Merge remote-tracking branch 'origin/Agent_Hub_Dev' into Agent_Hub_Dev

pilot/base_modules/agent/commands/command.py

@@ -87,7 +87,8 @@ def execute_command(
         try:
             return cmd(**arguments)
         except Exception as e:
-            return f"Error: {str(e)}"
+            raise ValueError(f"Error: {str(e)}")
+            # return f"Error: {str(e)}"
     # TODO: Change these to take in a file rather than pasted code, if
     # non-file is given, return instructions "Input should be a python
     # filepath, write your code to file and try again

pilot/connections/rdbms/base.py

@@ -349,7 +349,7 @@ class RDBMSDatabase(BaseConnect):
     def run(self, command: str, fetch: str = "all") -> List:
         """Execute a SQL command and return a string representing the results."""
         print("SQL:" + command)
-        if not command:
+        if not command or len(command) <0:
             return []
         parsed, ttype, sql_type, table_name = self.__sql_parse(command)
         if ttype == sqlparse.tokens.DML:

pilot/scene/chat_db/auto_execute/chat.py

@@ -5,6 +5,7 @@ from pilot.scene.base import ChatScene
 from pilot.common.sql_database import Database
 from pilot.configs.config import Config
 from pilot.scene.chat_db.auto_execute.prompt import prompt
+from pilot.base_modules.agent.commands.command_mange import ApiCall
 
 CFG = Config()
 
@@ -37,6 +38,7 @@ class ChatWithDbAutoExecute(BaseChat):
 
         self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name)
         self.top_k: int = 200
+        self.api_call = ApiCall(display_registry=CFG.command_disply)
 
     def generate_input_values(self):
         """
@@ -69,6 +71,11 @@ class ChatWithDbAutoExecute(BaseChat):
         }
         return input_values
 
-    def do_action(self, prompt_response):
-        print(f"do_action:{prompt_response}")
-        return self.database.run(prompt_response.sql)
+    def stream_plugin_call(self, text):
+        text = text.replace("\n", " ")
+        print(f"stream_plugin_call:{text}")
+        return self.api_call.run_display_sql(text, self.database.run_to_df)
+    #
+    # def do_action(self, prompt_response):
+    #     print(f"do_action:{prompt_response}")
+    #     return self.database.run(prompt_response.sql)

pilot/scene/chat_db/auto_execute/prompt.py

@@ -8,24 +8,51 @@ from pilot.scene.chat_db.auto_execute.example import sql_data_example
 
 CFG = Config()
 
-PROMPT_SCENE_DEFINE = "You are a SQL expert. "
 
-_DEFAULT_TEMPLATE = """
+_PROMPT_SCENE_DEFINE_EN = "You are a database expert. "
+_PROMPT_SCENE_DEFINE_ZH = "你是一个数据库专家. "
+
+_DEFAULT_TEMPLATE_EN = """
 Given an input question, create a syntactically correct {dialect} sql.
-
-Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. 
-Use as few tables as possible when querying.
-Only use the following tables schema to generate sql:
-{table_info}
-Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
-
+Table structure information:
+             {table_info}
+Constraint:
+1. You can only use the table provided in the table structure information to generate sql. If you cannot generate sql based on the provided table structure, please say: "The table structure information provided is not enough to generate sql query." It is prohibited to fabricate information at will.
+2. Do not query columns that do not exist. Pay attention to which column is in which table.
+3. Replace the corresponding sql into the sql field in the returned result
+4. Unless the user specifies in the question a specific number of examples he wishes to obtain, always limit the query to a maximum of {top_k} results.
+5. Please output the Sql content in the following format to execute the corresponding SQL to display the data:<api-call><name>response_table</name><args><sql>SQL Query to run</sql></args></api-call>
+Please make sure to respond as following format:
+    thoughts summary to say to user.<api-call><name>response_table</name><args><sql>SQL Query to run</sql></args></api-call>
+    
 Question: {input}
-
-Respond in JSON format as following format:
-{response}
-Ensure the response is correct json and can be parsed by Python json.loads
 """
 
+_DEFAULT_TEMPLATE_ZH = """
+给定一个输入问题，创建一个语法正确的 {dialect} sql。
+已知表结构信息:
+    {table_info}
+
+约束:
+1. 只能使用表结构信息中提供的表来生成 sql，如果无法根据提供的表结构中生成 sql ，请说：“提供的表结构信息不足以生成 sql 查询。” 禁止随意捏造信息。
+2. 不要查询不存在的列，注意哪一列位于哪张表中。
+3.将对应的sql替换到返回结果中的sql字段中
+4.除非用户在问题中指定了他希望获得的具体示例数量，否则始终将查询限制为最多 {top_k} 个结果。
+
+请务必按照以下格式回复：
+   对用户说的想法摘要。<api-call><name>response_table</name><args><sql>要运行的 SQL</sql></args></api-call>
+   
+问题:{input}
+"""
+
+_DEFAULT_TEMPLATE = (
+    _DEFAULT_TEMPLATE_EN if CFG.LANGUAGE == "en" else _DEFAULT_TEMPLATE_ZH
+)
+
+PROMPT_SCENE_DEFINE = (
+    _PROMPT_SCENE_DEFINE_EN if CFG.LANGUAGE == "en" else _PROMPT_SCENE_DEFINE_ZH
+)
+
 RESPONSE_FORMAT_SIMPLE = {
     "thoughts": "thoughts summary to say to user",
     "sql": "SQL Query to run",
@@ -33,7 +60,7 @@ RESPONSE_FORMAT_SIMPLE = {
 
 PROMPT_SEP = SeparatorStyle.SINGLE.value
 
-PROMPT_NEED_NEED_STREAM_OUT = False
+PROMPT_NEED_NEED_STREAM_OUT = True
 
 # Temperature is a configuration hyperparameter that controls the randomness of language model output.
 # A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
@@ -43,7 +70,7 @@ PROMPT_TEMPERATURE = 0.5
 prompt = PromptTemplate(
     template_scene=ChatScene.ChatWithDbExecute.value(),
     input_variables=["input", "table_info", "dialect", "top_k", "response"],
-    response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
+    # response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
     template_define=PROMPT_SCENE_DEFINE,
     template=_DEFAULT_TEMPLATE,
     stream_out=PROMPT_NEED_NEED_STREAM_OUT,