698 lines
24 KiB
Python
698 lines
24 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, Form
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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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from app.services.template_fill_service import template_fill_service
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from app.services.word_ai_service import word_ai_service
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from app.services.txt_ai_service import txt_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: Optional[UploadFile] = File(None),
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doc_id: Optional[str] = Form(None, description="文档ID（从数据库读取）"),
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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（与 doc_id 二选一）
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doc_id: 文档ID（从数据库读取）
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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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filename = None
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# 从数据库读取模式
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if doc_id:
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try:
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from app.core.database.mongodb import mongodb
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doc = await mongodb.get_document(doc_id)
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if not doc:
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raise HTTPException(status_code=404, detail=f"文档不存在: {doc_id}")
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filename = doc.get("metadata", {}).get("original_filename", "unknown.xlsx")
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file_ext = filename.split('.')[-1].lower()
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if file_ext not in ['xlsx', 'xls']:
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raise HTTPException(status_code=400, detail=f"文档类型不是 Excel: {file_ext}")
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file_path = doc.get("metadata", {}).get("file_path")
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if not file_path:
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raise HTTPException(status_code=400, detail="文档没有存储文件路径，请重新上传")
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# 使用文件路径进行 AI 分析
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if parse_all_sheets:
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result = await excel_ai_service.batch_analyze_sheets_from_path(
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file_path=file_path,
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filename=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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result = await excel_ai_service.analyze_excel_file_from_path(
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file_path=file_path,
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filename=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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if result.get("success"):
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return result
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else:
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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"从数据库读取 Excel 文档失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"读取文档失败: {str(e)}")
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# 文件上传模式
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if not file:
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raise HTTPException(status_code=400, detail="请提供文件或文档ID")
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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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# 验证文件内容不为空
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if not content:
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raise HTTPException(status_code=400, detail="文件内容为空，请确保文件已正确上传")
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logger.info(f"开始分析文件: {file.filename}, 分析类型: {analysis_type}, 文件大小: {len(content)} bytes")
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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: Optional[UploadFile] = File(None),
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doc_id: Optional[str] = Form(None, description="文档ID（从数据库读取）"),
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analysis_type: str = Query("summary", description="分析类型: summary, outline, key_points, questions, tags, qa, statistics, section, charts"),
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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（与 doc_id 二选一）
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doc_id: 文档ID（从数据库读取）
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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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filename = None
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tmp_path = None
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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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if doc_id:
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# 从数据库读取文档
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try:
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from app.core.database.mongodb import mongodb
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doc = await mongodb.get_document(doc_id)
