实现真实的服装搜索逻辑，直接调用 Google Vertex AI Search API
- 实现 execute_vertex_ai_search 函数，直接调用 Vertex AI Search API - 添加 get_google_access_token 函数获取访问令牌 - 实现 convert_vertex_response_to_search_results 转换响应格式 - 添加 parse_vertex_result_to_search_result 解析单个搜索结果 - 添加 parse_vertex_product_info 解析产品信息 - 支持搜索过滤器和相关性阈值 - 添加搜索功能的单元测试 - 修复多个编译警告，移除未使用的导入和变量
This commit is contained in:
imeepos
parent
6d86cea892
commit
d935dca4e7
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@ -1,9 +1,9 @@
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use anyhow::{Result, anyhow};
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use std::sync::Arc;
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use rusqlite::{params, Row};
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use chrono::{DateTime, Utc};
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use chrono;
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use serde_json;
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use tracing::{info, warn, debug, error};
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use tracing::{info, warn, error};
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use crate::data::models::template::{
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Template, TemplateMaterial, Track, TrackSegment, CanvasConfig,
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@ -8,7 +8,7 @@ mod template_service_tests {
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};
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use crate::infrastructure::database::Database;
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use std::sync::Arc;
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use tempfile::TempDir;
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use chrono::Utc;
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/// 创建测试数据库
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@ -3,7 +3,7 @@ use std::path::PathBuf;
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use std::sync::{Arc, Mutex};
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use anyhow::{Result, anyhow};
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use std::ops::{Deref, DerefMut};
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use uuid;
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use crate::infrastructure::connection_pool::{ConnectionPool, ConnectionPoolConfig, PooledConnectionHandle};
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mod migrations;
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@ -1,6 +1,6 @@
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use crate::infrastructure::markdown_parser::{
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MarkdownParser, MarkdownParserConfig, MarkdownParseResult,
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OutlineItem, LinkInfo, ValidationResult, MarkdownNode, Range, Position
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MarkdownParser, MarkdownParserConfig, MarkdownParseResult,
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OutlineItem, LinkInfo, ValidationResult, MarkdownNode
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};
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use anyhow::Result;
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use serde::{Deserialize, Serialize};
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@ -5,7 +5,7 @@ use crate::app_state::AppState;
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use crate::data::models::gemini_analysis::{AnalyzeImageRequest, AnalyzeImageResponse};
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use crate::data::models::outfit_search::{
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SearchRequest, SearchResponse, LLMQueryRequest, LLMQueryResponse,
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OutfitSearchGlobalConfig
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OutfitSearchGlobalConfig, SearchResult, ProductInfo, SearchFilterBuilder
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};
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use crate::infrastructure::gemini_service::{GeminiService, GeminiConfig};
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@ -46,17 +46,29 @@ pub async fn analyze_outfit_image(
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#[tauri::command]
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pub async fn search_similar_outfits(
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_state: State<'_, AppState>,
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_request: SearchRequest,
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request: SearchRequest,
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) -> Result<SearchResponse, String> {
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// TODO: 实现真实的搜索逻辑
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let start_time = std::time::Instant::now();
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// 创建Gemini服务实例用于获取访问令牌
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let config = GeminiConfig::default();
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let mut gemini_service = GeminiService::new(Some(config))
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.map_err(|e| format!("Failed to create GeminiService: {}", e))?;
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// 执行直接的 Vertex AI Search 搜索
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let search_results = execute_vertex_ai_search(&mut gemini_service, &request).await
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.map_err(|e| {
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eprintln!("搜索失败: {}", e);
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format!("搜索失败: {}", e)
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})?;
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let search_time_ms = start_time.elapsed().as_millis() as u64;
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// 执行搜索（暂时返回模拟数据）
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// TODO: 实现真实的搜索逻辑
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Ok(SearchResponse {
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results: vec![],
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total_size: 0,
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next_page_token: None,
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search_time_ms: 100,
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results: search_results.results,
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total_size: search_results.total_size,
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next_page_token: search_results.next_page_token,
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search_time_ms,
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searched_at: chrono::Utc::now(),
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})
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}
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@ -264,10 +276,321 @@ mod tests {
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storage_bucket_name: "test-bucket".to_string(),
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data_store_id: "test-store".to_string(),
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};
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let serialized = serde_json::to_string(&config_info).unwrap();
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assert!(serialized.contains("test-project"));
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}
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#[test]
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fn test_search_request_creation() {
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use crate::data::models::outfit_search::{SearchConfig, RelevanceThreshold};
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let request = SearchRequest {
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query: "牛仔裤搭配".to_string(),
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config: SearchConfig {
