修改了单元测试的问题生成代码

backend/.env.example

@@ -1,3 +1,8 @@
+JIEBA_DATA=./nltk_data
+NLTK_DATA=./nltk_data
+SQLITE_DATABASE_URL=sqlite:///./source.db
+DATA_SOURCE_CACHE=./restapi
+
 # The Llama Cloud API key.
 # LLAMA_CLOUD_API_KEY=
 SQL_DATABASE_URL=mysql+pymysql://zjinfo1:Dy2Bcr53Hm5xRkba@110.42.234.166:3306/zjinfo1
@@ -80,3 +85,4 @@ SYSTEM_PROMPT="You are a weather forecast agent. You help users to get the weath
 - You can install any pip package (if it exists) by running a cell with pip install.
 "
 
+PROJECT_TITLE = "您好，我是博微工程理解小助手，您可以问我有关[线路工程]工程数据的相关问题！"

backend/.env.xinference

@@ -1,3 +1,8 @@
+JIEBA_DATA=./nltk_data
+NLTK_DATA=./nltk_data
+SQLITE_DATABASE_URL=sqlite:///./source.db
+DATA_SOURCE_CACHE=./restapi
+
 # The Llama Cloud API key.
 # LLAMA_CLOUD_API_KEY=
 SQL_DATABASE_URL=mysql+pymysql://zjinfo1:Dy2Bcr53Hm5xRkba@110.42.234.166:3306/zjinfo1
@@ -111,3 +116,4 @@ SYSTEM_PROMPT="You are a weather forecast agent. You help users to get the weath
 - You can install any pip package (if it exists) by running a cell with pip install.
 "
 
+PROJECT_TITLE = "您好，我是博微工程理解小助手，您可以问我有关[线路工程]工程数据的相关问题！"

backend/app/api/routers/app.py

@@ -3,6 +3,7 @@ import json
 import logging
 import time
 from typing import Dict, List, Any, Optional, AsyncGenerator
+from collections import deque
 
 from aiostream import stream
 from fastapi import APIRouter, Request
@@ -13,7 +14,8 @@ from llama_index.core.callbacks import CBEventType
 from llama_index.core.chat_engine.types import StreamingAgentChatResponse
 from llama_index.core.tools import ToolOutput
 from pydantic import BaseModel
-from app.api.routers.request.base import userMng, conversations,message,parameter
+from app.api.routers.request.base import userMng, conversations,message,parameter,feedback
+from app.api.routers.request.baseConfig import *
 from app.api.routers.request.models import ChatRequestData,ChatFileUploadRequest
 from app.engine import get_chat_engine
 import uuid
@@ -24,78 +26,138 @@ api_router = r = APIRouter()
 v1_router = v = APIRouter()
 
 class ChatCallbackEvent(BaseModel):
-    event_type: CBEventType
+    event_type: ChatEventType
     payload: Optional[Dict[str, Any]] = None
-    event_id: str = ""
 
-    def get_retrieval_message(self) -> dict | None:
-        if self.payload:
-            nodes = self.payload.get("nodes")
-            if nodes:
-                msg = f"根据查询检索到 {len(nodes)} 源文件"
-            else:
-                msg = f"查询检索中: '{self.payload.get('query_str')}'"
-            return {
-                "type": "events",
-                "data": {"title": msg},
-            }
-        else:
-            return None
+    def get_common_param(self)-> dict:
+        return {
+            'event': self.event_type.name,
+            'conversation_id':self.payload.get("conversation_id"),
+            'message_id': self.payload.get("message_id"), 
+            'created_at':  int(time.time()),
+            'task_id': self.payload.get("task_id")
+        }
 
-    def get_tool_message(self) -> dict | None:
-        func_call_args = self.payload.get("function_call")
-        if func_call_args is not None and "tool" in self.payload:
-            tool = self.payload.get("tool")
-            return {
-                "type": "events",
-                "data": {
-                    "title": f"调用工具 {tool.name} ，参数: {func_call_args}",
+    def get_WorkflowStart_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'workflow_run_id':self.payload.get('workflow_run_id'),
+            'data':{
+                "id": self.payload.get('workflow_run_id'),
+                "workflow_id": self.payload.get('workflow_id'),
+                "sequence_number": 1709,
+                "inputs": {
+                    "sys.query": self.payload.get('query'),
+                    "sys.files": [],
+                    "sys.conversation_id": self.payload.get('conversation_id'),
+                    "sys.user_id": self.payload.get('use_id')
                 },
+                "created_at": int(time.time())
             }
+        })
+        return params
 
-    def _is_output_serializable(self, output: Any) -> bool:
-        try:
-            json.dumps(output)
-            return True
-        except TypeError:
-            return False
+    def get_WorkflowFinished_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'workflow_run_id':self.payload.get('workflow_run_id'),
+            'data':{
+                "id": self.payload.get('workflow_run_id'),
+                "workflow_id": self.payload.get('workflow_id'),
+                "sequence_number": 1709,
+                "status": "succeeded",
+                "outputs": {
+                    "answer": self.payload.get('response')
+                },
+                "error": '',
+                "elapsed_time": 36.03764106379822,
+                "total_tokens": 11707,
+                "total_steps": 10,
+                "created_by": {
+                "id": str(uuid.uuid4()),
+                "user": self.payload.get('use_id')
+                },
+                "created_at": int(time.time()),
+                "finished_at": int(time.time()),
+                "files": []
+            }
+        })
+        return params
     
-    def get_agent_tool_response(self) -> dict | None:
-        response = self.payload.get("response")
-        if response is not None:
-            sources = response.sources
-            for source in sources:
-                # Return the tool response here to include the toolCall information
-                if isinstance(source, ToolOutput):
-                    if self._is_output_serializable(source.raw_output):
-                        output = source.raw_output
-                    else:
-                        output = source.content
+    def get_NodeStart_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'workflow_run_id':self.payload.get('workflow_run_id'),
+            'data':{
+                "id": self.payload.get('nodeid'),
+                "node_id": self.payload.get('nodeid'),
+                "node_type": "http-request",
+                "title": self.payload.get('title'),
+                "index": self.payload.get('index'),
+                "predecessor_node_id": self.payload.get('predecessor_node_id'),
+                "inputs": '',
+                "created_at": 1724398751,
+                "extras": {}
+            }
+        })
+        return params     
 
