doc:update readme

README.md

@@ -56,8 +56,7 @@ DB-GPT is an experimental open-source project that uses localized GPT large mode
 ## Demo
 Run on an RTX 4090 GPU.
 ##### Chat Excel
-![cx_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/659259dc-c3ba-41c8-8bc3-179cd4385dbe)
-![chatecl_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/2ebfdee2-2262-4d32-8933-4fb27f969180)
+![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0474d220-2a9f-449f-a940-92c8a25af390)
 ##### Chat Plugin
 ![auto_plugin_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/7d95c347-f4b7-4fb6-8dd2-c1c02babaa56)
 ##### LLM Management

README.zh.md

@@ -58,8 +58,7 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地
 示例通过 RTX 4090 GPU 演示
 
 ##### Chat Excel
-![cx_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/659259dc-c3ba-41c8-8bc3-179cd4385dbe)
-![chatecl_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/2ebfdee2-2262-4d32-8933-4fb27f969180)
+![excel](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/0474d220-2a9f-449f-a940-92c8a25af390)
 ##### Chat Plugin
 ![auto_plugin_new](https://github.com/eosphoros-ai/DB-GPT/assets/13723926/7d95c347-f4b7-4fb6-8dd2-c1c02babaa56)
 ##### LLM Management

pilot/base_modules/agent/commands/command_mange.py

@@ -227,7 +227,7 @@ def __is_need_wait_plugin_call(self, api_call_context):
                     i += 1
         return False
 
-    def __check_last_plugin_call_ready(self, all_context):
+    def check_last_plugin_call_ready(self, all_context):
         start_agent_count = all_context.count(self.agent_prefix)
         end_agent_count = all_context.count(self.agent_end)
 
@@ -359,7 +359,7 @@ def to_view_text(self, api_status: PluginStatus):
     def run(self, llm_text):
         if self.__is_need_wait_plugin_call(llm_text):
             # wait api call generate complete
-            if self.__check_last_plugin_call_ready(llm_text):
+            if self.check_last_plugin_call_ready(llm_text):
                 self.update_from_context(llm_text)
                 for key, value in self.plugin_status_map.items():
                     if value.status == Status.TODO.value:
@@ -379,7 +379,7 @@ def run(self, llm_text):
     def run_display_sql(self, llm_text, sql_run_func):
         if self.__is_need_wait_plugin_call(llm_text):
             # wait api call generate complete
-            if self.__check_last_plugin_call_ready(llm_text):
+            if self.check_last_plugin_call_ready(llm_text):
                 self.update_from_context(llm_text)
                 for key, value in self.plugin_status_map.items():
                     if value.status == Status.TODO.value:

pilot/openapi/api_v1/editor/api_editor_v1.py

@@ -29,6 +29,8 @@
 from pilot.scene.chat_dashboard.data_loader import DashboardDataLoader
 from pilot.scene.chat_db.data_loader import DbDataLoader
 from pilot.memory.chat_history.chat_hisotry_factory import ChatHistory
+from pilot.base_modules.agent.commands.command_mange import ApiCall
+
 
 router = APIRouter()
 CFG = Config()
@@ -101,12 +103,19 @@ async def get_editor_sql(con_uid: str, round: int):
                         logger.info(
                             f'history ai json resp:{element["data"]["content"]}'
                         )
-                        context = (
+                        api_call = ApiCall()
+                        result = {}
+                        result["thoughts"] = element["data"]["content"]
+                        if api_call.check_last_plugin_call_ready(
                             element["data"]["content"]
-                            .replace("\\n", " ")
-                            .replace("\n", " ")
-                        )
-                        return Result.succ(json.loads(context))
+                        ):
+                            api_call.update_from_context(element["data"]["content"])
+                            if len(api_call.plugin_status_map) > 0:
+                                first_item = next(
+                                    iter(api_call.plugin_status_map.items())
+                                )[1]
+                                result["sql"] = first_item.args["sql"]
+                        return Result.succ(result)
     return Result.faild(msg="not have sql!")
 
 
@@ -156,17 +165,22 @@ async def sql_editor_submit(sql_edit_context: ChatSqlEditContext = Body()):
             )
         )[0]
         if edit_round:
+            new_ai_text = ""
             for element in edit_round["messages"]:
                 if element["type"] == "ai":
-                    db_resp = json.loads(element["data"]["content"])
-                    db_resp["thoughts"] = sql_edit_context.new_speak
-                    db_resp["sql"] = sql_edit_context.new_sql
-                    element["data"]["content"] = json.dumps(db_resp)
+                    new_ai_text = element["data"]["content"]
+                    new_ai_text.replace(
+                        sql_edit_context.old_sql, sql_edit_context.new_sql
+                    )
+                    element["data"]["content"] = new_ai_text
+
+            for element in edit_round["messages"]:
                 if element["type"] == "view":
-                    data_loader = DbDataLoader()
-                    element["data"]["content"] = data_loader.get_table_view_by_conn(
-                        conn.run(sql_edit_context.new_sql), sql_edit_context.new_speak
+                    api_call = ApiCall()
+                    new_view_text = api_call.run_display_sql(
+                        new_ai_text, conn.run_to_df
                     )
+                    element["data"]["content"] = new_view_text
             history_mem.update(history_messages)
             return Result.succ(None)
     return Result.faild(msg="Edit Faild!")

pilot/scene/chat_db/auto_execute/chat.py

@@ -5,6 +5,7 @@
 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 @@ def __init__(self, chat_param: Dict):
 
         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 +71,12 @@ def generate_input_values(self):
         }
         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

@@ -8,32 +8,59 @@
 
 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.
+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}
+"""
 
-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.
+_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}
 
-Question: {input}
+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。
 
-Respond in JSON format as following format:
-{response}
-Ensure the response is correct json and can be parsed by Python json.loads
+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",
 }
 
 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 = 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,