* feat: 增加无引用分段设置

apps/application/chat_pipeline/step/chat_step/i_chat_step.py

@@ -15,9 +15,9 @@
 
 from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
 from application.chat_pipeline.pipeline_manage import PipelineManage
+from application.serializers.application_serializers import NoReferencesSetting
 from common.field.common import InstanceField
 from common.util.field_message import ErrMessage
-from dataset.models import Paragraph
 
 
 class ModelField(serializers.Field):
@@ -70,6 +70,8 @@ class InstanceSerializer(serializers.Serializer):
         stream = serializers.BooleanField(required=False, error_messages=ErrMessage.base("流式输出"))
         client_id = serializers.CharField(required=True, error_messages=ErrMessage.char("客户端id"))
         client_type = serializers.CharField(required=True, error_messages=ErrMessage.char("客户端类型"))
+        # 未查询到引用分段
+        no_references_setting = NoReferencesSetting(required=True, error_messages=ErrMessage.base("无引用分段设置"))
 
         def is_valid(self, *, raise_exception=False):
             super().is_valid(raise_exception=True)
@@ -92,5 +94,6 @@ def execute(self, message_list: List[BaseMessage],
                 chat_model: BaseChatModel = None,
                 paragraph_list=None,
                 manage: PipelineManage = None,
-                padding_problem_text: str = None, stream: bool = True, client_id=None, client_type=None, **kwargs):
+                padding_problem_text: str = None, stream: bool = True, client_id=None, client_type=None,
+                no_references_setting=None, **kwargs):
         pass

apps/application/chat_pipeline/step/chat_step/impl/base_chat_step.py

@@ -17,7 +17,8 @@
 from django.http import StreamingHttpResponse
 from langchain.chat_models.base import BaseChatModel
 from langchain.schema import BaseMessage
-from langchain.schema.messages import BaseMessageChunk, HumanMessage, AIMessage
+from langchain.schema.messages import HumanMessage, AIMessage
+from langchain_core.messages import AIMessageChunk
 
 from application.chat_pipeline.I_base_chat_pipeline import ParagraphPipelineModel
 from application.chat_pipeline.pipeline_manage import PipelineManage
@@ -47,7 +48,8 @@ def event_content(response,
                   message_list: List[BaseMessage],
                   problem_text: str,
                   padding_problem_text: str = None,
-                  client_id=None, client_type=None):
+                  client_id=None, client_type=None,
+                  is_ai_chat: bool = None):
     all_text = ''
     try:
         for chunk in response:
@@ -56,8 +58,12 @@ def event_content(response,
                                          'content': chunk.content, 'is_end': False}) + "\n\n"
 
         # 获取token
-        request_token = chat_model.get_num_tokens_from_messages(message_list)
-        response_token = chat_model.get_num_tokens(all_text)
+        if is_ai_chat:
+            request_token = chat_model.get_num_tokens_from_messages(message_list)
+            response_token = chat_model.get_num_tokens(all_text)
+        else:
+            request_token = 0
+            response_token = 0
         step.context['message_tokens'] = request_token
         step.context['answer_tokens'] = response_token
         current_time = time.time()
@@ -88,15 +94,16 @@ def execute(self, message_list: List[BaseMessage],
                 padding_problem_text: str = None,
                 stream: bool = True,
                 client_id=None, client_type=None,
+                no_references_setting=None,
                 **kwargs):
         if stream:
             return self.execute_stream(message_list, chat_id, problem_text, post_response_handler, chat_model,
                                        paragraph_list,
-                                       manage, padding_problem_text, client_id, client_type)
+                                       manage, padding_problem_text, client_id, client_type, no_references_setting)
         else:
             return self.execute_block(message_list, chat_id, problem_text, post_response_handler, chat_model,
                                       paragraph_list,
-                                      manage, padding_problem_text, client_id, client_type)
+                                      manage, padding_problem_text, client_id, client_type, no_references_setting)
 
