add user_feedback_downvote

application/api/main.py

@@ -353,8 +353,9 @@ def user_feedback(input_data: FeedBackInput):
  input_data.query_intent, input_data.query_answer_list)
  return upvote_res
  else:
- logger.info("user downvote {}", input_data)
- return True
+ downvote_res = service.user_feedback_downvote(input_data.data_profiles, input_data.query,
+ input_data.query_intent, input_data.query_answer_list)
+ return downvote_res
 
 
 @router.websocket("/ws")

application/api/service.py

@@ -209,6 +209,7 @@ def ask(question: Question) -> Answer:
  database_profile['db_type'] = ConnectionManagement.get_db_type_by_name(conn_name)
  prompt_map = database_profile['prompt_map']
 
+ entity_slot = []
  # 通过标志位控制后续的逻辑
  # 主要的意图有4个, 拒绝, 查询, 思维链, 知识问答
  if intent_ner_recognition_flag:
@@ -292,7 +293,7 @@ def ask(question: Question) -> Answer:
  search_intent_result = get_sql_result_tool(database_profile,
  current_nlq_chain.get_generated_sql())
  if search_intent_result["status_code"] == 500:
- sql_search_result.data_analyse = "-1"
+ sql_search_result.data_analyse = "The query results are temporarily unavailable, please switch to debugging webpage to try the same query and check the log file for more information."
  else:
  if search_intent_result["data"] is not None and len(search_intent_result["data"]) > 0:
  search_intent_analyse_result = data_analyse_tool(model_type, prompt_map, search_box,
@@ -315,7 +316,6 @@ def ask(question: Question) -> Answer:
  intent="normal_search",
  log_info=log_info,
  time_str=current_time)
-
  answer = Answer(query=search_box, query_intent="normal_search", knowledge_search_result=knowledge_search_result,
  sql_search_result=sql_search_result, agent_search_result=agent_search_response,
  suggested_question=generate_suggested_question_list)
@@ -382,6 +382,31 @@ def user_feedback_upvote(data_profiles: str, query: str, query_intent: str, quer
  except Exception as e:
  return False
 
+def user_feedback_downvote(data_profiles: str, query: str, query_intent: str, query_answer_list):
+ try:
+ if query_intent == "normal_search":
+ if len(query_answer_list) > 0:
+ log_id = generate_log_id()
+ current_time = get_current_time()
+ LogManagement.add_log_to_database(log_id=log_id, profile_name=data_profiles,
+ sql=query_answer_list[0].sql, query=query,
+ intent="normal_search_user_downvote",
+ log_info="",
+ time_str=current_time)
+ elif query_intent == "agent_search":
+ for each in query_answer_list:
+ log_id = generate_log_id()
+ current_time = get_current_time()
+ LogManagement.add_log_to_database(log_id=log_id, profile_name=data_profiles,
+ sql=each.sql, query=query + "; The sub task is " + each.query,
+ intent="agent_search_user_downvote",
+ log_info="",
+ time_str=current_time)
+ return True
+ except Exception as e:
+ return False
+
+
 
 def get_nlq_chain(question: Question) -> NLQChain:
  logger.debug(question)

application/utils/prompts/check_prompt.py

@@ -75,26 +75,19 @@
  },
  'agent': {
  'system_prompt': {
- 'mixtral-8x7b-instruct-0': [
- 'table_schema_data',
- 'sql_guidance',
- 'example_data'
- ],
- 'llama3-70b-instruct-0': [
- 'table_schema_data',
- 'sql_guidance',
- 'example_data'
- ],
- 'haiku-20240307v1-0': [
- 'table_schema_data',
- 'sql_guidance',
- 'example_data'
- ],
- 'sonnet-20240229v1-0': [
- 'table_schema_data',
- 'sql_guidance',
- 'example_data'
- ]
+ 'mixtral-8x7b-instruct-0': ['table_schema_data',
+ 'sql_guidance',
+ 'example_data'
+ ],
+ 'llama3-70b-instruct-0': ['table_schema_data',
+ 'sql_guidance',
+ 'example_data'],
+ 'haiku-20240307v1-0': ['table_schema_data',
+ 'sql_guidance',
+ 'example_data'],
+ 'sonnet-20240229v1-0': ['table_schema_data',
+ 'sql_guidance',
+ 'example_data']
  },
  'user_prompt': {
  'mixtral-8x7b-instruct-0': ['question'],

