excel识别

yuthon_ai_agent/__manifest__.py

@@ -26,6 +26,7 @@
         'views/ai_user_profile_views.xml',
         'views/ai_token_usage_views.xml',
         'views/ai_conversation_views.xml',
+        'views/ai_import_record_views.xml',
         'data/demo_provider.xml',
         'data/demo_rule.xml',
         'data/demo_manual.xml',

yuthon_ai_agent/controllers/main.py

@@ -187,10 +187,11 @@ class AiChatController(http.Controller):
 
     @http.route('/ai/attachment/upload', type='http', auth='user', methods=['POST'], csrf=False)
     def attachment_upload(self, **kwargs):
-        """上传聊天附件（图片/PDF），调 OCR 提取文字并返回结果。
+        """上传聊天附件（图片/PDF/Excel），调 OCR 或 Excel 解析器提取文字并返回结果。
 
         请求：multipart/form-data，字段名 'file'
-        返回：JSON { success, attachment_id, filename, mimetype, ocr_text, error }
+        可能携带 conversation_id 用于关联对话
+        返回：JSON { success, attachment_id, filename, mimetype, ocr_text, error, meta }
         """
         err = self._check_ai_access()
         if err:
@@ -205,30 +206,50 @@ class AiChatController(http.Controller):
         filename = uploaded.filename or 'untitled'
         mimetype = uploaded.content_type or 'application/octet-stream'
 
-        # 限制文件类型
+        # 文件类型分类
-        allowed = {'image/png', 'image/jpeg', 'image/jpg', 'image/bmp',
+        IMG_MIMES = {'image/png', 'image/jpeg', 'image/jpg', 'image/bmp', 'image/gif'}
-                   'image/gif', 'application/pdf'}
+        PDF_MIMES = {'application/pdf'}
-        if mimetype not in allowed:
+        EXCEL_MIMES = {
+            'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet',  # .xlsx
+            'application/vnd.ms-excel',                                            # .xls
+            'text/csv',                                                            # .csv
+            'application/vnd.ms-excel.sheet.macroEnabled.12',                      # .xlsm
+        }
+
+        is_image = mimetype in IMG_MIMES
+        is_pdf = mimetype in PDF_MIMES
+        is_excel = mimetype in EXCEL_MIMES
+
+        # 对于 Excel，也接受通过文件扩展名判断（企微/浏览器可能给错 MIME）
+        if not is_excel and not is_image and not is_pdf:
+            lower_name = filename.lower()
+            if lower_name.endswith(('.xlsx', '.xls', '.csv', '.xlsm')):
+                is_excel = True
+                mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+
+        if not (is_image or is_pdf or is_excel):
             return request.make_json_response({
                 'success': False,
-                'error': f'不支持的文件格式：{mimetype}，仅支持 PNG/JPG/BMP/GIF/PDF'
+                'error': f'不支持的文件格式（{mimetype}），支持：PNG/JPG/BMP/GIF/PDF/Excel/CSV'
             })
 
-        # 限制文件大小 (10MB)
+        # 限制文件大小
-        max_size = 10 * 1024 * 1024
+        max_size = 20 * 1024 * 1024 if is_excel else 10 * 1024 * 1024
         if len(raw_bytes) > max_size:
+            size_mb = len(raw_bytes) / 1024 / 1024
             return request.make_json_response({
                 'success': False,
-                'error': f'文件过大（{len(raw_bytes) / 1024 / 1024:.1f}MB），上限 10MB'
+                'error': f'文件过大（{size_mb:.1f}MB），上限 {max_size // 1024 // 1024}MB'
             })
 
-        # 获取当前对话的 Provider OCR 配置
+        # 获取当前对话的 Provider 配置
         conv_id = kwargs.get('conversation_id')
         provider = None
+        conversation = None
         if conv_id:
-            conv = request.env['ai.conversation'].sudo().browse(int(conv_id))
+            conversation = request.env['ai.conversation'].sudo().browse(int(conv_id))
-            if conv.exists():
+            if conversation.exists():
-                provider = conv.provider_id
+                provider = conversation.provider_id
 
         # 查找或创建 AI 聊天附件专用文件夹
         Folder = request.env['documents.folder'].sudo()
@@ -236,7 +257,7 @@ class AiChatController(http.Controller):
         if not folder:
             folder = Folder.create({'name': 'AI 聊天附件'})
 
-        # 创建 documents.document（raw 字段接受原始二进制，内部通过 attachment_id 存储）
+        # 创建 documents.document
         document = request.env['documents.document'].sudo().create({
             'name': filename,
             'mimetype': mimetype,
@@ -244,10 +265,54 @@ class AiChatController(http.Controller):
             'folder_id': folder.id,
         })
 
