Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,125 +1,489 @@
+# # # import streamlit as st
+# # # import pandas as pd
+# # # import numpy as np
+# # # import jieba
+# # # import requests
+# # # import os
+# # # import sys
+# # # import subprocess
+# # # from openai import OpenAI
+# # # from rank_bm25 import BM25Okapi
+# # # from sklearn.metrics.pairwise import cosine_similarity
+
+# # # # ================= 1. 全局配置与 CSS注入 =================
+
+# # # API_KEY = os.getenv("SILICONFLOW_API_KEY")
+# # # API_BASE = "https://api.siliconflow.cn/v1"
+# # # EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-4B"
+# # # RERANK_MODEL = "Qwen/Qwen3-Reranker-4B"
+# # # GEN_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
+# # # DATA_FILENAME = "comsol_embedded.parquet"
+# # # DATA_URL = "https://share.leezhu.cn/graduation_design_data/comsol_embedded.parquet"
+
+# # # st.set_page_config(
+# # #     page_title="COMSOL Dark Expert",
+# # #     page_icon="🌌",
+# # #     layout="wide",
+# # #     initial_sidebar_state="expanded"
+# # # )
+
+# # # # --- 注入自定义 CSS (保持之前的审美) ---
+# # # st.markdown("""
+# # # <style>
+# # #     /* 1. 整体背景 - 深空黑 */
+# # #     .stApp {
+# # #         background-color: #050505;
+# # #         background-image: radial-gradient(circle at 50% 0%, #1a1f35 0%, #050505 60%);
+# # #         color: #e0e0e0;
+# # #         font-family: 'Inter', system-ui, -apple-system, sans-serif;
+# # #     }
+
+# # #     /* 2. 隐藏默认组件 */
+# # #     #MainMenu {visibility: hidden;}
+# # #     footer {visibility: hidden;}
+# # #     header {visibility: hidden;}
+
+# # #     /* 3. 聊天气泡 */
+# # #     [data-testid="stChatMessage"] {
+# # #         background: rgba(255, 255, 255, 0.03);
+# # #         border: 1px solid rgba(255, 255, 255, 0.08);
+# # #         border-radius: 16px;
+# # #         backdrop-filter: blur(12px);
+# # #         box-shadow: 0 4px 20px rgba(0,0,0,0.2);
+# # #         padding: 1.2rem;
+# # #     }
+    
+# # #     /* 用户气泡 */
+# # #     [data-testid="stChatMessage"][data-testid="user"] {
+# # #         background: rgba(41, 181, 232, 0.1);
+# # #         border-color: rgba(41, 181, 232, 0.2);
+# # #     }
+
+# # #     /* 4. 自定义标题栏 */
+# # #     .custom-header {
+# # #         border-bottom: 1px solid rgba(255,255,255,0.1);
+# # #         padding-bottom: 1rem;
+# # #         margin-bottom: 2rem;
+# # #         display: flex;
+# # #         align-items: center;
+# # #         gap: 1rem;
+# # #     }
+# # #     .glitch-text {
+# # #         font-size: 2rem;
+# # #         font-weight: 800;
+# # #         background: linear-gradient(120deg, #fff, #29B5E8);
+# # #         -webkit-background-clip: text;
+# # #         -webkit-text-fill-color: transparent;
+# # #         letter-spacing: -1px;
+# # #     }
+    
+# # #     /* 5. 快捷按钮 */
+# # #     div.stButton > button {
+# # #         background: rgba(255,255,255,0.05);
+# # #         color: #aaa;
+# # #         border: 1px solid rgba(255,255,255,0.1);
+# # #         border-radius: 20px;
+# # #         padding: 0.5rem 1rem;
+# # #         font-size: 0.85rem;
+# # #         transition: all 0.3s;
+# # #         width: 100%;
+# # #     }
+# # #     div.stButton > button:hover {
+# # #         background: rgba(41, 181, 232, 0.2);
+# # #         color: #fff;
+# # #         border-color: #29B5E8;
+# # #         transform: translateY(-2px);
+# # #     }
+
+# # #     /* 6. 输入框 */
+# # #     .stChatInputContainer textarea {
+# # #         background-color: #0f1115 !important;
+# # #         border: 1px solid #333 !important;
+# # #         color: white !important;
+# # #         border-radius: 12px !important;
+# # #     }
+    
+# # #     /* 7. Expander */
+# # #     .streamlit-expanderHeader {
+# # #         background-color: rgba(255,255,255,0.02);
+# # #         border: 1px solid rgba(255,255,255,0.05);
+# # #         border-radius: 8px;
+# # #         color: #bbb;
+# # #     }
+# # # </style>
+# # # """, unsafe_allow_html=True)
+
+# # # # ================= 2. 核心逻辑（数据与RAG） =================
+
+# # # if not API_KEY:
+# # #     st.error("⚠️ 未检测到 API Key。请在 Settings -> Secrets 中配置 `SILICONFLOW_API_KEY`。")
+# # #     st.stop()
+
+# # # def download_with_curl(url, output_path):
+# # #     try:
+# # #         cmd = [
+# # #             "curl", "-L", 
+# # #             "-A", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
+# # #             "-o", output_path,
+# # #             "--fail",
+# # #             url
+# # #         ]
+# # #         result = subprocess.run(cmd, capture_output=True, text=True)
+# # #         if result.returncode != 0: raise Exception(f"Curl failed: {result.stderr}")
+# # #         return True
+# # #     except Exception as e:
+# # #         print(f"Curl download error: {e}")
+# # #         return False
+
+# # # def get_data_file_path():
+# # #     possible_paths = [
+# # #         DATA_FILENAME, os.path.join("/app", DATA_FILENAME),
+# # #         os.path.join("processed_data", DATA_FILENAME),
+# # #         os.path.join("src", DATA_FILENAME),
+# # #         os.path.join("..", DATA_FILENAME), "/tmp/" + DATA_FILENAME
+# # #     ]
+# # #     for path in possible_paths:
+# # #         if os.path.exists(path): return path
+            
+# # #     download_target = "/app/" + DATA_FILENAME
+# # #     try: os.makedirs(os.path.dirname(download_target), exist_ok=True)
+# # #     except: download_target = "/tmp/" + DATA_FILENAME
+
+# # #     status_container = st.empty()
+# # #     status_container.info("📡 正在接入神经元网络... (下载核心数据中)")
+    
+# # #     if download_with_curl(DATA_URL, download_target):
+# # #         status_container.empty()
+# # #         return download_target
+        
+# # #     try:
+# # #         headers = {'User-Agent': 'Mozilla/5.0'}
+# # #         r = requests.get(DATA_URL, headers=headers, stream=True)
+# # #         r.raise_for_status()
+# # #         with open(download_target, 'wb') as f:
+# # #             for chunk in r.iter_content(chunk_size=8192): f.write(chunk)
+# # #         status_container.empty()
+# # #         return download_target
+# # #     except Exception as e:
+# # #         st.error(f"❌ 数据链路中断。Error: {e}")
+# # #         st.stop()
+
+# # # class FullRetriever:
+# # #     def __init__(self, parquet_path):
+# # #         try: self.df = pd.read_parquet(parquet_path)
+# # #         except Exception as e: st.error(f"Memory Matrix Load Failed: {e}"); st.stop()
+# # #         self.documents = self.df['content'].tolist()
+# # #         self.embeddings = np.stack(self.df['embedding'].values)
+# # #         self.bm25 = BM25Okapi([jieba.lcut(str(d).lower()) for d in self.documents])
+# # #         self.client = OpenAI(base_url=API_BASE, api_key=API_KEY)
+# # #         # Reranker 初始化移到这里，减少重复调用
+# # #         self.rerank_headers = {"Content-Type": "application/json", "Authorization": f"Bearer {API_KEY}"}
+# # #         self.rerank_url = f"{API_BASE}/rerank"
+
+# # #     def _get_emb(self, q):
+# # #         try: return self.client.embeddings.create(model=EMBEDDING_MODEL, input=[q]).data[0].embedding
+# # #         except: return [0.0] * 1024
+
+# # #     def hybrid_search(self, query: str, top_k=5):
+# # #         # 1. Vector
+# # #         q_emb = self._get_emb(query)
+# # #         vec_scores = cosine_similarity([q_emb], self.embeddings)[0]
+# # #         vec_idx = np.argsort(vec_scores)[-100:][::-1]
+# # #         # 2. Keyword
+# # #         kw_idx = np.argsort(self.bm25.get_scores(jieba.lcut(query.lower())))[-100:][::-1]
+# # #         # 3. RRF Fusion
+# # #         fused = {}
+# # #         for r, i in enumerate(vec_idx): fused[i] = fused.get(i, 0) + 1/(60+r+1)
+# # #         for r, i in enumerate(kw_idx): fused[i] = fused.get(i, 0) + 1/(60+r+1)
+# # #         c_idxs = [x[0] for x in sorted(fused.items(), key=lambda x:x[1], reverse=True)[:50]]
