添加临时文件清理异常处理和修改大纲接口为POST方法
- 在analyze_markdown、analyze_markdown_stream和get_markdown_outline函数中添加了 try-catch块来处理临时文件清理过程中的异常 - 将/analyze/md/outline接口从GET方法改为POST方法以支持文件上传 - 确保在所有情况下都能正确清理临时文件，并记录清理失败的日志 refactor(health): 改进健康检查逻辑验证实际数据库连接 - 修改MySQL健康检查，实际执行SELECT 1查询来验证连接 - 修改MongoDB健康检查，执行ping命令来验证连接 - 修改Redis健康检查，执行ping命令来验证连接 - 添加异常捕获并记录具体的错误日志 refactor(upload): 使用os.path.basename优化文件名提取 - 替换手动字符串分割为os.path.basename来获取文件名 - 统一Excel上传和导出中文件名的处理方式 feat(instruction): 新增指令执行框架模块 - 创建instruction包包含意图解析和指令执行的基础架构 - 添加IntentParser和InstructionExecutor抽象基类 - 提供默认实现但标记为未完成，为未来功能扩展做准备 refactor(frontend): 调整AuthContext导入路径并移除重复文件 - 将AuthContext从src/context移动到src/contexts目录 - 更新App.tsx和RouteGuard.tsx中的导入路径 - 移除旧的AuthContext.tsx文件 fix(backend-api): 修复AI分析API的HTTP方法错误 - 将aiApi中的fetch请求方法从GET改为POST以支持文件上传
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@@ -215,9 +215,12 @@ async def analyze_markdown(
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return result
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finally:
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# 清理临时文件
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if os.path.exists(tmp_path):
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os.unlink(tmp_path)
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# 清理临时文件，确保在所有情况下都能清理
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try:
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if tmp_path and os.path.exists(tmp_path):
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os.unlink(tmp_path)
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except Exception as cleanup_error:
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logger.warning(f"临时文件清理失败: {tmp_path}, error: {cleanup_error}")
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except HTTPException:
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raise
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@@ -279,8 +282,12 @@ async def analyze_markdown_stream(
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)
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finally:
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if os.path.exists(tmp_path):
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os.unlink(tmp_path)
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# 清理临时文件，确保在所有情况下都能清理
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try:
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if tmp_path and os.path.exists(tmp_path):
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os.unlink(tmp_path)
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except Exception as cleanup_error:
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logger.warning(f"临时文件清理失败: {tmp_path}, error: {cleanup_error}")
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except HTTPException:
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raise
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@@ -289,7 +296,7 @@ async def analyze_markdown_stream(
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raise HTTPException(status_code=500, detail=f"流式分析失败: {str(e)}")
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@router.get("/analyze/md/outline")
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@router.post("/analyze/md/outline")
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async def get_markdown_outline(
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file: UploadFile = File(...)
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):
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@@ -323,8 +330,12 @@ async def get_markdown_outline(
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result = await markdown_ai_service.extract_outline(tmp_path)
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return result
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finally:
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if os.path.exists(tmp_path):
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os.unlink(tmp_path)
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# 清理临时文件，确保在所有情况下都能清理
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try:
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if tmp_path and os.path.exists(tmp_path):
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os.unlink(tmp_path)
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except Exception as cleanup_error:
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logger.warning(f"临时文件清理失败: {tmp_path}, error: {cleanup_error}")
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except Exception as e:
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logger.error(f"获取 Markdown 大纲失败: {str(e)}")
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@@ -19,26 +19,43 @@ async def health_check() -> Dict[str, Any]:
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返回各数据库连接状态和应用信息
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"""
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# 检查各数据库连接状态
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mysql_status = "connected"
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mongodb_status = "connected"
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redis_status = "connected"
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mysql_status = "unknown"
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mongodb_status = "unknown"
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redis_status = "unknown"
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try:
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if mysql_db.async_engine is None:
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mysql_status = "disconnected"
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except Exception:
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else:
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# 实际执行一次查询验证连接
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from sqlalchemy import text
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async with mysql_db.async_engine.connect() as conn:
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await conn.execute(text("SELECT 1"))
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mysql_status = "connected"
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except Exception as e:
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logger.warning(f"MySQL 健康检查失败: {e}")
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mysql_status = "error"
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try:
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if mongodb.client is None:
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mongodb_status = "disconnected"
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except Exception:
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else:
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# 实际 ping 验证
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await mongodb.client.admin.command('ping')
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mongodb_status = "connected"
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except Exception as e:
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logger.warning(f"MongoDB 健康检查失败: {e}")
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mongodb_status = "error"
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try:
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if not redis_db.is_connected:
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if not redis_db.is_connected or redis_db.client is None:
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redis_status = "disconnected"
