Merge remote-tracking branch 'origin/dev_ty_06_end' into llm_framework

pilot/connections/rdbms/py_study/study_enum.py

@@ -0,0 +1,44 @@
+from enum import Enum
+from typing import List
+
+class Test(Enum):
+    XXX = ("x", "1", True)
+    YYY =("Y", "2", False)
+    ZZZ = ("Z", "3")
+    def __init__(self, code, v, flag= False):
+        self.code = code
+        self.v = v
+        self.flag = flag
+
+class Scene:
+    def __init__(self, code, name, describe, param_types:List=[], is_inner: bool = False):
+        self.code = code
+        self.name = name
+        self.describe = describe
+        self.param_types = param_types
+        self.is_inner = is_inner
+
+
+class ChatScene(Enum):
+
+    ChatWithDbExecute = Scene("chat_with_db_execute", "Chat Data", "Dialogue with your private data through natural language.", ["DB Select"])
+    ChatWithDbQA = Scene("chat_with_db_qa", "Chat Meta Data", "Have a Professional Conversation with Metadata.", ["DB Select"])
+    ChatExecution = Scene("chat_execution", "Chat Plugin", "Use tools through dialogue to accomplish your goals.", ["Plugin Select"])
+    ChatDefaultKnowledge = Scene("chat_default_knowledge", "Chat Default Knowledge", "Dialogue through natural language and private documents and knowledge bases.")
+    ChatNewKnowledge = Scene("chat_new_knowledge", "Chat New Knowledge", "Dialogue through natural language and private documents and knowledge bases.", ["Knowledge Select"])
+    ChatUrlKnowledge = Scene("chat_url_knowledge", "Chat URL", "Dialogue through natural language and private documents and knowledge bases.", ["Url Input"])
+    InnerChatDBSummary = Scene("inner_chat_db_summary", "DB Summary", "Db Summary.", True)
+
+    ChatNormal = Scene("chat_normal", "Chat Normal", "Native LLM large model AI dialogue.")
+    ChatDashboard = Scene("chat_dashboard", "Chat Dashboard", "Provide you with professional analysis reports through natural language.", ["DB Select"])
+    ChatKnowledge = Scene("chat_knowledge", "Chat Knowledge", "Dialogue through natural language and private documents and knowledge bases.", ["Knowledge Space Select"])
+
+    def scene_value(self):
+        return self.value.code;
+
+    def scene_name(self):
+       return self._value_.name;
+
+if __name__ == "__main__":
+    print(ChatScene.ChatWithDbExecute.scene_value())
+    # print(ChatScene.ChatWithDbExecute.value.describe)

pilot/memory/chat_history/duckdb_history.py

@@ -36,8 +36,9 @@ class DuckdbHistoryMemory(BaseChatHistoryMemory):
         if not result:
             # 如果表不存在，则创建新表
             self.connect.execute(
-                "CREATE TABLE chat_history (conv_uid VARCHAR(100) PRIMARY KEY, chat_mode VARCHAR(50), summary VARCHAR(255),  user_name VARCHAR(100), messages TEXT)"
+                "CREATE TABLE chat_history (id integer primary key, conv_uid VARCHAR(100) UNIQUE, chat_mode VARCHAR(50), summary VARCHAR(255),  user_name VARCHAR(100), messages TEXT)"
             )
+            self.connect.execute("CREATE SEQUENCE seq_id START 1;")
 