-                    return {
-                        "type": "tools",
-                        "data": {
-                            "toolOutput": {
-                                "output": output,
-                                "isError": source.is_error,
-                            },
-                            "toolCall": {
-                                "id": None,  # There is no tool id in the ToolOutput
-                                "name": source.tool_name,
-                                "input": source.raw_input,
-                            },
-                        },
-                    }
+    def get_NodeFinished_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'workflow_run_id':self.payload.get('workflow_run_id'),
+            'data':{
+                "id": self.payload.get('nodeid'),
+                "node_id": self.payload.get('nodeid'),
+                "node_type": "http-request",
+                "title": self.payload.get('title'),
+                "index": self.payload.get('index'),
+                "predecessor_node_id": self.payload.get('predecessor_node_id'),
+                "inputs": '',
+                "process_data": '',
+                "outputs": '',
+                "status": "succeeded",
+                "error": '',
+                "elapsed_time": 0.10402441816404462,
+                "execution_metadata": '',
+                "created_at": 1724398751,
+                "finished_at": 1724398751,
+                "files": []
+            }
+        })
+        return params  
 
-    def to_response(self):
+    def get_Message_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'id':self.payload.get('message_id'),
+            'answer':self.payload.get('answer')
+        })
+        return params 
+    
+    def get_MessageEnd_param(self) -> dict:
+        params = self.get_common_param()
+        params.update({
+            'id':self.payload.get('message_id'),
+            'metadata':self.payload.get('metadata')
+        })
+        return params 
+
+    def to_response(self)-> dict|None:
         try:
             match self.event_type:
-                case "retrieve":
-                    return self.get_retrieval_message()
-                case "function_call":
-                    return self.get_tool_message()
-                case "agent_step":
-                    return self.get_agent_tool_response()
+                case "workflow_started":
+                    return self.get_WorkflowStart_param()
+                case "workflow_finished":
+                    return self.get_WorkflowFinished_param()
+                case "node_started":
+                    return self.get_NodeStart_param()
+                case 'node_finished':
+                    return self.get_NodeFinished_param()
+                case 'message':
+                    return self.get_Message_param()
+                case 'message_end':
+                    return self.get_MessageEnd_param()
                 case _:
                     return None
         except Exception as e:
@@ -106,9 +168,7 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
     _aqueue: asyncio.Queue
     is_done: bool = False
 
-    def __init__(
-            self,
-    ):
+    def __init__(self,**params):
         """Initialize the base callback handler."""
         ignored_events = [
             # CBEventType.CHUNKING,
@@ -119,6 +179,23 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
         ]
         super().__init__(ignored_events, ignored_events)
         self._aqueue = asyncio.Queue()
+        self._response:str = ''
+        self._params:Dict[str,Any] = params
+        self._nodeStack:deque = deque()
+
+        #添加工作流开始事件     
+        data:ChatRequestData = self._params['data']
+        args:Dict[str,Any] = self._params['ids']
+        args.update(
+            {
+                'use_id': data.user,
+                'query': data.query,
+                'conversation_id': data.conversation_id  
+            }
+        )
+        wf_event = ChatCallbackEvent(event_type = ChatEventType.WORKFLOW_START,payload = args)
+        if wf_event.to_response() is not None:
+            self._aqueue.put_nowait(wf_event)
 
     def on_event_start(
             self,
@@ -129,9 +206,21 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
     ) -> str:
         logger.info("event_start:{} type:{} payload:{}\n".format(event_id, event_type, payload))
 
-        event = ChatCallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
-        if event.to_response() is not None:
-            self._aqueue.put_nowait(event)
+        self._nodeStack.append(event_id)      
+        nindex = self._nodeStack.count() - 1
+        args:Dict[str,Any] = self._params['ids']
+        args.update(
+            {
+                'nodeid':event_id,
+                'title':event_type.name,
+                'index':nindex + 1,
+                'predecessor_node_id': self._nodeStack[nindex - 1] if nindex > 0 else ''
+            }
+        )
+        nd_event = ChatCallbackEvent(event_type = ChatEventType.NODE_START,payload = args)
+        if nd_event.to_response() is not None:
+            self._aqueue.put_nowait(nd_event)
+
 
     def on_event_end(
             self,
@@ -141,9 +230,25 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
             **kwargs: Any,
     ) -> None:
         logger.info("event_end:{} type:{} payload:{}\n".format(event_id, event_type, payload))
-        event = ChatCallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
-        if event.to_response() is not None:
-            self._aqueue.put_nowait(event)
+        
+        #self.response = payload.get("response","")
+        args:Dict[str,Any] = self._params['ids']
+        nodeID = self._nodeStack[-1]
+        if nodeID == event_id:
+            nindex = self._nodeStack.count() - 1
+            args.update(
+                {
+                    'nodeid':event_id,
+                    'title':event_type.name,
+                    'index':nindex + 1,
+                    'predecessor_node_id':self._nodeStack[nindex - 1] if nindex > 0 else ''
+                }
+            )
+            nd_event = ChatCallbackEvent(event_type = ChatEventType.NODE_FINISHED,payload = args)
+            if nd_event.to_response() is not None:
+                self._aqueue.put_nowait(nd_event)
+            self._nodeStack.pop()
+
 
     def start_trace(self, trace_id: Optional[str] = None) -> None:
         """No-op."""
@@ -156,6 +261,23 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """No-op."""
         logger.info("trace_end:{} trace_map:{}\n".format(trace_id, trace_map))
+        data:ChatRequestData = self._params['data']    
+        args:Dict[str,Any] = self._params['ids']
+        args.update(
+            {
+                'response':self._response,
+                'conversation_id': data.conversation_id
+            }
+        )
+        wf_event = ChatCallbackEvent(event_type = ChatEventType.WORKFLOW_FINISHED,payload = args)
+        if wf_event.to_response() is not None:
+            self._aqueue.put_nowait(wf_event)
+
+        
+        args:Dict[str,Any] = self._params['ids']
+        msgEnt_event = ChatCallbackEvent(event_type = ChatEventType.MESSAGE_END,payload = args)
+        if msgEnt_event.to_response() is not None:
+            self._aqueue.put_nowait(msgEnt_event)                 
 
     async def async_event_gen(self) -> AsyncGenerator[ChatCallbackEvent, None]:
         while not self._aqueue.empty() or not self.is_done:
@@ -173,95 +295,26 @@ class IDManager:
             "workflow_id": str(uuid.uuid4())
         }
 