+# # #         c_docs = [self.documents[i] for i in c_idxs]
+        
+# # #         # 4. Rerank
+# # #         try:
+# # #             payload = {"model": RERANK_MODEL, "query": query, "documents": c_docs, "top_n": top_k}
+# # #             resp = requests.post(self.rerank_url, headers=self.rerank_headers, json=payload, timeout=10)
+# # #             results = resp.json().get('results', [])
+# # #         except:
+# # #             results = [{"index": i, "relevance_score": 0.0} for i in range(len(c_docs))][:top_k]
+            
+# # #         final_res = []
+# # #         context = ""
+# # #         for i, item in enumerate(results):
+# # #             orig_idx = c_idxs[item['index']]
+# # #             row = self.df.iloc[orig_idx]
+# # #             final_res.append({
+# # #                 "score": item['relevance_score'],
+# # #                 "filename": row['filename'],
+# # #                 "content": row['content']
+# # #             })
+# # #             context += f"[文档{i+1}]: {row['content']}\n\n"
+# # #         return final_res, context
+
+# # # @st.cache_resource
+# # # def load_engine():
+# # #     real_path = get_data_file_path()
+# # #     return FullRetriever(real_path)
+
+# # # # ================= 3. UI 主程序 =================
+
+# # # def main():
+# # #     st.markdown("""
+# # #     <div class="custom-header">
+# # #         <div style="font-size: 3rem;">🌌</div>
+# # #         <div>
+# # #             <div class="glitch-text">COMSOL DARK EXPERT</div>
+# # #             <div style="color: #666; font-size: 0.9rem; letter-spacing: 1px;">
+# # #                 NEURAL SIMULATION ASSISTANT <span style="color:#29B5E8">V4.1 Fixed</span>
+# # #             </div>
+# # #         </div>
+# # #     </div>
+# # #     """, unsafe_allow_html=True)
+
+# # #     retriever = load_engine()
+
+# # #     with st.sidebar:
+# # #         st.markdown("### ⚙️ 控制台")
+# # #         top_k = st.slider("检索深度", 1, 10, 4)
+# # #         temp = st.slider("发散度", 0.0, 1.0, 0.3)
+# # #         st.markdown("---")
+# # #         if st.button("🗑️ 清空记忆 (Clear)", use_container_width=True):
+# # #             st.session_state.messages = []
+# # #             st.session_state.current_refs = []
+# # #             st.rerun()
+
+# # #     if "messages" not in st.session_state: st.session_state.messages = []
+# # #     if "current_refs" not in st.session_state: st.session_state.current_refs = []
+
+# # #     col_chat, col_evidence = st.columns([0.65, 0.35], gap="large")
+
+# # #     # ------------------ 处理输入源 ------------------
+# # #     # 我们定义一个变量 user_input，不管它来自按钮还是输入框
+# # #     user_input = None
+
+# # #     with col_chat:
+# # #         # 1. 如果历史为空，显示快捷按钮
+# # #         if not st.session_state.messages:
+# # #             st.markdown("##### 💡 初始化提问序列 (Starter Sequence)")
+# # #             c1, c2, c3 = st.columns(3)
+# # #             # 点击按钮直接赋值给 user_input
+# # #             if c1.button("🌊 流固耦合接口设置"):
+# # #                 user_input = "怎么设置流固耦合接口？"
+# # #             elif c2.button("⚡ 低频电磁场网格"):
+# # #                 user_input = "低频电磁场网格划分有哪些技巧？"
+# # #             elif c3.button("📉 求解器不收敛"):
+# # #                 user_input = "求解器不收敛通常怎么解决？"
+        
+# # #         # 2. 渲染历史消息
+# # #         for msg in st.session_state.messages:
+# # #             with st.chat_message(msg["role"]):
+# # #                 st.markdown(msg["content"])
+
+# # #         # 3. 处理底部输入框 (如果有按钮输入，这里会被跳过，因为 user_input 已经有值了)
+# # #         if not user_input:
+# # #             user_input = st.chat_input("输入指令或物理参数问题...")
+
+# # #     # ------------------ 统一处理消息追加 ------------------
+# # #     if user_input:
+# # #         st.session_state.messages.append({"role": "user", "content": user_input})
+# # #         # 强制刷新以立即在 UI 上显示用户的提问（对于按钮点击尤为重要）
+# # #         st.rerun()
+
+# # #     # ------------------ 统一触发生成 (修复的核心) ------------------
+# # #     # 检查：如果有消息，且最后一条是 User 发的，说明需要 Assistant 回答
+# # #     if st.session_state.messages and st.session_state.messages[-1]["role"] == "user":
+        
+# # #         # 获取最后一条用户消息
+# # #         last_query = st.session_state.messages[-1]["content"]
+        
+# # #         with col_chat: # 确保在聊天栏显示
+# # #             with st.spinner("🔍 正在扫描向量空间..."):
+# # #                 refs, context = retriever.hybrid_search(last_query, top_k=top_k)
+# # #                 st.session_state.current_refs = refs
+            
+# # #             system_prompt = f"""你是一个COMSOL高级仿真专家。请基于提供的文档回答问题。
+# # #             要求：
+# # #             1. 语气专业、客观，逻辑严密。
+# # #             2. 涉及物理公式时，**必须**使用 LaTeX 格式（例如 $E = mc^2$）。
+# # #             3. 涉及步骤或参数对比时，优先使用 Markdown 列表或表格。
+            
+# # #             参考文档：
+# # #             {context}
+# # #             """
+            
+# # #             with st.chat_message("assistant"):
+# # #                 resp_cont = st.empty()
+# # #                 full_resp = ""
+# # #                 client = OpenAI(base_url=API_BASE, api_key=API_KEY)
+                
+# # #                 try:
+# # #                     stream = client.chat.completions.create(
+# # #                         model=GEN_MODEL_NAME,
+# # #                         messages=[{"role": "system", "content": system_prompt}] + st.session_state.messages[-6:], # 除去当前的System
+# # #                         temperature=temp,
+# # #                         stream=True
+# # #                     )
+# # #                     for chunk in stream:
+# # #                         txt = chunk.choices[0].delta.content
+# # #                         if txt:
+# # #                             full_resp += txt
+# # #                             resp_cont.markdown(full_resp + " ▌")
+# # #                     resp_cont.markdown(full_resp)
+# # #                     st.session_state.messages.append({"role": "assistant", "content": full_resp})
+# # #                 except Exception as e:
+# # #                     st.error(f"Neural Generation Failed: {e}")
+
+# # #     # ------------------ 渲染右侧证据栏 ------------------
+# # #     with col_evidence:
+# # #         st.markdown("### 📚 神经记忆 (Evidence)")
+# # #         if st.session_state.current_refs:
+# # #             for i, ref in enumerate(st.session_state.current_refs):
+# # #                 score = ref['score']
+# # #                 score_color = "#00ff41" if score > 0.6 else "#ffb700" if score > 0.4 else "#ff003c"
+                
+# # #                 with st.expander(f"📄 Doc {i+1}: {ref['filename'][:20]}...", expanded=(i==0)):
+# # #                     st.markdown(f"""
+# # #                     <div style="margin-bottom:5px;">
+# # #                         <span style="color:#888;">Relevance:</span> 
+# # #                         <span style="color:{score_color}; font-weight:bold;">{score:.4f}</span>
+# # #                     </div>
+# # #                     """, unsafe_allow_html=True)
+# # #                     st.code(ref['content'], language="text")
+# # #         else:
+# # #             st.info("等待输入指令以检索知识库...")
+# # #             st.markdown("""
+# # #             <div style="opacity:0.3; font-size:0.8rem; margin-top:20px;">
+# # #             Waiting for query signal...<br>
+# # #             Index Status: Ready<br>
+# # #             Awaiting Input
+# # #             </div>
+# # #             """, unsafe_allow_html=True)
+
+# # # if __name__ == "__main__":
+# # #     main()
+
+
+
 # # import streamlit as st
 # # import pandas as pd
 # # import numpy as np
 # # import jieba
 # # import requests
 # # import os
-# # import sys
+# # import time
+# # import json
+# # import re
+# # import random
 # # import subprocess
 # # from openai import OpenAI
 # # from rank_bm25 import BM25Okapi
 # # from sklearn.metrics.pairwise import cosine_similarity
+# # from typing import List, Dict, Tuple, Any
 
