insertResponse = milvusClient.insert(insertParam);
-
- if (insertResponse.getStatus() != 0) {
- throw new RuntimeException("插入向量失败: " + insertResponse.getMessage());
- }
-
- logger.debug("向量插入成功: id={}, source={}, chunk={}", id, source, chunkIndex);
-
- } catch (Exception e) {
- logger.error("插入向量到 Milvus 失败", e);
- throw e;
- }
- }
-
- /**
- * 索引结果类
- */
@Getter
public static class IndexingResult {
@Setter
diff --git a/src/main/java/com/superbiz/agent/service/VectorSearchService.java b/src/main/java/com/superbiz/agent/service/VectorSearchService.java
index c717993..3aa7105 100644
--- a/src/main/java/com/superbiz/agent/service/VectorSearchService.java
+++ b/src/main/java/com/superbiz/agent/service/VectorSearchService.java
@@ -1,53 +1,23 @@
package com.superbiz.agent.service;
-import com.fasterxml.jackson.core.JsonProcessingException;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.superbiz.agent.constant.MilvusConstants;
-import io.milvus.client.MilvusServiceClient;
-import io.milvus.grpc.SearchResults;
-import io.milvus.param.R;
-import io.milvus.param.dml.SearchParam;
-import io.milvus.response.SearchResultsWrapper;
+import com.superbiz.agent.service.milvus.MilvusHybridKnowledgeStore;
import lombok.Getter;
import lombok.Setter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
-import org.springframework.ai.document.Document;
-import org.springframework.ai.vectorstore.SearchRequest;
-import org.springframework.ai.vectorstore.VectorStore;
-import org.springframework.beans.factory.ObjectProvider;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
-import java.util.ArrayList;
-import java.util.Collections;
import java.util.List;
-import java.util.Map;
+import java.util.Locale;
/**
- * L1 向量检索门面。
+ * Knowledge vector retrieval facade.
*
- * 对上层（{@link KnowledgeDocumentRetriever}）只暴露稳定 API：
- * {@link #searchSimilarDocuments(String, int, String)}。底层实现可切换：
- *
- * - {@code sdk}：Milvus SDK 直连
- * - {@code spring} / {@code spring-ai}：Spring AI VectorStore
- * - {@code auto}（默认）：先 Spring AI，失败再 fallback 到 SDK
- *
- *
- * 分数兼容约定
- * 后处理 {@code KnowledgeEvidencePostProcessor} 按“L2 距离越小越相似”归一化。
- * 因此本类统一把 {@link SearchResult#score} 填成兼容 L2 距离：
- *
- * - SDK 路径：直接用 Milvus L2 score
- * - Spring AI 路径：优先 metadata.distance；否则把 similarity 映射为
- * {@code (1 - similarity) * maxL2Distance}
- *
- *
- * 过滤
- * 可选 category + 全局 {@code retrieval.kb-scope}。两条实现路径的 filter 语法不同，
- * 但语义一致：只在对应 metadata 字段上收窄。
+ * Single backend: {@link MilvusHybridKnowledgeStore} (Milvus Java SDK v2).
+ * Legacy {@code MilvusServiceClient} search and Spring AI VectorStore routing for
+ * {@code lookup_knowledge} have been removed.
*/
@Service
public class VectorSearchService {
@@ -55,276 +25,42 @@ public class VectorSearchService {
private static final Logger logger = LoggerFactory.getLogger(VectorSearchService.class);
@Autowired
- private MilvusServiceClient milvusClient;
+ private MilvusHybridKnowledgeStore knowledgeStore;
@Autowired
private VectorEmbeddingService embeddingService;
- @Autowired
- private ObjectProvider vectorStoreProvider;
-
- @Autowired
- private ObjectMapper objectMapper;
-
- /** 检索实现路由：auto / spring / spring-ai / sdk。 */
- @Value("${retrieval.vector-store.mode:auto}")
- private String vectorStoreMode = "auto";
-
- /** similarity -> 兼容 L2 时使用的距离上限，需与后处理归一化配置一致。 */
- @Value("${retrieval.normalization.max-l2-distance:2.0}")
- private double maxL2Distance = 2.0;
-
- /** 非空时只检索该 kb_scope 下的 chunk（多租户/多知识库隔离）。 */
- @Value("${retrieval.kb-scope:}")
- private String kbScope = "";
+ /**
+ * dense | hybrid
+ */
+ @Value("${retrieval.search.mode:dense}")
+ private String searchMode = "dense";
public List searchSimilarDocuments(String query, int topK) {
return searchSimilarDocuments(query, topK, null);
}
- /**
- * 按 query 召回 topK 相似文档片段。
- *
- * @param query 检索文本（会再 embedding）
- * @param topK 返回条数
- * @param category 可选 category 过滤；null/blank 表示不过滤
- */
public List searchSimilarDocuments(String query, int topK, String category) {
- String mode = vectorStoreMode == null ? "auto" : vectorStoreMode.trim().toLowerCase();
- return switch (mode) {
- case "sdk" -> searchSimilarDocumentsWithSdk(query, topK, category);
- case "spring", "spring-ai" -> searchSimilarDocumentsWithVectorStore(query, topK, category);
- case "auto" -> searchWithAutoFallback(query, topK, category);
- default -> {
- logger.warn("Unknown retrieval.vector-store.mode={}, using auto mode", vectorStoreMode);
- yield searchWithAutoFallback(query, topK, category);
- }
- };
+ String mode = searchMode == null ? "dense" : searchMode.trim().toLowerCase(Locale.ROOT);
+ List queryVector = embeddingService.generateQueryVector(query);
+ if ("hybrid".equals(mode)) {
+ logger.info("Hybrid dense+BM25 search topK={} category={} collection={}",
+ topK, category, knowledgeStore.collectionName());
+ return knowledgeStore.searchHybrid(query, queryVector, topK, category);
+ }
+ logger.info("Dense search topK={} category={} collection={}",
+ topK, category, knowledgeStore.collectionName());
+ return knowledgeStore.searchDense(query, queryVector, topK, category);
}
- /** auto：Spring AI 优先，任意异常则降级 SDK（保证检索可用性）。 */
- private List searchWithAutoFallback(String query, int topK, String category) {
- try {
- return searchSimilarDocumentsWithVectorStore(query, topK, category);
- } catch (Exception e) {
- logger.warn("Spring AI VectorStore retrieval failed, falling back to Milvus SDK: errorType={}",
- e.getClass().getSimpleName());
- return searchSimilarDocumentsWithSdk(query, topK, category);
- }
- }
-
- /**
- * Spring AI VectorStore 路径。
- * 注意：写入索引仍主要由 Milvus SDK 完成；这里是读适配，依赖双方 metadata schema 一致。
- */
- List searchSimilarDocumentsWithVectorStore(String query, int topK, String category) {
- VectorStore vectorStore = vectorStoreProvider != null ? vectorStoreProvider.getIfAvailable() : null;
- if (vectorStore == null) {
- throw new IllegalStateException("Spring AI VectorStore bean is unavailable");
- }
-
- logger.info("Starting Spring AI VectorStore search: topK={}, category={}, kbScope={}",
- topK, category, effectiveKbScope());
- SearchRequest.Builder builder = SearchRequest.builder()
- .query(query)
- .topK(topK)
- // 不做框架层阈值截断，相关性判断交给后处理
- .similarityThresholdAll();