-class DifyChatResponseEvent(BaseModel):
-    event: str
-    conversation_id: str 
-    message_id: str 
-    created_at: int = int(time.time())
-    task_id: str 
-
-class Workflow_started_DifyChatResponseEvent(DifyChatResponseEvent):
-    workflow_run_id:str 
-    data:Dict[str,Any]
-    def __init__(self,**args):
-        args['data'] = {
-            "id": args['workflow_run_id'],
-            "workflow_id": args['workflow_id'],
-            "sequence_number": 1709,
-            "inputs": {
-                "sys.query": args['query'],
-                "sys.files": [],
-                "sys.conversation_id":  args['conversation_id'],
-                "sys.user_id": args['use_id']
-            },
-            "created_at": int(time.time())
-        }
-        args['event'] = 'workflow_started'
-        super().__init__(**args)
-       
-class Workflow_finished_DifyChatResponseEvent(DifyChatResponseEvent):
-    workflow_run_id:str 
-    data:Dict[str,Any]
-    def __init__(self,**args):
-        args['event'] = 'workflow_finished'
-        args['data'] =  {
-		    "id": args['workflow_run_id'],
-		    "workflow_id": args['workflow_id'],
-		    "sequence_number": 1709,
-		    "status": "succeeded",
-		    "outputs": {
-			    "answer": args['response']
-		    },
-		    "error": '',
-		    "elapsed_time": 36.03764106379822,
-		    "total_tokens": 11707,
-		    "total_steps": 10,
-		    "created_by": {
-			"id": str(uuid.uuid4()),
-			"user": args['use_id']
-		    },
-		    "created_at": int(time.time()),
-		    "finished_at": int(time.time()),
-		    "files": []
-        }
-        super().__init__(**args)
-
-class Message_DifyChatResponseEvent(DifyChatResponseEvent):
-    id:str 
-    answer:str
-    def __init__(self,**args):
-        args['id'] = args['message_id']
-        args['event'] = 'message'
-        super().__init__(**args)
-
-class MessageEnd_DifyChatResponseEvent(DifyChatResponseEvent):
-    id:str
-    metadata:Dict[str,Any] = {}
-    def __init__(self,**args):
-        args['id'] = args['message_id']
-        args['event'] = 'message_end'
-        super().__init__(**args)
-        
 class ChatStreamResponse(StreamingResponse):
     TEXT_PREFIX = "data: "
     DATA_PREFIX = "data: "
+    ids:Dict[str,Any] = {}
+    data:ChatRequestData = None
 
     @classmethod
-    def convert_text(cls, token: str):
-        # Escape newlines and double quotes to avoid breaking the stream
-        #token = json.dumps(token)
-
-        #return f"data: {{"event": "message", "conversation_id": "80d85523-de92-4b9d-aca0-c48a5eacb068", "message_id": "16a06b1b-a89b-49c0-bc15-123bd999f6d6", "created_at": 1724406492, "task_id": "802f3064-030d-42ac-a882-0e1293712d04", "id": "16a06b1b-a89b-49c0-bc15-123bd999f6d6", "answer": "{token}"}}"
-        return "\n"
-
-    @classmethod
-    def convert_data(cls, data: dict):
-        data_str = json.dumps(data)
+    def convert_Message(cls, token: str):
+        params = cls.ids
+        params.update({
+            'answer':token,
+            'conversation_id':cls.data.conversation_id
+        })
+        event = ChatCallbackEvent(event_type = ChatEventType.MESSAGE,payload = params)
+        data_str = json.dumps(event.to_response())
         return f"{cls.DATA_PREFIX}{data_str}\n\n"
 
     @classmethod
-    def convert_event(cls, event: DifyChatResponseEvent):
-        data_str = json.dumps(event.dict())
+    def convert_Event(cls, data: dict):
+        data_str = json.dumps(data)
         return f"{cls.DATA_PREFIX}{data_str}\n\n"
 
     def __init__(
@@ -269,8 +322,11 @@ class ChatStreamResponse(StreamingResponse):
             request: Request,
             event_handler: ChatEventCallbackHandler,
             response: StreamingAgentChatResponse,
-            data: ChatRequestData
+            data: ChatRequestData,
+            ids:Dict[str,Any]
     ):
+        ChatStreamResponse.ids = ids
+        ChatStreamResponse.data = data
         content = ChatStreamResponse.content_generator(
             request, event_handler, response, data
         )
@@ -284,41 +340,26 @@ class ChatStreamResponse(StreamingResponse):
             response: StreamingAgentChatResponse,
             data: ChatRequestData
     ):
-        ids = IDManager().createID()
+        
         # Yield the text response
         async def _chat_response_generator():
             final_response = ""
             async for token in response.async_response_gen():
                 final_response += token
-                args = ids
-                args['answer'] = token
-                args['conversation_id'] = data.conversation_id
-                event = Message_DifyChatResponseEvent(**args)
-                yield ChatStreamResponse.convert_event(event)
-                #yield ChatStreamResponse.convert_text(token)
+                yield ChatStreamResponse.convert_Message(token)
 
             # 存储消息历史
             message().add(user_id=data.user,conversation_id=data.conversation_id,query=data.query,answer=final_response)
 
             # the text_generator is the leading stream, once it's finished, also finish the event stream
             event_handler.is_done = True
-            # 发送工作流结束事件
-            args = ids
-            args['response'] = final_response
-            args['conversation_id'] = data.conversation_id
-            wf_event = Workflow_finished_DifyChatResponseEvent(**args)
-            yield ChatStreamResponse.convert_event(wf_event)
-
-            msgEnt_event = MessageEnd_DifyChatResponseEvent(**ids)
-            yield ChatStreamResponse.convert_event(msgEnt_event)         
-
        
         # Yield the events from the event handler
         async def _event_generator():
             async for event in event_handler.async_event_gen():
                 event_response = event.to_response()
                 if event_response is not None:
-                    yield ChatStreamResponse.convert_text("")
+                    yield ChatStreamResponse.convert_Event(event_response)
 
         combine = stream.merge(_chat_response_generator(), _event_generator())
         is_stream_started = False
@@ -327,25 +368,11 @@ class ChatStreamResponse(StreamingResponse):
                 if not is_stream_started:
                     is_stream_started = True
 
-                    # 发送工作流开始事件
-                    args = ids
-                    args['use_id'] = data.user
-                    args['query'] = data.query
-                    args['conversation_id'] = data.conversation_id
-                    wf_event = Workflow_started_DifyChatResponseEvent(**args)
-                    yield ChatStreamResponse.convert_event(wf_event)
-
-                    # Stream a blank message to start the stream
-                    # 发送一个空消息事件
-                    #yield ChatStreamResponse.convert_text("")
-
                 yield output
 
                 if await request.is_disconnected():
                     break
 
-
-
 @v.post("/chat-messages")
 async def post_conversations(request: Request, data: ChatRequestData):
     userMng.findNoExistCreate(data.user)
@@ -365,14 +392,15 @@ async def post_conversations(request: Request, data: ChatRequestData):
     chat_engine = get_chat_engine(filters=filters, params=params)
 
