階優(yōu)化)
一、混合檢索向量 關(guān)鍵詞1. 純向量檢索的局限語(yǔ)義相近但答案靠后向量相似度高不代表文檔真正包含答案。精確關(guān)鍵詞匹配弱用戶搜索特定術(shù)語(yǔ)時(shí)向量檢索可能漏掉精確匹配的文檔。多路召回更可靠結(jié)合向量檢索和關(guān)鍵詞檢索取并集后融合排序。2. 混合檢索的實(shí)現(xiàn)以PGVector為例-- PGVector 全文檢索 向量檢索 SQL SELECT * FROM knowledge_base WHERE tsv to_tsquery(chinese, :query) -- 關(guān)鍵詞匹配 ORDER BY ts_rank(tsv, to_tsquery(chinese, :query)) DESC, -- 關(guān)鍵詞相關(guān)性 embedding :query_vector -- 向量相似度 LIMIT :topK;3. Java端混合檢索邏輯核心思路向量檢索與關(guān)鍵詞檢索并行結(jié)果融合去重RRF排序。Service public class HybridSearchService { Resource private VectorStore vectorStore; Resource private JdbcTemplate jdbcTemplate; public ListDocument hybridSearch(String query, int topK) { // 1. 向量檢索 ListDocument vectorResults vectorStore.similaritySearch( SearchRequest.builder().query(query).topK(topK).build() ); // 2. 關(guān)鍵詞檢索PGVector全文搜索 String sql SELECT id, content, ts_rank(tsv, to_tsquery(chinese, ?)) AS rank FROM knowledge_base WHERE tsv to_tsquery(chinese, ?) ORDER BY rank DESC LIMIT ? ; ListDocument keywordResults jdbcTemplate.query(sql, ...); // 3. RRF融合排序Reciprocal Rank fusion return rrfMerge(vectorResults, keywordResults, topK); } /** * RRFReciprocal Rank Fusion融合排序 * 對(duì)兩個(gè)檢索結(jié)果按排名的倒數(shù)加權(quán)融合得到最終排序 */ private ListDocument rrfMerge( ListDocument vectorResults, ListDocument keywordResults, int topK) { double k 60.0; // RRF常量 MapString, Double scores new HashMap(); MapString, Document docMap new HashMap(); // 向量檢索結(jié)果按排名加權(quán) for (int i 0; i vectorResults.size(); i) { String id vectorResults.get(i).getId(); double score 1.0 / (k i 1); scores.merge(id, score, Double::sum); docMap.put(id, vectorResults.get(i)); } // 關(guān)鍵詞檢索結(jié)果按排名加權(quán) for (int i 0; i keywordResults.size(); i) { String id keywordResults.get(i).getId(); double score 1.0 / (k i 1); scores.merge(id, score, Double::sum); docMap.put(id, keywordResults.get(i)); } // 按融合分?jǐn)?shù)降序取topK return scores.entrySet().stream() .sorted(Map.Entry.String, DoublecomparingByValue().reversed()) .limit(topK) .map(e - docMap.get(e.getKey())) .toList(); } }4. 集成到RAG流程GetMapping(/ask-hybrid) public String askHybrid(RequestParam String question) { // Step 1: 混合檢索 ListDocument docs hybridSearchService.hybridSearch(question, 10); // Step 2: 生成答案 String context buildContext(docs); return chatClient.prompt() .user(參考資料\n context \n\n問題 question) .call().content(); }二、Rerank重排序Rerank 是對(duì)檢索結(jié)果按查詢相關(guān)性進(jìn)行二次精排的過程。粗檢索階段多召回topK20Rerank階段精排取Top5。1. DashScope Rerankqwen3-rerank通過阿里云 DashScope API 調(diào)用 qwen3-rerank 模型實(shí)現(xiàn)基于API的文檔重排序。Service public class DashScopeDocumentReranker implements DocumentPostProcessor { private final RestTemplate restTemplate; private final String apiKey; private final String modelName; private final int topN; public DashScopeDocumentReranker(String apiKey, String modelName, int topN) { this.restTemplate new RestTemplate(); this.apiKey apiKey; this.modelName modelName; this.topN topN; } Override public ListDocument process(Query query, ListDocument documents) { if (documents null || documents.size() 1) { return documents; } ListString texts documents.stream().map(Document::getText).toList(); MapString, Object body Map.of( model, modelName, query, query.text(), documents, texts, top_n, Math.min(topN, documents.size()) ); HttpHeaders headers new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set(Authorization, Bearer apiKey); try { ResponseEntityRerankResponse response restTemplate.postForEntity( https://dashscope.aliyuncs.com/api/v1/services/rerank, new HttpEntity(body, headers), RerankResponse.class ); return reorder(documents, response.getBody()); } catch (Exception e) { log.warn(DashScope rerank調(diào)用失敗: {}, e.getMessage()); return documents; } } /** * 按rerank返回的相關(guān)性順序重排文檔結(jié)果已按relevance_score降序排列 */ private ListDocument reorder(ListDocument documents, RerankResponse response) { if (response null || response.results null || response.results.isEmpty()) { return documents; } ListDocument reranked new ArrayList(); for (RerankResult result : response.results) { if (result.index ! null result.index 0 result.index documents.size()) { reranked.add(documents.get(result.index)); } } return reranked.isEmpty() ? documents : reranked; } // qwen3-rerank API 響應(yīng)結(jié)構(gòu) static class RerankResponse { public ListRerankResult results; } static class RerankResult { JsonProperty(relevance_score) public Double relevanceScore; public Integer index; } }2. 硅基流動(dòng) RerankBAAI/bge-reranker-v2-m3調(diào)用硅基流動(dòng)平臺(tái)的 bge-reranker-v2-m3 模型進(jìn)行文檔重排序。