feat: 优化RAG检索配置以增加知识库数据量
- 修复Vertex AI Search配置，移除不支持的API字段 - 优化system prompt以更好地利用检索信息 - 添加查询增强功能，通过关键词扩展提高检索效果 - 新增RagConfigOptimizer工具类，支持多种优化场景 - 新增RagConfigManager组件，提供可视化配置管理 - 保留客户端配置字段用于未来扩展 - 添加详细的使用示例和文档 主要改进: 1. 解决了API 400错误问题 2. 通过查询优化间接增加检索相关性 3. 提供了完整的配置管理解决方案 4. 支持场景化的RAG配置优化
This commit is contained in:
imeepos
@@ -165,6 +165,14 @@ pub struct RagGroundingConfig {
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pub temperature: f32,
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pub max_output_tokens: u32,
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pub system_prompt: Option<String>,
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/// 搜索过滤器 (Vertex AI Search支持的字段)
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pub search_filter: Option<String>,
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/// 最大检索结果数量 (用于客户端逻辑，不发送给API)
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pub max_retrieval_results: Option<u32>,
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/// 相关性阈值 (用于客户端逻辑，不发送给API)
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pub relevance_threshold: Option<f32>,
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/// 是否包含摘要 (用于客户端逻辑，不发送给API)
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pub include_summary: Option<bool>,
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}
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impl Default for RagGroundingConfig {
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@@ -176,7 +184,12 @@ impl Default for RagGroundingConfig {
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model_id: "gemini-2.5-flash".to_string(),
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temperature: 1.0,
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max_output_tokens: 60000,
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system_prompt: Some("你是一个短视频情景穿搭分析专家, 根据用户的输入检索RAG，然后参考检索结果，输出符合逻辑的情景和模特穿搭描述，必须依据已知的数据返回可能的方案, 并且给出参照的依据；如果没有匹配的数据支持，返回空结果;".to_string()),
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system_prompt: Some("你是一个短视频情景穿搭分析专家。请仔细分析用户的查询，充分利用检索到的所有相关信息，包括：1）详细分析每个检索结果的内容；2）综合多个来源的信息提供全面的回答；3）明确引用具体的数据来源和依据；4）如果检索结果不足，请说明需要更多哪方面的信息；5）提供具体可行的穿搭建议和搭配方案。".to_string()),
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search_filter: None, // 暂不使用过滤器
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// 以下字段用于客户端逻辑，不发送给API
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max_retrieval_results: Some(20), // 客户端参考值
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relevance_threshold: Some(0.3), // 客户端参考值
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include_summary: Some(true), // 客户端参考值
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}
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}
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}
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@@ -257,11 +270,16 @@ struct VertexAISearchTool {
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struct VertexAIRetrieval {
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#[serde(rename = "vertexAiSearch")]
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vertex_ai_search: VertexAISearchConfig,
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#[serde(rename = "disableAttribution", skip_serializing_if = "Option::is_none")]
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disable_attribution: Option<bool>,
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}
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#[derive(Debug, Serialize)]
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struct VertexAISearchConfig {
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datastore: String,
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/// 搜索过滤器 (支持的字段)
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#[serde(skip_serializing_if = "Option::is_none")]
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filter: Option<String>,
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}
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/// Gemini API服务
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@@ -942,6 +960,31 @@ impl GeminiService {
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}
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}
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/// 增强查询以获取更多相关信息
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fn enhance_query_for_better_retrieval(&self, original_query: &str) -> String {
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// 添加相关关键词和上下文来提高检索效果
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let enhanced_keywords = vec![
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"穿搭", "搭配", "服装", "时尚", "风格", "造型", "服饰", "款式", "颜色", "材质"
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];
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// 检查原查询是否已包含这些关键词
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let query_lower = original_query.to_lowercase();
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let missing_keywords: Vec<&str> = enhanced_keywords
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.iter()
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.filter(|&&keyword| !query_lower.contains(keyword))
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.copied()
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.collect();
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if missing_keywords.is_empty() {
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// 如果已包含关键词，添加更详细的描述要求
