diff --git a/ruoyi-modules-api/ruoyi-knowledge-api/pom.xml b/ruoyi-modules-api/ruoyi-knowledge-api/pom.xml
index 90933a7d..11360516 100644
--- a/ruoyi-modules-api/ruoyi-knowledge-api/pom.xml
+++ b/ruoyi-modules-api/ruoyi-knowledge-api/pom.xml
@@ -80,6 +80,12 @@
2.6.4
+
+
+ dev.langchain4j
+ langchain4j-milvus
+
+
dev.langchain4j
langchain4j-open-ai
diff --git a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/AbstractVectorStoreStrategy.java b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/AbstractVectorStoreStrategy.java
index 4f7616c0..07f0866f 100644
--- a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/AbstractVectorStoreStrategy.java
+++ b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/AbstractVectorStoreStrategy.java
@@ -14,7 +14,7 @@ import org.ruoyi.common.core.utils.StringUtils;
* 向量库策略抽象基类
* 提供公共的方法实现，如embedding模型获取等
*
- * @author ageer
+ * @author Yzm
*/
@Slf4j
@RequiredArgsConstructor
diff --git a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategy.java b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategy.java
index bd93e6fa..b0c965d6 100644
--- a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategy.java
+++ b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategy.java
@@ -6,7 +6,7 @@ import org.ruoyi.service.VectorStoreService;
* 向量库策略接口
* 继承VectorStoreService以避免重复定义相同的方法
*
- * @author ageer
+ * @author Yzm
*/
public interface VectorStoreStrategy extends VectorStoreService {
diff --git a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategyFactory.java b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategyFactory.java
index fbd5b27b..579b989f 100644
--- a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategyFactory.java
+++ b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/VectorStoreStrategyFactory.java
@@ -15,7 +15,7 @@ import java.util.Map;
* 向量库策略工厂
* 根据配置动态选择向量库实现
*
- * @author ageer
+ * @author Yzm
*/
@Slf4j
@Component
diff --git a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java
index f8e58733..07ee2ba6 100644
--- a/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java
+++ b/ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java
@@ -1,20 +1,17 @@
package org.ruoyi.service.strategy.impl;
-import org.ruoyi.common.core.exception.ServiceException;
import dev.langchain4j.data.embedding.Embedding;
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.data.document.Metadata;
import dev.langchain4j.model.embedding.EmbeddingModel;
-import io.milvus.client.MilvusServiceClient;
-import io.milvus.common.clientenum.ConsistencyLevelEnum;
-import io.milvus.grpc.*;
-import io.milvus.param.*;
-import io.milvus.param.collection.*;
-import io.milvus.param.dml.DeleteParam;
-import io.milvus.param.dml.InsertParam;
-import io.milvus.param.dml.SearchParam;
-import io.milvus.param.index.CreateIndexParam;
-import io.milvus.param.index.DescribeIndexParam;
-import io.milvus.response.DescCollResponseWrapper;
-import io.milvus.response.SearchResultsWrapper;
+import dev.langchain4j.store.embedding.EmbeddingMatch;
+import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
+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.milvus.MilvusEmbeddingStore;
+import io.milvus.param.IndexType;
+import io.milvus.param.MetricType;
import lombok.SneakyThrows;
import lombok.extern.slf4j.Slf4j;
import org.ruoyi.common.core.config.VectorStoreProperties;
@@ -23,19 +20,19 @@ import org.ruoyi.domain.bo.StoreEmbeddingBo;
import org.ruoyi.service.strategy.AbstractVectorStoreStrategy;
import org.springframework.stereotype.Component;
-import java.util.*;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.stream.IntStream;
/**
* Milvus向量库策略实现
*
- * @author ageer
+ * @author Yzm
*/
@Slf4j
@Component
public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
- private MilvusServiceClient milvusClient;
-
