perf: 应用的AI模型修改为不必填 (#297)

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

@@ -54,7 +54,7 @@ class IChatStep(IBaseChatPipelineStep):
         message_list = serializers.ListField(required=True, child=MessageField(required=True),
                                              error_messages=ErrMessage.list("对话列表"))
         # 大语言模型
-        chat_model = ModelField(error_messages=ErrMessage.list("大语言模型"))
+        chat_model = ModelField(required=False, allow_null=True, error_messages=ErrMessage.list("大语言模型"))
         # 段落列表
         paragraph_list = serializers.ListField(error_messages=ErrMessage.list("段落列表"))
         # 对话id

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

@@ -126,6 +126,26 @@ class BaseChatStep(IChatStep):
         result.append({'role': 'ai', 'content': answer_text})
         return result
 
+    @staticmethod
+    def get_stream_result(message_list: List[BaseMessage],
+                          chat_model: BaseChatModel = None,
+                          paragraph_list=None,
+                          no_references_setting=None):
+        if paragraph_list is None:
+            paragraph_list = []
+        directly_return_chunk_list = [AIMessageChunk(content=paragraph.content)
+                                      for paragraph in paragraph_list if
+                                      paragraph.hit_handling_method == 'directly_return']
+        if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
+            return iter(directly_return_chunk_list), False
+        elif no_references_setting.get(
+                'status') == 'designated_answer':
+            return iter([AIMessageChunk(content=no_references_setting.get('value'))]), False
+        if chat_model is None:
+            return iter([AIMessageChunk('抱歉，没有在知识库中查询到相关信息。')]), False
+        else:
+            return chat_model.stream(message_list), True
+
     def execute_stream(self, message_list: List[BaseMessage],
                        chat_id,
                        problem_text,
@@ -136,29 +156,8 @@ class BaseChatStep(IChatStep):
                        padding_problem_text: str = None,
                        client_id=None, client_type=None,
                        no_references_setting=None):
-        is_ai_chat = False
+        chat_result, is_ai_chat = self.get_stream_result(message_list, chat_model, paragraph_list,
-        # 调用模型
+                                                         no_references_setting)
-        if chat_model is None:
-            chat_result = iter(
-                [AIMessageChunk(content=paragraph.title + "\n" + paragraph.content) for paragraph in paragraph_list])
-        else:
-            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:
-                if paragraph_list is not None and len(paragraph_list) > 0:
-                    directly_return_chunk_list = [AIMessageChunk(content=paragraph.title + "\n" + paragraph.content)
-                                                  for paragraph in paragraph_list if
-                                                  paragraph.hit_handling_method == 'directly_return']
-                    if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
-                        chat_result = iter(directly_return_chunk_list)
-                    else:
-                        chat_result = chat_model.stream(message_list)
-                        is_ai_chat = True
-                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,
@@ -169,6 +168,27 @@ class BaseChatStep(IChatStep):
         r['Cache-Control'] = 'no-cache'
         return r
 
+    @staticmethod
+    def get_block_result(message_list: List[BaseMessage],
+                         chat_model: BaseChatModel = None,
+                         paragraph_list=None,
+                         no_references_setting=None):
+        if paragraph_list is None:
+            paragraph_list = []
+
+        directly_return_chunk_list = [AIMessage(content=paragraph.content)
+                                      for paragraph in paragraph_list if
+                                      paragraph.hit_handling_method == 'directly_return']
+        if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
+            return directly_return_chunk_list[0], False
+        elif no_references_setting.get(
+                'status') == 'designated_answer':
+            return AIMessage(no_references_setting.get('value')), False
+        if chat_model is None:
+            return AIMessage('抱歉，没有在知识库中查询到相关信息。'), False
+        else:
+            return chat_model.invoke(message_list), True
+
     def execute_block(self, message_list: List[BaseMessage],
                       chat_id,
                       problem_text,
@@ -178,28 +198,8 @@ class BaseChatStep(IChatStep):
                       manage: PipelineManage = None,
                       padding_problem_text: str = 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, is_ai_chat = self.get_block_result(message_list, chat_model, paragraph_list, no_references_setting)
-            chat_result = AIMessage(
-                content="\n\n".join([paragraph.title + "\n" + paragraph.content for paragraph in paragraph_list]))
-        else:
-            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:
-                if paragraph_list is not None and len(paragraph_list) > 0:
-                    directly_return_chunk_list = [AIMessageChunk(content=paragraph.title + "\n" + paragraph.content)
-                                                  for paragraph in paragraph_list if
-                                                  paragraph.hit_handling_method == 'directly_return']
-                    if directly_return_chunk_list is not None and len(directly_return_chunk_list) > 0:
-                        chat_result = iter(directly_return_chunk_list)
-                    else:
-                        chat_result = chat_model.invoke(message_list)
-                        is_ai_chat = True
-                else:
-                    chat_result = chat_model.invoke(message_list)
-                    is_ai_chat = True
         chat_record_id = uuid.uuid1()
         if is_ai_chat:
             request_token = chat_model.get_num_tokens_from_messages(message_list)