Service public class RerankService { private static final String API_URL https://api.siliconflow.cn/v1/rerank; private final RestTemplate restTemplate; private final ObjectMapper objectMapper; Value(${spring.ai.siliconflow-api-key}) private String apiKey; public RerankService(RestTemplateBuilder restTemplateBuilder) { this.restTemplate restTemplateBuilder .connectTimeout(Duration.ofSeconds(10)) .readTimeout(Duration.ofSeconds(30)) .build(); this.objectMapper new ObjectMapper(); } public ListDocument rerank(String query, ListDocument documents, int topN) { if (documents null || documents.isEmpty()) { return List.of(); } if (documents.size() 1) { return documents; } ListString texts documents.stream().map(Document::getText).toList(); MapString, Object body Map.of( model, BAAI/bge-reranker-v2-m3, query, query, documents, texts, top_n, Math.min(topN, documents.size()) ); try { HttpHeaders headers new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set(Authorization, Bearer apiKey); HttpEntityMapString, Object request new HttpEntity(body, headers); ResponseEntityMap response restTemplate.postForEntity( API_URL, request, Map.class ); SuppressWarnings(unchecked) ListMapString, Object results (ListMapString, Object) response.getBody().get(results); if (results null || results.isEmpty()) { return documents; } return results.stream().map(r - { int index (int) r.get(index); Document doc documents.get(index); doc.getMetadata().put(rerankScore, r.get(relevance_score)); return doc; }).toList(); } catch (Exception e) { log.warn(硅基流動(dòng)Rerank調(diào)用失敗: {}, e.getMessage()); return documents.stream().limit(topN).toList(); } } }3. LLM文檔重排器自定義實(shí)現(xiàn)Spring AI 2.0.1 沒有內(nèi)置 reranker可以用 LLM 對(duì)檢索結(jié)果按相關(guān)性重新排序。/** * 基于LLM的文檔重排器實(shí)現(xiàn)DocumentPostProcessor接口 */ public class LlmDocumentReranker implements DocumentPostProcessor { private static final Pattern INDEX_PATTERN Pattern.compile(\\d); private final ChatClient chatClient; private final int maxDocPreviewLength; public LlmDocumentReranker(ChatClient.Builder chatClientBuilder, int maxDocPreviewLength) { this.chatClient chatClientBuilder.build(); this.maxDocPreviewLength maxDocPreviewLength; } Override public ListDocument process(Query query, ListDocument documents) { if (documents null || documents.size() 1) { return documents; } StringBuilder docList new StringBuilder(); for (int i 0; i documents.size(); i) { String text documents.get(i).getText(); if (text null) { text ; } if (text.length() maxDocPreviewLength) { text text.substring(0, maxDocPreviewLength) ...; } docList.append([).append(i).append(] ).append(text).append(\n\n); } String prompt 你是一個(gè)文檔相關(guān)性排序助手。請(qǐng)根據(jù)文檔與查詢的相關(guān)程度重新排序。 查詢%s 文檔列表 %s 請(qǐng)只返回文檔編號(hào)的排序結(jié)果用英文逗號(hào)分隔。 例如2,0,3,1 .formatted(query.text(), docList); String result chatClient.prompt().user(prompt).call().content(); return reorder(documents, result); } /** * 解析LLM返回的編號(hào)序列按順序重排文檔。解析失敗時(shí)返回原順序。 */ private ListDocument reorder(ListDocument documents, String llmOutput) { if (llmOutput null || llmOutput.isBlank()) { return documents; } Matcher matcher INDEX_PATTERN.matcher(llmOutput); ListInteger indices new ArrayList(); while (matcher.find()) { int idx Integer.parseInt(matcher.group()); if (idx 0 idx documents.size() !indices.contains(idx)) { indices.add(idx); } } if (indices.size() ! documents.size()) { return documents; } ListDocument reranked new ArrayList(documents.size()); for (int idx : indices) { reranked.add(documents.get(idx)); } return reranked; } }4. Rerank集成方式方式一手動(dòng)編排GetMapping(/ask-rerank) public String askWithRerank(RequestParam String question) { // Step 1: 粗檢索多召回 ListDocument candidates vectorStore.similaritySearch( SearchRequest.builder().query(question).topK(20).build() ); // Step 2: Rerank精排取Top5 ListDocument reranked rerankService.rerank(question, candidates, 5); // Step 3: 生成答案 String context buildContext(reranked); return chatClient.prompt() .user(參考資料\n context \n\n問題 question) .call().content(); }方式二Advisor集成RetrievalAugmentationAdvisor ragAdvisor RetrievalAugmentationAdvisor.builder() .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .documentPostProcessors(new DashScopeDocumentReranker(apiKey, qwen3-rerank, 3)) .build();三、Query改寫與多查詢擴(kuò)展1. Query改寫的必要性用戶提問可能口語(yǔ)化、模糊或含指代詞直接用于檢索效果差。通過LLM改寫為更適合知識(shí)庫(kù)檢索的形式提升召回率。2. RewriteQueryTransformerSpring AI內(nèi)置Spring AI 2.0 提供了內(nèi)置的查詢改寫器可直接集成到 Advisor 中。