- String filterExpression = buildSpringAiFilterExpression(category);
- if (filterExpression != null) {
- builder.filterExpression(filterExpression);
- logger.info("Spring AI VectorStore metadata filter: {}", filterExpression);
- }
-
- List documents = vectorStore.similaritySearch(builder.build());
- List results = new ArrayList<>();
- for (Document document : documents) {
- SearchResult result = new SearchResult();
- result.setId(document.getId());
- result.setContent(document.getText());
- result.setMetadata(toJson(document.getMetadata()));
- result.setRawScore(document.getScore());
- result.setScoreLabel("similarity");
- // 下游统一按 L2 距离消费，这里做兼容映射
- result.setScore(toCompatibleL2Distance(document));
- results.add(result);
- }
- logger.info("Spring AI VectorStore search complete, candidates={}", results.size());
- return results;
- }
-
- /** Milvus SDK 原生 L2 检索路径。 */
- List searchSimilarDocumentsWithSdk(String query, int topK, String category) {
- try {
- logger.info("Starting Milvus SDK search: topK={}, category={}, kbScope={}",
- topK, category, effectiveKbScope());
-
- List queryVector = embeddingService.generateQueryVector(query);
- logger.debug("Query vector generated, dimension={}", queryVector.size());
-
- SearchParam.Builder searchParamBuilder = SearchParam.newBuilder()
- .withCollectionName(MilvusConstants.MILVUS_COLLECTION_NAME)
- .withVectorFieldName("vector")
- .withVectors(Collections.singletonList(queryVector))
- .withTopK(topK)
- .withMetricType(io.milvus.param.MetricType.L2)
- .withOutFields(List.of("id", "content", "metadata"))
- .withParams("{\"nprobe\":10}");
-
- String expr = buildSdkFilterExpression(category);
- if (expr != null) {
- searchParamBuilder.withExpr(expr);
- logger.info("Milvus SDK metadata filter: {}", expr);
- }
-
- R searchResponse = milvusClient.search(searchParamBuilder.build());
- if (searchResponse.getStatus() != 0) {
- throw new RuntimeException("Vector search failed: " + searchResponse.getMessage());
- }
-
- SearchResultsWrapper wrapper = new SearchResultsWrapper(searchResponse.getData().getResults());
- List results = new ArrayList<>();
- for (int i = 0; i < wrapper.getRowRecords(0).size(); i++) {
- SearchResult result = new SearchResult();
- result.setId((String) wrapper.getIDScore(0).get(i).get("id"));
- result.setContent((String) wrapper.getFieldData("content", 0).get(i));
- // Milvus L2：数值越小越相似
- result.setScore(wrapper.getIDScore(0).get(i).getScore());
- result.setRawScore((double) result.getScore());
- result.setScoreLabel("l2_distance");
-
- Object metadataObj = wrapper.getFieldData("metadata", 0).get(i);
- if (metadataObj != null) {
- result.setMetadata(metadataObj.toString());
- }
-
- results.add(result);
- }
-
- logger.info("Milvus SDK search complete, candidates={}", results.size());
- return results;
- } catch (Exception e) {
- logger.error("Milvus SDK vector search failed: errorType={}", e.getClass().getSimpleName());
- throw new RuntimeException("Vector search failed: " + e.getMessage(), e);
- }
- }
-
- /** 优先使用 metadata.distance；否则把 similarity 映射为兼容 L2。 */
- private float toCompatibleL2Distance(Document document) {
- Double distance = extractDistance(document.getMetadata());
- if (distance != null) {
- return distance.floatValue();
- }
- return toCompatibleL2Distance(document.getScore());
- }
-
- /**
- * similarity ∈ [0,1] 越大越相似 -> 兼容 L2 距离。
- * 映射：distance = (1 - similarity) * maxL2Distance
- */
- private float toCompatibleL2Distance(Double similarity) {
- if (similarity == null) {
- return (float) maxL2Distance;
- }
- double bounded = Math.max(0.0, Math.min(1.0, similarity));
- return (float) ((1.0 - bounded) * maxL2Distance);
- }
-
- private Double extractDistance(Map metadata) {
- if (metadata == null) {
- return null;
- }
- Object value = metadata.get("distance");
- if (value instanceof Number number) {
- return number.doubleValue();
- }
- if (value instanceof String text) {
- try {
- return Double.parseDouble(text);
- } catch (NumberFormatException ignored) {
- return null;
- }
- }
- return null;
- }
-
- private String toJson(Map metadata) {
- if (metadata == null || metadata.isEmpty()) {
- return null;
- }
- try {
- return objectMapper.writeValueAsString(metadata);
- } catch (JsonProcessingException e) {
- return metadata.toString();
- }
- }
-
- private String escapeFilterValue(String value) {
- return value.replace("'", "\\'");
- }
-
- /** Spring AI filter DSL，例如：category == 'mysql' && kb_scope == 'prod' */
- String buildSpringAiFilterExpression(String category) {
- List parts = new ArrayList<>();
- String categoryFilter = trimToNull(category);
- if (categoryFilter != null) {
- parts.add("category == '" + escapeFilterValue(categoryFilter) + "'");
- }
- String scopeFilter = effectiveKbScope();
- if (scopeFilter != null) {
- parts.add("kb_scope == '" + escapeFilterValue(scopeFilter) + "'");
- }
- return parts.isEmpty() ? null : String.join(" && ", parts);
- }
-
- /** Milvus boolean expr，字段在 JSON metadata 内。 */
- String buildSdkFilterExpression(String category) {
- List parts = new ArrayList<>();
- String categoryFilter = trimToNull(category);
- if (categoryFilter != null) {
- parts.add("metadata[\"category\"] == \"" + escapeMilvusString(categoryFilter) + "\"");
- }
- String scopeFilter = effectiveKbScope();
- if (scopeFilter != null) {
- parts.add("metadata[\"kb_scope\"] == \"" + escapeMilvusString(scopeFilter) + "\"");
- }
- return parts.isEmpty() ? null : String.join(" && ", parts);
- }
-
- private String effectiveKbScope() {
- return trimToNull(kbScope);
- }
-
- private String trimToNull(String value) {
- if (value == null || value.isBlank()) {
- return null;
- }
- return value.trim();
- }
-
- private String escapeMilvusString(String value) {
- return value.replace("\\", "\\\\").replace("\"", "\\\"");
- }
-
- /**
- * 统一检索命中结构。
- *
- *
- * - {@code score}：兼容 L2 距离，供后处理 normalizeL2
- * - {@code rawScore}：底层原始分（similarity 或 l2）
- * - {@code scoreLabel}：解释 rawScore 语义
- * - {@code metadata}：JSON 字符串，含 docId/chunkIndex/title 等
- *
- */
@Setter
@Getter
public static class SearchResult {
private String id;
private String content;
/**
- * Compatibility score used by existing lookup relevance normalization.
