add log store

application/api/schemas.py

@@ -67,11 +67,7 @@ class SQLSearchResult(BaseModel):
 
 class TaskSQLSearchResult(BaseModel):
     sub_task_query: str
-    sql: str
-    sql_data: list[Any]
-    data_show_type: str
-    sql_gen_process: str
-    data_analyse: str
+    sql_search_result: SQLSearchResult
 
 
 class KnowledgeSearchResult(BaseModel):

application/api/service.py

@@ -8,6 +8,7 @@
 from nlq.business.nlq_chain import NLQChain
 from nlq.business.profile import ProfileManagement
 from nlq.business.vector_store import VectorStore
+from nlq.business.log_store import LogManagement
 from utils.apis import get_sql_result_tool
 from utils.database import get_db_url_dialect
 from nlq.business.suggested_question import SuggestedQuestionManagement as sqm
@@ -16,6 +17,7 @@
     generate_suggested_question, data_visualization
 from utils.opensearch import get_retrieve_opensearch
 from utils.text_search import normal_text_search, agent_text_search
+from utils.tool import generate_log_id, get_current_time
 from .schemas import Question, Answer, Example, Option, SQLSearchResult, AgentSearchResult, KnowledgeSearchResult, \
     TaskSQLSearchResult
 from .exception_handler import BizException
@@ -179,6 +181,10 @@ def ask(question: Question) -> Answer:
 
     filter_deep_dive_sql_result = []
 
+    log_id = generate_log_id()
+    current_time = get_current_time()
+    log_info = ""
+
     all_profiles = ProfileManagement.get_all_profiles_with_info()
     database_profile = all_profiles[selected_profile]
 
@@ -188,7 +194,7 @@ def ask(question: Question) -> Answer:
                                         sql_gen_process="",
                                         data_analyse="")
 
-    agent_search_response = AgentSearchResult(agent_summary="", agent_sql_search_result=[], sub_search_task=[])
+    agent_search_response = AgentSearchResult(agent_summary="", agent_sql_search_result=[])
 
     knowledge_search_result = KnowledgeSearchResult(knowledge_response="")
 
@@ -232,6 +238,8 @@ def ask(question: Question) -> Answer:
         answer = Answer(query=search_box, query_intent="reject_search", knowledge_search_result=knowledge_search_result,
                         sql_search_result=sql_search_result, agent_search_result=agent_search_response,
                         suggested_question=[])
+        LogManagement.add_log_to_database(log_id=log_id, profile_name=selected_profile, sql="", query=search_box,
+                                          intent="reject_search", log_info="", time_str=current_time)
         return answer
     elif search_intent_flag:
         normal_search_result = normal_text_search(search_box, model_type,
@@ -246,6 +254,10 @@ def ask(question: Question) -> Answer:
                         knowledge_search_result=knowledge_search_result,
                         sql_search_result=sql_search_result, agent_search_result=agent_search_response,
                         suggested_question=[])
+
+        LogManagement.add_log_to_database(log_id=log_id, profile_name=selected_profile, sql="", query=search_box,
+                                          intent="knowledge_search", log_info=knowledge_search_result.knowledge_response,
+                                          time_str=current_time)
         return answer
 
     else:
@@ -298,6 +310,12 @@ def ask(question: Question) -> Answer:
                 # sql_search_result.sql_data = [list(search_intent_result["data"].columns)] + search_intent_result[
                 #     "data"].values.tolist()
 
+        log_info = search_intent_result["error_info"] + ";" + sql_search_result.data_analyse
+        LogManagement.add_log_to_database(log_id=log_id, profile_name=selected_profile, sql=sql_search_result.sql, query=search_box,
+                                          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)
@@ -311,13 +329,30 @@ def ask(question: Question) -> Answer:
                     orient='records')
                 filter_deep_dive_sql_result.append(agent_search_result[i])
                 each_task_sql_res = [list(each_task_res["data"].columns)] + each_task_res["data"].values.tolist()
+
+                model_select_type, show_select_data = data_visualization(model_type, agent_search_result[i]["query"],
+                                                                         each_task_res["data"],
+                                                                         database_profile['prompt_map'])
+
+                each_task_sql_response = agent_search_result[i]["response"]
+                sub_task_sql_result = SQLSearchResult(sql_data=show_select_data, sql=each_task_res["sql"], data_show_type=model_select_type,
+                                                    sql_gen_process=each_task_sql_response,
+                                                    data_analyse="")
+
                 each_task_sql_search_result = TaskSQLSearchResult(sub_task_query=agent_search_result[i]["query"],
-                                                                  sql_data=each_task_sql_res,
-                                                                  sql=each_task_res["sql"], data_show_type="table",
-                                                                  sql_gen_process="",
-                                                                  data_analyse="")
+                                                                  sql_search_result=sub_task_sql_result)
                 agent_sql_search_result.append(each_task_sql_search_result)
+
                 sub_search_task.append(agent_search_result[i]["query"])
+                log_info = ""
+            else:
+                log_info = agent_search_result[i]["query"] + "The SQL error Info: " + each_task_res["error_info"] + "。"
+            log_id = generate_log_id()
+            LogManagement.add_log_to_database(log_id=log_id, profile_name=selected_profile, sql=each_task_res["sql"],
+                                              query=search_box + "; The sub task is " + agent_search_result[i]["query"],
+                                              intent="agent_search",
+                                              log_info=log_info,
+                                              time_str=current_time)
         agent_data_analyse_result = data_analyse_tool(model_type, prompt_map, search_box,
                                                       json.dumps(filter_deep_dive_sql_result, ensure_ascii=False),
                                                       "agent")

