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Refactor OpenSearch/Dashboards front page (opensearch-project#8114)
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* Refactor OpenSearch/Dashboards front page

Signed-off-by: Fanit Kolchina <[email protected]>

* Editorial comments

Signed-off-by: Fanit Kolchina <[email protected]>

---------

Signed-off-by: Fanit Kolchina <[email protected]>
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kolchfa-aws authored and epugh committed Sep 12, 2024
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52 changes: 30 additions & 22 deletions _about/index.md
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Expand Up @@ -22,16 +22,21 @@ This section contains documentation for OpenSearch and OpenSearch Dashboards.

## Getting started

- [Intro to OpenSearch]({{site.url}}{{site.baseurl}}/intro/)
- [Quickstart]({{site.url}}{{site.baseurl}}/quickstart/)
To get started, explore the following documentation:

- [Getting started guide]({{site.url}}{{site.baseurl}}/getting-started/):
- [Intro to OpenSearch]({{site.url}}{{site.baseurl}}/getting-started/intro/)
- [Installation quickstart]({{site.url}}{{site.baseurl}}/getting-started/quickstart/)
- [Communicate with OpenSearch]({{site.url}}{{site.baseurl}}/getting-started/communicate/)
- [Ingest data]({{site.url}}{{site.baseurl}}/getting-started/ingest-data/)
- [Search data]({{site.url}}{{site.baseurl}}/getting-started/search-data/)
- [Getting started with OpenSearch security]({{site.url}}{{site.baseurl}}/getting-started/security/)
- [Install OpenSearch]({{site.url}}{{site.baseurl}}/install-and-configure/install-opensearch/index/)
- [Install OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/install-and-configure/install-dashboards/index/)
- [See the FAQ](https://opensearch.org/faq)
- [FAQ](https://opensearch.org/faq)

## Why use OpenSearch?

With OpenSearch, you can perform the following use cases:

<table style="table-layout: auto ; width: 100%;">
<tbody>
<tr style="text-align: center; vertical-align:center;">
Expand All @@ -41,35 +46,38 @@ With OpenSearch, you can perform the following use cases:
<td><img src="{{site.url}}{{site.baseurl}}/images/4_tracking.png" class="no-border" alt="Operational health tracking" height="100"/></td>
</tr>
<tr style="text-align: left; vertical-align:top; font-weight: bold; color: rgb(0,59,92)">
<td>Fast, Scalable Full-text Search</td>
<td>Application and Infrastructure Monitoring</td>
<td>Security and Event Information Management</td>
<td>Operational Health Tracking</td>
<td>Fast, scalable full-text search</td>
<td>Application and infrastructure monitoring</td>
<td>Security and event information management</td>
<td>Operational health tracking</td>
</tr>
<tr style="text-align: left; vertical-align:top;">
<td>Help users find the right information within your application, website, or data lake catalog. </td>
<td>Easily store and analyze log data, and set automated alerts for underperformance.</td>
<td>Easily store and analyze log data, and set automated alerts for performance issues.</td>
<td>Centralize logs to enable real-time security monitoring and forensic analysis.</td>
<td>Use observability logs, metrics, and traces to monitor your applications and business in real time.</td>
<td>Use observability logs, metrics, and traces to monitor your applications in real time.</td>
</tr>
</tbody>
</table>

**Additional features and plugins:**
## Key features

OpenSearch provides several features to help index, secure, monitor, and analyze your data:

