KiriAky 107
5fca4eb094
- 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
343 lines
11 KiB
Python
343 lines
11 KiB
Python
"""
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AI 分析 API 接口
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"""
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from fastapi import APIRouter, UploadFile, File, HTTPException, Query, Body
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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 tempfile
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import os
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from app.services.excel_ai_service import excel_ai_service
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from app.services.markdown_ai_service import markdown_ai_service
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/ai", tags=["AI 分析"])
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@router.post("/analyze/excel")
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async def analyze_excel(
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file: UploadFile = File(...),
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user_prompt: str = Query("", description="用户自定义提示词"),
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analysis_type: str = Query("general", description="分析类型: general, summary, statistics, insights"),
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parse_all_sheets: bool = Query(False, description="是否分析所有工作表")
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):
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"""
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上传并使用 AI 分析 Excel 文件
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Args:
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file: 上传的 Excel 文件
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user_prompt: 用户自定义提示词
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analysis_type: 分析类型
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parse_all_sheets: 是否分析所有工作表
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Returns:
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dict: 分析结果，包含 Excel 数据和 AI 分析结果
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"""
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# 检查文件类型
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if not file.filename:
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raise HTTPException(status_code=400, detail="文件名为空")
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file_ext = file.filename.split('.')[-1].lower()
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if file_ext not in ['xlsx', 'xls']:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的文件类型: {file_ext}，仅支持 .xlsx 和 .xls"
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)
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# 验证分析类型
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supported_types = ['general', 'summary', 'statistics', 'insights']
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if analysis_type not in supported_types:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的分析类型: {analysis_type}，支持的类型: {', '.join(supported_types)}"
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)
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try:
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# 读取文件内容
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content = await file.read()
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logger.info(f"开始分析文件: {file.filename}, 分析类型: {analysis_type}")
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# 调用 AI 分析服务
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if parse_all_sheets:
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result = await excel_ai_service.batch_analyze_sheets(
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content,
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file.filename,
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user_prompt=user_prompt,
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analysis_type=analysis_type
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)
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else:
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# 解析选项
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parse_options = {"header_row": 0}
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result = await excel_ai_service.analyze_excel_file(
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content,
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file.filename,
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user_prompt=user_prompt,
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analysis_type=analysis_type,
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parse_options=parse_options
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)
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logger.info(f"文件分析完成: {file.filename}, 成功: {result['success']}")
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return result
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"AI 分析过程中出错: {str(e)}")
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raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
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@router.get("/analysis/types")
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async def get_analysis_types():
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"""
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获取支持的分析类型列表
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Returns:
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dict: 支持的分析类型（包含 Excel 和 Markdown）
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"""
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return {
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"excel_types": excel_ai_service.get_supported_analysis_types(),
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"markdown_types": markdown_ai_service.get_supported_analysis_types()
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}
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@router.post("/analyze/text")
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async def analyze_text(
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excel_data: dict = Body(..., description="Excel 解析后的数据"),
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user_prompt: str = Body("", description="用户提示词"),
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analysis_type: str = Body("general", description="分析类型")
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):
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"""
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对已解析的 Excel 数据进行 AI 分析
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Args:
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excel_data: Excel 数据
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user_prompt: 用户提示词
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analysis_type: 分析类型
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Returns:
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dict: 分析结果
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"""
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try:
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logger.info(f"开始文本分析, 分析类型: {analysis_type}")
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# 调用 LLM 服务
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from app.services.llm_service import llm_service
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if user_prompt and user_prompt.strip():
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result = await llm_service.analyze_with_template(
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excel_data,
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user_prompt
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)
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else:
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result = await llm_service.analyze_excel_data(
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excel_data,
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user_prompt,
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analysis_type
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)
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logger.info(f"文本分析完成, 成功: {result['success']}")
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return result
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except Exception as e:
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logger.error(f"文本分析失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
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@router.post("/analyze/md")
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async def analyze_markdown(
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file: UploadFile = File(...),
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analysis_type: str = Query("summary", description="分析类型: summary, outline, key_points, questions, tags, qa, statistics, section"),
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user_prompt: str = Query("", description="用户自定义提示词"),
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section_number: Optional[str] = Query(None, description="指定章节编号，如 '一' 或 '（一）'")
