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feat(milvus): 实现Milvus向量数据库集成

Browse Source 
- 添加Milvus Java SDK依赖
- 实现MilvusVectorStoreStrategy核心功能
- 支持集合管理、数据存储、向量搜索和数据删除
- 添加Milvus实现指南文档
- 更新数据库连接配置
- 修改VectorStoreService接口添加异常声明
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237
MILVUS_IMPLEMENTATION_GUIDE.md Normal file
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@@ -0,0 +1,237 @@
# Milvus向量库实现指南
## 概述
本项目已完成Milvus向量库的集成，基于Milvus SDK 2.6.4版本实现。Milvus是一个开源的向量数据库，专为AI应用和相似性搜索而设计。
## 实现特性
### ✅ 已实现功能
1. **集合管理**
- 自动创建集合（Collection）
- 检查集合是否存在
- 删除集合
2. **数据存储**
- 批量插入向量数据
- 支持文本、fid、kid、docId等元数据
- 自动生成向量嵌入
3. **向量搜索**
- 基于相似性的向量搜索
- 支持TopK结果返回
- 返回相关文本内容
4. **数据删除**
- 按文档ID删除
- 按片段ID删除
- 删除整个集合
## 架构设计
### 策略模式实现
```
AbstractVectorStoreStrategy (抽象基类)
↓
MilvusVectorStoreStrategy (Milvus实现)
WeaviateVectorStoreStrategy (Weaviate实现)
```
### 核心类说明
- **MilvusVectorStoreStrategy**: Milvus向量库策略实现
- **VectorStoreStrategyFactory**: 向量库策略工厂，支持动态切换
- **VectorStoreService**: 向量库服务接口
## 配置说明
### 必需配置项
在系统配置中需要设置以下Milvus相关配置：
```properties
# Milvus服务地址
milvus.url=http://localhost:19530
# 集合名称前缀
milvus.collectionname=LocalKnowledge
# 向量库类型选择
vector.store_type=milvus
```
### 集合Schema设计
每个集合包含以下字段：
| 字段名 | 类型 | 说明 |
|--------|------|------|
| id | Int64 | 主键，自动生成 |
| text | VarChar(65535) | 文本内容 |
| fid | VarChar(255) | 片段ID |
| kid | VarChar(255) | 知识库ID |
| docId | VarChar(255) | 文档ID |
| vector | FloatVector(1024) | 向量数据 |
### 索引配置
- **索引类型**: IVF_FLAT
- **距离度量**: L2 (欧几里得距离)
- **参数**: nlist=1024
## 使用示例
### 1. 创建集合
```java
MilvusVectorStoreStrategy strategy = new MilvusVectorStoreStrategy(configService);
strategy.createSchema("bge-large-zh-v1.5", "test001", "test-model");
```
### 2. 存储向量数据
```java
StoreEmbeddingBo storeEmbeddingBo = new StoreEmbeddingBo();
storeEmbeddingBo.setVectorModelName("bge-large-zh-v1.5");
storeEmbeddingBo.setKid("test001");
storeEmbeddingBo.setDocId("doc001");
storeEmbeddingBo.setChunkList(Arrays.asList("文本1", "文本2"));
storeEmbeddingBo.setFids(Arrays.asList("fid001", "fid002"));
strategy.storeEmbeddings(storeEmbeddingBo);
```
### 3. 查询向量数据
```java
QueryVectorBo queryVectorBo = new QueryVectorBo();
queryVectorBo.setQuery("查询文本");
queryVectorBo.setKid("test001");
queryVectorBo.setMaxResults(5);
List<String> results = strategy.getQueryVector(queryVectorBo);
```
### 4. 删除数据
```java
// 按文档ID删除
strategy.removeByDocId("doc001", "test001");
// 按片段ID删除
strategy.removeByFid("fid001", "test001");
// 删除整个集合
strategy.removeById("test001", "model");
```
## 部署要求
### Milvus服务部署
1. **Docker部署** (推荐)
```bash
# 下载docker-compose文件
wget https://github.com/milvus-io/milvus/releases/download/v2.6.4/milvus-standalone-docker-compose.yml -O docker-compose.yml
# 启动Milvus
docker-compose up -d
```
2. **验证部署**
```bash
# 检查服务状态
docker-compose ps
# 查看日志
docker-compose logs milvus-standalone
```
### 系统要求
- **内存**: 最少8GB，推荐16GB+
- **存储**: SSD推荐，至少50GB可用空间
- **CPU**: 4核心以上
- **网络**: 确保19530端口可访问
## 性能优化
### 1. 索引优化
根据数据量调整索引参数：
- 小数据集(<100万): nlist=1024
- 中等数据集(100万-1000万): nlist=4096
- 大数据集(>1000万): nlist=16384
### 2. 批量操作
- 批量插入：建议每批1000-10000条记录
- 批量查询：避免频繁的单条查询
### 3. 内存管理
```yaml
# docker-compose.yml中的内存配置
environment:
MILVUS_CONFIG_PATH: /milvus/configs/milvus.yaml
volumes:
- ./milvus.yaml:/milvus/configs/milvus.yaml
```
## 故障排除
### 常见问题
1. **连接失败**
- 检查Milvus服务是否启动
- 验证网络连接和端口
- 确认配置中的URL正确
2. **集合创建失败**
- 检查集合名称是否符合规范
- 验证字段定义是否正确
- 查看Milvus日志获取详细错误
3. **插入数据失败**
- 确认向量维度与schema一致
- 检查数据格式是否正确
- 验证集合是否已加载
4. **查询无结果**
- 确认集合中有数据
- 检查查询参数设置
