后端完成异步和rag设置

backend/.env.example

@@ -37,8 +37,8 @@ LLM_MODEL_NAME="MiniMax-Text-01"
 # 上传文件存储目录 (相对于项目根目录)
 UPLOAD_DIR="./data/uploads"
 
-# ChromaDB 向量数据库持久化目录
+# Faiss 向量数据库持久化目录 (LangChain + Faiss 实现)
-CHROMADB_PERSIST_DIR="./data/chromadb"
+FAISS_INDEX_DIR="./data/faiss"
 
 # ==================== RAG 配置 ====================
 # Embedding 模型名称

backend/app/api/endpoints/documents.py

@@ -2,17 +2,20 @@
 文档管理 API 接口
 
 支持多格式文档(docx/xlsx/md/txt)上传、解析、存储和RAG索引
+集成 Excel 存储和 AI 生成字段描述
 """
+import logging
 import uuid
-from datetime import datetime
 from typing import List, Optional
 
 from fastapi import APIRouter, UploadFile, File, HTTPException, Query, BackgroundTasks
 from pydantic import BaseModel
 
 from app.services.file_service import file_service
-from app.core.database import mongodb, mysql_db
+from app.core.database import mongodb, redis_db
 from app.services.rag_service import rag_service
+from app.services.table_rag_service import table_rag_service
+from app.services.excel_storage_service import excel_storage_service
 from app.core.document_parser import ParserFactory, ParseResult
 
 logger = logging.getLogger(__name__)
@@ -31,7 +34,7 @@ class UploadResponse(BaseModel):
 
 class TaskStatusResponse(BaseModel):
     task_id: str
-    status: str  # pending, processing, success, failure
+    status: str
     progress: int = 0
     message: Optional[str] = None
     result: Optional[dict] = None
@@ -44,7 +47,6 @@ class TaskStatusResponse(BaseModel):
 async def upload_document(
     background_tasks: BackgroundTasks,
     file: UploadFile = File(...),
-    doc_type: Optional[str] = Query(None, description="文档类型: docx/xlsx/md/txt"),
     parse_all_sheets: bool = Query(False, description="是否解析所有工作表(仅Excel)"),
     sheet_name: Optional[str] = Query(None, description="指定工作表(仅Excel)"),
     header_row: int = Query(0, description="表头行号(仅Excel)")
@@ -56,13 +58,15 @@ async def upload_document(
     1. 保存到本地存储
     2. 解析内容
     3. 存入 MongoDB (原始内容)
-    4. 如果是 Excel，存入 MySQL (结构化数据)
+    4. 如果是 Excel：
-    5. 建立 RAG 索引
+       - 存入 MySQL (结构化数据)
+       - AI 生成字段描述
+       - 建立 RAG 索引
+    5. 建立 RAG 索引 (非结构化文档)
     """
     if not file.filename:
         raise HTTPException(status_code=400, detail="文件名为空")
 
-    # 根据扩展名确定文档类型
     file_ext = file.filename.split('.')[-1].lower()
     if file_ext not in ['docx', 'xlsx', 'xls', 'md', 'txt']:
         raise HTTPException(
@@ -70,21 +74,16 @@ async def upload_document(
             detail=f"不支持的文件类型: {file_ext}，仅支持 docx/xlsx/xls/md/txt"
         )
 
-    # 生成任务ID
     task_id = str(uuid.uuid4())
 
     try:
-        # 读取文件内容
         content = await file.read()
-
-        # 保存文件
         saved_path = file_service.save_uploaded_file(
             content,
             file.filename,
             subfolder=file_ext
         )
 
-        # 后台处理文档
         background_tasks.add_task(
             process_document,
             task_id=task_id,
@@ -114,13 +113,8 @@ async def upload_document(
 async def upload_documents(
     background_tasks: BackgroundTasks,
     files: List[UploadFile] = File(...),
-    doc_type: Optional[str] = Query(None, description="文档类型")
 ):
-    """
+    """批量上传文档"""
-    批量上传文档
-
-    所有文档会异步处理，处理完成后可通过 task_id 查询状态
-    """
     if not files:
         raise HTTPException(status_code=400, detail="没有上传文件")
 