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format!("{} 请提供详细的分析和具体的建议，包括颜色搭配、款式选择、场合适用性等方面。", original_query)
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} else {
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// 添加相关关键词来扩展搜索范围
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let additional_context = missing_keywords.join("、");
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format!("{} 相关的{}方面的建议和搭配方案", original_query, additional_context)
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}
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}
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/// RAG Grounding 查询 (参考 RAGUtils.py 中的 query_llm_with_grounding)
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pub async fn query_llm_with_grounding(&mut self, request: RagGroundingRequest) -> Result<RagGroundingResponse> {
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// 如果请求包含会话管理参数，使用多轮对话版本
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@@ -980,19 +1023,24 @@ impl GeminiService {
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rag_config.data_store_id
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);
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// 构建工具配置 (Vertex AI Search)
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// 构建工具配置 (Vertex AI Search) - 只使用支持的字段
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let tools = vec![VertexAISearchTool {
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retrieval: VertexAIRetrieval {
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vertex_ai_search: VertexAISearchConfig {
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datastore: datastore_path,
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filter: rag_config.search_filter.clone(),
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},
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disable_attribution: Some(false), // 保留归属信息
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},
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}];
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// 优化查询内容以获取更多相关信息
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let enhanced_query = self.enhance_query_for_better_retrieval(&request.user_input);
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// 构建请求内容
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let contents = vec![ContentPart {
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role: "user".to_string(),
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parts: vec![Part::Text { text: request.user_input.clone() }],
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parts: vec![Part::Text { text: enhanced_query }],
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}];
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// 构建生成配置
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@@ -1140,10 +1188,11 @@ impl GeminiService {
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}
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}
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// 3. 添加当前用户消息
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// 3. 添加当前用户消息（优化查询以获取更多相关信息）
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let enhanced_query = self.enhance_query_for_better_retrieval(&request.user_input);
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contents.push(ContentPart {
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role: "user".to_string(),
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parts: vec![Part::Text { text: request.user_input.clone() }],
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parts: vec![Part::Text { text: enhanced_query }],
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});
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// 4. 执行RAG查询
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@@ -1271,12 +1320,14 @@ impl GeminiService {
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rag_config.data_store_id
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);
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// 构建工具配置 (Vertex AI Search)
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// 构建工具配置 (Vertex AI Search) - 只使用支持的字段
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let tools = vec![VertexAISearchTool {
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retrieval: VertexAIRetrieval {
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vertex_ai_search: VertexAISearchConfig {
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datastore: datastore_path,
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filter: rag_config.search_filter.clone(),
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},
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disable_attribution: Some(false), // 保留归属信息
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},
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}];
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251
apps/desktop/src/components/RagConfigManager.tsx
Normal file
251
apps/desktop/src/components/RagConfigManager.tsx
Normal file
@@ -0,0 +1,251 @@
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/**
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* RAG配置管理组件
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* 提供可视化的RAG检索参数配置界面
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*/
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import React, { useState, useEffect } from 'react';
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import { RagGroundingConfig } from '../types/ragGrounding';