OpenSearch has several features and plugins to help index, secure, monitor, and analyze your data. Most OpenSearch plugins have corresponding OpenSearch Dashboards plugins that provide a convenient, unified user interface.
- [Anomaly detection]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/) - Identify atypical data and receive automatic notifications
- [KNN]({{site.url}}{{site.baseurl}}/search-plugins/knn/) - Find “nearest neighbors” in your vector data
- [Performance Analyzer]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/) - Monitor and optimize your cluster
- [SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/index/) - Use SQL or a piped processing language to query your data
- [Index State Management]({{site.url}}{{site.baseurl}}/im-plugin/) - Automate index operations
- [ML Commons plugin]({{site.url}}{{site.baseurl}}/ml-commons-plugin/index/) - Train and execute machine-learning models
- [Asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/) - Run search requests in the background
- [Cross-cluster replication]({{site.url}}{{site.baseurl}}/replication-plugin/index/) - Replicate your data across multiple OpenSearch clusters
- [Anomaly detection]({{site.url}}{{site.baseurl}}/monitoring-plugins/ad/) -- Identify atypical data and receive automatic notifications.
- [SQL]({{site.url}}{{site.baseurl}}/search-plugins/sql/index/) -- Use SQL or a Piped Processing Language (PPL) to query your data.
- [Index State Management]({{site.url}}{{site.baseurl}}/im-plugin/) -- Automate index operations.
- [Search methods]({{site.url}}{{site.baseurl}}/search-plugins/knn/) -- From traditional lexical search to advanced vector and hybrid search, discover the optimal search method for your use case.
- [Machine learning]({{site.url}}{{site.baseurl}}/ml-commons-plugin/index/) -- Integrate machine learning models into your workloads.
- [Workflow automation]({{site.url}}{{site.baseurl}}/automating-configurations/index/) -- Automate complex OpenSearch setup and preprocessing tasks.
- [Performance evaluation]({{site.url}}{{site.baseurl}}/monitoring-plugins/pa/) -- Monitor and optimize your cluster.
- [Asynchronous search]({{site.url}}{{site.baseurl}}/search-plugins/async/) -- Run search requests in the background.
- [Cross-cluster replication]({{site.url}}{{site.baseurl}}/replication-plugin/index/) -- Replicate your data across multiple OpenSearch clusters.


## The secure path forward
OpenSearch includes a demo configuration so that you can get up and running quickly, but before using OpenSearch in a production environment, you must [configure the Security plugin manually]({{site.url}}{{site.baseurl}}/security/configuration/index/) with your own certificates, authentication method, users, and passwords.

OpenSearch includes a demo configuration so that you can get up and running quickly, but before using OpenSearch in a production environment, you must [configure the Security plugin manually]({{site.url}}{{site.baseurl}}/security/configuration/index/) with your own certificates, authentication method, users, and passwords. To get started, see [Getting started with OpenSearch security]({{site.url}}{{site.baseurl}}/getting-started/security/).

## Looking for the Javadoc?

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1 change: 1 addition & 0 deletions _getting-started/intro.md
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Expand Up @@ -56,6 +56,7 @@ ID | Name | GPA | Graduation year
1 | John Doe | 3.89 | 2022
2 | Jonathan Powers | 3.85 | 2025
3 | Jane Doe | 3.52 | 2024
... | | |

## Clusters and nodes

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4 changes: 4 additions & 0 deletions _ml-commons-plugin/index.md
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Expand Up @@ -32,6 +32,10 @@ ML Commons supports various algorithms to help train ML models and make predicti

ML Commons provides its own set of REST APIs. For more information, see [ML Commons API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api/index/).

## ML-powered search

For information about available ML-powered search types, see [ML-powered search]({{site.url}}{{site.baseurl}}/search-plugins/index/#ml-powered-search).

## Tutorials

Using the OpenSearch ML framework, you can build various applications, from implementing conversational search to building your own chatbot. For more information, see [Tutorials]({{site.url}}{{site.baseurl}}/ml-commons-plugin/tutorials/index/).
47 changes: 25 additions & 22 deletions _search-plugins/index.md
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Expand Up @@ -12,37 +12,40 @@ redirect_from:

# Search

OpenSearch provides many features for customizing your search use cases and improving search relevance.
OpenSearch provides many features for customizing your search use cases and improving search relevance.

## Search methods

OpenSearch supports the following search methods:
OpenSearch supports the following search methods.

- **Traditional lexical search**
### Traditional lexical search

- [Keyword (BM25) search]({{site.url}}{{site.baseurl}}/search-plugins/keyword-search/): Searches the document corpus for words that appear in the query.
OpenSearch supports [keyword (BM25) search]({{site.url}}{{site.baseurl}}/search-plugins/keyword-search/), which searches the document corpus for words that appear in the query.