     # 启动聊天事件监听
-    event_handler = ChatEventCallbackHandler()
+    ids = IDManager().createID()
+    event_handler = ChatEventCallbackHandler(ids = ids,data = data)
     chat_engine.callback_manager.handlers.append(event_handler)  # type: ignore
 
     # 执行异步聊天
     response = await chat_engine.astream_chat(data.query)
 
     # 返回异步消息回应
-    return ChatStreamResponse(request, event_handler, response, data)
+    return ChatStreamResponse(request, event_handler, response, data,ids)
 
 @v.get("/messages")
 async def query_messages(user:str, conversation_id:str):
@@ -388,8 +416,9 @@ async def query_messages(user:str, conversation_id:str):
 
     for record in records:
         res = record.dict()
+        feeds = feedback().query(res['id'])  
         res["message_files"] = []
-        res["feedback"] = ''
+        res["feedback"] = {'rating':feeds['rating'] } if feeds != None else ''
         res["retriever_resources"] = []
         res["created_at"] = 1723444905
         res["agent_thoughts"] = []
@@ -440,48 +469,22 @@ async def query_conversations(user:str, first_id:str = None, limit:str = None, p
 async def query_parameters(user:str):
     params = parameter().get(user)
     if len(params) == 0:
-        params = {
-                "opening_statement": "您好，我是配网D3造价软件小助手，您可以问我有关配网造价软件的相关问题！",
-                "suggested_questions": [],
-                "suggested_questions_after_answer": {
-                    "enabled": False
-                },
-                "speech_to_text": {
-                    "enabled": False
-                },
-                "text_to_speech": {
-                    "enabled": False,
-                    "language": "",
-                    "voice": ""
-                },
-                "retriever_resource": {
-                    "enabled": True
-                },
-                "annotation_reply": {
-                    "enabled": False
-                },
-                "more_like_this": {
-                    "enabled": False
-                },
-                "user_input_form": [],
-                "sensitive_word_avoidance": {
-                    "enabled": False
-                },
-                "file_upload": {
-                    "image": {
-                        "enabled": False,
-                        "number_limits": 3,
-                        "transfer_methods": [
-                            "remote_url"
-                        ]
-                    }
-                },
-                "system_parameters": {
-                    "image_file_size_limit": "10"
-                }
-            }  
+        params = BaseConfig().ParamterCfg() 
     return params
 
+@v.post("/messages/{message_id}/feedbacks")
+async def post_feedbacks(request: Request,message_id:str,params:Dict[str,Any]):
+    if params['rating'] =='null':
+        feedback().delete(message_id)
+    else:
+        condition = {'id':message_id}
+        results = message().query(**condition)
+        if len(results) > 0:
+            result = results[0]
+            feedback().add(message_id=message_id,query=result['query'],
+                        answer=result['answer'],rating=params['rating'])
+
 @r.post("")
 def upload_file(request: ChatFileUploadRequest) -> List[str]:
     pass
+

backend/app/api/routers/request/base.py

@@ -25,7 +25,7 @@ class conversations:
         return None
 
     def add(self,id:str, user_id:str, name:str):
-        template = BaseConfig.ConversationCfg
+        template = BaseConfig().ConversationCfg()
         template['id'] = id
         template['user_id'] = user_id
         template['name'] = name
@@ -111,7 +111,7 @@ class message:
         return datas
 
     def add(self,user_id:str,conversation_id:str,query:str,answer:str):
-        template = BaseConfig.MessageCfg
+        template = BaseConfig.MessageCfg()
         template['id'] = str(uuid.uuid4())
         template['user_id'] = user_id
         template['conversation_id'] = conversation_id
@@ -122,4 +122,34 @@ class message:
     def delete(self,user_id:str):
         dbManage.delete(self._tableName,user_id = user_id)
 
+    def query(self,**condition):
+        results = []
+        records = dbManage.query(self._tableName,**condition)
+        for record in records:
+            results.append(record.dict())
+        return results        
 
+class feedback:
+    def __init__(self) -> None:
+        self._tableName = 'feedbacks'
+        dbManage.createTable(self._tableName)
+
+    def add(self,message_id:str,query:str,answer:str,rating:str):
+        record = {
+            'message_id': message_id,
+            'query': query,  
+            'answer': answer,  
+            'rating': rating,  
+        }
+        dbManage.addRecord(self._tableName,record)
+ 
+    def delete(self,message_id:str):
+        cond = {'message_id':message_id}
+        dbManage.delete(self._tableName,**cond)
+    
+    def query(self,message_id:str):
+        cond = {'message_id':message_id}
+        records = dbManage.query(self._tableName,**cond)
+        if len(records) > 0:
+            return records[0].dict()    
+        return None

backend/app/api/routers/request/baseConfig.py

@@ -1,62 +1,80 @@
+from pydantic import BaseModel
+import os
+from enum import Enum
 
-class BaseConfig:
-	ParamterCfg = {
-		"opening_statement": "您好，我是配网D3造价软件小助手，您可以问我有关配网造价软件的相关问题！",
-		"suggested_questions": [],
-		"suggested_questions_after_answer": {
-			"enabled": False
-		},
-		"speech_to_text": {
-			"enabled": False
-		},
-		"text_to_speech": {
-			"enabled": False,
-			"language": "",
-			"voice": ""
-		},
-		"retriever_resource": {
-			"enabled": True
-		},
-		"annotation_reply": {
-			"enabled": False
-		},
-		"more_like_this": {
-			"enabled": False
-		},
-		"user_input_form": [],
-		"sensitive_word_avoidance": {
-			"enabled": False
-		},
-		"file_upload": {
-			"image": {
+class BaseConfig(BaseModel):
+	projectInfo:str = os.getenv("PROJECT_TITLE","您好，我是博微工程理解小助手，您可以问我有关[线路工程]工程数据的相关问题！")
+
+	def ParamterCfg(self):
+		questions = os.getenv("CONVERSATION_STARTERS", "dev")
+		return{
+			"opening_statement": self.projectInfo,
+			"suggested_questions": questions.split('\n'),
+			"suggested_questions_after_answer": {
+				"enabled": False
+			},
+			"speech_to_text": {
+				"enabled": False
+			},
+			"text_to_speech": {
 				"enabled": False,
-				"number_limits": 3,
-				"transfer_methods": [
-					"remote_url"
-				]
+				"language": "",
+				"voice": ""
+			},
+			"retriever_resource": {
+				"enabled": True
+			},
+			"annotation_reply": {
+				"enabled": False
+			},
+			"more_like_this": {
+				"enabled": False
+			},
+			"user_input_form": [],
+			"sensitive_word_avoidance": {
+				"enabled": False
+			},
+			"file_upload": {
+				"image": {
+					"enabled": False,
+					"number_limits": 3,
+					"transfer_methods": [
+						"remote_url"
+					]
+				}
+			},
+			"system_parameters": {
+				"image_file_size_limit": "10"
 			}
-		},
-		"system_parameters": {
-			"image_file_size_limit": "10"
 		}
-	}
 	