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if not doc:
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raise HTTPException(status_code=404, detail=f"文档不存在: {doc_id}")
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filename = doc.get("metadata", {}).get("original_filename", "unknown.md")
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file_ext = filename.split('.')[-1].lower()
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if file_ext not in ['md', 'markdown']:
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raise HTTPException(status_code=400, detail=f"文档类型不是 Markdown: {file_ext}")
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content = doc.get("content") or ""
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if not content:
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raise HTTPException(status_code=400, detail="文档内容为空")
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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.encode('utf-8'))
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tmp_path = tmp.name
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logger.info(f"从数据库加载 Markdown 文档: {filename}, 长度: {len(content)}")
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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"从数据库读取 Markdown 文档失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"读取文档失败: {str(e)}")
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else:
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# 文件上传模式
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if not file:
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raise HTTPException(status_code=400, detail="请提供文件或文档ID")
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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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# 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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filename = file.filename
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except Exception as e:
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logger.error(f"读取 Markdown 文件失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"读取文件失败: {str(e)}")
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try:
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logger.info(f"开始分析 Markdown 文件: {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: {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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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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finally:
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# 清理临时文件
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if tmp_path and os.path.exists(tmp_path):
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try:
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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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@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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|
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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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@router.post("/analyze/txt")
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async def analyze_txt(
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file: Optional[UploadFile] = File(None),
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doc_id: Optional[str] = Form(None, description="文档ID（从数据库读取）"),
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analysis_type: str = Query("structured", description="分析类型: structured, charts")
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):
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"""
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上传并使用 AI 分析 TXT 文本文件，提取结构化数据或生成图表
|
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将非结构化文本转换为结构化表格数据，便于后续填表使用
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当 analysis_type=charts 时，可生成可视化图表
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Args:
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file: 上传的 TXT 文件（与 doc_id 二选一）
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doc_id: 文档ID（从数据库读取）
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analysis_type: 分析类型 - "structured"（默认，提取结构化数据）或 "charts"（生成图表）
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Returns:
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dict: 分析结果，包含结构化表格数据或图表数据
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"""
|
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filename = None
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text_content = None
|
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|
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if doc_id:
|
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# 从数据库读取文档
|
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try:
|
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from app.core.database.mongodb import mongodb
|
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doc = await mongodb.get_document(doc_id)
|
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if not doc:
|
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raise HTTPException(status_code=404, detail=f"文档不存在: {doc_id}")
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filename = doc.get("metadata", {}).get("original_filename", "unknown.txt")
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file_ext = filename.split('.')[-1].lower()