- 验证向量化模型一致性
### 日志调试
启用详细日志：
```properties
logging.level.org.ruoyi.service.strategy.impl.MilvusVectorStoreStrategy=DEBUG
logging.level.io.milvus=DEBUG
```
## 与Weaviate对比
| 特性 | Milvus | Weaviate |
|------|--------|----------|
| 性能 | 高性能，专为大规模设计 | 中等性能 |
| 部署 | 需要独立部署 | 可独立部署或云服务 |
| 生态 | 专注向量搜索 | 集成更多AI功能 |
| 学习成本 | 中等 | 较低 |
| 扩展性 | 优秀 | 良好 |
## 后续优化建议
1. **连接池管理**: 实现MilvusClient连接池
2. **异步操作**: 支持异步插入和查询
3. **分片策略**: 大数据集的分片管理
4. **监控告警**: 集成性能监控
5. **备份恢复**: 数据备份和恢复机制
## 参考资料
- [Milvus官方文档](https://milvus.io/docs)
- [Milvus Java SDK](https://github.com/milvus-io/milvus-sdk-java)
- [向量数据库最佳实践](https://milvus.io/docs/performance_faq.md)

4
ruoyi-admin/src/main/resources/application-dev.yml
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@@ -16,9 +16,9 @@ spring:
master:
type: ${spring.datasource.type}
driverClassName: com.mysql.cj.jdbc.Driver
url: jdbc:mysql://127.0.0.1:3306/ruoyi-ai?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8&autoReconnect=true&rewriteBatchedStatements=true
url: jdbc:mysql://127.0.0.1:3306/ruoyi-ai-github?useUnicode=true&characterEncoding=utf8&zeroDateTimeBehavior=convertToNull&useSSL=true&serverTimezone=GMT%2B8&autoReconnect=true&rewriteBatchedStatements=true
username: root
password: root
password: qxyg1010
hikari:
# 最大连接池数量

6
ruoyi-modules-api/ruoyi-knowledge-api/pom.xml
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@@ -74,6 +74,12 @@
<version>1.19.6</version>
</dependency>
<dependency>
<groupId>io.milvus</groupId>
<artifactId>milvus-sdk-java</artifactId>
<version>2.6.4</version>
</dependency>
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-open-ai</artifactId>

9
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/VectorStoreService.java
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@@ -1,5 +1,6 @@
package org.ruoyi.service;
import com.google.protobuf.ServiceException;
import org.ruoyi.domain.bo.QueryVectorBo;
import org.ruoyi.domain.bo.StoreEmbeddingBo;
@@ -11,15 +12,15 @@ import java.util.List;
*/
public interface VectorStoreService {
void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo);
void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) throws ServiceException;
List<String> getQueryVector(QueryVectorBo queryVectorBo);
void createSchema(String vectorModelName, String kid,String modelName);
void removeById(String id,String modelName);
void removeById(String id,String modelName) throws ServiceException;
void removeByDocId(String docId, String kid);
void removeByDocId(String docId, String kid) throws ServiceException;
void removeByFid(String fid, String kid);
void removeByFid(String fid, String kid) throws ServiceException;
}

417
ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java
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@@ -1,8 +1,28 @@
package org.ruoyi.service.strategy.impl;
import com.google.gson.Gson;
import com.google.gson.JsonObject;
import com.google.protobuf.ServiceException;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.embedding.EmbeddingModel;
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.DescribeCollectionReq;
import io.milvus.v2.service.collection.request.DropCollectionReq;
import io.milvus.v2.service.collection.request.HasCollectionReq;
import io.milvus.v2.service.collection.response.DescribeCollectionResp;
import io.milvus.v2.service.vector.request.DeleteReq;
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.FloatVec;
import io.milvus.v2.service.vector.response.DeleteResp;
import io.milvus.v2.service.vector.response.InsertResp;
import io.milvus.v2.service.vector.response.SearchResp;
import lombok.extern.slf4j.Slf4j;
import org.ruoyi.common.core.service.ConfigService;
import org.ruoyi.domain.bo.QueryVectorBo;
@@ -10,10 +30,7 @@ import org.ruoyi.domain.bo.StoreEmbeddingBo;
import org.ruoyi.service.strategy.AbstractVectorStoreStrategy;
import org.springframework.stereotype.Component;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.*;
/**
* Milvus向量库策略实现
@@ -24,8 +41,7 @@ import java.util.Map;
@Component
public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
// Milvus客户端和相关配置
// private MilvusClient milvusClient;
private MilvusClientV2 client;
public MilvusVectorStoreStrategy(ConfigService configService) {
super(configService);
@@ -41,106 +57,89 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
log.info("Milvus创建schema: vectorModelName={}, kid={}, modelName={}", vectorModelName, kid, modelName);
// 1. 获取Milvus配置
String host = configService.getConfigValue("milvus", "host");
String port = configService.getConfigValue("milvus", "port");
String host = configService.getConfigValue("milvus", "url");
String collectionName = configService.getConfigValue("milvus", "collectionname") + kid;
// 2. 初始化Milvus客户端
// ConnectParam connectParam = ConnectParam.newBuilder()
// .withHost(host)
// .withPort(Integer.parseInt(port))
// .build();
// milvusClient = new MilvusClient(connectParam);
ConnectConfig config = ConnectConfig.builder()
.uri(host)
.build();
client = new MilvusClientV2(config);
// 3. 检查集合是否存在，如果不存在则创建
// HasCollectionParam hasCollectionParam = HasCollectionParam.newBuilder()
// .withCollectionName(collectionName)
// .build();
// R<Boolean> hasCollectionResponse = milvusClient.hasCollection(hasCollectionParam);
//
// if (!hasCollectionResponse.getData()) {
// // 创建集合
// List<FieldType> fieldsSchema = new ArrayList<>();
//
// // 主键字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("id")
// .withDataType(DataType.Int64)
// .withPrimaryKey(true)
// .withAutoID(true)
// .build());
//
// // 文本字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("text")
// .withDataType(DataType.VarChar)
// .withMaxLength(65535)
// .build());
//
// // fid字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("fid")
// .withDataType(DataType.VarChar)
// .withMaxLength(255)
// .build());
//
// // kid字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("kid")
// .withDataType(DataType.VarChar)
// .withMaxLength(255)
// .build());
//
// // docId字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("docId")
// .withDataType(DataType.VarChar)
// .withMaxLength(255)
// .build());
//
// // 向量字段
// fieldsSchema.add(FieldType.newBuilder()
// .withName("vector")
// .withDataType(DataType.FloatVector)
// .withDimension(1536) // 根据实际embedding维度调整
// .build());
//
// CreateCollectionParam createCollectionParam = CreateCollectionParam.newBuilder()
// .withCollectionName(collectionName)
// .withDescription("Knowledge base collection for " + kid)
// .withShardsNum(2)
// .withFieldTypes(fieldsSchema)
// .build();
//
// R<RpcStatus> createCollectionResponse = milvusClient.createCollection(createCollectionParam);
// if (createCollectionResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus集合创建成功: {}", collectionName);
//
// // 创建索引
// IndexParam indexParam = IndexParam.newBuilder()
// .withCollectionName(collectionName)
// .withFieldName("vector")
// .withIndexType(IndexType.IVF_FLAT)
// .withMetricType(MetricType.L2)
// .withExtraParam("{\"nlist\":1024}")
// .build();
//
// R<RpcStatus> createIndexResponse = milvusClient.createIndex(indexParam);
// if (createIndexResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus索引创建成功: {}", collectionName);
// } else {
// log.error("Milvus索引创建失败: {}", createIndexResponse.getMessage());
// }
// } else {
// log.error("Milvus集合创建失败: {}", createCollectionResponse.getMessage());
// }
// }
// 2. 检查集合是否存在
HasCollectionReq hasCollectionReq = HasCollectionReq.builder()
.collectionName(collectionName)
.build();
log.info("Milvus schema创建完成: {}", collectionName);
Boolean hasCollection = client.hasCollection(hasCollectionReq);
if (!hasCollection) {
// 3. 创建集合schema
CreateCollectionReq.CollectionSchema schema = CreateCollectionReq.CollectionSchema.builder()
.build();
// 添加字段定义
schema.addField(AddFieldReq.builder()
.fieldName("id")
.dataType(DataType.Int64)
.isPrimaryKey(true)
.autoID(true)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("text")
.dataType(DataType.VarChar)
.maxLength(65535)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("fid")
.dataType(DataType.VarChar)
.maxLength(255)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("kid")
.dataType(DataType.VarChar)
.maxLength(255)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("docId")
.dataType(DataType.VarChar)
.maxLength(255)
.build());
schema.addField(AddFieldReq.builder()
.fieldName("vector")
.dataType(DataType.FloatVector)
.dimension(1024) // 根据实际embedding维度调整
.build());
// 4. 创建索引参数
List<IndexParam> indexParams = new ArrayList<>();
indexParams.add(IndexParam.builder()
.fieldName("vector")
.indexType(IndexParam.IndexType.IVF_FLAT)
.metricType(IndexParam.MetricType.L2)
.extraParams(Map.of("nlist", 1024))
.build());
// 5. 创建集合
CreateCollectionReq createCollectionReq = CreateCollectionReq.builder()
.collectionName(collectionName)
.collectionSchema(schema)
.indexParams(indexParams)
.build();
client.createCollection(createCollectionReq);
log.info("Milvus集合创建成功: {}", collectionName);
} else {
log.info("Milvus集合已存在: {}", collectionName);
}
}
@Override
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) throws ServiceException {
createSchema(storeEmbeddingBo.getVectorModelName(), storeEmbeddingBo.getKid(), storeEmbeddingBo.getVectorModelName());
EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(),
@@ -155,48 +154,56 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
log.info("Milvus向量存储条数记录: " + chunkList.size());
long startTime = System.currentTimeMillis();
// List<InsertParam.Field> fields = new ArrayList<>();
// List<String> textList = new ArrayList<>();
// List<String> fidListData = new ArrayList<>();
// List<String> kidList = new ArrayList<>();
// List<String> docIdList = new ArrayList<>();
// List<List<Float>> vectorList = new ArrayList<>();
// 准备批量插入数据
List<String> textList = new ArrayList<>();
List<String> fidListData = new ArrayList<>();
List<String> kidList = new ArrayList<>();
List<String> docIdList = new ArrayList<>();
List<List<Float>> vectorList = new ArrayList<>();
for (int i = 0; i < chunkList.size(); 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<Float> vector = new ArrayList<>();
// for (float f : embedding.vector()) {
// vector.add(f);
// }
// vectorList.add(vector);
textList.add(text);
fidListData.add(fid);
kidList.add(kid);
docIdList.add(docId);
List<Float> vector = new ArrayList<>();
for (float f : embedding.vector()) {
vector.add(f);
}
vectorList.add(vector);
}
// 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));
//
// InsertParam insertParam = InsertParam.newBuilder()
// .withCollectionName(collectionName)
// .withFields(fields)
// .build();
//
// R<MutationResult> insertResponse = milvusClient.insert(insertParam);
// if (insertResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus向量存储成功，插入条数: {}", insertResponse.getData().getInsertCnt());
// } else {
// log.error("Milvus向量存储失败: {}", insertResponse.getMessage());
// throw new ServiceException("Milvus向量存储失败: " + insertResponse.getMessage());
// }
// 构建插入数据
List<JsonObject> data = new ArrayList<>();
Gson gson = new Gson();
for (int i = 0; i < textList.size(); i++) {
JsonObject row = new JsonObject();
row.addProperty("text", textList.get(i));
row.addProperty("fid", fidListData.get(i));
row.addProperty("kid", kidList.get(i));
row.addProperty("docId", docIdList.get(i));
row.add("vector", gson.toJsonTree(vectorList.get(i)));
data.add(row);
}
// 执行插入
InsertReq insertReq = InsertReq.builder()
.collectionName(collectionName)
.data(data)
.build();
InsertResp insertResp = client.insert(insertReq);
if (insertResp.getInsertCnt() > 0) {
log.info("Milvus向量存储成功，插入条数: {}", insertResp.getInsertCnt());
} else {
log.error("Milvus向量存储失败");
throw new ServiceException("Milvus向量存储失败");
}
long endTime = System.currentTimeMillis();
log.info("Milvus向量存储完成消耗时间：" + (endTime - startTime) / 1000 + "秒");
@@ -214,99 +221,95 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
List<String> resultList = new ArrayList<>();
// List<String> searchOutputFields = List.of("text", "fid", "kid", "docId");
// List<List<Float>> searchVectors = new ArrayList<>();
// List<Float> queryVector = new ArrayList<>();
// for (float f : queryEmbedding.vector()) {
// queryVector.add(f);
// }
// searchVectors.add(queryVector);
//
// SearchParam searchParam = SearchParam.newBuilder()
// .withCollectionName(collectionName)
// .withMetricType(MetricType.L2)
// .withOutFields(searchOutputFields)
// .withTopK(queryVectorBo.getMaxResults())
// .withVectors(searchVectors)
// .withVectorFieldName("vector")
// .withParams("{\"nprobe\":10}")
// .build();
//
// R<SearchResults> searchResponse = milvusClient.search(searchParam);
// if (searchResponse.getStatus() == R.Status.Success.getCode()) {
// SearchResults searchResults = searchResponse.getData();
// List<SearchResults.QueryResult> queryResults = searchResults.getResults();
//
// for (SearchResults.QueryResult queryResult : queryResults) {
// List<SearchResults.QueryResult.Row> rows = queryResult.getRows();
// for (SearchResults.QueryResult.Row row : rows) {
// String text = (String) row.get("text");
// resultList.add(text);
// }
// }
// } else {
// log.error("Milvus查询失败: {}", searchResponse.getMessage());
// }
// 准备查询向量
List<BaseVector> searchVectors = new ArrayList<>();
float[] queryVectorArray = new float[queryEmbedding.vector().length];
for (int i = 0; i < queryEmbedding.vector().length; i++) {
queryVectorArray[i] = queryEmbedding.vector()[i];
}
searchVectors.add(new FloatVec(queryVectorArray));
// 构建搜索请求
SearchReq searchReq = SearchReq.builder()
.collectionName(collectionName)
.data(searchVectors)
.topK(queryVectorBo.getMaxResults())
.outputFields(Arrays.asList("text", "fid", "kid", "docId"))
.build();
SearchResp searchResp = client.search(searchReq);
if (searchResp != null && searchResp.getSearchResults() != null) {
List<List<SearchResp.SearchResult>> searchResults = searchResp.getSearchResults();
for (List<SearchResp.SearchResult> results : searchResults) {
for (SearchResp.SearchResult result : results) {
Map<String, Object> entity = result.getEntity();