-        # OCR 提取文字
         ocr_text = ''
         ocr_error = ''
-        ocr_diag = {}  # 诊断信息
+        ocr_diag = {}
+        file_type_code = 'image'
+
+        # —— Excel 解析 ——
+        if is_excel:
+            file_type_code = 'excel'
+            ocr_diag['parser'] = 'excel_parser'
+            # 创建导入记录
+            import_record = request.env['ai.import.record'].sudo().create({
+                'document_id': document.id,
+                'filename': filename,
+                'file_type': 'excel',
+                'user_id': request.env.uid,
+                'ocr_status': 'pending',
+            })
+            if conversation:
+                import_record.write({'conversation_id': conversation.id})
+
+            from ..utils.excel_parser import parse_excel_to_markdown, MAX_ROWS
+            ocr_diag['max_rows'] = MAX_ROWS
+            markdown_text, meta = parse_excel_to_markdown(raw_bytes, filename)
+            ocr_diag['meta'] = meta
+            if meta.get('error'):
+                ocr_error = meta['error']
+                import_record.write({'ocr_status': 'failed', 'ocr_error': ocr_error})
+            else:
+                ocr_text = markdown_text
+                document.sudo().write({'ai_ocr_content': ocr_text})
+                ocr_status = 'success' if not meta.get('truncated') else 'success'
+                import_record.write({
+                    'ocr_status': ocr_status,
+                    'ocr_content': ocr_text,
+                    'ocr_error': ''
+                })
+                # 查找匹配的导入规则
+                rule = request.env['ai.import.rule'].sudo().find_matching_rule(
+                    filename, 'excel', mimetype
+                )
+                if rule:
+                    import_record.write({'import_rule_id': rule.id})
+                    ocr_diag['matched_rule'] = rule.name
+                    _logger.info('[Excel-Upload] 匹配导入规则: %s', rule.name)
+
+        # —— 图片/PDF OCR ——
+        elif is_image or is_pdf:
+            file_type_code = 'image' if is_image else 'pdf'
             if provider and provider.ocr_enabled:
                 from ..models.ocr_provider import get_ocr_instance
                 ocr, init_err = get_ocr_instance(provider)
@@ -255,12 +320,9 @@ class AiChatController(http.Controller):
                 ocr_diag['ocr_enabled'] = True
                 if ocr is None:
                     ocr_error = init_err or 'OCR 实例创建失败'
-                _logger.error('[AI-OCR] get_ocr_instance 失败: %s', ocr_error)
                     ocr_diag['init_error'] = ocr_error
                 else:
                     ocr_diag['init_ok'] = True
-                # 直接用通用文字识别（不先走票据识别，避免不必要的 API 调用）
-                _logger.info('[AI-OCR] 开始识别 %s (%d bytes, %s)', filename, len(raw_bytes), mimetype)
                     result = ocr.recognize_general(raw_bytes, mimetype)
                     ocr_diag['method'] = 'GeneralBasicOCR'
                     ocr_diag['success'] = result.get('success')
@@ -269,20 +331,13 @@ class AiChatController(http.Controller):
                         ocr_diag['text_len'] = len(ocr_text)
                         if ocr_text:
                             document.sudo().write({'ai_ocr_content': ocr_text})
-                        _logger.info('[AI-OCR] ✅ 识别成功，%d 字符: %s', len(ocr_text), ocr_text[:200])
                         else:
                             ocr_error = '图片中未识别到文字'
-                        _logger.info('[AI-OCR] ⚠️ 识别完成但无文字 — 图片中可能确实没有印刷文字')
                     else:
                         ocr_error = result.get('error', 'OCR 调用失败')
-                    ocr_diag['error'] = ocr_error
-                    _logger.error('[AI-OCR] ❌ %s', ocr_error)
             else:
                 ocr_diag['ocr_enabled'] = False
-            if not provider:
+                ocr_diag['reason'] = 'OCR 未启用' if provider else '未找到 AI 服务商'
-                ocr_diag['reason'] = '未找到 AI 服务商（conversation_id 可能无效）'
-            else:
-                ocr_diag['reason'] = 'OCR 未启用'
 
         return request.make_json_response({
             'success': True,
@@ -291,5 +346,6 @@ class AiChatController(http.Controller):
             'mimetype': mimetype,
             'ocr_text': ocr_text,
             'ocr_error': ocr_error,
-            '_ocr_diag': ocr_diag,  # 前端 console 可查看
+            'file_type': file_type_code,
+            '_ocr_diag': ocr_diag,
         })