-	ConversationCfg = {
-		"id": "",
-		'user_id':'',
-		"name": "",
-		"inputs": {},
-		"status": "normal",
-		"introduction": ParamterCfg['opening_statement'],
-		"created_at":''
-	}
+	def ConversationCfg(self):
+		return{
+			"id": "",
+			'user_id':'',
+			"name": "",
+			"inputs": {},
+			"status": "normal",
+			"introduction": self.projectInfo,
+			"created_at":''
+		}
 
-
-	MessageCfg = {
+	@classmethod 
+	def MessageCfg(cls):
+		return {
 		    "id": "",
 			'user_id':'',
             "conversation_id": "",
             "inputs": {},
             "query": "",
             "answer": ""
-	}
+		}
+
+
+class ChatEventType(str, Enum):
+    WORKFLOW_START = "workflow_started"
+    WORKFLOW_FINISHED = "workflow_finished"
+    NODE_START = "node_started"
+    NODE_FINISHED = "node_finished"
+    MESSAGE = "message"
+    MESSAGE_END = "message_end"

backend/app/api/routers/request/dbOrm.py

@@ -2,7 +2,7 @@ import os
 from typing import Dict, List, Any
 
 from pydantic import BaseModel
-from sqlalchemy import create_engine, Column, String, Integer, JSON
+from sqlalchemy import create_engine, Column, String, Integer, JSON,Float
 from sqlalchemy.engine.reflection import Inspector
 from sqlalchemy.orm import sessionmaker, declarative_base
 
@@ -24,10 +24,6 @@ class ConversationOrm(Base):
         if 'name' in data:
             self.name = data['name']
 
-
-
-
-
 class UserOrm(Base):
     __tablename__ = "user"
 
@@ -51,6 +47,14 @@ class MessagesOrm(Base):
     query = Column(String)
     answer = Column(String)
 
+class FeedBackOrm(Base):
+    __tablename__ = "feedbacks"
+
+    message_id = Column(String,primary_key=True)
+    query = Column(String)
+    answer = Column(String)
+    rating = Column(String)
+
 #数据结构
 class ConversationModel(BaseModel):
     id: str
@@ -61,7 +65,6 @@ class ConversationModel(BaseModel):
     created_at: int
 
     class Config:
-        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -73,7 +76,6 @@ class UserModel(BaseModel):
     createtime: str
 
     class Config:
-        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -86,7 +88,6 @@ class ParametersModel(BaseModel):
     value : Dict[str, Any]
 
     class Config:
-        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -101,13 +102,25 @@ class MessagesModel(BaseModel):
     answer : str
 
     class Config:
-        #orm_mode = True
         from_attributes=True
 
     @classmethod
     def orm(cls):
         return MessagesOrm
 
+class FeedBackModel(BaseModel):
+    message_id :str
+    query :str
+    answer :str
+    rating :str
+
+    class Config:
+        from_attributes=True
+
+    @classmethod
+    def orm(cls):
+        return FeedBackOrm
+
 class DBManager:
     def __init__(self) -> None:
         DATABASE_URL = os.getenv("SQLITE_DATABASE_URL")

backend/app/api/routers/request/models.py

@@ -1,7 +1,7 @@
 
 from typing import Dict, Any
 from pydantic import BaseModel
-
+from typing import Optional
 
 class ChatRequestData(BaseModel):
     inputs: Dict[str,Any]
@@ -13,3 +13,5 @@ class ChatRequestData(BaseModel):
 
 class ChatFileUploadRequest(BaseModel):
     base64: str
+
+

backend/app/engine/retriever/CHTokener.py

@@ -1,3 +1,4 @@
+import os
 from typing import Any, Dict, List, Union, Callable, NamedTuple
 from bm25s.tokenization import *
 
@@ -8,9 +9,12 @@ except ImportError:
     def tqdm(iterable, *args, **kwargs):
         return iterable
 
+import jieba
+jiebapath = os.environ.get("JIEBA_DATA", "")
+jieba.set_dictionary(os.path.join(jiebapath, 'dict.txt')) #设置字典
+jieba.initialize() #初始化jeiba
 
 def chinese_tokenizer(text: str) -> List[str]:
-    import jieba
     from nltk.corpus import stopwords
     tokens = jieba.lcut(text)
     return [token for token in tokens if token not in stopwords.words('chinese')]

backend/app/settings.py

@@ -3,11 +3,10 @@ from typing import Dict
 
 from llama_index.core.constants import DEFAULT_TEMPERATURE
 from llama_index.core.settings import Settings
+from app.xinference.base import XinferenceEmbedding, XinferenceRerank
 from llama_index.llms.xinference import Xinference
 from llama_index.llms.xinference.base import DEFAULT_XINFERENCE_TEMP
 
-from app.xinference.base import XinferenceEmbedding, XinferenceRerank
-
 
 def get_node_postprocessors():
     rerank_enabled = os.getenv("RERANK_ENABLED").title()

backend/main.py

@@ -1,7 +1,5 @@
 
 from dotenv import load_dotenv
-from llama_index.core.node_parser import SentenceSplitter
-
 load_dotenv()
 
 import logging

backend/pyproject.toml

@@ -17,7 +17,7 @@ aiostream = "^0.6.2"
 llama-index = "0.10.63"
 cachetools = "^5.3.3"
 protobuf = "4.25.4"
-nltk = "^3.8.2"
+nltk = "^3.9.1"
 jieba = "^0.42.1"
 
 #arize-phoenix = "^4.12.0"
@@ -35,6 +35,7 @@ chroma="^0.2.0"
 llama-index-vector-stores-chroma = "^0.1.10"
 llama-index-readers-json = "^0.1.5"
 llama-index-retrievers-bm25 = "^0.2.2"
+llama-index-experimental = "^0.2.0"
 
 duckduckgo_search = "^6.2.6"
 