Bean public RetrievalAugmentationAdvisor ragAdvisorWithRewrite( ChatClient.Builder chatClientBuilder, VectorStore vectorStore) { RewriteQueryTransformer rewriteTransformer RewriteQueryTransformer.builder() .chatClientBuilder(chatClientBuilder) .targetSearchSystem(vector store) .build(); return RetrievalAugmentationAdvisor.builder() .queryTransformers(rewriteTransformer) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .topK(5) .build()) .build(); }3. 自定義多查詢改寫服務(wù)生成多個(gè)改寫版本分別檢索后取并集進(jìn)一步提升召回率。Service public class QueryRewriteService { Resource private ChatClient ragManualChatClient; private final ObjectMapper objectMapper; public QueryRewriteService() { this.objectMapper new ObjectMapper(); } public ListString rewrite(String originalQuery, String conversationHistory) { String prompt String.format( 你是一個(gè)搜索查詢優(yōu)化專家。請(qǐng)將用戶的問題改寫為更適合在知識(shí)庫(kù)中檢索的形式。 要求 1. 生成3個(gè)不同角度的改寫版本 2. 用JSON數(shù)組格式返回 3. 保留原始問題的核心意圖 4. 如果有對(duì)話歷史結(jié)合上下文補(bǔ)全指代 對(duì)話歷史%s 用戶問題%s 請(qǐng)直接返回JSON數(shù)組不要包含其他文字。 , conversationHistory, originalQuery); String result ragManualChatClient.prompt().user(prompt).call().content(); return parseJsonArray(result); } /** * 將大模型返回的JSON數(shù)組字符串解析為L(zhǎng)istString * 大模型返回格式示例[改寫版本1, 改寫版本2, 改寫版本3] */ private ListString parseJsonArray(String json) { if (json null || json.isBlank()) { return List.of(); } try { String cleaned json.trim(); if (cleaned.startsWith(json)) { cleaned cleaned.substring(7); } else if (cleaned.startsWith()) { cleaned cleaned.substring(3); } if (cleaned.endsWith()) { cleaned cleaned.substring(0, cleaned.length() - 3); } cleaned cleaned.trim(); return objectMapper.readValue(cleaned, new TypeReferenceListString() {}); } catch (Exception e) { return Arrays.stream(json.split(\n)) .map(String::trim) .filter(s - !s.isEmpty()) .map(s - s.replaceAll(^\\d[\\.\\)\u3001]\u0020*, )) .filter(s - !s.startsWith([)) .toList(); } } }4. 多查詢檢索流程GetMapping(/ask-multi-query) public FluxString askMultiQuery( RequestParam String message, RequestParam(defaultValue default) String conversationId) { // Step 1: 改寫問題生成3個(gè)版本 ListString queries queryRewriteService.rewrite(message, ); // Step 2: 分別檢索并去重 SetString seen new HashSet(); ListDocument allDocs new ArrayList(); for (String q : queries) { ListDocument docs knowledgeService.search(q, 5, 0.6); for (Document doc : docs) { if (seen.add(doc.getId())) { allDocs.add(doc); } } } // Step 3: 生成答案 String context buildContext(allDocs); return chatClient.prompt() .user(參考資料\n context \n\n問題 message) .stream().content(); }四、閑聊感知查詢改寫問題背景用戶可能輸入閑聊內(nèi)容如 你好此時(shí)不應(yīng)觸發(fā)RAG檢索流程避免浪費(fèi)資源和產(chǎn)生無關(guān)檢索結(jié)果。public class ChitchatAwareQueryTransformer implements QueryTransformer { private final RewriteQueryTransformer delegate; public ChitchatAwareQueryTransformer(ChatClient.Builder chatClientBuilder) { this.delegate RewriteQueryTransformer.builder() .chatClientBuilder(chatClientBuilder) .targetSearchSystem(vector store) .build(); } Override public Query transform(Query query) { String text query.text(); if (isChitchat(text)) { return query; // 閑聊直接透?jìng)?} return delegate.transform(query); // 知識(shí)性問題交給改寫器 } private boolean isChitchat(String text) { if (text null || text.isBlank()) return true; String trimmed text.trim(); if (trimmed.length() 6) return true; String lower trimmed.toLowerCase(); return lower.matches(.*(你好|您好|hi|hello|謝謝|感謝|再見|你是誰(shuí)).*) !lower.matches(.*(什么是|怎么|如何|為什么|區(qū)別|原理|實(shí)現(xiàn)).*); } }集成到RAG AdvisorRetrievalAugmentationAdvisor ragAdvisor RetrievalAugmentationAdvisor.builder() .queryTransformers(new ChitchatAwareQueryTransformer(ChatClient.builder(chatModel))) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .build();五、查詢?cè)鰪?qiáng)與空上下文處理ContextualQueryAugmenter負(fù)責(zé)將檢索到的文檔與用戶查詢拼裝為完整的 Prompt。allowEmptyContext 配置默認(rèn)情況下無檢索結(jié)果時(shí)會(huì)拋出異常。設(shè)置 allowEmptyContext(true) 后無檢索結(jié)果時(shí)仍能正常生成回答。Bean public RetrievalAugmentationAdvisor ragAdvisor( VectorStore vectorStore) { return RetrievalAugmentationAdvisor.builder() .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .topK(5) .build()) .queryAugmenter(ContextualQueryAugmenter.builder() .allowEmptyContext(true) // 允許空上下文 .build()) .build(); }六、幻覺檢測(cè)與答案質(zhì)量保障1. 基于引用驗(yàn)證的幻覺檢測(cè)幻覺檢測(cè)的核心思路檢查模型生成的答案是否完全基于參考資料識(shí)別不受支持的說法。Service public class HallucinationDetector { private final ChatClient chatClient; private final ObjectMapper objectMapper; public HallucinationDetector(ChatClient.Builder builder) { this.chatClient builder .defaultSystem(你是一個(gè)嚴(yán)格的事實(shí)核查專家。) .build(); this.objectMapper new ObjectMapper(); } public MapString, Object detect(String answer, String context) { String prompt String.format( 請(qǐng)判斷以下答案是否完全基于參考資料得出。 