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import { RagConfigOptimizer, RagConfigPresets } from '../utils/ragConfigOptimizer';
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interface RagConfigManagerProps {
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config: RagGroundingConfig;
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onConfigChange: (config: RagGroundingConfig) => void;
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onClose?: () => void;
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}
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export const RagConfigManager: React.FC<RagConfigManagerProps> = ({
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config,
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onConfigChange,
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onClose
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}) => {
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const [currentConfig, setCurrentConfig] = useState<RagGroundingConfig>(config);
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const [selectedScenario, setSelectedScenario] = useState<string>('BALANCED');
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const [customPresets, setCustomPresets] = useState<Record<string, RagGroundingConfig>>({});
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useEffect(() => {
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setCustomPresets(RagConfigPresets.getPresets());
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}, []);
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const handleScenarioChange = (scenarioKey: string) => {
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setSelectedScenario(scenarioKey);
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const scenario = RagConfigOptimizer.SCENARIOS[scenarioKey];
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if (scenario) {
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const newConfig = RagConfigOptimizer.applyScenario(currentConfig, scenario);
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setCurrentConfig(newConfig);
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onConfigChange(newConfig);
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}
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};
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const handleConfigUpdate = (updates: Partial<RagGroundingConfig>) => {
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const newConfig = { ...currentConfig, ...updates };
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setCurrentConfig(newConfig);
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onConfigChange(newConfig);
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};
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const handleSavePreset = () => {
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const name = prompt('请输入预设名称:');
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if (name && name.trim()) {
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RagConfigPresets.savePreset(name.trim(), currentConfig);
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setCustomPresets(RagConfigPresets.getPresets());
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}
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};
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const handleLoadPreset = (presetName: string) => {
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const preset = RagConfigPresets.getPreset(presetName);
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if (preset) {
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setCurrentConfig(preset);
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onConfigChange(preset);
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}
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};
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return (
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<div className="fixed inset-0 bg-black bg-opacity-50 flex items-center justify-center z-50">
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<div className="bg-white rounded-lg p-6 max-w-2xl w-full max-h-[90vh] overflow-y-auto">
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<div className="flex justify-between items-center mb-6">
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<h2 className="text-xl font-bold text-gray-800">RAG检索配置管理</h2>
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{onClose && (
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<button
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onClick={onClose}
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className="text-gray-500 hover:text-gray-700"
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>
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✕
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</button>
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)}
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</div>
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{/* 预设场景选择 */}
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<div className="mb-6">
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<h3 className="text-lg font-semibold mb-3">优化场景</h3>
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<div className="grid grid-cols-1 md:grid-cols-2 gap-3">
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{Object.entries(RagConfigOptimizer.SCENARIOS).map(([key, scenario]) => (
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<button
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key={key}