@@ -131,25 +125,15 @@ async def upload_documents(
         for file in files:
             if not file.filename:
                 continue
-
             content = await file.read()
-            saved_path = file_service.save_uploaded_file(
+            saved_path = file_service.save_uploaded_file(content, file.filename, subfolder="batch")
-                content,
-                file.filename,
-                subfolder="batch"
-            )
             saved_paths.append({
                 "path": saved_path,
                 "filename": file.filename,
                 "ext": file.filename.split('.')[-1].lower()
             })
 
-        # 后台处理所有文档
+        background_tasks.add_task(process_documents_batch, task_id=task_id, files=saved_paths)
-        background_tasks.add_task(
-            process_documents_batch,
-            task_id=task_id,
-            files=saved_paths
-        )
 
         return UploadResponse(
             task_id=task_id,
@@ -173,13 +157,10 @@ async def process_document(
     parse_options: dict
 ):
     """处理单个文档"""
-    from app.core.database import redis_db
-
     try:
-        # 更新状态: 处理中
+        # 状态: 解析中
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="processing",
-            status="processing",
             meta={"progress": 10, "message": "正在解析文档"}
         )
 
@@ -190,11 +171,10 @@ async def process_document(
         if not result.success:
             raise Exception(result.error or "解析失败")
 
-        # 更新状态: 存储数据
+        # 状态: 存储中
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="processing",
-            status="processing",
+            meta={"progress": 30, "message": "正在存储数据"}
-            meta={"progress": 40, "message": "正在存储数据"}
         )
 
         # 存储到 MongoDB
@@ -209,24 +189,53 @@ async def process_document(
             structured_data=result.data.get("structured_data")
         )
 
-        # 如果是 Excel，存储到 MySQL
+        # 如果是 Excel，存储到 MySQL + AI生成描述 + RAG索引
         if doc_type in ["xlsx", "xls"]:
-            await store_excel_to_mysql(file_path, original_filename, result)
-
-        # 更新状态: 建立 RAG 索引
             await redis_db.set_task_status(
-            task_id,
+                task_id, status="processing",
-            status="processing",
+                meta={"progress": 50, "message": "正在存储到MySQL并生成字段描述"}
-            meta={"progress": 70, "message": "正在建立索引"}
             )
 
-        # 建立 RAG 索引
+            # 使用 TableRAG 服务完成建表和RAG索引
+            rag_result = await table_rag_service.build_table_rag_index(
+                file_path=file_path,
+                filename=original_filename,
+                sheet_name=parse_options.get("sheet_name"),
+                header_row=parse_options.get("header_row", 0)
+            )
+
+            if rag_result.get("success"):
+                logger.info(f"RAG索引构建成功: {original_filename}")
+            else:
+                logger.warning(f"RAG索引构建失败: {rag_result.get('error')}")
+
+        else:
+            # 非结构化文档
+            await redis_db.set_task_status(
+                task_id, status="processing",
+                meta={"progress": 60, "message": "正在建立索引"}
+            )
+
+            # 如果文档中有表格数据，提取并存储到 MySQL + RAG
+            structured_data = result.data.get("structured_data", {})
+            tables = structured_data.get("tables", [])
+
+            if tables:
+                # 对每个表格建立 MySQL 表和 RAG 索引
+                for table_info in tables:
+                    await table_rag_service.index_document_table(
+                        doc_id=doc_id,
+                        filename=original_filename,
+                        table_data=table_info,
+                        source_doc_type=doc_type
+                    )
+
+            # 同时对文档内容建立 RAG 索引
             await index_document_to_rag(doc_id, original_filename, result, doc_type)
 