application/utils/prompts/generate_prompt.py

@@ -650,7 +650,7 @@
 # agent任务拆分
 agent_system_prompt_dict['mixtral-8x7b-instruct-0'] = """
 you are a data analysis expert as well as a retail expert. 
-Your current task is to conduct an in-depth analysis of the data.
+Your current task is to break down the current problem into multiple word problems based on the problem and the provided data table structure.
 
 <instructions>
 1. Fully understand the problem raised by the user
@@ -673,7 +673,8 @@
 
 </guidelines> 
 
-here is a example:
+Here are some examples of breaking down complex problems into subtasks:
+
 <example>
 
 {example_data}
@@ -682,11 +683,12 @@
 
 Please conduct a thorough analysis of the user's question according to the above instructions, and finally only output the JSON structure without outputting any other content.
 
+
 """
 
 agent_system_prompt_dict['llama3-70b-instruct-0'] = """
 you are a data analysis expert as well as a retail expert. 
-Your current task is to conduct an in-depth analysis of the data.
+Your current task is to break down the current problem into multiple word problems based on the problem and the provided data table structure.
 
 <instructions>
 1. Fully understand the problem raised by the user
@@ -709,7 +711,8 @@
 
 </guidelines> 
 
-here is a example:
+Here are some examples of breaking down complex problems into subtasks:
+
 <example>
 
 {example_data}
@@ -722,7 +725,7 @@
 
 agent_system_prompt_dict['haiku-20240307v1-0'] = """
 you are a data analysis expert as well as a retail expert. 
-Your current task is to conduct an in-depth analysis of the data.
+Your current task is to break down the current problem into multiple word problems based on the problem and the provided data table structure.
 
 <instructions>
 1. Fully understand the problem raised by the user
@@ -745,7 +748,8 @@
 
 </guidelines> 
 
-here is a example:
+Here are some examples of breaking down complex problems into subtasks:
+
 <example>
 
 {example_data}
@@ -758,7 +762,7 @@
 
 agent_system_prompt_dict['sonnet-20240229v1-0'] = """
 you are a data analysis expert as well as a retail expert. 
-Your current task is to conduct an in-depth analysis of the data.
+Your current task is to break down the current problem into multiple word problems based on the problem and the provided data table structure.
 
 <instructions>
 1. Fully understand the problem raised by the user
@@ -781,7 +785,8 @@
 
 </guidelines> 
 
-here is a example:
+Here are some examples of breaking down complex problems into subtasks:
+
 <example>
 
 {example_data}
@@ -1624,8 +1629,12 @@ def generate_agent_cot_system_prompt(ddl, prompt_map, search_box, model_id, agen
  user_prompt = prompt_map.get('agent', {}).get('user_prompt', {}).get(name)
 
  # reformat prompts
- system_prompt = system_prompt.format(table_schema_data=ddl, sql_guidance=agent_cot_example_str,
- example_data=AGENT_COT_EXAMPLE)
+ if agent_cot_example_str != "":
+ system_prompt = system_prompt.format(table_schema_data=ddl, sql_guidance="",
+ example_data=agent_cot_example_str)
+ else:
+ system_prompt = system_prompt.format(table_schema_data=ddl, sql_guidance="",
+ example_data=AGENT_COT_EXAMPLE)
  user_prompt = user_prompt.format(question=search_box)
 
  return user_prompt, system_prompt