參考資料%s 模型答案%s 請(qǐng)用JSON格式返回 {reliable: true/false, reason: 判斷理由, unsupportedClaims: [...]} , context, answer); String result chatClient.prompt().user(prompt).call().content(); return parseResult(result); } /** * 將大模型返回的幻覺檢測(cè)結(jié)果JSON字符串解析為Map */ private MapString, Object parseResult(String json) { MapString, Object defaultResult Map.of( reliable, true, reason, 解析失敗默認(rèn)標(biāo)記為可信 ); if (json null || json.isBlank()) { return defaultResult; } try { String cleaned json.trim(); if (cleaned.startsWith(json)) { cleaned cleaned.substring(7); } else if (cleaned.startsWith()) { cleaned cleaned.substring(3); } if (cleaned.endsWith()) { cleaned cleaned.substring(0, cleaned.length() - 3); } cleaned cleaned.trim(); JsonNode node objectMapper.readTree(cleaned); boolean reliable node.has(reliable) node.get(reliable).asBoolean(); String reason node.has(reason) ? node.get(reason).asText() : ; ListString unsupportedClaims new ArrayList(); if (node.has(unsupportedClaims) node.get(unsupportedClaims).isArray()) { for (JsonNode claim : node.get(unsupportedClaims)) { unsupportedClaims.add(claim.asText()); } } return Map.of(reliable, reliable, reason, reason, unsupportedClaims, unsupportedClaims); } catch (Exception e) { return defaultResult; } } }2. RAG流程集成GetMapping(/ask-safe) public MapString, Object askSafe( RequestParam String message, RequestParam(defaultValue default) String conversationId) { // Step 1: 檢索文檔 ListDocument docs vectorStore.similaritySearch( SearchRequest.builder().query(message).topK(5).build() ); // Step 2: 生成答案 String context buildContext(docs); String answer chatClient.prompt() .user(參考資料\n context \n\n問題 message) .call().content(); // Step 3: 幻覺檢測(cè) MapString, Object result hallucinationDetector.detect(answer, context); // Step 4: 組裝返回 MapString, Object response new HashMap(); response.put(answer, answer); response.put(reliable, result.get(reliable)); response.put(reason, result.get(reason)); response.put(unsupportedClaims, result.get(unsupportedClaims)); return response; }3. System Prompt約束建議只基于提供的參考資料回答問題不要編造信息。如果參考資料中沒有相關(guān)內(nèi)容請(qǐng)明確告知用戶。引用參考資料時(shí)標(biāo)注來源。七、RAG Advisor完整優(yōu)化流程核心流程查詢改寫 → 檢索 → 重排 → 增強(qiáng) → 生成完整配置示例Bean public ChatClient ragOptimizedChatClient( ChatClient.Builder builder, VectorStore vectorStore, ChatModel chatModel) { String promptTemplate 你是一個(gè)知識(shí)庫(kù)助手。請(qǐng)嚴(yán)格遵循以下規(guī)則 1. 只基于提供的參考資料回答問題不要編造信息 2. 如果參考資料中沒有相關(guān)內(nèi)容請(qǐng)明確告知用戶 3. 引用參考資料時(shí)標(biāo)注來源 參考資料 {context} 問題{question} ; RetrievalAugmentationAdvisor advisor RetrievalAugmentationAdvisor.builder() .queryTransformers(new ChitchatAwareQueryTransformer(builder)) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .documentPostProcessors(new DashScopeDocumentReranker(apiKey, qwen3-rerank, 3)) .queryAugmenter(ContextualQueryAugmenter.builder() .allowEmptyContext(true) .build()) .build(); return builder.defaultAdvisors(advisor).build(); }八、常用工具方法buildContext文檔列表拼接為上下文字符串將檢索到的 Document 列表拼接為一段完整的上下文文本。private String buildContext(ListDocument documents) { if (documents null || documents.isEmpty()) { return ; } StringBuilder sb new StringBuilder(); for (int i 0; i documents.size(); i) { Document doc documents.get(i); sb.append(--- 文檔).append(i 1).append( ---\n); Object fileName doc.getMetadata().get(fileName); if (fileName ! null) { sb.append(來源).append(fileName).append(\n); } sb.append(doc.getText()).append(\n\n); } return sb.toString(); }extractCitations從響應(yīng)中提取引用來源解析 Spring AI ChatResponse 中的 citations 信息。/** * 從ChatResponse中提取引用的文檔信息 */ private ListMapString, Object extractCitations(ChatResponse response) { ListMapString, Object citations new ArrayList(); if (response null || response.getMetadata() null) { return citations; } MapString, Object metadata response.getMetadata(); Object docsObj metadata.get(documents); if (docsObj instanceof List?) { for (Object obj : (List?) docsObj) { if (obj instanceof Document doc) { MapString, Object citation new HashMap(); citation.put(content, doc.getText()); citation.put(metadata, doc.getMetadata()); citations.add(citation); } } } return citations; }總結(jié)結(jié)合混合檢索、Rerank重排序、Query改寫、閑聊感知、查詢?cè)鰪?qiáng)和幻覺檢測(cè)可顯著提升知識(shí)庫(kù)問答系統(tǒng)的檢索效果與答案質(zhì)量。建議在實(shí)際應(yīng)用中分階段引入上述機(jī)制持續(xù)優(yōu)化用戶體驗(yàn)和結(jié)果可靠性。 第二章：5、RAG進(jìn)階優(yōu)化
