAgentRouter:
                     "model_name": api_key_config.model_name
                 }
             )
-            temperature = 0.3  # 决策任务使用较低温度
+            # temperature = 0.3  # 决策任务使用较低温度
-            max_tokens = 1000            
+            # max_tokens = 1000            
-            if self.model_parameters:
+            # if self.model_parameters:
-                temperature = self.model_parameters.temperature
+            #     temperature = self.model_parameters["temperature"]
-                max_tokens = self.model_parameters.max_tokens
+            #     max_tokens = self.model_parameters["max_tokens"]
                 
-            extra_params = {"temperature": temperature,
+            extra_params = {"temperature": self.model_parameters.get("temperature", 0.3),
-                                "max_tokens":max_tokens
+                                "max_tokens":self.model_parameters.get("max_tokens", 1000)
                                 }
             # 创建 RedBearModelConfig
             model_config = RedBearModelConfig(

api/app/services/multi_agent_service.py

@@ -290,12 +290,22 @@ class MultiAgentService:
         else:
             execution_config_data = convert_uuids_to_str(data.execution_config.model_dump())
 
+        # 处理 model_parameters（可能是 None、字典或 Pydantic 模型）
+        if data.model_parameters is None:
+            model_parameters_data = None
+        elif isinstance(data.model_parameters, dict):
+            # 过滤掉值为 None 的字段
+            model_parameters_data = {k: v for k, v in data.model_parameters.items() if v is not None}
+        else:
+            # 过滤掉值为 None 的字段
+            model_parameters_data = {k: v for k, v in data.model_parameters.model_dump().items() if v is not None}
+
         config = MultiAgentConfig(
                 app_id=app_id,
                 master_agent_id=data.master_agent_id,
                 master_agent_name=data.master_agent_name,
                 default_model_config_id=data.default_model_config_id,
-                model_parameters=data.model_parameters,
+                model_parameters=model_parameters_data,
                 orchestration_mode=data.orchestration_mode,
                 sub_agents=sub_agents_data,
                 # routing_rules=routing_rules_data,

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

@@ -41,8 +41,6 @@ nodes:
             - 使用友好、礼貌的语气
             - 适当使用格式化（如列表、段落）提高可读性
         
-        - role: user
-          content: "{{sys.message}}"
       
       model_id: null
       temperature: 0.7