铭感词

dbgpt/app/apps/vadmin/word/crud.py

@@ -28,7 +28,7 @@ from dbgpt.app.apps.utils.tools import test_password
 from . import models, schemas
 from .params.similar import SimilarParams
 from .schemas.similar import SimilarIn,SimilarUpdate
-from .schemas.sensitive import SensitiveIn
+from .schemas.sensitive import SensitiveIn,SensitiveUpdate
 from .models import sensitive
 
 from dbgpt.app.apps.config import settings
@@ -158,4 +158,15 @@ class SensitiveDal(DalBase):
         """
         return list(queryset.all())
 
+    async def update_sensitives(self, ids: [], data: SensitiveUpdate):
+        """
+        修改敏感词
+        """
+        await self.db.execute(
+            update(self.model).where(self.model.id.in_(ids)).values(
+                update_datetime=datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                word_name=data.word_name
+            )
+        )
+
 

dbgpt/app/apps/vadmin/word/schemas/sensitive.py

@@ -32,12 +32,8 @@ class SensitiveOut(SensitiveSchemas):
     update_datetime: datetime | None = None
     delete_datetime: datetime | None = None
 
-class SensitiveUpdate(BaseModel):
+class SensitiveUpdate(SensitiveSchemas):
     """
     更新敏感词信息
     """
     word_name: str | None = ""
-    is_delete: int | None = 0
-    create_datetime: datetime | None = None
-    update_datetime: datetime | None = None
-    delete_datetime: datetime | None = None

dbgpt/app/apps/vadmin/word/views.py

@@ -10,7 +10,7 @@ from . import schemas, crud, models
 from dbgpt.app.apps.vadmin.word.models.similar import VadminWordSimilar
 from dbgpt.app.apps.vadmin.word.params.similar import SimilarParams
 from dbgpt.app.apps.vadmin.word.schemas.similar import SimilarSchemas, SimilarIn,SimilarUpdate
-from dbgpt.app.apps.vadmin.word.schemas.sensitive import SensitiveSchemas, SensitiveIn
+from dbgpt.app.apps.vadmin.word.schemas.sensitive import SensitiveSchemas, SensitiveIn,SensitiveUpdate
 from dbgpt.app.apps.vadmin.word import crud
 from fastapi.encoders import jsonable_encoder
 
@@ -59,12 +59,11 @@ async def delete_similars(data: Request, auth: Auth = Depends(OpenAuth())):
     if data is None:
         print('SimilarIn is None')
         return ErrorResponse("不能删除当前近义词")
-    """
     else:
         print(f"req词条：{data.query_params['word_name']} req近义词：{data.query_params['similar_name']} ")
-    """
 
-    similarids = [7]
+    data_id = data.query_params['word_id']
+    similarids = [data_id]
     await crud.SimilarDal(auth.db).delete_datas(ids=similarids, v_soft=True)
     return SuccessResponse("删除成功")
 
@@ -74,7 +73,7 @@ async def update_similar_byid(data: Request, auth: Auth = Depends(OpenAuth())):
         print('SimilarIn is None')
         return ErrorResponse("不能修改当前近义词")
     else:
-        print(f"word_id：{data.query_params['word_id']} req词条：{data.query_params['word_name']} req词条：{data.query_params['word_name']} req近义词：{data.query_params['similar_name']} ")
+        print(f"word_id：{data.query_params['word_id']} req词条：{data.query_params['word_name']} req近义词：{data.query_params['similar_name']} ")
 
     data_id = data.query_params['word_id']
 
