kangda-robot-backend/ruoyi-fastapi-backend/module_admin/controller/ragflow_controller.py

# from datetime import datetime
import hashlib
import json
import time
from typing import List
from fastapi import APIRouter, Depends, Request, UploadFile, File
from fastapi.responses import StreamingResponse
# from pydantic_validation_decorator import ValidateFields
# from sqlalchemy.ext.asyncio import AsyncSession
# from config.enums import BusinessType
# from config.get_db import get_db
from module_admin.aspect.interface_auth import CheckRoleInterfaceAuth
# from module_admin.entity.vo.notice_vo import DeleteNoticeModel, NoticeModel, NoticePageQueryModel
# from module_admin.entity.vo.user_vo import CurrentUserModel
from module_admin.service.login_service import LoginService
from module_admin.service.ragflow_service import RAGFlowService
from module_admin.service.search_service import SearchService, SearchServiceError

from utils.log_util import logger
# from utils.page_util import PageResponseModel
from utils.response_util import ResponseUtil
from module_admin.entity.vo.ragflow_vo import RagflowListQueryModel, ListDocumentsQueryModel, UpdateFileModel, DeleteFileModel, CreateDatasetModel, DocumentIdsModel, UpdateChatAssistantModel,\
    CreateSessionWithChatModel, ConverseWithChatAssistantModel
# from config.env import RAGFlowConfig



ragflowController = APIRouter(prefix="/system/ragflow", dependencies=[Depends(LoginService.get_current_user)])



# 查看数据集列表
@ragflowController.post("/dataset_list"
                        # , response_model=PageResponseModel
                        # , dependencies=[Depends(CheckUserInterfaceAuth("system:ragflow:list"))]"
                        )
async def get_system_ragflow_list(
    request: Request,
    rage_flow_dastset_query: RagflowListQueryModel ,
    # query_db: AsyncSession = Depends(get_db),
):
    
    result = await RAGFlowService.get_ragflow_dataset_list_services(None, rage_flow_dastset_query)
    
    return parse_result(result)
    
# 创建数据集
@ragflowController.post('/create_dataset')
async def create_dataset(
    request: Request,
    create_dataset_params: CreateDatasetModel,
):
    
    result = await RAGFlowService.create_dataset_services(create_dataset_params)
    return parse_result(result)

# 更新数据集
@ragflowController.post('/update_dataset/{dataset_id}')
async def update_dataset(
    request: Request,
    dataset_id: str,
    update_dataset_params: CreateDatasetModel,
):
    result = await RAGFlowService.update_dataset_services(dataset_id, update_dataset_params)
    return parse_result(result)


# 列出数据集中文档列表
@ragflowController.get("/list_documents/{dataset_id}")
async def list_documents_by_dataset_id(
    request: Request,
    dataset_id: str,
    list_documents_query: ListDocumentsQueryModel = Depends(ListDocumentsQueryModel.as_query),
    
    # query_db: AsyncSession = Depends(get_db),
):
    """
    列出数据集中文档列表
    """
    print(list_documents_query)
    result = await RAGFlowService.list_documents_services(None, dataset_id, list_documents_query)
    
    return parse_result(result)
    

# 上传文件到数据集
@ragflowController.post("/upload_file/{dataset_id}")
async def upload_file_dataset(
    dataset_id: str,
    files: List[UploadFile] = File(...),
    # query_db: AsyncSession = Depends(get_db),
):
    """
    上传文件到数据集
    """
    # print(file)
    result = await RAGFlowService.upload_file_dataset_services(None, dataset_id ,files)
    
    return parse_result(result)
    
# 更新文档
@ragflowController.post("/update_file/{dataset_id}/{document_id}")
async def update_file_dataset(
    dataset_id: str,
    document_id: str,
    update_params: UpdateFileModel,
    # query_db: AsyncSession = Depends(get_db),
):
    """
    更新文件到数据集
    """
    # print(file)
    result = await RAGFlowService.update_file_dataset_services(dataset_id ,document_id, update_params)
    
    return parse_result(result)

# 开始解析文档
@ragflowController.post('/parse_documents/{dataset_id}')
async def parse_documents(
    dataset_id: str,
    parse_params: DocumentIdsModel,
    # query_db: AsyncSession = Depends(get_db),
):
    result = await RAGFlowService.parse_documents_services(dataset_id, parse_params)
    return parse_result(result)

# 停止解析文档
@ragflowController.post('/stop_parse_documents/{dataset_id}')
async def stop_parse_documents(
    dataset_id: str,
    parse_params: DocumentIdsModel,
    # query_db: AsyncSession = Depends(get_db),
):
    result = await RAGFlowService.stop_parse_documents_services(dataset_id, parse_params)
    return parse_result(result)

# 删除文档
@ragflowController.post('/delete_file/{dataset_id}')
async def delete_file(
    dataset_id: str,
    delete_params: DeleteFileModel,
    # query_db: AsyncSession = Depends(get_db),
):
    """
    删除文件
    """
    result = await RAGFlowService.delete_file_services(dataset_id, delete_params)
    
    return parse_result(result)
    
    
# 删除数据集
@ragflowController.post('/delete_datasets')
async def delete_datasets(
    delete_params: DeleteFileModel,
    # query_db: AsyncSession = Depends(get_db),
):
    """
    删除数据集
    """
    result = await RAGFlowService.delete_datasets_services(delete_params)
    return parse_result(result)

# 查看聊天助手列表
@ragflowController.post('/get_chat_assistant_list')
async def get_chat_assistant_list(
    query_params: RagflowListQueryModel,
):
    """
    查看聊天助手列表
    """
    
    result = await RAGFlowService.get_chat_assistant_list_services(query_params)
    return parse_result(result)
    