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relevance_threshold: RelevanceThreshold::High,
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categories: vec!["上装".to_string(), "下装".to_string()],
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environments: vec!["Outdoor".to_string()],
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color_filters: std::collections::HashMap::new(),
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design_styles: std::collections::HashMap::new(),
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max_keywords: 10,
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},
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page_size: 9,
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page_offset: 0,
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};
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assert_eq!(request.query, "牛仔裤搭配");
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assert_eq!(request.page_size, 9);
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assert_eq!(request.config.categories.len(), 2);
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}
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#[test]
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fn test_search_filter_builder() {
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use crate::data::models::outfit_search::SearchConfig;
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let mut config = SearchConfig::default();
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config.categories = vec!["上装".to_string()];
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config.environments = vec!["Outdoor".to_string()];
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let filters = SearchFilterBuilder::build_filters(&config);
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assert!(filters.contains("上装") || filters.contains("Outdoor"));
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}
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#[test]
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fn test_query_keywords_builder() {
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use crate::data::models::outfit_search::SearchConfig;
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let mut config = SearchConfig::default();
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config.environments = vec!["Outdoor".to_string()];
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let keywords = SearchFilterBuilder::build_query_keywords(&config);
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assert!(keywords.contains(&"Outdoor".to_string()));
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}
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}
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/// 执行 Vertex AI Search 搜索
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async fn execute_vertex_ai_search(
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_gemini_service: &mut GeminiService,
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request: &SearchRequest,
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) -> Result<SearchResponse, anyhow::Error> {
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// 1. 获取访问令牌（通过直接调用API）
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let access_token = get_google_access_token().await?;
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// 2. 获取全局配置
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let global_config = OutfitSearchGlobalConfig::default();
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// 3. 构建搜索过滤器
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let search_filter = SearchFilterBuilder::build_filters(&request.config);
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// 4. 构建查询关键词
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let query_keywords = SearchFilterBuilder::build_query_keywords(&request.config);
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// 5. 组合查询字符串
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let enhanced_query = if query_keywords.is_empty() {
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request.query.clone()
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} else {
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format!("{} {}", request.query, query_keywords.join(" "))
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};
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// 6. 构建请求负载
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let mut payload = serde_json::json!({
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"query": enhanced_query,
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"relevanceThreshold": request.config.relevance_threshold.to_value().to_string(),
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"relevanceScoreSpec": {
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"returnRelevanceScore": true
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},
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"pageSize": request.page_size,
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"offset": request.page_offset
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});
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// 7. 添加过滤器（如果有）
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if !search_filter.is_empty() {
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payload["filter"] = serde_json::Value::String(search_filter);
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}
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// 8. 构建请求URL
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let search_url = format!(
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"https://discoveryengine.googleapis.com/v1beta/projects/{}/locations/global/collections/default_collection/engines/{}/servingConfigs/default_search:search",
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global_config.google_project_id,
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global_config.vertex_ai_app_id
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);
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// 9. 发送HTTP请求
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let client = reqwest::Client::new();
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let response = client
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.post(&search_url)
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.header("Authorization", format!("Bearer {}", access_token))
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.header("Content-Type", "application/json")
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.json(&payload)
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.send()
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.await?;
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let status = response.status();
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let response_text = response.text().await?;
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if !status.is_success() {
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return Err(anyhow::anyhow!("Vertex AI Search 请求失败: {} - {}", status, response_text));
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}
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// 10. 解析响应
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let vertex_response: serde_json::Value = serde_json::from_str(&response_text)?;
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// 11. 转换为我们的搜索结果格式
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let search_results = convert_vertex_response_to_search_results(&vertex_response, request)?;
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Ok(search_results)
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}
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/// 获取 Google 访问令牌
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async fn get_google_access_token() -> Result<String, anyhow::Error> {
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let config = GeminiConfig::default();
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let client = reqwest::Client::new();