yuthon_ai_agent/models/__init__.py

@@ -9,3 +9,4 @@ from . import ai_report
 from . import ai_report_template
 from . import ai_report_template_config
 from . import ocr_provider
+from . import ai_import_record

yuthon_ai_agent/security/ir.model.access.csv

@@ -31,3 +31,7 @@ access_ai_report_template_domain_line_user,查询条件行-用户只读,model_ai
 access_ai_report_template_domain_line_admin,查询条件行-管理员,model_ai_report_template_domain_line,yuthon_ai_agent.group_ai_agent_admin,1,1,1,1
 access_ai_report_template_chart_user,图表配置-用户只读,model_ai_report_template_chart,yuthon_ai_agent.group_ai_agent_user,1,0,0,0
 access_ai_report_template_chart_admin,图表配置-管理员,model_ai_report_template_chart,yuthon_ai_agent.group_ai_agent_admin,1,1,1,1
+access_ai_import_record_user,附件导入记录-用户,model_ai_import_record,yuthon_ai_agent.group_ai_agent_user,1,0,1,0
+access_ai_import_record_admin,附件导入记录-管理员,model_ai_import_record,yuthon_ai_agent.group_ai_agent_admin,1,1,1,1
+access_ai_import_rule_admin,导入规则-管理员,model_ai_import_rule,yuthon_ai_agent.group_ai_agent_admin,1,1,1,1
+access_ai_import_rule_user,导入规则-用户只读,model_ai_import_rule,yuthon_ai_agent.group_ai_agent_user,1,0,0,0

yuthon_ai_agent/static/src/js/ai_chat_action.js

@@ -83,14 +83,21 @@ export class AiChatAction extends Component {
     onFileSelected(ev) {
         const file = ev.target.files[0];
         if (!file) return;
-        const allowed = ["image/png", "image/jpeg", "image/jpg", "image/bmp", "image/gif", "application/pdf"];
+        const IMG_TYPES = ["image/png", "image/jpeg", "image/jpg", "image/bmp", "image/gif"];
-        if (!allowed.includes(file.type)) {
+        const EXCEL_TYPES = ["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            alert("仅支持 PNG、JPG、BMP、GIF、PDF 文件");
+                            "application/vnd.ms-excel", "text/csv"];
+        const ALLOWED = IMG_TYPES.concat(["application/pdf"], EXCEL_TYPES);
+        const isExcelByName = file.name && /\\.(xlsx|xls|csv)$/i.test(file.name);
+        if (!ALLOWED.includes(file.type) && !isExcelByName) {
+            alert("仅支持 PNG、JPG、BMP、GIF、PDF、Excel（.xlsx/.xls/.csv）文件");
             ev.target.value = "";
             return;
         }
-        if (file.size > 10 * 1024 * 1024) {
+        const isExcel = EXCEL_TYPES.includes(file.type) || isExcelByName;
-            alert("文件不能超过 10MB");
+        const maxSize = isExcel ? 20 * 1024 * 1024 : 10 * 1024 * 1024;
+        const sizeLabel = isExcel ? "20MB" : "10MB";
+        if (file.size > maxSize) {
+            alert("文件不能超过 " + sizeLabel);
             ev.target.value = "";
             return;
         }

