463 lines
19 KiB
Python
463 lines
19 KiB
Python
"""
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简化的RAGFlow控制器 - 集成语义缓存
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"""
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import asyncio
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import hashlib
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import json
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import time
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from typing import Any, Dict, Generator, Optional, Tuple
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from fastapi import APIRouter, Depends, Request
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from fastapi.responses import StreamingResponse
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from module_admin.service.login_service import LoginService
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from module_admin.service.ragflow_service import RAGFlowService
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from module_admin.service.search_service import SearchService
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from module_admin.entity.vo.ragflow_vo import (
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ConverseWithChatAssistantModel,
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CreateSessionWithChatModel,
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UpdateChatAssistantModel,
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RagflowListQueryModel
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)
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from utils.log_util import logger
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from utils.response_util import ResponseUtil
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from utils.semantic_cache_service import get_semantic_cache_service, lookup_question, store_qa_pair
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def _get_question_hash(question: str) -> str:
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"""计算问题的hash值"""
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import hashlib
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normalized = question.lower().strip()
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return hashlib.md5(normalized.encode('utf-8')).hexdigest()[:16]
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from utils.static_qa_service import get_static_qa_service
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async def _async_store_qa(chat_id: str, question: str, answer: str, redis) -> None:
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"""
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异步存储问答对到语义缓存
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"""
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store_hash = _get_question_hash(question)
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logger.info(f"[SemanticCache] 存储QA | chat_id={chat_id} | question={question} | hash={store_hash} | answer_length={len(answer)}")
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try:
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await store_qa_pair(chat_id, question, answer, redis)
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except Exception as e:
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logger.warning(f"[SemanticCache] 异步存储失败: {e}")
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# 使用标准的APIRouter，增加认证依赖
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ragflowController = APIRouter(prefix="/system/ragflow", dependencies=[Depends(LoginService.get_current_user)])
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def format_sse(data: dict, event: str | None = None) -> str:
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"""格式化SSE数据"""
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payload = json.dumps(data, ensure_ascii=False)
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prefix = f'event: {event}\n' if event else ''
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return f'{prefix}data: {payload}\n\n'
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def parse_result(result: Dict[str, Any]) -> Dict[str, Any]:
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"""统一结果解析"""
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code = result.get('code', 0)
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if code != 0:
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msg = result.get('message') or result.get('msg') or '接口异常'
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return ResponseUtil.error(msg=msg, data=result.get('data', None))
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return ResponseUtil.success(data=result.get('data', None))
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def build_chat_cache_key(chat_id: str, question: str) -> str:
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"""构建聊天缓存键"""
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digest = hashlib.sha256(question.encode('utf-8')).hexdigest()
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return f'ragflow:chat:{chat_id}:{digest}'
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def remove_style_hint(question: str) -> Tuple[str, bool]:
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"""
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移除语言风格提示词
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Args:
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question: 原始问题
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Returns:
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(处理后的问题, 是否移除了风格提示词)
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"""
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if not question:
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return question, False
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import re
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pattern = r'语言风格\s*[:：]\s*[^，。！？\n]*'
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match = re.search(pattern, question)
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if match:
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style_hint = match.group(0)
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cleaned_question = re.sub(pattern, '', question).strip()
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cleaned_question = re.sub(r'\s+', ' ', cleaned_question)
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logger.info(f'[StyleHint] 移除风格提示词: {style_hint} | 原始问题: {question} | 处理后: {cleaned_question}')
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return cleaned_question, True
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return question, False
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# 非流式对话接口
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@ragflowController.post('/converse_with_chat_assistant')
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async def converse_with_chat_assistant(
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request: Request,
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converse_params: ConverseWithChatAssistantModel,
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):
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"""
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与聊天助手进行对话 - 集成语义缓存版本（支持流式和非流式）
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匹配流程：
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1. 移除语言风格提示词
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2. 静态问答匹配 (threshold=0.70)
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3. RAG历史缓存匹配 (threshold=0.60)
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4. RAG服务调用（使用原始问题）
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"""
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start_time = time.perf_counter()
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original_question = converse_params.question
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cleaned_question, style_removed = remove_style_hint(original_question)