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let url = format!("{}/google/access-token", config.base_url);
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let response = client
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.get(&url)
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.header("Authorization", format!("Bearer {}", config.bearer_token))
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.send()
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.await?;
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let status = response.status();
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if !status.is_success() {
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let error_body = response.text().await.unwrap_or_default();
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return Err(anyhow::anyhow!("获取访问令牌失败: {} - {}", status, error_body));
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}
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let response_text = response.text().await?;
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let token_response: serde_json::Value = serde_json::from_str(&response_text)?;
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let access_token = token_response
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.get("access_token")
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.and_then(|v| v.as_str())
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.ok_or_else(|| anyhow::anyhow!("访问令牌响应中未找到 access_token 字段"))?;
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Ok(access_token.to_string())
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}
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/// 将 Vertex AI Search 响应转换为我们的搜索结果格式
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fn convert_vertex_response_to_search_results(
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vertex_response: &serde_json::Value,
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request: &SearchRequest,
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) -> Result<SearchResponse, anyhow::Error> {
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let mut results = Vec::new();
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// 解析 Vertex AI Search 响应
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if let Some(vertex_results) = vertex_response.get("results").and_then(|v| v.as_array()) {
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for vertex_result in vertex_results {
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if let Ok(search_result) = parse_vertex_result_to_search_result(vertex_result) {
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// 应用相关性阈值过滤
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if search_result.relevance_score >= request.config.relevance_threshold.to_value() {
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results.push(search_result);
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}
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}
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}
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}
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let total_size = vertex_response
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.get("totalSize")
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.and_then(|v| v.as_u64())
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.unwrap_or(results.len() as u64) as usize;
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let next_page_token = vertex_response
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.get("nextPageToken")
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.and_then(|v| v.as_str())
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.map(|s| s.to_string());
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Ok(SearchResponse {
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results,
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total_size,
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next_page_token,
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search_time_ms: 0, // 将在调用方设置
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searched_at: chrono::Utc::now(),
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})
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}
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/// 解析单个 Vertex AI Search 结果为我们的搜索结果格式
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fn parse_vertex_result_to_search_result(vertex_result: &serde_json::Value) -> Result<SearchResult, anyhow::Error> {
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// 获取文档数据
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let document = vertex_result
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.get("document")
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.ok_or_else(|| anyhow::anyhow!("Vertex result missing document field"))?;
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// 获取结构化数据
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let struct_data = document
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.get("structData")
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.ok_or_else(|| anyhow::anyhow!("Document missing structData field"))?;
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// 解析基本信息
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let id = document
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.get("id")
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.and_then(|v| v.as_str())
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.unwrap_or(&format!("result_{}", chrono::Utc::now().timestamp()))
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.to_string();
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// 从 structData 中提取信息
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let style_description = struct_data
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.get("style_description")
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.and_then(|v| v.as_str())
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.unwrap_or("时尚搭配")
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.to_string();
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let environment_tags = struct_data
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.get("environment_tags")
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.and_then(|v| v.as_array())
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.map(|arr| {
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arr.iter()
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.filter_map(|v| v.as_str())
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.map(|s| s.to_string())
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.collect()
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})
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.unwrap_or_else(|| vec!["日常".to_string()]);
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// 解析产品信息
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let products = struct_data
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.get("products")
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.and_then(|v| v.as_array())
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.map(|arr| {
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arr.iter()
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.filter_map(|v| parse_vertex_product_info(v).ok())
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.collect()
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})
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.unwrap_or_else(Vec::new);
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// 获取图片URL（可能在不同的字段中）
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let image_url = struct_data
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.get("uri")
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.or_else(|| struct_data.get("image_url"))
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.or_else(|| struct_data.get("url"))
