Merge pull request #281 from Anush008/main

feat: Adds support for Qdrant vector search

ruoyi-admin/src/main/resources/application.yml

@@ -275,7 +275,7 @@ warm-flow:
 
 # 向量库配置
 vector-store:
-  # 向量存储类型 可选(weaviate/milvus)
+  # 向量存储类型 可选(weaviate/milvus/qdrant)
   # 如需修改向量库类型，请修改此配置值!
   type: milvus
   # Weaviate配置
@@ -287,3 +287,10 @@ vector-store:
   milvus:
     url: http://localhost:19530
     collectionname: LocalKnowledge
+  # Qdrant配置
+  qdrant:
+    host: localhost
+    port: 6334
+    collectionname: LocalKnowledge
+    api-key:
+    use-tls: false

ruoyi-modules/ruoyi-chat/pom.xml

@@ -91,6 +91,12 @@
             <version>${langchain4j.community.version}</version>
         </dependency>
 
+        <dependency>
+            <groupId>dev.langchain4j</groupId>
+            <artifactId>langchain4j-qdrant</artifactId>
+            <version>${langchain4j.community.version}</version>
+        </dependency>
+
         <dependency>
             <groupId>dev.langchain4j</groupId>
             <artifactId>langchain4j-mcp</artifactId>

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/config/VectorStoreProperties.java

@@ -59,4 +59,37 @@ public class VectorStoreProperties {
          */
         private String collectionname;
     }
+
+    /**
+     * Qdrant配置
+     */
+    private Qdrant qdrant = new Qdrant();
+
+    @Data
+    public static class Qdrant {
+        /**
+         * 主机地址
+         */
+        private String host = "localhost";
+
+        /**
+         * gRPC端口
+         */
+        private int port = 6334;
+
+        /**
+         * 集合名称
+         */
+        private String collectionname = "LocalKnowledge";
+
+        /**
+         * API密钥（可选）
+         */
+        private String apiKey;
+
+        /**
+         * 是否启用TLS
+         */
+        private boolean useTls = false;
+    }
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/factory/VectorStoreStrategyFactory.java

@@ -7,6 +7,7 @@ import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.config.VectorStoreProperties;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.ruoyi.service.vector.impl.MilvusVectorStoreStrategy;
+import org.ruoyi.service.vector.impl.QdrantVectorStoreStrategy;
 import org.ruoyi.service.vector.impl.WeaviateVectorStoreStrategy;
 import org.springframework.stereotype.Component;
 
@@ -27,6 +28,7 @@ public class VectorStoreStrategyFactory {
     private final VectorStoreProperties vectorStoreProperties;
     private final WeaviateVectorStoreStrategy weaviateStrategy;
     private final MilvusVectorStoreStrategy milvusStrategy;
+    private final QdrantVectorStoreStrategy qdrantStrategy;
 
     private Map<String, VectorStoreService> strategies;
 
@@ -35,6 +37,7 @@ public class VectorStoreStrategyFactory {
         strategies = new HashMap<>();
         strategies.put("weaviate", weaviateStrategy);
         strategies.put("milvus", milvusStrategy);
+        strategies.put("qdrant", qdrantStrategy);
         log.info("向量库策略工厂初始化完成，支持的策略: {}", strategies.keySet());
     }
 

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/QdrantVectorStoreStrategy.java