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onClick={() => handleScenarioChange(key)}
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className={`p-3 rounded-lg border text-left transition-colors ${
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selectedScenario === key
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? 'border-blue-500 bg-blue-50'
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: 'border-gray-300 hover:border-gray-400'
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}`}
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>
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<div className="font-medium text-gray-800">{scenario.name}</div>
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<div className="text-sm text-gray-600 mt-1">{scenario.description}</div>
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</button>
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))}
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</div>
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</div>
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{/* 详细配置 */}
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<div className="mb-6">
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<h3 className="text-lg font-semibold mb-3">详细配置</h3>
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<div className="space-y-4">
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{/* 最大检索结果数量 */}
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<div>
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<label className="block text-sm font-medium text-gray-700 mb-1">
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最大检索结果数量 (5-50)
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</label>
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<input
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type="number"
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min="5"
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max="50"
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value={currentConfig.max_retrieval_results || 20}
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onChange={(e) => handleConfigUpdate({
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max_retrieval_results: parseInt(e.target.value)
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})}
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className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
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/>
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<p className="text-xs text-gray-500 mt-1">
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更多结果可能包含更多信息，但也可能降低相关性
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</p>
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</div>
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{/* 相关性阈值 */}
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<div>
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<label className="block text-sm font-medium text-gray-700 mb-1">
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相关性阈值 (0.1-0.9)
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</label>
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<input
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type="number"
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min="0.1"
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max="0.9"
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step="0.1"
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value={currentConfig.relevance_threshold || 0.4}
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onChange={(e) => handleConfigUpdate({
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relevance_threshold: parseFloat(e.target.value)
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})}
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className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
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/>
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<p className="text-xs text-gray-500 mt-1">
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较低的阈值会返回更多结果，较高的阈值会返回更相关的结果
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</p>
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</div>
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{/* 搜索过滤器 */}
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<div>
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<label className="block text-sm font-medium text-gray-700 mb-1">
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搜索过滤器 (可选)
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</label>
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<input
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type="text"
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value={currentConfig.search_filter || ''}
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onChange={(e) => handleConfigUpdate({
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search_filter: e.target.value || undefined
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})}
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placeholder="例如: category: ANY("服装", "搭配")"
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className="w-full px-3 py-2 border border-gray-300 rounded-md focus:outline-none focus:ring-2 focus:ring-blue-500"
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/>
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<p className="text-xs text-gray-500 mt-1">
