添加 TXT 和 Word 文件 AI 分析功能支持图表生成

- 新增 txt_ai_service 服务，支持 TXT 文件的结构化数据提取和图表生成
- 为 Word 分析添加图表生成功能，扩展 word_ai_service.generate_charts 方法
- 在前端添加 TXT 和 Word AI 分析界面，支持 structured 和 charts 两种分析模式
- 更新后端 API 接口，添加 analysis_type 参数控制分析类型
- 优化分析结果显示逻辑，区分结构化数据和图表结果展示

backend/app/api/endpoints/ai_analyze.py

@@ -12,6 +12,7 @@ from app.services.excel_ai_service import excel_ai_service
 from app.services.markdown_ai_service import markdown_ai_service
 from app.services.template_fill_service import template_fill_service
 from app.services.word_ai_service import word_ai_service
+from app.services.txt_ai_service import txt_ai_service
 
 logger = logging.getLogger(__name__)
 
@@ -347,17 +348,20 @@ async def get_markdown_outline(
 @router.post("/analyze/txt")
 async def analyze_txt(
     file: UploadFile = File(...),
+    analysis_type: str = Query("structured", description="分析类型: structured, charts")
 ):
     """
-    上传并使用 AI 分析 TXT 文本文件，提取结构化数据
+    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
 
     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
+    当 analysis_type=charts 时，可生成可视化图表
 
     Args:
         file: 上传的 TXT 文件
+        analysis_type: 分析类型 - "structured"（默认，提取结构化数据）或 "charts"（生成图表）
 
     Returns:
-        dict: 分析结果，包含结构化表格数据
+        dict: 分析结果，包含结构化表格数据或图表数据
     """
     if not file.filename:
         raise HTTPException(status_code=400, detail="文件名为空")
@@ -372,6 +376,7 @@ async def analyze_txt(
     try:
         # 读取文件内容
         content = await file.read()
+        text_content = content.decode('utf-8', errors='replace')
 
         # 保存到临时文件
         with tempfile.NamedTemporaryFile(mode='wb', suffix='.txt', delete=False) as tmp:
@@ -379,20 +384,22 @@ async def analyze_txt(
             tmp_path = tmp.name
 
         try:
-            logger.info(f"开始 AI 分析 TXT 文件: {file.filename}")
+            logger.info(f"开始 AI 分析 TXT 文件: {file.filename}, analysis_type={analysis_type}")
 
-            # 使用 template_fill_service 的 AI 分析方法
+            # 使用 txt_ai_service 的 AI 分析方法
-            result = await template_fill_service.analyze_txt_with_ai(
+            result = await txt_ai_service.analyze_txt_with_ai(
-                content=content.decode('utf-8', errors='replace'),
+                content=text_content,
-                filename=file.filename
+                filename=file.filename,
+                analysis_type=analysis_type
             )
 
             if result:
                 logger.info(f"TXT AI 分析成功: {file.filename}")
                 return {
-                    "success": True,
+                    "success": result.get("success", True),
                     "filename": file.filename,
-                    "structured_data": result
+                    "analysis_type": analysis_type,
+                    "result": result
                 }
             else:
                 logger.warning(f"TXT AI 分析返回空结果: {file.filename}")
@@ -400,7 +407,7 @@ async def analyze_txt(
                     "success": False,
                     "filename": file.filename,
                     "error": "AI 分析未能提取到结构化数据",
-                    "structured_data": None
+                    "result": None
                 }
 
         finally:
@@ -420,19 +427,22 @@ async def analyze_txt(
 @router.post("/analyze/word")
 async def analyze_word(
     file: UploadFile = File(...),
-    user_hint: str = Query("", description="用户提示词，如'请提取表格数据'")
+    user_hint: str = Query("", 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
+    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
         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
+        dict: 包含结构化数据的解析结果或图表数据
     """
     if not file.filename:
         raise HTTPException(status_code=400, detail="文件名为空")
@@ -453,16 +463,25 @@ async def analyze_word(
             tmp_path = tmp.name
 
         try:
-            # 使用 AI 解析 Word 文档
+            # 根据 analysis_type 选择处理方式
-            result = await word_ai_service.parse_word_with_ai(
+            if analysis_type == "charts":
-                file_path=tmp_path,
+                # 生成图表
-                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"
+                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:

backend/app/services/txt_ai_service.py

@@ -0,0 +1,352 @@
+"""
+TXT 文档 AI 分析服务
+
+使用 LLM 对 TXT 文本文件进行深度分析，提取结构化数据并生成可视化图表
+"""
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+from app.services.llm_service import llm_service
+from app.services.visualization_service import visualization_service
+from app.core.document_parser.txt_parser import TxtParser
+
+logger = logging.getLogger(__name__)
+
+
+class TxtAIService:
+    """TXT 文档 AI 分析服务"""
+
+    def __init__(self):
+        self.parser = TxtParser()
+
+    async def analyze_txt_with_ai(
+        self,
+        content: str,
+        filename: str = "",
+        user_hint: str = "",
+        analysis_type: str = "structured"
+    ) -> Dict[str, Any]:
+        """
+        使用 AI 解析 TXT 文本文件
+
+        Args:
+            content: 文本内容
+            filename: 文件名（可选）
+            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: 包含结构化数据的分析结果
+        """
+        try:
+            if not content or not content.strip():
+                return {
+                    "success": False,
+                    "error": "文档内容为空"
+                }
+
+            # 根据分析类型选择处理方式
+            if analysis_type == "charts":
+                return await self.generate_charts(content, filename, user_hint)
+
+            # 默认：提取结构化数据
+            return await self._extract_structured_data(content, filename, user_hint)
+
+        except Exception as e:
+            logger.error(f"TXT AI 分析失败: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+    async def _extract_structured_data(
+        self,
+        content: str,
+        filename: str = "",
+        user_hint: str = ""
+    ) -> Dict[str, Any]:
+        """
+        从文本中提取结构化数据
+
+        Args:
+            content: 文本内容
+            filename: 文件名
+            user_hint: 用户提示词
+
+        Returns:
+            结构化数据
+        """
+        try:
+            # 截断内容避免超出 token 限制
+            max_content_len = 8000
+            text_preview = content[:max_content_len] if len(content) > max_content_len else content
+
+            prompt = f"""你是一个专业的数据提取专家。请从以下文本中提取结构化数据。
+
+【用户需求】
+{user_hint if user_hint else "请提取文档中的所有结构化数据，包括表格数据、键值对、列表项等。"}
+
+【文档内容】（{"前" + str(max_content_len) + "字符，仅显示部分" if len(content) > max_content_len else "全文"}）
+{text_preview}
+
+请按照以下 JSON 格式输出：
+{{
+    "type": "structured_text",
+    "tables": [{{"headers": [...], "rows": [...]}}],
+    "key_values": {{"键1": "值1", "键2": "值2", ...}},
+    "list_items": ["项1", "项2", ...],
+    "summary": "文档内容摘要"
+}}
+
+重点：
+- 如果文档包含表格数据（制表符、空格对齐等），提取到 tables 中
+- 如果文档包含键值对（如 名称: 张三），提取到 key_values 中
+- 如果文档包含列表项，提取到 list_items 中
+- 如果无法提取到结构化数据，至少提供一个详细的摘要
+"""
+
+            messages = [
+                {"role": "system", "content": "你是一个专业的数据提取助手。请严格按JSON格式输出。"},
+                {"role": "user", "content": prompt}
+            ]
+
+            response = await self.llm.chat(
+                messages=messages,
+                temperature=0.1,
+                max_tokens=50000
+            )
+
+            content_text = self.llm.extract_message_content(response)
+            result = self._parse_json_response(content_text)
+
+            if result:
+                logger.info(f"TXT 结构化数据提取成功: type={result.get('type')}")
+                return {
+                    "success": True,
+                    "type": result.get("type", "structured_text"),
+                    "tables": result.get("tables", []),
+                    "key_values": result.get("key_values", {}),
+                    "list_items": result.get("list_items", []),
+                    "summary": result.get("summary", "")
+                }
+            else:
+                return {
+                    "success": True,
+                    "type": "text",
+                    "summary": text_preview[:500],
+                    "raw_text_preview": text_preview[:500]
+                }
+
+        except Exception as e:
+            logger.error(f"TXT 结构化数据提取失败: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+    async def generate_charts(
+        self,
+        content: str,
+        filename: str = "",
+        user_hint: str = ""
+    ) -> Dict[str, Any]:
+        """
+        从文本中提取数据并生成可视化图表
+
+        Args:
+            content: 文本内容
+            filename: 文件名
+            user_hint: 用户提示词
+
+        Returns:
+            包含图表数据和统计信息的结果
+        """
+        try:
+            # 截断内容避免超出 token 限制
+            max_content_len = 8000
+            text_preview = content[:max_content_len] if len(content) > max_content_len else content
+
+            # 使用 LLM 提取可用于图表的数据
+            prompt = f"""你是一个专业的数据可视化助手。请从以下文本中提取可用于可视化的数据。
+
+文档标题：{filename}
+
+文档内容：
+{text_preview}
+
+请完成以下任务：
+1. 识别文本中的表格数据（制表符分隔、空格对齐的表格等）
+2. 识别文本中的关键统计数据（百分比、数量、趋势等）
+3. 识别可用于比较的分类数据
+
+请用 JSON 格式返回以下结构的数据（如果没有表格数据，返回空结构）：
+{{
+  "tables": [
+    {{
+      "description": "表格的描述",
+      "columns": ["列名1", "列名2", ...],