- * SDK mode keeps L2 distance; VectorStore mode prefers the Milvus
- * distance metadata and falls back to similarity mapping.
+ * Compatibility score for post-process normalizeL2.
+ * Dense path: L2 distance. Hybrid path: dense L2 when available.
*/
private float score;
private Double rawScore;
diff --git a/src/main/java/com/superbiz/agent/service/milvus/MilvusHybridKnowledgeStore.java b/src/main/java/com/superbiz/agent/service/milvus/MilvusHybridKnowledgeStore.java
new file mode 100644
index 0000000..1622157
--- /dev/null
+++ b/src/main/java/com/superbiz/agent/service/milvus/MilvusHybridKnowledgeStore.java
@@ -0,0 +1,426 @@
+package com.superbiz.agent.service.milvus;
+
+import com.google.gson.Gson;
+import com.google.gson.JsonObject;
+import com.superbiz.agent.config.MilvusProperties;
+import com.superbiz.agent.constant.MilvusConstants;
+import com.superbiz.agent.service.VectorSearchService;
+import io.milvus.common.clientenum.FunctionType;
+import io.milvus.v2.client.ConnectConfig;
+import io.milvus.v2.client.MilvusClientV2;
+import io.milvus.v2.common.DataType;
+import io.milvus.v2.common.IndexParam;
+import io.milvus.v2.service.collection.request.AddFieldReq;
+import io.milvus.v2.service.collection.request.CreateCollectionReq;
+import io.milvus.v2.service.collection.request.HasCollectionReq;
+import io.milvus.v2.service.collection.request.LoadCollectionReq;
+import io.milvus.v2.service.index.request.CreateIndexReq;
+import io.milvus.v2.service.vector.request.AnnSearchReq;
+import io.milvus.v2.service.vector.request.DeleteReq;
+import io.milvus.v2.service.vector.request.HybridSearchReq;
+import io.milvus.v2.service.vector.request.InsertReq;
+import io.milvus.v2.service.vector.request.SearchReq;
+import io.milvus.v2.service.vector.request.data.BaseVector;
+import io.milvus.v2.service.vector.request.data.EmbeddedText;
+import io.milvus.v2.service.vector.request.data.FloatVec;
+import io.milvus.v2.service.vector.request.ranker.RRFRanker;
+import io.milvus.v2.service.vector.response.SearchResp;
+import jakarta.annotation.PreDestroy;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.LinkedHashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.UUID;
+
+/**
+ * Single knowledge vector backend (Milvus Java SDK v2).
+ *
+ * Supports dense ANN and dense+BM25 hybrid search via {@code hybridSearch} + {@link RRFRanker}.
+ * Legacy {@code MilvusServiceClient} search is not used.
+ */
+@Service
+public class MilvusHybridKnowledgeStore {
+
+ private static final Logger log = LoggerFactory.getLogger(MilvusHybridKnowledgeStore.class);
+ private static final Gson GSON = new Gson();
+
+ public static final String FIELD_ID = "id";
+ public static final String FIELD_CONTENT = "content";
+ public static final String FIELD_SEARCH_TEXT = "search_text";
+ public static final String FIELD_SPARSE = "sparse_vector";
+ public static final String FIELD_DENSE = "vector";
+ public static final String FIELD_METADATA = "metadata";
+
+ private final MilvusProperties milvusProperties;
+
+ @Value("${milvus.collection:biz_hybrid}")
+ private String collectionName = "biz_hybrid";
+
+ @Value("${retrieval.hybrid.rrf-k:60}")
+ private int rrfK = 60;
+
+ @Value("${retrieval.normalization.max-l2-distance:2.0}")
+ private double maxL2Distance = 2.0;
+
+ @Value("${retrieval.kb-scope:}")
+ private String kbScope = "";
+
+ private volatile MilvusClientV2 client;
+
+ public MilvusHybridKnowledgeStore(MilvusProperties milvusProperties) {
+ this.milvusProperties = milvusProperties;
+ }
+
+ public synchronized MilvusClientV2 client() {
+ if (client == null) {
+ client = connect();
+ ensureCollection(client);
+ loadCollection(client);
+ }
+ return client;
+ }
+
+ public String collectionName() {
+ return collectionName;
+ }
+
+ public void upsertChunk(String content,
+ String searchText,
+ List denseVector,
+ Map metadata,
+ int chunkIndex) {
+ String source = metadata == null ? null : stringVal(metadata.get("_source"));
+ if (source == null) {
+ source = metadata == null ? null : stringVal(metadata.get("source"));
+ }
+ if (source == null) {
+ source = metadata == null ? null : stringVal(metadata.get("docId"));
+ }
+ String idSeed = (source == null ? "chunk" : source) + "_" + chunkIndex;
+ String id = UUID.nameUUIDFromBytes(idSeed.getBytes()).toString();
+
+ JsonObject row = new JsonObject();
+ row.addProperty(FIELD_ID, id);
+ row.addProperty(FIELD_CONTENT, content == null ? "" : content);
+ row.addProperty(FIELD_SEARCH_TEXT, searchText == null ? "" : searchText);
+ row.add(FIELD_DENSE, GSON.toJsonTree(denseVector));
+ row.add(FIELD_METADATA, GSON.toJsonTree(metadata == null ? Map.of() : metadata));
+
+ client().insert(InsertReq.builder()
+ .collectionName(collectionName)
+ .data(List.of(row))
+ .build());
+ }
+
+ public void deleteByDocId(String docId) {
+ if (docId == null || docId.isBlank()) {
+ return;
+ }
+ String filter = "metadata[\"docId\"] == \"" + escapeFilter(docId) + "\"";
+ client().delete(DeleteReq.builder()
+ .collectionName(collectionName)
+ .filter(filter)
+ .build());
+ }
+
+ public void deleteBySource(String sourcePath) {
+ if (sourcePath == null || sourcePath.isBlank()) {
+ return;
+ }
+ String normalized = sourcePath.replace('\\', '/');
+ String filter = "metadata[\"_source\"] == \"" + escapeFilter(normalized) + "\"";
+ client().delete(DeleteReq.builder()
+ .collectionName(collectionName)
+ .filter(filter)
+ .build());
+ }
+
+ public List searchDense(String queryEmbeddingText,
+ List queryVector,
+ int topK,
+ String category) {
+ String filter = buildFilter(category);
+ SearchReq.SearchReqBuilder builder = SearchReq.builder()
+ .collectionName(collectionName)
+ .annsField(FIELD_DENSE)
+ .data(List.of(new FloatVec(queryVector)))
+ .topK(topK)
+ .outputFields(List.of(FIELD_ID, FIELD_CONTENT, FIELD_METADATA))
+ .metricType(IndexParam.MetricType.L2);
+ if (filter != null) {
+ builder.filter(filter);
+ }
+ SearchResp resp = client().search(builder.build());
+ return toSearchResults(resp, "l2_distance", false);
+ }
+
+ /**
+ * Dense + BM25 hybrid fused by RRF. Dense L2 scores are attached when the same id
+ * appears in a parallel dense search so quality thresholds stay meaningful.