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if style_removed:
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converse_params.question = cleaned_question
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# 获取redis实例
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redis = getattr(request.app.state, 'redis', None)
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cache_key = None
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cached_answer = None
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cache_similarity = 0.0
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cache_source = None
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# ========== 1. 静态问答匹配 ==========
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try:
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static_qa_service = get_static_qa_service()
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logger.info(f'[StaticQA] 开始匹配 | chat_id={converse_params.chat_id} | question={converse_params.question} | threshold=0.70')
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static_match, static_sim = static_qa_service.find_match(converse_params.question, threshold=0.70)
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logger.info(f'[StaticQA] 匹配结果 | similarity={static_sim:.2f} | matched={static_match is not None}')
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if static_match:
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cached_answer = static_match.get('answer', '')
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cache_similarity = static_sim
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cache_source = 'static_qa'
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logger.info(f'[RAG_SOURCE] 命中静态FAQ | chat_id={converse_params.chat_id} | question={converse_params.question} | similarity={cache_similarity:.2f} | answer_length={len(cached_answer)}')
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logger.info(f'[StaticQA] 流式响应使用静态问答答案，chat_id={converse_params.chat_id}')
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return StreamingResponse(
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stream_cached_response(cached_answer, converse_params.chat_id, start_time, cache_source='static_qa'),
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media_type='text/event-stream',
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headers={
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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'X-Accel-Buffering': 'no',
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'Transfer-Encoding': 'chunked'
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}
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)
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except Exception as e:
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logger.warning(f'[StaticQA] 静态问答匹配失败: {e}')
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# ========== 2. RAG历史缓存查找 ==========
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logger.info(f'[SemanticCache] 准备执行RAG历史缓存查找 | redis={redis is not None} | chat_id={converse_params.chat_id}')
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if redis:
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lookup_hash = _get_question_hash(converse_params.question)
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logger.info(f'[SemanticCache] 开始查找 | chat_id={converse_params.chat_id} | question={converse_params.question} | hash={lookup_hash} | threshold=0.60')
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cache_result = await lookup_question(
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converse_params.chat_id,
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converse_params.question,
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redis
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)
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logger.info(f'[SemanticCache] 查找结果 | found={cache_result is not None}')
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if cache_result:
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cached_answer, cache_similarity = cache_result
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cache_source = 'rag_history'
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logger.info(f'[RAG_SOURCE] 命中RAG会话历史 | chat_id={converse_params.chat_id} | question={converse_params.question} | similarity={cache_similarity:.2f} | answer_length={len(cached_answer)}')
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logger.info(f'[SemanticCache] 流式响应使用RAG历史缓存答案，chat_id={converse_params.chat_id}')
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return StreamingResponse(
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stream_cached_response(cached_answer, converse_params.chat_id, start_time, cache_source='rag_history'),
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media_type='text/event-stream',
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headers={
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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'X-Accel-Buffering': 'no',
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'Transfer-Encoding': 'chunked'
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}
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)
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# 检查是否应该使用搜索服务
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try:
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intent = await SearchService.classify_intent(converse_params.question)
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logger.info(f"[RAG_PROFILE] Intent Router ({converse_params.chat_id}): {intent}")
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if intent == 'SEARCH':
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return await SearchService.handle_search_chat(converse_params, redis)
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except Exception as e:
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logger.warning(f"意图分类失败，使用RAG服务: {e}")
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# 直接使用同步RAGFlow服务
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try:
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# 恢复原始问题（包含语言风格提示词）
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if style_removed:
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converse_params.question = original_question
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logger.info(f'[StyleHint] 恢复原始问题: {original_question}')
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logger.info(f'[RAG_SOURCE] 调用原生RAG服务 | chat_id={converse_params.chat_id} | question={converse_params.question}')
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result = RAGFlowService.converse_with_chat_assistant_services(converse_params)
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cache_question = cleaned_question if style_removed else converse_params.question
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store_hash = _get_question_hash(cache_question)
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logger.info(f'[RAG_CACHE] 准备存储 | chat_id={converse_params.chat_id} | question={cache_question} | hash={store_hash}')
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async def make_cache_store(chat_id: str, question: str):
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async def store_answer(answer: str):
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if redis and answer and len(answer.strip()) >= 10:
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try:
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await _async_store_qa(chat_id, question, answer, redis)
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logger.info(f'[RAG_CACHE] 语义缓存存储成功 | chat_id={chat_id} | answer_length={len(answer)}')
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except Exception as e:
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logger.warning(f'语义缓存存储失败: {e}')
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return store_answer
