新增工程配置信息

backend/.env.example

@@ -80,3 +80,4 @@ 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

@@ -111,3 +111,4 @@ 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
@@ -102,77 +104,6 @@ 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 
@@ -180,7 +111,11 @@ 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):
@@ -196,14 +131,13 @@ 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'],
@@ -227,41 +161,219 @@ 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_text(cls, token: str):
+    def convert_Message(cls, token: str):
-        # Escape newlines and double quotes to avoid breaking the stream
+        params = cls.ids
-        #token = json.dumps(token)
+        params.update({
-
+            'answer':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}"}}"
+            'conversation_id':cls.data.conversation_id
-        return "\n"
+        })
-
+        event = Message_DifyChatResponseEvent(**params)
-    @classmethod
+        data_str = json.dumps(event.dict())
-    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, event: DifyChatResponseEvent):
+    def convert_Event(cls, data: dict):
-        data_str = json.dumps(event.dict())
+        data_str = json.dumps(data)
         return f"{cls.DATA_PREFIX}{data_str}\n\n"
 
     def __init__(
@@ -269,8 +381,11 @@ 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 +399,26 @@ 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
+                yield ChatStreamResponse.convert_Message(token)
-                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_text("")
+                    yield ChatStreamResponse.convert_Event(event_response)
 
         combine = stream.merge(_chat_response_generator(), _event_generator())
         is_stream_started = False
@@ -327,25 +427,11 @@ 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 +451,15 @@ 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 +475,9 @@ 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 +528,22 @@ 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 = {
+        params = 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,
-                        "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
@@ -122,4 +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

@@ -1,8 +1,13 @@
+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！")
 
-class BaseConfig:
+	def ParamterCfg(self):
-	ParamterCfg = {
+		questions = os.getenv("CONVERSATION_STARTERS", "dev")
-		"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！",
+		return{
-		"suggested_questions": [],
+			"opening_statement": self.projectInfo,
+			"suggested_questions": questions.split('\n'),
 			"suggested_questions_after_answer": {
 				"enabled": False
 			},
@@ -41,18 +46,20 @@ class BaseConfig:
 			}
 		}
 	
-	ConversationCfg = {
+	def ConversationCfg(self):
+		return{
 			"id": "",
 			'user_id':'',
 			"name": "",
 			"inputs": {},
 			"status": "normal",
-		"introduction": ParamterCfg['opening_statement'],
+			"introduction": self.projectInfo,
 			"created_at":''
 		}
 
-
+	@classmethod 
-	MessageCfg = {
+	def MessageCfg(cls):
+		return {
 		    "id": "",
 			'user_id':'',
             "conversation_id": "",
@@ -60,3 +67,5 @@ class BaseConfig:
             "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
+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/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}")