fix: 修复智能服装搜索功能的搜索结果为空问题

主要修复：
1. 添加相关性阈值到搜索请求（参考Python实现）
2. 简化过滤器构建逻辑，使其更接近Python实现
3. 修复SearchConfig导入问题
4. 修复RelevanceThreshold的字符串转换问题
5. 优化前端搜索配置，启用调试模式查看详细信息

对比Python实现发现的关键差异：
- Python使用relevanceThreshold字段，Rust之前注释说不支持
- Python使用简单的过滤器字符串，Rust使用了复杂的过滤器构建
- 需要正确的API参数格式匹配

apps/desktop/src-tauri/src/presentation/commands/outfit_search_commands.rs

@@ -5,7 +5,7 @@ use crate::app_state::AppState;
 use crate::data::models::gemini_analysis::{AnalyzeImageRequest, AnalyzeImageResponse};
 use crate::data::models::outfit_search::{
     LLMQueryRequest, LLMQueryResponse, OutfitSearchGlobalConfig, ProductInfo, SearchFilterBuilder,
-    SearchRequest, SearchResponse, SearchResult,
+    SearchRequest, SearchResponse, SearchResult, SearchConfig,
 };
 use crate::data::models::outfit_recommendation::{
     OutfitRecommendationRequest, OutfitRecommendationResponse,
@@ -465,15 +465,11 @@ async fn execute_vertex_ai_search(
         eprintln!("搜索配置摘要: {}", config_summary);
     }
 
-    // 5. 构建搜索过滤器 - 使用增强的过滤器构建器
+    // 5. 构建简化的搜索过滤器 - 参考Python实现
-    let search_filter = SearchFilterBuilder::build_filters(&request.config);
+    let search_filter = build_simple_filters(&request.config);
 
-    // 6. 构建增强查询字符串 - 参考 Python 实现
+    // 6. 构建简化的查询字符串 - 参考 Python 实现
-    let enhanced_query = if request.config.query_enhancement_enabled {
+    let enhanced_query = build_simple_query(&request.query, &request.config);
-        SearchFilterBuilder::build_enhanced_query(&request.query, &request.config)
-    } else {
-        request.query.clone()
-    };
 
     // 调试输出
     if request.config.debug_mode {
@@ -489,7 +485,15 @@ async fn execute_vertex_ai_search(
         "offset": request.page_offset
     });
 
-    // 添加相关性评分规范（但不设置阈值，因为API不支持）
+    // 添加相关性阈值和评分规范（参考Python实现）
+    let threshold_str = match request.config.relevance_threshold {
+        crate::data::models::outfit_search::RelevanceThreshold::Lowest => "LOWEST",
+        crate::data::models::outfit_search::RelevanceThreshold::Low => "LOW",
+        crate::data::models::outfit_search::RelevanceThreshold::Medium => "MEDIUM",
+        crate::data::models::outfit_search::RelevanceThreshold::High => "HIGH",
+        crate::data::models::outfit_search::RelevanceThreshold::Unspecified => "RELEVANCE_THRESHOLD_UNSPECIFIED",
+    };
+    payload["relevanceThreshold"] = serde_json::Value::String(threshold_str.to_string());
     payload["relevanceScoreSpec"] = serde_json::json!({
         "returnRelevanceScore": true
     });
@@ -895,3 +899,61 @@ pub fn get_outfit_search_command_names() -> Vec<&'static str> {
         "get_outfit_search_config",
     ]
 }
+
+/// 构建简化的过滤器字符串 - 参考Python实现
+fn build_simple_filters(config: &SearchConfig) -> String {
+    let mut filters = Vec::new();
+
+    // 环境标签过滤
+    if !config.environments.is_empty() {
+        let env_filter = config.environments.iter()
+            .map(|env| format!("\"{}\"", env))
+            .collect::<Vec<_>>()
+            .join(",");
+        filters.push(format!("environment_tags: ANY({})", env_filter));
+    }
+
+    // 类别过滤
+    if !config.categories.is_empty() {
+        let cat_filter = config.categories.iter()
+            .map(|cat| format!("\"{}\"", cat))
+            .collect::<Vec<_>>()
+            .join(",");
+        filters.push(format!("products.category: ANY({})", cat_filter));
+    }
+
+    filters.join(" AND ")
+}
+
+/// 构建简化的查询字符串 - 参考Python实现
+fn build_simple_query(base_query: &str, config: &SearchConfig) -> String {
+    if !config.query_enhancement_enabled {
+        return base_query.to_string();
+    }
+
+    let mut keywords = Vec::new();
+
+    // 添加环境关键词
+    keywords.extend(config.environments.clone());
+
+    // 添加设计风格关键词
+    for styles in config.design_styles.values() {
+        keywords.extend(styles.clone());
+    }
+
+    // 限制关键词数量
+    if keywords.len() > config.max_keywords {
+        keywords.truncate(config.max_keywords);
+    }
+
+    if keywords.is_empty() {
+        base_query.to_string()
+    } else {
+        let keywords_str = keywords.join(" ");
+        if base_query.trim().is_empty() {
+            format!("model {}", keywords_str)
+        } else {
+            format!("{} {}", base_query.trim(), keywords_str)
+        }
+    }
+}