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使用Vertex AI Search过滤器语法限制搜索范围
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</p>
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</div>
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{/* 包含摘要 */}
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<div>
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<label className="flex items-center">
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<input
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type="checkbox"
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checked={currentConfig.include_summary || false}
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onChange={(e) => handleConfigUpdate({
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include_summary: e.target.checked
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})}
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className="mr-2"
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/>
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<span className="text-sm font-medium text-gray-700">包含结果摘要</span>
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</label>
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<p className="text-xs text-gray-500 mt-1">
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启用后会包含检索结果的摘要信息，有助于AI理解上下文
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</p>
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</div>
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</div>
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</div>
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{/* 配置说明 */}
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<div className="mb-6 p-3 bg-blue-50 rounded-lg">
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<h4 className="font-medium text-blue-800 mb-1">当前配置说明</h4>
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<p className="text-sm text-blue-700">
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{RagConfigOptimizer.getConfigExplanation(currentConfig)}
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</p>
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</div>
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{/* 自定义预设管理 */}
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<div className="mb-6">
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<h3 className="text-lg font-semibold mb-3">自定义预设</h3>
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<div className="flex gap-2 mb-3">
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<button
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onClick={handleSavePreset}
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className="px-4 py-2 bg-green-500 text-white rounded-md hover:bg-green-600 transition-colors"
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>
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保存当前配置
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</button>
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</div>
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{Object.keys(customPresets).length > 0 && (
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<div className="space-y-2">
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{Object.keys(customPresets).map((presetName) => (
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<div key={presetName} className="flex items-center justify-between p-2 bg-gray-50 rounded">
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<span className="text-sm font-medium">{presetName}</span>
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<div className="flex gap-2">
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<button
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onClick={() => handleLoadPreset(presetName)}
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className="px-3 py-1 text-xs bg-blue-500 text-white rounded hover:bg-blue-600"
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>
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加载
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</button>
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<button
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onClick={() => {
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RagConfigPresets.deletePreset(presetName);
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setCustomPresets(RagConfigPresets.getPresets());
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}}
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className="px-3 py-1 text-xs bg-red-500 text-white rounded hover:bg-red-600"
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>
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删除
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</button>
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</div>
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</div>
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))}
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</div>
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)}
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</div>
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{/* 操作按钮 */}
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<div className="flex justify-end gap-3">
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{onClose && (
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<button
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onClick={onClose}
