feat(core): Support system code for feedback and prompt (#873)

README.zh.md

@@ -127,6 +127,7 @@ DB-GPT是一个开源的数据库领域大模型框架。目的是构建大模
   - [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b)
   - [qwen-7b-chat](https://huggingface.co/Qwen/Qwen-7B-Chat)
   - [qwen-14b-chat](https://huggingface.co/Qwen/Qwen-14B-Chat)
+  - [qwen-72b-chat](https://huggingface.co/Qwen/Qwen-72B-Chat)
   - [wizardlm-13b](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)
   - [orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
   - [orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)

pilot/configs/model_config.py

@@ -88,6 +88,18 @@ LLM_MODEL_CONFIG = {
     "qwen-14b-chat-int8": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int8"),
     # https://huggingface.co/Qwen/Qwen-14B-Chat-Int4
     "qwen-14b-chat-int4": os.path.join(MODEL_PATH, "Qwen-14B-Chat-Int4"),
+    # https://huggingface.co/Qwen/Qwen-72B-Chat
+    "qwen-72b-chat": os.path.join(MODEL_PATH, "Qwen-72B-Chat"),
+    # https://huggingface.co/Qwen/Qwen-72B-Chat-Int8
+    "qwen-72b-chat-int8": os.path.join(MODEL_PATH, "Qwen-72B-Chat-Int8"),
+    # https://huggingface.co/Qwen/Qwen-72B-Chat-Int4
+    "qwen-72b-chat-int4": os.path.join(MODEL_PATH, "Qwen-72B-Chat-Int4"),
+    # https://huggingface.co/Qwen/Qwen-1_8B-Chat
+    "qwen-1.8b-chat": os.path.join(MODEL_PATH, "Qwen-1_8B-Chat"),
+    # https://huggingface.co/Qwen/Qwen-1_8B-Chat-Int8
+    "qwen-1.8b-chat-int8": os.path.join(MODEL_PATH, "wen-1_8B-Chat-Int8"),
+    # https://huggingface.co/Qwen/Qwen-1_8B-Chat-Int4
+    "qwen-1.8b-chat-int4": os.path.join(MODEL_PATH, "Qwen-1_8B-Chat-Int4"),
     # (Llama2 based) We only support WizardLM-13B-V1.2 for now, which is trained from Llama-2 13b, see https://huggingface.co/WizardLM/WizardLM-13B-V1.2
     "wizardlm-13b": os.path.join(MODEL_PATH, "WizardLM-13B-V1.2"),
     # wget https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF/resolve/main/vicuna-13b-v1.5.Q4_K_M.gguf -O models/ggml-model-q4_0.gguf

pilot/openapi/api_v1/feedback/feed_back_db.py

@@ -49,7 +49,6 @@ class ChatFeedBackDao(BaseDao):
 
     def create_or_update_chat_feed_back(self, feed_back: FeedBackBody):
         # Todo: We need to have user information first.
-        def_user_name = ""
 
         session = self.get_session()
         chat_feed_back = ChatFeedBackEntity(
@@ -60,7 +59,7 @@ class ChatFeedBackDao(BaseDao):
             question=feed_back.question,
             knowledge_space=feed_back.knowledge_space,
             messages=feed_back.messages,
-            user_name=def_user_name,
+            user_name=feed_back.user_name,
             gmt_created=datetime.now(),
             gmt_modified=datetime.now(),
         )
@@ -76,7 +75,7 @@ class ChatFeedBackDao(BaseDao):
             result.question = feed_back.question
             result.knowledge_space = feed_back.knowledge_space
             result.messages = feed_back.messages
-            result.user_name = def_user_name
+            result.user_name = feed_back.user_name
             result.gmt_created = datetime.now()
             result.gmt_modified = datetime.now()
         else:

pilot/openapi/api_v1/feedback/feed_back_model.py

@@ -1,4 +1,5 @@
 from pydantic.main import BaseModel
+from typing import Optional
 
 
 class FeedBackBody(BaseModel):
@@ -12,14 +13,16 @@ class FeedBackBody(BaseModel):
     """question: human question"""
     question: str
 