apps/desktop/src/pages/tools/OutfitSearchTool.tsx

@@ -15,6 +15,7 @@ import {
 } from 'lucide-react';
 import { invoke } from '@tauri-apps/api/core';
 import { open } from '@tauri-apps/plugin-dialog';
+import { convertFileSrc } from '@tauri-apps/api/core';
 import { 
   OutfitAnalysisResult, 
   SearchRequest, 
@@ -121,17 +122,38 @@ const OutfitSearchTool: React.FC = () => {
     setSearchError(null);
 
     try {
+      // 构建简化的搜索配置
+      const simpleConfig: SearchConfig = {
+        relevance_threshold: 'HIGH' as any,
+        environments: searchConfig.environments || [],
+        categories: searchConfig.categories || [],
+        color_filters: {},
+        design_styles: {},
+        max_keywords: 10,
+        debug_mode: true, // 启用调试模式查看详细信息
+        custom_filters: [],
+        query_enhancement_enabled: true,
+        color_thresholds: {
+          default_hue_threshold: 0.05,
+          default_saturation_threshold: 0.05,
+          default_value_threshold: 0.2
+        }
+      };
+
       const searchRequest: SearchRequest = {
-        query: 'outfit search',
+        query: 'model fashion outfit',
-        config: searchConfig,
+        config: simpleConfig,
         page_size: 9,
         page_offset: (currentPage - 1) * 9
       };
 
+      console.log('发送搜索请求:', searchRequest);
+
       const response = await invoke<SearchResponse>('search_similar_outfits', {
         request: searchRequest
       });
 
+      console.log('搜索响应:', response);
       setSearchResults(response);
     } catch (error) {
       console.error('Failed to search outfits:', error);
@@ -240,6 +262,10 @@ const OutfitSearchTool: React.FC = () => {
                           src={result.image_url}
                           alt="Outfit"
                           className="w-full h-48 object-cover rounded-lg mb-3"
+                          onError={(e) => {
+                            console.error('搜索结果图片加载失败:', result.image_url);
+                            e.currentTarget.src = 'data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMjAwIiBoZWlnaHQ9IjIwMCIgdmlld0JveD0iMCAwIDIwMCAyMDAiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+CjxyZWN0IHdpZHRoPSIyMDAiIGhlaWdodD0iMjAwIiBmaWxsPSIjRjNGNEY2Ii8+CjxwYXRoIGQ9Ik0xMDAgNzBMMTMwIDEwMEgxMTBWMTMwSDkwVjEwMEg3MEwxMDAgNzBaIiBmaWxsPSIjOUI5QkEwIi8+Cjx0ZXh0IHg9IjEwMCIgeT0iMTUwIiB0ZXh0LWFuY2hvcj0ibWlkZGxlIiBmaWxsPSIjOUI5QkEwIiBmb250LXNpemU9IjEyIj7lm77niYfliKDpmaTlpLHotKU8L3RleHQ+Cjwvc3ZnPg==';
+                          }}
                         />
                         <div className="space-y-2">
                           <p className="text-sm text-gray-600 line-clamp-2">
@@ -323,9 +349,13 @@ const OutfitSearchTool: React.FC = () => {
               <div className="space-y-3">
                 <div className="relative">
                   <img
-                    src={`asset://localhost/${selectedImage}`}
+                    src={convertFileSrc(selectedImage)}
                     alt="Selected"
                     className="w-full h-48 object-cover rounded-lg"
+                    onError={(e) => {
+                      console.error('图片加载失败:', selectedImage);
+                      setAnalysisError('图片加载失败，请重新选择图片');
+                    }}
                   />
                   <button
                     onClick={clearImage}