@@ -62,6 +63,12 @@ version = "^0.8"
 version = "0.0.7"
 
 
+
+[[tool.poetry.source]]
+name = "mirrors"
+url = "https://pypi.tuna.tsinghua.edu.cn/simple/"
+priority = "default"
+
 [build-system]
 requires = [ "poetry-core" ]
 build-backend = "poetry.core.masonry.api"

backend/test1/ParamTuner.py

@@ -0,0 +1,138 @@
+import nest_asyncio
+nest_asyncio.apply()
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import VectorStoreIndex
+from llama_index.core.evaluation import (
+    FaithfulnessEvaluator, 
+    DatasetGenerator, 
+    CorrectnessEvaluator, 
+    SemanticSimilarityEvaluator,
+)
+from llama_index.experimental.param_tuner import ParamTuner
+from llama_index.experimental.param_tuner.base import RunResult
+from llama_index.llms.openai import OpenAI
+
+import asyncio
+
+# 初始化环境
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+load_dotenv()
+init_settings()
+init_observability()
+
+# 读取文档
+documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()
+
+# 参数字典
+param_dict = {
+    "chunk_size": [512, 1024],
+    "top_k": [1, 5],
+    "temperature": [0.1, 1.0]
+}
+
+# 辅助函数
+def _build_index(chunk_size, documents):
+    # 构建索引
+    splitter = SentenceSplitter(chunk_size=chunk_size)
+    vector_index = VectorStoreIndex.from_documents(
+        documents, transformations=[splitter],
+    )
+    return vector_index
+
+# 评估函数
+def evaluate_query_engine(query_engine, questions):
+    loop = asyncio.get_event_loop()
+    correct, total = loop.run_until_complete(_evaluate_query_engine_async(query_engine, questions))
+    return correct, total
+
+async def _evaluate_query_engine_async(query_engine, questions):
+    c = [query_engine.aquery(q) for q in questions]
+    gathering_future = asyncio.gather(*c)
+    results = await gathering_future
+
+    total_correct = 0
+    for r in results:
+        eval_result = (
+            1 if FaithfulnessEvaluator().evaluate_response(response=r).passing else 0
+        )
+        total_correct += eval_result
+
+    return total_correct, len(results)
+
+
+
+# 生成问题
+question_generator = DatasetGenerator.from_documents(documents)
+eval_questions = question_generator.generate_questions_from_nodes(1)  # 假设生成10个问题
+
+# 打印生成的问题
+for i, q in enumerate(eval_questions, start=1):
+    print(f"问题 {i}: {q}")
+
+# 目标函数
+def objective_function(params_dict, documents, questions):
+    chunk_size = params_dict["chunk_size"]
+    top_k = params_dict["top_k"]
+    temperature = params_dict["temperature"]
+
+    # 构建索引
+    vector_index = _build_index(chunk_size, documents)
+    
+    # 查询引擎
+    query_engine = vector_index.as_query_engine(
+        similarity_top_k=top_k, temperature=temperature
+    )
+
+    # 评估查询引擎
+    correct, total = 0, len(questions)
+    question_answers = []  # 添加列表来收集问题和答案
+    
+    for question in questions:
+        response = query_engine.query(question)
+        if response is not None:
+            question_answers.append((question, response.response))
+            eval_result = FaithfulnessEvaluator().evaluate_response(response=response, query_str=question)
+            if eval_result.passing:
+                correct += 1
+
+    # 计算分数
+    score = correct / total if total > 0 else 0
+    return RunResult(score=score, params=params_dict, question_answers=question_answers)
+
+# 创建 ParamTuner 实例
+param_tuner = ParamTuner(
+    param_fn=lambda params_dict: objective_function(params_dict, documents, eval_questions),
+    param_dict=param_dict,
+    show_progress=True,
+)
+
+# 调用 tune 方法
+results = param_tuner.tune()
+best_result = results.best_run_result
+best_top_k = best_result.params["top_k"]
+best_chunk_size = best_result.params["chunk_size"]
+best_temperature = best_result.params["temperature"]
+print(f"得分: {best_result.score}")
+print(f"Top-k: {best_top_k}")
+print(f"文本块大小: {best_chunk_size}")
+print(f"温度: {best_temperature}")
+
+# 使用最佳参数再次运行查询引擎，并打印问题与答案
+best_vector_index = _build_index(best_chunk_size, documents)
+best_query_engine = best_vector_index.as_query_engine(
+    similarity_top_k=best_top_k, temperature=best_temperature
+)
+
+best_question_answers = []
+for question in eval_questions:
+    response = best_query_engine.query(question)
+    if response is not None:
+        best_question_answers.append((question, response.response))
+
+# 打印最佳参数下的问题与答案
+for i, (question, answer) in enumerate(best_question_answers, start=1):
+    print(f"最佳参数 - 问题 {i}: {question}\n答案: {answer}\n")

backend/test1/evaluator.py

@@ -0,0 +1,81 @@
+from app.observability import init_observability
+from app.settings import init_settings
+from dotenv import load_dotenv
+
+import nest_asyncio
+nest_asyncio.apply()
+
+load_dotenv()
+
+
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import (
+    VectorStoreIndex,
+    SimpleDirectoryReader,
+    Response,
+)
+from llama_index.core.evaluation import (
+    FaithfulnessEvaluator, 
+    DatasetGenerator, 
+    CorrectnessEvaluator, 
+    SemanticSimilarityEvaluator,)
+
+
+
+init_settings()
+init_observability()
+
+faith_evaluator_qwen = FaithfulnessEvaluator() #诚实度评测
+corr_evaluator_qwen = CorrectnessEvaluator() #准确率评测
+Seman_evaluator_qwen = SemanticSimilarityEvaluator()#嵌入相似度评估
+
+documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()
+
+splitter = SentenceSplitter(chunk_size=512)
+
+
+vector_index = VectorStoreIndex.from_documents(
+    documents, transformations=[splitter],
+)
+
+
+# # 运行评估
+# query_engine = vector_index.as_query_engine()
+# response_vector = query_engine.query("工程监理费的金额是多少？")
+# eval_result = evaluator_qwen.evaluate_response(response=response_vector)
+
+# print(response_vector)
+# print(eval_result)
+
+
+question_generator = DatasetGenerator.from_documents(documents)
+eval_questions = question_generator.generate_questions_from_nodes(5)
+print(eval_questions)
+
+import asyncio
+
+async def evaluate_query_engine_async(query_engine, questions):
+    c = [query_engine.aquery(q) for q in questions]
+    gathering_future = asyncio.gather(*c)
+    results = await gathering_future
+    #print(results)
+    
+    total_correct = 0
+    for r in results:
+        eval_result = (
+            1 if faith_evaluator_qwen.evaluate_response(response=r).passing else 0
+        )
+        total_correct += eval_result
+
+    return total_correct, len(results)
+
+def evaluate_query_engine(query_engine, questions):
+    loop = asyncio.get_event_loop()
+    correct, total = loop.run_until_complete(evaluate_query_engine_async(query_engine, questions))
+    return correct, total
+
+# 使用 evaluate_query_engine 函数
+vector_query_engine = vector_index.as_query_engine()
+correct, total = evaluate_query_engine(vector_query_engine, eval_questions[:5])
+
+print(f"score: {correct}/{total}")