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except Exception:
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else:
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# 实际执行 ping 验证
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await redis_db.client.ping()
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redis_status = "connected"
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except Exception as e:
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logger.warning(f"Redis 健康检查失败: {e}")
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redis_status = "error"
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return {
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@@ -5,6 +5,7 @@ from fastapi import APIRouter, UploadFile, File, HTTPException, Query
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from fastapi.responses import StreamingResponse
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from typing import Optional
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import logging
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import os
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import pandas as pd
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import io
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@@ -126,7 +127,7 @@ async def upload_excel(
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content += f"... (共 {len(sheet_data['rows'])} 行)\n\n"
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doc_metadata = {
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"filename": saved_path.split("/")[-1] if "/" in saved_path else saved_path.split("\\")[-1],
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"filename": os.path.basename(saved_path),
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"original_filename": file.filename,
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"saved_path": saved_path,
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"file_size": len(content),
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@@ -253,7 +254,7 @@ async def export_excel(
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output.seek(0)
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# 生成文件名
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original_name = file_path.split('/')[-1] if '/' in file_path else file_path
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original_name = os.path.basename(file_path)
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if columns:
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export_name = f"export_{sheet_name or 'data'}_{len(column_list) if columns else 'all'}_cols.xlsx"
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else:
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@@ -0,0 +1,15 @@
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"""
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指令执行模块
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注意: 此模块为可选功能，当前尚未实现。
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如需启用，请实现 intent_parser.py 和 executor.py
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"""
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from .intent_parser import IntentParser, DefaultIntentParser
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from .executor import InstructionExecutor, DefaultInstructionExecutor
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__all__ = [
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"IntentParser",
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"DefaultIntentParser",
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"InstructionExecutor",
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"DefaultInstructionExecutor",
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]
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@@ -0,0 +1,35 @@
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"""
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指令执行器模块
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将自然语言指令转换为可执行操作
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注意: 此模块为可选功能，当前尚未实现。
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"""
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from abc import ABC, abstractmethod
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from typing import Any, Dict
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class InstructionExecutor(ABC):
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"""指令执行器抽象基类"""
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@abstractmethod
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async def execute(self, instruction: str, context: Dict[str, Any]) -> Dict[str, Any]:
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"""
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执行指令
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Args:
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instruction: 解析后的指令
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context: 执行上下文
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Returns:
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执行结果
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"""
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pass
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class DefaultInstructionExecutor(InstructionExecutor):
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"""默认指令执行器"""
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async def execute(self, instruction: str, context: Dict[str, Any]) -> Dict[str, Any]:
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"""暂未实现"""
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raise NotImplementedError("指令执行功能暂未实现")
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@@ -0,0 +1,34 @@
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"""
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意图解析器模块
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解析用户自然语言指令，识别意图和参数
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注意: 此模块为可选功能，当前尚未实现。
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"""
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from abc import ABC, abstractmethod
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from typing import Any, Dict, Tuple
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class IntentParser(ABC):
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"""意图解析器抽象基类"""
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@abstractmethod
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async def parse(self, text: str) -> Tuple[str, Dict[str, Any]]:
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"""
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解析自然语言指令
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Args:
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text: 用户输入的自然语言
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Returns:
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(意图类型, 参数字典)
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"""
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pass
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class DefaultIntentParser(IntentParser):
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"""默认意图解析器"""
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async def parse(self, text: str) -> Tuple[str, Dict[str, Any]]:
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"""暂未实现"""
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raise NotImplementedError("意图解析功能暂未实现")
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