+      "rows": [
+        ["值1", "值2", ...],
+        ["值1", "值2", ...]
+      ]
+    }}
+  ],
+  "key_statistics": [
+    {{
+      "name": "指标名称",
+      "value": "数值",
+      "trend": "增长/下降/持平",
+      "description": "指标说明"
+    }}
+  ],
+  "chart_suggestions": [
+    {{
+      "chart_type": "bar/line/pie",
+      "title": "图表标题",
+      "data_source": "数据来源说明"
+    }}
+  ]
+}}
+
+如果没有表格数据，返回空结构：{{"tables": [], "key_statistics": [], "chart_suggestions": []}}
+请确保返回的是合法的 JSON 格式。"""
+
+            messages = [
+                {"role": "system", "content": "你是一个专业的数据可视化助手，擅长从文本中提取数据并生成图表。"},
+                {"role": "user", "content": prompt}
+            ]
+
+            response = await self.llm.chat(
+                messages=messages,
+                temperature=0.1,
+                max_tokens=50000
+            )
+
+            content_text = self.llm.extract_message_content(response)
+            chart_data = self._parse_json_response(content_text)
+
+            if not chart_data:
+                return {
+                    "success": False,
+                    "error": "无法从文本中提取有效的数据结构"
+                }
+
+            # 检查是否有表格数据
+            tables = chart_data.get("tables", [])
+            key_statistics = chart_data.get("key_statistics", [])
+
+            if not tables:
+                return {
+                    "success": False,
+                    "error": "文档中没有可用于图表的表格数据",
+                    "key_statistics": key_statistics,
+                    "chart_suggestions": chart_data.get("chart_suggestions", [])
+                }
+
+            # 使用第一个表格生成图表
+            first_table = tables[0]
+            columns = first_table.get("columns", [])
+            rows = first_table.get("rows", [])
+
+            if not columns or not rows:
+                return {
+                    "success": False,
+                    "error": "表格数据为空"
+                }
+
+            # 转换为 visualization_service 需要的格式
+            viz_data = {
+                "columns": columns,
+                "rows": rows
+            }
+
+            # 生成可视化图表
+            logger.info(f"开始生成图表，列数: {len(columns)}, 行数: {len(rows)}")
+            vis_result = visualization_service.analyze_and_visualize(viz_data)
+
+            if vis_result.get("success"):
+                return {
+                    "success": True,
+                    "charts": vis_result.get("charts", {}),
+                    "statistics": vis_result.get("statistics", {}),
+                    "distributions": vis_result.get("distributions", {}),
+                    "row_count": vis_result.get("row_count", 0),
+                    "column_count": vis_result.get("column_count", 0),
+                    "key_statistics": key_statistics,
+                    "chart_suggestions": chart_data.get("chart_suggestions", []),
+                    "table_description": first_table.get("description", "")
+                }
+            else:
+                return {
+                    "success": False,
+                    "error": vis_result.get("error", "可视化生成失败"),
+                    "key_statistics": key_statistics
+                }
+
+        except Exception as e:
+            logger.error(f"TXT 图表生成失败: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e)
+            }
+
+    def _parse_json_response(self, content: str) -> Optional[Dict]:
+        """解析 JSON 响应，处理各种格式问题"""
+        if not content:
+            return None
+
+        import json
+
+        # 清理 markdown 标记
+        cleaned = content.strip()
+        cleaned = re.sub(r'^```json\s*', '', cleaned, flags=re.MULTILINE)
+        cleaned = re.sub(r'^```\s*', '', cleaned, flags=re.MULTILINE)
+        cleaned = cleaned.strip()
+
+        # 找到 JSON 开始位置
+        json_start = -1
+        for i, c in enumerate(cleaned):
+            if c == '{':
+                json_start = i
+                break
+
+        if json_start == -1:
+            logger.warning("无法找到 JSON 开始位置")
+            return None
+
+        json_text = cleaned[json_start:]
+
+        # 尝试直接解析
+        try:
+            return json.loads(json_text)
+        except json.JSONDecodeError:
+            pass
+
+        # 尝试修复并解析
+        try:
+            # 找到闭合括号
+            depth = 0
+            end_pos = -1
+            for i, c in enumerate(json_text):
+                if c == '{':
+                    depth += 1
+                elif c == '}':
+                    depth -= 1
+                    if depth == 0:
+                        end_pos = i + 1
+                        break
+
+            if end_pos > 0:
+                fixed = json_text[:end_pos]
+                # 移除末尾逗号
+                fixed = re.sub(r',\s*([}]])', r'\1', fixed)
+                return json.loads(fixed)
+        except Exception as e:
+            logger.warning(f"JSON 修复失败: {e}")
+
+        return None
+
+
+# 全局单例
+txt_ai_service = TxtAIService()

