'commit'

dsLightRag/Routes/QA.py

@@ -0,0 +1,203 @@
+import json
+import logging
+import time
+import uuid
+
+import fastapi
+import jieba
+from fastapi import APIRouter
+from fastapi import HTTPException
+from openai import AsyncOpenAI
+from sse_starlette.sse import EventSourceResponse
+
+from Config import Config
+from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil
+
+# 创建路由路由器
+router = APIRouter(prefix="/qa", tags=["答疑"])
+
+# 配置日志
+logger = logging.getLogger(__name__)
+
+# 初始化异步 OpenAI 客户端
+client = AsyncOpenAI(
+    api_key=Config.ALY_LLM_API_KEY,
+    base_url=Config.ALY_LLM_BASE_URL
+)
+# 初始化 ElasticSearch 工具
+search_util = EsSearchUtil(Config.ES_CONFIG)
+@router.post("/chat")
+async def chat(request: fastapi.Request):
+    """
+    根据用户输入的语句，查询相关历史对话
+    然后调用大模型进行回答
+    """
+    try:
+        data = await request.json()
+        user_id = data.get('user_id', 'anonymous')
+        query = data.get('query', '')
+        session_id = data.get('session_id', str(uuid.uuid4()))  # 获取或生成会话ID
+        include_history = data.get('include_history', True)
+
+        if not query:
+            raise HTTPException(status_code=400, detail="查询内容不能为空")
+
+        # 1. 初始化会话历史和学生信息
+        if session_id not in search_util.conversation_history:
+            search_util.conversation_history[session_id] = []
+
+        # 检查是否已有学生信息，如果没有则从ES加载
+        if user_id not in search_util.student_info:
+            # 从ES加载学生信息
+            info_from_es = search_util.get_student_info_from_es(user_id)
+            if info_from_es:
+                search_util.student_info[user_id] = info_from_es
+                logger.info(f"从ES加载用户 {user_id} 的信息: {info_from_es}")
+            else:
+                search_util.student_info[user_id] = {}
+
+        # 2. 使用jieba分词提取学生信息
+        search_util.extract_student_info(query, user_id)
+
+        # 输出调试信息
+        logger.info(f"当前学生信息: {search_util.student_info.get(user_id, {})}")
+
+        # 为用户查询生成标签并存储到ES
+        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        tags = [user_id, f"time:{current_time.split()[0]}", f"session:{session_id}"]
+
+        # 提取查询中的关键词作为额外标签 - 使用jieba分词
+        try:
+            seg_list = jieba.cut(query, cut_all=False)  # 精确模式
+            keywords = [kw for kw in seg_list if kw.strip() and kw not in search_util.STOPWORDS and len(kw) > 1]
+            keywords = keywords[:5]
+            tags.extend([f"keyword:{kw}" for kw in keywords])
+            logger.info(f"使用jieba分词提取的关键词: {keywords}")
+        except Exception as e:
+            logger.error(f"分词失败: {str(e)}")
+            keywords = query.split()[:5]
+            tags.extend([f"keyword:{kw}" for kw in keywords if kw.strip()])
+
+        # 存储查询到ES
+        try:
+            search_util.insert_long_text_to_es(query, tags)
+            logger.info(f"用户 {user_id} 的查询已存储到ES，标签: {tags}")
+        except Exception as e:
+            logger.error(f"存储用户查询到ES失败: {str(e)}")
+
+        # 3. 构建对话历史上下文
+        history_context = ""
+        if include_history and session_id in search_util.conversation_history:
+            # 获取最近的几次对话历史
+            recent_history = search_util.conversation_history[session_id][-search_util.MAX_HISTORY_ROUNDS:]
+            if recent_history:
+                history_context = "\n\n以下是最近的对话历史，可供参考：\n"
+                for i, (user_msg, ai_msg) in enumerate(recent_history, 1):
+                    history_context += f"[对话 {i}] 用户: {user_msg}\n"
+                    history_context += f"[对话 {i}] 老师: {ai_msg}\n"
+
+        # 4. 构建学生信息上下文
+        student_context = ""
+        if user_id in search_util.student_info and search_util.student_info[user_id]:
+            student_context = "\n\n学生基础信息：\n"
+            for key, value in search_util.student_info[user_id].items():
+                if key == 'grade':
+                    student_context += f"- 年级: {value}\n"
+                else:
+                    student_context += f"- {key}: {value}\n"
+
+        # 5. 构建提示词
+        system_prompt = """
+    你是一位平易近人且教学方法灵活的教师，通过引导学生自主学习来帮助他们掌握知识。
+
+    严格遵循以下教学规则：
+    1. 基于学生情况调整教学：如果已了解学生的年级水平和知识背景，应基于此调整教学内容和难度。
+    2. 基于现有知识构建：将新思想与学生已有的知识联系起来。
+    3. 引导而非灌输：使用问题、提示和小步骤，让学生自己发现答案。
+    4. 检查和强化：在讲解难点后，确认学生能够重述或应用这些概念。
+    5. 变化节奏：混合讲解、提问和互动活动，让教学像对话而非讲座。
+
+    最重要的是：不要直接给出答案，而是通过合作和基于学生已有知识的引导，帮助学生自己找到答案。