+ */
+ public List searchHybrid(String queryText,
+ List queryVector,
+ int topK,
+ String category) {
+ String filter = buildFilter(category);
+ int pathTopK = Math.max(topK, 10);
+
+ AnnSearchReq.AnnSearchReqBuilder denseAnn = AnnSearchReq.builder()
+ .vectorFieldName(FIELD_DENSE)
+ .vectors(List.of((BaseVector) new FloatVec(queryVector)))
+ .topK(pathTopK)
+ .metricType(IndexParam.MetricType.L2)
+ .params("{\"nprobe\":10}");
+ if (filter != null) {
+ denseAnn.filter(filter);
+ }
+
+ AnnSearchReq.AnnSearchReqBuilder sparseAnn = AnnSearchReq.builder()
+ .vectorFieldName(FIELD_SPARSE)
+ .vectors(List.of((BaseVector) new EmbeddedText(queryText == null ? "" : queryText)))
+ .topK(pathTopK)
+ .metricType(IndexParam.MetricType.BM25);
+ if (filter != null) {
+ sparseAnn.filter(filter);
+ }
+
+ HybridSearchReq hybridReq = HybridSearchReq.builder()
+ .collectionName(collectionName)
+ .searchRequests(List.of(denseAnn.build(), sparseAnn.build()))
+ .ranker(new RRFRanker(Math.max(1, rrfK)))
+ .topK(topK)
+ .outFields(List.of(FIELD_ID, FIELD_CONTENT, FIELD_METADATA))
+ .build();
+
+ SearchResp hybridResp = client().hybridSearch(hybridReq);
+ List fused = toSearchResults(hybridResp, "rrf_fused", true);
+
+ // Attach dense-compatible L2 when available.
+ Map denseScores = new HashMap<>();
+ try {
+ for (VectorSearchService.SearchResult denseHit :
+ searchDense(queryText, queryVector, pathTopK, category)) {
+ if (denseHit.getId() != null) {
+ denseScores.put(denseHit.getId(), denseHit.getScore());
+ }
+ }
+ } catch (Exception e) {
+ log.warn("Dense score enrichment failed: {}", e.getMessage());
+ }
+ for (VectorSearchService.SearchResult hit : fused) {
+ Float dense = denseScores.get(hit.getId());
+ if (dense != null) {
+ hit.setScore(dense);
+ hit.setScoreLabel("l2_distance");
+ } else {
+ // BM25-only hit: treat as weak for legacy thresholds
+ hit.setScore((float) maxL2Distance);
+ hit.setScoreLabel("bm25_only_no_dense");
+ }
+ }
+ return fused;
+ }
+
+ private List toSearchResults(SearchResp resp,
+ String scoreLabel,
+ boolean fused) {
+ List out = new ArrayList<>();
+ if (resp == null || resp.getSearchResults() == null || resp.getSearchResults().isEmpty()) {
+ return out;
+ }
+ List first = resp.getSearchResults().get(0);
+ if (first == null) {
+ return out;
+ }
+ for (SearchResp.SearchResult row : first) {
+ VectorSearchService.SearchResult mapped = new VectorSearchService.SearchResult();
+ Object id = row.getId();
+ mapped.setId(id == null ? null : String.valueOf(id));
+ Map entity = row.getEntity() == null ? Map.of() : row.getEntity();
+ Object content = entity.get(FIELD_CONTENT);
+ mapped.setContent(content == null ? null : String.valueOf(content));
+ Object metadata = entity.get(FIELD_METADATA);
+ if (metadata instanceof JsonObject jsonObject) {
+ mapped.setMetadata(jsonObject.toString());
+ } else if (metadata instanceof Map map) {
+ mapped.setMetadata(GSON.toJson(map));
+ } else if (metadata != null) {
+ mapped.setMetadata(String.valueOf(metadata));
+ }
+ Float score = row.getScore();
+ mapped.setRawScore(score == null ? null : score.doubleValue());
+ mapped.setScoreLabel(scoreLabel);
+ if (fused) {
+ // temporary; may be overwritten with dense L2
+ mapped.setScore(score == null ? (float) maxL2Distance : invertUnknownScore(score));
+ } else {
+ mapped.setScore(score == null ? (float) maxL2Distance : score);
+ }
+ out.add(mapped);
+ }
+ return out;
+ }
+
+ private float invertUnknownScore(float score) {
+ // RRF-like small scores: map higher better -> small L2-like distance
+ double bounded = Math.max(0.0, Math.min(1.0, score));
+ if (score > 1.0f) {
+ // already distance-like
+ return score;
+ }
+ return (float) ((1.0 - bounded) * maxL2Distance);
+ }
+
+ private String buildFilter(String category) {
+ List parts = new ArrayList<>();
+ String categoryFilter = trimToNull(category);
+ if (categoryFilter != null) {
+ parts.add("metadata[\"category\"] == \"" + escapeFilter(categoryFilter) + "\"");
+ }
+ String scope = trimToNull(kbScope);
+ if (scope != null) {
+ parts.add("metadata[\"kb_scope\"] == \"" + escapeFilter(scope) + "\"");
+ }
+ return parts.isEmpty() ? null : String.join(" && ", parts);
+ }
+
+ private MilvusClientV2 connect() {
+ String uri;
+ if (milvusProperties.isSecure() || milvusProperties.getPort() == 443) {
+ uri = "https://" + milvusProperties.getHost() + ":" + milvusProperties.getPort();
+ } else {
+ uri = "http://" + milvusProperties.getHost() + ":" + milvusProperties.getPort();
+ }
+ ConnectConfig.ConnectConfigBuilder builder = ConnectConfig.builder()
+ .uri(uri)
+ .connectTimeoutMs(milvusProperties.getTimeout() == null ? 10000L : milvusProperties.getTimeout());
+ if (milvusProperties.getToken() != null && !milvusProperties.getToken().isBlank()) {
+ builder.token(milvusProperties.getToken());
+ builder.secure(true);
+ } else if (milvusProperties.getUsername() != null && !milvusProperties.getUsername().isBlank()) {
+ builder.username(milvusProperties.getUsername());
+ builder.password(milvusProperties.getPassword());
+ }
+ if (milvusProperties.getDatabase() != null && !milvusProperties.getDatabase().isBlank()) {
+ builder.dbName(milvusProperties.getDatabase());
+ }
+ log.info("Connecting MilvusClientV2 uri={} db={} collection={}",
+ uri, milvusProperties.getDatabase(), collectionName);
+ return new MilvusClientV2(builder.build());
+ }
+
+ private void ensureCollection(MilvusClientV2 milvusClient) {
+ Boolean exists = milvusClient.hasCollection(HasCollectionReq.builder()
+ .collectionName(collectionName)
+ .build());
+ if (Boolean.TRUE.equals(exists)) {
