43 lines
3.3 KiB
Markdown
43 lines
3.3 KiB
Markdown
---
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layout: default
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title: k-NN
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nav_order: 50
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has_children: true
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has_toc: false
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---
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# k-NN
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Short for *k-nearest neighbors*, the k-NN plugin enables users to search for the k-nearest neighbors to a query point across an index of vectors. To determine the neighbors, you can specify the space (the distance function) you want to use to measure the distance between points.
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Use cases include recommendations (for example, an "other songs you might like" feature in a music application), image recognition, and fraud detection. For more background information on k-NN search, see [Wikipedia](https://en.wikipedia.org/wiki/Nearest_neighbor_search).
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This plugin supports three different methods for obtaining the k-nearest neighbors from an index of vectors:
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1. **Approximate k-NN**
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The first method takes an approximate nearest neighbor approach---it uses the HNSW algorithm to return the approximate k-nearest neighbors to a query vector. This algorithm sacrifices indexing speed and search accuracy in return for lower latency and more scalable search. To learn more about the algorithm, please refer to [nmslib's documentation](https://github.com/nmslib/nmslib/) or [the paper introducing the algorithm](https://arxiv.org/abs/1603.09320).
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Approximate k-NN is the best choice for searches over large indices (i.e. hundreds of thousands of vectors or more) that require low latency. You should not use approximate k-NN if you want to apply a filter on the index before the k-NN search, which greatly reduces the number of vectors to be searched. In this case, you should use either the script scoring method or painless extensions.
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For more details about this method, see [Approximate k-NN search](approximate-knn).
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2. **Script Score k-NN**
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The second method extends OpenSearch's script scoring functionality to execute a brute force, exact k-NN search over "knn_vector" fields or fields that can represent binary objects. With this approach, you can run k-NN search on a subset of vectors in your index (sometimes referred to as a pre-filter search).
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Use this approach for searches over smaller bodies of documents or when a pre-filter is needed. Using this approach on large indices may lead to high latencies.
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For more details about this method, see [Exact k-NN with scoring script](knn-score-script).
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3. **Painless extensions**
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The third method adds the distance functions as painless extensions that you can use in more complex combinations. Similar to the k-NN Script Score, you can use this method to perform a brute force, exact k-NN search across an index, which also supports pre-filtering.
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This approach has slightly slower query performance compared to the k-NN Script Score. If your use case requires more customization over the final score, you should use this approach over Script Score k-NN.
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For more details about this method, see [Painless scripting functions](painless-functions).
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Overall, for larger data sets, you should generally choose the approximate nearest neighbor method because it scales significantly better. For smaller data sets, where you may want to apply a filter, you should choose the custom scoring approach. If you have a more complex use case where you need to use a distance function as part of their scoring method, you should use the painless scripting approach.
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