refactor(agent): 移除未使用的通用问题检测逻辑

清理不再使用的通用问题检测函数及相关正则表达式模式，简化代码结构

orchestrator/agent.py

@@ -8,7 +8,6 @@ from __future__ import annotations
 import asyncio
 import json
 import logging
-import re
 from collections import defaultdict
 from typing import Dict, List, Optional
 
@@ -86,35 +85,6 @@ Guidelines:
 MAX_ITERATIONS = 10
 _TOOL_MAP = {t.name: t for t in TOOLS}
 
-QUESTION_PATTERNS = [
-    r'\?$',  # ends with ?
-    r'？$',  # ends with Chinese ?
-    r'\b(what|how|why|when|where|who|which|explain|describe|tell me|can you|could you|is there|are there|do you know)\b',
-    r'(什么|怎么|为什么|何时|哪里|谁|哪个|解释|描述|告诉我|能否|可以|有没有|是不是)',
-]
-
-
-def _is_general_question(text: str) -> bool:
-    """Check if text looks like a general knowledge question (not a project task)."""
-    text_lower = text.lower().strip()
-    
-    project_indicators = [
-        'create', 'make', 'build', 'fix', 'update', 'delete', 'remove', 'add',
-        'implement', 'refactor', 'test', 'run', 'execute', 'start', 'stop',
-        'project', 'folder', 'directory', 'file', 'code', 'session',
-        '创建', '制作', '构建', '修复', '更新', '删除', '添加', '实现', '重构', '测试', '运行', '项目', '文件夹', '文件', '代码',
-    ]
-    
-    for indicator in project_indicators:
-        if indicator in text_lower:
-            return False
-    
-    for pattern in QUESTION_PATTERNS:
-        if re.search(pattern, text_lower, re.IGNORECASE):
-            return True
-    
-    return False
-
 
 class OrchestrationAgent:
     """Per-user agent with conversation history and active session tracking."""
@@ -186,23 +156,6 @@ class OrchestrationAgent:
                 logger.exception("Passthrough error for user=%s", user_id)
                 return f"[Error] {exc}"
 
-        # Direct Q&A: if no active session and message looks like a general question, answer directly
-        if not active_conv and _is_general_question(text):
-            logger.debug("    → direct Q&A (no tools)")
-            llm_no_tools = ChatOpenAI(
-                base_url=OPENAI_BASE_URL,
-                api_key=OPENAI_API_KEY,
-                model=OPENAI_MODEL,
-                temperature=0.7,
-            )
-            qa_prompt = (
-                "You are a helpful assistant. Answer the user's question concisely and accurately. "
-                "Reply in the same language the user uses.\n\n"
-                f"Question: {text}"
-            )
-            response = await llm_no_tools.ainvoke([HumanMessage(content=qa_prompt)])
-            return response.content or ""
-
         messages: list[BaseMessage] = (
             [SystemMessage(content=self._build_system_prompt(user_id))]
             + self._history[user_id]