+ log.info("Hybrid collection '{}' already exists", collectionName);
+ return;
+ }
+ log.info("Creating hybrid collection '{}'", collectionName);
+
+ CreateCollectionReq.CollectionSchema schema = milvusClient.createSchema();
+ schema.setEnableDynamicField(false);
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_ID)
+ .dataType(DataType.VarChar)
+ .maxLength(MilvusConstants.ID_MAX_LENGTH)
+ .isPrimaryKey(true)
+ .autoID(false)
+ .build());
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_CONTENT)
+ .dataType(DataType.VarChar)
+ .maxLength(MilvusConstants.CONTENT_MAX_LENGTH)
+ .build());
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_SEARCH_TEXT)
+ .dataType(DataType.VarChar)
+ .maxLength(MilvusConstants.CONTENT_MAX_LENGTH)
+ .enableAnalyzer(true)
+ .build());
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_SPARSE)
+ .dataType(DataType.SparseFloatVector)
+ .build());
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_DENSE)
+ .dataType(DataType.FloatVector)
+ .dimension(milvusProperties.getVectorDim())
+ .build());
+ schema.addField(AddFieldReq.builder()
+ .fieldName(FIELD_METADATA)
+ .dataType(DataType.JSON)
+ .build());
+ schema.addFunction(CreateCollectionReq.Function.builder()
+ .functionType(FunctionType.BM25)
+ .name("bm25_fn")
+ .inputFieldNames(List.of(FIELD_SEARCH_TEXT))
+ .outputFieldNames(List.of(FIELD_SPARSE))
+ .build());
+
+ milvusClient.createCollection(CreateCollectionReq.builder()
+ .collectionName(collectionName)
+ .collectionSchema(schema)
+ .description("Knowledge hybrid dense+BM25 collection")
+ .numShards(MilvusConstants.DEFAULT_SHARD_NUMBER)
+ .build());
+
+ List indexes = List.of(
+ IndexParam.builder()
+ .fieldName(FIELD_DENSE)
+ .indexType(IndexParam.IndexType.IVF_FLAT)
+ .metricType(IndexParam.MetricType.L2)
+ .extraParams(Map.of("nlist", 128))
+ .build(),
+ IndexParam.builder()
+ .fieldName(FIELD_SPARSE)
+ .indexType(IndexParam.IndexType.SPARSE_INVERTED_INDEX)
+ .metricType(IndexParam.MetricType.BM25)
+ .build()
+ );
+ milvusClient.createIndex(CreateIndexReq.builder()
+ .collectionName(collectionName)
+ .indexParams(indexes)
+ .build());
+ log.info("Hybrid collection '{}' created with dense+BM25 indexes", collectionName);
+ }
+
+ private void loadCollection(MilvusClientV2 milvusClient) {
+ milvusClient.loadCollection(LoadCollectionReq.builder()
+ .collectionName(collectionName)
+ .build());
+ }
+
+ @PreDestroy
+ public void close() {
+ if (client != null) {
+ try {
+ client.close();
+ } catch (Exception e) {
+ log.warn("Error closing MilvusClientV2: {}", e.getMessage());
+ }
+ }
+ }
+
+ private static String escapeFilter(String value) {
+ return value.replace("\\", "\\\\").replace("\"", "\\\"");
+ }
+
+ private static String trimToNull(String value) {
+ if (value == null || value.isBlank()) {
+ return null;
+ }
+ return value.trim();
+ }
+
+ private static String stringVal(Object value) {
+ return value == null ? null : String.valueOf(value);
+ }
+}
diff --git a/src/main/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapter.java b/src/main/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapter.java
index fca46dc..c7d2245 100644
--- a/src/main/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapter.java
+++ b/src/main/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapter.java
@@ -2,22 +2,19 @@ package com.superbiz.agent.service.retrieval;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.superbiz.agent.service.VectorSearchService;
-import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Component;
import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
-import java.util.Locale;
import java.util.Map;
/**
- * {@link KnowledgeSearchPort} backed by the existing vector search facade.
+ * {@link KnowledgeSearchPort} adapter.
*
- *
- * - {@code DENSE}: single dense path (legacy behavior)
- * - {@code HYBRID}: dense unfiltered + optional dense filtered + lexical rank, fused by RRF
- *
+ * Delegates to {@link VectorSearchService}, which is backed solely by
+ * Milvus V2 dense / dense+BM25 hybrid store. Mode selection lives in
+ * {@code retrieval.search.mode}.
*/
@Component
public class VectorKnowledgeSearchAdapter implements KnowledgeSearchPort {
@@ -25,21 +22,6 @@ public class VectorKnowledgeSearchAdapter implements KnowledgeSearchPort {
private final VectorSearchService vectorSearchService;
private final ObjectMapper objectMapper;
- @Value("${retrieval.search.mode:dense}")
- private String configuredMode = "dense";
-
- @Value("${retrieval.hybrid.rrf-k:60}")
- private int rrfK = 60;
-
- @Value("${retrieval.hybrid.weight.dense-unfiltered:1.0}")
- private double weightDenseUnfiltered = 1.0;
-
- @Value("${retrieval.hybrid.weight.dense-filtered:1.0}")
- private double weightDenseFiltered = 1.0;
-
- @Value("${retrieval.hybrid.weight.lexical:1.0}")
- private double weightLexical = 1.0;
-
public VectorKnowledgeSearchAdapter(VectorSearchService vectorSearchService, ObjectMapper objectMapper) {
this.vectorSearchService = vectorSearchService;
this.objectMapper = objectMapper;
@@ -47,100 +29,14 @@ public class VectorKnowledgeSearchAdapter implements KnowledgeSearchPort {
@Override
public List search(KnowledgeSearchRequest request) {
- KnowledgeSearchMode mode = resolveMode(request.mode());
- if (mode == KnowledgeSearchMode.HYBRID) {
- return searchHybrid(request);
- }
- return searchDense(request.query(), request.topK(), request.categoryFilter());
- }
-
- private List searchDense(String query, int topK, String categoryFilter) {
+ // Mode is global on VectorSearchService; request.mode is advisory for future per-call overrides.
