v0.2.0: CRM/ERP 系统升级 - 清理 .gitignore 并移除误提交的 venv/env/db 文件

- 更新 .gitignore：全面覆盖环境变量、数据库、日志、缓存、上传文件
- 移除误跟踪的 server/venv/、crm_data.db、.env 文件
- 新增 server/.env.example 模板
- 新增合同管理、利润核算、AI教练等功能模块
- 新增 Playwright e2e 测试套件
- 前后端多项功能升级和 bug 修复

server/app/services/ocr_service.py

@@ -72,11 +72,12 @@ async def ocr_image(
         "messages": [
             {
                 "role": "user",
-                "content": "/no_think\n" + prompt,
+                "content": prompt,
                 "images": [image_base64],  # Ollama vision 格式
             },
         ],
         "stream": False,
+        "think": False,  # 关闭思考模式：稳定输出、避免死循环、提速 2-5x
         "options": {
             "temperature": 0.1,
             "num_predict": 2000,
@@ -87,19 +88,18 @@ async def ocr_image(
         async with httpx.AsyncClient(timeout=120.0) as client:
             resp = await client.post(url, json=payload)
             if resp.status_code != 200:
-                print(f"[OCR] 3090 返回 {resp.status_code}: {resp.text[:200]}")
-                return {"success": False, "data": {}, "error": f"VL 模型返回 {resp.status_code}"}
+                detail = resp.text[:200]
+                print(f"[OCR] 3090 返回 {resp.status_code}: {detail}")
+                if "model runner" in detail:
+                    return {"success": False, "data": {}, "error": "AI OCR 模型进程崩溃，请联系管理员重启 Ollama 服务"}
+                return {"success": False, "data": {}, "error": f"AI OCR 服务异常 (HTTP {resp.status_code})，请稍后重试"}
 
             data = resp.json()
-            # Qwen3.5 的 CoT 推理放在 message.thinking，最终结果在 message.content
             content = data.get("message", {}).get("content", "")
-            thinking = data.get("message", {}).get("thinking", "")
 
-            # 优先从 content 提取 JSON，回退到 thinking
-            for text_source in [content, thinking]:
-                if not text_source:
-                    continue
-                cleaned = re.sub(r'<think>.*?</think>', '', text_source, flags=re.DOTALL).strip()
+            # 关闭思考模式后，结果直接在 content（无 thinking 字段）
+            if content:
+                cleaned = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
                 json_match = re.search(r'\{[\s\S]*\}', cleaned)
                 if json_match:
                     try:
@@ -107,16 +107,14 @@ async def ocr_image(
                         print(f"[OCR] 解析成功: {list(result.keys())}")
                         return {"success": True, "data": result}
                     except json.JSONDecodeError:
-                        continue
+                        pass
 
-            # 没有提取到 JSON，返回原始文本
-            raw = content or thinking
-            print(f"[OCR] 未能提取 JSON, 内容长度: content={len(content)}, thinking={len(thinking)}")
-            return {"success": True, "data": {"raw_text": raw[:2000]}}
+            print(f"[OCR] 未能提取 JSON, content 长度: {len(content)}")
+            return {"success": True, "data": {"raw_text": content[:2000]}}
 
     except httpx.TimeoutException:
-        print("[OCR] 3090 超时（60s）")
-        return {"success": False, "data": {}, "error": "VL 模型响应超时"}
+        print("[OCR] 3090 超时（120s）")
+        return {"success": False, "data": {}, "error": "AI OCR 响应超时（120s），模型可能负载过高，请稍后重试"}
     except json.JSONDecodeError as e:
         print(f"[OCR] JSON 解析失败: {e}")
         return {"success": False, "data": {}, "error": f"JSON 解析失败: {e}"}
@@ -172,11 +170,11 @@ async def extract_invoice_from_text(
         "messages": [
             {
                 "role": "user",
-                "content": f"/no_think\n{prompt}\n\n--- 以下是发票文本内容 ---\n\n{truncated}",
-                # 不传 images —— 纯文本模式
+                "content": f"{prompt}\n\n--- 以下是发票文本内容 ---\n\n{truncated}",
             },
         ],
         "stream": False,
+        "think": False,  # 关闭思考模式
         "options": {
             "temperature": 0.1,
             "num_predict": 2000,
@@ -192,12 +190,9 @@ async def extract_invoice_from_text(
 
             data = resp.json()
             content = data.get("message", {}).get("content", "")
-            thinking = data.get("message", {}).get("thinking", "")
 
-            for text_source in [content, thinking]:
-                if not text_source:
-                    continue
-                cleaned = re.sub(r'<think>.*?</think>', '', text_source, flags=re.DOTALL).strip()
+            if content:
+                cleaned = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
                 json_match = re.search(r'\{[\s\S]*\}', cleaned)
                 if json_match:
                     try:
@@ -205,11 +200,10 @@ async def extract_invoice_from_text(
                         print(f"[TextExtract] AI 提取成功: {list(result.keys())}")
                         return {"success": True, "data": result}
                     except json.JSONDecodeError:
-                        continue
+                        pass
 
-            raw = content or thinking
-            print(f"[TextExtract] 未能提取 JSON, 内容: {raw[:200]}")
-            return {"success": True, "data": {"raw_text": raw[:2000]}}
+            print(f"[TextExtract] 未能提取 JSON, content: {content[:200]}")
+            return {"success": True, "data": {"raw_text": content[:2000]}}
 
     except httpx.TimeoutException:
         print("[TextExtract] 3090 超时")