public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties) {
super(vectorStoreProperties);
}
@@ -49,290 +46,164 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
public void createSchema(String vectorModelName, String kid) {
String url = vectorStoreProperties.getMilvus().getUrl();
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-
- // 创建Milvus客户端连接
- ConnectParam connectParam = ConnectParam.newBuilder()
- .withUri(url)
+ // 使用 LangChain4j 的 MilvusEmbeddingStore 来确保集合存在（按需创建）
+ MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
+ .uri(url)
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(true)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
.build();
- milvusClient = new MilvusServiceClient(connectParam);
-
- // 检查集合是否存在
- HasCollectionParam hasCollectionParam = HasCollectionParam.newBuilder()
- .withCollectionName(collectionName)
- .build();
-
- R hasCollectionResponse = milvusClient.hasCollection(hasCollectionParam);
- if (hasCollectionResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("检查集合是否存在失败: {}", hasCollectionResponse.getMessage());
- return;
- }
-
- if (!hasCollectionResponse.getData()) {
- // 创建字段
- List fields = new ArrayList<>();
-
- // ID字段 (主键)
- fields.add(FieldType.newBuilder()
- .withName("id")
- .withDataType(DataType.Int64)
- .withPrimaryKey(true)
- .withAutoID(true)
- .build());
-
- // 文本字段
- fields.add(FieldType.newBuilder()
- .withName("text")
- .withDataType(DataType.VarChar)
- .withMaxLength(65535)
- .build());
-
- // fid字段
- fields.add(FieldType.newBuilder()
- .withName("fid")
- .withDataType(DataType.VarChar)
- .withMaxLength(255)
- .build());
-
- // kid字段
- fields.add(FieldType.newBuilder()
- .withName("kid")
- .withDataType(DataType.VarChar)
- .withMaxLength(255)
- .build());
-
- // docId字段
- fields.add(FieldType.newBuilder()
- .withName("docId")
- .withDataType(DataType.VarChar)
- .withMaxLength(255)
- .build());
-
- // 向量字段
- fields.add(FieldType.newBuilder()
- .withName("vector")
- .withDataType(DataType.FloatVector)
- .withDimension(2048) // 根据实际embedding维度调整
- .build());
-
- // 创建集合
- CreateCollectionParam createCollectionParam = CreateCollectionParam.newBuilder()
- .withCollectionName(collectionName)
- .withDescription("Knowledge base collection for " + kid)
- .withShardsNum(2)
- .withFieldTypes(fields)
- .build();
-
- R createCollectionResponse = milvusClient.createCollection(createCollectionParam);
- if (createCollectionResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("创建集合失败: {}", createCollectionResponse.getMessage());
- return;
- }
-
- // 创建索引
- CreateIndexParam createIndexParam = CreateIndexParam.newBuilder()
- .withCollectionName(collectionName)
- .withFieldName("vector")
- .withIndexType(IndexType.IVF_FLAT)
- .withMetricType(MetricType.L2)
- .withExtraParam("{\"nlist\":1024}")
- .build();
-
- R createIndexResponse = milvusClient.createIndex(createIndexParam);
- if (createIndexResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("创建索引失败: {}", createIndexResponse.getMessage());
- } else {
- log.info("Milvus集合和索引创建成功: {}", collectionName);
- }
- } else {
- log.info("Milvus集合已存在: {}", collectionName);
- }
+ log.info("Milvus集合初始化完成: {}", collectionName);
}
@Override
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid());
-
+
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
storeEmbeddingBo.getApiKey(), storeEmbeddingBo.getBaseUrl());
-
+
List chunkList = storeEmbeddingBo.getChunkList();
List fidList = storeEmbeddingBo.getFids();
String kid = storeEmbeddingBo.getKid();
String docId = storeEmbeddingBo.getDocId();
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-
- log.info("Milvus向量存储条数记录: " + chunkList.size());
+
+ log.info("Milvus向量存储条数记录: {}", chunkList.size());
long startTime = System.currentTimeMillis();
-
- // 准备批量插入数据
- List fields = new ArrayList<>();
- List textList = new ArrayList<>();