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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// 获取相关性评分
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let relevance_score = vertex_result
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.get("modelScores")
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.and_then(|scores| scores.get("relevance"))
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.and_then(|score| score.get("values"))
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.and_then(|values| values.as_array())
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.and_then(|arr| arr.first())
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.and_then(|v| v.as_f64())
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.unwrap_or(0.8);
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Ok(SearchResult {
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id,
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image_url,
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style_description,
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environment_tags,
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products,
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relevance_score,
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})
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}
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/// 解析 Vertex AI Search 中的产品信息
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fn parse_vertex_product_info(value: &serde_json::Value) -> Result<ProductInfo, anyhow::Error> {
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use crate::data::models::gemini_analysis::ColorHSV;
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let category = value
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.get("category")
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.and_then(|v| v.as_str())
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.unwrap_or("服装")
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.to_string();
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let description = value
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.get("description")
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.and_then(|v| v.as_str())
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.unwrap_or("时尚单品")
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.to_string();
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let color_pattern = value
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.get("color_pattern")
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.and_then(|v| {
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let hue = v.get("Hue").or_else(|| v.get("hue")).and_then(|h| h.as_f64()).unwrap_or(0.0);
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let saturation = v.get("Saturation").or_else(|| v.get("saturation")).and_then(|s| s.as_f64()).unwrap_or(0.5);
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let value = v.get("Value").or_else(|| v.get("value")).and_then(|val| val.as_f64()).unwrap_or(0.8);
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Some(ColorHSV::new(hue, saturation, value))
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})
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.unwrap_or_else(|| ColorHSV::new(0.0, 0.5, 0.8));
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let design_styles = value
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.get("design_styles")
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.and_then(|v| v.as_array())
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.map(|arr| {
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arr.iter()
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.filter_map(|v| v.as_str())
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.map(|s| s.to_string())
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.collect()
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})
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.unwrap_or_else(|| vec!["时尚".to_string()]);
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Ok(ProductInfo {
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category,
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description,
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color_pattern,
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design_styles,
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})
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}
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/// 获取所有服装搜索相关的Tauri命令名称
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|
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@ -284,3 +607,5 @@ pub fn get_outfit_search_command_names() -> Vec<&'static str> {
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"get_outfit_search_config",
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]
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}
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|
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@ -117,7 +117,7 @@ pub async fn get_rag_grounding_config(
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::infrastructure::gemini_service::RagGroundingConfig;
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#[test]
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|
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@ -0,0 +1,190 @@
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# 服装搭配搜索功能实现
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|
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## 概述
|
||||
|
||||
本文档描述了服装搭配搜索功能的实现，该功能直接调用 Google Vertex AI Search API 进行搜索，而不是通过 RAG Grounding。
|
||||
|
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## 核心功能
|
||||
|
||||
### 1. 直接 Vertex AI Search 调用
|
||||
|
||||
- **API 端点**: `https://discoveryengine.googleapis.com/v1beta/projects/{project_id}/locations/global/collections/default_collection/engines/{app_id}/servingConfigs/default_search:search`
|
||||
- **认证**: 通过 Google 访问令牌进行认证
|
||||
- **数据存储**: 使用配置的 `vertex_ai_app_id` 和 `data_store_id`
|
||||
|
||||
### 2. 搜索配置
|
||||
|
||||
```rust
|
||||
pub struct SearchRequest {
|
||||
pub query: String, // 搜索查询字符串
|
||||
pub config: SearchConfig, // 搜索配置
|
||||
pub page_size: usize, // 页面大小
|
||||
pub page_offset: usize, // 页面偏移量
|
||||
}
|
||||
|
||||
pub struct SearchConfig {
|
||||
pub relevance_threshold: RelevanceThreshold, // 相关性阈值
|
||||
pub environments: Vec<String>, // 环境标签过滤
|
||||
pub categories: Vec<String>, // 类别过滤
|
||||
pub color_filters: HashMap<String, ColorFilter>, // 颜色过滤器
|
||||
pub design_styles: HashMap<String, Vec<String>>, // 设计风格过滤
|
||||
pub max_keywords: usize, // 最大关键词数量
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 搜索流程
|
||||
|
||||
1. **获取访问令牌**: 通过 Modal.run API 获取 Google 访问令牌
|
||||
2. **构建搜索过滤器**: 根据搜索配置构建 Vertex AI Search 过滤器
|
||||
3. **组合查询字符串**: 将用户查询与关键词组合
|
||||
4. **发送 HTTP 请求**: 直接调用 Vertex AI Search API
|
||||
5. **解析响应**: 将 Vertex AI 响应转换为应用程序格式
|
||||
6. **应用过滤**: 根据相关性阈值过滤结果
|
||||
|
||||
### 4. 响应格式转换
|
||||
|
||||
```rust
|
||||
pub struct SearchResult {
|
||||
pub id: String, // 结果ID
|
||||
pub image_url: String, // 图片URL
|
||||
pub style_description: String, // 风格描述
|
||||
pub environment_tags: Vec<String>, // 环境标签
|
||||
pub products: Vec<ProductInfo>, // 产品信息列表
|
||||
pub relevance_score: f64, // 相关性评分
|
||||
}
|
||||
|
||||
pub struct ProductInfo {
|
||||
pub category: String, // 产品类别
|
||||
pub description: String, // 产品描述
|
||||
pub color_pattern: ColorHSV, // 主要颜色
|
||||
pub design_styles: Vec<String>, // 设计风格
|
||||
}
|
||||
```
|
||||
|
||||
## 实现细节
|
||||
|
||||
### 1. 访问令牌获取
|
||||
|
||||
```rust
|
||||
async fn get_google_access_token() -> Result<String, anyhow::Error> {
|
||||
let config = GeminiConfig::default();
|
||||
let client = reqwest::Client::new();
|
||||
|
||||
let url = format!("{}/google/access-token", config.base_url);
|
||||
|
||||
let response = client
|
||||
.get(&url)
|
||||
.header("Authorization", format!("Bearer {}", config.bearer_token))
|
||||
.send()
|
||||
.await?;
|
||||
|
||||
// 解析访问令牌...