     def get_details(self, manage, **kwargs):
         return {
@@ -127,19 +134,26 @@ def execute_stream(self, message_list: List[BaseMessage],
                        paragraph_list=None,
                        manage: PipelineManage = None,
                        padding_problem_text: str = None,
-                       client_id=None, client_type=None):
+                       client_id=None, client_type=None,
+                       no_references_setting=None):
+        is_ai_chat = False
         # 调用模型
         if chat_model is None:
             chat_result = iter(
-                [BaseMessageChunk(content=paragraph.title + "\n" + paragraph.content) for paragraph in paragraph_list])
+                [AIMessageChunk(content=paragraph.title + "\n" + paragraph.content) for paragraph in paragraph_list])
         else:
-            chat_result = chat_model.stream(message_list)
+            if (paragraph_list is None or len(paragraph_list) == 0) and no_references_setting.get(
+                    'status') == 'designated_answer':
+                chat_result = iter([AIMessageChunk(content=no_references_setting.get('value'))])
+            else:
+                chat_result = chat_model.stream(message_list)
+                is_ai_chat = True
 
         chat_record_id = uuid.uuid1()
         r = StreamingHttpResponse(
             streaming_content=event_content(chat_result, chat_id, chat_record_id, paragraph_list,
                                             post_response_handler, manage, self, chat_model, message_list, problem_text,
-                                            padding_problem_text, client_id, client_type),
+                                            padding_problem_text, client_id, client_type, is_ai_chat),
             content_type='text/event-stream;charset=utf-8')
 
         r['Cache-Control'] = 'no-cache'
@@ -153,16 +167,26 @@ def execute_block(self, message_list: List[BaseMessage],
                       paragraph_list=None,
                       manage: PipelineManage = None,
                       padding_problem_text: str = None,
-                      client_id=None, client_type=None):
+                      client_id=None, client_type=None, no_references_setting=None):
+        is_ai_chat = False
         # 调用模型
         if chat_model is None:
             chat_result = AIMessage(
                 content="\n\n".join([paragraph.title + "\n" + paragraph.content for paragraph in paragraph_list]))
         else:
-            chat_result = chat_model.invoke(message_list)
+            if (paragraph_list is None or len(paragraph_list) == 0) and no_references_setting.get(
+                    'status') == 'designated_answer':
+                chat_result = AIMessage(content=no_references_setting.get('value'))
+            else:
+                chat_result = chat_model.invoke(message_list)
+                is_ai_chat = True
         chat_record_id = uuid.uuid1()
-        request_token = chat_model.get_num_tokens_from_messages(message_list)
-        response_token = chat_model.get_num_tokens(chat_result.content)
+        if is_ai_chat:
+            request_token = chat_model.get_num_tokens_from_messages(message_list)
+            response_token = chat_model.get_num_tokens(chat_result.content)
+        else:
+            request_token = 0
+            response_token = 0
         self.context['message_tokens'] = request_token
         self.context['answer_tokens'] = response_token
         current_time = time.time()

apps/application/chat_pipeline/step/generate_human_message_step/i_generate_human_message_step.py

@@ -15,9 +15,9 @@
 from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
 from application.chat_pipeline.pipeline_manage import PipelineManage
 from application.models import ChatRecord
+from application.serializers.application_serializers import NoReferencesSetting
 from common.field.common import InstanceField
 from common.util.field_message import ErrMessage
-from dataset.models import Paragraph
 
 
 class IGenerateHumanMessageStep(IBaseChatPipelineStep):
@@ -39,6 +39,8 @@ class InstanceSerializer(serializers.Serializer):
         prompt = serializers.CharField(required=True, error_messages=ErrMessage.char("提示词"))
         # 补齐问题
         padding_problem_text = serializers.CharField(required=False, error_messages=ErrMessage.char("补齐问题"))
+        # 未查询到引用分段
+        no_references_setting = NoReferencesSetting(required=True, error_messages=ErrMessage.base("无引用分段设置"))
 
     def get_step_serializer(self, manage: PipelineManage) -> Type[serializers.Serializer]:
         return self.InstanceSerializer
@@ -56,6 +58,7 @@ def execute(self,
                 max_paragraph_char_number: int,
                 prompt: str,
                 padding_problem_text: str = None,
+                no_references_setting=None,
                 **kwargs) -> List[BaseMessage]:
         """
 