+    """
+
+        # 添加学生信息到系统提示词
+        if user_id in search_util.student_info and search_util.student_info[user_id]:
+            student_info_str = "\n\n学生基础信息:\n"
+            for key, value in search_util.student_info[user_id].items():
+                if key == 'grade':
+                    student_info_str += f"- 年级: {value}\n"
+                else:
+                    student_info_str += f"- {key}: {value}\n"
+            system_prompt += student_info_str
+
+        # 6. 流式调用大模型生成回答
+        async def generate_response_stream():
+            try:
+                # 构建消息列表
+                messages = [{'role': 'system', 'content': system_prompt.strip()}]
+
+                # 添加学生信息（如果有）
+                if student_context:
+                    messages.append({'role': 'user', 'content': student_context.strip()})
+
+                # 添加历史对话（如果有）
+                if history_context:
+                    messages.append({'role': 'user', 'content': history_context.strip()})
+
+                # 添加当前问题
+                messages.append({'role': 'user', 'content': query})
+
+                stream = await client.chat.completions.create(
+                    model=Config.ALY_LLM_MODEL_NAME,
+                    messages=messages,
+                    max_tokens=8000,
+                    stream=True
+                )
+
+                # 收集完整回答用于保存
+                full_answer = []
+                async for chunk in stream:
+                    if chunk.choices[0].delta.content:
+                        full_answer.append(chunk.choices[0].delta.content)
+                        yield f"data: {json.dumps({'reply': chunk.choices[0].delta.content}, ensure_ascii=False)}\n\n"
+
+                # 保存回答到ES和对话历史
+                if full_answer:
+                    answer_text = ''.join(full_answer)
+                    search_util.extract_student_info(answer_text, user_id)
+                    try:
+                        # 为回答添加标签
+                        answer_tags = [f"{user_id}_answer", f"time:{current_time.split()[0]}", f"session:{session_id}"]
+                        try:
+                            seg_list = jieba.cut(answer_text, cut_all=False)
+                            answer_keywords = [kw for kw in seg_list if kw.strip() and kw not in search_util.STOPWORDS and len(kw) > 1]
+                            answer_keywords = answer_keywords[:5]
+                            answer_tags.extend([f"keyword:{kw}" for kw in answer_keywords])
+                        except Exception as e:
+                            logger.error(f"回答分词失败: {str(e)}")
+
+                        search_util.insert_long_text_to_es(answer_text, answer_tags)
+                        logger.info(f"用户 {user_id} 的回答已存储到ES")
+
+                        # 更新对话历史
+                        search_util.conversation_history[session_id].append((query, answer_text))
+                        # 保持历史记录不超过最大轮数
+                        if len(search_util.conversation_history[session_id]) > search_util.MAX_HISTORY_ROUNDS:
+                            search_util.conversation_history[session_id].pop(0)
+
+                    except Exception as e:
+                        logger.error(f"存储回答到ES失败: {str(e)}")
+
+            except Exception as e:
+                logger.error(f"大模型调用失败: {str(e)}")
+                yield f"data: {json.dumps({'error': f'生成回答失败: {str(e)}'})}\n\n"
+
+        return EventSourceResponse(generate_response_stream())
+
+    except HTTPException as e:
+        logger.error(f"聊天接口错误: {str(e.detail)}")
+        raise e
+    except Exception as e:
+        logger.error(f"聊天接口异常: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"处理请求失败: {str(e)}")
+

dsLightRag/Routes/TeachingModel/api/LoginController.py

@@ -1,19 +1,20 @@
+# pip install captcha
 # routes/LoginController.py
 import base64
-import os
 import json
+import os
 import random
 import string
-import jwt
 
+import jwt
 from captcha.image import ImageCaptcha
-from fastapi import APIRouter, Request, Response, status, HTTPException
+from fastapi import APIRouter
+
 from Util.CommonUtil import *
 from Util.CookieUtil import *
 from Util.Database import *
 from Util.JwtUtil import *
 from Util.ParseRequest import *
-from Config.Config import *
 
 # 创建一个路由实例
 router = APIRouter()