-        # 更新状态: 完成
+        # 完成
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="success",
-            status="success",
             meta={
                 "progress": 100,
                 "message": "处理完成",
@@ -244,20 +253,16 @@ async def process_document(
     except Exception as e:
         logger.error(f"文档处理失败: {str(e)}")
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="failure",
-            status="failure",
             meta={"error": str(e)}
         )
 
 
 async def process_documents_batch(task_id: str, files: List[dict]):
     """批量处理文档"""
-    from app.core.database import redis_db
-
     try:
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="processing",
-            status="processing",
             meta={"progress": 0, "message": "开始批量处理"}
         )
 
@@ -278,6 +283,29 @@ async def process_documents_batch(task_id: str, files: List[dict]):
                         },
                         structured_data=result.data.get("structured_data")
                     )
+
+                    # Excel 处理
+                    if file_info["ext"] in ["xlsx", "xls"]:
+                        await table_rag_service.build_table_rag_index(
+                            file_path=file_info["path"],
+                            filename=file_info["filename"]
+                        )
+                    else:
+                        # 非结构化文档：处理其中的表格 + 内容索引
+                        structured_data = result.data.get("structured_data", {})
+                        tables = structured_data.get("tables", [])
+
+                        if tables:
+                            for table_info in tables:
+                                await table_rag_service.index_document_table(
+                                    doc_id=doc_id,
+                                    filename=file_info["filename"],
+                                    table_data=table_info,
+                                    source_doc_type=file_info["ext"]
+                                )
+
+                        await index_document_to_rag(doc_id, file_info["filename"], result, file_info["ext"])
+
                     results.append({"filename": file_info["filename"], "doc_id": doc_id, "success": True})
                 else:
                     results.append({"filename": file_info["filename"], "success": False, "error": result.error})
@@ -285,56 +313,33 @@ async def process_documents_batch(task_id: str, files: List[dict]):
             except Exception as e:
                 results.append({"filename": file_info["filename"], "success": False, "error": str(e)})
 
-            # 更新进度
             progress = int((i + 1) / len(files) * 100)
             await redis_db.set_task_status(
-                task_id,
+                task_id, status="processing",
-                status="processing",
                 meta={"progress": progress, "message": f"已处理 {i+1}/{len(files)}"}
             )
 
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="success",
-            status="success",
             meta={"progress": 100, "message": "批量处理完成", "results": results}
         )
 
     except Exception as e:
         logger.error(f"批量处理失败: {str(e)}")
         await redis_db.set_task_status(
-            task_id,
+            task_id, status="failure",
-            status="failure",
             meta={"error": str(e)}
         )
 
 
-async def store_excel_to_mysql(file_path: str, filename: str, result: ParseResult):
-    """将 Excel 数据存储到 MySQL"""
-    # TODO: 实现 Excel 数据到 MySQL 的转换和存储
-    # 需要根据表头动态创建表结构
-    pass
-
-
 async def index_document_to_rag(doc_id: str, filename: str, result: ParseResult, doc_type: str):
-    """将文档索引到 RAG"""
+    """将非结构化文档索引到 RAG"""
     try:
-        if doc_type in ["xlsx", "xls"]:
-            # Excel 文件: 索引字段信息
-            columns = result.metadata.get("columns", [])
-            for col in columns:
-                rag_service.index_field(
-                    table_name=filename,
-                    field_name=col,
-                    field_description=f"Excel表格 {filename} 的列 {col}",
-                    sample_values=None
-                )
-        else:
-            # 其他文档: 索引文档内容
         content = result.data.get("content", "")
         if content:
             rag_service.index_document_content(
                 doc_id=doc_id,
-                    content=content[:5000],  # 限制长度
+                content=content[:5000],
                 metadata={
                     "filename": filename,
                     "doc_type": doc_type
@@ -365,7 +370,3 @@ async def parse_uploaded_document(
     except Exception as e:
         logger.error(f"解析文档失败: {str(e)}")
         raise HTTPException(status_code=500, detail=f"解析失败: {str(e)}")
-
-
-# 需要添加 import
-import logging