-# # # ================= 1. 全局配置与 CSS注入 =================
+# # # ================= 1. 全局配置与样式 =================
 
-# # API_KEY = os.getenv("SILICONFLOW_API_KEY")
+# # # API 配置 (从 HF 环境变量获取)
 # # API_BASE = "https://api.siliconflow.cn/v1"
+# # API_KEY = os.getenv("SILICONFLOW_API_KEY")
+
+# # # 模型名称配置
 # # EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-4B"
 # # RERANK_MODEL = "Qwen/Qwen3-Reranker-4B"
-# # GEN_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
+# # GEN_MODEL_NAME = "MiniMaxAI/MiniMax-M2"  
+# # QE_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+# # SUGGEST_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+
+# # # 预置问题池
+# # PRESET_QUESTIONS = [
+# #     "如何设置流固耦合接口？",
+# #     "求解器不收敛怎么办？",
+# #     "网格划分有哪些技巧？",
+# #     "如何定义随时间变化的边界条件？",
+# #     "计算结果如何导出数据？",
+# #     "什么是完美匹配层 (PML)？",
+# #     "低频电磁场仿真注意事项",
+# #     "如何提高瞬态计算速度？",
+# #     "参数化扫描如何设置？",
+# #     "多物理场耦合的收敛性优化"
+# # ]
+
+# # # 数据文件配置
 # # DATA_FILENAME = "comsol_embedded.parquet"
 # # DATA_URL = "https://share.leezhu.cn/graduation_design_data/comsol_embedded.parquet"
 
+# # # 页面配置
 # # st.set_page_config(
-# #     page_title="COMSOL Dark Expert",
-# #     page_icon="🌌",
+# #     page_title="COMSOL RAG 策略控制台",
+# #     page_icon="🎛️",
 # #     layout="wide",
 # #     initial_sidebar_state="expanded"
 # # )
 
-# # # --- 注入自定义 CSS (保持之前的审美) ---
+# # # 自定义CSS样式
 # # st.markdown("""
 # # <style>
-# #     /* 1. 整体背景 - 深空黑 */
+# #     /* 深色主题 */
 # #     .stApp {
-# #         background-color: #050505;
-# #         background-image: radial-gradient(circle at 50% 0%, #1a1f35 0%, #050505 60%);
-# #         color: #e0e0e0;
-# #         font-family: 'Inter', system-ui, -apple-system, sans-serif;
+# #         background-color: #0E1117;
+# #         color: #E0E0E0;
 # #     }
 
-# #     /* 2. 隐藏默认组件 */
-# #     #MainMenu {visibility: hidden;}
-# #     footer {visibility: hidden;}
-# #     header {visibility: hidden;}
-
-# #     /* 3. 聊天气泡 */
+# #     /* 聊天消息样式 */
 # #     [data-testid="stChatMessage"] {
-# #         background: rgba(255, 255, 255, 0.03);
-# #         border: 1px solid rgba(255, 255, 255, 0.08);
-# #         border-radius: 16px;
-# #         backdrop-filter: blur(12px);
-# #         box-shadow: 0 4px 20px rgba(0,0,0,0.2);
-# #         padding: 1.2rem;
-# #     }
-    
-# #     /* 用户气泡 */
-# #     [data-testid="stChatMessage"][data-testid="user"] {
-# #         background: rgba(41, 181, 232, 0.1);
-# #         border-color: rgba(41, 181, 232, 0.2);
+# #         background-color: #1E1E1E;
+# #         border: 1px solid #333;
+# #         border-radius: 10px;
+# #         box-shadow: 0 2px 4px rgba(0,0,0,0.3);
 # #     }
 
-# #     /* 4. 自定义标题栏 */
-# #     .custom-header {
-# #         border-bottom: 1px solid rgba(255,255,255,0.1);
-# #         padding-bottom: 1rem;
-# #         margin-bottom: 2rem;
-# #         display: flex;
-# #         align-items: center;
-# #         gap: 1rem;
-# #     }
-# #     .glitch-text {
-# #         font-size: 2rem;
-# #         font-weight: 800;
-# #         background: linear-gradient(120deg, #fff, #29B5E8);
-# #         -webkit-background-clip: text;
-# #         -webkit-text-fill-color: transparent;
-# #         letter-spacing: -1px;
-# #     }
-    
-# #     /* 5. 快捷按钮 */
-# #     div.stButton > button {
-# #         background: rgba(255,255,255,0.05);
-# #         color: #aaa;
-# #         border: 1px solid rgba(255,255,255,0.1);
-# #         border-radius: 20px;
-# #         padding: 0.5rem 1rem;
-# #         font-size: 0.85rem;
-# #         transition: all 0.3s;
-# #         width: 100%;
-# #     }
-# #     div.stButton > button:hover {
-# #         background: rgba(41, 181, 232, 0.2);
-# #         color: #fff;
-# #         border-color: #29B5E8;
-# #         transform: translateY(-2px);
+# #     /* 侧边栏样式 */
+# #     [data-testid="stSidebar"] {
+# #         background-color: #161B22;
+# #         border-right: 1px solid #30363D;
 # #     }
 
-# #     /* 6. 输入框 */
-# #     .stChatInputContainer textarea {
-# #         background-color: #0f1115 !important;
-# #         border: 1px solid #333 !important;
-# #         color: white !important;
-# #         border-radius: 12px !important;
+# #     /* 策略标签 */
+# #     .strat-tag {
+# #         font-size: 0.75rem;
+# #         padding: 3px 8px;
+# #         border-radius: 4px;
+# #         margin-right: 6px;
+# #         font-weight: bold;
+# #         display: inline-block;
+# #         margin-bottom: 4px;
+# #         border: 1px solid rgba(255,255,255,0.2);
 # #     }
-    
-# #     /* 7. Expander */
-# #     .streamlit-expanderHeader {
-# #         background-color: rgba(255,255,255,0.02);
-# #         border: 1px solid rgba(255,255,255,0.05);
+# #     .tag-vec { background-color: rgba(31, 119, 180, 0.3); color: #4EA8DE; border-color: #1f77b4; }
+# #     .tag-bm25 { background-color: rgba(255, 127, 14, 0.3); color: #FFAB5E; border-color: #ff7f0e; }
+# #     .tag-qe { background-color: rgba(44, 160, 44, 0.3); color: #69DB7C; border-color: #2ca02c; }
+# #     .tag-rerank { background-color: rgba(214, 39, 40, 0.3); color: #FF6B6B; border-color: #d62728; }
+
+# #     /* 过程展示框 */
+# #     .process-box {
+# #         background-color: #0D1117;
+# #         border: 1px solid #30363D;
+# #         padding: 15px;
 # #         border-radius: 8px;
-# #         color: #bbb;
+# #         font-size: 0.9rem;
+# #         color: #8B949E;
+# #         margin-bottom: 15px;
+# #     }
+
+# #     /* 策略矩阵标题 */
+# #     .strategy-title {
+# #         background: linear-gradient(45deg, #667eea 0%, #764ba2 100%);
+# #         -webkit-background-clip: text;
+# #         -webkit-text-fill-color: transparent;
+# #         background-clip: text;
+# #         font-weight: bold;
+# #         font-size: 1.2rem;
 # #     }
 # # </style>
 # # """, unsafe_allow_html=True)
 
-# # # ================= 2. 核心逻辑（数据与RAG） =================
-
-# # if not API_KEY:
-# #     st.error("⚠️ 未检测到 API Key。请在 Settings -> Secrets 中配置 `SILICONFLOW_API_KEY`。")
-# #     st.stop()
+# # # ================= 2. 数据下载工具 (HF 适配) =================
 
 # # def download_with_curl(url, output_path):
+# #     """使用 curl 下载文件，增加鲁棒性"""
 # #     try:
 # #         cmd = [
 # #             "curl", "-L", 
@@ -129,240 +493,485 @@
 # #             url
 # #         ]
 # #         result = subprocess.run(cmd, capture_output=True, text=True)
-# #         if result.returncode != 0: raise Exception(f"Curl failed: {result.stderr}")
+# #         if result.returncode != 0: 
+# #             print(f"Curl stderr: {result.stderr}")
+# #             return False
 # #         return True
 # #     except Exception as e:
 # #         print(f"Curl download error: {e}")
 # #         return False
 
 # # def get_data_file_path():
+# #     """获取数据文件路径，如果不存在则自动下载"""
+# #     # 优先检查本地可能存在的路径
 # #     possible_paths = [
-# #         DATA_FILENAME, os.path.join("/app", DATA_FILENAME),
+# #         DATA_FILENAME, 
+# #         os.path.join("/app", DATA_FILENAME),
 # #         os.path.join("processed_data", DATA_FILENAME),
-# #         os.path.join("src", DATA_FILENAME),
-# #         os.path.join("..", DATA_FILENAME), "/tmp/" + DATA_FILENAME
+# #         os.path.join(os.getcwd(), DATA_FILENAME)
 # #     ]
+    
 # #     for path in possible_paths:
-# #         if os.path.exists(path): return path
+# #         if os.path.exists(path):
+# #             return path
             
-# #     download_target = "/app/" + DATA_FILENAME
-# #     try: os.makedirs(os.path.dirname(download_target), exist_ok=True)
-# #     except: download_target = "/tmp/" + DATA_FILENAME
-
+# #     # 如果都没找到，准备下载
+# #     # HF Spaces 通常在 /home/user/app 下运行，直接下载到当前目录
+# #     download_target = os.path.join(os.getcwd(), DATA_FILENAME)
+    
 # #     status_container = st.empty()
-# #     status_container.info("📡 正在接入神经元网络... (下载核心数据中)")
+# #     status_container.info("📡 正在接入神经元网络... (下载核心数据中，首次运行可能需要几十秒)")
     
+# #     # 尝试 Curl 下载
 # #     if download_with_curl(DATA_URL, download_target):
 # #         status_container.empty()
 # #         return download_target
         
+# #     # 降级尝试 Requests 下载
 # #     try:
 # #         headers = {'User-Agent': 'Mozilla/5.0'}
 # #         r = requests.get(DATA_URL, headers=headers, stream=True)
 # #         r.raise_for_status()
 # #         with open(download_target, 'wb') as f:
-# #             for chunk in r.iter_content(chunk_size=8192): f.write(chunk)
+# #             for chunk in r.iter_content(chunk_size=8192): 
+# #                 f.write(chunk)
 # #         status_container.empty()
 # #         return download_target
 # #     except Exception as e:
-# #         st.error(f"❌ 数据链路中断。Error: {e}")
+# #         st.error(f"❌ 数据下载失败。Error: {e}")
 # #         st.stop()
 
-# # class FullRetriever:
-# #     def __init__(self, parquet_path):
-# #         try: self.df = pd.read_parquet(parquet_path)
-# #         except Exception as e: st.error(f"Memory Matrix Load Failed: {e}"); st.stop()
-# #         self.documents = self.df['content'].tolist()
-# #         self.embeddings = np.stack(self.df['embedding'].values)
-# #         self.bm25 = BM25Okapi([jieba.lcut(str(d).lower()) for d in self.documents])
+# # # ================= 3. 核心 RAG 控制器 =================
+
+# # class RAGController:
+# #     """RAG系统控制器 - 实现策略矩阵"""
+
+# #     def __init__(self):
+# #         """初始化控制器"""
+# #         if not API_KEY:
+# #             st.error("⚠️ 未检测到 API Key。请在 Space Settings -> Secrets 中配置 `SILICONFLOW_API_KEY`。")
+# #             st.stop()
+            
 # #         self.client = OpenAI(base_url=API_BASE, api_key=API_KEY)
-# #         # Reranker 初始化移到这里，减少重复调用
-# #         self.rerank_headers = {"Content-Type": "application/json", "Authorization": f"Bearer {API_KEY}"}
-# #         self.rerank_url = f"{API_BASE}/rerank"
-
-# #     def _get_emb(self, q):
-# #         try: return self.client.embeddings.create(model=EMBEDDING_MODEL, input=[q]).data[0].embedding
-# #         except: return [0.0] * 1024
-
-# #     def hybrid_search(self, query: str, top_k=5):
-# #         # 1. Vector
-# #         q_emb = self._get_emb(query)
-# #         vec_scores = cosine_similarity([q_emb], self.embeddings)[0]
-# #         vec_idx = np.argsort(vec_scores)[-100:][::-1]
-# #         # 2. Keyword
-# #         kw_idx = np.argsort(self.bm25.get_scores(jieba.lcut(query.lower())))[-100:][::-1]
-# #         # 3. RRF Fusion
-# #         fused = {}
-# #         for r, i in enumerate(vec_idx): fused[i] = fused.get(i, 0) + 1/(60+r+1)
-# #         for r, i in enumerate(kw_idx): fused[i] = fused.get(i, 0) + 1/(60+r+1)
-# #         c_idxs = [x[0] for x in sorted(fused.items(), key=lambda x:x[1], reverse=True)[:50]]
-# #         c_docs = [self.documents[i] for i in c_idxs]
+# #         self.df = None
+# #         self.documents = []
+# #         self.embeddings = None
+# #         self.bm25 = None
+# #         self.filenames = []
+# #         self._load_data()
+
+# #     def _load_data(self):
+# #         """加载COMSOL文档数据"""
+# #         real_path = get_data_file_path()
+
+# #         try:
+# #             # 加载数据
+# #             self.df = pd.read_parquet(real_path)
+# #             self.documents = self.df['content'].tolist()
+# #             self.filenames = self.df['filename'].tolist()
+
+# #             # 加载向量嵌入
+# #             self.embeddings = np.stack(self.df['embedding'].values)
+
+# #             # 初始化BM25
+# #             tokenized_corpus = [jieba.lcut(str(doc).lower()) for doc in self.documents]
+# #             self.bm25 = BM25Okapi(tokenized_corpus)
+
+# #             st.success(f"✅ 成功加载 {len(self.documents)} 条文档")
+
+# #         except Exception as e:
+# #             st.error(f"❌ 数据加载失败: {str(e)}")
+# #             st.stop()
+
+# #     def get_embedding(self, text: str) -> List[float]:
+# #         """获取文本向量嵌入"""
+# #         try:
+# #             resp = self.client.embeddings.create(
+# #                 model=EMBEDDING_MODEL,
+# #                 input=[text]
+# #             )
+# #             return resp.data[0].embedding
+# #         except Exception as e:
+# #             st.warning(f"向量获取失败: {e}")
+# #             return [0.0] * 2560  # Qwen3-Embedding-4B dimension fallback
+
+# #     def expand_query(self, query: str) -> Tuple[str, float]:
+# #         """查询扩展 - 使用LLM优化查询"""
+# #         prompt = f"""你是COMSOL仿真专家。请将用户的口语化问题改写为专业的检索查询。
+        
+# #         要求：
+# #         1. 补充COMSOL专业术语（物理场、模块、边界条件等）
+# #         2. 保持问题核心意图不变
+# #         3. 输出简洁，仅返回改写后的查询
         
-# #         # 4. Rerank
+# #         用户问题: {query}
+# #         专业查询:"""
+
 # #         try:
-# #             payload = {"model": RERANK_MODEL, "query": query, "documents": c_docs, "top_n": top_k}
-# #             resp = requests.post(self.rerank_url, headers=self.rerank_headers, json=payload, timeout=10)
-# #             results = resp.json().get('results', [])
-# #         except:
-# #             results = [{"index": i, "relevance_score": 0.0} for i in range(len(c_docs))][:top_k]
-            
-# #         final_res = []
-# #         context = ""
-# #         for i, item in enumerate(results):
-# #             orig_idx = c_idxs[item['index']]
-# #             row = self.df.iloc[orig_idx]
-# #             final_res.append({
-# #                 "score": item['relevance_score'],
-# #                 "filename": row['filename'],
-# #                 "content": row['content']
+# #             start_time = time.time()
+# #             resp = self.client.chat.completions.create(
+# #                 model=QE_MODEL_NAME,
+# #                 messages=[{"role": "user", "content": prompt}],
+# #                 temperature=0.3
+# #             )
+# #             expanded = resp.choices[0].message.content.strip()
+# #             elapsed = time.time() - start_time
+# #             return expanded, elapsed
+# #         except Exception as e:
+# #             print(f"QE Error: {e}")
+# #             return query, 0
+
+# #     def vector_search(self, query: str, top_k: int = 100) -> List[Tuple[int, float]]:
+# #         """向量检索"""
+# #         q_vec = self.get_embedding(query)
+# #         similarities = cosine_similarity([q_vec], self.embeddings)[0]
+# #         top_indices = np.argsort(similarities)[-top_k:][::-1]
+# #         return [(idx, similarities[idx]) for idx in top_indices]
+
+# #     def bm25_search(self, query: str, top_k: int = 100) -> List[Tuple[int, float]]:
+# #         """BM25关键词检索"""
+# #         tokenized_query = jieba.lcut(query.lower())
+# #         scores = self.bm25.get_scores(tokenized_query)
+# #         top_indices = np.argsort(scores)[-top_k:][::-1]
+# #         return [(idx, scores[idx]) for idx in top_indices]
+
+# #     def reciprocal_rank_fusion(self, vector_results: List[Tuple[int, float]],
+# #                               bm25_results: List[Tuple[int, float]], k: int = 60) -> Dict[int, float]:
+# #         """RRF融合算法"""
+# #         scores = {}
+# #         for rank, (idx, score) in enumerate(vector_results):
+# #             scores[idx] = scores.get(idx, 0) + 1.0 / (k + rank + 1)
+# #         for rank, (idx, score) in enumerate(bm25_results):
+# #             scores[idx] = scores.get(idx, 0) + 1.0 / (k + rank + 1)
+# #         return scores
+
+# #     def rerank_documents(self, query: str, documents: List[Dict], top_n: int) -> Tuple[List[Dict], float]:
+# #         """使用重排序模型"""
+# #         if not documents: return [], 0
+
+# #         url = f"{API_BASE}/rerank"
+# #         headers = {
+# #             "Authorization": f"Bearer {API_KEY}",
+# #             "Content-Type": "application/json"
+# #         }
+
+# #         # 截断文档内容以符合 Context Window
+# #         docs_content = [doc["content"][:2048] for doc in documents]
+        
+# #         payload = {
+# #             "model": RERANK_MODEL,
+# #             "query": query,
+# #             "documents": docs_content,
+# #             "top_n": top_n
+# #         }
+
+# #         try:
+# #             start_time = time.time()
+# #             response = requests.post(url, headers=headers, json=payload, timeout=20)
+# #             elapsed = time.time() - start_time
+
+# #             if response.status_code == 200:
+# #                 results = response.json().get("results", [])
+# #                 reranked_docs = []
+# #                 for result in results:
+# #                     original_doc = documents[result["index"]]
+# #                     original_doc["rerank_score"] = result["relevance_score"]
+# #                     original_doc["final_score"] = result["relevance_score"]
+# #                     reranked_docs.append(original_doc)
+# #                 return reranked_docs, elapsed
+# #             else:
+# #                 print(f"Rerank API Error: {response.text}")
+# #                 return documents[:top_n], elapsed
+# #         except Exception as e:
+# #             print(f"Rerank Exception: {e}")
+# #             return documents[:top_n], 0
+
+# #     def execute_strategy(self, query: str, config: Dict[str, Any]) -> Dict[str, Any]:
+# #         """执行策略矩阵"""
+# #         start_time = time.time()
+# #         result = {
+# #             'original_query': query,
+# #             'final_query': query,
+# #             'documents': [],
+# #             'steps': [],
+# #             'metrics': {'qe_time': 0, 'retrieval_time': 0, 'rerank_time': 0, 'total_time': 0},
+# #             'strategy_tags': []
+# #         }
+
+# #         # 1. 查询扩展
+# #         if config['use_qe']:
+# #             expanded_q, qe_time = self.expand_query(query)
+# #             result['final_query'] = expanded_q
+# #             result['metrics']['qe_time'] = qe_time
+# #             result['steps'].append(f"🧠 查询扩展 ({qe_time:.2f}s): {query} → **{expanded_q}**")
+# #             result['strategy_tags'].append("QE")
+
+# #         # 2. 检索
+# #         retrieval_start = time.time()
+# #         query_to_search = result['final_query']
+
+# #         if config['strategy'] == 'Vector':
+# #             results = self.vector_search(query_to_search)
+# #             result['steps'].append(f"🔍 向量检索: 找到 {len(results)} 个候选")
+# #             result['strategy_tags'].append("Vector")
+# #         elif config['strategy'] == 'BM25':
+# #             results = self.bm25_search(query_to_search)
+# #             result['steps'].append(f"🔍 BM25检索: 找到 {len(results)} 个候选")
+# #             result['strategy_tags'].append("BM25")
+# #         elif config['strategy'] == 'Hybrid':
+# #             vec_results = self.vector_search(query_to_search)
+# #             bm25_results = self.bm25_search(query_to_search)
+# #             fused_scores = self.reciprocal_rank_fusion(vec_results, bm25_results)
+# #             results = sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)
+# #             results = [(idx, score) for idx, score in results]
+# #             result['steps'].append(f"🔍 混合检索: Vector + BM25 → {len(results)} 个融合候选")
+# #             result['strategy_tags'].extend(["Vector", "BM25"])
+
+# #         result['metrics']['retrieval_time'] = time.time() - retrieval_start
+
+# #         # 3. 构建候选列表
+# #         recall_k = config['top_k'] * 3 if config['use_rerank'] else config['top_k']
+# #         top_results = results[:recall_k]
+        
+# #         documents = []
+# #         for idx, score in top_results:
+# #             documents.append({
+# #                 'content': self.documents[idx],
+# #                 'filename': self.filenames[idx],
+# #                 'retrieval_score': score,
+# #                 'final_score': score,
+# #                 'type': 'retrieval'
 # #             })
-# #             context += f"[文档{i+1}]: {row['content']}\n\n"
-# #         return final_res, context
 
-# # @st.cache_resource
-# # def load_engine():
-# #     real_path = get_data_file_path()
-# #     return FullRetriever(real_path)
+# #         # 4. 重排序
+# #         if config['use_rerank']:
+# #             reranked_docs, rerank_time = self.rerank_documents(
+# #                 result['final_query'], documents, config['top_k']
+# #             )
+# #             result['documents'] = reranked_docs
+# #             result['metrics']['rerank_time'] = rerank_time
+# #             result['steps'].append(f"⚖️ 重排序 ({rerank_time:.2f}s): 精选 Top-{config['top_k']}")
+# #             result['strategy_tags'].append("Rerank")
+# #         else:
+# #             result['documents'] = documents[:config['top_k']]
+
+# #         result['metrics']['total_time'] = time.time() - start_time
+# #         result['steps'].append(f"⏱️ 总耗时: {result['metrics']['total_time']:.2f}s")
+# #         return result
 
-# # # ================= 3. UI 主程序 =================
+# # def generate_suggestions(controller, query: str, answer: str) -> List[str]:
+# #     """生成3个后续引导问题"""
+# #     prompt = f"""基于以下技术问答，预测用户可能感兴趣的3个后续COMSOL专业问题。
+# #     用户问题：{query}
+# #     专家回答：{answer[:800]}...
+    
+# #     要求：
+# #     1. 问题简短（15字以内）。
+# #     2. 紧扣当前话题。
+# #     3. 严格输出 JSON 字符串数组格式，例如：["问题1", "问题2", "问题3"]。
+# #     4. 不要包含任何 Markdown 标记。
+# #     """
+    
+# #     try:
+# #         resp = controller.client.chat.completions.create(
+# #             model=SUGGEST_MODEL_NAME, 
+# #             messages=[{"role": "user", "content": prompt}],
+# #             temperature=0.5
+# #         )
+# #         content = resp.choices[0].message.content.strip()
+# #         match = re.search(r'\[.*\]', content, re.DOTALL)
+# #         if match:
+# #             sugs = json.loads(match.group())
+# #             return sugs[:3]
+# #         return []
+# #     except Exception as e:
+# #         print(f"Suggestion Error: {e}")
+# #         return []
+
+# # def generate_answer(controller, query: str, documents: List[Dict], history: List[Dict], max_rounds: int) -> str:
+# #     """流式生成回答"""
+# #     if not documents:
+# #         return "抱歉，没有找到相关的文档来回答您的问题。"
+
+# #     context_text = "\n\n".join([f"[文档{i+1}] {doc['content'][:800]}..." for i, doc in enumerate(documents)])
+    
+# #     system_prompt = f"""你是一个COMSOL Multiphysics仿真专家。请基于提供的文档回答用户问题。
+# #     要求：
+# #     1. 语气专业，使用COMSOL术语。
+# #     2. 物理公式使用 LaTeX（如 $E=mc^2$）。
+# #     3. 如果文档信息不足，请如实告知，不要编造。
+    
+# #     【参考文档】：
+# #     {context_text}
+# #     """
+
+# #     # 构建历史记录
+# #     keep_messages = max_rounds * 2
+# #     history_to_send = history[:-1][-keep_messages:] if keep_messages > 0 else []
+    
+# #     api_messages = [{"role": "system", "content": system_prompt}] + history_to_send + [{"role": "user", "content": query}]
+
+# #     try:
+# #         response = controller.client.chat.completions.create(
+# #             model=GEN_MODEL_NAME,
+# #             messages=api_messages,
+# #             temperature=0.3,
+# #             stream=True
+# #         )
+
+# #         answer = ""
+# #         placeholder = st.empty()
+# #         for chunk in response:
+# #             if chunk.choices[0].delta.content:
+# #                 answer += chunk.choices[0].delta.content
+# #                 placeholder.markdown(answer + "▌")
+# #         placeholder.markdown(answer)
+# #         return answer
+# #     except Exception as e:
+# #         return f"生成遇到错误: {e}"
+
+# # # ================= 4. 初始化与组件渲染 =================
+
+# # @st.cache_resource(show_spinner="🚀 正在初始化 RAG 引擎...")
+# # def initialize_controller():
+# #     return RAGController()
+
+# # def render_strategy_matrix():
+# #     st.markdown('<p class="strategy-title">🎯 策略矩阵配置</p>', unsafe_allow_html=True)
+# #     st.markdown("""<div style="background-color: #161B22; padding: 10px; border-radius: 8px; margin-bottom: 20px;">
+# #         <p style="font-size: 0.85rem; color: #8B949E; margin: 0;">⚙️ <b>参数调节</b>：控制检索片段数量和模型记忆深度。</p>
+# #     </div>""", unsafe_allow_html=True)
+
+# #     col1, col2 = st.columns(2)
+# #     with col1:
+# #         use_qe = st.toggle("🔄 查询扩展 (QE)", value=False)
+# #         use_rerank = st.toggle("⚖️ 深度重排序 (Rerank)", value=True)
+# #         max_history_rounds = st.slider("🧠 记忆轮数", 0, 50, 10, help="发给模型的对话历史轮数")
+
+# #     with col2:
+# #         strategy = st.radio("🔍 检索策略", ["Vector", "BM25", "Hybrid"], index=2)
+# #         top_k = st.slider("📊 检索数量", 1, 50, 10, help="从知识库召回的片段数量")
+
+# #     return {'use_qe': use_qe, 'strategy': strategy, 'use_rerank': use_rerank, 'top_k': top_k, 'max_history_rounds': max_history_rounds}
+
+# # def render_metrics(metrics):
+# #     st.markdown("### 📊 性能指标")
+# #     cols = st.columns(4)
+# #     with cols[0]: st.metric("查询扩展", f"{metrics['qe_time']:.2f}s" if metrics['qe_time']>0 else "N/A", delta="QE" if metrics['qe_time']>0 else None)
+# #     with cols[1]: st.metric("检索耗时", f"{metrics['retrieval_time']:.2f}s")
+# #     with cols[2]: st.metric("重排序", f"{metrics['rerank_time']:.2f}s" if metrics['rerank_time']>0 else "N/A", delta="Rerank" if metrics['rerank_time']>0 else None)
+# #     with cols[3]: st.metric("总耗时", f"{metrics['total_time']:.2f}s", delta="⚡")
+
+# # def render_documents(documents, strategy_tags):
+# #     st.markdown("### 📄 检索结果")
+# #     if not documents:
+# #         st.warning("未找到相关文档")
+# #         return
+
+# #     tags_html = "".join([f'<span class="strat-tag {{"QE":"tag-qe","Vector":"tag-vec","BM25":"tag-bm25","Rerank":"tag-rerank"}}.get(t, "")}}">{t}</span>' for t in strategy_tags]) # Simplified for brevity, use full logic if copying
+# #     # Manual mapping for safety
+# #     html_tags = ""
+# #     for tag in strategy_tags:
+# #         cls = "tag-vec" if tag=="Vector" else "tag-bm25" if tag=="BM25" else "tag-qe" if tag=="QE" else "tag-rerank"
+# #         html_tags += f'<span class="strat-tag {cls}">{tag}</span>'
+    
+# #     st.markdown(f"**策略组合:** {html_tags}", unsafe_allow_html=True)
+
+# #     for i, doc in enumerate(documents):
+# #         score = doc.get('final_score', 0)
+# #         with st.expander(f"📄 文档 {i+1} | Score: {score:.4f} | {doc['filename'][:40]}...", expanded=i<2):
+# #             st.code(doc['content'], language="markdown")
+
+# # # ================= 5. 主程序 =================
 