- **Machine learning (ML)-powered search**
### ML-powered search

- **LTR search**

- [LTR search]({{site.url}}{{site.baseurl}}/search-plugins/ltr/): Uses ML to learn a ranking function that can effectively order a set of search results based on their relevance to the user's query that are query dependent.

- **Vector search**

- [k-NN search]({{site.url}}{{site.baseurl}}/search-plugins/knn/): Searches for k-nearest neighbors to a search term across an index of vectors.
OpenSearch supports the following machine learning (ML)-powered search methods:

- **Neural search**: [Neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) facilitates generating vector embeddings at ingestion time and searching them at search time. Neural search lets you integrate ML models into your search and serves as a framework for implementing other search methods. The following search methods are built on top of neural search:
- **LTR search**

- [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/): Considers the meaning of the words in the search context. Uses dense retrieval based on text embedding models to search text data.
- [LTR search]({{site.url}}{{site.baseurl}}/search-plugins/ltr/): Uses ML to learn a ranking function that can effectively order a set of search results based on their relevance to the user's query that are query dependent.

- [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/): Uses multimodal embedding models to search text and image data.
- **Vector search**

- [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/): Uses sparse retrieval based on sparse embedding models to search text data.
- [k-NN search]({{site.url}}{{site.baseurl}}/search-plugins/knn/): Searches for the k-nearest neighbors to a search term across an index of vectors.

- [Hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/): Combines traditional search and vector search to improve search relevance.
- **Neural search**: [Neural search]({{site.url}}{{site.baseurl}}/search-plugins/neural-search/) facilitates generating vector embeddings at ingestion time and searching them at search time. Neural search lets you integrate ML models into your search and serves as a framework for implementing other search methods. The following search methods are built on top of neural search:

- [Conversational search]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/): Implements a retrieval-augmented generative search.
- [Semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/): Considers the meaning of the words in the search context. Uses dense retrieval based on text embedding models to search text data.

- [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/): Uses multimodal embedding models to search text and image data.

- [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/): Uses sparse retrieval based on sparse embedding models to search text data.

- [Hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/): Combines traditional search and vector search to improve search relevance.

- [Conversational search]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/): Implements a retrieval-augmented generative search.

## Query languages

Expand All @@ -56,7 +59,7 @@ In OpenSearch, you can use the following query languages to search your data:

- [Piped Processing Language (PPL)]({{site.url}}{{site.baseurl}}/search-plugins/sql/ppl/index/): The primary language used with observability in OpenSearch. PPL uses a pipe syntax that chains commands into a query.

- [Dashboards Query Language (DQL)]({{site.url}}{{site.baseurl}}/dashboards/dql/): A simple text-based query language for filtering data in OpenSearch Dashboards.
- [Dashboards Query Language (DQL)]({{site.url}}{{site.baseurl}}/dashboards/dql/): A simple text-based query language for filtering data in OpenSearch Dashboards.

## Search performance

Expand All @@ -70,22 +73,22 @@ OpenSearch offers several ways to improve search performance:

OpenSearch provides the following search relevance features:

- [Compare Search Results]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/compare-search-results/): A search comparison tool in OpenSearch Dashboards that you can use to compare results from two queries side by side.
- [Compare Search Results]({{site.url}}{{site.baseurl}}/search-plugins/search-relevance/compare-search-results/): A search comparison tool in OpenSearch Dashboards that you can use to compare results from two queries side by side.

- [Querqy]({{site.url}}{{site.baseurl}}/search-plugins/querqy/): Offers query rewriting capability.

- [User Behavior Insights]({{site.url}}{{site.baseurl}}/search-plugins/ubi/): Links user behavior to user queries to improve search quality.

## Search results

OpenSearch supports the following commonly used operations on search results:

- [Paginate]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/paginate/)
- [Paginate with Point in Time]({{site.url}}{{site.baseurl}}/search-plugins/point-in-time/)
- [Sort]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/sort/)
- [Highlight search terms]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/highlight/)
- [Highlight search terms]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/highlight/)
- [Autocomplete]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/autocomplete/)
- [Did-you-mean]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/did-you-mean/)
- [Did-you-mean]({{site.url}}{{site.baseurl}}/search-plugins/searching-data/did-you-mean/)

## Search pipelines

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