-    """knowledge_space: knowledge space"""
-    knowledge_space: str
-
     """score: rating of the llm's answer"""
     score: int
 
     """ques_type: question type"""
     ques_type: str
 
+    user_name: Optional[str] = None
+
     """messages: rating detail"""
-    messages: str
+    messages: Optional[str] = None
+
+    """knowledge_space: knowledge space"""
+    knowledge_space: Optional[str] = None

pilot/server/prompt/prompt_manage_db.py

@@ -55,6 +55,7 @@ class PromptManageDao(BaseDao):
             prompt_name=prompt.prompt_name,
             content=prompt.content,
             user_name=prompt.user_name,
+            sys_code=prompt.sys_code,
             gmt_created=datetime.now(),
             gmt_modified=datetime.now(),
         )
@@ -83,6 +84,8 @@ class PromptManageDao(BaseDao):
             prompts = prompts.filter(
                 PromptManageEntity.prompt_name == query.prompt_name
             )
+        if query.sys_code is not None:
+            prompts = prompts.filter(PromptManageEntity.sys_code == query.sys_code)
 
         prompts = prompts.order_by(PromptManageEntity.gmt_created.desc())
         result = prompts.all()

pilot/server/prompt/request/request.py

@@ -1,24 +1,44 @@
 from typing import List
 
 from pydantic import BaseModel
+from typing import Optional
+from pydantic import BaseModel
 
 
 class PromptManageRequest(BaseModel):
-    """chat_scene: for example: chat_with_db_execute, chat_excel, chat_with_db_qa"""
+    """Model for managing prompts."""
 
-    chat_scene: str = None
+    chat_scene: Optional[str] = None
+    """
+    The chat scene, e.g. chat_with_db_execute, chat_excel, chat_with_db_qa.
+    """
 
-    """sub_chat_scene: sub chat scene"""
+    sub_chat_scene: Optional[str] = None
-    sub_chat_scene: str = None
+    """
+    The sub chat scene.
+    """
 
-    """prompt_type: common or private"""
+    prompt_type: Optional[str] = None
-    prompt_type: str = None
+    """
+    The prompt type, either common or private.
+    """
 
-    """content: prompt content"""
+    content: Optional[str] = None
-    content: str = None
+    """
+    The prompt content.
+    """
 
-    """user_name: user name"""
+    user_name: Optional[str] = None
-    user_name: str = None
+    """
+    The user name.
+    """
 
-    """prompt_name: prompt name"""
+    sys_code: Optional[str] = None
-    prompt_name: str = None
+    """
+    System code
+    """
+
+    prompt_name: Optional[str] = None
+    """
+    The prompt name.
+    """

pilot/server/prompt/service.py

@@ -17,9 +17,15 @@ class PromptManageService:
         query = PromptManageRequest(
             prompt_name=request.prompt_name,
         )
+        err_sys_str = ""
+        if query.sys_code:
+            query.sys_code = request.sys_code
+            err_sys_str = f" and sys_code: {request.sys_code}"
         prompt_name = prompt_manage_dao.get_prompts(query)
         if len(prompt_name) > 0:
-            raise Exception(f"prompt name:{request.prompt_name} have already named")
+            raise Exception(
+                f"prompt name: {request.prompt_name}{err_sys_str} have already named"
+            )
         prompt_manage_dao.create_prompt(request)
         return True
 
@@ -32,6 +38,7 @@ class PromptManageService:
             prompt_type=request.prompt_type,
             prompt_name=request.prompt_name,
             user_name=request.user_name,
+            sys_code=request.sys_code,
         )
         responses = []
         prompts = prompt_manage_dao.get_prompts(query)