     def __get_messages_by_conv_uid(self, conv_uid: str):
         cursor = self.connect.cursor()
@@ -59,7 +60,7 @@ class DuckdbHistoryMemory(BaseChatHistoryMemory):
         try:
             cursor = self.connect.cursor()
             cursor.execute(
-                "INSERT INTO chat_history(conv_uid, chat_mode summary, user_name, messages)VALUES(?,?,?,?,?)",
+                "INSERT INTO chat_history(id, conv_uid, chat_mode summary, user_name, messages)VALUES(nextval('seq_id'),?,?,?,?,?)",
                 [self.chat_seesion_id, chat_mode, summary, user_name, ""],
             )
             cursor.commit()
@@ -81,7 +82,7 @@ class DuckdbHistoryMemory(BaseChatHistoryMemory):
             )
         else:
             cursor.execute(
-                "INSERT INTO chat_history(conv_uid, chat_mode,  summary, user_name, messages)VALUES(?,?,?,?,?)",
+                "INSERT INTO chat_history(id, conv_uid, chat_mode,  summary, user_name, messages)VALUES(nextval('seq_id'),?,?,?,?,?)",
                 [
                     self.chat_seesion_id,
                     once_message.chat_mode,
@@ -115,10 +116,10 @@ class DuckdbHistoryMemory(BaseChatHistoryMemory):
             cursor = duckdb.connect(duckdb_path).cursor()
             if user_name:
                 cursor.execute(
-                    "SELECT * FROM chat_history where user_name=? limit 20", [user_name]
+                    "SELECT * FROM chat_history where user_name=? order by id desc limit 20", [user_name]
                 )
             else:
-                cursor.execute("SELECT * FROM chat_history limit 20")
+                cursor.execute("SELECT * FROM chat_history order by id desc limit 20")
             # 获取查询结果字段名
             fields = [field[0] for field in cursor.description]
             data = []

pilot/openapi/api_v1/api_v1.py

@@ -66,7 +66,7 @@ def __get_conv_user_message(conversations: dict):
 
 def __new_conversation(chat_mode, user_id) -> ConversationVo:
     unique_id = uuid.uuid1()
-    history_mem = DuckdbHistoryMemory(str(unique_id))
+    # history_mem = DuckdbHistoryMemory(str(unique_id))
     return ConversationVo(conv_uid=str(unique_id), chat_mode=chat_mode)
 
 
@@ -102,12 +102,7 @@ async def read_main():
 
 
 @router.get("/v1/chat/dialogue/list", response_model=Result[ConversationVo])
-async def dialogue_list(response: Response, user_id: str = None):
+async def dialogue_list( user_id: str = None):
-    # 设置CORS头部信息
-    response.headers["Access-Control-Allow-Origin"] = "*"
-    response.headers["Access-Control-Allow-Methods"] = "GET"
-    response.headers["Access-Control-Request-Headers"] = "content-type"
-
     dialogues: List = []
     datas = DuckdbHistoryMemory.conv_list(user_id)
 
@@ -132,19 +127,17 @@ async def dialogue_scenes():
     new_modes: List[ChatScene] = [
         ChatScene.ChatWithDbExecute,
         ChatScene.ChatWithDbQA,
-        ChatScene.ChatDashboard,
         ChatScene.ChatKnowledge,
+        ChatScene.ChatDashboard,
         ChatScene.ChatExecution,
     ]
     for scene in new_modes:
-        if not scene.value in [
+
-            ChatScene.ChatNormal.value,
-            ChatScene.InnerChatDBSummary.value,
-        ]:
         scene_vo = ChatSceneVo(
-                chat_scene=scene.value,
+            chat_scene=scene.value(),
-                scene_name=scene.name,
+            scene_name=scene.scene_name(),
-                param_title="Selection Param",
+            scene_describe= scene.describe(),
+            param_title=",".join(scene.param_types()),
         )
         scene_vos.append(scene_vo)
     return Result.succ(scene_vos)
@@ -152,23 +145,23 @@ async def dialogue_scenes():
 
 @router.post("/v1/chat/dialogue/new", response_model=Result[ConversationVo])
 async def dialogue_new(
-    chat_mode: str = ChatScene.ChatNormal.value, user_id: str = None
+    chat_mode: str = ChatScene.ChatNormal.value(), user_id: str = None
 ):
     conv_vo = __new_conversation(chat_mode, user_id)
     return Result.succ(conv_vo)
 