backend/unit_test/corr_test.py

@@ -0,0 +1,121 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from llama_index.core.evaluation import CorrectnessEvaluator
+from app.engine import get_chat_engine  
+from app.engine.index import get_index
+from app.observability import init_observability
+from app.settings import init_settings
+
+init_settings()
+init_observability()
+
+index = get_index()
+
+
+import os
+import json
+import asyncio
+import nest_asyncio
+nest_asyncio.apply()
+from llama_index.core.prompts import (
+    ChatMessage,
+    ChatPromptTemplate,
+    MessageRole
+)
+
+DEFAULT_SYSTEM_TEMPLATE = """
+您是一个问答聊天机器人的专业评估系统。
+
+您将获得以下信息：
+
+- 用户查询，
+- 生成的回答，
+
+也可能提供一个参考答案作为评估的依据。
+
+您的任务是判断生成回答的相关性和正确性。
+输出一个代表全面评估的单一分数。
+您必须在一行中仅返回该分数。
+不要以其他任何格式返回答案。
+在单独的一行提供给定分数的理由。
+
+请遵循以下评分指南：
+
+- 您的分数必须在1到5之间，其中1是最差，5是最好的。
+-如果生成的回答与用户查询不相关，您应该给出1分。
+-如果生成的回答相关但包含错误，您应该给出2到3分之间的分数。
+-如果生成的回答相关且完全正确，您应该给出4到5分之间的分数。
+示例响应：
+4.0
+生成的回答与参考答案的指标完全相同，但不够精炼。
+
+"""
+
+DEFAULT_USER_TEMPLATE = """
+## User Query
+{query}
+
+## Reference Answer
+{reference_answer}
+
+## Generated Answer
+{generated_answer}
+"""
+
+DEFAULT_EVAL_TEMPLATE = ChatPromptTemplate(
+    message_templates=[
+        ChatMessage(role=MessageRole.SYSTEM, content=DEFAULT_SYSTEM_TEMPLATE),
+        ChatMessage(role=MessageRole.USER, content=DEFAULT_USER_TEMPLATE),
+    ]
+)
+
+
+# 初始化聊天引擎和评估器
+chat_engine = get_chat_engine()
+corr_evaluator_qwen = CorrectnessEvaluator()
+
+# 加载本地问题回答文件
+script_dir = os.path.dirname(os.path.abspath(__file__))
+file_path = os.path.join(script_dir, 'questions_and_answers.json')
+output_file_path = file_path.replace('.json', '_test.json')
+
+with open(file_path, 'r', encoding='utf-8') as f:
+    data = json.load(f)
+
+# 异步函数用于评估查询
+async def evaluate_query(question, answer, index, output_file):
+    response = await chat_engine.astream_chat(question)
+
+    # 检查sources是否为空
+    if response.sources:
+        content_str = str(response.sources[0])
+    else:
+        content_str = "<无回答>"
+
+    result = corr_evaluator_qwen.evaluate(
+        query=question,
+        response=content_str, 
+        reference=answer,
+    )
+    
+    result_dict = {
+        "编号": index,
+        "问题": question,
+        "答案": answer,
+        "回答": result.response,
+        "得分(1~5)": result.score,
+        "评价": result.feedback
+    }
+    
+    with open(output_file, 'a', encoding='utf-8') as f:
+        f.write(json.dumps(result_dict, ensure_ascii=False, indent=4))
+        f.write(',\n')
+
+# 主异步函数
+async def main():
+    for index, item in enumerate(data, start=1):
+        await evaluate_query(item['question'], item['answer'], index, output_file_path)
+
+# 运行主协程
+asyncio.run(main())

backend/unit_test/prompts.py

@@ -0,0 +1,55 @@
+Attribute_Prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+	"现在需要你针对数据中属性一列进行提问和回答。"
+    "问题和回答的示例应该是这种类型的，示例：'工程总投资（万元）,工程总投资（万元）是77469835.590045万元','尖峰及施工基面土石方量,尖峰及施工基面土石方量是8377.6','截止阀的编码,截止阀的编码是F01010203',"
+    "你生成的回答必须严格按照示例中的格式（'问题, 回答'），不允许有丝毫的变动。问题和回答应该在一个单引号内。"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+
+Amount_Prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+	"现在需要你针对上下文信息中的金额或者合价进行提问和回答。" 	
+    "问题和回答的示例应该是这种类型的，示例：'项目建设技术服务费的金额,项目建设技术服务费的金额是16855957065.4302','项目后评价费的费率,项目后评价费的费率是0.5','架空输电线路本体工程的金额,架空输电线路本体工程的金额是55105688268.5176','工程静态投资的金额,工程静态投资的金额是715035853336.391'"
+    "你生成的回答必须严格按照示例中的格式（'问题, 回答'），不允许有丝毫的变动。问题和回答应该在一个单引号内。"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+
+
+Units_Prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+	"现在需要你针对上下文信息来进行单位转化问题提问和回答。"  
+    "问题和回答的示例应该是这种类型的，示例：'工程总投资（万元）结果用元表示,工程总投资（万元）是774698355900.45元','本体工程（元）结果用万元表示,本体工程（元）是5490494.261046万元'"
+    "你生成的回答必须严格按照示例中的格式（'问题, 回答'），不允许有丝毫的变动。问题和回答应该在一个单引号内。"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+Name_Prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+	"现在需要你针对上下文信息中的重名问题进行提问和回答。"    
+    "问题和回答的示例应该是这种类型的，示例：'专业类型为线路的杆塔工程项目划分的合价,专业类型为线路的杆塔工程项目划分的合价是220969744.905856','专业类型为线路清理的杆塔工程项目划分的合价,电缆工程的合价是0'"
+    "你生成的回答必须严格按照示例中的格式（'问题, 回答'），不允许有丝毫的变动。问题和回答应该在一个单引号内。"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+
+All_Amount_Prompt = (
+    "你是一个电力造价工程相关的项目经理，现在给你一些上下文信息，"
+    "你需要根据现有的上下文信息，来生成{num_questions_per_chunk}个电力造价工程相关的问题和对应的回答，"
+	"现在需要你针对上下文信息中的总体金额进行提问和回答。" 	
+    "问题和回答的示例应该是这种类型的，示例：'架空输电线路本体工程的总体金额,架空输电线路本体工程的总体金额是7.706703','工程静态投资的总体金额,工程静态投资的总体金额是100'"
+    "你生成的回答必须严格按照示例中的格式（'问题, 回答'），不允许有丝毫的变动。问题和回答应该在一个单引号内。"
+    "这种类似的问题和答案，生成的问题和答案必须一一对应，要符合文件里的内容，不要生成一些无关的问题，不要生成一些重复的问题，"
+    "不要生成一些过于简单的问题，不要生成一些过于复杂的问题。"
+)
+
+