@@ -125,3 +124,35 @@ async def get_sensitives(auth: Auth = Depends(OpenAuth())):
     for row in datas:
         print(f"编号：{row.id} 词条：{row.word_name} ")
     return SuccessResponse(jsondatas, count=1)
+
+@router.post("/update_sensitive_byid", summary="根据id修改敏感词")
+async def update_sensitive_byid(data: Request, auth: Auth = Depends(OpenAuth())):
+    if data is None:
+        print('SimilarIn is None')
+        return ErrorResponse("不能修改当前敏感词")
+    else:
+        print(f"word_id：{data.query_params['word_id']} req词条：{data.query_params['word_name']}  ")
+
+    data_id = data.query_params['word_id']
+
+    sdata = SensitiveUpdate
+    sdata.word_name = data.query_params['word_name']
+
+    sensitiveids = []
+    sensitiveids.append(data_id)
+
+    await crud.SensitiveDal(auth.db).update_sensitives(sensitiveids, sdata)
+    return SuccessResponse("修改成功")
+
+@router.post("/delete_sensitives", summary="根据id删除敏感词")
+async def delete_sensitives(data: Request, auth: Auth = Depends(OpenAuth())):
+    if data is None:
+        print('sensitive request is None')
+        return ErrorResponse("不能删除当前敏感词")
+    else:
+        print(f"req词条：{data.query_params['word_name']} ")
+
+    data_id = data.query_params['word_id']
+    sensitiveids = [data_id]
+    await crud.SensitiveDal(auth.db).delete_datas(ids=sensitiveids, v_soft=True)
+    return SuccessResponse("删除成功")
\ No newline at end of file

dbgpt/app/scene/chat_knowledge/v1/prompt.py

@@ -16,24 +16,25 @@ The assistant gives helpful, detailed, professional and polite answers to the us
 
 _DEFAULT_TEMPLATE_ZH = """ 基于以下给出的已知信息, 准守规范约束，专业、简要回答用户的问题.
 规范约束:
-     1.如果已知信息包含的图片、链接、表格、代码块等特殊markdown标签格式的信息，确保在答案中包含原文这些图片、链接、表格和代码标签，不要丢弃不要修改，如:图片格式：![image.png](xxx), 链接格式:[xxx](xxx), 表格格式:|xxx|xxx|xxx|, 代码格式:```xxx```.
-     2.如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造.
-     3.回答的时候最好按照1.2.3.点进行总结, 并以markdwon格式显示.
+     1.如果已知信息包含的链接、表格、代码块等特殊markdown标签格式的信息，确保在答案中包含原文这链接、表格和代码标签，不要丢弃不要修改，如:链接格式:[xxx](xxx), 表格格式:|xxx|xxx|xxx|, 代码格式:```xxx```.
+     2.不要在答案中包含图片.
+     3.如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造.
+     4.回答的时候最好按照1.2.3.点进行总结, 并以markdwon格式显示.
             已知内容: 
             {context}
             问题:
-            {question},请使用和用户相同的语言进行回答.
+            {question},请使用和用户相同的语言进行回答..
 """
-_DEFAULT_TEMPLATE_EN = """ Based on the known information below, provide users with professional and concise answers to their questions.
-constraints:
-    1.Ensure to include original markdown formatting elements such as images, links, tables, or code blocks without alteration in the response if they are present in the provided information.
-        For example, image format should be ![image.png](xxx), link format [xxx](xxx), table format should be represented with |xxx|xxx|xxx|, and code format with xxx.
-    2.If the information available in the knowledge base is insufficient to answer the question, state clearly: "The content provided in the knowledge base is not enough to answer this question," and avoid making up answers.
-    3.When responding, it is best to summarize the points in the order of 1, 2, 3, And displayed in markdwon format.
-            known information: 
+_DEFAULT_TEMPLATE_EN = """ 基于以下给出的已知信息, 准守规范约束，专业、简要回答用户的问题.
+规范约束:
+     1.如果已知信息包含的链接、表格、代码块等特殊markdown标签格式的信息，确保在答案中包含原文这链接、表格和代码标签，不要丢弃不要修改，如:链接格式:[xxx](xxx), 表格格式:|xxx|xxx|xxx|, 代码格式:```xxx```.
+     2.不要在答案中包含图片.
+     3.如果无法从提供的内容中获取答案, 请说: "知识库中提供的内容不足以回答此问题" 禁止胡乱编造.
+     4.回答的时候最好按照1.2.3.点进行总结, 并以markdwon格式显示.
+            已知内容: 
             {context}
-            question:
-            {question},when answering, use the same language as the "user".
+            问题:
+            {question},请使用和用户相同的语言进行回答..
 """
 
 _DEFAULT_TEMPLATE = (