|
||||
}
|
||||
```
|
||||
|
||||
### 2. 搜索请求构建
|
||||
|
||||
```rust
|
||||
let mut payload = serde_json::json!({
|
||||
"query": enhanced_query,
|
||||
"relevanceThreshold": request.config.relevance_threshold.to_value().to_string(),
|
||||
"relevanceScoreSpec": {
|
||||
"returnRelevanceScore": true
|
||||
},
|
||||
"pageSize": request.page_size,
|
||||
"offset": request.page_offset
|
||||
});
|
||||
|
||||
// 添加过滤器（如果有）
|
||||
if !search_filter.is_empty() {
|
||||
payload["filter"] = serde_json::Value::String(search_filter);
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 响应解析
|
||||
|
||||
- 从 Vertex AI Search 响应中提取 `results` 数组
|
||||
- 解析每个结果的 `document.structData` 字段
|
||||
- 提取图片URL、风格描述、环境标签等信息
|
||||
- 解析产品信息和颜色模式
|
||||
- 获取相关性评分并应用阈值过滤
|
||||
|
||||
## 配置参数
|
||||
|
||||
### 全局配置
|
||||
|
||||
```rust
|
||||
impl Default for OutfitSearchGlobalConfig {
|
||||
fn default() -> Self {
|
||||
Self {
|
||||
google_project_id: "gen-lang-client-0413414134".to_string(),
|
||||
vertex_ai_app_id: "jeans-search_1751353769585".to_string(),
|
||||
storage_bucket_name: "fashion_image_block".to_string(),
|
||||
data_store_id: "jeans_pattern_data_store".to_string(),
|
||||
cloudflare_project_id: "67720b647ff2b55cf37ba3ef9e677083".to_string(),
|
||||
cloudflare_gateway_id: "bowong-dev".to_string(),
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 相关性阈值
|
||||
|
||||
- **LOWEST**: 0.3 - 显示更多相关结果
|
||||
- **LOW**: 0.5 - 包含较多相关结果
|
||||
- **MEDIUM**: 0.7 - 平衡相关性和数量
|
||||
- **HIGH**: 0.9 - 只显示高度相关结果
|
||||
|
||||
## 错误处理
|
||||
|
||||
- 访问令牌获取失败
|
||||
- HTTP 请求失败
|
||||
- JSON 解析错误
|
||||
- 数据格式不匹配
|
||||
- 网络超时
|
||||
|
||||
## 测试
|
||||
|
||||
实现了以下测试用例：
|
||||
|
||||
1. `test_search_request_creation` - 测试搜索请求创建
|
||||
2. `test_search_filter_builder` - 测试搜索过滤器构建
|
||||
3. `test_query_keywords_builder` - 测试查询关键词构建
|
||||
|
||||
## 使用示例
|
||||
|
||||
```typescript
|
||||
import { OutfitSearchService } from '../services/outfitSearchService';
|
||||
|
||||
const searchRequest = {
|
||||
query: "牛仔裤搭配",
|
||||
config: {
|
||||
relevance_threshold: "HIGH",
|
||||
categories: ["上装", "下装"],
|
||||
environments: ["Outdoor"],
|
||||
color_filters: {},
|
||||
design_styles: {},
|
||||
max_keywords: 10
|
||||
},
|
||||
page_size: 9,
|
||||
page_offset: 0
|
||||
};
|
||||
|
||||
const response = await OutfitSearchService.searchSimilarOutfits(searchRequest);
|
||||
console.log(`找到 ${response.total_size} 个搜索结果`);
|
||||
```
|
||||
|
||||
## 性能优化
|
||||
|
||||
- 使用连接池减少HTTP连接开销
|
||||
- 实现访问令牌缓存机制
|
||||
- 支持分页查询避免大量数据传输
|
||||
- 客户端相关性过滤减少无效结果
|
||||
|
||||
## 未来改进
|
||||
|
||||
1. 添加向量搜索支持
|
||||
2. 实现搜索结果缓存
|
||||
3. 支持更复杂的过滤条件
|
||||
4. 添加搜索分析和统计
|
||||
5. 实现搜索建议优化
|
||||
Loading…
Reference in New Issue