 
 @router.post("/v1/chat/mode/params/list", response_model=Result[dict])
-async def params_list(chat_mode: str = ChatScene.ChatNormal.value):
+async def params_list(chat_mode: str = ChatScene.ChatNormal.value()):
-    if ChatScene.ChatWithDbQA.value == chat_mode:
+    if ChatScene.ChatWithDbQA.value() == chat_mode:
         return Result.succ(get_db_list())
-    elif ChatScene.ChatWithDbExecute.value == chat_mode:
+    elif ChatScene.ChatWithDbExecute.value() == chat_mode:
         return Result.succ(get_db_list())
-    elif ChatScene.ChatDashboard.value == chat_mode:
+    elif ChatScene.ChatDashboard.value() == chat_mode:
         return Result.succ(get_db_list())
-    elif ChatScene.ChatExecution.value == chat_mode:
+    elif ChatScene.ChatExecution.value() == chat_mode:
         return Result.succ(plugins_select_info())
-    elif ChatScene.ChatKnowledge.value == chat_mode:
+    elif ChatScene.ChatKnowledge.value() == chat_mode:
         return Result.succ(knowledge_list())
     else:
         return Result.succ(None)
@@ -201,7 +194,7 @@ async def dialogue_history_messages(con_uid: str):
 async def chat_completions(dialogue: ConversationVo = Body()):
     print(f"chat_completions:{dialogue.chat_mode},{dialogue.select_param}")
     if not dialogue.chat_mode:
-        dialogue.chat_mode = ChatScene.ChatNormal.value
+        dialogue.chat_mode = ChatScene.ChatNormal.value()
     if not dialogue.conv_uid:
         conv_vo = __new_conversation(dialogue.chat_mode, dialogue.user_name)
         dialogue.conv_uid = conv_vo.conv_uid
@@ -222,17 +215,17 @@ async def chat_completions(dialogue: ConversationVo = Body()):
         "user_input": dialogue.user_input,
     }
 
-    if ChatScene.ChatWithDbQA.value == dialogue.chat_mode:
+    if ChatScene.ChatWithDbQA.value() == dialogue.chat_mode:
         chat_param.update({"db_name": dialogue.select_param})
-    elif ChatScene.ChatWithDbExecute.value == dialogue.chat_mode:
+    elif ChatScene.ChatWithDbExecute.value() == dialogue.chat_mode:
         chat_param.update({"db_name": dialogue.select_param})
-    elif ChatScene.ChatDashboard.value == dialogue.chat_mode:
+    elif ChatScene.ChatDashboard.value() == dialogue.chat_mode:
         chat_param.update({"db_name": dialogue.select_param})
         ## DEFAULT
         chat_param.update({"report_name": "sales_report"})
-    elif ChatScene.ChatExecution.value == dialogue.chat_mode:
+    elif ChatScene.ChatExecution.value() == dialogue.chat_mode:
         chat_param.update({"plugin_selector": dialogue.select_param})
-    elif ChatScene.ChatKnowledge.value == dialogue.chat_mode:
+    elif ChatScene.ChatKnowledge.value() == dialogue.chat_mode:
         chat_param.update({"knowledge_space": dialogue.select_param})
 
     chat: BaseChat = CHAT_FACTORY.get_implementation(dialogue.chat_mode, **chat_param)

pilot/openapi/api_v1/api_view_model.py

@@ -26,7 +26,8 @@ class Result(Generic[T], BaseModel):
 class ChatSceneVo(BaseModel):
     chat_scene: str = Field(..., description="chat_scene")
     scene_name: str = Field(..., description="chat_scene name show for user")
-    param_title: str = Field(..., description="chat_scene required parameter title")
+    scene_describe: str = Field("", description="chat_scene describe ")
+    param_title: str = Field("", description="chat_scene required parameter title")
 
 
 class ConversationVo(BaseModel):

pilot/scene/base.py

@@ -1,28 +1,52 @@
 from enum import Enum
+from typing import List
 
 
 class Scene:
-    def __init__(self, code, describe, is_inner):
+    def __init__(self, code, name, describe, param_types: List = [], is_inner: bool = False):
         self.code = code
+        self.name = name
         self.describe = describe
+        self.param_types = param_types
         self.is_inner = is_inner
 