backend/unit_test/question.py

@@ -0,0 +1,144 @@
+
+from dotenv import load_dotenv
+load_dotenv()
+
+import json
+import sys
+
+
+from app.observability import init_observability
+from app.settings import init_settings
+
+import nest_asyncio
+nest_asyncio.apply()
+
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core.evaluation import DatasetGenerator
+
+import prompts  
+
+init_settings()
+init_observability()
+
+# 读取所有文档（即所有表格）
+documents = SimpleDirectoryReader("D:/LLM_model/text2sql/zjdataai-app-test/backend/data-test").load_data()
+
+# 定义表格名称和索引的对应关系
+table_names = {
+    "工程信息表": 0,
+    "其他费用表": 1,
+    "取费表": 2,
+    "项目划分表": 3,
+    "项目划分_费用预览表": 4,
+    "总算表": 5,
+    "工程量表": 6
+}
+
+# 定义中文提示词和Python代码中提示词名称的映射
+prompt_mapping = {
+    "普通属性": "Attribute_Prompt",
+    "金额查询": "Amount_Prompt",
+    "单位换算": "Units_Prompt",
+    "重名项目划分": "Name_Prompt",
+    "总体金额查询": "All_Amount_Prompt"
+}
+
+# 定义表格与其对应的查询类别
+table_prompt_mapping = {
+    "工程信息表": ["普通属性", "单位换算"],
+    "其他费用表": ["金额查询", "单位换算"],
+    "取费表": ["金额查询"],
+    "总算表": ["金额查询", "总体金额查询"],
+    "工程量表": ["普通属性", "重名项目划分"]
+}
+
+# 根据表格名称选择特定的表格
+def select_document(documents, table_name):
+    if table_name not in table_names:
+        raise ValueError(f"未找到名为 '{table_name}' 的表格")
+    index = table_names[table_name]
+    return [documents[index]]  # 返回一个包含所选表格的列表
+
+# 选择提示词
+def select_prompt(prompt_category):
+    prompt_name = prompt_mapping.get(prompt_category)
+    if not prompt_name:
+        raise ValueError(f"未找到名为 '{prompt_category}' 的提示词")
+    try:
+        return getattr(prompts, prompt_name)
+    except AttributeError:
+        raise ValueError(f"未找到提示词 '{prompt_name}' 对应的函数")
+
+# 生成问题和答案
+def generate_questions_from_document(document, quest_prompt, num_questions):
+    question_generator = DatasetGenerator.from_documents(
+        documents=document,
+        question_gen_query=quest_prompt,
+        num_questions_per_chunk=num_questions
+    )
+
+    eval_questions = question_generator.generate_questions_from_nodes(num_questions)
+    print(eval_questions)
+
+    qa_pairs = []
+    for qa in eval_questions:
+        if ',' in qa:
+            question, answer = qa.split(",", 1)
+            qa_pairs.append({
+                "question": question.strip(),
+                "answer": answer.strip()
+            })
+        else:
+            print(f"无法处理的问题和答案: {qa}")
+
+    return qa_pairs
+
+# 主函数，控制生成多个表格的问题和使用多个提示词，并将结果合并到一个文件中
+def main(documents, table_names_input, prompt_categories_input, num_questions_per_prompt):
+    if table_names_input == "all":
+        selected_tables = list(table_prompt_mapping.keys())
+    else:
+        selected_tables = table_names_input.strip('[]').split(',')
+
+    all_results = {}
+
+    for table_name in selected_tables:
+        table_name = table_name.strip()  # 去掉前后空格
+        document = select_document(documents, table_name)
+
+        if prompt_categories_input == "all":
+            selected_prompts = table_prompt_mapping[table_name]
+        else:
+            selected_prompts = prompt_categories_input.strip('[]').split(',')
+            selected_prompts = [p.strip() for p in selected_prompts]  # 去掉前后空格
+
+        for prompt_category in selected_prompts:
+            if prompt_category not in table_prompt_mapping[table_name]:
+                print(f"跳过表格 '{table_name}' 的提示词 '{prompt_category}'，因为该表中不包含该类别的信息")
+                continue
+
+            quest_prompt = select_prompt(prompt_category).format(num_questions_per_chunk=num_questions_per_prompt)
+            qa_pairs = generate_questions_from_document(document, quest_prompt, num_questions_per_prompt)
+
+            label = f"test:{table_name}_{prompt_category}"
+            all_results[label] = qa_pairs
+
+    # 自动生成输出文件名
+    output_file = "combined_test.json"
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        json.dump(all_results, f, ensure_ascii=False, indent=4)
+
+    print(f"All questions and answers have been saved to '{output_file}'")
+
+# 获取命令行参数
+if __name__ == "__main__":
+    if len(sys.argv) != 4:
+        print("Usage: python script.py <table_names_input> <prompt_categories_input> <num_questions_per_prompt>")
+    else:
+        table_names_input = sys.argv[1]
+        prompt_categories_input = sys.argv[2]
+        num_questions_per_prompt = int(sys.argv[3])
+
+        main(documents, table_names_input, prompt_categories_input, num_questions_per_prompt)

phoenixserver/phoenixserver.py

@@ -1,9 +1,10 @@
 import os
+from dotenv import load_dotenv
+load_dotenv()
+
 import phoenix as px
 
 
-os.environ['PHOENIX_HOST'] = "0.0.0.0"
-
 session = px.launch_app(use_temp_dir=False)
 
 import msvcrt