|
|||||||
| Fast, Scalable Full-text Search | -Application and Infrastructure Monitoring | -Security and Event Information Management | -Operational Health Tracking | +Fast, scalable full-text search | +Application and infrastructure monitoring | +Security and event information management | +Operational health tracking |
| Help users find the right information within your application, website, or data lake catalog. | -Easily store and analyze log data, and set automated alerts for underperformance. | +Easily store and analyze log data, and set automated alerts for performance issues. | Centralize logs to enable real-time security monitoring and forensic analysis. | -Use observability logs, metrics, and traces to monitor your applications and business in real time. | +Use observability logs, metrics, and traces to monitor your applications in real time. |
{:/} icon.
+
+## Creating an index pattern
+
+After creating a data source connection, the next step is to create an index pattern for that data source. For more information and a tutorial on index patterns, refer to [Index patterns]({{site.url}}{{site.baseurl}}/dashboards/management/index-patterns/).
diff --git a/_dashboards/management/data-sources.md b/_dashboards/management/data-sources.md
index fdd4edc150..62d3a5aab2 100644
--- a/_dashboards/management/data-sources.md
+++ b/_dashboards/management/data-sources.md
@@ -13,67 +13,37 @@ This documentation focuses on using the OpenSeach Dashboards interface to connec
## Prerequisites
-The first step in connecting your data sources to OpenSearch is to install OpenSearch and OpenSearch Dashboards on your system. You can follow the installation instructions in the [OpenSearch documentation]({{site.url}}{{site.baseurl}}/install-and-configure/index/) to install these tools.
+The first step in connecting your data sources to OpenSearch is to install OpenSearch and OpenSearch Dashboards on your system. Refer to the [installation instructions]({{site.url}}{{site.baseurl}}/install-and-configure/index/) for information.
Once you have installed OpenSearch and OpenSearch Dashboards, you can use Dashboards to connect your data sources to OpenSearch and then use Dashboards to manage data sources, create index patterns based on those data sources, run queries against a specific data source, and combine visualizations in one dashboard.
Configuration of the [YAML files]({{site.url}}{{site.baseurl}}/install-and-configure/configuring-opensearch/#configuration-file) and installation of the `dashboards-observability` and `opensearch-sql` plugins is necessary. For more information, see [OpenSearch plugins]({{site.url}}{{site.baseurl}}/install-and-configure/plugins/).
-## Permissions
-
-To work with data sources in OpenSearch Dashboards, make sure that the user has been assigned the correct cluster-level [data source permission]({{site.url}}{{site.baseurl}}/security/access-control/permissions#data-source-permissions).
-
-
-
-## Create a data source connection
-
-A data source connection specifies the parameters needed to connect to a data source. These parameters form a connection string for the data source. Using Dashboards, you can add new data source connections or manage existing ones.
-
-The following steps guide you through the basics of creating a data source connection:
-
-1. From the OpenSearch Dashboards main menu, select **Management** > **Data sources** > **Create data source connection**. The UI is shown in the following image.
+To securely store and encrypt data source connections in OpenSearch, you must add the following configuration to the `opensearch.yml` file on all the nodes:
- 
+`plugins.query.datasources.encryption.masterkey: "YOUR_GENERATED_MASTER_KEY_HERE"`
-2. Create the data source connection by entering the appropriate information into the **Connection Details** and **Authentication Method** fields.
-
- - Under **Connection Details**, enter a title and endpoint URL. For this tutorial, use the URL `http://localhost:5601/app/management/opensearch-dashboards/dataSources`. Entering a description is optional.
+The key must be 16, 24, or 32 characters. You can use the following command to generate a 24-character key:
- - Under **Authentication Method**, select an authentication method from the dropdown list. Once an authentication method is selected, the applicable fields for that method appear. You can then enter the required details. The authentication method options are:
- - **No authentication**: No authentication is used to connect to the data source.
- - **Username & Password**: A basic username and password are used to connect to the data source.
- - **AWS SigV4**: An AWS Signature Version 4 authenticating request is used to connect to the data source. AWS Signature Version 4 requires an access key and a secret key.
- - For AWS Signature Version 4 authentication, first specify the **Region**. Next, select the OpenSearch service in the **Service Name** list. The options are **Amazon OpenSearch Service** and **Amazon OpenSearch Serverless**. Lastly, enter the **Access Key** and **Secret Key** for authorization.
+`openssl rand -hex 12`
- After you have populated the required fields, the **Test connection** and **Create data source** buttons become active. You can select **Test connection** to confirm that the connection is valid.
+Generating 12 bytes results in a hexadecimal string that is 12 * 2 = 24 characters.
+{: .note}
-3. Select **Create data source** to save your settings. The connection is created. The active window returns to the **Data sources** main page, and the new connection appears in the list of data sources.
-
-4. To delete a data source connection, select the checkbox to the left of the data source **Title** and then select the **Delete 1 connection** button. Selecting multiple checkboxes for multiple connections is supported. An example UI is shown in the following image.
-
- 
-
-### Modify a data source connection
-
-To make changes to a data source connection, select a connection in the list on the **Data sources** main page. The **Connection Details** window opens.