 
 class ChatScene(Enum):
-    ChatWithDbExecute = "chat_with_db_execute"
+    ChatWithDbExecute = Scene("chat_with_db_execute", "Chat Data",
-    ChatWithDbQA = "chat_with_db_qa"
+                              "Dialogue with your private data through natural language.", ["DB Select"])
-    ChatExecution = "chat_execution"
+    ChatWithDbQA = Scene("chat_with_db_qa", "Chat Meta Data", "Have a Professional Conversation with Metadata.",
-    ChatDefaultKnowledge = "chat_default_knowledge"
+                         ["DB Select"])
-    ChatNewKnowledge = "chat_new_knowledge"
+    ChatExecution = Scene("chat_execution", "Plugin", "Use tools through dialogue to accomplish your goals.",
-    ChatUrlKnowledge = "chat_url_knowledge"
+                          ["Plugin Select"])
-    InnerChatDBSummary = "inner_chat_db_summary"
+    ChatDefaultKnowledge = Scene("chat_default_knowledge", "Chat Default Knowledge",
+                                 "Dialogue through natural language and private documents and knowledge bases.")
+    ChatNewKnowledge = Scene("chat_new_knowledge", "Chat New Knowledge",
+                             "Dialogue through natural language and private documents and knowledge bases.",
+                             ["Knowledge Select"])
+    ChatUrlKnowledge = Scene("chat_url_knowledge", "Chat URL",
+                             "Dialogue through natural language and private documents and knowledge bases.",
+                             ["Url Input"])
+    InnerChatDBSummary = Scene("inner_chat_db_summary", "DB Summary", "Db Summary.", True)
 
-    ChatNormal = "chat_normal"
+    ChatNormal = Scene("chat_normal", "Chat Normal", "Native LLM large model AI dialogue.")
-    ChatDashboard = "chat_dashboard"
+    ChatDashboard = Scene("chat_dashboard", "Dashboard",
-    ChatKnowledge = "chat_knowledge"
+                          "Provide you with professional analysis reports through natural language.", ["DB Select"])
-    # ChatDb = "chat_db"
+    ChatKnowledge = Scene("chat_knowledge", "Chat Knowledge",
-    # ChatData= "chat_data"
+                          "Dialogue through natural language and private documents and knowledge bases.",
+                          ["Knowledge Space Select"])
 
     @staticmethod
     def is_valid_mode(mode):
-        return any(mode == item.value for item in ChatScene)
+        return any(mode == item.value() for item in ChatScene)
+
+    def value(self):
+        return self._value_.code;
+
+    def scene_name(self):
+        return self._value_.name;
+
+    def describe(self):
+        return self._value_.describe;
+
+    def param_types(self):
+        return self._value_.param_types

pilot/scene/base_chat.py

@@ -73,10 +73,10 @@ class BaseChat(ABC):
 
         ### load prompt template
         self.prompt_template: PromptTemplate = CFG.prompt_templates[
-            self.chat_mode.value
+            self.chat_mode.value()
         ]
         self.history_message: List[OnceConversation] = self.memory.messages()
-        self.current_message: OnceConversation = OnceConversation(chat_mode.value)
+        self.current_message: OnceConversation = OnceConversation(chat_mode.value())
         self.current_tokens_used: int = 0
 
     class Config:

pilot/scene/chat_dashboard/chat.py

@@ -23,7 +23,7 @@ CFG = Config()
 
 
 class ChatDashboard(BaseChat):
-    chat_scene: str = ChatScene.ChatDashboard.value
+    chat_scene: str = ChatScene.ChatDashboard.value()
     report_name: str
     """Number of results to return from the query"""
 

pilot/scene/chat_dashboard/prompt.py

@@ -17,7 +17,7 @@ Provide professional data analysis, use as few dimensions as possible, but no le
 Used to support goal: {input}
 
 Pay attention to the length of the output content of the analysis result, do not exceed 4000tokens
-According to the characteristics of the analyzed data, choose the best one from the charts provided below to display，chart types:
+According to the characteristics of the analyzed data, choose the best one from the charts provided below to display, use different types of charts as much as possible，chart types:
 {supported_chat_type}
 
 Give {dialect} data analysis SQL, analysis title, display method and analytical thinking,respond in the following json format:
@@ -40,7 +40,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = False
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatDashboard.value,
+    template_scene=ChatScene.ChatDashboard.value(),
     input_variables=["input", "table_info", "dialect", "supported_chat_type"],
     response_format=json.dumps(RESPONSE_FORMAT, indent=4),
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_dashboard/template/sales_report/dashboard.json