backend/app/api/endpoints/templates.py

@@ -4,6 +4,7 @@
 提供模板上传、解析和填写功能
 """
 import io
+import logging
 from typing import List, Optional
 
 from fastapi import APIRouter, File, HTTPException, Query, UploadFile
@@ -222,7 +223,3 @@ async def export_filled_template(
     except Exception as e:
         logger.error(f"导出失败: {str(e)}")
         raise HTTPException(status_code=500, detail=f"导出失败: {str(e)}")
-
-
-# ==================== 需要添加的 import ====================
-import logging

backend/app/config.py

@@ -34,7 +34,7 @@ class Settings(BaseSettings):
     UPLOAD_DIR: str = "data/uploads"
 
     # ==================== RAG/向量数据库配置 ====================
-    CHROMADB_PERSIST_DIR: str = "data/chromadb"
+    FAISS_INDEX_DIR: str = "data/faiss"
 
     # 允许 Pydantic 从 .env 文件读取
     model_config = SettingsConfigDict(

backend/app/services/excel_storage_service.py

@@ -246,6 +246,150 @@ class ExcelStorageService:
             logger.error(f"存储 Excel 到 MySQL 失败: {str(e)}")
             return {"success": False, "error": str(e)}
 
+    async def store_structured_data(
+        self,
+        table_name: str,
+        data: Dict[str, Any],
+        source_doc_id: str = None
+    ) -> Dict[str, Any]:
+        """
+        将结构化数据（从非结构化文档提取的表格）存储到 MySQL
+
+        Args:
+            table_name: 表名
+            data: 结构化数据，格式为:
+                {
+                    "columns": ["col1", "col2"],  # 列名
+                    "rows": [["val1", "val2"], ["val3", "val4"]]  # 数据行
+                }
+            source_doc_id: 源文档 ID
+
+        Returns:
+            存储结果
+        """
+        results = {
+            "success": True,
+            "table_name": table_name,
+            "row_count": 0,
+            "columns": []
+        }
+
+        try:
+            columns = data.get("columns", [])
+            rows = data.get("rows", [])
+
+            if not columns or not rows:
+                return {"success": False, "error": "数据为空"}
+
+            # 清理列名
+            sanitized_columns = [self._sanitize_column_name(c) for c in columns]
+
+            # 推断列类型
+            column_types = {}
+            for i, col in enumerate(columns):
+                col_values = [row[i] for row in rows if i < len(row)]
+                # 根据数据推断类型
+                col_type = self._infer_type_from_values(col_values)
+                column_types[col] = col_type
+                results["columns"].append({
+                    "original_name": col,
+                    "sanitized_name": self._sanitize_column_name(col),
+                    "type": col_type
+                })
+
+            # 创建表
+            model_class = self._create_table_model(table_name, columns, column_types)
+
+            # 创建表结构
+            async with self.mysql_db.get_session() as session:
+                model_class.__table__.create(session.bind, checkfirst=True)
+
+            # 插入数据
+            records = []
+            for row in rows:
+                record = {}
+                for i, col in enumerate(columns):
+                    if i >= len(row):
+                        continue
+                    col_name = self._sanitize_column_name(col)
+                    value = row[i]
+                    col_type = column_types.get(col, "TEXT")
+
+                    # 处理空值
+                    if value is None or str(value).strip() == '':
+                        record[col_name] = None
+                    elif col_type == "INTEGER":
+                        try:
+                            record[col_name] = int(value)
+                        except (ValueError, TypeError):
+                            record[col_name] = None
+                    elif col_type == "FLOAT":
+                        try:
+                            record[col_name] = float(value)
+                        except (ValueError, TypeError):
+                            record[col_name] = None
+                    else:
+                        record[col_name] = str(value)
+
+                records.append(record)
+
+            # 批量插入
+            async with self.mysql_db.get_session() as session:
+                for record in records:
+                    session.add(model_class(**record))
+                await session.commit()
+
+            results["row_count"] = len(records)
+            logger.info(f"结构化数据已存储到 MySQL 表 {table_name}，共 {len(records)} 行")
+
+            return results
+
+        except Exception as e:
+            logger.error(f"存储结构化数据到 MySQL 失败: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    def _infer_type_from_values(self, values: List[Any]) -> str:
+        """
+        根据值列表推断列类型
+
+        Args:
+            values: 值列表
+
+        Returns:
+            类型名称
+        """
+        non_null_values = [v for v in values if v is not None and str(v).strip() != '']
+        if not non_null_values:
+            return "TEXT"
+
+        # 检查是否全是整数
+        is_integer = all(self._is_integer(v) for v in non_null_values)
+        if is_integer:
+            return "INTEGER"
+
+        # 检查是否全是浮点数
+        is_float = all(self._is_float(v) for v in non_null_values)
+        if is_float:
+            return "FLOAT"
+
+        return "TEXT"
+
+    def _is_integer(self, value: Any) -> bool:
+        """判断值是否可以转为整数"""
+        try:
+            int(value)
+            return True
+        except (ValueError, TypeError):
+            return False
+
+    def _is_float(self, value: Any) -> bool:
+        """判断值是否可以转为浮点数"""
+        try:
+            float(value)
+            return True
+        except (ValueError, TypeError):
+            return False
+
     async def query_table(
         self,
         table_name: str,

