diff --git a/dsSchoolBuddy/Start.py b/dsSchoolBuddy/Start.py index 454a8a22..1e51d78d 100644 --- a/dsSchoolBuddy/Start.py +++ b/dsSchoolBuddy/Start.py @@ -1,13 +1,14 @@ +# pip install jieba import json import logging -import uuid -from datetime import datetime - +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 @@ -16,6 +17,8 @@ from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil 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( @@ -26,6 +29,12 @@ client = AsyncOpenAI( # 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 = {} + +# 最大对话历史轮数 +MAX_HISTORY_ROUNDS = 10 + def get_system_prompt(): """获取系统提示""" @@ -50,34 +59,76 @@ app = FastAPI(lifespan=lifespan) @app.post("/api/chat") async def chat(request: fastapi.Request): """ - 根据用户输入的语句，通过关键字和向量两种方式查询相关信息 + 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") - # 获取系统提示词 + # 1. 初始化会话历史 + if session_id not in conversation_history: + conversation_history[session_id] = [] + + # 2. 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. 构建提示词 system_prompt = get_system_prompt() - prompt = f""" - {system_prompt.strip()} - - 用户现在的问题是: '{query}' - """ - # 5. 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=[ - {'role': 'user', 'content': prompt} - ], + messages=messages, max_tokens=8000, stream=True ) @@ -89,6 +140,32 @@ async def chat(request: fastapi.Request): 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) + 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" @@ -104,6 +181,5 @@ async def chat(request: fastapi.Request): - if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/dsSchoolBuddy/TestStart.py b/dsSchoolBuddy/TestStart.py index f3e01a8d..6bca92cf 100644 --- a/dsSchoolBuddy/TestStart.py +++ b/dsSchoolBuddy/TestStart.py @@ -105,6 +105,7 @@ def main(): print("请输入您的问题，比如：帮我讲解一下勾股定理的证明。输入'退出'结束对话") print("===========================") logger.info("教育助手对话系统已启动") + logger.info(f"当前会话ID: {SESSION_ID}") while True: try: