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���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���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", 
@@ -129,234 +493,478 @@ def download_with_curl(url, output_path):
             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
+
+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="⚡")
 
-# ================= 3. UI 主程序 =================
+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)
+                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})
                 
-                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")
+                # 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()