backend/app/services/table_rag_service.py

@@ -0,0 +1,491 @@
+"""
+表结构 RAG 索引服务
+
+AI 自动生成表字段的语义描述，并建立向量索引
+"""
+import logging
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from app.services.llm_service import llm_service
+from app.services.rag_service import rag_service
+from app.services.excel_storage_service import excel_storage_service
+from app.core.database.mysql import mysql_db
+
+logger = logging.getLogger(__name__)
+
+
+class TableRAGService:
+    """
+    表结构 RAG 索引服务
+
+    核心功能：
+    1. AI 根据表头和数据生成字段语义描述
+    2. 将字段描述存入向量数据库 (RAG)
+    3. 支持自然语言查询表字段
+    """
+
+    def __init__(self):
+        self.llm = llm_service
+        self.rag = rag_service
+        self.excel_storage = excel_storage_service
+
+    async def generate_field_description(
+        self,
+        table_name: str,
+        field_name: str,
+        sample_values: List[Any],
+        all_fields: Dict[str, List[Any]] = None
+    ) -> str:
+        """
+        使用 AI 生成字段的语义描述
+
+        Args:
+            table_name: 表名
+            field_name: 字段名
+            sample_values: 字段示例值 (前10个)
+            all_fields: 其他字段的示例值，用于上下文理解
+
+        Returns:
+            字段的语义描述
+        """
+        # 构建 Prompt
+        context = ""
+        if all_fields:
+            context = "\n其他字段示例：\n"
+            for fname, values in all_fields.items():
+                if fname != field_name and values:
+                    context += f"- {fname}: {', '.join([str(v) for v in values[:3]])}\n"
+
+        prompt = f"""你是一个数据语义分析专家。请根据字段名和示例值，推断该字段的语义含义。
+
+表名：{table_name}
+字段名：{field_name}
+示例值：{', '.join([str(v) for v in sample_values[:10] if v is not None])}
+{context}
+
+请生成一段简洁的字段语义描述（不超过50字），说明：
+1. 该字段代表什么含义
+2. 数据格式或单位（如果有）
+3. 可能的业务用途
+
+只输出描述文字，不要其他内容。"""
+
+        try:
+            messages = [
+                {"role": "system", "content": "你是一个专业的数据分析师。"},
+                {"role": "user", "content": prompt}
+            ]
+
+            response = await self.llm.chat(
+                messages=messages,
+                temperature=0.3,
+                max_tokens=200
+            )
+
+            description = self.llm.extract_message_content(response)
+            return description.strip()
+
+        except Exception as e:
+            logger.error(f"生成字段描述失败: {str(e)}")
+            return f"{field_name}: 数据字段"
+
+    async def build_table_rag_index(
+        self,
+        file_path: str,
+        filename: str,
+        sheet_name: Optional[str] = None,
+        header_row: int = 0,
+        sample_size: int = 10
+    ) -> Dict[str, Any]:
+        """
+        为 Excel 表构建完整的 RAG 索引
+
+        流程：
+        1. 读取 Excel 获取字段信息
+        2. AI 生成每个字段的语义描述
+        3. 将字段描述存入向量数据库
+
+        Args:
+            file_path: Excel 文件路径
+            filename: 原始文件名
+            sheet_name: 工作表名称
+            header_row: 表头行号
+            sample_size: 每个字段采样的数据条数
+
+        Returns:
+            索引构建结果
+        """
+        results = {
+            "success": True,
+            "table_name": "",
+            "field_count": 0,
+            "indexed_fields": [],
+            "errors": []
+        }
+
+        try:
+            # 1. 读取 Excel
+            if sheet_name:
+                df = pd.read_excel(file_path, sheet_name=sheet_name, header=header_row)
+            else:
+                df = pd.read_excel(file_path, header=header_row)
+
+            if df.empty:
+                return {"success": False, "error": "Excel 文件为空"}
+
+            # 清理列名
+            df.columns = [str(c) for c in df.columns]
+            table_name = excel_storage._sanitize_table_name(filename)
+            results["table_name"] = table_name
+            results["field_count"] = len(df.columns)
+
+            # 2. 初始化 RAG (如果需要)
+            if not self.rag._initialized:
+                self.rag._init_vector_store()
+
+            # 3. 为每个字段生成描述并索引