一、混合檢索向量 關(guān)鍵詞1. 純向量檢索的局限語(yǔ)義相近但答案靠后向量相似度高不代表文檔真正包含答案。精確關(guān)鍵詞匹配弱用戶搜索特定術(shù)語(yǔ)時(shí)向量檢索可能漏掉精確匹配的文檔。多路召回更可靠結(jié)合向量檢索和關(guān)鍵詞檢索取并集后融合排序。2. 混合檢索的實(shí)現(xiàn)以PGVector為例-- PGVector 全文檢索 向量檢索 SQL SELECT * FROM knowledge_base WHERE tsv to_tsquery(chinese, :query) -- 關(guān)鍵詞匹配 ORDER BY ts_rank(tsv, to_tsquery(chinese, :query)) DESC, -- 關(guān)鍵詞相關(guān)性 embedding :query_vector -- 向量相似度 LIMIT :topK;3. Java端混合檢索邏輯核心思路向量檢索與關(guān)鍵詞檢索并行結(jié)果融合去重RRF排序。Service public class HybridSearchService { Resource private VectorStore vectorStore; Resource private JdbcTemplate jdbcTemplate; public ListDocument hybridSearch(String query, int topK) { // 1. 向量檢索 ListDocument vectorResults vectorStore.similaritySearch( SearchRequest.builder().query(query).topK(topK).build() ); // 2. 關(guān)鍵詞檢索PGVector全文搜索 String sql SELECT id, content, ts_rank(tsv, to_tsquery(chinese, ?)) AS rank FROM knowledge_base WHERE tsv to_tsquery(chinese, ?) ORDER BY rank DESC LIMIT ? ; ListDocument keywordResults jdbcTemplate.query(sql, ...); // 3. RRF融合排序Reciprocal Rank fusion return rrfMerge(vectorResults, keywordResults, topK); } /** * RRFReciprocal Rank Fusion融合排序 * 對(duì)兩個(gè)檢索結(jié)果按排名的倒數(shù)加權(quán)融合得到最終排序 */ private ListDocument rrfMerge( ListDocument vectorResults, ListDocument keywordResults, int topK) { double k 60.0; // RRF常量 MapString, Double scores new HashMap(); MapString, Document docMap new HashMap(); // 向量檢索結(jié)果按排名加權(quán) for (int i 0; i vectorResults.size(); i) { String id vectorResults.get(i).getId(); double score 1.0 / (k i 1); scores.merge(id, score, Double::sum); docMap.put(id, vectorResults.get(i)); } // 關(guān)鍵詞檢索結(jié)果按排名加權(quán) for (int i 0; i keywordResults.size(); i) { String id keywordResults.get(i).getId(); double score 1.0 / (k i 1); scores.merge(id, score, Double::sum); docMap.put(id, keywordResults.get(i)); } // 按融合分?jǐn)?shù)降序取topK return scores.entrySet().stream() .sorted(Map.Entry.String, DoublecomparingByValue().reversed()) .limit(topK) .map(e - docMap.get(e.getKey())) .toList(); } }4. 集成到RAG流程GetMapping(/ask-hybrid) public String askHybrid(RequestParam String question) { // Step 1: 混合檢索 ListDocument docs hybridSearchService.hybridSearch(question, 10); // Step 2: 生成答案 String context buildContext(docs); return chatClient.prompt() .user(參考資料\n context \n\n問題 question) .call().content(); }二、Rerank重排序Rerank 是對(duì)檢索結(jié)果按查詢相關(guān)性進(jìn)行二次精排的過程。粗檢索階段多召回topK20Rerank階段精排取Top5。1. DashScope Rerankqwen3-rerank通過阿里云 DashScope API 調(diào)用 qwen3-rerank 模型實(shí)現(xiàn)基于API的文檔重排序。Service public class DashScopeDocumentReranker implements DocumentPostProcessor { private final RestTemplate restTemplate; private final String apiKey; private final String modelName; private final int topN; public DashScopeDocumentReranker(String apiKey, String modelName, int topN) { this.restTemplate new RestTemplate(); this.apiKey apiKey; this.modelName modelName; this.topN topN; } Override public ListDocument process(Query query, ListDocument documents) { if (documents null || documents.size() 1) { return documents; } ListString texts documents.stream().map(Document::getText).toList(); MapString, Object body Map.of( model, modelName, query, query.text(), documents, texts, top_n, Math.min(topN, documents.size()) ); HttpHeaders headers new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set(Authorization, Bearer apiKey); try { ResponseEntityRerankResponse response restTemplate.postForEntity( https://dashscope.aliyuncs.com/api/v1/services/rerank, new HttpEntity(body, headers), RerankResponse.class ); return reorder(documents, response.getBody()); } catch (Exception e) { log.warn(DashScope rerank調(diào)用失敗: {}, e.getMessage()); return documents; } } /** * 按rerank返回的相關(guān)性順序重排文檔結(jié)果已按relevance_score降序排列 */ private ListDocument reorder(ListDocument documents, RerankResponse response) { if (response null || response.results null || response.results.isEmpty()) { return documents; } ListDocument reranked new ArrayList(); for (RerankResult result : response.results) { if (result.index ! null result.index 0 result.index documents.size()) { reranked.add(documents.get(result.index)); } } return reranked.isEmpty() ? documents : reranked; } // qwen3-rerank API 響應(yīng)結(jié)構(gòu) static class RerankResponse { public ListRerankResult results; } static class RerankResult { JsonProperty(relevance_score) public Double relevanceScore; public Integer index; } }2. 硅基流動(dòng) RerankBAAI/bge-reranker-v2-m3調(diào)用硅基流動(dòng)平臺(tái)的 bge-reranker-v2-m3 模型進(jìn)行文檔重排序。