186 lines
7.7 KiB
Python
186 lines
7.7 KiB
Python
# pip install jieba
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import json
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import logging
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import time
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import jieba
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import fastapi
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import uvicorn
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from fastapi import FastAPI, HTTPException
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from openai import AsyncOpenAI
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from sse_starlette import EventSourceResponse
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import uuid
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from Config import Config
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from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil
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# 初始化日志
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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# 初始化停用词表
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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'])
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# 初始化异步 OpenAI 客户端
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client = AsyncOpenAI(
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api_key=Config.ALY_LLM_API_KEY,
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base_url=Config.ALY_LLM_BASE_URL
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)
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# 初始化 ElasticSearch 工具
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search_util = EsSearchUtil(Config.ES_CONFIG)
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# 存储对话历史的字典，键为会话ID，值为对话历史列表
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conversation_history = {}
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# 最大对话历史轮数
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MAX_HISTORY_ROUNDS = 10
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def get_system_prompt():
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"""获取系统提示"""
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return """
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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。
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严格遵循以下教学规则：
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1. 首先了解学生情况：在开始讲解前，询问学生的年级水平和对询问知识的了解程度。
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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。
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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。
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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。
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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。
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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。
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"""
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async def lifespan(_: FastAPI):
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yield
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app = FastAPI(lifespan=lifespan)
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@app.post("/api/chat")
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async def chat(request: fastapi.Request):
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"""
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根据用户输入的语句，查询相关历史对话
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然后调用大模型进行回答
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"""
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try:
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data = await request.json()
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user_id = data.get('user_id', 'anonymous')
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query = data.get('query', '')
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session_id = data.get('session_id', str(uuid.uuid4())) # 获取或生成会话ID
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include_history = data.get('include_history', True)
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if not query:
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raise HTTPException(status_code=400, detail="查询内容不能为空")
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# 1. 初始化会话历史
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if session_id not in conversation_history:
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conversation_history[session_id] = []
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# 2. 为用户查询生成标签并存储到ES
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current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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tags = [user_id, f"time:{current_time.split()[0]}", f"session:{session_id}"]
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# 提取查询中的关键词作为额外标签 - 使用jieba分词
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try:
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seg_list = jieba.cut(query, cut_all=False) # 精确模式
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keywords = [kw for kw in seg_list if kw.strip() and kw not in STOPWORDS and len(kw) > 1]
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keywords = keywords[:5]
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tags.extend([f"keyword:{kw}" for kw in keywords])
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logger.info(f"使用jieba分词提取的关键词: {keywords}")
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except Exception as e:
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logger.error(f"分词失败: {str(e)}")
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keywords = query.split()[:5]
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tags.extend([f"keyword:{kw}" for kw in keywords if kw.strip()])
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# 存储查询到ES
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try:
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search_util.insert_long_text_to_es(query, tags)
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logger.info(f"用户 {user_id} 的查询已存储到ES，标签: {tags}")
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except Exception as e:
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logger.error(f"存储用户查询到ES失败: {str(e)}")
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# 3. 构建对话历史上下文
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history_context = ""
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if include_history and session_id in conversation_history:
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# 获取最近的几次对话历史
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recent_history = conversation_history[session_id][-MAX_HISTORY_ROUNDS:]
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if recent_history:
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history_context = "\n\n以下是最近的对话历史，可供参考：\n"
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for i, (user_msg, ai_msg) in enumerate(recent_history, 1):
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history_context += f"[对话 {i}] 用户: {user_msg}\n"
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history_context += f"[对话 {i}] 老师: {ai_msg}\n"
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# 4. 构建提示词
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system_prompt = get_system_prompt()
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# 5. 流式调用大模型生成回答
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async def generate_response_stream():
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try:
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# 构建消息列表
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messages = [{'role': 'system', 'content': system_prompt.strip()}]
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# 添加历史对话（如果有）
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if history_context:
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messages.append({'role': 'user', 'content': history_context.strip()})
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# 添加当前问题
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messages.append({'role': 'user', 'content': query})
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stream = await client.chat.completions.create(
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model=Config.ALY_LLM_MODEL_NAME,
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messages=messages,
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max_tokens=8000,
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stream=True
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)
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# 收集完整回答用于保存
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full_answer = []
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async for chunk in stream:
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if chunk.choices[0].delta.content:
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full_answer.append(chunk.choices[0].delta.content)
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yield f"data: {json.dumps({'reply': chunk.choices[0].delta.content}, ensure_ascii=False)}\n\n"
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# 保存回答到ES和对话历史
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if full_answer:
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answer_text = ''.join(full_answer)
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try:
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# 为回答添加标签
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answer_tags = [f"{user_id}_answer", f"time:{current_time.split()[0]}", f"session:{session_id}"]
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try:
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seg_list = jieba.cut(answer_text, cut_all=False)
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answer_keywords = [kw for kw in seg_list if kw.strip() and kw not in STOPWORDS and len(kw) > 1]
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answer_keywords = answer_keywords[:5]
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answer_tags.extend([f"keyword:{kw}" for kw in answer_keywords])
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except Exception as e:
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logger.error(f"回答分词失败: {str(e)}")
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search_util.insert_long_text_to_es(answer_text, answer_tags)
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logger.info(f"用户 {user_id} 的回答已存储到ES")
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# 更新对话历史
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conversation_history[session_id].append((query, answer_text))
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# 保持历史记录不超过最大轮数
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if len(conversation_history[session_id]) > MAX_HISTORY_ROUNDS:
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conversation_history[session_id].pop(0)
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except Exception as e:
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logger.error(f"存储回答到ES失败: {str(e)}")
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except Exception as e:
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logger.error(f"大模型调用失败: {str(e)}")
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yield f"data: {json.dumps({'error': f'生成回答失败: {str(e)}'})}\n\n"
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return EventSourceResponse(generate_response_stream())
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except HTTPException as e:
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logger.error(f"聊天接口错误: {str(e.detail)}")
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raise e
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except Exception as e:
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logger.error(f"聊天接口异常: {str(e)}")
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raise HTTPException(status_code=500, detail=f"处理请求失败: {str(e)}")
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8000)
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