into an OpenSearch index and perform different kinds of k-NN search. The `knn_vector` field is highly configurable and can serve many different k-NN workloads. For more information, see [k-NN vector]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector/).
Starting with k-NN plugin version 2.9, you can use `byte` vectors with the `lucene` engine in order to reduce the amount of storage space needed. For more information, see [Lucene byte vector]({{site.url}}{{site.baseurl}}/field-types/supported-field-types/knn-vector#lucene-byte-vector).
A method definition refers to the underlying configuration of the Approximate k-NN algorithm you want to use. Method definitions are used to either create a `knn_vector` field (when the method does not require training) or [create a model during training]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model) that can then be used to [create a `knn_vector` field]({{site.url}}{{site.baseurl}}/search-plugins/knn/approximate-knn/#building-a-k-nn-index-from-a-model).
`space_type` | false | l2 | false | The vector space used to calculate the distance between vectors.
`engine` | false | nmslib | false | The approximate k-NN library to use for indexing and search. The available libraries are faiss, nmslib, and Lucene.
`hnsw` | false | l2, innerproduct, cosinesimil, l1, linf | Hierarchical proximity graph approach to Approximate k-NN search. For more details on the algorithm, see this [abstract](https://arxiv.org/abs/1603.09320).
`ef_construction` | false | 512 | false | The size of the dynamic list used during k-NN graph creation. Higher values lead to a more accurate graph but slower indexing speed.
`m` | false | 16 | false | The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.
`ivf` | true | l2, innerproduct | Bucketing approach where vectors are assigned different buckets based on clustering and, during search, only a subset of the buckets is searched.
`ef_construction` | false | 512 | false | The size of the dynamic list used during k-NN graph creation. Higher values lead to a more accurate graph but slower indexing speed.
`m` | false | 16 | false | The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.
`encoder` | false | flat | false | Encoder definition for encoding vectors. Encoders can reduce the memory footprint of your index, at the expense of search accuracy.
`nlist` | false | 4 | false | Number of buckets to partition vectors into. Higher values may lead to more accurate searches at the expense of memory and training latency. For more information about choosing the right value, refer to [Guidelines to choose an index](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index).
`nprobes` | false | 1 | false | Number of buckets to search during query. Higher values lead to more accurate but slower searches.
`encoder` | false | flat | false | Encoder definition for encoding vectors. Encoders can reduce the memory footprint of your index, at the expense of search accuracy.
For more information about setting these parameters, please refer to [*faiss*'s documentation](https://github.com/facebookresearch/faiss/wiki/Faiss-indexes).
The IVF algorithm requires a training step. To create an index that uses IVF, you need to train a model with the
[Train API]({{site.url}}{{site.baseurl}}/search-plugins/knn/api#train-model), passing the IVF method definition. IVF requires that, at a minimum, there should be `nlist` training
data points, but it is [recommended that you use more](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#how-big-is-the-dataset).
Training data can be composed of either the same data that is going to be ingested or a separate dataset.
`ef_construction` | false | 512 | false | The size of the dynamic list used during k-NN graph creation. Higher values lead to a more accurate graph but slower indexing speed.<br>The Lucene engine uses the proprietary term "beam_width" to describe this function, which corresponds directly to "ef_construction". To be consistent throughout OpenSearch documentation, we retain the term "ef_construction" to label this parameter.
`m` | false | 16 | false | The number of bidirectional links that the plugin creates for each new element. Increasing and decreasing this value can have a large impact on memory consumption. Keep this value between 2 and 100.<br>The Lucene engine uses the proprietary term "max_connections" to describe this function, which corresponds directly to "m". To be consistent throughout OpenSearch documentation, we retain the term "m" to label this parameter.
Lucene HNSW implementation ignores `ef_search` and dynamically sets it to the value of "k" in the search request. Therefore, there is no need to make settings for `ef_search` when using the Lucene engine.
`pq` | true | Short for product quantization, it is a lossy compression technique that encodes a vector into a fixed size of bytes using clustering, with the goal of minimizing the drop in k-NN search accuracy. From a high level, vectors are broken up into `m` subvectors, and then each subvector is represented by a `code_size` code obtained from a code book produced during training. For more details on product quantization, here is a [great blog post](https://medium.com/dotstar/understanding-faiss-part-2-79d90b1e5388)!
`m` | false | 1 | false | Determine how many many sub-vectors to break the vector into. sub-vectors are encoded independently of each other. This dimension of the vector must be divisible by `m`. Max value is 1024.
`code_size` | false | 8 | false | Determines the number of bits to encode a sub-vector into. Max value is 8. **Note** --- for IVF, this value must be less than or equal to 8. For HNSW, this value can only be 8.
There are a lot of options to choose from when building your `knn_vector` field. To determine the correct methods and parameters to choose, you should first understand what requirements you have for your workload and what trade-offs you are willing to make. Factors to consider are (1) query latency, (2) query quality, (3) memory limits, (4) indexing latency.
At the moment, several parameters defined in the settings are in the deprecation process. Those parameters should be set in the mapping instead of the index settings. Parameters set in the mapping will override the parameters set in the index settings. Setting the parameters in the mapping allows an index to have multiple `knn_vector` fields with different parameters.
`index.knn` | false | false | Whether the index should build native library indexes for the `knn_vector` fields. If set to false, the `knn_vector` fields will be stored in doc values, but Approximate k-NN search functionality will be disabled.
`index.knn.algo_param.ef_search` | 512 | true | The size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches. Only available for nmslib.
`index.knn.algo_param.ef_construction` | 512 | false | Deprecated in 1.0.0. Use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value instead.
`index.knn.algo_param.m` | 16 | false | Deprecated in 1.0.0. Use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value instead.
`index.knn.space_type` | l2 | false | Deprecated in 1.0.0. Use the [mapping parameters](https://opensearch.org/docs/latest/search-plugins/knn/knn-index/#method-definitions) to set this value instead.
