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[Backport 2.17] Fix broken links #8353

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Sep 23, 2024
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[Backport 2.17] Fix broken links #8353

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4 changes: 2 additions & 2 deletions _clients/index.md
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Expand Up @@ -53,8 +53,8 @@ To view the compatibility matrix for a specific client, see the `COMPATIBILITY.m

Client | Recommended version
:--- | :---
[Elasticsearch Java low-level REST client](https://search.maven.org/artifact/org.elasticsearch.client/elasticsearch-rest-client/7.13.4/jar) | 7.13.4
[Elasticsearch Java high-level REST client](https://search.maven.org/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client/7.13.4/jar) | 7.13.4
[Elasticsearch Java low-level REST client](https://central.sonatype.com/artifact/org.elasticsearch.client/elasticsearch-rest-client/7.13.4) | 7.13.4
[Elasticsearch Java high-level REST client](https://central.sonatype.com/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client/7.13.4) | 7.13.4
[Elasticsearch Python client](https://pypi.org/project/elasticsearch/7.13.4/) | 7.13.4
[Elasticsearch Node.js client](https://www.npmjs.com/package/@elastic/elasticsearch/v/7.13.0) | 7.13.0
[Elasticsearch Ruby client](https://rubygems.org/gems/elasticsearch/versions/7.13.0) | 7.13.0
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4 changes: 2 additions & 2 deletions _field-types/supported-field-types/knn-vector.md
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Expand Up @@ -175,7 +175,7 @@ By default, k-NN vectors are `float` vectors, in which each dimension is 4 bytes
Byte vectors are supported only for the `lucene` and `faiss` engines. They are not supported for the `nmslib` engine.
{: .note}

In [k-NN benchmarking tests](https://github.com/opensearch-project/k-NN/tree/main/benchmarks/perf-tool), the use of `byte` rather than `float` vectors resulted in a significant reduction in storage and memory usage as well as improved indexing throughput and reduced query latency. Additionally, precision on recall was not greatly affected (note that recall can depend on various factors, such as the [quantization technique](#quantization-techniques) and data distribution).
In [k-NN benchmarking tests](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch), the use of `byte` rather than `float` vectors resulted in a significant reduction in storage and memory usage as well as improved indexing throughput and reduced query latency. Additionally, precision on recall was not greatly affected (note that recall can depend on various factors, such as the [quantization technique](#quantization-techniques) and data distribution).

When using `byte` vectors, expect some loss of precision in the recall compared to using `float` vectors. Byte vectors are useful in large-scale applications and use cases that prioritize a reduced memory footprint in exchange for a minimal loss of recall.
{: .important}
Expand Down Expand Up @@ -411,7 +411,7 @@ As an example, assume that you have 1 million vectors with a dimension of 256 an

### Quantization techniques

If your vectors are of the type `float`, you need to first convert them to the `byte` type before ingesting the documents. This conversion is accomplished by _quantizing the dataset_---reducing the precision of its vectors. There are many quantization techniques, such as scalar quantization or product quantization (PQ), which is used in the Faiss engine. The choice of quantization technique depends on the type of data you're using and can affect the accuracy of recall values. The following sections describe the scalar quantization algorithms that were used to quantize the [k-NN benchmarking test](https://github.com/opensearch-project/k-NN/tree/main/benchmarks/perf-tool) data for the [L2](#scalar-quantization-for-the-l2-space-type) and [cosine similarity](#scalar-quantization-for-the-cosine-similarity-space-type) space types. The provided pseudocode is for illustration purposes only.
If your vectors are of the type `float`, you need to first convert them to the `byte` type before ingesting the documents. This conversion is accomplished by _quantizing the dataset_---reducing the precision of its vectors. There are many quantization techniques, such as scalar quantization or product quantization (PQ), which is used in the Faiss engine. The choice of quantization technique depends on the type of data you're using and can affect the accuracy of recall values. The following sections describe the scalar quantization algorithms that were used to quantize the [k-NN benchmarking test](https://github.com/opensearch-project/opensearch-benchmark-workloads/tree/main/vectorsearch) data for the [L2](#scalar-quantization-for-the-l2-space-type) and [cosine similarity](#scalar-quantization-for-the-cosine-similarity-space-type) space types. The provided pseudocode is for illustration purposes only.

#### Scalar quantization for the L2 space type

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2 changes: 1 addition & 1 deletion _install-and-configure/plugins.md
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Expand Up @@ -181,7 +181,7 @@ Continue with installation? [y/N]y

### Install a plugin using Maven coordinates

The `opensearch-plugin install` tool also allows you to specify Maven coordinates for available artifacts and versions hosted on [Maven Central](https://search.maven.org/search?q=org.opensearch.plugin). The tool parses the Maven coordinates you provide and constructs a URL. As a result, the host must be able to connect directly to the Maven Central site. The plugin installation fails if you pass coordinates to a proxy or local repository.
The `opensearch-plugin install` tool also allows you to specify Maven coordinates for available artifacts and versions hosted on [Maven Central](https://central.sonatype.com/namespace/org.opensearch.plugin). The tool parses the Maven coordinates you provide and constructs a URL. As a result, the host must be able to connect directly to the Maven Central site. The plugin installation fails if you pass coordinates to a proxy or local repository.

#### Usage
```bash
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2 changes: 1 addition & 1 deletion _monitoring-your-cluster/pa/index.md
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Expand Up @@ -60,7 +60,7 @@ private-key-file-path = specify_path

The Performance Analyzer plugin is included in the installations for [Docker]({{site.url}}{{site.baseurl}}/opensearch/install/docker/) and [tarball]({{site.url}}{{site.baseurl}}/opensearch/install/tar/), but you can also install the plugin manually.

To install the Performance Analyzer plugin manually, download the plugin from [Maven](https://search.maven.org/search?q=org.opensearch.plugin) and install it using the standard [plugin installation]({{site.url}}{{site.baseurl}}/opensearch/install/plugins/) process. Performance Analyzer runs on each node in a cluster.
To install the Performance Analyzer plugin manually, download the plugin from [Maven](https://central.sonatype.com/namespace/org.opensearch.plugin) and install it using the standard [plugin installation]({{site.url}}{{site.baseurl}}/opensearch/install/plugins/) process. Performance Analyzer runs on each node in a cluster.

To start the Performance Analyzer root cause analysis (RCA) agent on a tarball installation, run the following command:

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