mccullocht commented on code in PR #16030: URL: https://github.com/apache/lucene/pull/16030#discussion_r3961977495
########## lucene/core/src/java/org/apache/lucene/codecs/lucene106/Lucene106ScalarQuantizedVectorsFormat.java: ########## @@ -0,0 +1,170 @@ +/* + * 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.codecs.lucene106; + +import java.io.IOException; +import org.apache.lucene.codecs.hnsw.FlatVectorScorerUtil; +import org.apache.lucene.codecs.hnsw.FlatVectorsFormat; +import org.apache.lucene.codecs.hnsw.FlatVectorsReader; +import org.apache.lucene.codecs.hnsw.FlatVectorsWriter; +import org.apache.lucene.codecs.lucene99.Lucene99FlatVectorsFormat; +import org.apache.lucene.index.SegmentReadState; +import org.apache.lucene.index.SegmentWriteState; +import org.apache.lucene.util.quantization.QuantizedByteVectorValues.ScalarEncoding; + +/** + * The quantization format used here is a per-vector optimized scalar quantization. These ideas are + * evolutions of LVQ proposed in <a href="https://arxiv.org/abs/2304.04759";>Similarity search in the + * blink of an eye with compressed indices</a> by Cecilia Aguerrebere et al., the previous work on + * globally optimized scalar quantization in Apache Lucene, and <a + * href="https://arxiv.org/abs/1908.10396";>Accelerating Large-Scale Inference with Anisotropic + * Vector Quantization </a> by Ruiqi Guo et. al. Also see {@link + * org.apache.lucene.util.quantization.OptimizedScalarQuantizer}. Some of key features are: + * + * <ul> + * <li>Estimating the distance between two vectors using their centroid centered distance. This + * requires some additional corrective factors, but allows for centroid centering to occur. + * <li>Optimized scalar quantization to single bit level of centroid centered vectors. + * <li>Asymmetric quantization of vectors, where query vectors are quantized to half-byte (4 bits) + * precision (normalized to the centroid) and then compared directly against the single bit + * quantized vectors in the index. + * <li>Transforming the half-byte quantized query vectors in such a way that the comparison with + * single bit vectors can be done with bit arithmetic. + * </ul> + * + * A previous work related to improvements over regular LVQ is <a + * href="https://arxiv.org/abs/2409.09913";>Practical and Asymptotically Optimal Quantization of + * High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search </a> by + * Jianyang Gao, et. al. + * + * <p>The format is stored within two files: + * + * <h2>.veq (vector data) file</h2> + * + * <p>Stores the quantized vectors in a flat format. Additionally, it stores each vector's + * corrective factors. At the end of the file, additional information is stored for vector ordinal + * to centroid ordinal mapping and sparse vector information. + * + * <ul> + * <li>For each vector: + * <ul> + * <li><b>[byte]</b> the quantized values. Each dimension may be up to 8 bits, and multiple + * dimensions may be packed into a single byte. + * <li><b>[float]</b> the optimized quantiles and an additional similarity dependent + * corrective factor. + * <li><b>[int]</b> the sum of the quantized components + * </ul> + * <li>After the vectors, sparse vector information keeping track of monotonic blocks. + * </ul> + * + * <h2>.vemq (vector metadata) file</h2> + * + * <p>Stores the metadata for the vectors. This includes the number of vectors, the number of + * dimensions, and file offset information. + * + * <ul> + * <li><b>int</b> the field number + * <li><b>int</b> the vector encoding ordinal + * <li><b>int</b> the vector similarity ordinal + * <li><b>vint</b> the vector dimensions + * <li><b>vlong</b> the offset to the vector data in the .veq file + * <li><b>vlong</b> the length of the vector data in the .veq file + * <li><b>vint</b> the number of vectors + * <li><b>vint</b> the wire number for ScalarEncoding + * <li><b>[float]</b> the centroid + * <li><b>float</b> the centroid square magnitude + * <li>The sparse vector information, if required, mapping vector ordinal to doc ID Review Comment: Pushed everything back into the Lucene104 codec. Disabling centering results in an internal VERSION bump that omits the centroid and derived data and replaces it with zeros if needed. Writers with centering enable (default) won't see anything different at all, writers with centering disabled will experience the usually one way door. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
