前后端基本架构和完全excel表的解析及统计图表的生成以及excel表的到出

2026-03-19 01:51:34 +08:00
parent c23b93bb70
commit 2f630695ff
194 changed files with 23354 additions and 174 deletions
--- a/backend/app/services/excel_ai_service.py
+++ b/backend/app/services/excel_ai_service.py
@@ -0,0 +1,253 @@
+"""
+Excel AI 分析服务 - 集成 Excel 解析和 LLM 分析
+"""
+import logging
+from typing import Dict, Any, Optional, List
+
+from app.core.document_parser import XlsxParser
+from app.services.file_service import file_service
+from app.services.llm_service import llm_service
+
+logger = logging.getLogger(__name__)
+
+
+class ExcelAIService:
+    """Excel AI 分析服务"""
+
+    def __init__(self):
+        self.parser = XlsxParser()
+        self.file_service = file_service
+        self.llm_service = llm_service
+
+    async def analyze_excel_file(
+        self,
+        file_content: bytes,
+        filename: str,
+        user_prompt: str = "",
+        analysis_type: str = "general",
+        parse_options: Optional[Dict[str, Any]] = None
+    ) -> Dict[str, Any]:
+        """
+        分析 Excel 文件
+
+        Args:
+            file_content: 文件内容字节
+            filename: 文件名
+            user_prompt: 用户自定义提示词
+            analysis_type: 分析类型
+            parse_options: 解析选项
+
+        Returns:
+            Dict[str, Any]: 分析结果
+        """
+        # 1. 保存文件
+        try:
+            saved_path = self.file_service.save_uploaded_file(
+                file_content,
+                filename,
+                subfolder="excel"
+            )
+            logger.info(f"文件已保存: {saved_path}")
+        except Exception as e:
+            logger.error(f"文件保存失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"文件保存失败: {str(e)}",
+                "analysis": None
+            }
+
+        # 2. 解析 Excel 文件
+        try:
+            parse_options = parse_options or {}
+            parse_result = self.parser.parse(saved_path, **parse_options)
+
+            if not parse_result.success:
+                return {
+                    "success": False,
+                    "error": parse_result.error,
+                    "analysis": None
+                }
+
+            excel_data = parse_result.data
+            logger.info(f"Excel 解析成功: {parse_result.metadata}")
+
+        except Exception as e:
+            logger.error(f"Excel 解析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Excel 解析失败: {str(e)}",
+                "analysis": None
+            }
+
+        # 3. 调用 LLM 进行分析
+        try:
+            # 如果有自定义提示词，使用模板分析
+            if user_prompt and user_prompt.strip():
+                llm_result = await self.llm_service.analyze_with_template(
+                    excel_data,
+                    user_prompt
+                )
+            else:
+                # 否则使用标准分析
+                llm_result = await self.llm_service.analyze_excel_data(
+                    excel_data,
+                    user_prompt,
+                    analysis_type
+                )
+
+            logger.info(f"AI 分析完成: {llm_result['success']}")
+
+            # 4. 组合结果
+            return {
+                "success": True,
+                "excel": {
+                    "data": excel_data,
+                    "metadata": parse_result.metadata,
+                    "saved_path": saved_path
+                },
+                "analysis": llm_result
+            }
+
+        except Exception as e:
+            logger.error(f"AI 分析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"AI 分析失败: {str(e)}",
+                "excel": {
+                    "data": excel_data,
+                    "metadata": parse_result.metadata
+                },
+                "analysis": None
+            }
+
+    async def batch_analyze_sheets(
+        self,
+        file_content: bytes,
+        filename: str,
+        user_prompt: str = "",
+        analysis_type: str = "general"
+    ) -> Dict[str, Any]:
+        """
+        批量分析 Excel 文件的所有工作表
+
+        Args:
+            file_content: 文件内容字节
+            filename: 文件名
+            user_prompt: 用户自定义提示词
+            analysis_type: 分析类型
+
+        Returns:
+            Dict[str, Any]: 分析结果
+        """
+        # 1. 保存文件
+        try:
+            saved_path = self.file_service.save_uploaded_file(
+                file_content,
+                filename,
+                subfolder="excel"
+            )
+            logger.info(f"文件已保存: {saved_path}")
+        except Exception as e:
+            logger.error(f"文件保存失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"文件保存失败: {str(e)}",
+                "analysis": None
+            }
+
+        # 2. 解析所有工作表
+        try:
+            parse_result = self.parser.parse_all_sheets(saved_path)
+
+            if not parse_result.success:
+                return {
+                    "success": False,
+                    "error": parse_result.error,
+                    "analysis": None
+                }
+
+            sheets_data = parse_result.data.get("sheets", {})
+            logger.info(f"Excel 解析成功，共 {len(sheets_data)} 个工作表")
+
+        except Exception as e:
+            logger.error(f"Excel 解析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": f"Excel 解析失败: {str(e)}",
+                "analysis": None
+            }
+
+        # 3. 批量分析每个工作表
+        sheet_analyses = {}
+        errors = {}
+
+        for sheet_name, sheet_data in sheets_data.items():
+            try:
+                # 调用 LLM 分析
+                if user_prompt and user_prompt.strip():
+                    llm_result = await self.llm_service.analyze_with_template(
+                        sheet_data,
+                        user_prompt
+                    )
+                else:
+                    llm_result = await self.llm_service.analyze_excel_data(
+                        sheet_data,
+                        user_prompt,
+                        analysis_type
+                    )
+
+                sheet_analyses[sheet_name] = llm_result
+
+                if not llm_result["success"]:
+                    errors[sheet_name] = llm_result.get("error", "未知错误")
+
+                logger.info(f"工作表 '{sheet_name}' 分析完成")
+
+            except Exception as e:
+                logger.error(f"工作表 '{sheet_name}' 分析失败: {str(e)}")
+                errors[sheet_name] = str(e)
+
+        # 4. 组合结果
+        return {
+            "success": len(errors) == 0,
+            "excel": {
+                "sheets": sheets_data,
+                "metadata": parse_result.metadata,
+                "saved_path": saved_path
+            },
+            "analysis": {
+                "sheets": sheet_analyses,
+                "total_sheets": len(sheets_data),
+                "successful": len(sheet_analyses) - len(errors),
+                "errors": errors
+            }
+        }
+
+    def get_supported_analysis_types(self) -> List[str]:
+        """获取支持的分析类型"""
+        return [
+            {
+                "value": "general",
+                "label": "综合分析",
+                "description": "提供数据概览、关键发现、质量评估和建议"
+            },
+            {
+                "value": "summary",
+                "label": "数据摘要",
+                "description": "快速了解数据的结构、范围和主要内容"
+            },
+            {
+                "value": "statistics",
+                "label": "统计分析",
+                "description": "数值型列的统计信息和分类列的分布"
+            },
+            {
+                "value": "insights",
+                "label": "深度洞察",
+                "description": "深入挖掘数据，提供异常值和业务建议"
+            }
+        ]
+
+
+# 全局单例
+excel_ai_service = ExcelAIService()