 # # def main():
-# #     st.markdown("""
-# #     <div class="custom-header">
-# #         <div style="font-size: 3rem;">🌌</div>
-# #         <div>
-# #             <div class="glitch-text">COMSOL DARK EXPERT</div>
-# #             <div style="color: #666; font-size: 0.9rem; letter-spacing: 1px;">
-# #                 NEURAL SIMULATION ASSISTANT <span style="color:#29B5E8">V4.1 Fixed</span>
-# #             </div>
-# #         </div>
-# #     </div>
-# #     """, unsafe_allow_html=True)
-
-# #     retriever = load_engine()
+# #     # 状态初始化
+# #     if "messages" not in st.session_state: st.session_state.messages = []
+# #     if "last_result" not in st.session_state: st.session_state.last_result = None
+# #     if "suggestions" not in st.session_state: st.session_state.suggestions = random.sample(PRESET_QUESTIONS, 3)
+# #     if "prompt_trigger" not in st.session_state: st.session_state.prompt_trigger = None
 
+# #     # 加载控制器
+# #     controller = initialize_controller()
+
+# #     # 侧边栏
 # #     with st.sidebar:
-# #         st.markdown("### ⚙️ 控制台")
-# #         top_k = st.slider("检索深度", 1, 10, 4)
-# #         temp = st.slider("发散度", 0.0, 1.0, 0.3)
+# #         config = render_strategy_matrix()
 # #         st.markdown("---")
-# #         if st.button("🗑️ 清空记忆 (Clear)", use_container_width=True):
+# #         if st.button("🗑️ 清空当前对话", use_container_width=True):
 # #             st.session_state.messages = []
-# #             st.session_state.current_refs = []
+# #             st.session_state.last_result = None
 # #             st.rerun()
 
-# #     if "messages" not in st.session_state: st.session_state.messages = []
-# #     if "current_refs" not in st.session_state: st.session_state.current_refs = []
-
-# #     col_chat, col_evidence = st.columns([0.65, 0.35], gap="large")
-
-# #     # ------------------ 处理输入源 ------------------
-# #     # 我们定义一个变量 user_input，不管它来自按钮还是输入框
-# #     user_input = None
-
-# #     with col_chat:
-# #         # 1. 如果历史为空，显示快捷按钮
-# #         if not st.session_state.messages:
-# #             st.markdown("##### 💡 初始化提问序列 (Starter Sequence)")
-# #             c1, c2, c3 = st.columns(3)
-# #             # 点击按钮直接赋值给 user_input
-# #             if c1.button("🌊 流固耦合接口设置"):
-# #                 user_input = "怎么设置流固耦合接口？"
-# #             elif c2.button("⚡ 低频电磁场网格"):
-# #                 user_input = "低频电磁场网格划分有哪些技巧？"
-# #             elif c3.button("📉 求解器不收敛"):
-# #                 user_input = "求解器不收敛通常怎么解决？"
+# #     # 主界面布局
+# #     main_col, debug_col = st.columns([0.6, 0.4], gap="large")
+
+# #     with main_col:
+# #         st.markdown("### 💬 智能仿真问答")
         
-# #         # 2. 渲染历史消息
+# #         # 1. 历史消息
 # #         for msg in st.session_state.messages:
 # #             with st.chat_message(msg["role"]):
 # #                 st.markdown(msg["content"])
 
-# #         # 3. 处理底部输入框 (如果有按钮输入，这里会被跳过，因为 user_input 已经有值了)
-# #         if not user_input:
-# #             user_input = st.chat_input("输入指令或物理参数问题...")
+# #         # 2. 建议区
+# #         if st.session_state.suggestions:
+# #             st.markdown("##### 💡 您可能想问：")
+# #             cols = st.columns(3)
+# #             for i, sug in enumerate(st.session_state.suggestions):
+# #                 if cols[i].button(sug, use_container_width=True, key=f"sug_{i}"):
+# #                     st.session_state.prompt_trigger = sug
+# #                     st.rerun()
+
+# #         # 3. 输入处理
+# #         user_input = None
+# #         if st.session_state.prompt_trigger:
+# #             user_input = st.session_state.prompt_trigger
+# #             st.session_state.prompt_trigger = None
+# #         else:
+# #             user_input = st.chat_input("请输入您关于 COMSOL 的问题...")
 
-# #     # ------------------ 统一处理消息追加 ------------------
-# #     if user_input:
-# #         st.session_state.messages.append({"role": "user", "content": user_input})
-# #         # 强制刷新以立即在 UI 上显示用户的提问（对于按钮点击尤为重要）
-# #         st.rerun()
+# #         # 4. 执行逻辑
+# #         if user_input:
+# #             st.session_state.messages.append({"role": "user", "content": user_input})
+# #             with st.chat_message("user"): st.markdown(user_input)
 
-# #     # ------------------ 统一触发生成 (修复的核心) ------------------
-# #     # 检查：如果有消息，且最后一条是 User 发的，说明需要 Assistant 回答
-# #     if st.session_state.messages and st.session_state.messages[-1]["role"] == "user":
-        
-# #         # 获取最后一条用户消息
-# #         last_query = st.session_state.messages[-1]["content"]
-        
-# #         with col_chat: # 确保在聊天栏显示
-# #             with st.spinner("🔍 正在扫描向量空间..."):
-# #                 refs, context = retriever.hybrid_search(last_query, top_k=top_k)
-# #                 st.session_state.current_refs = refs
-            
-# #             system_prompt = f"""你是一个COMSOL高级仿真专家。请基于提供的文档回答问题。
-# #             要求：
-# #             1. 语气专业、客观，逻辑严密。
-# #             2. 涉及物理公式时，**必须**使用 LaTeX 格式（例如 $E = mc^2$）。
-# #             3. 涉及步骤或参数对比时，优先使用 Markdown 列表或表格。
-            
-# #             参考文档：
-# #             {context}
-# #             """
-            
+# #             # 检索
+# #             with st.spinner("🔍 检索知识库中..."):
+# #                 result = controller.execute_strategy(user_input, config)
+# #                 st.session_state.last_result = result
+
+# #             # 生成
 # #             with st.chat_message("assistant"):
-# #                 resp_cont = st.empty()
-# #                 full_resp = ""
-# #                 client = OpenAI(base_url=API_BASE, api_key=API_KEY)
-                
-# #                 try:
-# #                     stream = client.chat.completions.create(
-# #                         model=GEN_MODEL_NAME,
-# #                         messages=[{"role": "system", "content": system_prompt}] + st.session_state.messages[-6:], # 除去当前的System
-# #                         temperature=temp,
-# #                         stream=True
-# #                     )
-# #                     for chunk in stream:
-# #                         txt = chunk.choices[0].delta.content
-# #                         if txt:
-# #                             full_resp += txt
-# #                             resp_cont.markdown(full_resp + " ▌")
-# #                     resp_cont.markdown(full_resp)
-# #                     st.session_state.messages.append({"role": "assistant", "content": full_resp})
-# #                 except Exception as e:
-# #                     st.error(f"Neural Generation Failed: {e}")
-
-# #     # ------------------ 渲染右侧证据栏 ------------------
-# #     with col_evidence:
-# #         st.markdown("### 📚 神经记忆 (Evidence)")
-# #         if st.session_state.current_refs:
-# #             for i, ref in enumerate(st.session_state.current_refs):
-# #                 score = ref['score']
-# #                 score_color = "#00ff41" if score > 0.6 else "#ffb700" if score > 0.4 else "#ff003c"
+# #                 answer = generate_answer(
+# #                     controller, user_input, result['documents'], 
+# #                     st.session_state.messages, config['max_history_rounds']
+# #                 )
+# #                 st.session_state.messages.append({"role": "assistant", "content": answer})
                 
