'commit'

dsSchoolBuddy/Start.py

@@ -1,13 +1,14 @@
+# pip install jieba
 import json
 import logging
-import uuid
+import time
-from datetime import datetime
+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=[
+                    messages=messages,
-                        {'role': 'user', 'content': prompt}
-                    ],
                     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)

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: