【lucene】实现knn
在 Lucene 中，可以通过 `KnnFloatVectorQuery` 和 `KnnFloatVectorField` 来实现 KNN（k-Nearest Neighbors）搜索。以下是具体介绍：
1. 功能原理
`KnnFloatVectorQuery` 是 Lucene 用于执行最近邻搜索的查询类，它可以在一个字段中搜索与目标向量最相似的 k 个向量。其核心是基于 HNSW（Hierarchical Navigable Small World）算法，构建图索引以实现高效的近似最近邻（Approximate Nearest Neighbor，ANN）搜索。
2. 代码示例
2.1 索引向量字段
```java
import org.apache.lucene.document.Document;
import org.apache.lucene.document.KnnFloatVectorField;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.ByteBuffersDirectory;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
public class LuceneKNNExample {
public static float[] generateFVector(int dim) {
float[] vector = new float[dim];
Random random = new Random();
for (int i = 0; i < dim; i++) {
vector[i] = random.nextFloat();
}
return vector;
}
public static void main(String[] args) throws IOException {
Directory directory = new ByteBuffersDirectory();
IndexWriterConfig config = new IndexWriterConfig(null);
IndexWriter indexWriter = new IndexWriter(directory, config);
int count = 10000;
int dim = 128;
List<Document> docs = new ArrayList<>();
for (int i = 0; i < count; i++) {
Document doc = new Document();
doc.add(new KnnFloatVectorField("fvecs", generateFVector(dim)));
docs.add(doc);
}
indexWriter.addDocuments(docs);
indexWriter.commit();
System.out.println("索引写入成功");
}
}
```
2.2 执行 KNN 查询
```java
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.Directory;
import org.apache.lucene.util.BytesRef;
import java.io.IOException;
import java.nio.file.Path;
import java.util.Random;
public class KNNQueryExample {
public static float[] generateFVector(int dim) {
float[] vector = new float[dim];
Random random = new Random();
for (int i = 0; i < dim; i++) {
vector[i] = random.nextFloat();
}
return vector;
}
public static void main(String[] args) throws IOException {
Directory readDirectory = new ByteBuffersDirectory();
IndexReader indexReader = DirectoryReader.open(readDirectory);
IndexSearcher indexSearcher = new IndexSearcher(indexReader);
float[] queryVector = generateFVector(128);
int k = 3;
TopDocs topDocs = indexSearcher.search(new KnnFloatVectorQuery("fvecs", queryVector, k), k);
for (ScoreDoc scoreDoc : topDocs.scoreDocs) {
System.out.println("doc: " + scoreDoc.doc + ", score: " + scoreDoc.score);
}
}
}
```
3. 查询原理
- `KnnFloatVectorQuery` 的 rewrite 过程：在 rewrite 之后，`KnnFloatVectorQuery` 会变成 `DocAndScoreQuery`，它内部已经存储了符合条件的 `docId` 和 `score`。
- HNSW 算法：HNSW 算法将新节点链接到 M 个最近邻，通过反向链接和修剪来保留多样性。M 值越大，精度越高，成本也越高。Beam-width 控制搜索范围。