backend/app/services/word_ai_service.py

@@ -8,6 +8,7 @@ from typing import Dict, Any, List, Optional
 import json
 
 from app.services.llm_service import llm_service
+from app.services.visualization_service import visualization_service
 from app.core.document_parser.docx_parser import DocxParser
 
 logger = logging.getLogger(__name__)
@@ -634,6 +635,127 @@ class WordAIService:
 
         return values
 
+    async def generate_charts(
+        self,
+        file_path: str,
+        user_hint: str = ""
+    ) -> Dict[str, Any]:
+        """
+        使用 AI 解析 Word 文档并生成可视化图表
+
+        从 Word 文档中提取表格数据，然后生成统计图表
+
+        Args:
+            file_path: Word 文件路径
+            user_hint: 用户提示词，指定要提取的内容类型
+
+        Returns:
+            Dict: 包含图表数据和统计信息的结果
+        """
+        try:
+            # 1. 先用基础解析器提取原始内容
+            parse_result = self.parser.parse(file_path)
+
+            if not parse_result.success:
+                return {
+                    "success": False,
+                    "error": parse_result.error,
+                    "structured_data": None
+                }
+
+            # 2. 获取原始数据
+            raw_data = parse_result.data
+            paragraphs = raw_data.get("paragraphs", [])
+            tables = raw_data.get("tables", [])
+            content = raw_data.get("content", "")
+
+            logger.info(f"Word 基础解析完成: {len(paragraphs)} 个段落, {len(tables)} 个表格")
+
+            # 3. 优先处理表格数据
+            if tables and len(tables) > 0:
+                structured_data = await self._extract_tables_with_ai(
+                    tables, paragraphs, 0, user_hint, parse_result.metadata
+                )
+            elif paragraphs and len(paragraphs) > 0:
+                structured_data = await self._extract_from_text_with_ai(
+                    paragraphs, content, 0, [], user_hint
+                )
+            else:
+                return {
+                    "success": False,
+                    "error": "文档内容为空",
+                    "structured_data": None
+                }
+
+            # 4. 检查是否有表格数据用于可视化
+            if not structured_data.get("success"):
+                return {
+                    "success": False,
+                    "error": structured_data.get("error", "解析失败"),
+                    "structured_data": None
+                }
+
+            parse_type = structured_data.get("type", "")
+
+            # 5. 提取可用于图表的数据
+            chart_data = None
+
+            if parse_type == "table_data":
+                headers = structured_data.get("headers", [])
+                rows = structured_data.get("rows", [])
+                if headers and rows:
+                    chart_data = {
+                        "columns": headers,
+                        "rows": rows
+                    }
+            elif parse_type == "structured_text":
+                tables = structured_data.get("tables", [])
+                if tables and len(tables) > 0:
+                    first_table = tables[0]
+                    headers = first_table.get("headers", [])
+                    rows = first_table.get("rows", [])
+                    if headers and rows:
+                        chart_data = {
+                            "columns": headers,
+                            "rows": rows
+                        }
+
+            # 6. 生成可视化图表
+            if chart_data:
+                logger.info(f"开始生成图表，列数: {len(chart_data['columns'])}, 行数: {len(chart_data['rows'])}")
+                vis_result = visualization_service.analyze_and_visualize(chart_data)
+
+                if vis_result.get("success"):
+                    return {
+                        "success": True,
+                        "charts": vis_result.get("charts", {}),
+                        "statistics": vis_result.get("statistics", {}),
+                        "distributions": vis_result.get("distributions", {}),
+                        "structured_data": structured_data,
+                        "row_count": vis_result.get("row_count", 0),
+                        "column_count": vis_result.get("column_count", 0)
+                    }
+                else:
+                    return {
+                        "success": False,
+                        "error": vis_result.get("error", "可视化生成失败"),
+                        "structured_data": structured_data
+                    }
+            else:
+                return {
+                    "success": False,
+                    "error": "文档中没有可用于图表的表格数据",
+                    "structured_data": structured_data
+                }
+
+        except Exception as e:
+            logger.error(f"Word 文档图表生成失败: {str(e)}")
+            return {
+                "success": False,
+                "error": str(e),
+                "structured_data": None
+            }
+
 
