Apache
/
lucene
mirror of https://github.com/apache/lucene.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

remove the mv sim interface

This commit is contained in:
Vigya Sharma 2024-11-12 09:54:07 -08:00
parent f1c73528fd
commit 2554e05ef4
2 changed files with 23 additions and 69 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

45
lucene/core/src/java/org/apache/lucene/index/MultiVectorSimilarity.java
View File

@ -1,45 +0,0 @@
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.lucene.index;
/** Defines comparison functions for multi-vector similarity */
public interface MultiVectorSimilarity {
/**
* Calculates a similarity score between the two multi-vectors with a specified function. Higher
* similarity scores correspond to closer vectors.
*
* @param t1 a multi-vector with non-empty vectors All vector values are concatenated in a single
* packed array.
* @param t2 another multi-vector, vectors of the same dimension as t1. All vector values are
* concatenated in a single packed array.
* @return the value of the similarity function applied to the two multi-vectors
*/
float compare(float[] t1, float[] t2, int dimension);
/**
* Calculates a similarity score between the two multi-vectors with a specified function. Higher
* similarity scores correspond to closer vectors.
*
* @param t1 a multi-vector with non-empty vectors. All vector values are concatenated in a single
* packed array.
* @param t2 another multi-vector, vectors of the same dimension as t1. All vector values are
* concatenated in a single packed array.
* @return the value of the similarity function applied to the two multi-vector
*/
float compare(byte[] t1, byte[] t2, int dimension);
}

47
lucene/core/src/java/org/apache/lucene/index/MultiVectorSimilarityFunction.java
View File

@ -21,16 +21,17 @@ import java.util.List;
import org.apache.lucene.util.ArrayUtil;
/**
* Multi-vector similarity function; used in search to return top K most similar multi-vectors to a
* target multi-vector. This method is used during indexing and searching of the multi-vectors in
* order to determine the nearest neighbors.
* Computes similarity between two multi-vectors.
* <p>
* A multi-vector is a collection of multiple vectors that represent a single document or query.
* MultiVectorSimilarityFunction is used to determine nearest neighbors during
* indexing and search on multi-vectors.
*/
// no commit
public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
public class MultiVectorSimilarityFunction {
/** Aggregation function to combine similarity across multiple vector values */
public enum Aggregation {
/** Placeholder aggregation that is not intended to be used. */
/** Selecting this aggregation indicates that the field does not have multi-vector values */
NONE {
@Override
public float aggregate(
@ -52,7 +53,7 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
},
/**
* SumMaxSimilarity between two multi-vectors. Aggregates using the sum of maximum similarity
* SumMaxSimilarity between two multi-vectors. Computes the sum of maximum similarity
* found for each vector in the first multi-vector against all vectors in the second
* multi-vector.
*/
@ -64,17 +65,16 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
VectorSimilarityFunction vectorSimilarityFunction,
int dimension) {
if (outer.length % dimension != 0 || inner.length % dimension != 0) {
throw new IllegalArgumentException("Multi vectors do not match provided dimensions");
throw new IllegalArgumentException("Multi vectors do not match provided dimension value");
}
// TODO: can we avoid making vector copies?
List<float[]> outerList = new ArrayList<>();
List<float[]> innerList = new ArrayList<>();
for (int i = 0; i < outer.length; i += dimension) {
// System.out.println("copy subArray - " + i + ":" + i+dimension);
outerList.add(ArrayUtil.copyOfSubArray(outer, i, i + dimension));
}
for (int i = 0; i < inner.length; i += dimension) {
// System.out.println("copy subArray - " + i + ":" + i+dimension);
innerList.add(ArrayUtil.copyOfSubArray(inner, i, i + dimension));
}
@ -96,18 +96,16 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
VectorSimilarityFunction vectorSimilarityFunction,
int dimension) {
if (outer.length % dimension != 0 || inner.length % dimension != 0) {
throw new IllegalArgumentException("Multi vectors do not match provided dimensions");
throw new IllegalArgumentException("Multi vectors do not match provided dimension value");
}
// TODO: can we avoid making vector copies?
List<byte[]> outerList = new ArrayList<>();
List<byte[]> innerList = new ArrayList<>();
// System.out.println("...handling outer list");
for (int i = 0; i < outer.length; i += dimension) {
// System.out.println("copy subArray - " + i + ":" + dimension);
outerList.add(ArrayUtil.copyOfSubArray(outer, i, i + dimension));
}
// System.out.println("...handling inner list");
for (int i = 0; i < inner.length; i += dimension) {
// System.out.println("copy subArray - " + i + ":" + dimension);
innerList.add(ArrayUtil.copyOfSubArray(inner, i, i + dimension));
}
@ -124,12 +122,15 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
};
/**
* Computes and aggregates similarity over multiple vector values
* Computes and aggregates similarity over multiple vector values.
*
* Assumes that all vector values in both provided multi-vectors have the same dimensions. Slices
* inner and outer float[] multi-vectors into dimension sized vector values for comparison.
*
* @param outer first multi-vector
* @param inner second multi-vector
* @param vectorSimilarityFunction distance function for vector proximity
* @param dimension dimension for each vector value in the multi-vector
* @param dimension dimension for each vector in the provided multi-vectors
* @return similarity between the two multi-vectors
*/
public abstract float aggregate(
@ -139,12 +140,15 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
int dimension);
/**
* Computes and aggregates similarity over multiple vector values
* Computes and aggregates similarity over multiple vector values.
*
* Assumes that all vector values in both provided multi-vectors have the same dimensions. Slices
* inner and outer byte[] multi-vectors into dimension sized vector values for comparison.
*
* @param outer first multi-vector
* @param inner second multi-vector
* @param vectorSimilarityFunction distance function for vector proximity
* @param dimension dimension for each vector value in the multi-vector
* @param dimension dimension for each vector in the provided multi-vectors
* @return similarity between the two multi-vectors
*/
public abstract float aggregate(
@ -172,17 +176,14 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
this.aggregation = aggregation;
}
@Override
public float compare(float[] t1, float[] t2, int dimension) {
return aggregation.aggregate(t1, t2, similarityFunction, dimension);
}
@Override
public float compare(byte[] t1, byte[] t2, int dimension) {
return aggregation.aggregate(t1, t2, similarityFunction, dimension);
}
@Override
public boolean equals(Object obj) {
if (obj instanceof MultiVectorSimilarityFunction == false) {
return false;
@ -191,14 +192,12 @@ public class MultiVectorSimilarityFunction implements MultiVectorSimilarity {
return this.similarityFunction == o.similarityFunction && this.aggregation == o.aggregation;
}
@Override
public int hashCode() {
int result = Integer.hashCode(similarityFunction.ordinal());
result = 31 * result + Integer.hashCode(aggregation.ordinal());
return result;
}
@Override
public String toString() {
return "MultiVectorSimilarityFunction(similarity="
+ similarityFunction