- List fidListData = new ArrayList<>();
- List kidList = new ArrayList<>();
- List docIdList = new ArrayList<>();
- List> vectorList = new ArrayList<>();
-
- for (int i = 0; i < chunkList.size(); i++) {
+
+ EmbeddingStore embeddingStore = MilvusEmbeddingStore.builder()
+ .uri(vectorStoreProperties.getMilvus().getUrl())
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(false)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
+ .build();
+
+ IntStream.range(0, chunkList.size()).forEach(i -> {
String text = chunkList.get(i);
String fid = fidList.get(i);
Embedding embedding = embeddingModel.embed(text).content();
-
- textList.add(text);
- fidListData.add(fid);
- kidList.add(kid);
- docIdList.add(docId);
-
- List vector = new ArrayList<>();
- for (float f : embedding.vector()) {
- vector.add(f);
- }
- vectorList.add(vector);
- }
-
- // 构建字段数据
- fields.add(new InsertParam.Field("text", textList));
- fields.add(new InsertParam.Field("fid", fidListData));
- fields.add(new InsertParam.Field("kid", kidList));
- fields.add(new InsertParam.Field("docId", docIdList));
- fields.add(new InsertParam.Field("vector", vectorList));
-
- // 执行插入
- InsertParam insertParam = InsertParam.newBuilder()
- .withCollectionName(collectionName)
- .withFields(fields)
- .build();
-
- R insertResponse = milvusClient.insert(insertParam);
- if (insertResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("Milvus向量存储失败: {}", insertResponse.getMessage());
- throw new ServiceException("Milvus向量存储失败");
- } else {
- log.info("Milvus向量存储成功，插入条数: {}", insertResponse.getData().getInsertCnt());
- }
-
+ Metadata metadata = new Metadata()
+ .put("fid", fid)
+ .put("kid", kid)
+ .put("docId", docId);
+ TextSegment segment = TextSegment.from(text, metadata);
+ embeddingStore.add(embedding, segment);
+ });
+
long endTime = System.currentTimeMillis();
- log.info("Milvus向量存储完成消耗时间：" + (endTime - startTime) / 1000 + "秒");
+ log.info("Milvus向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
}
@Override
public List getQueryVector(QueryVectorBo queryVectorBo) {
createSchema(queryVectorBo.getVectorModelName(), queryVectorBo.getKid());
-
+
EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(),
queryVectorBo.getApiKey(), queryVectorBo.getBaseUrl());
-
+
Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
-
+
+ EmbeddingStore embeddingStore = MilvusEmbeddingStore.builder()
+ .uri(vectorStoreProperties.getMilvus().getUrl())
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(true)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
+ .build();
+
List resultList = new ArrayList<>();
-
- // 加载集合到内存
- LoadCollectionParam loadCollectionParam = LoadCollectionParam.newBuilder()
- .withCollectionName(collectionName)
+ EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
+ .queryEmbedding(queryEmbedding)
+ .maxResults(queryVectorBo.getMaxResults())
.build();
- milvusClient.loadCollection(loadCollectionParam);
-
- // 准备查询向量
- List> searchVectors = new ArrayList<>();
- List queryVector = new ArrayList<>();
- for (float f : queryEmbedding.vector()) {
- queryVector.add(f);
- }
- searchVectors.add(queryVector);
-
- // 构建搜索参数
- SearchParam searchParam = SearchParam.newBuilder()
- .withCollectionName(collectionName)
- // 匹配方法
- .withMetricType(MetricType.L2)
- .withOutFields(Arrays.asList("text", "fid", "kid", "docId"))
- .withTopK(queryVectorBo.getMaxResults())
- .withVectors(searchVectors)
- .withVectorFieldName("vector")
- .withParams("{\"nprobe\":10}")
- .build();
-
- R searchResponse = milvusClient.search(searchParam);
- if (searchResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("Milvus查询失败: {}", searchResponse.getMessage());
- return resultList;
- }
-
- SearchResultsWrapper wrapper = new SearchResultsWrapper(searchResponse.getData().getResults());
-
- // 遍历搜索结果