List results = vectorSearchService.searchSimilarDocuments(
- query, topK, categoryFilter);
+ request.query(),
+ request.topK(),
+ request.categoryFilter());
return toHits(results);
}
- private List searchHybrid(KnowledgeSearchRequest request) {
- String query = request.query();
- int topK = request.topK();
- String category = trimToNull(request.categoryFilter());
-
- List unfiltered = searchDense(query, topK, null);
- List filtered = category == null
- ? List.of()
- : searchDense(query, topK, category);
-
- Map unionByKey = new LinkedHashMap<>();
- for (KnowledgeSearchHit hit : unfiltered) {
- unionByKey.putIfAbsent(hit.evidenceKey(), hit);
- }
- for (KnowledgeSearchHit hit : filtered) {
- unionByKey.putIfAbsent(hit.evidenceKey(), hit);
- }
- List union = new ArrayList<>(unionByKey.values());
- List lexical = LexicalRanker.rank(query, union);
-
- List> paths = new ArrayList<>();
- paths.add(new RrfFusion.RankedPath<>("dense_unfiltered", unfiltered, weightDenseUnfiltered));
- if (!filtered.isEmpty()) {
- paths.add(new RrfFusion.RankedPath<>("dense_filtered", filtered, weightDenseFiltered));
- }
- if (!lexical.isEmpty()) {
- paths.add(new RrfFusion.RankedPath<>("lexical", lexical, weightLexical));
- }
-
- List> fused = RrfFusion.fuse(
- paths, rrfK, KnowledgeSearchHit::evidenceKey);
-
- List ordered = new ArrayList<>();
- int rank = 1;
- for (RrfFusion.Scored scored : fused) {
- if (ordered.size() >= topK) {
- break;
- }
- KnowledgeSearchHit base = scored.item();
- Map metadata = new LinkedHashMap<>(
- base.metadata() == null ? Map.of() : base.metadata());
- metadata.put("fusedScore", Double.toString(scored.rrfScore()));
- metadata.put("fusionRanks", scored.ranks().toString());
- metadata.put("fusionRank", Integer.toString(rank));
- ordered.add(new KnowledgeSearchHit(
- base.id(),
- base.content(),
- base.score(),
- base.rawScore(),
- base.scoreLabel(),
- base.metadataJson(),
- metadata,
- base.docId(),
- base.chunkIndex(),
- base.evidenceKey(),
- base.source(),
- base.title(),
- base.breadcrumb(),
- rank
- ));
- rank++;
- }
- return ordered;
- }
-
- private KnowledgeSearchMode resolveMode(KnowledgeSearchMode requestMode) {
- if (requestMode == KnowledgeSearchMode.HYBRID) {
- return KnowledgeSearchMode.HYBRID;
- }
- if (requestMode == KnowledgeSearchMode.DENSE) {
- // Allow global config to force hybrid even if caller passes DENSE default.
- String configured = configuredMode == null ? "dense" : configuredMode.trim().toLowerCase(Locale.ROOT);
- if ("hybrid".equals(configured)) {
- return KnowledgeSearchMode.HYBRID;
- }
- return KnowledgeSearchMode.DENSE;
- }
- String configured = configuredMode == null ? "dense" : configuredMode.trim().toLowerCase(Locale.ROOT);
- return "hybrid".equals(configured) ? KnowledgeSearchMode.HYBRID : KnowledgeSearchMode.DENSE;
- }
-
private List toHits(List results) {
if (results == null || results.isEmpty()) {
return List.of();
@@ -201,11 +97,4 @@ public class VectorKnowledgeSearchAdapter implements KnowledgeSearchPort {
return Map.of();
}
}
-
- private static String trimToNull(String value) {
- if (value == null || value.isBlank()) {
- return null;
- }
- return value.trim();
- }
}
diff --git a/src/main/resources/application.yml b/src/main/resources/application.yml
index 648d355..f3cf67e 100644
--- a/src/main/resources/application.yml
+++ b/src/main/resources/application.yml
@@ -25,6 +25,8 @@ milvus:
token: ${MILVUS_TOKEN}
secure: true
vector-dim: 1024 # BGE-M3 = 1024，换模型时同步改
+ # dense+BM25 hybrid collection (requires reindex; legacy "biz" is not used by knowledge path)
+ collection: biz_hybrid
# =====================================================
# 模型路由配置
@@ -162,16 +164,24 @@ rag:
enabled: false
content-preview-limit: 300
-# 检索归一化配置
+# 检索配置（单一 Milvus V2 后端；已移除 sdk/spring/auto 路由）
retrieval:
- kb-scope: "" # empty means search all legacy documents; use rag-eval for eval seed docs
- vector-store:
- mode: auto # auto | spring-ai | sdk
+ kb-scope: "" # empty means search all documents in hybrid collection
+ search:
+ mode: hybrid # dense | hybrid (dense + BM25 RRF)
+ hybrid:
+ rrf-k: 60
normalization:
max-l2-distance: 2.0 # L2 距离上界（BGE-M3 单位向量 = 2.0）
highly-relevant-threshold: 0.75 # similarity >= 0.75 → HIGHLY_RELEVANT
reference-threshold: 0.5 # similarity >= 0.5 → REFERENCE
+# RAG 召回宽度（Delivery 1）
+rag:
+ retrieve-k: 20
+ return-n: 5
+ max-chunks-per-document: 2
+
# Prometheus 配置
prometheus:
base-url: http://localhost:9090
diff --git a/src/test/java/com/superbiz/agent/service/VectorSearchServiceTest.java b/src/test/java/com/superbiz/agent/service/VectorSearchServiceTest.java
index 5a5cc5f..9945b21 100644
--- a/src/test/java/com/superbiz/agent/service/VectorSearchServiceTest.java
+++ b/src/test/java/com/superbiz/agent/service/VectorSearchServiceTest.java
@@ -1,208 +1,61 @@
package com.superbiz.agent.service;
-import com.fasterxml.jackson.databind.ObjectMapper;
+import com.superbiz.agent.service.milvus.MilvusHybridKnowledgeStore;
import org.junit.jupiter.api.Test;
-import org.mockito.ArgumentCaptor;
-import org.springframework.ai.document.Document;
-import org.springframework.ai.vectorstore.SearchRequest;
-import org.springframework.ai.vectorstore.VectorStore;
-import org.springframework.beans.factory.ObjectProvider;
import org.springframework.test.util.ReflectionTestUtils;
import java.util.List;
-import java.util.Map;
import static org.junit.jupiter.api.Assertions.assertEquals;
-import static org.junit.jupiter.api.Assertions.assertNull;
-import static org.junit.jupiter.api.Assertions.assertTrue;
-import static org.mockito.ArgumentMatchers.any;
import static org.mockito.ArgumentMatchers.eq;
-import static org.mockito.Mockito.doReturn;
+import static org.mockito.ArgumentMatchers.isNull;
import static org.mockito.Mockito.mock;
-import static org.mockito.Mockito.never;
-import static org.mockito.Mockito.spy;