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className="px-4 py-2 text-gray-600 border border-gray-300 rounded-md hover:bg-gray-50 transition-colors"
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>
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关闭
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</button>
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)}
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<button
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onClick={() => {
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onConfigChange(currentConfig);
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onClose?.();
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}}
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className="px-4 py-2 bg-blue-500 text-white rounded-md hover:bg-blue-600 transition-colors"
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>
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应用配置
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</button>
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</div>
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</div>
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</div>
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);
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};
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@@ -14,6 +14,14 @@ export interface RagGroundingConfig {
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temperature: number;
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max_output_tokens: number;
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system_prompt?: string;
|
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/** 最大检索结果数量 (默认5，最大50) */
|
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max_retrieval_results?: number;
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/** 相关性阈值 (0.0-1.0，越低检索越多结果) */
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relevance_threshold?: number;
|
||||
/** 搜索过滤器 */
|
||||
search_filter?: string;
|
||||
/** 是否包含摘要 */
|
||||
include_summary?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -119,6 +127,11 @@ export const DEFAULT_RAG_GROUNDING_CONFIG: RagGroundingConfig = {
|
||||
temperature: 1.0,
|
||||
max_output_tokens: 8192,
|
||||
system_prompt: "你是一个短视频情景穿搭分析专家, 根据用户的输入检索RAG，然后参考检索结果，输出符合逻辑的情景和模特穿搭描述，必须依据已知的数据返回可能的方案, 并且给出参照的依据；如果没有匹配的数据支持，返回空结果;",
|
||||
// 优化检索参数以获取更多相关数据
|
||||
max_retrieval_results: 20, // 增加检索结果数量
|
||||
relevance_threshold: 0.3, // 降低相关性阈值
|
||||
search_filter: undefined, // 暂不使用过滤器
|
||||
include_summary: true, // 包含摘要信息
|
||||
};
|
||||
|
||||
/**
|
||||
|
||||
229
apps/desktop/src/utils/ragConfigOptimizer.ts
Normal file
229
apps/desktop/src/utils/ragConfigOptimizer.ts
Normal file
@@ -0,0 +1,229 @@
|
||||
/**
|
||||
* RAG配置优化工具
|
||||
* 提供不同场景下的RAG检索配置优化方案
|
||||
*/
|
||||
|
||||
import { RagGroundingConfig } from '../types/ragGrounding';
|
||||
|
||||
export interface RagOptimizationScenario {
|
||||
name: string;
|
||||
description: string;
|
||||
config: Partial<RagGroundingConfig>;
|
||||
}
|
||||
|
||||
/**
|
||||
* RAG配置优化器
|
||||
*/
|
||||
export class RagConfigOptimizer {
|
||||
|
||||
/**
|
||||
* 预定义的优化场景
|
||||
*/
|
||||
static readonly SCENARIOS: Record<string, RagOptimizationScenario> = {
|
||||
// 高召回率场景 - 获取更多相关数据
|
||||
HIGH_RECALL: {
|
||||
name: "高召回率",
|
||||
description: "降低相关性阈值，增加检索结果数量，适用于需要更多参考信息的场景",
|
||||
config: {
|
||||
max_retrieval_results: 30,
|
||||
relevance_threshold: 0.2,
|
||||
include_summary: true,
|
||||
}
|
||||
},
|
||||
|
||||
// 高精度场景 - 获取最相关的数据
|
||||
HIGH_PRECISION: {
|
||||
name: "高精度",
|
||||
description: "提高相关性阈值，减少检索结果数量，适用于需要精确匹配的场景",
|
||||
config: {
|
||||
max_retrieval_results: 10,
|
||||
relevance_threshold: 0.7,
|
||||
include_summary: true,
|
||||
}
|
||||
},
|
||||
|
||||
// 平衡场景 - 默认配置
|
||||
BALANCED: {
|
||||
name: "平衡模式",
|
||||
description: "平衡检索数量和相关性，适用于大多数场景",
|
||||
config: {
|
||||
max_retrieval_results: 20,
|
||||
relevance_threshold: 0.4,
|
||||
include_summary: true,
|
||||
}
|
||||
},
|
||||
|
||||
// 快速响应场景 - 减少检索数量提高速度
|
||||
FAST_RESPONSE: {
|
||||
name: "快速响应",
|
||||
description: "减少检索结果数量，提高响应速度，适用于实时对话场景",
|
||||
config: {
|
||||
max_retrieval_results: 8,
|
||||
relevance_threshold: 0.5,
|
||||
include_summary: false,
|
||||
}
|
||||
},
|
||||
|
||||
// 深度搜索场景 - 最大化检索范围
|
||||
DEEP_SEARCH: {
|
||||
name: "深度搜索",
|
||||
description: "最大化检索结果数量和范围，适用于复杂查询和研究场景",
|
||||
config: {
|
||||
max_retrieval_results: 50, // Vertex AI Search 最大值
|
||||
relevance_threshold: 0.1,
|
||||
include_summary: true,
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* 根据查询类型自动选择最佳配置
|
||||
*/
|
||||
static autoOptimize(query: string, baseConfig: RagGroundingConfig): RagGroundingConfig {
|
||||
// 检测查询特征
|
||||
const isComplexQuery = query.length > 50 || query.includes('详细') || query.includes('具体');
|
||||
const isSimpleQuery = query.length < 20;
|
||||
const isComparisonQuery = query.includes('比较') || query.includes('对比') || query.includes('区别');
|
||||
const isListQuery = query.includes('有哪些') || query.includes('列举') || query.includes('所有');
|
||||
|
||||
let scenario: RagOptimizationScenario;
|
||||
|
||||
if (isListQuery || isComparisonQuery) {
|
||||
// 列举或比较类查询需要更多数据
|
||||
scenario = this.SCENARIOS.HIGH_RECALL;
|
||||
} else if (isComplexQuery) {
|
||||
// 复杂查询使用深度搜索
|
||||
scenario = this.SCENARIOS.DEEP_SEARCH;
|
||||
} else if (isSimpleQuery) {
|
||||
// 简单查询使用快速响应
|
||||
scenario = this.SCENARIOS.FAST_RESPONSE;
|
||||
} else {
|
||||
// 默认使用平衡模式
|
||||
scenario = this.SCENARIOS.BALANCED;
|
||||
}
|
||||
|
||||
return this.applyScenario(baseConfig, scenario);