application/nlq/business/log_store.py

@@ -0,0 +1,13 @@
+import logging
+
+from nlq.data_access.dynamo_query_log import DynamoQueryLogDao
+
+logger = logging.getLogger(__name__)
+
+
+class LogManagement:
+    query_log_dao = DynamoQueryLogDao()
+
+    @classmethod
+    def add_log_to_database(cls, log_id, profile_name, sql, query, intent, log_info, time_str):
+        cls.query_log_dao.add_log(log_id, profile_name, sql, query, intent, log_info, time_str)

application/nlq/data_access/dynamo_query_log.py

@@ -11,7 +11,7 @@
 DYNAMODB_AWS_REGION = os.environ.get('DYNAMODB_AWS_REGION')
 
 
-class DynamoQueryLog:
+class DynamoQueryLogEntity:
     def __init__(self, log_id, profile_name, sql, query, intent, log_info, time_str):
         self.log_id = log_id
         self.profile_name = profile_name
@@ -101,7 +101,14 @@ def create_table(self):
             raise
 
     def add(self, entity):
-        self.table.put_item(Item=entity.to_dict())
+        try:
+            self.table.put_item(Item=entity.to_dict())
+        except Exception as e:
+            logger.error("add log entity is error {}",e)
 
     def update(self, entity):
         self.table.put_item(Item=entity.to_dict())
+
+    def add_log(self, log_id, profile_name, sql, query, intent, log_info, time_str):
+        entity = DynamoQueryLogEntity(log_id, profile_name, sql, query, intent, log_info, time_str)
+        self.add(entity)

application/utils/constant.py

@@ -2,6 +2,5 @@
 PROFILE_QUESTION_TABLE_NAME = 'NlqSuggestedQuestion'
 DEFAULT_PROMPT_NAME = 'suggested_question_prompt_default'
 ACTIVE_PROMPT_NAME = 'suggested_question_prompt_active'
-BEDROCK_MODEL_IDS = ['anthropic.claude-3-sonnet-20240229-v1:0', 'anthropic.claude-3-opus-20240229-v1:0',
-                 'anthropic.claude-3-haiku-20240307-v1:0', 'mistral.mixtral-8x7b-instruct-v0:1',
-                 'meta.llama3-70b-instruct-v1:0']
+BEDROCK_MODEL_IDS = ['anthropic.claude-3-sonnet-20240229-v1:0', 'anthropic.claude-3-haiku-20240307-v1:0',
+                     'mistral.mixtral-8x7b-instruct-v0:1', 'meta.llama3-70b-instruct-v1:0']

application/utils/tool.py

@@ -1,17 +1,44 @@
 import logging
+import time
+import random
+from datetime import datetime
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 
-
-
-
 def get_generated_sql(generated_sql_response):
     sql = ""
     try:
         return generated_sql_response.split("<sql>")[1].split("</sql>")[0]
     except IndexError:
         logger.error("No SQL found in the LLM's response")
         logger.error(generated_sql_response)
-    return sql
+    return sql
+
+
+def generate_log_id():
+    # 获取当前时间戳，精确到微秒
+    timestamp = int(time.time() * 1000000)
+    # 添加随机数以增加唯一性
+    random_part = random.randint(0, 9999)
+    # 拼接时间戳和随机数生成logID
+    log_id = f"{timestamp}{random_part:04d}"
+    return log_id
+
+
+def get_current_time():
+    # 获取当前时间
+    now = datetime.now()
+    # 格式化时间，包括毫秒部分
+    # 注意：strftime默认不直接支持毫秒，需要单独处理
+    formatted_time = now.strftime('%Y-%m-%d %H:%M:%S')
+    return formatted_time
+
+
+def get_generated_sql_explain(generated_sql_response):
+    index = generated_sql_response.find("</sql>")
+    if index != -1:
+        return generated_sql_response[index + len("</sql>"):]
+    else:
+        return generated_sql_response