@@ -0,0 +1,204 @@
+package org.ruoyi.service.vector.impl;
+
+import dev.langchain4j.data.document.Metadata;
+import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.store.embedding.EmbeddingStore;
+import dev.langchain4j.store.embedding.filter.Filter;
+import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder;
+import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
+import io.qdrant.client.QdrantClient;
+import io.qdrant.client.QdrantGrpcClient;
+import io.qdrant.client.grpc.Collections.Distance;
+import io.qdrant.client.grpc.Collections.VectorParams;
+import io.qdrant.client.grpc.JsonWithInt;
+import io.qdrant.client.grpc.Points.DenseVector;
+import io.qdrant.client.grpc.Points.Query;
+import io.qdrant.client.grpc.Points.QueryPoints;
+import io.qdrant.client.grpc.Points.ScoredPoint;
+import io.qdrant.client.grpc.Points.VectorInput;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.service.chat.IChatModelService;
+import org.ruoyi.common.core.exception.ServiceException;
+import org.ruoyi.config.VectorStoreProperties;
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.factory.EmbeddingModelFactory;
+import org.springframework.stereotype.Component;
+
+import static io.qdrant.client.VectorInputFactory.vectorInput;
+import static io.qdrant.client.WithPayloadSelectorFactory.enable;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.stream.IntStream;
+
+/**
+ * Qdrant向量库策略实现
+ */
+@Slf4j
+@Component
+public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
+
+    private static final String VECTOR_STORE_TYPE   = "qdrant";
+    private static final String TEXT_SEGMENT_KEY    = "text_segment";
+    private static final String METADATA_FID_KEY    = "fid";
+    private static final String METADATA_KID_KEY    = "kid";
+    private static final String METADATA_DOC_ID_KEY = "doc_id";
+
+    public QdrantVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
+                                     IChatModelService chatModelService,
+                                     EmbeddingModelFactory embeddingModelFactory) {
+        super(vectorStoreProperties, embeddingModelFactory, chatModelService);
+    }
+
+    private EmbeddingStore<TextSegment> getQdrantStore(String collectionName) {
+        VectorStoreProperties.Qdrant cfg = vectorStoreProperties.getQdrant();
+        QdrantEmbeddingStore.Builder builder = QdrantEmbeddingStore.builder()
+                .host(cfg.getHost())
+                .port(cfg.getPort())
+                .collectionName(collectionName)
+                .useTls(cfg.isUseTls());
+        if (cfg.getApiKey() != null && !cfg.getApiKey().isEmpty()) {
+            builder.apiKey(cfg.getApiKey());
+        }
+        return builder.build();
+    }
+
+    private QdrantClient buildQdrantClient() {
+        VectorStoreProperties.Qdrant cfg = vectorStoreProperties.getQdrant();
+        QdrantGrpcClient.Builder grpcBuilder = QdrantGrpcClient.newBuilder(cfg.getHost(), cfg.getPort(), cfg.isUseTls());
+        if (cfg.getApiKey() != null && !cfg.getApiKey().isEmpty()) {
+            grpcBuilder.withApiKey(cfg.getApiKey());
+        }
+        return new QdrantClient(grpcBuilder.build());
+    }
+
+    private int getModelDimension(String modelName) {
+        return chatModelService.selectModelByName(modelName).getModelDimension();
+    }
+
+    @Override
+    public String getVectorStoreType() {
+        return VECTOR_STORE_TYPE;
+    }
+
+    @Override
+    public void createSchema(String kid, String modelName) {
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + kid;
+        int dimension = getModelDimension(modelName);
+        try (QdrantClient client = buildQdrantClient()) {
+            Boolean exists = client.collectionExistsAsync(collectionName).get();
+            if (!exists) {
+                VectorParams params = VectorParams.newBuilder()
+                        .setSize(dimension)
+                        .setDistance(Distance.Cosine)
+                        .build();
+                client.createCollectionAsync(collectionName, params).get();
+                log.info("Qdrant集合创建成功: {}, dimension: {}", collectionName, dimension);
+            } else {
+                log.info("Qdrant集合已存在: {}", collectionName);
+            }
+        } catch (Exception e) {
+            log.error("Qdrant集合创建失败: {}", collectionName, e);
+            throw new ServiceException("Qdrant集合创建失败: " + collectionName);
+        }
+    }
+
+    @Override
+    public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
+        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName());
+        List<String> chunkList = storeEmbeddingBo.getChunkList();
+        List<String> fidList = storeEmbeddingBo.getFids();
+        String kid = storeEmbeddingBo.getKid();
+        String docId = storeEmbeddingBo.getDocId();
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + kid;
+
+        EmbeddingStore<TextSegment> embeddingStore = getQdrantStore(collectionName);
+
+        log.info("Qdrant向量存储条数记录: {}", chunkList.size());
+        long startTime = System.currentTimeMillis();
+
+        IntStream.range(0, chunkList.size()).forEach(i -> {
+            String text = chunkList.get(i);
+            String fid = fidList.get(i);
+            Metadata metadata = new Metadata();
+            metadata.put(METADATA_FID_KEY, fid);
+            metadata.put(METADATA_KID_KEY, kid);
+            metadata.put(METADATA_DOC_ID_KEY, docId);
+            TextSegment textSegment = TextSegment.from(text, metadata);
+            Embedding embedding = embeddingModel.embed(text).content();
+            embeddingStore.add(embedding, textSegment);
+        });
+
+        long endTime = System.currentTimeMillis();
+        log.info("Qdrant向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
+    }
+
+    @Override
+    public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
+        Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + queryVectorBo.getKid();
+
+        List<Float> vector = new ArrayList<>();
+        for (float f : queryEmbedding.vector()) {
+            vector.add(f);
+        }
+
+        try (QdrantClient client = buildQdrantClient()) {
+            QueryPoints request = QueryPoints.newBuilder()
+                    .setCollectionName(collectionName)
+                    .setQuery(Query.newBuilder()
+                            .setNearest(vectorInput(vector))
+                            .build())
+                    .setLimit(queryVectorBo.getMaxResults())
+                    .setWithPayload(enable(true))
+                    .build();
+
+            List<ScoredPoint> results = client.queryAsync(request).get();
+            List<String> resultList = new ArrayList<>();
+            for (ScoredPoint point : results) {
+                JsonWithInt.Value textValue = point.getPayloadMap().get(TEXT_SEGMENT_KEY);
+                if (textValue != null && textValue.hasStringValue()) {
+                    resultList.add(textValue.getStringValue());
+                }
+            }
+            return resultList;
+        } catch (Exception e) {
+            log.error("Qdrant查询失败: {}", collectionName, e);
+            throw new ServiceException("Qdrant向量查询失败");
+        }
+    }
+
+    @Override
+    public void removeById(String id, String modelName) {
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + id;
+        try (QdrantClient client = buildQdrantClient()) {
+            client.deleteCollectionAsync(collectionName).get();
+            log.info("Qdrant成功删除集合: {}", collectionName);
+        } catch (Exception e) {
+            log.error("Qdrant删除集合失败: {}", collectionName, e);
+            throw new ServiceException("失败删除向量数据!");
+        }
+    }
+
+    @Override
+    public void removeByDocId(String docId, String kid) {
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + kid;
+        EmbeddingStore<TextSegment> embeddingStore = getQdrantStore(collectionName);
+        Filter filter = MetadataFilterBuilder.metadataKey(METADATA_DOC_ID_KEY).isEqualTo(docId);
+        embeddingStore.removeAll(filter);
+        log.info("Qdrant成功删除 docId={} 的所有向量数据", docId);
+    }
+
+    @Override
+    public void removeByFid(String fid, String kid) {
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + kid;
+        EmbeddingStore<TextSegment> embeddingStore = getQdrantStore(collectionName);
+        Filter filter = MetadataFilterBuilder.metadataKey(METADATA_FID_KEY).isEqualTo(fid);
+        embeddingStore.removeAll(filter);
+        log.info("Qdrant成功删除 fid={} 的所有向量数据", fid);
+    }
+}