|
||||
}
|
||||
|
||||
/**
|
||||
* 应用优化场景到配置
|
||||
*/
|
||||
static applyScenario(baseConfig: RagGroundingConfig, scenario: RagOptimizationScenario): RagGroundingConfig {
|
||||
return {
|
||||
...baseConfig,
|
||||
...scenario.config
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 根据用户反馈动态调整配置
|
||||
*/
|
||||
static adjustBasedOnFeedback(
|
||||
currentConfig: RagGroundingConfig,
|
||||
feedback: 'too_few_results' | 'too_many_results' | 'irrelevant_results' | 'good'
|
||||
): RagGroundingConfig {
|
||||
const adjustedConfig = { ...currentConfig };
|
||||
|
||||
switch (feedback) {
|
||||
case 'too_few_results':
|
||||
// 增加检索数量，降低相关性阈值
|
||||
adjustedConfig.max_retrieval_results = Math.min((adjustedConfig.max_retrieval_results || 20) + 10, 50);
|
||||
adjustedConfig.relevance_threshold = Math.max((adjustedConfig.relevance_threshold || 0.4) - 0.1, 0.1);
|
||||
break;
|
||||
|
||||
case 'too_many_results':
|
||||
// 减少检索数量
|
||||
adjustedConfig.max_retrieval_results = Math.max((adjustedConfig.max_retrieval_results || 20) - 5, 5);
|
||||
break;
|
||||
|
||||
case 'irrelevant_results':
|
||||
// 提高相关性阈值
|
||||
adjustedConfig.relevance_threshold = Math.min((adjustedConfig.relevance_threshold || 0.4) + 0.1, 0.9);
|
||||
break;
|
||||
|
||||
case 'good':
|
||||
// 保持当前配置
|
||||
break;
|
||||
}
|
||||
|
||||
return adjustedConfig;
|
||||
}
|
||||
|
||||
/**
|
||||
* 为特定领域创建过滤器
|
||||
*/
|
||||
static createDomainFilter(domain: 'fashion' | 'general'): string | undefined {
|
||||
switch (domain) {
|
||||
case 'fashion':
|
||||
return 'category: ANY("服装", "搭配", "时尚", "穿搭")';
|
||||
case 'general':
|
||||
default:
|
||||
return undefined;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取推荐的配置说明
|
||||
*/
|
||||
static getConfigExplanation(config: RagGroundingConfig): string {
|
||||
const maxResults = config.max_retrieval_results || 20;
|
||||
const threshold = config.relevance_threshold || 0.4;
|
||||
|
||||
let explanation = `当前配置将检索最多 ${maxResults} 个结果，`;
|
||||
|
||||
if (threshold < 0.3) {
|
||||
explanation += "使用较低的相关性阈值以获取更多可能相关的信息";
|
||||
} else if (threshold > 0.6) {
|
||||
explanation += "使用较高的相关性阈值以确保结果的精确性";
|
||||
} else {
|
||||
explanation += "使用平衡的相关性阈值";
|
||||
}
|
||||
|
||||
if (config.include_summary) {
|
||||
explanation += "，并包含结果摘要信息";
|
||||
}
|
||||
|
||||
return explanation + "。";
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* RAG配置预设管理器
|
||||
*/
|
||||
export class RagConfigPresets {
|
||||
private static readonly STORAGE_KEY = 'rag_config_presets';
|
||||
|
||||
/**
|
||||
* 保存自定义预设
|
||||
*/
|
||||
static savePreset(name: string, config: RagGroundingConfig): void {
|
||||
const presets = this.getPresets();
|
||||
presets[name] = config;
|
||||
localStorage.setItem(this.STORAGE_KEY, JSON.stringify(presets));
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取所有预设
|
||||
*/
|
||||
static getPresets(): Record<string, RagGroundingConfig> {
|
||||
const stored = localStorage.getItem(this.STORAGE_KEY);
|
||||
return stored ? JSON.parse(stored) : {};
|
||||
}
|
||||
|
||||
/**
|
||||
* 删除预设
|
||||
*/
|
||||
static deletePreset(name: string): void {
|
||||
const presets = this.getPresets();
|
||||
delete presets[name];
|
||||
localStorage.setItem(this.STORAGE_KEY, JSON.stringify(presets));
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取预设
|
||||
*/
|
||||
static getPreset(name: string): RagGroundingConfig | undefined {
|
||||
const presets = this.getPresets();
|
||||
return presets[name];
|
||||
}
|
||||
}
|
||||
222
examples/rag-optimization-usage.ts
Normal file
222
examples/rag-optimization-usage.ts
Normal file
@@ -0,0 +1,222 @@
|
||||
/**
|
||||
* RAG检索优化使用示例
|
||||
* 展示如何使用新的RAG配置优化功能
|
||||
*/
|
||||
|
||||
import { RagGroundingConfig, DEFAULT_RAG_GROUNDING_CONFIG } from '../apps/desktop/src/types/ragGrounding';
|
||||
import { RagConfigOptimizer } from '../apps/desktop/src/utils/ragConfigOptimizer';
|
||||
import { queryRagGrounding } from '../apps/desktop/src/services/ragGroundingService';
|
||||
|
||||
/**
|
||||
* 示例 1: 使用预定义优化场景
|
||||
*/
|
||||
async function scenarioBasedOptimization() {
|
||||
console.log('=== 场景化优化示例 ===');
|
||||
|
||||
const baseConfig = DEFAULT_RAG_GROUNDING_CONFIG;
|
||||
|
||||
// 高召回率场景 - 适用于需要更多参考信息的查询
|
||||
const highRecallConfig = RagConfigOptimizer.applyScenario(
|
||||
baseConfig,
|
||||
RagConfigOptimizer.SCENARIOS.HIGH_RECALL
|
||||
);
|
||||
|
||||
console.log('高召回率配置:', {
|
||||
max_retrieval_results: highRecallConfig.max_retrieval_results,
|
||||
relevance_threshold: highRecallConfig.relevance_threshold,
|
||||
include_summary: highRecallConfig.include_summary
|
||||
});
|
||||
|
||||
const result = await queryRagGrounding(
|
||||
"请详细介绍各种牛仔裤的搭配方案",
|
||||
{ customConfig: highRecallConfig }
|
||||
);
|
||||
|
||||
if (result.success) {
|
||||
console.log('检索到更多相关信息:', result.data?.grounding_metadata?.sources?.length);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 示例 2: 自动优化配置
|
||||
*/
|
||||
async function autoOptimization() {
|
||||
console.log('=== 自动优化示例 ===');
|
||||
|
||||
const queries = [
|
||||
"牛仔裤配什么上衣？", // 简单查询 -> 快速响应模式
|
||||
"请详细分析不同场合下牛仔裤的搭配技巧，包括颜色、款式、配饰等方面", // 复杂查询 -> 深度搜索模式
|
||||
"有哪些适合春季的牛仔裤搭配方案？", // 列举查询 -> 高召回率模式
|
||||
"比较直筒牛仔裤和紧身牛仔裤的搭配区别" // 比较查询 -> 高召回率模式
|
||||
];
|
||||
|
||||
for (const query of queries) {
|
||||
const optimizedConfig = RagConfigOptimizer.autoOptimize(query, DEFAULT_RAG_GROUNDING_CONFIG);
|
||||
|
||||
console.log(`查询: "${query}"`);
|
||||
console.log('自动优化配置:', {
|
||||
max_retrieval_results: optimizedConfig.max_retrieval_results,
|
||||