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cache_store = await make_cache_store(converse_params.chat_id, cache_question)
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return StreamingResponse(
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stream_ragflow_response(result, converse_params.chat_id, start_time, cache_store_func=cache_store),
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media_type='text/event-stream',
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headers={
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'Cache-Control': 'no-cache',
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'Connection': 'keep-alive',
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'X-Accel-Buffering': 'no',
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'Transfer-Encoding': 'chunked'
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}
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)
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except Exception as e:
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logger.exception(f'[RAG_PROFILE] ragflow对话异常: {e}')
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return ResponseUtil.error(msg=f'对话服务异常: {str(e)}')
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def stream_ragflow_response(result: Generator, chat_id: str, start_time: float, cache_store_func=None) -> Generator[str, None, None]:
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"""
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流式处理RAGFlow响应 - 同步版本，修复首token延迟
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"""
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import time
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server_stream_start = time.time()
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logger.info(f"[RAG_SERVER {server_stream_start:.3f}] 🚀 开始流式响应处理，chat_id: {chat_id}")
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last_answer = ""
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first_token_received = False
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start_stream_time = time.perf_counter()
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# 立即发送连接建立消息，解决首token延迟
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connection_time = time.time()
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ping_message = "event: ping\ndata: {\"status\": \"connected\"}\n\n"
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yield ping_message
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logger.info(f"[RAG_SERVER {connection_time:.3f}] 📡 连接建立ping发送，耗时: {connection_time - server_stream_start:.3f}s")
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chunk_count = 0
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try:
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for chunk in result:
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chunk_time = time.time()
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chunk_count += 1
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# 检查第一个token的延迟
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if not first_token_received:
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first_token_received = True
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latency = time.perf_counter() - start_stream_time
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logger.info(f"[RAG_SERVER {chunk_time:.3f}] 🎯 首token到达，耗时: {latency:.3f}s")
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# 处理chunk数据
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payload = chunk.get('data') if isinstance(chunk, dict) else chunk
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if not payload:
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logger.info(f"[RAG_SERVER {chunk_time:.3f}] ⚠️ 空chunk跳过")
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continue
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body = payload if isinstance(payload, dict) else {'data': payload}
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# 处理错误
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if isinstance(chunk, dict) and chunk.get('code') and chunk.get('code') != 0:
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logger.error(f"[RAG_SERVER {chunk_time:.3f}] ❌ RAGFlow Stream Error: {chunk}")
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error_message = format_sse({'message': chunk.get('message', '流式处理异常')}, event='error')
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yield error_message
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continue
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# 处理answer字段的增量更新
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if isinstance(body, dict) and 'answer' in body:
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current_answer = body['answer']
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# 安全检查：确保current_answer不为None且为字符串
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if current_answer is None:
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logger.warning(f"[RAG_SERVER {chunk_time:.3f}] ⚠️ current_answer为None，跳过处理")
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continue
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if not isinstance(current_answer, str):
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logger.warning(f"[RAG_SERVER {chunk_time:.3f}] ⚠️ current_answer不是字符串，类型: {type(current_answer)}")
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current_answer = str(current_answer)
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# 计算增量内容
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if current_answer.startswith(last_answer):
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delta = current_answer[len(last_answer):]
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if delta:
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body['answer'] = delta
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last_answer = current_answer
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logger.info(f"[RAG_SERVER {chunk_time:.3f}] 📝 Chunk #{chunk_count} 处理完成，delta长度: {len(delta)}")
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yield format_sse(body)
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else:
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# 上下文重置的备用处理
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last_answer = current_answer
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logger.info(f"[RAG_SERVER {chunk_time:.3f}] 🔄 Chunk #{chunk_count} 上下文重置")
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yield format_sse(body)
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else:
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logger.info(f"[RAG_SERVER {chunk_time:.3f}] 📦 Chunk #{chunk_count} 其他数据: {body}")
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yield format_sse(body)
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# 流结束
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stream_end_time = time.time()
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end_message = format_sse({'status': 'completed'}, event='end')
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yield end_message
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logger.info(f'[RAG_SERVER {stream_end_time:.3f}] 流式响应完成，chat_id: {chat_id}')
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logger.info(f'[RAG_SERVER {stream_end_time:.3f}] 总共处理chunk数量: {chunk_count}')
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logger.info(f'[RAG_SOURCE] 原生RAG流式响应完成 | chat_id={chat_id} | total_chunks={chunk_count} | answer_length={len(last_answer)}')
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except Exception as exc:
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error_time = time.time()
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logger.exception(f'[RAG_SERVER {error_time:.3f}] ragflow流式对话异常: {exc}')
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error_message = format_sse({'message': str(exc)}, event='error')