@@ -67,6 +70,7 @@ def execute(self,
         :param prompt:                     模板
         :param padding_problem_text        用户修改文本
         :param kwargs:                     其他参数
+        :param no_references_setting:     无引用分段设置
         :return:
         """
         pass

apps/application/chat_pipeline/step/generate_human_message_step/impl/base_generate_human_message_step.py

@@ -6,7 +6,7 @@
     @date：2024/1/10 17:50
     @desc:
 """
-from typing import List
+from typing import List, Dict
 
 from langchain.schema import BaseMessage, HumanMessage
 
@@ -26,22 +26,31 @@ def execute(self, problem_text: str,
                 max_paragraph_char_number: int,
                 prompt: str,
                 padding_problem_text: str = None,
+                no_references_setting=None,
                 **kwargs) -> List[BaseMessage]:
+        prompt = prompt if no_references_setting.get('status') == 'designated_answer' else no_references_setting.get(
+            'value')
         exec_problem_text = padding_problem_text if padding_problem_text is not None else problem_text
         start_index = len(history_chat_record) - dialogue_number
         history_message = [[history_chat_record[index].get_human_message(), history_chat_record[index].get_ai_message()]
                            for index in
                            range(start_index if start_index > 0 else 0, len(history_chat_record))]
         return [*flat_map(history_message),
-                self.to_human_message(prompt, exec_problem_text, max_paragraph_char_number, paragraph_list)]
+                self.to_human_message(prompt, exec_problem_text, max_paragraph_char_number, paragraph_list,
+                                      no_references_setting)]
 
     @staticmethod
     def to_human_message(prompt: str,
                          problem: str,
                          max_paragraph_char_number: int,
-                         paragraph_list: List[ParagraphPipelineModel]):
+                         paragraph_list: List[ParagraphPipelineModel],
+                         no_references_setting: Dict):
         if paragraph_list is None or len(paragraph_list) == 0:
-            return HumanMessage(content=prompt.format(**{'data': "<data></data>", 'question': problem}))
+            if no_references_setting.get('status') == 'ai_questioning':
+                return HumanMessage(
+                    content=no_references_setting.get('value').format(**{'question': problem}))
+            else:
+                return HumanMessage(content=prompt.format(**{'data': "", 'question': problem}))
         temp_data = ""
         data_list = []
         for p in paragraph_list:

apps/application/models/application.py

@@ -19,7 +19,11 @@
 
 
 def get_dataset_setting_dict():
-    return {'top_n': 3, 'similarity': 0.6, 'max_paragraph_char_number': 5000, 'search_mode': 'embedding'}
+    return {'top_n': 3, 'similarity': 0.6, 'max_paragraph_char_number': 5000, 'search_mode': 'embedding',
+            'no_references_setting': {
+                'status': 'ai_questioning',
+                'value': '{question}'
+            }}
 
 
 def get_model_setting_dict():

apps/application/serializers/application_serializers.py

@@ -73,6 +73,18 @@ class Meta:
         fields = "__all__"
 
 
+class NoReferencesChoices(models.TextChoices):
+    """订单类型"""
+    ai_questioning = 'ai_questioning', 'ai回答'
+    designated_answer = 'designated_answer', '指定回答'
+
+
+class NoReferencesSetting(serializers.Serializer):
+    status = serializers.ChoiceField(required=True, choices=NoReferencesChoices.choices,
+                                     error_messages=ErrMessage.char("无引用状态"))
+    value = serializers.CharField(required=True, error_messages=ErrMessage.char("提示词"))
+
+
 class DatasetSettingSerializer(serializers.Serializer):
     top_n = serializers.FloatField(required=True, max_value=100, min_value=1,
                                    error_messages=ErrMessage.float("引用分段数"))
@@ -85,6 +97,8 @@ class DatasetSettingSerializer(serializers.Serializer):
                                   message="类型只支持register|reset_password", code=500)
     ], error_messages=ErrMessage.char("检索模式"))
 