-# #                 with st.expander(f"📄 Doc {i+1}: {ref['filename'][:20]}...", expanded=(i==0)):
-# #                     st.markdown(f"""
-# #                     <div style="margin-bottom:5px;">
-# #                         <span style="color:#888;">Relevance:</span> 
-# #                         <span style="color:{score_color}; font-weight:bold;">{score:.4f}</span>
-# #                     </div>
-# #                     """, unsafe_allow_html=True)
-# #                     st.code(ref['content'], language="text")
+# #                 # 生成新建议
+# #                 new_sugs = generate_suggestions(controller, user_input, answer)
+# #                 st.session_state.suggestions = new_sugs if new_sugs else random.sample(PRESET_QUESTIONS, 3)
+# #                 st.rerun()
+
+# #     with debug_col:
+# #         st.markdown("### 🔍 系统调试视图")
+# #         if st.session_state.last_result:
+# #             res = st.session_state.last_result
+# #             st.info(f"当前查询: {res.get('original_query', 'N/A')}")
+# #             render_metrics(res['metrics'])
+# #             with st.expander("🔧 检索链路详情", expanded=True):
+# #                 for step in res['steps']: st.markdown(f"- {step}")
+# #             render_documents(res['documents'], res['strategy_tags'])
 # #         else:
-# #             st.info("等待输入指令以检索知识库...")
-# #             st.markdown("""
-# #             <div style="opacity:0.3; font-size:0.8rem; margin-top:20px;">
-# #             Waiting for query signal...<br>
-# #             Index Status: Ready<br>
-# #             Awaiting Input
-# #             </div>
-# #             """, unsafe_allow_html=True)
+# #             st.info("等待交互...")
 
 # # if __name__ == "__main__":
 # #     main()
 
 
 
+
 # import streamlit as st
 # import pandas as pd
 # import numpy as np
@@ -389,8 +998,10 @@
 # EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-4B"
 # RERANK_MODEL = "Qwen/Qwen3-Reranker-4B"
 # GEN_MODEL_NAME = "MiniMaxAI/MiniMax-M2"  
-# QE_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
-# SUGGEST_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+# # QE_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+# # SUGGEST_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+# QE_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
+# SUGGEST_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
 
 # # 预置问题池
 # PRESET_QUESTIONS = [
@@ -418,30 +1029,19 @@
 #     initial_sidebar_state="expanded"
 # )
 
-# # 自定义CSS样式
+# # 自定义CSS样式 (适配深色/浅色模式)
 # st.markdown("""
 # <style>
-#     /* 深色主题 */
-#     .stApp {
-#         background-color: #0E1117;
-#         color: #E0E0E0;
-#     }
+#     /* 移除强制背景色，改用透明或半透明，从而适配系统主题 */
 
-#     /* 聊天消息样式 */
+#     /* 聊天消息样式 - 使用半透明背景以适配两种模式 */
 #     [data-testid="stChatMessage"] {
-#         background-color: #1E1E1E;
-#         border: 1px solid #333;
+#         background-color: rgba(128, 128, 128, 0.1); /* 10%透明度的灰色，深浅模式都适用 */
+#         border: 1px solid rgba(128, 128, 128, 0.2);
 #         border-radius: 10px;
-#         box-shadow: 0 2px 4px rgba(0,0,0,0.3);
-#     }
-
-#     /* 侧边栏样式 */
-#     [data-testid="stSidebar"] {
-#         background-color: #161B22;
-#         border-right: 1px solid #30363D;
 #     }
 
-#     /* 策略标签 */
+#     /* 策略标签 - 保持原有的 rgba 设置，因为它们是半��明的，在浅色模式下也好看 */
 #     .strat-tag {
 #         font-size: 0.75rem;
 #         padding: 3px 8px;
@@ -450,33 +1050,54 @@
 #         font-weight: bold;
 #         display: inline-block;
 #         margin-bottom: 4px;
-#         border: 1px solid rgba(255,255,255,0.2);
+#         border: 1px solid rgba(128, 128, 128, 0.2);
 #     }
-#     .tag-vec { background-color: rgba(31, 119, 180, 0.3); color: #4EA8DE; border-color: #1f77b4; }
-#     .tag-bm25 { background-color: rgba(255, 127, 14, 0.3); color: #FFAB5E; border-color: #ff7f0e; }
-#     .tag-qe { background-color: rgba(44, 160, 44, 0.3); color: #69DB7C; border-color: #2ca02c; }
-#     .tag-rerank { background-color: rgba(214, 39, 40, 0.3); color: #FF6B6B; border-color: #d62728; }
+
+#     /* 调整标签颜色适配 */
+#     .tag-vec { background-color: rgba(31, 119, 180, 0.15); color: #1f77b4; border-color: #1f77b4; }
+#     .tag-bm25 { background-color: rgba(255, 127, 14, 0.15); color: #d66a00; border-color: #ff7f0e; }
+#     .tag-qe { background-color: rgba(44, 160, 44, 0.15); color: #2ca02c; border-color: #2ca02c; }
+#     .tag-rerank { background-color: rgba(214, 39, 40, 0.15); color: #d62728; border-color: #d62728; }
 
 #     /* 过程展示框 */
 #     .process-box {
-#         background-color: #0D1117;
-#         border: 1px solid #30363D;
+#         background-color: rgba(128, 128, 128, 0.05); /* 极淡的背景 */
+#         border: 1px solid rgba(128, 128, 128, 0.2);
 #         padding: 15px;
 #         border-radius: 8px;
 #         font-size: 0.9rem;
-#         color: #8B949E;
 #         margin-bottom: 15px;
 #     }
 
-#     /* 策略矩阵标题 */
+#     /* 策略矩阵标题 - 渐变色文字通常在两种背景下都可见，微调一下 */
 #     .strategy-title {
-#         background: linear-gradient(45deg, #667eea 0%, #764ba2 100%);
+#         background: linear-gradient(45deg, #4A90E2 0%, #9013FE 100%);
 #         -webkit-background-clip: text;
 #         -webkit-text-fill-color: transparent;
 #         background-clip: text;
 #         font-weight: bold;
 #         font-size: 1.2rem;
 #     }
+
+#     /* 性能指标框 */
+#     .metric-box {
+#         background-color: rgba(128, 128, 128, 0.05);
+#         border: 1px solid rgba(128, 128, 128, 0.2);
+#         border-radius: 6px;
+#         padding: 10px;
+#         margin: 5px 0;
+#         text-align: center;
+#     }
+
+#     /* 动画效果保持不变 */
+#     @keyframes pulse {
+#         0% { opacity: 1; }
+#         50% { opacity: 0.7; }
+#         100% { opacity: 1; }
+#     }
+#     .processing {
+#         animation: pulse 1.5s infinite;
+#     }
 # </style>
 # """, unsafe_allow_html=True)
 
@@ -838,8 +1459,8 @@
 
 # def render_strategy_matrix():
 #     st.markdown('<p class="strategy-title">🎯 策略矩阵配置</p>', unsafe_allow_html=True)
-#     st.markdown("""<div style="background-color: #161B22; padding: 10px; border-radius: 8px; margin-bottom: 20px;">
-#         <p style="font-size: 0.85rem; color: #8B949E; margin: 0;">⚙️ <b>参数调节</b>：控制检索片段数量和模型记忆深度。</p>
+#     st.markdown("""<div style="background-color: rgba(128, 128, 128, 0.05); padding: 10px; border-radius: 8px; margin-bottom: 20px; border: 1px solid rgba(128, 128, 128, 0.2);">
+#         <p style="font-size: 0.85rem; margin: 0;">⚙️ <b>参数调节</b>：控制检索片段数量和模型记忆深度。</p>
 #     </div>""", unsafe_allow_html=True)
 