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yield error_message
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finally:
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total_time = time.perf_counter() - start_time
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logger.info(f'[RAG_SERVER {time.time():.3f}] ⏱️ Total Stream Duration ({chat_id}): {total_time:.3f}s')
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# 流结束后存储缓存
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if cache_store_func and last_answer and len(last_answer.strip()) >= 10:
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try:
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import asyncio
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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loop.run_until_complete(cache_store_func(last_answer))
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logger.info(f'[RAG_CACHE] 缓存存储完成 | chat_id={chat_id} | answer_length={len(last_answer)}')
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finally:
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loop.close()
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except Exception as cache_err:
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logger.warning(f'[RAG_CACHE] 缓存存储失败: {cache_err}')
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# 聊天助手列表
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@ragflowController.post('/get_chat_assistant_list')
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async def get_chat_assistant_list(
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query_params: RagflowListQueryModel, # 使用正确的Pydantic模型
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):
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"""获取聊天助手列表"""
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result = RAGFlowService.get_chat_assistant_list_services(query_params)
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return parse_result(result)
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# 创建会话
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@ragflowController.post('/create_session_with_chat')
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async def create_session_with_chat(
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create_params: CreateSessionWithChatModel, # 使用正确的Pydantic模型
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):
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"""创建聊天会话"""
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result = RAGFlowService.create_session_with_chat_services(create_params)
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return parse_result(result)
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# 更新聊天助手
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@ragflowController.post('/update_chat_assistant')
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async def update_chat_assistant(
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update_params: UpdateChatAssistantModel, # 使用正确的Pydantic模型
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):
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"""更新聊天助手"""
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result = RAGFlowService.update_chat_assistant_services(update_params)
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return parse_result(result)
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# 数据集相关接口
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@ragflowController.post('/dataset_list')
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async def get_system_ragflow_list(
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request: Request,
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ragflow_list_query: Any, # 使用Any避免导入具体模型类
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):
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"""获取数据集列表"""
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result = RAGFlowService.get_ragflow_dataset_list_services(None, ragflow_list_query)
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return parse_result(result)
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@ragflowController.post('/create_dataset')
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async def create_dataset(
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create_dataset_params: Any, # 使用Any避免导入具体模型类
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):
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"""创建数据集"""
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result = RAGFlowService.create_dataset_services(create_dataset_params)
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return parse_result(result)
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@ragflowController.post('/delete_datasets')
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async def delete_datasets(
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delete_params: Any, # 使用Any避免导入具体模型类
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):
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"""删除数据集"""
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result = RAGFlowService.delete_datasets_services(delete_params)
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return parse_result(result)
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def stream_cached_response(cached_answer: str, chat_id: str, start_time: float, cache_source: str = 'cache') -> Generator[str, None, None]:
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"""
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流式返回缓存的答案
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Args:
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cached_answer: 缓存的答案文本
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chat_id: 会话ID
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start_time: 请求开始时间
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cache_source: 缓存来源 ('static_qa' 或 'rag_history')
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"""
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import time
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server_stream_start = time.time()
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logger.info(f"[CACHE_STREAM {server_stream_start:.3f}] 🚀 开始流式返回缓存答案，chat_id: {chat_id}")
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# 立即发送连接建立消息
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connection_time = time.time()
|
||
ping_message = "event: ping\ndata: {\"status\": \"connected\"}\n\n"
|
||
yield ping_message
|
||
logger.info(f"[CACHE_STREAM {connection_time:.3f}] 📡 连接建立ping发送，耗时: {connection_time - server_stream_start:.3f}s")
|
||
|
||
# 模拟流式输出效果（可选：如果是完整答案，可以选择立即返回或模拟流式）
|
||
# 这里我们选择模拟流式输出，每次发送一小部分，模拟打字效果
|
||
if cached_answer:
|
||
# 将答案分块发送，模拟打字效果
|
||
chunk_size = 10 # 每个块10个字符
|
||
answer_chunks = [cached_answer[i:i+chunk_size] for i in range(0, len(cached_answer), chunk_size)]
|
||
|
||
first_token_time = time.time()
|
||
for i, chunk in enumerate(answer_chunks):
|
||
chunk_time = time.time()
|
||
|
||
# 首token延迟
|
||
if i == 0:
|
||
latency = time.perf_counter() - start_time
|
||
logger.info(f"[CACHE_STREAM {chunk_time:.3f}] 🎯 首token到达（缓存），耗时: {latency:.3f}s")
|
||
|
||
body = {'answer': chunk, 'chunk_index': i, 'total_chunks': len(answer_chunks)}
|
||
yield format_sse(body)
|
||
|
||
# 小延迟模拟打字效果（可调整或移除）
|
||
# time.sleep(0.01)
|
||
|
||
logger.info(f"[CACHE_STREAM {chunk_time:.3f}] 📝 缓存答案流式发送完成，共 {len(answer_chunks)} 个chunk")
|
||
|
||
# 流结束
|
||
stream_end_time = time.time()
|
||
end_message = format_sse({
|
||
'status': 'completed',
|
||
'from_cache': True,
|
||
'source': cache_source,
|
||
'total_time': stream_end_time - server_stream_start
|
||
}, event='end')
|
||
yield end_message
|
||
|
||
logger.info(f"[CACHE_STREAM {stream_end_time:.3f}] 🏁 缓存流式响应完成")
|
||
if cached_answer:
|
||
logger.info(f"[CACHE_STREAM {stream_end_time:.3f}] 📊 缓存答案长度: {len(cached_answer)} 字符")
|
||
|
||
total_time = time.perf_counter() - start_time
|
||
logger.info(f'[CACHE_STREAM {time.time():.3f}] ⏱️ Total Cache Stream Duration ({chat_id}): {total_time:.3f}s') |