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

Reviewed-on: #4

backend/.env.example

@@ -80,4 +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

@@ -111,4 +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
@@ -104,6 +102,77 @@ class ChatCallbackEvent(BaseModel):
             logger.error(f"转换回应时间时发生错误，原因: {e}")
             return None
 
+class ChatEventCallbackHandler(BaseCallbackHandler):
+    _aqueue: asyncio.Queue
+    is_done: bool = False
+
+    def __init__(
+            self,
+    ):
+        """Initialize the base callback handler."""
+        ignored_events = [
+            # CBEventType.CHUNKING,
+            # CBEventType.NODE_PARSING,
+            # CBEventType.EMBEDDING,
+            # CBEventType.LLM,
+            # CBEventType.TEMPLATING,
+        ]
+        super().__init__(ignored_events, ignored_events)
+        self._aqueue = asyncio.Queue()
+
+    def on_event_start(
+            self,
+            event_type: CBEventType,
+            payload: Optional[Dict[str, Any]] = None,
+            event_id: str = "",
+            **kwargs: Any,
+    ) -> 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)
+
+    def on_event_end(
+            self,
+            event_type: CBEventType,
+            payload: Optional[Dict[str, Any]] = None,
+            event_id: str = "",
+            **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)
+
+    def start_trace(self, trace_id: Optional[str] = None) -> None:
+        """No-op."""
+        logger.info("trace_start:{}\n".format(trace_id))
+
+    def end_trace(
+            self,
+            trace_id: Optional[str] = None,
+            trace_map: Optional[Dict[str, List[str]]] = None,
+    ) -> None:
+        """No-op."""
+        logger.info("trace_end:{} trace_map:{}\n".format(trace_id, trace_map))
+
+    async def async_event_gen(self) -> AsyncGenerator[ChatCallbackEvent, None]:
+        while not self._aqueue.empty() or not self.is_done:
+            try:
+                yield await asyncio.wait_for(self._aqueue.get(), timeout=0.1)
+            except asyncio.TimeoutError:
+                pass
+
+class IDManager:
+    def createID(self):
+        return {
+            "message_id" : str(uuid.uuid4()),
+            'task_id':str(uuid.uuid4()),
+            'workflow_run_id': str(uuid.uuid4()),
+            "workflow_id": str(uuid.uuid4())
+        }
+
 class DifyChatResponseEvent(BaseModel):
     event: str
     conversation_id: str 
@@ -111,11 +180,7 @@ class DifyChatResponseEvent(BaseModel):
     created_at: int = int(time.time())
     task_id: str 
 
-    def to_response(self):
-        return self.dict()
-
 class Workflow_started_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'workflow_started'
     workflow_run_id:str 
     data:Dict[str,Any]
     def __init__(self,**args):
@@ -131,13 +196,14 @@ class Workflow_started_DifyChatResponseEvent(DifyChatResponseEvent):
             },
             "created_at": int(time.time())
         }
+        args['event'] = 'workflow_started'
         super().__init__(**args)
        
 class Workflow_finished_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'workflow_finished'
     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'],
@@ -161,219 +227,41 @@ class Workflow_finished_DifyChatResponseEvent(DifyChatResponseEvent):
         super().__init__(**args)
 
 class Message_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'message'
     id:str 
     answer:str
     def __init__(self,**args):
         args['id'] = args['message_id']
+        args['event'] = 'message'
         super().__init__(**args)
 
 class MessageEnd_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'message_end'
     id:str
     metadata:Dict[str,Any] = {}
     def __init__(self,**args):
         args['id'] = args['message_id']
+        args['event'] = 'message_end'
         super().__init__(**args)
         