 #     col1, col2 = st.columns(2)
@@ -983,11 +1604,30 @@ import json
 import re
 import random
 import subprocess
+import logging
+import psutil
 from openai import OpenAI
 from rank_bm25 import BM25Okapi
 from sklearn.metrics.pairwise import cosine_similarity
 from typing import List, Dict, Tuple, Any
 
+# ================= 0. 日志与内存监控配置 =================
+
+# 配置日志格式 - 日志会显示在 HF Space 的 "Logs" 标签页
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+def log_memory():
+    """记录当前内存占用情况 (单位: MB)"""
+    process = psutil.Process(os.getpid())
+    mem_info = process.memory_info()
+    res_mem = mem_info.rss / (1024 * 1024)
+    logger.info(f"💾 Current Memory Usage: {res_mem:.2f} MB")
+    return res_mem
+
 # ================= 1. 全局配置与样式 =================
 
 # API 配置 (从 HF 环境变量获取)
@@ -998,10 +1638,8 @@ API_KEY = os.getenv("SILICONFLOW_API_KEY")
 EMBEDDING_MODEL = "Qwen/Qwen3-Embedding-4B"
 RERANK_MODEL = "Qwen/Qwen3-Reranker-4B"
 GEN_MODEL_NAME = "MiniMaxAI/MiniMax-M2"  
-# QE_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
-# SUGGEST_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
-QE_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
-SUGGEST_MODEL_NAME = "MiniMaxAI/MiniMax-M2"
+QE_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
+SUGGEST_MODEL_NAME = "Qwen/Qwen3-Next-80B-A3B-Instruct"
 
 # 预置问题池
 PRESET_QUESTIONS = [
@@ -1107,19 +1745,19 @@ def download_with_curl(url, output_path):
     """使用 curl 下载���件，增加鲁棒性"""
     try:
         cmd = [
-            "curl", "-L", 
+            "curl", "-L",
             "-A", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
             "-o", output_path,
             "--fail",
             url
         ]
         result = subprocess.run(cmd, capture_output=True, text=True)
-        if result.returncode != 0: 
-            print(f"Curl stderr: {result.stderr}")
+        if result.returncode != 0:
+            logger.warning(f"Curl stderr: {result.stderr}")
             return False
         return True
     except Exception as e:
-        print(f"Curl download error: {e}")
+        logger.error(f"Curl download error: {e}")
         return False
 
 def get_data_file_path():
@@ -1186,21 +1824,34 @@ class RAGController:
         real_path = get_data_file_path()
 
         try:
+            logger.info("🚀 Initializing RAG Controller...")
+            log_memory()
+
             # 加载数据
+            logger.info(f"📂 Loading parquet from: {real_path}")
             self.df = pd.read_parquet(real_path)
             self.documents = self.df['content'].tolist()
             self.filenames = self.df['filename'].tolist()
+            logger.info(f"✅ Dataframe loaded. Shape: {self.df.shape}")
+            log_memory()
 
             # 加载向量嵌入
+            logger.info("🧠 Stacking embeddings matrix...")
             self.embeddings = np.stack(self.df['embedding'].values)
+            logger.info(f"✅ Embeddings stacked. Shape: {self.embeddings.shape}")
+            log_memory()
 
             # 初始化BM25
+            logger.info("🔍 Initializing BM25 index (Tokenizing)...")
             tokenized_corpus = [jieba.lcut(str(doc).lower()) for doc in self.documents]
             self.bm25 = BM25Okapi(tokenized_corpus)
+            logger.info("✅ BM25 Index ready.")
+            log_memory()
 
             st.success(f"✅ 成功加载 {len(self.documents)} 条文档")
 
         except Exception as e:
+            logger.error(f"❌ Critical error during data load: {str(e)}", exc_info=True)
             st.error(f"❌ 数据加载失败: {str(e)}")
             st.stop()
 
@@ -1219,12 +1870,12 @@ class RAGController:
     def expand_query(self, query: str) -> Tuple[str, float]:
         """查询扩展 - 使用LLM优化查询"""
         prompt = f"""你是COMSOL仿真专家。请将用户的口语化问题改写为专业的检索查询。
-        
+
         要求：
         1. 补充COMSOL专业术语（物理场、模块、边界条件等）
         2. 保持问题核心意图不变
         3. 输出简洁，仅返回改写后的查询
-        
+
         用户问题: {query}
         专业查询:"""
 
@@ -1237,9 +1888,10 @@ class RAGController:
             )
             expanded = resp.choices[0].message.content.strip()
             elapsed = time.time() - start_time
+            logger.info(f"🔧 QE completed in {elapsed:.2f}s")
             return expanded, elapsed
         except Exception as e:
-            print(f"QE Error: {e}")
+            logger.error(f"❌ QE Error: {e}")
             return query, 0
 
     def vector_search(self, query: str, top_k: int = 100) -> List[Tuple[int, float]]:
@@ -1278,7 +1930,7 @@ class RAGController:
 
         # 截断文档内容以符合 Context Window
         docs_content = [doc["content"][:2048] for doc in documents]
-        
+
         payload = {
             "model": RERANK_MODEL,
             "query": query,
@@ -1288,7 +1940,8 @@ class RAGController:
 
         try:
             start_time = time.time()
-            response = requests.post(url, headers=headers, json=payload, timeout=20)
+            # 设置 timeout 为 15 秒，防止长时间挂起导致 WebSocket 断开
+            response = requests.post(url, headers=headers, json=payload, timeout=15)
             elapsed = time.time() - start_time
 
             if response.status_code == 200:
@@ -1301,10 +1954,13 @@ class RAGController:
                     reranked_docs.append(original_doc)
                 return reranked_docs, elapsed
             else:
-                print(f"Rerank API Error: {response.text}")
+                logger.warning(f"Rerank API Error: {response.text}")
                 return documents[:top_n], elapsed
+        except requests.exceptions.Timeout:
+            logger.warning("⚠️ Rerank API timed out, falling back to original order.")
+            return documents[:top_n], 0
         except Exception as e:
-            print(f"Rerank Exception: {e}")
+            logger.error(f"❌ Rerank error: {e}")
             return documents[:top_n], 0
 
     def execute_strategy(self, query: str, config: Dict[str, Any]) -> Dict[str, Any]:
@@ -1406,7 +2062,7 @@ def generate_suggestions(controller, query: str, answer: str) -> List[str]:
             return sugs[:3]
         return []
     except Exception as e:
-        print(f"Suggestion Error: {e}")
+        logger.error(f"Suggestion Error: {e}")
         return []
 
 def generate_answer(controller, query: str, documents: List[Dict], history: List[Dict], max_rounds: int) -> str:
@@ -1541,6 +2197,7 @@ def main():
             cols = st.columns(3)
             for i, sug in enumerate(st.session_state.suggestions):
                 if cols[i].button(sug, use_container_width=True, key=f"sug_{i}"):
+                    logger.info(f"🔘 Triggered by button: {sug}")
                     st.session_state.prompt_trigger = sug
                     st.rerun()
 
@@ -1548,9 +2205,12 @@ def main():
         user_input = None
         if st.session_state.prompt_trigger:
             user_input = st.session_state.prompt_trigger
-            st.session_state.prompt_trigger = None
+            st.session_state.prompt_trigger = None  # 立即清除，防止重复触发
+            logger.info(f"🔘 Triggered by button: {user_input}")
         else:
             user_input = st.chat_input("请输入您关于 COMSOL 的问题...")
+            if user_input:
+                logger.info(f"⌨️ Triggered by chat input: {user_input}")
 
         # 4. 执行逻辑
         if user_input:
@@ -1559,21 +2219,25 @@ def main():
 
             # 检索
             with st.spinner("🔍 检索知识库中..."):
+                logger.info(f"🔎 Starting retrieval for: {user_input[:50]}...")
                 result = controller.execute_strategy(user_input, config)
                 st.session_state.last_result = result
+                logger.info(f"✅ Retrieval done in {result['metrics']['total_time']:.2f}s")
 
             # 生成
             with st.chat_message("assistant"):
+                logger.info("🤖 Generating answer...")
                 answer = generate_answer(
                     controller, user_input, result['documents'], 
                     st.session_state.messages, config['max_history_rounds']
                 )
                 st.session_state.messages.append({"role": "assistant", "content": answer})
-                
-                # 生成新建议
+
+                # 生成新建议 (不需要 rerun，Streamlit 会在脚本结束后自动刷新 UI)
+                logger.info("✨ Generating follow-up questions...")
                 new_sugs = generate_suggestions(controller, user_input, answer)
                 st.session_state.suggestions = new_sugs if new_sugs else random.sample(PRESET_QUESTIONS, 3)
-                st.rerun()
+                logger.info(f"✅ Response completed in {result['metrics']['total_time']:.2f}s")
 
     with debug_col:
         st.markdown("### 🔍 系统调试视图")