import static org.mockito.Mockito.verify;
import static org.mockito.Mockito.when;
class VectorSearchServiceTest {
@Test
- void sdkModeBypassesVectorStore() {
- VectorSearchService service = spy(new VectorSearchService());
- setMode(service, "sdk");
- VectorSearchService.SearchResult expected = result("sdk-doc", 0.2f);
- doReturn(List.of(expected))
- .when(service).searchSimilarDocumentsWithSdk("query", 3, null);
+ void denseModeCallsHybridStoreDenseSearch() {
+ MilvusHybridKnowledgeStore store = mock(MilvusHybridKnowledgeStore.class);
+ VectorEmbeddingService embeddingService = mock(VectorEmbeddingService.class);
+ when(embeddingService.generateQueryVector("query")).thenReturn(List.of(0.1f, 0.2f));
+ VectorSearchService.SearchResult expected = result("doc-1", 0.2f);
+ when(store.searchDense(eq("query"), eq(List.of(0.1f, 0.2f)), eq(3), isNull()))
+ .thenReturn(List.of(expected));
+ when(store.collectionName()).thenReturn("biz_hybrid");
+
+ VectorSearchService service = new VectorSearchService();
+ ReflectionTestUtils.setField(service, "knowledgeStore", store);
+ ReflectionTestUtils.setField(service, "embeddingService", embeddingService);
+ ReflectionTestUtils.setField(service, "searchMode", "dense");
List results = service.searchSimilarDocuments("query", 3, null);
assertEquals(List.of(expected), results);
- verify(service, never()).searchSimilarDocumentsWithVectorStore(any(), eq(3), any());
+ verify(store).searchDense(eq("query"), eq(List.of(0.1f, 0.2f)), eq(3), isNull());
}
@Test
- void autoModeUsesVectorStoreWhenAvailable() {
- VectorStore vectorStore = mock(VectorStore.class);
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(vectorStore);
- when(vectorStore.similaritySearch(any(SearchRequest.class))).thenReturn(List.of(
- Document.builder()
- .id("spring-doc")
- .text("spring content")
- .metadata(Map.of("_source", "spring.md", "category", "api"))
- .score(0.8)
- .build()
- ));
+ void hybridModeCallsHybridStoreHybridSearch() {
+ MilvusHybridKnowledgeStore store = mock(MilvusHybridKnowledgeStore.class);
+ VectorEmbeddingService embeddingService = mock(VectorEmbeddingService.class);
+ when(embeddingService.generateQueryVector("pool")).thenReturn(List.of(0.3f));
+ VectorSearchService.SearchResult expected = result("doc-h", 0.4f);
+ when(store.searchHybrid(eq("pool"), eq(List.of(0.3f)), eq(5), eq("mysql")))
+ .thenReturn(List.of(expected));
+ when(store.collectionName()).thenReturn("biz_hybrid");
VectorSearchService service = new VectorSearchService();
- setMode(service, "auto");
- setVectorStore(service, provider);
+ ReflectionTestUtils.setField(service, "knowledgeStore", store);
+ ReflectionTestUtils.setField(service, "embeddingService", embeddingService);
+ ReflectionTestUtils.setField(service, "searchMode", "hybrid");
- List results = service.searchSimilarDocuments("query", 3, null);
+ List results = service.searchSimilarDocuments("pool", 5, "mysql");
assertEquals(1, results.size());
- assertEquals("spring-doc", results.get(0).getId());
- assertEquals("similarity", results.get(0).getScoreLabel());
- assertEquals(0.8, results.get(0).getRawScore(), 0.0001);
- assertEquals(0.4f, results.get(0).getScore(), 0.0001);
- assertTrue(results.get(0).getMetadata().contains("spring.md"));
- }
-
- @Test
- void vectorStoreSearchUsesDistanceMetadataAsCompatibleScore() {
- VectorStore vectorStore = mock(VectorStore.class);
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(vectorStore);
- when(vectorStore.similaritySearch(any(SearchRequest.class))).thenReturn(List.of(
- Document.builder()
- .id("spring-doc")
- .text("spring content")
- .metadata(Map.of("distance", 0.5659486, "category", "api"))
- .score(0.4340514)
- .build()
- ));
-
- VectorSearchService service = new VectorSearchService();
- setMode(service, "auto");
- setVectorStore(service, provider);
-
- List results = service.searchSimilarDocuments("query", 3, null);
-
- assertEquals(1, results.size());
- assertEquals("similarity", results.get(0).getScoreLabel());
- assertEquals(0.4340514, results.get(0).getRawScore(), 0.0001);
- assertEquals(0.5659486f, results.get(0).getScore(), 0.0001);
- }
-
- @Test
- void autoModeFallsBackToSdkWhenVectorStoreFails() {
- VectorStore vectorStore = mock(VectorStore.class);
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(vectorStore);
- when(vectorStore.similaritySearch(any(SearchRequest.class))).thenThrow(new RuntimeException("vectorstore down"));
-
- VectorSearchService service = spy(new VectorSearchService());
- setMode(service, "auto");
- setVectorStore(service, provider);
- VectorSearchService.SearchResult fallback = result("sdk-doc", 0.3f);
- doReturn(List.of(fallback))
- .when(service).searchSimilarDocumentsWithSdk("query", 3, null);
-
- List results = service.searchSimilarDocuments("query", 3, null);
-
- assertEquals(List.of(fallback), results);
- }
-
- @Test
- void autoModeFallsBackToSdkWhenVectorStoreUnavailable() {
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(null);
-
- VectorSearchService service = spy(new VectorSearchService());
- setMode(service, "auto");
- setVectorStore(service, provider);
- VectorSearchService.SearchResult fallback = result("sdk-doc", 0.3f);
- doReturn(List.of(fallback))
- .when(service).searchSimilarDocumentsWithSdk("query", 3, null);
-
- List results = service.searchSimilarDocuments("query", 3, null);
-
- assertEquals(List.of(fallback), results);
- }
-
- @Test
- void vectorStoreSearchUsesCategoryFilter() {
- VectorStore vectorStore = mock(VectorStore.class);
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(vectorStore);
- when(vectorStore.similaritySearch(any(SearchRequest.class))).thenReturn(List.of());
- VectorSearchService service = new VectorSearchService();
- setMode(service, "spring-ai");
- setVectorStore(service, provider);
-
- service.searchSimilarDocuments("query", 5, "api");