-class Node_started_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'node_started'
-    workflow_run_id:str
-    data:Dict[str,Any]
-    def __init__(self,**args):
-        args['data'] = {
-            "id": args['nodeid'],
-            "node_id": args['nodeid'],
-            "node_type": "http-request",
-            "title": args['title'],
-            "index": args['index'],
-            "predecessor_node_id": args['predecessor_node_id'],
-            "inputs": '',
-            "created_at": 1724398751,
-            "extras": {}
-        }
-        super().__init__(**args)
-
-class Node_finished_DifyChatResponseEvent(DifyChatResponseEvent):
-    event: str = 'node_finished'
-    workflow_run_id:str
-    data:Dict[str,Any]
-    def __init__(self,**args):
-        args['data'] = {
-            "id": args['nodeid'],
-            "node_id": args['nodeid'],
-            "node_type": "http-request",
-            "title": args['title'],
-            "index": args['index'],
-            "predecessor_node_id": args['predecessor_node_id'],
-            "inputs": '',
-            "process_data": '',
-            "outputs": '',
-            "status": "succeeded",
-            "error": '',
-            "elapsed_time": 0.10402441816404462,
-            "execution_metadata": '',
-            "created_at": 1724398751,
-            "finished_at": 1724398751,
-            "files": []
-        }
-        super().__init__(**args)
-
-class ChatEventCallbackHandler(BaseCallbackHandler):
-    _aqueue: asyncio.Queue
-    is_done: bool = False
-
-    def __init__(self,**params):
-        """Initialize the base callback handler."""
-        ignored_events = [
-            # CBEventType.CHUNKING,
-            # CBEventType.NODE_PARSING,
-            # CBEventType.EMBEDDING,
-            # CBEventType.LLM,
-            # CBEventType.TEMPLATING,
-        ]
-        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     
-        ids:Dict[str,Any] = self._params['ids']
-        data:ChatRequestData = self._params['data']
-        args = ids
-        args.update(
-            {
-                'use_id': data.user,
-                'query': data.query,
-                'conversation_id': data.conversation_id  
-            }
-        )
-        wf_event = Workflow_started_DifyChatResponseEvent(**args)
-        if wf_event.to_response() is not None:
-            self._aqueue.put_nowait(wf_event)
-
-    def on_event_start(
-            self,
-            event_type: CBEventType,
-            payload: Optional[Dict[str, Any]] = None,
-            event_id: str = "",
-            **kwargs: Any,
-    ) -> str:
-        logger.info("event_start:{} type:{} payload:{}\n".format(event_id, event_type, payload))
-
-        self._nodeStack.append(event_id)      
-        nindex = self._nodeStack.count() - 1
-
-        ids:Dict[str,Any] = self._params['ids']
-        args = 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 = Node_started_DifyChatResponseEvent(**args)
-        if nd_event.to_response() is not None:
-            self._aqueue.put_nowait(nd_event)
-
-
-    def on_event_end(
-            self,
-            event_type: CBEventType,
-            payload: Optional[Dict[str, Any]] = None,
-            event_id: str = "",
-            **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 = Node_finished_DifyChatResponseEvent(**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."""
-        logger.info("trace_start:{}\n".format(trace_id))
-
-    def end_trace(
-            self,
-            trace_id: Optional[str] = None,
-            trace_map: Optional[Dict[str, List[str]]] = None,
-    ) -> None:
-        """No-op."""
-        logger.info("trace_end:{} trace_map:{}\n".format(trace_id, trace_map))
-        ids:Dict[str,Any] = self._params['ids']
-        data:ChatRequestData = self._params['data']    
-        args = ids
-        args.update(
-            {
-                'response':self._response,
-                'conversation_id': data.conversation_id
-            }
-        )
-        wf_event = Workflow_finished_DifyChatResponseEvent(**args)
-        if wf_event.to_response() is not None:
-            self._aqueue.put_nowait(wf_event)
-
-        
-        args = ids
-        msgEnt_event = MessageEnd_DifyChatResponseEvent(**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:
-            try:
-                yield await asyncio.wait_for(self._aqueue.get(), timeout=0.1)
-            except asyncio.TimeoutError:
-                pass
-
-class IDManager:
-    def createID(self):
-        return {
-            "message_id" : str(uuid.uuid4()),
-            'task_id':str(uuid.uuid4()),
-            'workflow_run_id': str(uuid.uuid4()),
-            "workflow_id": str(uuid.uuid4())
-        }
-
 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 = Message_DifyChatResponseEvent(**params)
-        data_str = json.dumps(event.dict())
+    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__(
@@ -381,11 +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
         )
@@ -399,26 +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
@@ -427,11 +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)
@@ -451,15 +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):
@@ -475,9 +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"] = []
@@ -528,22 +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
-

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

@@ -1,13 +1,8 @@
-from pydantic import BaseModel
-import os
-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'),
+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
 		},
@@ -46,20 +41,18 @@ class BaseConfig(BaseModel):
 		}
 	}
 
-	def ConversationCfg(self):
-		return{
+	ConversationCfg = {
 		"id": "",
 		'user_id':'',
 		"name": "",
 		"inputs": {},
 		"status": "normal",
-			"introduction": self.projectInfo,
+		"introduction": ParamterCfg['opening_statement'],
 		"created_at":''
 	}
 
-	@classmethod 
-	def MessageCfg(cls):
-		return {
+
+	MessageCfg = {
 		    "id": "",
 			'user_id':'',
             "conversation_id": "",
@@ -67,5 +60,3 @@ class BaseConfig(BaseModel):
             "query": "",
             "answer": ""
 	}
-
-

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]
@@ -13,5 +13,3 @@ class ChatRequestData(BaseModel):
 
 class ChatFileUploadRequest(BaseModel):
     base64: str
-
-

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}")