Service public class RerankService { private static final String API_URL https://api.siliconflow.cn/v1/rerank; private final RestTemplate restTemplate; private final ObjectMapper objectMapper; Value(${spring.ai.siliconflow-api-key}) private String apiKey; public RerankService(RestTemplateBuilder restTemplateBuilder) { this.restTemplate restTemplateBuilder .connectTimeout(Duration.ofSeconds(10)) .readTimeout(Duration.ofSeconds(30)) .build(); this.objectMapper new ObjectMapper(); } public ListDocument rerank(String query, ListDocument documents, int topN) { if (documents null || documents.isEmpty()) { return List.of(); } if (documents.size() 1) { return documents; } ListString texts documents.stream().map(Document::getText).toList(); MapString, Object body Map.of( model, BAAI/bge-reranker-v2-m3, query, query, documents, texts, top_n, Math.min(topN, documents.size()) ); try { HttpHeaders headers new HttpHeaders(); headers.setContentType(MediaType.APPLICATION_JSON); headers.set(Authorization, Bearer apiKey); HttpEntityMapString, Object request new HttpEntity(body, headers); ResponseEntityMap response restTemplate.postForEntity( API_URL, request, Map.class ); SuppressWarnings(unchecked) ListMapString, Object results (ListMapString, Object) response.getBody().get(results); if (results null || results.isEmpty()) { return documents; } return results.stream().map(r - { int index (int) r.get(index); Document doc documents.get(index); doc.getMetadata().put(rerankScore, r.get(relevance_score)); return doc; }).toList(); } catch (Exception e) { log.warn(硅基流動(dòng)Rerank調(diào)用失敗: {}, e.getMessage()); return documents.stream().limit(topN).toList(); } } }3. LLM文檔重排器自定義實(shí)現(xiàn)Spring AI 2.0.1 沒有內(nèi)置 reranker可以用 LLM 對(duì)檢索結(jié)果按相關(guān)性重新排序。/** * 基于LLM的文檔重排器實(shí)現(xiàn)DocumentPostProcessor接口 */ public class LlmDocumentReranker implements DocumentPostProcessor { private static final Pattern INDEX_PATTERN Pattern.compile(\\d); private final ChatClient chatClient; private final int maxDocPreviewLength; public LlmDocumentReranker(ChatClient.Builder chatClientBuilder, int maxDocPreviewLength) { this.chatClient chatClientBuilder.build(); this.maxDocPreviewLength maxDocPreviewLength; } Override public ListDocument process(Query query, ListDocument documents) { if (documents null || documents.size() 1) { return documents; } StringBuilder docList new StringBuilder(); for (int i 0; i documents.size(); i) { String text documents.get(i).getText(); if (text null) { text ; } if (text.length() maxDocPreviewLength) { text text.substring(0, maxDocPreviewLength) ...; } docList.append([).append(i).append(] ).append(text).append(\n\n); } String prompt 你是一個(gè)文檔相關(guān)性排序助手。請(qǐng)根據(jù)文檔與查詢的相關(guān)程度重新排序。 查詢%s 文檔列表 %s 請(qǐng)只返回文檔編號(hào)的排序結(jié)果用英文逗號(hào)分隔。 例如2,0,3,1 .formatted(query.text(), docList); String result chatClient.prompt().user(prompt).call().content(); return reorder(documents, result); } /** * 解析LLM返回的編號(hào)序列按順序重排文檔。解析失敗時(shí)返回原順序。 */ private ListDocument reorder(ListDocument documents, String llmOutput) { if (llmOutput null || llmOutput.isBlank()) { return documents; } Matcher matcher INDEX_PATTERN.matcher(llmOutput); ListInteger indices new ArrayList(); while (matcher.find()) { int idx Integer.parseInt(matcher.group()); if (idx 0 idx documents.size() !indices.contains(idx)) { indices.add(idx); } } if (indices.size() ! documents.size()) { return documents; } ListDocument reranked new ArrayList(documents.size()); for (int idx : indices) { reranked.add(documents.get(idx)); } return reranked; } }4. Rerank集成方式方式一手動(dòng)編排GetMapping(/ask-rerank) public String askWithRerank(RequestParam String question) { // Step 1: 粗檢索多召回 ListDocument candidates vectorStore.similaritySearch( SearchRequest.builder().query(question).topK(20).build() ); // Step 2: Rerank精排取Top5 ListDocument reranked rerankService.rerank(question, candidates, 5); // Step 3: 生成答案 String context buildContext(reranked); return chatClient.prompt() .user(參考資料\n context \n\n問題 question) .call().content(); }方式二Advisor集成RetrievalAugmentationAdvisor ragAdvisor RetrievalAugmentationAdvisor.builder() .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .documentPostProcessors(new DashScopeDocumentReranker(apiKey, qwen3-rerank, 3)) .build();三、Query改寫與多查詢擴(kuò)展1. Query改寫的必要性用戶提問可能口語(yǔ)化、模糊或含指代詞直接用于檢索效果差。通過LLM改寫為更適合知識(shí)庫(kù)檢索的形式提升召回率。2. RewriteQueryTransformerSpring AI內(nèi)置Spring AI 2.0 提供了內(nèi)置的查詢改寫器可直接集成到 Advisor 中。