yuthon_ai_agent/static/src/js/ai_floating.js

@@ -145,14 +145,21 @@ function triggerFileInput() {
 function onFileSelected(e) {
     const file = e.target.files[0];
     if (!file) return;
-    const allowed = ["image/png", "image/jpeg", "image/jpg", "image/bmp", "image/gif", "application/pdf"];
+    const IMG_TYPES = ["image/png", "image/jpeg", "image/jpg", "image/bmp", "image/gif"];
-    if (!allowed.includes(file.type)) {
+    const EXCEL_TYPES = ["application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-        alert("仅支持 PNG、JPG、BMP、GIF、PDF 文件");
+                        "application/vnd.ms-excel", "text/csv"];
+    const ALLOWED = IMG_TYPES.concat(["application/pdf"], EXCEL_TYPES);
+    const isExcelByName = file.name && /\\.(xlsx|xls|csv)$/i.test(file.name);
+    if (!ALLOWED.includes(file.type) && !isExcelByName) {
+        alert("仅支持 PNG、JPG、BMP、GIF、PDF、Excel（.xlsx/.xls/.csv）文件");
         e.target.value = "";
         return;
     }
-    if (file.size > 10 * 1024 * 1024) {
+    const isExcel = EXCEL_TYPES.includes(file.type) || isExcelByName;
-        alert("文件不能超过 10MB");
+    const maxSize = isExcel ? 20 * 1024 * 1024 : 10 * 1024 * 1024;
+    const sizeLabel = isExcel ? "20MB" : "10MB";
+    if (file.size > maxSize) {
+        alert("文件不能超过 " + sizeLabel);
         e.target.value = "";
         return;
     }
@@ -345,11 +352,11 @@ function renderChat() {
                 <div id="yuthon_attach_preview" class="o_ai_attach_preview" style="display:${showConvList ? 'none' : 'flex'}">
                 </div>
                 <div class="o_ai_input_area" style="display:${showConvList ? 'none' : 'flex'}">
-                    <button id="yuthon_attach_btn" class="o_ai_attach_btn" title="上传图片或PDF">
+                    <button id="yuthon_attach_btn" class="o_ai_attach_btn" title="上传图片/PDF/Excel">
                         <i class="fa fa-paperclip"></i>
                     </button>
                     <input type="file" id="yuthon_file_input"
-                        accept="image/png,image/jpeg,image/jpg,image/bmp,image/gif,application/pdf"
+                        accept="image/png,image/jpeg,image/jpg,image/bmp,image/gif,application/pdf,.xlsx,.xls,.csv"
                         style="display:none"/>
                     <textarea id="yuthon_chat_input" class="o_ai_input"
                         placeholder="输入消息... 支持粘贴图片" rows="1"></textarea>

yuthon_ai_agent/static/src/xml/ai_chat_template.xml

@@ -82,11 +82,11 @@
             <!-- 输入区 -->
             <div class="ai-chat-input-area p-2 border-top d-flex gap-2 align-items-center">
                 <button class="btn btn-outline-secondary btn-sm ai-chat-attach-btn"
-                    t-on-click="triggerFileInput" title="上传图片或PDF（支持 Ctrl+V 粘贴图片）">
+                    t-on-click="triggerFileInput" title="上传图片/PDF/Excel（支持 Ctrl+V 粘贴图片）">
                     <i class="fa fa-paperclip"/>
                 </button>
                 <input type="file" id="ai_chat_file_input"
-                    accept="image/png,image/jpeg,image/jpg,image/bmp,image/gif,application/pdf"
+                    accept="image/png,image/jpeg,image/jpg,image/bmp,image/gif,application/pdf,.xlsx,.xls,.csv"
                     t-on-change="onFileSelected"
                     style="display:none"/>
                 <input type="text" class="form-control"

yuthon_wechat/controllers/ai_callback.py

@@ -135,21 +135,37 @@ class WecomAiCallback(http.Controller):
             db_name = request.env.cr.dbname
             threading.Thread(
                 target=self._async_process_ai_message,
-                args=(db_name, wecom_userid, "请帮我看看这张图片里有什么", media_id),
+                args=(db_name, wecom_userid, "请帮我看看这张图片里有什么", media_id, 'image'),
+                daemon=True,
+            ).start()
+            return Response("", content_type='text/plain')
+
+        # 文件消息（Excel/CSV）：下载 → 解析 → 交给 AI
+        if msg_type == 'file':
+            media_id = msg.get('media_id', '')
+            if not wecom_userid or not media_id:
+                return Response("", content_type='text/plain')
+
+            _logger.info("收到企微文件消息: user=%s, media_id=%s", wecom_userid, media_id[:20])
+            db_name = request.env.cr.dbname
+            threading.Thread(
+                target=self._async_process_ai_message,
+                args=(db_name, wecom_userid, "请帮我分析这个文件的内容", media_id, 'file'),
                 daemon=True,
             ).start()
             return Response("", content_type='text/plain')
 
         # 其他类型暂不处理
-        _logger.info("AI回调收到非文本/图片消息类型: %s，暂不处理", msg_type)
+        _logger.info("AI回调收到非文本/图片/文件消息类型: %s，暂不处理", msg_type)
         return Response("", content_type='text/plain')
 
-    def _async_process_ai_message(self, db_name, wecom_userid, content, media_id=None):
+    def _async_process_ai_message(self, db_name, wecom_userid, content, media_id=None, media_type='image'):
         """
         后台线程: 处理 AI 对话并推送回复
         使用独立的数据库游标和环境
 
