fix: VLM 食物识别全链路修复 + 用药 Agent Prompt 优化

- 图片上传自动压缩（max 860px, JPEG Q65），解决大图超 API 129KB 限制
- 修复 VLM 传 file:// 本地路径 Bug，改为 base64 data URL
- VLM Prompt 优化为中文食堂场景，附带常见中餐热量参考
- 千问 API 错误信息透传，方便调试
- 用药 Agent Prompt 加查询规则：先调 manage_medication 再回答
- 新增 System.Drawing.Common 依赖用于服务端图片压缩

backend/src/Health.Infrastructure/AI/ai_clients.cs

@@ -129,7 +129,11 @@ public sealed class QwenVisionClient(HttpClient http, IConfiguration config)
         var json = JsonSerializer.Serialize(request, _jsonOptions);
         var content = new StringContent(json, Encoding.UTF8, "application/json");
         var response = await _http.PostAsync("chat/completions", content, ct);
-        response.EnsureSuccessStatusCode();
+        if (!response.IsSuccessStatusCode)
+        {
+            var errorBody = await response.Content.ReadAsStringAsync(ct);
+            throw new HttpRequestException($"VLM API {response.StatusCode}: {errorBody}");
+        }
         var body = await response.Content.ReadAsStringAsync(ct);
         return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
     }

backend/src/Health.Infrastructure/AI/prompt_manager.cs

@@ -83,11 +83,12 @@ public sealed class PromptManager
         你是一个用药管理专家。
 
         规则：
-        1. 解析用户口中的药品信息（药名/剂量/频次/时间）
+        1. 用户询问当前用药时，必须先调用 manage_medication(action="query") 查询已有用药记录
-        2. "早饭后"等模糊时间→追问具体几点
+        2. 解析用户口中的药品信息（药名/剂量/频次/时间）
-        3. 解析完成后展示确认卡片
+        3. "早饭后"等模糊时间→追问具体几点
-        4. 处方拍照→提取药品信息→生成用药计划→让用户确认
+        4. 解析完成后展示确认卡片
-        5. 回答用药相关疑问
+        5. 处方拍照→提取药品信息→生成用药计划→让用户确认
+        6. 回答用药相关疑问
         """;
 
     private const string ReportPrompt = """

backend/src/Health.WebApi/Endpoints/ai_chat_endpoints.cs

@@ -1,3 +1,5 @@
+using System.Drawing;
+using System.Drawing.Imaging;
 using Health.Infrastructure.AI;
 
 namespace Health.WebApi.Endpoints;
@@ -256,20 +258,30 @@ public static class AiChatEndpoints
 
                 var safeName = $"{Guid.NewGuid()}_{Path.GetFileName(file.FileName)}";
                 var filePath = Path.Combine(uploadsDir, safeName);
-                using var stream = new FileStream(filePath, FileMode.Create);
+                using (var stream = new FileStream(filePath, FileMode.Create))
-                await file.CopyToAsync(stream, ct);
+                    await file.CopyToAsync(stream, ct);
-                imageUrls.Add($"file://{filePath}");
+
+                // 压缩图片后转 base64（VLM API 有请求体大小限制）
+                var compressedPath = Path.Combine(uploadsDir, $"compressed_{safeName}");
+                CompressImage(filePath, compressedPath, maxWidth: 860, quality: 65L);
+                var compressedBytes = await File.ReadAllBytesAsync(compressedPath, ct);
+                var base64 = Convert.ToBase64String(compressedBytes);
+                imageUrls.Add($"data:image/jpeg;base64,{base64}");
             }
 
             var prompt = """
-                识别图片中的所有食物，返回 JSON 格式：
+                你是一个中餐营养分析专家。请仔细识别图片中的所有食物。
+                注意：这是中国食堂的中式菜肴，请用中文名称。
+
+                返回 JSON 格式：
                 {
-                  "foods": [{"name":"食物名","portion":"份量描述","calories":数字,"proteinGrams":数字,"carbsGrams":数字,"fatGrams":数字,"warning":null或警告文字}],
+                  "foods": [{"name":"食物名（中文）","portion":"份量","calories":数字,"proteinGrams":数字,"carbsGrams":数字,"fatGrams":数字,"warning":null}],
-                  "totalCalories":总热量数字,
+                  "totalCalories":总热量,
-                  "warnings":["整体警告"],
+                  "warnings":["整体建议"],
-                  "score":1-5评分
+                  "score":1-5
                 }
-                请只返回 JSON，不要加任何其他文字。
+                常见中餐参考：米饭约200g/230卡，炒青菜约200g/80卡，青椒肉丝约200g/200卡，红烧肉约150g/400卡，番茄炒蛋约200g/180卡，麻婆豆腐约200g/220卡。
+                只返回JSON。
                 """;
 
             try
@@ -278,9 +290,9 @@ public static class AiChatEndpoints
                 var result = response.Choices?.FirstOrDefault()?.Message?.Content ?? "{}";
                 return Results.Ok(new { code = 0, data = result, message = (string?)null });
             }
-            catch (Exception)
+            catch (Exception ex)
             {
-                return Results.Ok(new { code = 50001, data = (object?)null, message = $"食物识别失败，请重试" });
+                return Results.Ok(new { code = 50001, data = (object?)null, message = $"食物识别失败：{ex.Message}" });
             }
         });
     }
@@ -561,6 +573,28 @@ public static class AiChatEndpoints
             Parameters = new { type = "object", properties = new { reason = new { type = "string" }, urgency_level = new { type = "string" } } }
         }
     };
+
+    /// <summary>压缩图片到合理大小供 VLM API 使用</summary>
+    private static void CompressImage(string inputPath, string outputPath, int maxWidth, long quality)
+    {
+        using var image = Image.FromFile(inputPath);
+        var width = image.Width;
+        var height = image.Height;
+        if (width > maxWidth)
+        {
+            height = (int)((double)height / width * maxWidth);
+            width = maxWidth;
+        }
+        using var bitmap = new Bitmap(width, height);
+        using var graphics = Graphics.FromImage(bitmap);
+        graphics.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
+        graphics.DrawImage(image, 0, 0, width, height);
+
+        var jpegCodec = ImageCodecInfo.GetImageEncoders().First(c => c.MimeType == "image/jpeg");
+        var parameters = new EncoderParameters(1);
+        parameters.Param[0] = new EncoderParameter(Encoder.Quality, quality);
+        bitmap.Save(outputPath, jpegCodec, parameters);
+    }
 }
 
 /// <summary>AI 对话请求</summary>

backend/src/Health.WebApi/Health.WebApi.csproj

@@ -10,6 +10,7 @@
     <PackageReference Include="Microsoft.AspNetCore.Authentication.JwtBearer" Version="10.0.8" />
     <PackageReference Include="Microsoft.AspNetCore.OpenApi" Version="10.0.8" />
     <PackageReference Include="Microsoft.EntityFrameworkCore" Version="10.0.8" />
+    <PackageReference Include="System.Drawing.Common" Version="10.0.8" />
   </ItemGroup>
 
   <ItemGroup>