Bean public RetrievalAugmentationAdvisor ragAdvisorWithRewrite( ChatClient.Builder chatClientBuilder, VectorStore vectorStore) { RewriteQueryTransformer rewriteTransformer RewriteQueryTransformer.builder() .chatClientBuilder(chatClientBuilder) .targetSearchSystem(vector store) .build(); return RetrievalAugmentationAdvisor.builder() .queryTransformers(rewriteTransformer) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .topK(5) .build()) .build(); }3. 自定義多查詢改寫服務(wù)生成多個(gè)改寫版本分別檢索后取并集進(jìn)一步提升召回率。Service public class QueryRewriteService { Resource private ChatClient ragManualChatClient; private final ObjectMapper objectMapper; public QueryRewriteService() { this.objectMapper new ObjectMapper(); } public ListString rewrite(String originalQuery, String conversationHistory) { String prompt String.format( 你是一個(gè)搜索查詢優(yōu)化專家。請(qǐng)將用戶的問題改寫為更適合在知識(shí)庫(kù)中檢索的形式。 要求 1. 生成3個(gè)不同角度的改寫版本 2. 用JSON數(shù)組格式返回 3. 保留原始問題的核心意圖 4. 如果有對(duì)話歷史結(jié)合上下文補(bǔ)全指代 對(duì)話歷史%s 用戶問題%s 請(qǐng)直接返回JSON數(shù)組不要包含其他文字。 , conversationHistory, originalQuery); String result ragManualChatClient.prompt().user(prompt).call().content(); return parseJsonArray(result); } /** * 將大模型返回的JSON數(shù)組字符串解析為L(zhǎng)istString * 大模型返回格式示例[改寫版本1, 改寫版本2, 改寫版本3] */ private ListString parseJsonArray(String json) { if (json null || json.isBlank()) { return List.of(); } try { String cleaned json.trim(); if (cleaned.startsWith(json)) { cleaned cleaned.substring(7); } else if (cleaned.startsWith()) { cleaned cleaned.substring(3); } if (cleaned.endsWith()) { cleaned cleaned.substring(0, cleaned.length() - 3); } cleaned cleaned.trim(); return objectMapper.readValue(cleaned, new TypeReferenceListString() {}); } catch (Exception e) { return Arrays.stream(json.split(\n)) .map(String::trim) .filter(s - !s.isEmpty()) .map(s - s.replaceAll(^\\d[\\.\\)\u3001]\u0020*, )) .filter(s - !s.startsWith([)) .toList(); } } }4. 多查詢檢索流程GetMapping(/ask-multi-query) public FluxString askMultiQuery( RequestParam String message, RequestParam(defaultValue default) String conversationId) { // Step 1: 改寫問題生成3個(gè)版本 ListString queries queryRewriteService.rewrite(message, ); // Step 2: 分別檢索并去重 SetString seen new HashSet(); ListDocument allDocs new ArrayList(); for (String q : queries) { ListDocument docs knowledgeService.search(q, 5, 0.6); for (Document doc : docs) { if (seen.add(doc.getId())) { allDocs.add(doc); } } } // Step 3: 生成答案 String context buildContext(allDocs); return chatClient.prompt() .user(參考資料\n context \n\n問題 message) .stream().content(); }四、閑聊感知查詢改寫問題背景用戶可能輸入閑聊內(nèi)容如 你好此時(shí)不應(yīng)觸發(fā)RAG檢索流程避免浪費(fèi)資源和產(chǎn)生無關(guān)檢索結(jié)果。public class ChitchatAwareQueryTransformer implements QueryTransformer { private final RewriteQueryTransformer delegate; public ChitchatAwareQueryTransformer(ChatClient.Builder chatClientBuilder) { this.delegate RewriteQueryTransformer.builder() .chatClientBuilder(chatClientBuilder) .targetSearchSystem(vector store) .build(); } Override public Query transform(Query query) { String text query.text(); if (isChitchat(text)) { return query; // 閑聊直接透?jìng)?} return delegate.transform(query); // 知識(shí)性問題交給改寫器 } private boolean isChitchat(String text) { if (text null || text.isBlank()) return true; String trimmed text.trim(); if (trimmed.length() 6) return true; String lower trimmed.toLowerCase(); return lower.matches(.*(你好|您好|hi|hello|謝謝|感謝|再見|你是誰(shuí)).*) !lower.matches(.*(什么是|怎么|如何|為什么|區(qū)別|原理|實(shí)現(xiàn)).*); } }集成到RAG AdvisorRetrievalAugmentationAdvisor ragAdvisor RetrievalAugmentationAdvisor.builder() .queryTransformers(new ChitchatAwareQueryTransformer(ChatClient.builder(chatModel))) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .build();五、查詢?cè)鰪?qiáng)與空上下文處理ContextualQueryAugmenter負(fù)責(zé)將檢索到的文檔與用戶查詢拼裝為完整的 Prompt。allowEmptyContext 配置默認(rèn)情況下無檢索結(jié)果時(shí)會(huì)拋出異常。設(shè)置 allowEmptyContext(true) 后無檢索結(jié)果時(shí)仍能正常生成回答。Bean public RetrievalAugmentationAdvisor ragAdvisor( VectorStore vectorStore) { return RetrievalAugmentationAdvisor.builder() .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .topK(5) .build()) .queryAugmenter(ContextualQueryAugmenter.builder() .allowEmptyContext(true) // 允許空上下文 .build()) .build(); }六、幻覺檢測(cè)與答案質(zhì)量保障1. 基于引用驗(yàn)證的幻覺檢測(cè)幻覺檢測(cè)的核心思路檢查模型生成的答案是否完全基于參考資料識(shí)別不受支持的說法。Service public class HallucinationDetector { private final ChatClient chatClient; private final ObjectMapper objectMapper; public HallucinationDetector(ChatClient.Builder builder) { this.chatClient builder .defaultSystem(你是一個(gè)嚴(yán)格的事實(shí)核查專家。) .build(); this.objectMapper new ObjectMapper(); } public MapString, Object detect(String answer, String context) { String prompt String.format( 請(qǐng)判斷以下答案是否完全基于參考資料得出。 