+    no_references_setting = NoReferencesSetting(required=True, error_messages=ErrMessage.base("未引用分段设置"))
+
 
 class ModelSettingSerializer(serializers.Serializer):
     prompt = serializers.CharField(required=True, max_length=2048, error_messages=ErrMessage.char("提示词"))
@@ -383,7 +397,9 @@ def reset_application(application: Dict):
             application['multiple_rounds_dialogue'] = True if application.get('dialogue_number') > 0 else False
             del application['dialogue_number']
             if 'dataset_setting' in application:
-                application['dataset_setting'] = {**application['dataset_setting'], 'search_mode': 'embedding'}
+                application['dataset_setting'] = {'search_mode': 'embedding', 'no_references_setting': {
+                    'status': 'ai_questioning',
+                    'value': '{question}'}, **application['dataset_setting']}
             return application
 
         def page(self, current_page: int, page_size: int, with_valid=True):

apps/application/serializers/chat_message_serializers.py

@@ -78,7 +78,11 @@ def to_base_pipeline_manage_params(self):
             'problem_optimization': self.application.problem_optimization,
             'stream': True,
             'search_mode': self.application.dataset_setting.get(
-                'search_mode') if 'search_mode' in self.application.dataset_setting else 'embedding'
+                'search_mode') if 'search_mode' in self.application.dataset_setting else 'embedding',
+            'no_references_setting': self.application.dataset_setting.get(
+                'no_references_setting') if 'no_references_setting' in self.application.dataset_setting else {
+                'status': 'ai_questioning',
+                'value': '{question}'}
 
         }
 

apps/application/swagger_api/application_api.py

@@ -176,6 +176,18 @@ def get_request_body_api():
                                                                 description="最多引用字符数", default=3000),
                     'search_mode': openapi.Schema(type=openapi.TYPE_STRING, title='检索模式',
                                                   description="embedding|keywords|blend", default='embedding'),
+                    'no_references_setting': openapi.Schema(type=openapi.TYPE_OBJECT, title='检索模式',
+                                                            required=['status', 'value'],
+                                                            properties={
+                                                                'status': openapi.Schema(type=openapi.TYPE_STRING,
+                                                                                         title="状态",
+                                                                                         description="ai作答:ai_questioning,指定回答:designated_answer",
+                                                                                         default='ai_questioning'),
+                                                                'value': openapi.Schema(type=openapi.TYPE_STRING,
+                                                                                        title="值",
+                                                                                        description="ai作答:就是题词,指定回答:就是指定回答内容",
+                                                                                        default='{question}'),
+                                                            }),
                 }
             )
 

ui/src/styles/element-plus.scss

@@ -335,3 +335,19 @@
 .auto-tooltip-popper {
   max-width: 500px;
 }
+
+// radio 一行一个样式
+.radio-block {
+  width: 100%;
+  display: block;
+  .el-radio {
+    align-items: flex-start;
+    height: 100%;
+    width: 100%;
+  }
+  .el-radio__label {
+    width: 100%;
+    margin-top: -8px;
+    line-height: 30px;
+  }
+}

ui/src/views/application-overview/component/EditAvatarDialog.vue

@@ -1,6 +1,6 @@
 <template>
   <el-dialog title="设置 Logo" v-model="dialogVisible">
-    <el-radio-group v-model="radioType" class="card__block mb-16">
+    <el-radio-group v-model="radioType" class="radio-block mb-16">
       <div>
         <el-radio value="default">
           <p>默认 Logo</p>
@@ -14,7 +14,7 @@
           />
         </el-radio>
       </div>
-      <div>
+      <div class="mt-8">
         <el-radio value="custom">
           <p>自定义上传</p>
           <div class="flex mt-8">
@@ -126,16 +126,4 @@ function submit() {
 
 defineExpose({ open })
 </script>
-<style lang="scss" scope>
-.card__block {
-  width: 100%;
-  display: block;
-  .el-radio {
-    align-items: flex-start;
-    height: 100%;
-  }
-  .el-radio__inner {
-    margin-top: 3px;
-  }
-}
-</style>
+<style lang="scss" scope></style>

ui/src/views/application/CreateAndSetting.vue

@@ -322,7 +322,11 @@ const applicationForm = ref<ApplicationFormType>({
     top_n: 3,
     similarity: 0.6,
     max_paragraph_char_number: 5000,
-    search_mode: 'embedding'
+    search_mode: 'embedding',
+    no_references_setting: {
+      status: 'ai_questioning',
+      value: '{question}'
+    }
   },
   model_setting: {
     prompt: defaultPrompt