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|
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if file_ext not in ['txt', 'text']:
|
||
raise HTTPException(status_code=400, detail=f"文档类型不是 TXT: {file_ext}")
|
||
|
||
# 使用数据库中的 content
|
||
text_content = doc.get("content") or ""
|
||
|
||
if not text_content:
|
||
raise HTTPException(status_code=400, detail="文档内容为空")
|
||
|
||
logger.info(f"从数据库加载 TXT 文档: {filename}, 长度: {len(text_content)}")
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"从数据库读取 TXT 文档失败: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"读取文档失败: {str(e)}")
|
||
else:
|
||
# 文件上传模式
|
||
if not file:
|
||
raise HTTPException(status_code=400, detail="请提供文件或文档ID")
|
||
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['txt', 'text']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .txt"
|
||
)
|
||
|
||
# 读取文件内容
|
||
content = await file.read()
|
||
text_content = content.decode('utf-8', errors='replace')
|
||
filename = file.filename
|
||
|
||
try:
|
||
logger.info(f"开始 AI 分析 TXT 文件: {filename}, analysis_type={analysis_type}")
|
||
|
||
# 使用 txt_ai_service 的 AI 分析方法
|
||
result = await txt_ai_service.analyze_txt_with_ai(
|
||
content=text_content,
|
||
filename=filename,
|
||
analysis_type=analysis_type
|
||
)
|
||
|
||
if result:
|
||
logger.info(f"TXT AI 分析成功: {filename}")
|
||
return {
|
||
"success": result.get("success", True),
|
||
"filename": filename,
|
||
"analysis_type": analysis_type,
|
||
"result": result
|
||
}
|
||
else:
|
||
logger.warning(f"TXT AI 分析返回空结果: {filename}")
|
||
return {
|
||
"success": False,
|
||
"filename": filename,
|
||
"error": "AI 分析未能提取到结构化数据",
|
||
"result": None
|
||
}
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"TXT AI 分析过程中出错: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
|
||
|
||
|
||
# ==================== Word 文档 AI 解析 ====================
|
||
|
||
@router.post("/analyze/word")
|
||
async def analyze_word(
|
||
file: Optional[UploadFile] = File(None),
|
||
doc_id: Optional[str] = Form(None, description="文档ID（从数据库读取）"),
|
||
user_hint: str = Form("", description="用户提示词，如'请提取表格数据'"),
|
||
analysis_type: str = Query("structured", description="分析类型: structured, charts")
|
||
):
|
||
"""
|
||
使用 AI 解析 Word 文档，提取结构化数据或生成图表
|
||
|
||
适用于从非结构化的 Word 文档中提取表格数据、键值对等信息
|
||
当 analysis_type=charts 时，可生成可视化图表
|
||
|
||
Args:
|
||
file: 上传的 Word 文件（与 doc_id 二选一）
|
||
doc_id: 文档ID（从数据库读取）
|
||
user_hint: 用户提示词
|
||
analysis_type: 分析类型 - "structured"（默认，提取结构化数据）或 "charts"（生成图表）
|
||
|
||
Returns:
|
||
dict: 包含结构化数据的解析结果或图表数据
|
||
"""
|
||
# 获取文件名和扩展名
|
||
filename = None
|
||
file_ext = None
|
||
|
||
if doc_id:
|
||
# 从数据库读取文档
|
||
try:
|
||
from app.core.database.mongodb import mongodb
|
||
doc = await mongodb.get_document(doc_id)
|
||
if not doc:
|
||
raise HTTPException(status_code=404, detail=f"文档不存在: {doc_id}")
|
||
|
||
filename = doc.get("metadata", {}).get("original_filename", "unknown.docx")
|
||
file_ext = filename.split('.')[-1].lower()
|
||
|
||
if file_ext not in ['docx']:
|
||
raise HTTPException(status_code=400, detail=f"文档类型不是 Word: {file_ext}")
|
||
|
||
# 使用数据库中的 content 进行分析
|
||
content = doc.get("content", "") or ""
|
||
structured_data = doc.get("structured_data") or {}
|
||
tables = structured_data.get("tables", [])
|
||
|
||
# 调用 AI 分析服务，传入数据库内容
|
||
if analysis_type == "charts":
|
||
result = await word_ai_service.generate_charts_from_db(
|
||
content=content,
|
||
tables=tables,
|
||
filename=filename,
|
||
user_hint=user_hint
|
||
)
|
||
else:
|
||
result = await word_ai_service.parse_word_with_ai_from_db(
|
||
content=content,
|
||
tables=tables,
|
||
filename=filename,
|
||
user_hint=user_hint or "请提取文档中的所有结构化数据，包括表格、键值对等"
|
||
)
|
||
|
||
if result.get("success"):
|
||
return {
|
||
"success": True,
|
||
"filename": filename,
|
||
"analysis_type": analysis_type,
|
||
"result": result
|
||
}
|
||
else:
|
||
return {
|
||
"success": False,
|
||
"filename": filename,
|
||
"error": result.get("error", "AI 解析失败"),
|
||
"result": None
|
||
}
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"从数据库读取 Word 文档失败: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"读取文档失败: {str(e)}")
|
||
|
||
# 文件上传模式
|
||
if not file:
|
||
raise HTTPException(status_code=400, detail="请提供文件或文档ID")
|
||
|
||
if not file.filename:
|
||
raise HTTPException(status_code=400, detail="文件名为空")
|
||
|
||
file_ext = file.filename.split('.')[-1].lower()
|
||
if file_ext not in ['docx']:
|
||
raise HTTPException(
|
||
status_code=400,
|
||
detail=f"不支持的文件类型: {file_ext}，仅支持 .docx"
|
||
)
|
||
|
||
try:
|
||
# 保存上传的文件
|
||
content = await file.read()
|
||
suffix = f".{file_ext}"
|
||
with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
|
||
tmp.write(content)
|
||
tmp_path = tmp.name
|
||
|
||
try:
|
||
# 根据 analysis_type 选择处理方式
|
||
if analysis_type == "charts":
|
||
# 生成图表
|
||
result = await word_ai_service.generate_charts(
|
||
file_path=tmp_path,
|
||
user_hint=user_hint
|
||
)
|
||
else:
|
||
# 提取结构化数据
|
||
result = await word_ai_service.parse_word_with_ai(
|
||
file_path=tmp_path,
|
||
user_hint=user_hint or "请提取文档中的所有结构化数据，包括表格、键值对等"
|
||
)
|
||
|
||
if result.get("success"):
|
||
return {
|
||
"success": True,
|
||
"filename": file.filename,
|
||
"analysis_type": analysis_type,
|
||
"result": result
|
||
}
|
||
else:
|
||
return {
|
||
"success": False,
|
||
"filename": file.filename,
|
||
"error": result.get("error", "AI 解析失败"),
|
||
"result": None
|
||
}
|
||
|
||
finally:
|
||
# 清理临时文件
|
||
if os.path.exists(tmp_path):
|
||
os.unlink(tmp_path)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Word AI 分析过程中出错: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"分析失败: {str(e)}")
|