String text = (String) entity.get("text");
if (text != null) {
resultList.add(text);
}
}
}
} else {
log.error("Milvus查询失败或无结果");
}
return resultList;
}
@Override
public void removeById(String id, String modelName) {
public void removeById(String id, String modelName) throws ServiceException {
String collectionName = configService.getConfigValue("milvus", "collectionname") + id;
// DropCollectionParam dropCollectionParam = DropCollectionParam.newBuilder()
// .withCollectionName(collectionName)
// .build();
//
// R<RpcStatus> dropResponse = milvusClient.dropCollection(dropCollectionParam);
// if (dropResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus集合删除成功: {}", collectionName);
// } else {
// log.error("Milvus集合删除失败: {}", dropResponse.getMessage());
// throw new ServiceException("Milvus集合删除失败: " + dropResponse.getMessage());
// }
// 删除整个集合
DropCollectionReq dropCollectionReq = DropCollectionReq.builder()
.collectionName(collectionName)
.build();
log.info("Milvus删除集合: {}", collectionName);
try {
client.dropCollection(dropCollectionReq);
log.info("Milvus集合删除成功: {}", collectionName);
} catch (Exception e) {
log.error("Milvus集合删除失败: {}", e.getMessage());
throw new ServiceException("Milvus集合删除失败: " + e.getMessage());
}
}
@Override
public void removeByDocId(String docId, String kid) {
public void removeByDocId(String docId, String kid) throws ServiceException {
String collectionName = configService.getConfigValue("milvus", "collectionname") + kid;
// String expr = "docId == \"" + docId + "\"";
// DeleteParam deleteParam = DeleteParam.newBuilder()
// .withCollectionName(collectionName)
// .withExpr(expr)
// .build();
//
// R<MutationResult> deleteResponse = milvusClient.delete(deleteParam);
// if (deleteResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus成功删除 docId={} 的所有向量数据，删除条数: {}", docId, deleteResponse.getData().getDeleteCnt());
// } else {
// log.error("Milvus删除失败: {}", deleteResponse.getMessage());
// }
String expr = "docId == \"" + docId + "\"";
DeleteReq deleteReq = DeleteReq.builder()
.collectionName(collectionName)
.filter(expr)
.build();
log.info("Milvus删除docId={}的数据", docId);
try {
DeleteResp deleteResp = client.delete(deleteReq);
log.info("Milvus成功删除 docId={} 的所有向量数据，删除条数: {}", docId, deleteResp.getDeleteCnt());
} catch (Exception e) {
log.error("Milvus删除失败: {}", e.getMessage());
throw new ServiceException(e.getMessage());
}
}
@Override
public void removeByFid(String fid, String kid) {
public void removeByFid(String fid, String kid) throws ServiceException {
String collectionName = configService.getConfigValue("milvus", "collectionname") + kid;
// String expr = "fid == \"" + fid + "\"";
// DeleteParam deleteParam = DeleteParam.newBuilder()
// .withCollectionName(collectionName)
// .withExpr(expr)
// .build();
//
// R<MutationResult> deleteResponse = milvusClient.delete(deleteParam);
// if (deleteResponse.getStatus() == R.Status.Success.getCode()) {
// log.info("Milvus成功删除 fid={} 的所有向量数据，删除条数: {}", fid, deleteResponse.getData().getDeleteCnt());
// } else {
// log.error("Milvus删除失败: {}", deleteResponse.getMessage());
// }
String expr = "fid == \"" + fid + "\"";
DeleteReq deleteReq = DeleteReq.builder()
.collectionName(collectionName)
.filter(expr)
.build();
log.info("Milvus删除fid={}的数据", fid);
try {
DeleteResp deleteResp = client.delete(deleteReq);
log.info("Milvus成功删除 fid={} 的所有向量数据，删除条数: {}", fid, deleteResp.getDeleteCnt());
} catch (Exception e) {
log.error("Milvus删除失败: {}", e.getMessage());
throw new ServiceException(e.getMessage());
}
}
}