Vector Similarity Search & Indexing Methods

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The document discusses various indexing methods for efficient vector similarity search on large datasets. It introduces graph-based indexes that use approximate nearest neighbor algorithms based on navigable small-world graphs. Space partition indexes are also discussed, including inverted multi-indexes and optimizations for billion-scale approximate nearest neighbors. Product quantization encoding is covered as another indexing approach. The document concludes by proposing a layered framework that decomposes vector search into space partitioning, candidate filtering, and result validation layers to balance accuracy, speed and system requirements.

Technology

Vector Similarity Search & Indexing Methods
Xiaomeng Yi
Senior Researcher, Zilliz

© 2020 Zilliz. All rights reserved.
Vector Similarity Search

© 2020 Zilliz. All rights reserved.
Information Retrieval: from text to versatile data types
How to measure similarity between data?

© 2020 Zilliz. All rights reserved.
Embeddings: represent data as vectors
a b c
a
b
c

© 2020 Zilliz. All rights reserved.
Efficiency problem for big data
• Trade accuracy for efficiency
• Indexing method

© 2020 Zilliz. All rights reserved.
Indexing Methods

© 2020 Zilliz. All rights reserved.
Graph based Index: general idea
Approximate nearest neighbor algorithm based on navigable small world graphs

© 2020 Zilliz. All rights reserved.
Graph: optimizations
Efficient and robust approximate nearest neighbor search
using Hierarchical Navigable Small world graphs
Approximate nearest neighbor algorithm based on
navigable small world graphs

© 2020 Zilliz. All rights reserved.
Example:
Space Partition based index
Approximate nearest neighbor
methods and vector models The inverted Multi-Index

© 2020 Zilliz. All rights reserved.
Optimization for space partition
Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors

© 2020 Zilliz. All rights reserved.
Encoding based index: general idea
Product quantization for nearest neighbor search

© 2020 Zilliz. All rights reserved.
Encoding: product quantization
Similarity Query Processing for High-Dimensional Data

© 2020 Zilliz. All rights reserved.
Comparison
Fast, accurate, and small,
never reached at the same time…
Fast
Accurate Small
HNSW L&C
IVF_PQ
IVF
_SQ
FLAT ∅

© 2020 Zilliz. All rights reserved.
Flexible indexes: A layered framework

© 2020 Zilliz. All rights reserved.
Layers: function decomposition
Layer
Function
Data Size Candidates
for a query
Requireme
nt
Space
Partition
Regions Small Full Accurate,
Fast
Candidate
Filtering
Compress
ed vectors
Mediu
m
Small
portion
Fast
Result
Validation
Original
vectors
Large Very small
portion
Accurate

© 2020 Zilliz. All rights reserved.
Layer
Function
Size Require
ment
Index Type
(Adjustable)
Optimization
Opportunity
Space
Partition
Small Accurate,
fast
Graph Cache-based
optimization
Candidate
Filtering
Medi
um
Small Coarse
encoding
Data locality,
inter/intra query
parallelism
Result
Validation
Large Accurate Flat SSD-based Storage,
compute-read pipeline
Layers: optimization opportunity

© 2020 Zilliz. All rights reserved.
Thank you!

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Vector Similarity Search & Indexing Methods

1. Vector Similarity Search & Indexing Methods Xiaomeng Yi Senior Researcher, Zilliz

10. © 2020 Zilliz. All rights reserved. Graph: optimizations Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small world graphs Approximate nearest neighbor algorithm based on navigable small world graphs

17. © 2020 Zilliz. All rights reserved. Layers: function decomposition Layer Function Data Size Candidates for a query Requireme nt Space Partition Regions Small Full Accurate, Fast Candidate Filtering Compress ed vectors Mediu m Small portion Fast Result Validation Original vectors Large Very small portion Accurate

18. © 2020 Zilliz. All rights reserved. Layer Function Size Require ment Index Type (Adjustable) Optimization Opportunity Space Partition Small Accurate, fast Graph Cache-based optimization Candidate Filtering Medi um Small Coarse encoding Data locality, inter/intra query parallelism Result Validation Large Accurate Flat SSD-based Storage, compute-read pipeline Layers: optimization opportunity

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