 # 全局单例
 word_ai_service = WordAIService()

frontend/src/db/backend-api.ts

@@ -250,6 +250,98 @@ export interface AIExcelAnalyzeResult {
   error?: string;
 }
 
+// ==================== Word/TXT AI 分析类型 ====================
+
+export type WordAnalysisType = 'structured' | 'charts';
+export type TxtAnalysisType = 'structured' | 'charts';
+
+export interface WordAIStructuredResult {
+  success: boolean;
+  result?: {
+    success?: boolean;
+    type?: string;
+    headers?: string[];
+    rows?: string[][];
+    key_values?: Record<string, string>;
+    list_items?: string[];
+    summary?: string;
+    error?: string;
+  };
+  error?: string;
+}
+
+export interface WordAIChartsResult {
+  success: boolean;
+  result?: {
+    success?: boolean;
+    charts?: {
+      histograms?: Array<any>;
+      bar_charts?: Array<any>;
+      box_plots?: Array<any>;
+      correlation?: any;
+    };
+    statistics?: {
+      numeric?: Record<string, any>;
+      categorical?: Record<string, any>;
+    };
+    distributions?: Record<string, any>;
+    row_count?: number;
+    column_count?: number;
+    error?: string;
+  };
+  error?: string;
+}
+
+export interface TxtAIStructuredResult {
+  success: boolean;
+  result?: {
+    success?: boolean;
+    type?: string;
+    tables?: Array<{
+      headers?: string[];
+      rows?: string[][];
+    }>;
+    key_values?: Record<string, string>;
+    list_items?: string[];
+    summary?: string;
+    error?: string;
+  };
+  error?: string;
+}
+
+export interface TxtAIChartsResult {
+  success: boolean;
+  result?: {
+    success?: boolean;
+    charts?: {
+      histograms?: Array<any>;
+      bar_charts?: Array<any>;
+      box_plots?: Array<any>;
+      correlation?: any;
+    };
+    statistics?: {
+      numeric?: Record<string, any>;
+      categorical?: Record<string, any>;
+    };
+    distributions?: Record<string, any>;
+    row_count?: number;
+    column_count?: number;
+    key_statistics?: Array<{
+      name?: string;
+      value?: string;
+      trend?: string;
+      description?: string;
+    }>;
+    chart_suggestions?: Array<{
+      chart_type?: string;
+      title?: string;
+      data_source?: string;
+    }>;
+    error?: string;
+  };
+  error?: string;
+}
+
 // ==================== API 封装 ====================
 
 export const backendApi = {
@@ -1337,28 +1429,25 @@ export const aiApi = {
   },
 
   /**
-   * 上传并使用 AI 分析 TXT 文本文件，提取结构化数据
+   * 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
    */
   async analyzeTxt(
-    file: File
+    file: File,
+    analysisType: TxtAnalysisType = 'structured'
   ): Promise<{
     success: boolean;
     filename?: string;
-    structured_data?: {
+    analysis_type?: string;
-      table?: {
+    result?: any;
-        columns?: string[];
-        rows?: string[][];
-      };
-      summary?: string;
-      key_value_pairs?: Array<{ key: string; value: string }>;
-      numeric_data?: Array<{ name: string; value: number; unit?: string }>;
-    };
     error?: string;
   }> {
     const formData = new FormData();
     formData.append('file', file);
 
-    const url = `${BACKEND_BASE_URL}/ai/analyze/txt`;
+    const params = new URLSearchParams();
+    params.append('analysis_type', analysisType);
+
+    const url = `${BACKEND_BASE_URL}/ai/analyze/txt?${params.toString()}`;
 
     try {
       const response = await fetch(url, {
@@ -1480,19 +1569,17 @@ export const aiApi = {
   // ==================== Word AI 解析 ====================
 
   /**
-   * 使用 AI 解析 Word 文档，提取结构化数据
+   * 使用 AI 解析 Word 文档，提取结构化数据或生成图表
    */
   async analyzeWordWithAI(
     file: File,
-    userHint: string = ''
+    userHint: string = '',
+    analysisType: WordAnalysisType = 'structured'
   ): Promise<{
     success: boolean;
-    type?: string;
+    filename?: string;
-    headers?: string[];
+    analysis_type?: string;
-    rows?: string[][];
+    result?: any;
-    key_values?: Record<string, string>;
-    list_items?: string[];
-    summary?: string;
     error?: string;
   }> {
     const formData = new FormData();
@@ -1501,7 +1588,10 @@ export const aiApi = {
       formData.append('user_hint', userHint);
     }
 