-
-To make changes to **Connection Details**, edit one or both of the **Title** and **Description** fields and select **Save changes** in the lower-right corner of the screen. You can also cancel changes here. To change the **Authentication Method**, choose a different authentication method, enter your credentials (if applicable), and then select **Save changes** in the lower-right corner of the screen. The changes are saved.
-
-When **Username & Password** is the selected authentication method, you can update the password by choosing **Update stored password** next to the **Password** field. In the pop-up window, enter a new password in the first field and then enter it again in the second field to confirm. Select **Update stored password** in the pop-up window. The new password is saved. Select **Test connection** to confirm that the connection is valid.
+## Permissions
-When **AWS SigV4** is the selected authentication method, you can update the credentials by selecting **Update stored AWS credential**. In the pop-up window, enter a new access key in the first field and a new secret key in the second field. Select **Update stored AWS credential** in the pop-up window. The new credentials are saved. Select **Test connection** in the upper-right corner of the screen to confirm that the connection is valid.
+To work with data sources in OpenSearch Dashboards, you must be assigned the correct cluster-level [data source permissions]({{site.url}}{{site.baseurl}}/security/access-control/permissions#data-source-permissions).
-To delete the data source connection, select the delete icon ({::nomarkdown}{:/}).
+## Types of data streams
-## Create an index pattern
+To configure data sources through OpenSearch Dashboards, go to **Management** > **Dashboards Management** > **Data sources**. This flow can be used for OpenSearch data stream connections. See [Configuring and using multiple data sources]({{site.url}}{{site.baseurl}}/dashboards/management/multi-data-sources/).
-Once you've created a data source connection, you can create an index pattern for the data source. An _index pattern_ is a template that OpenSearch uses to create indexes for data from the data source. See [Index patterns]({{site.url}}{{site.baseurl}}/dashboards/management/index-patterns/) for more information and a tutorial.
+Alternatively, if you are running OpenSearch Dashboards 2.16 or later, go to **Management** > **Data sources**. This flow can be used to connect Amazon Simple Storage Service (Amazon S3) and Prometheus. See [Connecting Amazon S3 to OpenSearch]({{site.url}}{{site.baseurl}}/dashboards/management/S3-data-source/) and [Connecting Prometheus to OpenSearch]({{site.url}}{{site.baseurl}}/dashboards/management/connect-prometheus/) for more information.
## Next steps
- Learn about [managing index patterns]({{site.url}}{{site.baseurl}}/dashboards/management/index-patterns/) through OpenSearch Dashboards.
- Learn about [indexing data using Index Management]({{site.url}}{{site.baseurl}}/dashboards/im-dashboards/index/) through OpenSearch Dashboards.
- Learn about how to connect [multiple data sources]({{site.url}}{{site.baseurl}}/dashboards/management/multi-data-sources/).
-- Learn about how to [connect OpenSearch and Amazon S3 through OpenSearch Dashboards]({{site.url}}{{site.baseurl}}/dashboards/management/S3-data-source/).
-- Learn about the [Integrations]({{site.url}}{{site.baseurl}}/integrations/index/) tool, which gives you the flexibility to use various data ingestion methods and connect data from the Dashboards UI.
-
+- Learn about how to connect [OpenSearch and Amazon S3]({{site.url}}{{site.baseurl}}/dashboards/management/S3-data-source/) and [OpenSearch and Prometheus]({{site.url}}{{site.baseurl}}/dashboards/management/connect-prometheus/) using the OpenSearch Dashboards interface.
+- Learn about the [Integrations]({{site.url}}{{site.baseurl}}/integrations/index/) plugin, which gives you the flexibility to use various data ingestion methods and connect data to OpenSearch Dashboards.
diff --git a/_getting-started/intro.md b/_getting-started/intro.md
index edd178a23f..f5eb24ba2b 100644
--- a/_getting-started/intro.md
+++ b/_getting-started/intro.md
@@ -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
diff --git a/_ml-commons-plugin/index.md b/_ml-commons-plugin/index.md
index f0355b6be3..50d637379e 100644
--- a/_ml-commons-plugin/index.md
+++ b/_ml-commons-plugin/index.md
@@ -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/).
\ No newline at end of file
diff --git a/_search-plugins/index.md b/_search-plugins/index.md
index 79e0e715d0..3604245f11 100644
--- a/_search-plugins/index.md
+++ b/_search-plugins/index.md
@@ -16,29 +16,31 @@ OpenSearch provides many features for customizing your search use cases and impr
## 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
- - **Vector search**
+OpenSearch supports the following machine learning (ML)-powered search methods:
- - [k-NN search]({{site.url}}{{site.baseurl}}/search-plugins/knn/): Searches for k-nearest neighbors to a search term across an index of vectors.
+- **Vector search**
- - **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:
+ - [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.
- - [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.
+- **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:
- - [Multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/): Uses multimodal embedding models to search text and image data.
+ - [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.
- - [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/): Uses sparse retrieval based on sparse 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.
- - [Hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/): Combines traditional search and vector search to improve search relevance.
+ - [Neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/): Uses sparse retrieval based on sparse embedding models to search text data.
- - [Conversational search]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/): Implements a retrieval-augmented generative search.
+ - [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
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