+            all_fields_data = {}
+            for col in df.columns:
+                # 采样示例值
+                sample_values = df[col].dropna().head(sample_size).tolist()
+                all_fields_data[col] = sample_values
+
+            # 批量生成描述（避免过多 API 调用）
+            indexed_count = 0
+            for col in df.columns:
+                try:
+                    sample_values = all_fields_data[col]
+
+                    # 生成描述
+                    description = await self.generate_field_description(
+                        table_name=table_name,
+                        field_name=col,
+                        sample_values=sample_values,
+                        all_fields=all_fields_data
+                    )
+
+                    # 存入 RAG
+                    self.rag.index_field(
+                        table_name=table_name,
+                        field_name=col,
+                        field_description=description,
+                        sample_values=[str(v) for v in sample_values[:5]]
+                    )
+
+                    indexed_count += 1
+                    results["indexed_fields"].append({
+                        "field": col,
+                        "description": description
+                    })
+
+                    logger.info(f"字段已索引: {table_name}.{col}")
+
+                except Exception as e:
+                    error_msg = f"字段 {col} 索引失败: {str(e)}"
+                    logger.error(error_msg)
+                    results["errors"].append(error_msg)
+
+            # 4. 存储到 MySQL
+            store_result = await self.excel_storage.store_excel(
+                file_path=file_path,
+                filename=filename,
+                sheet_name=sheet_name,
+                header_row=header_row
+            )
+
+            if store_result.get("success"):
+                results["mysql_table"] = store_result.get("table_name")
+                results["row_count"] = store_result.get("row_count")
+            else:
+                results["mysql_warning"] = "MySQL 存储失败: " + str(store_result.get("error"))
+
+            results["indexed_count"] = indexed_count
+            logger.info(f"表 {table_name} RAG 索引构建完成，共 {indexed_count} 个字段")
+
+            return results
+
+        except Exception as e:
+            logger.error(f"构建 RAG 索引失败: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def index_document_table(
+        self,
+        doc_id: str,
+        filename: str,
+        table_data: Dict[str, Any],
+        source_doc_type: str
+    ) -> Dict[str, Any]:
+        """
+        为非结构化文档中提取的表格建立 MySQL 存储和 RAG 索引
+
+        Args:
+            doc_id: 源文档 ID
+            filename: 源文件名
+            table_data: 表格数据，支持两种格式:
+                1. docx/txt格式: {"rows": [["col1", "col2"], ["val1", "val2"]], ...}
+                2. md格式: {"headers": [...], "rows": [...], ...}
+            source_doc_type: 源文档类型 (docx/md/txt)
+
+        Returns:
+            索引构建结果
+        """
+        results = {
+            "success": True,
+            "table_name": "",
+            "field_count": 0,
+            "indexed_fields": [],
+            "errors": []
+        }
+
+        try:
+            # 兼容两种格式
+            if "headers" in table_data:
+                # md 格式：headers 和 rows 分开
+                columns = table_data.get("headers", [])
+                data_rows = table_data.get("rows", [])
+            else:
+                # docx/txt 格式：第一行作为表头
+                rows = table_data.get("rows", [])
+                if not rows or len(rows) < 2:
+                    return {"success": False, "error": "表格数据不足"}
+                columns = rows[0]
+                data_rows = rows[1:]
+
+            # 生成表名：源文件 + 表格索引
+            base_name = self.excel_storage._sanitize_table_name(filename)
+            table_name = f"{base_name}_table{table_data.get('table_index', 0)}"