-    const url = `${BACKEND_BASE_URL}/ai/analyze/word`;
+    const params = new URLSearchParams();
+    params.append('analysis_type', analysisType);
+
+    const url = `${BACKEND_BASE_URL}/ai/analyze/word?${params.toString()}`;
 
     try {
       const response = await fetch(url, {

frontend/src/pages/Documents.tsx

@@ -107,6 +107,15 @@ const Documents: React.FC = () => {
   const [mdStreaming, setMdStreaming] = useState(false);
   const [mdStreamingContent, setMdStreamingContent] = useState('');
 
+  // Word AI 分析相关状态
+  const [wordAnalysis, setWordAnalysis] = useState<any>(null);
+  const [wordAnalysisType, setWordAnalysisType] = useState<'structured' | 'charts'>('structured');
+  const [wordUserHint, setWordUserHint] = useState('');
+
+  // TXT AI 分析相关状态
+  const [txtAnalysis, setTxtAnalysis] = useState<any>(null);
+  const [txtAnalysisType, setTxtAnalysisType] = useState<'structured' | 'charts'>('structured');
+
   // RAG 向量检索相关状态
   const [ragStatus, setRagStatus] = useState<{ vector_count: number; collections: string[] } | null>(null);
   const [ragSearchQuery, setRagSearchQuery] = useState('');
@@ -701,6 +710,62 @@ const Documents: React.FC = () => {
     }
   };
 
+  // Word AI 分析处理
+  const handleWordAnalyze = async () => {
+    if (!uploadedFile || !isWordFile(uploadedFile.name)) {
+      toast.error('请先上传 Word 文件');
+      return;
+    }
+
+    setAnalyzing(true);
+    setWordAnalysis(null);
+
+    try {
+      const result = await aiApi.analyzeWordWithAI(
+        uploadedFile,
+        wordUserHint,
+        wordAnalysisType
+      );
+
+      if (result.success) {
+        toast.success('Word AI 分析完成');
+        setWordAnalysis(result);
+      } else {
+        toast.error(result.error || 'AI 分析失败');
+      }
+    } catch (error: any) {
+      toast.error(error.message || 'AI 分析失败');
+    } finally {
+      setAnalyzing(false);
+    }
+  };
+
+  // TXT AI 分析处理
+  const handleTxtAnalyze = async () => {
+    if (!uploadedFile || !isTxtFile(uploadedFile.name)) {
+      toast.error('请先上传 TXT 文件');
+      return;
+    }
+
+    setAnalyzing(true);
+    setTxtAnalysis(null);
+
+    try {
+      const result = await aiApi.analyzeTxt(uploadedFile, txtAnalysisType);
+
+      if (result.success) {
+        toast.success('TXT AI 分析完成');
+        setTxtAnalysis(result);
+      } else {
+        toast.error(result.error || 'AI 分析失败');
+      }
+    } catch (error: any) {
+      toast.error(error.message || 'AI 分析失败');
+    } finally {
+      setAnalyzing(false);
+    }
+  };
+
   const getMdAnalysisIcon = (type: string) => {
     switch (type) {
       case 'summary': return <FileText size={20} />;
@@ -739,6 +804,16 @@ const Documents: React.FC = () => {
     return ext === 'xlsx' || ext === 'xls';
   };
 
+  const isWordFile = (filename: string) => {
+    const ext = filename.split('.').pop()?.toLowerCase();
+    return ext === 'docx';
+  };
+
+  const isTxtFile = (filename: string) => {
+    const ext = filename.split('.').pop()?.toLowerCase();
+    return ext === 'txt';
+  };
+
   return (
     <div className="space-y-8 pb-10">
       <section className="flex flex-col md:flex-row md:items-center justify-between gap-4">
@@ -1238,6 +1313,115 @@ const Documents: React.FC = () => {
             </Card>
           )}
 
