# pip install jieba import json import logging import time import jieba import fastapi import uvicorn from fastapi import FastAPI, HTTPException from openai import AsyncOpenAI from sse_starlette import EventSourceResponse import uuid from Config import Config from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil # 初始化日志 logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) # 初始化停用词表 STOPWORDS = set(['的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这']) # 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) # 存储对话历史的字典，键为会话ID，值为对话历史列表 conversation_history = {} # 存储学生信息的字典，键为用户ID，值为学生信息 student_info = {} # 年级关键词词典 GRADE_KEYWORDS = { '一年级': ['一年级', '初一'], '二年级': ['二年级', '初二'], '三年级': ['三年级', '初三'], '四年级': ['四年级'], '五年级': ['五年级'], '六年级': ['六年级'], '七年级': ['七年级', '初一'], '八年级': ['八年级', '初二'], '九年级': ['九年级', '初三'], '高一': ['高一'], '高二': ['高二'], '高三': ['高三'] } # 最大对话历史轮数 MAX_HISTORY_ROUNDS = 10 # 添加函数：保存学生信息到ES def save_student_info_to_es(user_id, info): """将学生信息保存到Elasticsearch""" try: # 使用用户ID作为文档ID doc_id = f"student_{user_id}" # 准备文档内容 doc = { "user_id": user_id, "info": info, "update_time": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) } # 从连接池获取连接 es_conn = search_util.es_pool.get_connection() try: # 确保索引存在，如果不存在则创建 if not es_conn.indices.exists(index="student_info"): es_conn.indices.create( index="student_info", body={ "mappings": { "properties": { "user_id": {"type": "keyword"}, "info": {"type": "object"}, "update_time": {"type": "date", "format": "yyyy-MM-dd HH:mm:ss"} } } } ) logger.info("创建student_info索引成功") es_conn.index(index="student_info", id=doc_id, document=doc) logger.info(f"学生 {user_id} 的信息已保存到ES: {info}") finally: # 释放连接回连接池 search_util.es_pool.release_connection(es_conn) except Exception as e: logger.error(f"保存学生信息到ES失败: {str(e)}", exc_info=True) # 添加函数：从ES获取学生信息 def get_student_info_from_es(user_id): """从Elasticsearch获取学生信息""" try: doc_id = f"student_{user_id}" # 从连接池获取连接 es_conn = search_util.es_pool.get_connection() try: # 确保索引存在 if es_conn.indices.exists(index="student_info"): result = es_conn.get(index="student_info", id=doc_id) if result and '_source' in result: logger.info(f"从ES获取到学生 {user_id} 的信息: {result['_source']['info']}") return result['_source']['info'] else: logger.info(f"ES中没有找到学生 {user_id} 的信息") else: logger.info("student_info索引不存在") finally: # 释放连接回连接池 search_util.es_pool.release_connection(es_conn) except Exception as e: # 如果文档不存在，返回空字典 if "not_found" in str(e).lower(): logger.info(f"学生 {user_id} 的信息在ES中不存在") return {} logger.error(f"从ES获取学生信息失败: {str(e)}", exc_info=True) return {} def extract_student_info(text, user_id): """使用jieba分词提取学生信息""" try: # 提取年级信息 seg_list = jieba.cut(text, cut_all=False) # 精确模式 seg_set = set(seg_list) # 检查是否已有学生信息，如果没有则从ES加载 if user_id not in student_info: # 从ES加载学生信息 info_from_es = get_student_info_from_es(user_id) if info_from_es: 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: student_info[user_id] = {} # 提取并更新年级信息 grade_found = False for grade, keywords in GRADE_KEYWORDS.items(): for keyword in keywords: if keyword in seg_set: if 'grade' not in student_info[user_id] or student_info[user_id]['grade'] != grade: student_info[user_id]['grade'] = grade logger.info(f"提取到用户 {user_id} 的年级信息: {grade}") # 保存到ES save_student_info_to_es(user_id, student_info[user_id]) grade_found = True break if grade_found: break # 如果文本中明确提到年级，但没有匹配到关键词，尝试直接提取数字 if not grade_found: import re # 匹配"我是X年级"格式 match = re.search(r'我是(\d+)年级', text) if match: grade_num = match.group(1) grade = f"{grade_num}年级" if 'grade' not in student_info[user_id] or student_info[user_id]['grade'] != grade: student_info[user_id]['grade'] = grade logger.info(f"通过正则提取到用户 {user_id} 的年级信息: {grade}") # 保存到ES save_student_info_to_es(user_id, student_info[user_id]) except Exception as e: logger.error(f"提取学生信息失败: {str(e)}", exc_info=True) def get_system_prompt(): """获取系统提示""" return """ 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。 """ async def lifespan(_: FastAPI): yield app = FastAPI(lifespan=lifespan) @app.post("/api/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 conversation_history: 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 student_info: # 从ES加载学生信息 info_from_es = get_student_info_from_es(user_id) if info_from_es: 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: student_info[user_id] = {} # 2. 使用jieba分词提取学生信息 extract_student_info(query, user_id) # 输出调试信息 logger.info(f"当前学生信息: {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 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 conversation_history: # 获取最近的几次对话历史 recent_history = conversation_history[session_id][-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 student_info and student_info[user_id]: student_context = "\n\n学生基础信息：\n" for key, value in 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 = get_system_prompt() # 添加学生信息到系统提示词 if user_id in student_info and student_info[user_id]: student_info_str = "\n\n学生基础信息:\n" for key, value in 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) 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 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 conversation_history[session_id].append((query, answer_text)) # 保持历史记录不超过最大轮数 if len(conversation_history[session_id]) > MAX_HISTORY_ROUNDS: 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)}") if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8000)