    # pass

# 更新聊天助手
@ragflowController.post('/update_chat_assistant')
async def update_chat_assistant(
    update_params: UpdateChatAssistantModel,
):
    """
    更新聊天助手
    """
    result = await RAGFlowService.update_chat_assistant_services(update_params)
    return parse_result(result)

# 创建属于聊天助手的会话
@ragflowController.post('/create_session_with_chat')
async def create_session_with_chat(
    create_params: CreateSessionWithChatModel,
):
    """
    创建属于聊天助手的会话
    """
    
    result = await RAGFlowService.create_session_with_chat_services(create_params)
    return parse_result(result)

# 与聊天助手进行对话
@ragflowController.post('/converse_with_chat_assistant')
async def converse_with_chat_assistant(
    request: Request,
    converse_params: ConverseWithChatAssistantModel,
):
    """
    与聊天助手进行对话
    """

    start_time = time.perf_counter()
    redis = getattr(request.app.state, 'redis', None)
    cache_key = None

    # 检查是否应该使用搜索服务 (Router Strategy)
    # Using 'glm-4-flash' to classify intent: SEARCH vs RAG
    t0 = time.perf_counter()
    intent = await SearchService.classify_intent(converse_params.question)
    logger.info(f"[RAG_PROFILE] Intent Router ({converse_params.chat_id}): {intent} | Time: {time.perf_counter() - t0:.3f}s | Query: {converse_params.question}")
    
    if intent == 'SEARCH':
        return await SearchService.handle_search_chat(converse_params, redis)

    if not converse_params.stream and redis:
        cache_key = build_chat_cache_key(converse_params.chat_id, converse_params.question)
        cached = await redis.get(cache_key)
        if cached:
            logger.info('ragflow对话命中缓存: chat=%s', converse_params.chat_id)
            return ResponseUtil.success(json.loads(cached))

    # Revert to Native RAGFlow Service (OpenAI endpoint failed with Auth error)
    t1 = time.perf_counter()
    # 修复：await async方法获取AsyncGenerator
    result = await RAGFlowService.converse_with_chat_assistant_services(converse_params)
    logger.info(f"[RAG_PROFILE] RAG Init/Connect ({converse_params.chat_id}): {time.perf_counter() - t1:.3f}s")

    if converse_params.stream:
        async def stream_response():
            last_answer = ""
            first_token_received = False
            start_stream_time = time.perf_counter()
            
            try:
                async for chunk in result:
                    if not first_token_received:
                        first_token_received = True
                        latency = time.perf_counter() - start_stream_time
                        logger.info(f"[RAG_PROFILE] Time to First Token ({converse_params.chat_id}): {latency:.3f}s")
                    
                    # Native RAGFlow returns cumulative text in 'answer' field
                    # Chunk structure: {'data': {'answer': 'Cumulative Text...'}}
                    payload = chunk.get('data') if isinstance(chunk, dict) else chunk
                    if not payload:
                        continue

                    body = payload if isinstance(payload, dict) else {'data': payload}
                    
                    if isinstance(body, dict) and 'answer' in body:
                        # Inspect for errors even in stream
                        if isinstance(chunk, dict) and chunk.get('code') and chunk.get('code') != 0:
                             logger.error(f"RAGFlow Stream Error: {chunk}")
                        
                        current_answer = body['answer']
                        # Calculate Delta
                        if current_answer.startswith(last_answer):
                            delta = current_answer[len(last_answer):]
                            if delta:
                                body['answer'] = delta # Send only the new part
                                last_answer = current_answer
                                yield format_sse(body)
                        else:
                            # Context reset (unlikely but safe fallback)
                            last_answer = current_answer
                            yield format_sse(body)
                    else:
                        yield format_sse(body)
                        
                yield format_sse({'status': 'completed'}, event='end')
            except Exception as exc:
                logger.exception('[RAG_PROFILE] ragflow流式对话异常: %s', exc)
                yield format_sse({'message': str(exc)}, event='error')
            finally:
                total_time = time.perf_counter() - start_time
                logger.info(f'[RAG_PROFILE] Total Stream Duration ({converse_params.chat_id}): {total_time:.3f}s')

        return StreamingResponse(stream_response(), media_type='text/event-stream')

    response = parse_result(result)
    if redis and cache_key and isinstance(result, dict) and result.get('code') == 0:
        await redis.set(cache_key, json.dumps(result.get('data'), ensure_ascii=False), ex=60)
    logger.info(f'[RAG_PROFILE] Total Sync Duration ({converse_params.chat_id}): {time.perf_counter() - start_time:.3f}s')
    return response
    
   
 
    # return parse_result(result)
    
# 获取用户权限
@ragflowController.get('/get_user_permission', dependencies=[Depends(CheckRoleInterfaceAuth('pad'))])
async def get_user_permission(current_user = Depends(LoginService.get_current_user)):
    """
    获取用户权限
    """
    
    user_auth_list = current_user.permissions
    print(user_auth_list)

    
    return ResponseUtil.success(data=user_auth_list)

def parse_result(result):
    code = result.get('code', 0)
    if code != 0:
        msg = result.get('message') or result.get('msg') or '接口异常'
        return ResponseUtil.error(msg=msg, data=result.get('data', None))
    return ResponseUtil.success(data=result.get('data', None))


def build_chat_cache_key(chat_id: str, question: str) -> str:
    digest = hashlib.sha256(question.encode('utf-8')).hexdigest()
    return f'ragflow:chat:{chat_id}:{digest}'


def format_sse(data: dict, event: str | None = None) -> str:
    payload = json.dumps(data, ensure_ascii=False)
    prefix = f'event: {event}\n' if event else ''
    return f'{prefix}data: {payload}\n\n'