參考資料%s 模型答案%s 請(qǐng)用JSON格式返回 {reliable: true/false, reason: 判斷理由, unsupportedClaims: [...]} , context, answer); String result chatClient.prompt().user(prompt).call().content(); return parseResult(result); } /** * 將大模型返回的幻覺檢測(cè)結(jié)果JSON字符串解析為Map */ private MapString, Object parseResult(String json) { MapString, Object defaultResult Map.of( reliable, true, reason, 解析失敗默認(rèn)標(biāo)記為可信 ); if (json null || json.isBlank()) { return defaultResult; } try { String cleaned json.trim(); if (cleaned.startsWith(json)) { cleaned cleaned.substring(7); } else if (cleaned.startsWith()) { cleaned cleaned.substring(3); } if (cleaned.endsWith()) { cleaned cleaned.substring(0, cleaned.length() - 3); } cleaned cleaned.trim(); JsonNode node objectMapper.readTree(cleaned); boolean reliable node.has(reliable) node.get(reliable).asBoolean(); String reason node.has(reason) ? node.get(reason).asText() : ; ListString unsupportedClaims new ArrayList(); if (node.has(unsupportedClaims) node.get(unsupportedClaims).isArray()) { for (JsonNode claim : node.get(unsupportedClaims)) { unsupportedClaims.add(claim.asText()); } } return Map.of(reliable, reliable, reason, reason, unsupportedClaims, unsupportedClaims); } catch (Exception e) { return defaultResult; } } }2. RAG流程集成GetMapping(/ask-safe) public MapString, Object askSafe( RequestParam String message, RequestParam(defaultValue default) String conversationId) { // Step 1: 檢索文檔 ListDocument docs vectorStore.similaritySearch( SearchRequest.builder().query(message).topK(5).build() ); // Step 2: 生成答案 String context buildContext(docs); String answer chatClient.prompt() .user(參考資料\n context \n\n問題 message) .call().content(); // Step 3: 幻覺檢測(cè) MapString, Object result hallucinationDetector.detect(answer, context); // Step 4: 組裝返回 MapString, Object response new HashMap(); response.put(answer, answer); response.put(reliable, result.get(reliable)); response.put(reason, result.get(reason)); response.put(unsupportedClaims, result.get(unsupportedClaims)); return response; }3. System Prompt約束建議只基于提供的參考資料回答問題不要編造信息。如果參考資料中沒有相關(guān)內(nèi)容請(qǐng)明確告知用戶。引用參考資料時(shí)標(biāo)注來源。七、RAG Advisor完整優(yōu)化流程核心流程查詢改寫 → 檢索 → 重排 → 增強(qiáng) → 生成完整配置示例Bean public ChatClient ragOptimizedChatClient( ChatClient.Builder builder, VectorStore vectorStore, ChatModel chatModel) { String promptTemplate 你是一個(gè)知識(shí)庫(kù)助手。請(qǐng)嚴(yán)格遵循以下規(guī)則 1. 只基于提供的參考資料回答問題不要編造信息 2. 如果參考資料中沒有相關(guān)內(nèi)容請(qǐng)明確告知用戶 3. 引用參考資料時(shí)標(biāo)注來源 參考資料 {context} 問題{question} ; RetrievalAugmentationAdvisor advisor RetrievalAugmentationAdvisor.builder() .queryTransformers(new ChitchatAwareQueryTransformer(builder)) .documentRetriever(VectorStoreDocumentRetriever.builder() .vectorStore(vectorStore) .similarityThreshold(0.7) .topK(6) .build()) .documentPostProcessors(new DashScopeDocumentReranker(apiKey, qwen3-rerank, 3)) .queryAugmenter(ContextualQueryAugmenter.builder() .allowEmptyContext(true) .build()) .build(); return builder.defaultAdvisors(advisor).build(); }八、常用工具方法buildContext文檔列表拼接為上下文字符串將檢索到的 Document 列表拼接為一段完整的上下文文本。private String buildContext(ListDocument documents) { if (documents null || documents.isEmpty()) { return ; } StringBuilder sb new StringBuilder(); for (int i 0; i documents.size(); i) { Document doc documents.get(i); sb.append(--- 文檔).append(i 1).append( ---\n); Object fileName doc.getMetadata().get(fileName); if (fileName ! null) { sb.append(來源).append(fileName).append(\n); } sb.append(doc.getText()).append(\n\n); } return sb.toString(); }extractCitations從響應(yīng)中提取引用來源解析 Spring AI ChatResponse 中的 citations 信息。/** * 從ChatResponse中提取引用的文檔信息 */ private ListMapString, Object extractCitations(ChatResponse response) { ListMapString, Object citations new ArrayList(); if (response null || response.getMetadata() null) { return citations; } MapString, Object metadata response.getMetadata(); Object docsObj metadata.get(documents); if (docsObj instanceof List?) { for (Object obj : (List?) docsObj) { if (obj instanceof Document doc) { MapString, Object citation new HashMap(); citation.put(content, doc.getText()); citation.put(metadata, doc.getMetadata()); citations.add(citation); } } } return citations; }總結(jié)結(jié)合混合檢索、Rerank重排序、Query改寫、閑聊感知、查詢?cè)鰪?qiáng)和幻覺檢測(cè)可顯著提升知識(shí)庫(kù)問答系統(tǒng)的檢索效果與答案質(zhì)量。建議在實(shí)際應(yīng)用中分階段引入上述機(jī)制持續(xù)優(yōu)化用戶體驗(yàn)和結(jié)果可靠性。