@@ -3,7 +3,7 @@
   "name": "sale_report",
   "introduce": "",
   "layout": "TODO",
-  "supported_chart_type":["HeatMap","sheet", "LineChart", "PieChart", "BarChart"],
+  "supported_chart_type":["HeatMap","sheet", "LineChart", "PieChart", "BarChart", "Scatterplot", "IndicatorValue", "Table"],
   "key_metrics":[],
   "trends": []
 }

pilot/scene/chat_db/auto_execute/chat.py

@@ -17,7 +17,7 @@ CFG = Config()
 
 
 class ChatWithDbAutoExecute(BaseChat):
-    chat_scene: str = ChatScene.ChatWithDbExecute.value
+    chat_scene: str = ChatScene.ChatWithDbExecute.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_db/auto_execute/out_parser.py

@@ -35,15 +35,15 @@ class DbChatOutputParser(BaseOutputParser):
         if len(data) <= 1:
             data.insert(0, ["result"])
         df = pd.DataFrame(data[1:], columns=data[0])
-        if CFG.NEW_SERVER_MODE:
+        if not CFG.NEW_SERVER_MODE:
-            html = df.to_html(index=False, escape=False, sparsify=False)
-            html = "".join(html.split())
-        else:
             table_style = """<style> 
                 table{border-collapse:collapse;width:100%;height:80%;margin:0 auto;float:center;border: 1px solid #007bff; background-color:#333; color:#fff}th,td{border:1px solid #ddd;padding:3px;text-align:center}th{background-color:#C9C3C7;color: #fff;font-weight: bold;}tr:nth-child(even){background-color:#444}tr:hover{background-color:#444}
              </style>"""
             html_table = df.to_html(index=False, escape=False)
             html = f"<html><head>{table_style}</head><body>{html_table}</body></html>"
+        else:
+            html = df.to_html(index=False, escape=False, sparsify=False)
+            html = "".join(html.split())
 
         view_text = f"##### {str(speak)}" + "\n" + html.replace("\n", " ")
         return view_text

pilot/scene/chat_db/auto_execute/prompt.py

@@ -37,7 +37,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = False
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatWithDbExecute.value,
+    template_scene=ChatScene.ChatWithDbExecute.value(),
     input_variables=["input", "table_info", "dialect", "top_k", "response"],
     response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, indent=4),
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_db/professional_qa/chat.py

@@ -15,7 +15,7 @@ CFG = Config()
 
 
 class ChatWithDbQA(BaseChat):
-    chat_scene: str = ChatScene.ChatWithDbQA.value
+    chat_scene: str = ChatScene.ChatWithDbQA.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_db/professional_qa/prompt.py

@@ -59,7 +59,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatWithDbQA.value,
+    template_scene=ChatScene.ChatWithDbQA.value(),
     input_variables=["input", "table_info"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_execution/chat.py

@@ -16,7 +16,7 @@ CFG = Config()
 
 
 class ChatWithPlugin(BaseChat):
-    chat_scene: str = ChatScene.ChatExecution.value
+    chat_scene: str = ChatScene.ChatExecution.value()
     plugins_prompt_generator: PluginPromptGenerator
     select_plugin: str = None
 

pilot/scene/chat_execution/prompt.py

@@ -40,7 +40,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_STREAM_OUT = False
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatExecution.value,
+    template_scene=ChatScene.ChatExecution.value(),
     input_variables=["input", "constraints", "commands_infos", "response"],
     response_format=json.dumps(RESPONSE_FORMAT, indent=4),
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_knowledge/custom/chat.py

@@ -23,7 +23,7 @@ CFG = Config()
 
 
 class ChatNewKnowledge(BaseChat):
-    chat_scene: str = ChatScene.ChatNewKnowledge.value
+    chat_scene: str = ChatScene.ChatNewKnowledge.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_knowledge/custom/prompt.py

@@ -38,7 +38,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatNewKnowledge.value,
+    template_scene=ChatScene.ChatNewKnowledge.value(),
     input_variables=["context", "question"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_knowledge/default/chat.py