apps/application/chat_pipeline/step/reset_problem_step/i_reset_problem_step.py

@@ -28,7 +28,7 @@ class IResetProblemStep(IBaseChatPipelineStep):
         history_chat_record = serializers.ListField(child=InstanceField(model_type=ChatRecord, required=True),
                                                     error_messages=ErrMessage.list("历史对答"))
         # 大语言模型
-        chat_model = ModelField(error_messages=ErrMessage.base("大语言模型"))
+        chat_model = ModelField(required=False, allow_null=True, error_messages=ErrMessage.base("大语言模型"))
 
     def get_step_serializer(self, manage: PipelineManage) -> Type[serializers.Serializer]:
         return self.InstanceSerializer

apps/application/chat_pipeline/step/reset_problem_step/impl/base_reset_problem_step.py

@@ -22,6 +22,8 @@ prompt = (
 class BaseResetProblemStep(IResetProblemStep):
     def execute(self, problem_text: str, history_chat_record: List[ChatRecord] = None, chat_model: BaseChatModel = None,
                 **kwargs) -> str:
+        if chat_model is None:
+            return problem_text
         start_index = len(history_chat_record) - 3
         history_message = [[history_chat_record[index].get_human_message(), history_chat_record[index].get_ai_message()]
                            for index in

apps/application/serializers/application_serializers.py

@@ -47,7 +47,8 @@ chat_cache = cache.caches['chat_cache']
 
 class ModelDatasetAssociation(serializers.Serializer):
     user_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid("用户id"))
-    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char("模型id"))
+    model_id = serializers.CharField(required=False, allow_null=True, allow_blank=True,
+                                     error_messages=ErrMessage.char("模型id"))
     dataset_id_list = serializers.ListSerializer(required=False, child=serializers.UUIDField(required=True,
                                                                                              error_messages=ErrMessage.uuid(
                                                                                                  "知识库id")),
@@ -57,6 +58,7 @@ class ModelDatasetAssociation(serializers.Serializer):
         super().is_valid(raise_exception=True)
         model_id = self.data.get('model_id')
         user_id = self.data.get('user_id')
+        if model_id is not None and len(model_id) > 0:
             if not QuerySet(Model).filter(id=model_id).exists():
                 raise AppApiException(500, f'模型不存在【{model_id}】')
         dataset_id_list = list(set(self.data.get('dataset_id_list')))
@@ -109,7 +111,8 @@ class ApplicationSerializer(serializers.Serializer):
     desc = serializers.CharField(required=False, allow_null=True, allow_blank=True,
                                  max_length=256, min_length=1,
                                  error_messages=ErrMessage.char("应用描述"))
-    model_id = serializers.CharField(required=True, error_messages=ErrMessage.char("模型"))
+    model_id = serializers.CharField(required=False, allow_null=True, allow_blank=True,
+                                     error_messages=ErrMessage.char("模型"))
     multiple_rounds_dialogue = serializers.BooleanField(required=True, error_messages=ErrMessage.char("多轮对话"))
     prologue = serializers.CharField(required=False, allow_null=True, allow_blank=True, max_length=1024,
                                      error_messages=ErrMessage.char("开场白"))
@@ -254,7 +257,8 @@ class ApplicationSerializer(serializers.Serializer):
                                      error_messages=ErrMessage.char("应用名称"))
         desc = serializers.CharField(required=False, max_length=256, min_length=1, allow_null=True, allow_blank=True,
                                      error_messages=ErrMessage.char("应用描述"))
-        model_id = serializers.CharField(required=False, error_messages=ErrMessage.char("模型"))
+        model_id = serializers.CharField(required=False, allow_blank=True, allow_null=True,
+                                         error_messages=ErrMessage.char("模型"))
         multiple_rounds_dialogue = serializers.BooleanField(required=False,
                                                             error_messages=ErrMessage.boolean("多轮会话"))
         prologue = serializers.CharField(required=False, allow_null=True, allow_blank=True, max_length=1024,
@@ -494,13 +498,14 @@ class ApplicationSerializer(serializers.Serializer):
             application_id = self.data.get("application_id")
 