+          {/* Word AI 分析选项 */}
+          {uploadedFile && isWordFile(uploadedFile.name) && (
+            <Card className="border-none shadow-md bg-gradient-to-br from-blue-500/5 to-cyan-500/5">
+              <CardHeader className="pb-4">
+                <CardTitle className="flex items-center gap-2">
+                  <Sparkles className="text-blue-500" size={20} />
+                  Word AI 分析
+                </CardTitle>
+              </CardHeader>
+              <CardContent className="space-y-4">
+                <div className="space-y-2">
+                  <Label htmlFor="word-analysis-type" className="text-sm">分析类型</Label>
+                  <Select value={wordAnalysisType} onValueChange={(value: any) => setWordAnalysisType(value)}>
+                    <SelectTrigger id="word-analysis-type" className="bg-background">
+                      <SelectValue />
+                    </SelectTrigger>
+                    <SelectContent>
+                      <SelectItem value="structured">
+                        <div className="flex items-center gap-2">
+                          <FileText size={16} />
+                          <div className="flex flex-col">
+                            <span className="font-medium">结构化提取</span>
+                            <span className="text-xs text-muted-foreground">提取表格、键值对等</span>
+                          </div>
+                        </div>
+                      </SelectItem>
+                      <SelectItem value="charts">
+                        <div className="flex items-center gap-2">
+                          <TrendingUp size={16} />
+                          <div className="flex flex-col">
+                            <span className="font-medium">数据图表</span>
+                            <span className="text-xs text-muted-foreground">生成可视化图表</span>
+                          </div>
+                        </div>
+                      </SelectItem>
+                    </SelectContent>
+                  </Select>
+                </div>
+                <div className="space-y-2">
+                  <Label htmlFor="word-user-prompt" className="text-sm">自定义提示词（可选）</Label>
+                  <Textarea
+                    id="word-user-prompt"
+                    placeholder="例如：请提取文档中的表格数据..."
+                    value={wordUserHint}
+                    onChange={(e) => setWordUserHint(e.target.value)}
+                    className="bg-background resize-none"
+                    rows={2}
+                  />
+                </div>
+                <Button
+                  onClick={handleWordAnalyze}
+                  disabled={analyzing}
+                  className="w-full bg-gradient-to-r from-blue-500 to-cyan-600 hover:from-blue-500/90 hover:to-cyan-600/90"
+                >
+                  {analyzing ? <><Loader2 className="mr-2 animate-spin" size={16} /> 分析中...</> : <><Sparkles className="mr-2" size={16} />开始 AI 分析</>}
+                </Button>
+              </CardContent>
+            </Card>
+          )}
+
+          {/* TXT AI 分析选项 */}
+          {uploadedFile && isTxtFile(uploadedFile.name) && (
+            <Card className="border-none shadow-md bg-gradient-to-br from-amber-500/5 to-orange-500/5">
+              <CardHeader className="pb-4">
+                <CardTitle className="flex items-center gap-2">
+                  <Sparkles className="text-amber-500" size={20} />
+                  TXT AI 分析
+                </CardTitle>
+              </CardHeader>
+              <CardContent className="space-y-4">
+                <div className="space-y-2">
+                  <Label htmlFor="txt-analysis-type" className="text-sm">分析类型</Label>
+                  <Select value={txtAnalysisType} onValueChange={(value: any) => setTxtAnalysisType(value)}>
+                    <SelectTrigger id="txt-analysis-type" className="bg-background">
+                      <SelectValue />
+                    </SelectTrigger>
+                    <SelectContent>
+                      <SelectItem value="structured">
+                        <div className="flex items-center gap-2">
+                          <FileText size={16} />
+                          <div className="flex flex-col">
+                            <span className="font-medium">结构化提取</span>
+                            <span className="text-xs text-muted-foreground">提取表格、键值对等</span>
+                          </div>
+                        </div>
+                      </SelectItem>
+                      <SelectItem value="charts">
+                        <div className="flex items-center gap-2">
+                          <TrendingUp size={16} />
+                          <div className="flex flex-col">
+                            <span className="font-medium">数据图表</span>
+                            <span className="text-xs text-muted-foreground">生成可视化图表</span>
+                          </div>
+                        </div>
+                      </SelectItem>
+                    </SelectContent>
+                  </Select>
+                </div>
+                <Button
+                  onClick={handleTxtAnalyze}
+                  disabled={analyzing}
+                  className="w-full bg-gradient-to-r from-amber-500 to-orange-600 hover:from-amber-500/90 hover:to-orange-600/90"
+                >
+                  {analyzing ? <><Loader2 className="mr-2 animate-spin" size={16} /> 分析中...</> : <><Sparkles className="mr-2" size={16} />开始 AI 分析</>}
+                </Button>
+              </CardContent>
+            </Card>
+          )}
+
           {/* 数据操作 */}
           {parseResult?.success && (
             <Card className="border-none shadow-md bg-gradient-to-br from-emerald-500/5 to-blue-500/5">
@@ -1338,6 +1522,114 @@ const Documents: React.FC = () => {
             </Card>
           )}
 