@@ -25,7 +25,7 @@ CFG = Config()
 
 
 class ChatDefaultKnowledge(BaseChat):
-    chat_scene: str = ChatScene.ChatDefaultKnowledge.value
+    chat_scene: str = ChatScene.ChatDefaultKnowledge.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_knowledge/default/prompt.py

@@ -39,7 +39,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatDefaultKnowledge.value,
+    template_scene=ChatScene.ChatDefaultKnowledge.value(),
     input_variables=["context", "question"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_knowledge/inner_db_summary/chat.py

@@ -8,7 +8,7 @@ CFG = Config()
 
 
 class InnerChatDBSummary(BaseChat):
-    chat_scene: str = ChatScene.InnerChatDBSummary.value
+    chat_scene: str = ChatScene.InnerChatDBSummary.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_knowledge/inner_db_summary/prompt.py

@@ -38,7 +38,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = False
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.InnerChatDBSummary.value,
+    template_scene=ChatScene.InnerChatDBSummary.value(),
     input_variables=["db_profile_summary", "db_input", "response"],
     response_format=json.dumps(RESPONSE_FORMAT, indent=4),
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_knowledge/url/chat.py

@@ -24,7 +24,7 @@ CFG = Config()
 
 
 class ChatUrlKnowledge(BaseChat):
-    chat_scene: str = ChatScene.ChatUrlKnowledge.value
+    chat_scene: str = ChatScene.ChatUrlKnowledge.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_knowledge/url/prompt.py

@@ -38,7 +38,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatUrlKnowledge.value,
+    template_scene=ChatScene.ChatUrlKnowledge.value(),
     input_variables=["context", "question"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_knowledge/v1/chat.py

@@ -61,4 +61,4 @@ class ChatKnowledge(BaseChat):
 
     @property
     def chat_type(self) -> str:
-        return ChatScene.ChatKnowledge.value
+        return ChatScene.ChatKnowledge.value()

pilot/scene/chat_knowledge/v1/prompt.py

@@ -39,7 +39,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatKnowledge.value,
+    template_scene=ChatScene.ChatKnowledge.value(),
     input_variables=["context", "question"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/scene/chat_normal/chat.py

@@ -14,7 +14,7 @@ CFG = Config()
 
 
 class ChatNormal(BaseChat):
-    chat_scene: str = ChatScene.ChatNormal.value
+    chat_scene: str = ChatScene.ChatNormal.value()
 
     """Number of results to return from the query"""
 

pilot/scene/chat_normal/prompt.py

@@ -17,7 +17,7 @@ PROMPT_SEP = SeparatorStyle.SINGLE.value
 PROMPT_NEED_NEED_STREAM_OUT = True
 
 prompt = PromptTemplate(
-    template_scene=ChatScene.ChatNormal.value,
+    template_scene=ChatScene.ChatNormal.value(),
     input_variables=["input"],
     response_format=None,
     template_define=PROMPT_SCENE_DEFINE,

pilot/server/dbgpt_server.py

@@ -91,15 +91,19 @@ if __name__ == "__main__":
     parser.add_argument("--port", type=int, default=5000)
     parser.add_argument("--concurrency-count", type=int, default=10)
     parser.add_argument("--share", default=False, action="store_true")
+    parser.add_argument("-light", "--light", default=False,action="store_true", help="enable light mode")
     signal.signal(signal.SIGINT, signal_handler)
 
     # init server config
     args = parser.parse_args()
+    server_init(args)
 
+    if not args.light:
+        print("Model Unified Deployment Mode!")
         from pilot.server.llmserver import worker
         worker.start_check()
-    server_init(args)
         CFG.NEW_SERVER_MODE = True
-    import uvicorn
 
+
+    import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=args.port)

pilot/server/webserver.py

@@ -690,6 +690,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--model_list_mode", type=str, default="once", choices=["once", "reload"]
     )
+    parser.add_argument(
+        "-new", "--new", action="store_true", help="enable new http mode"
+    )
 
     # old version server config
     parser.add_argument("--host", type=str, default="0.0.0.0")