-
- ArgumentCaptor requestCaptor = ArgumentCaptor.forClass(SearchRequest.class);
- verify(vectorStore).similaritySearch(requestCaptor.capture());
- SearchRequest request = requestCaptor.getValue();
- assertEquals("query", request.getQuery());
- assertEquals(5, request.getTopK());
- assertTrue(request.hasFilterExpression());
- assertTrue(request.toString().contains("category"));
- assertTrue(request.toString().contains("api"));
- }
-
- @Test
- void springModeUsesVectorStoreAlias() {
- VectorStore vectorStore = mock(VectorStore.class);
- ObjectProvider provider = mock(ObjectProvider.class);
- when(provider.getIfAvailable()).thenReturn(vectorStore);
- when(vectorStore.similaritySearch(any(SearchRequest.class))).thenReturn(List.of());
-
- VectorSearchService service = new VectorSearchService();
- setMode(service, "spring");
- setVectorStore(service, provider);
-
- service.searchSimilarDocuments("query", 5, "api");
-
- verify(vectorStore).similaritySearch(any(SearchRequest.class));
- }
-
- @Test
- void defaultScopeDoesNotAddMetadataFilter() {
- VectorSearchService service = new VectorSearchService();
- setKbScope(service, "");
-
- assertNull(service.buildSpringAiFilterExpression(null));
- assertNull(service.buildSdkFilterExpression(null));
- assertEquals("category == 'api'", service.buildSpringAiFilterExpression("api"));
- assertEquals("metadata[\"category\"] == \"api\"", service.buildSdkFilterExpression("api"));
- }
-
- @Test
- void configuredScopeCombinesWithCategoryFilter() {
- VectorSearchService service = new VectorSearchService();
- setKbScope(service, "rag-eval");
-
- assertEquals("kb_scope == 'rag-eval'", service.buildSpringAiFilterExpression(null));
- assertEquals("category == 'api' && kb_scope == 'rag-eval'",
- service.buildSpringAiFilterExpression("api"));
- assertEquals("metadata[\"category\"] == \"api\" && metadata[\"kb_scope\"] == \"rag-eval\"",
- service.buildSdkFilterExpression("api"));
- }
-
- private static void setMode(VectorSearchService service, String mode) {
- ReflectionTestUtils.setField(service, "vectorStoreMode", mode);
- }
-
- private static void setVectorStore(VectorSearchService service, ObjectProvider provider) {
- ReflectionTestUtils.setField(service, "vectorStoreProvider", provider);
- ReflectionTestUtils.setField(service, "objectMapper", new ObjectMapper());
- ReflectionTestUtils.setField(service, "maxL2Distance", 2.0);
- }
-
- private static void setKbScope(VectorSearchService service, String kbScope) {
- ReflectionTestUtils.setField(service, "kbScope", kbScope);
+ assertEquals("doc-h", results.get(0).getId());
+ verify(store).searchHybrid(eq("pool"), eq(List.of(0.3f)), eq(5), eq("mysql"));
}
private static VectorSearchService.SearchResult result(String id, float score) {
diff --git a/src/test/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapterHybridTest.java b/src/test/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapterHybridTest.java
index 5af03d1..4709633 100644
--- a/src/test/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapterHybridTest.java
+++ b/src/test/java/com/superbiz/agent/service/retrieval/VectorKnowledgeSearchAdapterHybridTest.java
@@ -3,41 +3,36 @@ package com.superbiz.agent.service.retrieval;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.superbiz.agent.service.VectorSearchService;
import org.junit.jupiter.api.Test;
-import org.springframework.test.util.ReflectionTestUtils;
import java.util.List;
import static org.junit.jupiter.api.Assertions.assertEquals;
-import static org.junit.jupiter.api.Assertions.assertTrue;
import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.verify;
import static org.mockito.Mockito.when;
class VectorKnowledgeSearchAdapterHybridTest {
@Test
- void hybridFusesFilteredAndUnfilteredDensePaths() {
+ void adapterMapsStoreHitsWithChunkIdentity() {
VectorSearchService vectorSearchService = mock(VectorSearchService.class);
- when(vectorSearchService.searchSimilarDocuments("pool timeout", 3, null)).thenReturn(List.of(
- result("u1", "{\"_source\":\"a.md\",\"docId\":\"a\",\"chunkIndex\":0,\"title\":\"generic\"}", "generic pool", 0.4f),
- result("u2", "{\"_source\":\"b.md\",\"docId\":\"b\",\"chunkIndex\":0,\"title\":\"other\"}", "other", 0.5f)
- ));
when(vectorSearchService.searchSimilarDocuments("pool timeout", 3, "mysql")).thenReturn(List.of(
- result("f1", "{\"_source\":\"c.md\",\"docId\":\"c\",\"chunkIndex\":0,\"title\":\"mysql pool timeout\"}", "mysql pool timeout runbook", 0.35f)
+ result("id-1",
+ "{\"_source\":\"c.md\",\"docId\":\"c\",\"chunkIndex\":0,\"title\":\"mysql pool timeout\"}",
+ "mysql pool timeout runbook",
+ 0.35f)
));
- VectorKnowledgeSearchAdapter adapter = new VectorKnowledgeSearchAdapter(vectorSearchService, new ObjectMapper());
- ReflectionTestUtils.setField(adapter, "configuredMode", "hybrid");
- ReflectionTestUtils.setField(adapter, "rrfK", 60);
- ReflectionTestUtils.setField(adapter, "weightDenseUnfiltered", 1.0);
- ReflectionTestUtils.setField(adapter, "weightDenseFiltered", 1.0);
- ReflectionTestUtils.setField(adapter, "weightLexical", 1.0);
+ VectorKnowledgeSearchAdapter adapter =
+ new VectorKnowledgeSearchAdapter(vectorSearchService, new ObjectMapper());
List hits = adapter.search(
new KnowledgeSearchRequest("pool timeout", 3, "mysql", KnowledgeSearchMode.HYBRID));
- assertEquals(3, hits.size());
- assertTrue(hits.stream().anyMatch(hit -> "c#chunk-0".equals(hit.evidenceKey())));
- assertTrue(hits.get(0).metadata().containsKey("fusedScore"));
+ assertEquals(1, hits.size());
+ assertEquals("c#chunk-0", hits.get(0).evidenceKey());
+ assertEquals("c.md", hits.get(0).source());
+ verify(vectorSearchService).searchSimilarDocuments("pool timeout", 3, "mysql");
}
private static VectorSearchService.SearchResult result(String id, String metadata, String content, float score) {