+          {/* Word AI 分析结果 */}
+          {wordAnalysis && (
+            <Card className="border-none shadow-md border-l-4 border-l-blue-500">
+              <CardHeader>
+                <div className="flex items-center justify-between">
+                  <div className="space-y-1">
+                    <CardTitle className="flex items-center gap-2">
+                      <Sparkles className="text-blue-500" size={20} />
+                      Word AI 分析结果
+                    </CardTitle>
+                    {wordAnalysis.filename && (
+                      <CardDescription>
+                        {wordAnalysis.filename} • {wordAnalysis.analysis_type}
+                      </CardDescription>
+                    )}
+                  </div>
+                </div>
+              </CardHeader>
+              <CardContent className="max-h-[500px] overflow-y-auto">
+                {wordAnalysis.analysis_type === 'charts' && wordAnalysis.result?.charts ? (
+                  <AIChartDisplay
+                    charts={wordAnalysis.result.charts}
+                    statistics={wordAnalysis.result.statistics}
+                    distributions={wordAnalysis.result.distributions}
+                  />
+                ) : wordAnalysis.result?.success === false ? (
+                  <p className="text-sm text-destructive">{wordAnalysis.result?.error || wordAnalysis.error || '分析失败'}</p>
+                ) : wordAnalysis.result?.summary ? (
+                  <Markdown content={wordAnalysis.result.summary} />
+                ) : wordAnalysis.result?.headers && wordAnalysis.result?.rows ? (
+                  <div className="space-y-2">
+                    <p className="text-sm font-medium">提取的表格数据：</p>
+                    <div className="border rounded-lg overflow-x-auto">
+                      <TableComponent>
+                        <TableHeader>
+                          <TableRow>
+                            {wordAnalysis.result.headers.map((header: string, idx: number) => (
+                              <TableHead key={idx}>{header}</TableHead>
+                            ))}
+                          </TableRow>
+                        </TableHeader>
+                        <TableBody>
+                          {wordAnalysis.result.rows.slice(0, 20).map((row: string[], idx: number) => (
+                            <TableRow key={idx}>
+                              {row.map((cell: string, cidx: number) => (
+                                <TableCell key={cidx}>{cell}</TableCell>
+                              ))}
+                            </TableRow>
+                          ))}
+                        </TableBody>
+                      </TableComponent>
+                    </div>
+                  </div>
+                ) : (
+                  <p className="text-sm text-muted-foreground">分析完成，无数据</p>
+                )}
+              </CardContent>
+            </Card>
+          )}
+
+          {/* TXT AI 分析结果 */}
+          {txtAnalysis && (
+            <Card className="border-none shadow-md border-l-4 border-l-amber-500">
+              <CardHeader>
+                <div className="flex items-center justify-between">
+                  <div className="space-y-1">
+                    <CardTitle className="flex items-center gap-2">
+                      <Sparkles className="text-amber-500" size={20} />
+                      TXT AI 分析结果
+                    </CardTitle>
+                    {txtAnalysis.filename && (
+                      <CardDescription>
+                        {txtAnalysis.filename} • {txtAnalysis.analysis_type}
+                      </CardDescription>
+                    )}
+                  </div>
+                </div>
+              </CardHeader>
+              <CardContent className="max-h-[500px] overflow-y-auto">
+                {txtAnalysis.analysis_type === 'charts' && txtAnalysis.result?.charts ? (
+                  <AIChartDisplay
+                    charts={txtAnalysis.result.charts}
+                    statistics={txtAnalysis.result.statistics}
+                    distributions={txtAnalysis.result.distributions}
+                  />
+                ) : txtAnalysis.result?.success === false ? (
+                  <p className="text-sm text-destructive">{txtAnalysis.result?.error || txtAnalysis.error || '分析失败'}</p>
+                ) : txtAnalysis.result?.summary ? (
+                  <Markdown content={txtAnalysis.result.summary} />
+                ) : txtAnalysis.result?.key_values && Object.keys(txtAnalysis.result.key_values || {}).length > 0 ? (
+                  <div className="space-y-2">
+                    <p className="text-sm font-medium">提取的键值对：</p>
+                    <div className="grid grid-cols-2 gap-2">
+                      {Object.entries(txtAnalysis.result.key_values || {}).map(([key, value]: [string, any]) => (
+                        <div key={key} className="flex gap-2 p-2 bg-muted/30 rounded-lg">
+                          <span className="font-medium text-sm">{key}:</span>
+                          <span className="text-sm text-muted-foreground">{String(value)}</span>
+                        </div>
+                      ))}
+                    </div>
+                  </div>
+                ) : (
+                  <p className="text-sm text-muted-foreground">分析完成，无数据</p>
+                )}
+              </CardContent>
+            </Card>
+          )}
+
           {/* 图表显示 */}
           {analysisCharts && (
             <Card className="border-none shadow-md border-l-4 border-l-indigo-500">