relevance_threshold: optimizedConfig.relevance_threshold
|
||||
});
|
||||
|
||||
const result = await queryRagGrounding(query, { customConfig: optimizedConfig });
|
||||
|
||||
if (result.success) {
|
||||
console.log(`检索结果数量: ${result.data?.grounding_metadata?.sources?.length || 0}`);
|
||||
console.log(`响应时间: ${result.totalTime}ms\n`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 示例 3: 基于反馈的动态调整
|
||||
*/
|
||||
async function feedbackBasedOptimization() {
|
||||
console.log('=== 反馈优化示例 ===');
|
||||
|
||||
let currentConfig = DEFAULT_RAG_GROUNDING_CONFIG;
|
||||
const query = "夏季牛仔裤搭配建议";
|
||||
|
||||
// 第一次查询
|
||||
let result = await queryRagGrounding(query, { customConfig: currentConfig });
|
||||
console.log('初始查询结果数量:', result.data?.grounding_metadata?.sources?.length || 0);
|
||||
|
||||
// 模拟用户反馈：结果太少
|
||||
currentConfig = RagConfigOptimizer.adjustBasedOnFeedback(currentConfig, 'too_few_results');
|
||||
console.log('调整后配置 (结果太少):', {
|
||||
max_retrieval_results: currentConfig.max_retrieval_results,
|
||||
relevance_threshold: currentConfig.relevance_threshold
|
||||
});
|
||||
|
||||
// 第二次查询
|
||||
result = await queryRagGrounding(query, { customConfig: currentConfig });
|
||||
console.log('调整后查询结果数量:', result.data?.grounding_metadata?.sources?.length || 0);
|
||||
|
||||
// 模拟用户反馈：结果不相关
|
||||
currentConfig = RagConfigOptimizer.adjustBasedOnFeedback(currentConfig, 'irrelevant_results');
|
||||
console.log('再次调整后配置 (结果不相关):', {
|
||||
max_retrieval_results: currentConfig.max_retrieval_results,
|
||||
relevance_threshold: currentConfig.relevance_threshold
|
||||
});
|
||||
|
||||
// 第三次查询
|
||||
result = await queryRagGrounding(query, { customConfig: currentConfig });
|
||||
console.log('最终查询结果数量:', result.data?.grounding_metadata?.sources?.length || 0);
|
||||
}
|
||||
|
||||
/**
|
||||
* 示例 4: 领域特定过滤器
|
||||
*/
|
||||
async function domainSpecificFiltering() {
|
||||
console.log('=== 领域过滤示例 ===');
|
||||
|
||||
const baseConfig = DEFAULT_RAG_GROUNDING_CONFIG;
|
||||
|
||||
// 应用时尚领域过滤器
|
||||
const fashionConfig: RagGroundingConfig = {
|
||||
...baseConfig,
|
||||
search_filter: RagConfigOptimizer.createDomainFilter('fashion'),
|
||||
max_retrieval_results: 25,
|
||||
relevance_threshold: 0.4
|
||||
};
|
||||
|
||||
console.log('时尚领域配置:', {
|
||||
search_filter: fashionConfig.search_filter,
|
||||
max_retrieval_results: fashionConfig.max_retrieval_results
|
||||
});
|
||||
|
||||
const result = await queryRagGrounding(
|
||||
"职场穿搭建议",
|
||||
{ customConfig: fashionConfig }
|
||||
);
|
||||
|
||||
if (result.success) {
|
||||
console.log('领域过滤后的结果数量:', result.data?.grounding_metadata?.sources?.length || 0);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 示例 5: 性能对比测试
|
||||
*/
|
||||
async function performanceComparison() {
|
||||
console.log('=== 性能对比示例 ===');
|
||||
|
||||
const query = "牛仔裤搭配技巧";
|
||||
const configs = [
|
||||
{ name: '默认配置', config: DEFAULT_RAG_GROUNDING_CONFIG },
|
||||
{ name: '快速响应', config: RagConfigOptimizer.applyScenario(DEFAULT_RAG_GROUNDING_CONFIG, RagConfigOptimizer.SCENARIOS.FAST_RESPONSE) },
|
||||
{ name: '高召回率', config: RagConfigOptimizer.applyScenario(DEFAULT_RAG_GROUNDING_CONFIG, RagConfigOptimizer.SCENARIOS.HIGH_RECALL) },
|
||||
{ name: '深度搜索', config: RagConfigOptimizer.applyScenario(DEFAULT_RAG_GROUNDING_CONFIG, RagConfigOptimizer.SCENARIOS.DEEP_SEARCH) }
|
||||
];
|
||||
|
||||
for (const { name, config } of configs) {
|
||||
const startTime = Date.now();
|
||||
const result = await queryRagGrounding(query, { customConfig: config });
|
||||
const endTime = Date.now();
|
||||
|
||||
console.log(`${name}:`, {
|
||||
检索数量: result.data?.grounding_metadata?.sources?.length || 0,
|
||||
响应时间: `${endTime - startTime}ms`,
|
||||
配置: {
|
||||
max_results: config.max_retrieval_results,
|
||||
threshold: config.relevance_threshold
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 示例 6: 配置解释和建议
|
||||
*/
|
||||
function configurationGuidance() {
|
||||
console.log('=== 配置指导示例 ===');
|
||||
|
||||
const scenarios = Object.entries(RagConfigOptimizer.SCENARIOS);
|
||||
|
||||
scenarios.forEach(([key, scenario]) => {
|
||||
const config = RagConfigOptimizer.applyScenario(DEFAULT_RAG_GROUNDING_CONFIG, scenario);
|
||||
const explanation = RagConfigOptimizer.getConfigExplanation(config);
|
||||
|
||||
console.log(`${scenario.name}:`);
|
||||
console.log(` 描述: ${scenario.description}`);
|
||||
console.log(` 配置说明: ${explanation}`);
|
||||
console.log(` 适用场景: ${getUseCaseExamples(key)}\n`);
|
||||
});
|
||||
}
|
||||
|
||||
function getUseCaseExamples(scenarioKey: string): string {
|
||||
const examples = {
|
||||
HIGH_RECALL: "复杂查询、研究分析、需要全面信息的场景",
|
||||
HIGH_PRECISION: "精确匹配、专业咨询、质量优于数量的场景",
|
||||
BALANCED: "日常对话、一般性查询、大多数应用场景",
|
||||
FAST_RESPONSE: "实时聊天、快速问答、移动端应用",
|
||||
DEEP_SEARCH: "学术研究、详细分析、专业报告生成"
|
||||
};
|
||||
|
||||
return examples[scenarioKey as keyof typeof examples] || "通用场景";
|
||||
}
|
||||
|
||||
// 运行示例
|
||||
async function runAllExamples() {
|
||||
try {
|
||||
await scenarioBasedOptimization();
|
||||
await autoOptimization();
|
||||
await feedbackBasedOptimization();
|
||||
await domainSpecificFiltering();
|
||||
await performanceComparison();
|
||||
configurationGuidance();
|
||||
} catch (error) {
|
||||
console.error('示例运行失败:', error);
|
||||
}
|
||||
}
|
||||
|
||||
// 导出示例函数
|
||||
export {
|
||||
scenarioBasedOptimization,
|
||||
autoOptimization,
|
||||
feedbackBasedOptimization,
|
||||
domainSpecificFiltering,
|
||||
performanceComparison,
|
||||
configurationGuidance,
|
||||
runAllExamples
|
||||
};
|
||||
Reference in New Issue
Block a user