refactor: Recommended questions display ten by default.

backend/apps/chat/api/chat.py

@@ -1,6 +1,7 @@
 import asyncio
 import io
 import traceback
+from typing import Optional
 
 import orjson
 import pandas as pd
@@ -107,7 +108,7 @@ async def start_chat(session: SessionDep, current_user: CurrentUser):
 
 @router.post("/recommend_questions/{chat_record_id}")
 async def recommend_questions(session: SessionDep, current_user: CurrentUser, chat_record_id: int,
-                              current_assistant: CurrentAssistant):
+                              current_assistant: CurrentAssistant, articles_number: Optional[int]  = 4):
     def _return_empty():
         yield 'data:' + orjson.dumps({'content': '[]', 'type': 'recommended_question'}).decode() + '\n\n'
 
@@ -121,6 +122,7 @@ def _return_empty():
 
         llm_service = await LLMService.create(session, current_user, request_question, current_assistant, True)
         llm_service.set_record(record)
+        llm_service.set_articles_number(articles_number)
         llm_service.run_recommend_questions_task_async()
     except Exception as e:
         traceback.print_exc()

backend/apps/chat/models/chat_model.py

@@ -212,7 +212,8 @@ def sql_sys_question(self, db_type: Union[str, DB], enable_query_limit: bool = T
 
     def sql_user_question(self, current_time: str, change_title: bool):
         return get_sql_template()['user'].format(engine=self.engine, schema=self.db_schema, question=self.question,
-                                                 rule=self.rule, current_time=current_time, error_msg=self.error_msg,change_title = change_title)
+                                                 rule=self.rule, current_time=current_time, error_msg=self.error_msg,
+                                                 change_title=change_title)
 
     def chart_sys_question(self):
         return get_chart_template()['system'].format(sql=self.sql, question=self.question, lang=self.lang)
@@ -240,8 +241,8 @@ def datasource_sys_question(self):
     def datasource_user_question(self, datasource_list: str = "[]"):
         return get_datasource_template()['user'].format(question=self.question, data=datasource_list)
 
-    def guess_sys_question(self):
-        return get_guess_question_template()['system'].format(lang=self.lang)
+    def guess_sys_question(self, articles_number: int = 4):
+        return get_guess_question_template()['system'].format(lang=self.lang, articles_number=articles_number)
 
     def guess_user_question(self, old_questions: str = "[]"):
         return get_guess_question_template()['user'].format(question=self.question, schema=self.db_schema,

backend/apps/chat/task/llm.py

@@ -84,6 +84,7 @@ class LLMService:
     future: Future
 
     last_execute_sql_error: str = None
+    articles_number: int = 4
 
     def __init__(self, session: Session, current_user: CurrentUser, chat_question: ChatQuestion,
                  current_assistant: Optional[CurrentAssistant] = None, no_reasoning: bool = False,
@@ -213,6 +214,9 @@ def get_record(self):
     def set_record(self, record: ChatRecord):
         self.record = record
 
+    def set_articles_number(self, articles_number: int):
+        self.articles_number = articles_number
+
     def get_fields_from_chart(self, _session: Session):
         chart_info = get_chart_config(_session, self.record.id)
         return format_chart_fields(chart_info)
@@ -330,7 +334,7 @@ def generate_recommend_questions_task(self, _session: Session):
                 embedding=False)
 
         guess_msg: List[Union[BaseMessage, dict[str, Any]]] = []
-        guess_msg.append(SystemMessage(content=self.chat_question.guess_sys_question()))
+        guess_msg.append(SystemMessage(content=self.chat_question.guess_sys_question(self.articles_number)))
 
         old_questions = list(map(lambda q: q.strip(), get_old_questions(_session, self.record.datasource)))
         guess_msg.append(

backend/templates/template.yaml

@@ -374,7 +374,7 @@ template:
       ### 请使用语言：{lang} 回答，不需要输出深度思考过程
 
       ### 说明：
-      您的任务是根据给定的表结构，用户问题以及以往用户提问，推测用户接下来可能提问的1-4个问题。
+      您的任务是根据给定的表结构，用户问题以及以往用户提问，推测用户接下来可能提问的1-{articles_number}个问题。
       请遵循以下规则：
       - 推测的问题需要与提供的表结构相关，生成的提问例子如：["查询所有用户数据","使用饼图展示各产品类型的占比","使用折线图展示销售额趋势",...]
       - 推测问题如果涉及图形展示，支持的图形类型为：表格(table)、柱状图(column)、条形图(bar)、折线图(line)或饼图(pie)
@@ -385,7 +385,7 @@ template:
       - 如果用户没有提问且没有以往用户提问，则仅根据提供的表结构推测问题
       - 生成的推测问题使用JSON格式返回：
       ["推测问题1", "推测问题2", "推测问题3", "推测问题4"]
-      - 最多返回4个你推测出的结果
+      - 最多返回{articles_number}个你推测出的结果
       - 若无法推测,则返回空数据JSON:
       []
       - 若你的给出的JSON不是{lang}的，则必须翻译为{lang}

frontend/src/api/chat.ts

@@ -328,8 +328,12 @@ export const chatApi = {
   predict: (record_id: number | undefined, controller?: AbortController) => {
     return request.fetchStream(`/chat/record/${record_id}/predict`, {}, controller)
   },
-  recommendQuestions: (record_id: number | undefined, controller?: AbortController) => {
-    return request.fetchStream(`/chat/recommend_questions/${record_id}`, {}, controller)
+  recommendQuestions: (
+    record_id: number | undefined,
+    controller?: AbortController,
+    params: any
+  ) => {
+    return request.fetchStream(`/chat/recommend_questions/${record_id}${params}`, {}, controller)
   },
   recentQuestions: (datasource_id?: number): Promise<any> => {
     return request.get(`/chat/recent_questions/${datasource_id}`)

frontend/src/views/chat/QuickQuestion.vue

@@ -11,7 +11,7 @@ const recommendQuestionRef = ref()
 const recentQuestionRef = ref()
 const popoverRef = ref()
 const getRecommendQuestions = () => {
-  recommendQuestionRef.value.getRecommendQuestions()
+  recommendQuestionRef.value.getRecommendQuestions(10)
 }
 
 const retrieveQuestions = () => {

frontend/src/views/chat/RecommendQuestion.vue

@@ -58,12 +58,13 @@ function clickQuestion(question: string): void {
 
 const stopFlag = ref(false)
 
-async function getRecommendQuestions() {
+async function getRecommendQuestions(articles_number: number) {
   stopFlag.value = false
   loading.value = true
   try {
     const controller: AbortController = new AbortController()
-    const response = await chatApi.recommendQuestions(props.recordId, controller)
+    const params = articles_number ? '?articles_number=' + articles_number : ''
+    const response = await chatApi.recommendQuestions(props.recordId, controller, params)
     const reader = response.body.getReader()
     const decoder = new TextDecoder('utf-8')