Merge pull request '增加新的前端子模块' (#4) from dev into main

Reviewed-on: #4

backend/.env.example

@@ -1,8 +1,3 @@
-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
@@ -85,4 +80,3 @@ 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

@@ -1,8 +1,3 @@
-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
@@ -116,4 +111,3 @@ 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

@@ -3,7 +3,6 @@ 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
@@ -14,8 +13,7 @@ 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,feedback
-from app.api.routers.request.baseConfig import *
+from app.api.routers.request.base import userMng, conversations,message,parameter
 from app.api.routers.request.models import ChatRequestData,ChatFileUploadRequest
 from app.engine import get_chat_engine
 import uuid
@@ -26,138 +24,78 @@ api_router = r = APIRouter()
 v1_router = v = APIRouter()
 
 class ChatCallbackEvent(BaseModel):
-    event_type: ChatEventType
+    event_type: CBEventType
     payload: Optional[Dict[str, Any]] = None
+    event_id: str = ""
 
-    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_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_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')
+    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}",
                 },
-                "created_at": int(time.time())
             }
-        })
-        return params
 
-    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_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     
+    def _is_output_serializable(self, output: Any) -> bool:
+        try:
+            json.dumps(output)
+            return True
+        except TypeError:
+            return False
 
-    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 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_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 
+                    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 to_response(self)-> dict|None:
+    def to_response(self):
         try:
             match self.event_type:
-                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 "retrieve":
+                    return self.get_retrieval_message()
+                case "function_call":
+                    return self.get_tool_message()
+                case "agent_step":
+                    return self.get_agent_tool_response()
                 case _:
                     return None
         except Exception as e:
@@ -168,7 +106,9 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
     _aqueue: asyncio.Queue
     is_done: bool = False
 
-    def __init__(self,**params):
+    def __init__(
+            self,
+    ):
         """Initialize the base callback handler."""
         ignored_events = [
             # CBEventType.CHUNKING,
@@ -179,23 +119,6 @@ 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,
@@ -206,21 +129,9 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
     ) -> str:
         logger.info("event_start:{} type:{} payload:{}\n".format(event_id, event_type, payload))
 
-        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)
-
+        event = ChatCallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
+        if event.to_response() is not None:
+            self._aqueue.put_nowait(event)
 
     def on_event_end(
             self,
@@ -230,25 +141,9 @@ class ChatEventCallbackHandler(BaseCallbackHandler):
             **kwargs: Any,
     ) -> None:
         logger.info("event_end:{} type:{} payload:{}\n".format(event_id, event_type, payload))
-        
-        #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()
-
+        event = ChatCallbackEvent(event_id=event_id, event_type=event_type, payload=payload)
+        if event.to_response() is not None:
+            self._aqueue.put_nowait(event)
 
     def start_trace(self, trace_id: Optional[str] = None) -> None:
         """No-op."""
@@ -261,23 +156,6 @@ 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:
@@ -295,26 +173,95 @@ 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_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())
+    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)
         return f"{cls.DATA_PREFIX}{data_str}\n\n"
 
     @classmethod
-    def convert_Event(cls, data: dict):
-        data_str = json.dumps(data)
+    def convert_event(cls, event: DifyChatResponseEvent):
+        data_str = json.dumps(event.dict())
         return f"{cls.DATA_PREFIX}{data_str}\n\n"
 
     def __init__(
@@ -322,11 +269,8 @@ class ChatStreamResponse(StreamingResponse):
             request: Request,
             event_handler: ChatEventCallbackHandler,
             response: StreamingAgentChatResponse,
-            data: ChatRequestData,
-            ids:Dict[str,Any]
+            data: ChatRequestData
     ):
-        ChatStreamResponse.ids = ids
-        ChatStreamResponse.data = data
         content = ChatStreamResponse.content_generator(
             request, event_handler, response, data
         )
@@ -340,26 +284,41 @@ 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
-                yield ChatStreamResponse.convert_Message(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)
 