- for (int i = 0; i < wrapper.getIDScore(0).size(); i++) {
- SearchResultsWrapper.IDScore idScore = wrapper.getIDScore(0).get(i);
-
- // 获取text字段数据
- List textFieldData = wrapper.getFieldData("text", 0);
- if (textFieldData != null && i < textFieldData.size()) {
- Object textObj = textFieldData.get(i);
- if (textObj != null) {
- resultList.add(textObj.toString());
- log.debug("找到相似文本，ID: {}, 距离: {}, 内容: {}",
- idScore.getLongID(), idScore.getScore(), textObj.toString());
- }
+ List> matches = embeddingStore.search(request).matches();
+ for (EmbeddingMatch match : matches) {
+ TextSegment segment = match.embedded();
+ if (segment != null) {
+ resultList.add(segment.text());
}
}
-
return resultList;
}
@Override
@SneakyThrows
public void removeById(String id, String modelName) {
+ String url = vectorStoreProperties.getMilvus().getUrl();
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + id;
-
- // 删除整个集合
- DropCollectionParam dropCollectionParam = DropCollectionParam.newBuilder()
- .withCollectionName(collectionName)
+ MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
+ .uri(url)
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(true)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
.build();
-
- R dropResponse = milvusClient.dropCollection(dropCollectionParam);
- if (dropResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("Milvus集合删除失败: {}", dropResponse.getMessage());
- throw new ServiceException("Milvus集合删除失败");
- } else {
- log.info("Milvus集合删除成功: {}", collectionName);
- }
+ // 修正：MilvusEmbeddingStore 的 dropCollection 需要传入集合名
+ store.dropCollection(collectionName);
+ log.info("Milvus集合删除成功: {}", collectionName);
}
@Override
public void removeByDocId(String docId, String kid) {
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-
- String expr = "docId == \"" + docId + "\"";
- DeleteParam deleteParam = DeleteParam.newBuilder()
- .withCollectionName(collectionName)
- .withExpr(expr)
+ EmbeddingStore embeddingStore = MilvusEmbeddingStore.builder()
+ .uri(vectorStoreProperties.getMilvus().getUrl())
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(false)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
.build();
-
- R deleteResponse = milvusClient.delete(deleteParam);
- if (deleteResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("Milvus删除失败: {}", deleteResponse.getMessage());
- throw new ServiceException("Milvus删除失败");
- } else {
- log.info("Milvus成功删除 docId={} 的所有向量数据，删除条数: {}", docId, deleteResponse.getData().getDeleteCnt());
- }
+ Filter filter = MetadataFilterBuilder.metadataKey("docId").isEqualTo(docId);
+ embeddingStore.removeAll(filter);
+ log.info("Milvus成功删除 docId={} 的所有向量数据", docId);
}
@Override
public void removeByFid(String fid, String kid) {
String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-
- String expr = "fid == \"" + fid + "\"";
- DeleteParam deleteParam = DeleteParam.newBuilder()
- .withCollectionName(collectionName)
- .withExpr(expr)
+ EmbeddingStore embeddingStore = MilvusEmbeddingStore.builder()
+ .uri(vectorStoreProperties.getMilvus().getUrl())
+ .collectionName(collectionName)
+ .dimension(2048)
+ .indexType(IndexType.IVF_FLAT)
+ .metricType(MetricType.L2)
+ .autoFlushOnInsert(false)
+ .idFieldName("id")
+ .textFieldName("text")
+ .metadataFieldName("metadata")
+ .vectorFieldName("vector")
.build();
-
- R deleteResponse = milvusClient.delete(deleteParam);
- if (deleteResponse.getStatus() != R.Status.Success.getCode()) {
- log.error("Milvus删除失败: {}", deleteResponse.getMessage());
- throw new ServiceException("Milvus删除失败");
- } else {
- log.info("Milvus成功删除 fid={} 的所有向量数据，删除条数: {}", fid, deleteResponse.getData().getDeleteCnt());
- }
+ Filter filter = MetadataFilterBuilder.metadataKey("fid").isEqualTo(fid);
+ embeddingStore.removeAll(filter);
+ log.info("Milvus成功删除 fid={} 的所有向量数据", fid);
}
-}
\ No newline at end of file
+}