+
+            results["table_name"] = table_name
+            results["field_count"] = len(columns)
+
+            # 1. 初始化 RAG
+            if not self.rag._initialized:
+                self.rag._init_vector_store()
+
+            # 2. 准备结构化数据
+            structured_data = {
+                "columns": columns,
+                "rows": data_rows
+            }
+
+            # 3. 存储到 MySQL
+            store_result = await self.excel_storage.store_structured_data(
+                table_name=table_name,
+                data=structured_data,
+                source_doc_id=doc_id
+            )
+
+            if store_result.get("success"):
+                results["mysql_table"] = store_result.get("table_name")
+                results["row_count"] = store_result.get("row_count")
+            else:
+                results["mysql_warning"] = "MySQL 存储失败: " + str(store_result.get("error"))
+
+            # 4. 为每个字段生成描述并索引
+            all_fields_data = {}
+            for i, col in enumerate(columns):
+                col_values = [row[i] for row in data_rows if i < len(row)]
+                all_fields_data[col] = col_values
+
+            indexed_count = 0
+            for col in columns:
+                try:
+                    col_values = all_fields_data.get(col, [])
+
+                    # 生成描述
+                    description = await self.generate_field_description(
+                        table_name=table_name,
+                        field_name=col,
+                        sample_values=col_values[:10],
+                        all_fields=all_fields_data
+                    )
+
+                    # 存入 RAG
+                    self.rag.index_field(
+                        table_name=table_name,
+                        field_name=col,
+                        field_description=description,
+                        sample_values=[str(v) for v in col_values[:5]]
+                    )
+
+                    indexed_count += 1
+                    results["indexed_fields"].append({
+                        "field": col,
+                        "description": description
+                    })
+
+                    logger.info(f"文档表格字段已索引: {table_name}.{col}")
+
+                except Exception as e:
+                    error_msg = f"字段 {col} 索引失败: {str(e)}"
+                    logger.error(error_msg)
+                    results["errors"].append(error_msg)
+
+            results["indexed_count"] = indexed_count
+            logger.info(f"文档表格 {table_name} RAG 索引构建完成，共 {indexed_count} 个字段")
+
+            return results
+
+        except Exception as e:
+            logger.error(f"构建文档表格 RAG 索引失败: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def query_table_by_natural_language(
+        self,
+        user_query: str,
+        top_k: int = 5
+    ) -> Dict[str, Any]:
+        """
+        根据自然语言查询相关表字段
+
+        Args:
+            user_query: 用户查询
+            top_k: 返回数量
+
+        Returns:
+            匹配的字段信息
+        """
+        try:
+            # 1. RAG 检索
+            rag_results = self.rag.retrieve(user_query, top_k=top_k)
+
+            # 2. 解析检索结果
+            matched_fields = []
+            for result in rag_results:
+                metadata = result.get("metadata", {})
+                matched_fields.append({
+                    "table_name": metadata.get("table_name", ""),
+                    "field_name": metadata.get("field_name", ""),
+                    "description": result.get("content", ""),
+                    "score": result.get("score", 0),
+                    "sample_values": []  # 可以后续补充
+                })
+
+            return {
+                "success": True,
+                "query": user_query,
+                "matched_fields": matched_fields,
+                "count": len(matched_fields)
+            }
+
+        except Exception as e:
+            logger.error(f"查询失败: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+    async def get_table_fields_with_description(
+        self,
+        table_name: str
+    ) -> List[Dict[str, Any]]:
+        """
+        获取表的字段及其描述
+
+        Args:
+            table_name: 表名
+
+        Returns:
+            字段列表
+        """
+        try:
+            # 从 RAG 检索该表的所有字段
+            results = self.rag.retrieve_by_table(table_name, top_k=50)
+
+            fields = []
+            for result in results:
+                metadata = result.get("metadata", {})
+                fields.append({
+                    "table_name": metadata.get("table_name", ""),
+                    "field_name": metadata.get("field_name", ""),
+                    "description": result.get("content", ""),
+                    "score": result.get("score", 0)
+                })
+
+            return fields
+
+        except Exception as e:
+            logger.error(f"获取字段失败: {str(e)}")
+            return []
+
+    async def rebuild_all_table_indexes(self) -> Dict[str, Any]:
+        """
+        重建所有表的 RAG 索引
+
+        从 MySQL 读取所有表结构，重新生成描述并索引
+        """
+        try:
+            # 清空现有索引
+            self.rag.clear()
+
+            # 获取所有表
+            tables = await self.excel_storage.list_tables()
+
+            results = {
+                "success": True,
+                "tables_processed": 0,
+                "total_fields": 0,
+                "errors": []
+            }
+
+            for table_name in tables:
+                try:
+                    # 获取表结构
+                    schema = await self.excel_storage.get_table_schema(table_name)
+
+                    if not schema:
+                        continue
+
+                    # 初始化 RAG
+                    if not self.rag._initialized:
+                        self.rag._init_vector_store()
+
+                    # 为每个字段生成描述并索引
+                    for col_info in schema:
+                        field_name = col_info.get("COLUMN_NAME", "")
+                        if field_name in ["id", "created_at", "updated_at"]:
+                            continue
+
+                        # 采样数据
+                        samples = await self.excel_storage.query_table(
+                            table_name,
+                            columns=[field_name],
+                            limit=10
+                        )
+                        sample_values = [r.get(field_name) for r in samples if r.get(field_name)]
+
+                        # 生成描述
+                        description = await self.generate_field_description(
+                            table_name=table_name,
+                            field_name=field_name,
+                            sample_values=sample_values
+                        )
+
+                        # 索引
+                        self.rag.index_field(
+                            table_name=table_name,
+                            field_name=field_name,
+                            field_description=description,
+                            sample_values=[str(v) for v in sample_values[:5]]
+                        )
+
+                        results["total_fields"] += 1
+
+                    results["tables_processed"] += 1
+                    logger.info(f"表 {table_name} 索引重建完成")
+
+                except Exception as e:
+                    error_msg = f"表 {table_name} 索引失败: {str(e)}"
+                    logger.error(error_msg)
+                    results["errors"].append(error_msg)
+
+            logger.info(f"全部 {results['tables_processed']} 个表索引重建完成")
+            return results
+
+        except Exception as e:
+            logger.error(f"重建索引失败: {str(e)}")
+            return {"success": False, "error": str(e)}
+
+
+# ==================== 全局单例 ====================
+
+table_rag_service = TableRAGService()