             # 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_Event(event_response)
+                    yield ChatStreamResponse.convert_text("")
 
         combine = stream.merge(_chat_response_generator(), _event_generator())
         is_stream_started = False
@@ -368,11 +327,25 @@ 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)
@@ -392,15 +365,14 @@ 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
-    ids = IDManager().createID()
-    event_handler = ChatEventCallbackHandler(ids = ids,data = data)
+    event_handler = ChatEventCallbackHandler()
     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,ids)
+    return ChatStreamResponse(request, event_handler, response, data)
 
 @v.get("/messages")
 async def query_messages(user:str, conversation_id:str):
@@ -416,9 +388,8 @@ 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"] = {'rating':feeds['rating'] } if feeds != None else ''
+        res["feedback"] = ''
         res["retriever_resources"] = []
         res["created_at"] = 1723444905
         res["agent_thoughts"] = []
@@ -469,22 +440,48 @@ 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 = BaseConfig().ParamterCfg() 
+        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"
+                }
+            }  
     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
-
+    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
@@ -122,34 +122,4 @@ 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

@@ -1,80 +1,62 @@
-from pydantic import BaseModel
-import os
-from enum import Enum
 
-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": {
+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": {
 				"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"
+				"number_limits": 3,
+				"transfer_methods": [
+					"remote_url"
+				]
 			}
+		},
+		"system_parameters": {
+			"image_file_size_limit": "10"
 		}
-	
-	def ConversationCfg(self):
-		return{
-			"id": "",
-			'user_id':'',
-			"name": "",
-			"inputs": {},
-			"status": "normal",
-			"introduction": self.projectInfo,
-			"created_at":''
-		}
+	}
 
-	@classmethod 
-	def MessageCfg(cls):
-		return {
+	ConversationCfg = {
+		"id": "",
+		'user_id':'',
+		"name": "",
+		"inputs": {},
+		"status": "normal",
+		"introduction": ParamterCfg['opening_statement'],
+		"created_at":''
+	}
+
+
+	MessageCfg = {
 		    "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,Float
+from sqlalchemy import create_engine, Column, String, Integer, JSON
 from sqlalchemy.engine.reflection import Inspector
 from sqlalchemy.orm import sessionmaker, declarative_base
 
@@ -24,6 +24,10 @@ class ConversationOrm(Base):
         if 'name' in data:
             self.name = data['name']
 
+
+
+
+
 class UserOrm(Base):
     __tablename__ = "user"
 
@@ -47,14 +51,6 @@ 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
@@ -65,6 +61,7 @@ class ConversationModel(BaseModel):
     created_at: int
 
     class Config:
+        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -76,6 +73,7 @@ class UserModel(BaseModel):
     createtime: str
 
     class Config:
+        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -88,6 +86,7 @@ class ParametersModel(BaseModel):
     value : Dict[str, Any]
 
     class Config:
+        #orm_mode = True
         from_attributes=True
 
     @classmethod
@@ -102,25 +101,13 @@ 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]
@@ -12,6 +12,4 @@ class ChatRequestData(BaseModel):
     conversation_id: str = None
 
 class ChatFileUploadRequest(BaseModel):
-    base64: str
-
-
+    base64: str

backend/app/engine/retriever/CHTokener.py

@@ -1,4 +1,3 @@
-import os
 from typing import Any, Dict, List, Union, Callable, NamedTuple
 from bm25s.tokenization import *
 
@@ -9,12 +8,9 @@ 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

@@ -3,10 +3,11 @@ 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()
@@ -231,4 +232,4 @@ def init_mistral():
     #
     # Settings.llm = MistralAI(model=os.getenv("MODEL"))
     # Settings.embed_model = MistralAIEmbedding(model_name=os.getenv("EMBEDDING_MODEL"))
-    pass
+    pass

backend/main.py

@@ -1,5 +1,7 @@
 
 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 = "^3.9.1"
+nltk = "^3.8.2"
 jieba = "^0.42.1"
 
 #arize-phoenix = "^4.12.0"
@@ -35,7 +35,6 @@ 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"
 
@@ -63,12 +62,6 @@ 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

@@ -1,138 +0,0 @@
-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

@@ -1,81 +0,0 @@
-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

@@ -1,121 +0,0 @@
-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

@@ -1,55 +0,0 @@
-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

@@ -1,144 +0,0 @@
-
-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

@@ -1,10 +1,9 @@
 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