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):
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"""
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上传并使用 AI 分析 Markdown 文件
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Args:
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file: 上传的 Markdown 文件
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analysis_type: 分析类型
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user_prompt: 用户自定义提示词
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section_number: 指定分析的章节编号
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Returns:
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dict: 分析结果
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"""
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# 检查文件类型
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if not file.filename:
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raise HTTPException(status_code=400, detail="文件名为空")
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file_ext = file.filename.split('.')[-1].lower()
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if file_ext not in ['md', 'markdown']:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
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)
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# 验证分析类型
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supported_types = markdown_ai_service.get_supported_analysis_types()
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if analysis_type not in supported_types:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的分析类型: {analysis_type}，支持的类型: {', '.join(supported_types)}"
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)
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try:
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# 读取文件内容
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content = await file.read()
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# 保存到临时文件
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with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
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tmp.write(content)
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tmp_path = tmp.name
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try:
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logger.info(f"开始分析 Markdown 文件: {file.filename}, 分析类型: {analysis_type}, 章节: {section_number}")
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# 调用 AI 分析服务
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result = await markdown_ai_service.analyze_markdown(
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file_path=tmp_path,
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analysis_type=analysis_type,
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user_prompt=user_prompt,
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section_number=section_number
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)
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logger.info(f"Markdown 分析完成: {file.filename}, 成功: {result['success']}")
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if not result['success']:
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raise HTTPException(status_code=500, detail=result.get('error', '分析失败'))
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return result
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finally:
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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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except Exception as e:
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logger.error(f"Markdown AI 分析过程中出错: {str(e)}")
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raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
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@router.post("/analyze/md/stream")
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async def analyze_markdown_stream(
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file: UploadFile = File(...),
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analysis_type: str = Query("summary", description="分析类型"),
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user_prompt: str = Query("", description="用户自定义提示词"),
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section_number: Optional[str] = Query(None, description="指定章节编号")
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):
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"""
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流式分析 Markdown 文件 (SSE)
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Returns:
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StreamingResponse: SSE 流式响应
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"""
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if not file.filename:
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raise HTTPException(status_code=400, detail="文件名为空")
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file_ext = file.filename.split('.')[-1].lower()
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if file_ext not in ['md', 'markdown']:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
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)
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try:
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content = await file.read()
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with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
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tmp.write(content)
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tmp_path = tmp.name
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try:
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logger.info(f"开始流式分析 Markdown 文件: {file.filename}, 分析类型: {analysis_type}")
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async def stream_generator():
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async for chunk in markdown_ai_service.analyze_markdown_stream(
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file_path=tmp_path,
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analysis_type=analysis_type,
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user_prompt=user_prompt,
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section_number=section_number
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):
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yield chunk
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return StreamingResponse(
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stream_generator(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no"
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}
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)
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finally:
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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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except Exception as e:
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logger.error(f"Markdown AI 流式分析出错: {str(e)}")
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raise HTTPException(status_code=500, detail=f"流式分析失败: {str(e)}")
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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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"""
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获取 Markdown 文档的大纲结构（分章节信息）
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Args:
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file: 上传的 Markdown 文件
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Returns:
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dict: 文档大纲结构
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"""
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if not file.filename:
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raise HTTPException(status_code=400, detail="文件名为空")
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file_ext = file.filename.split('.')[-1].lower()
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if file_ext not in ['md', 'markdown']:
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raise HTTPException(
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status_code=400,
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detail=f"不支持的文件类型: {file_ext}，仅支持 .md 和 .markdown"
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)
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try:
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content = await file.read()
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with tempfile.NamedTemporaryFile(mode='wb', suffix='.md', delete=False) as tmp:
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tmp.write(content)
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tmp_path = tmp.name
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try:
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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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# 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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raise HTTPException(status_code=500, detail=f"获取大纲失败: {str(e)}")
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