             application = QuerySet(Application).get(id=application_id)
-
+            if instance.get('model_id') is None or len(instance.get('model_id')) == 0:
+                application.model_id = None
+            else:
                 model = QuerySet(Model).filter(
-                id=instance.get('model_id') if 'model_id' in instance else application.model_id,
+                    id=instance.get('model_id'),
                     user_id=application.user_id).first()
                 if model is None:
                     raise AppApiException(500, "模型不存在")
-
             update_keys = ['name', 'desc', 'model_id', 'multiple_rounds_dialogue', 'prologue', 'status',
                            'dataset_setting', 'model_setting', 'problem_optimization',
                            'api_key_is_active', 'icon']

apps/application/serializers/chat_message_serializers.py

@@ -167,9 +167,11 @@ class ChatMessageSerializer(serializers.Serializer):
             chat_cache.set(chat_id,
                            chat_info, timeout=60 * 30)
         model = chat_info.application.model
+        if model is None:
+            return chat_info
         model = QuerySet(Model).filter(id=model.id).first()
         if model is None:
-            raise AppApiException(500, "模型不存在")
+            return chat_info
         if model.status == Status.ERROR:
             raise AppApiException(500, "当前模型不可用")
         if model.status == Status.DOWNLOAD:

apps/application/serializers/chat_serializers.py

@@ -213,7 +213,8 @@ class ChatSerializers(serializers.Serializer):
 
         id = serializers.UUIDField(required=False, allow_null=True,
                                    error_messages=ErrMessage.uuid("应用id"))
-        model_id = serializers.UUIDField(required=True, error_messages=ErrMessage.uuid("模型id"))
+        model_id = serializers.CharField(required=False, allow_null=True, allow_blank=True,
+                                         error_messages=ErrMessage.uuid("模型id"))
 
         multiple_rounds_dialogue = serializers.BooleanField(required=True,
                                                             error_messages=ErrMessage.boolean("多轮会话"))
@@ -246,14 +247,17 @@ class ChatSerializers(serializers.Serializer):
         def open(self):
             user_id = self.is_valid(raise_exception=True)
             chat_id = str(uuid.uuid1())
+            model_id = self.data.get('model_id')
+            if model_id is not None and len(model_id) > 0:
                 model = QuerySet(Model).filter(user_id=user_id, id=self.data.get('model_id')).first()
-            if model is None:
-                raise AppApiException(500, "模型不存在")
-            dataset_id_list = self.data.get('dataset_id_list')
                 chat_model = ModelProvideConstants[model.provider].value.get_model(model.model_type, model.model_name,
                                                                                    json.loads(
                                                                                        decrypt(model.credential)),
                                                                                    streaming=True)
+            else:
+                model = None
+                chat_model = None
+            dataset_id_list = self.data.get('dataset_id_list')
             application = Application(id=None, dialogue_number=3, model=model,
                                       dataset_setting=self.data.get('dataset_setting'),
                                       model_setting=self.data.get('model_setting'),

ui/src/components/ai-chat/index.vue

@@ -224,7 +224,7 @@ const chartOpenId = ref('')
 const chatList = ref<any[]>([])
 
 const isDisabledChart = computed(
-  () => !(inputValue.value.trim() && (props.appId || (props.data?.name && props.data?.model_id)))
+  () => !(inputValue.value.trim() && (props.appId || props.data?.name))
 )
 const isMdArray = (val: string) => val.match(/^-\s.*/m)
 const prologueList = computed(() => {
@@ -509,9 +509,7 @@ function regenerationChart(item: chatType) {
 }
 
 function getSourceDetail(row: any) {
-  logApi
+  logApi.getRecordDetail(id || props.appId, row.chat_id, row.record_id, loading).then((res) => {
-    .getRecordDetail(id || props.appId, row.chat_id, row.record_id, loading)
-    .then((res) => {
     const exclude_keys = ['answer_text', 'id']
     Object.keys(res.data).forEach((key) => {
       if (!exclude_keys.includes(key)) {

ui/src/views/application/CreateAndSetting.vue

@@ -48,7 +48,7 @@
               <el-form-item label="AI 模型" prop="model_id">
                 <template #label>
                   <div class="flex-between">
-                    <span>AI 模型 <span class="danger">*</span></span>
+                    <span>AI 模型 </span>
                   </div>
                 </template>
                 <el-select
@@ -56,6 +56,7 @@
                   placeholder="请选择 AI 模型"
                   class="w-full"
                   popper-class="select-model"
+                  :clearable="true"
                 >
                   <el-option-group
                     v-for="(value, label) in modelOptions"
@@ -338,7 +339,7 @@ const rules = reactive<FormRules<ApplicationFormType>>({
   name: [{ required: true, message: '请输入应用名称', trigger: 'blur' }],
   model_id: [
     {
-      required: true,
+      required: false,
       message: '请选择模型',
       trigger: 'change'
     }