-        :param media_id: 可选，图片消息的 media_id。不为空时先下载→OCR→拼入消息
+        :param media_id: 可选，图片/文件消息的 media_id
+        :param media_type: 'image' | 'file'
         """
         try:
             registry = api.Registry(db_name)
@@ -158,9 +174,17 @@ class WecomAiCallback(http.Controller):
 
                 attachment_ids = None
                 if media_id:
+                    if media_type == 'file':
+                        attachment_ids = self._download_and_parse_file(env, wecom_userid, media_id)
+                    else:
                         attachment_ids = self._download_and_ocr_media(env, wecom_userid, media_id)
+
                     if not attachment_ids:
-                        # 下载/OCR失败，给用户一个提示，不再调AI
+                        # 下载/解析失败，给用户一个提示，不再调AI
+                        if media_type == 'file':
+                            env['wecom.ai.chat']._send_wecom_reply(
+                                wecom_userid, "文件已收到，但解析失败。请确保发送的是 .xlsx/.xls/.csv 格式的 Excel 文件。")
+                        else:
                             env['wecom.ai.chat']._send_wecom_reply(
                                 wecom_userid, "图片已收到，但未能从中提取到文字信息。请尝试发送更清晰的图片（含印刷文字）。")
                         cr.commit()
@@ -245,3 +269,87 @@ class WecomAiCallback(http.Controller):
         except Exception as e:
             _logger.exception('[WECOM-OCR] 下载/OCR 异常: %s', e)
             return None
+
+    def _download_and_parse_file(self, env, wecom_userid, media_id):
+        """下载企微文件 → 创建 documents.document → Excel解析 → 返回 [document_id]
+
+        支持 .xlsx / .xls / .csv
+        :return: [int] 或 None
+        """
+        try:
+            # 1. 下载文件
+            content, error = env['wecom.apps'].sudo().download_agent_media(media_id)
+            if content is None:
+                _logger.error('[WECOM-FILE] 下载失败: %s', error)
+                return None
+
+            _logger.info('[WECOM-FILE] 下载成功: %d bytes', len(content))
+
+            # 2. 确定 mimetype（根据魔数）
+            mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+            ext = '.xlsx'
+            if content[:2] == b'PK':
+                mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
+                ext = '.xlsx'
+            elif content[:2] == b'\xd0\xcf':
+                mimetype = 'application/vnd.ms-excel'
+                ext = '.xls'
+            else:
+                # 尝试按文本解码 → CSV
+                try:
+                    _ = content.decode('utf-8-sig')
+                    mimetype = 'text/csv'
+                    ext = '.csv'
+                except:
+                    _logger.warning('[WECOM-FILE] 无法识别文件类型，降级为 xlsx')
+            filename = f'wecom_file_{media_id[:8]}{ext}'
+
+            # 3. 创建 documents.document
+            DocFolder = env['documents.folder'].sudo()
+            folder = DocFolder.search([('name', '=', 'AI 聊天附件')], limit=1)
+            if not folder:
+                folder = DocFolder.create({'name': 'AI 聊天附件'})
+
+            document = env['documents.document'].sudo().create({
+                'name': filename,
+                'folder_id': folder.id,
+                'mimetype': mimetype,
+                'raw': content,
+            })
+
+            _logger.info('[WECOM-FILE] 文档已创建: id=%s', document.id)
+
+            # 4. Excel 解析
+            try:
+                from odoo.addons.yuthon_ai_agent.utils.excel_parser import parse_excel_to_markdown
+            except ImportError:
+                _logger.warning('[WECOM-FILE] 无法导入 Excel 解析器')
+                return [document.id]
+
+            markdown_text, meta = parse_excel_to_markdown(content, filename)
+            if meta.get('error'):
+                _logger.error('[WECOM-FILE] 解析失败: %s', meta['error'])
+                return [document.id]
+
+            document.write({'ai_ocr_content': markdown_text})
+            _logger.info('[WECOM-FILE] ✅ 解析成功: %d字符 | sheets=%s | rows=%s',
+                         len(markdown_text), meta.get('sheets'), meta.get('total_rows'))
+
+            # 5. 创建导入记录
+            try:
+                env['ai.import.record'].sudo().create({
+                    'document_id': document.id,
+                    'filename': filename,
+                    'file_type': 'excel',
+                    'user_id': env.ref('base.user_root').id,
+                    'wecom_userid': wecom_userid,
+                    'ocr_status': 'success' if not meta.get('truncated') else 'success',
+                    'ocr_content': markdown_text,
+                })
+            except Exception as e:
+                _logger.warning('[WECOM-FILE] 创建导入记录失败: %s', e)
+
+            return [document.id]
+        except Exception as e:
+            _logger.exception('[WECOM-FILE] 下载/解析异常: %s', e)
+            return None