vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Or, you can choose Do not train on intent, which does not save this message as an example for training.



![Select intent](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/select_intent.png)
3. Select Save.

![Save intent](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/save_intent.png)

The Watson Assistant service supports adding user input as an example to an intent as-is. If you are using @entity references as examples in your intent training data, and a user message that you want to save contains an entity value or synonym from your training data, then you must edit the message later. After you save it, edit the message from the Intents page to replace the entity that it references. For more information, see [Directly referencing an @Entity as an intent example](https://cloud.ibm.com/docs/assistant?topic=assistant-intentsintents-entity-as-example).







 Adding an entity value or synonym 



1. To add an entity value or synonym, select the ![Edit](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/edit_icon.png) edit icon for the chosen @entity.
2. Select Add entity.

![Add entity](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/add_entity.png)
3.
[ibm_eventnotifications_devices](https://cloud.ibm.com/docs/event-notifications?topic=event-notifications-en-monitoringibm_eventnotifications_devices) 





 ibm_eventnotifications_total_ingested_notifications 

Total number of notifications that are ingested. Ingested notifications are events from a source that has at least one condition that is defined on them for filtering.



Table 3. Ingestion metadata

 Metadata Description 

 Metric Name ibm_eventnotifications_total_ingested_notifications 
 Metric Type gauge 
 Value Type none 
 Segment By ibm_scope, ibm_ctype, ibm_location, ibm_service_name, ibm_service_instance, ibm_eventnotifications_source 







 ibm_eventnotifications_total 

Total number of notifications for a particular type includes successful and failed notifications.



Table 4. Notifications metadata

 Metadata Description 

 Metric Name ibm_eventnotifications_total 
 Metric Type gauge 
 Value Type none 
 Segment By ibm_scope, ibm_ctype, ibm_location, ibm_service_name, ibm_service_instance, ibm_eventnotifications_source, ibm_eventnotifications_destination, ibm_eventnotifications_source, ibm_eventnotifications_type 







 ibm_eventnotifications_failed 

Total number of failed notifications of a particular type.



Table 5. Failed notifications metadata

 Metadata Description 

 Metric Name ibm_eventnotifications_failed 
 Metric Type gauge 
 Value Type none 
 Segment By ibm_scope, ibm_ctype, ibm_location, ibm_service_name, ibm_service_instance, ibm_eventnotifications_destination, ibm_eventnotifications_source, ibm_eventnotifications_type, 







 ibm_eventnotifications_devices 

Total number of push notification devices registered.



Table 6. Total devices registered

 Metadata Description 

 Metric Name ibm_eventnotifications_devices 
 Metric Type gauge 
 Value Type none
For example, agentb1-gsmforvpc-mar17.soA.115c.

Output

{
"name": "agentb1-gsmforvpc-mar17",
"description": "Create Agent",
"resource_group": "aac37f57b20142dba1a435c70aeb12df",
"tags": [
"env:prod",
"mytest"
],
"version": "v1.0.0",
"schematics_location": "eu-de",
"agent_location": "Frankfurt MZR",
"user_state": {
"state": "enable",
"set_by": "geetha_sathyamurthy@in.ibm.com",
"set_at": "2023-03-16T18:08:18.399224788Z"
},
"agent_crn": "crn:v1:bluemix:public:schematics:eu-de:a/1f7277194bb748cdb1d35fd8fb85a7cb:9ae7be42-0d59-415c-a6ce-0b662f520a4d:agent:agentb1-gsmforvpc-mar17.soA.115c",
"id": "agentb1-gsmforvpc-mar17.soA.115c",
"created_at": "2023-03-16T18:08:18.39924616Z",
"creation_by": "geetha_sathyamurthy@in.ibm.com",
"updated_at": "0001-01-01T00:00:00Z",
"system_state": {
"status_code": "draft"
},
"agent_kpi": {}
}
If any issue occurs with the Status page, contact [IBM Cloud Support](https://cloud.ibm.com/unifiedsupport/supportcenter).
4. You can filter the lists by selecting specific components, locations, dates, types of ongoing events, or by using keyword searches. Click each item to get more details about the changes and why, and how they impact your environment.



You can build URL search values by using query parameters from outside the console. For more information, see [Advanced status search](https://cloud.ibm.com/docs/get-support?topic=get-support-adv-search).



 Viewing the status of a dedicated multizone region (MZR) 

As a user in an account that has access to a dedicated

MZR, you are able to view incidents, planned maintenance, security bulletins, and announcements that are related to the resources within that MZR in addition to the public regions on the IBM Cloud Status page. Additionally, on the History page, users in the account can view past status-related items that impact the dedicated MZR.

To view the status of a dedicated MZR, complete the following steps:



1. In the IBM Cloud console, click the Notifications icon ![Notifications icon "Notifications"](https://cloud.ibm.com/docs-content/v1/content/9147bc2ffd9bafd03e4559b378e714fac17a4977/icons/Notification.svg) in the menu bar.
2. Click View cloud status.
3. Select from the services that are available within the dedicated MZR to view the status of the MZR in the Private location status section. To view planned maintenance, security bulletins and announcements that are related only to your dedicated MZR, use the location filter on the appropriate page.



If an incident occurs that affects the dedicated MZR, you can see the following status on the IBM Cloud Status page, which shows you the continent where your private MZR is located, and the status of your private region:

Zoom

!
Use the Ingress and Egress tabs to see the communications for the selected entity and time period.

You can use the ![Include icon](https://cloud.ibm.com/docs-content/v1/content/667adc0b8a7fdb0b349b1158d8e128640b5bc72b/workload-protection//images/include.png) to include a listed communication or the ![Exclude icon](https://cloud.ibm.com/docs-content/v1/content/667adc0b8a7fdb0b349b1158d8e128640b5bc72b/workload-protection//images/exclude.png) to exclude the communication from the policy.

You can also use General ingress rules to block all, allow all inside the namespace, or allow all in the policy.

Sometimes it might not be possible to resolve an endpoint using Kubernetes metadata and classification. For example, if a microservice is communicating with an external web server and the external IP address is not associated with Kubernetes metadata in your cluster. You will see these entities listed as UNRESOLVED IPs. By default, unresolved IP addresses are excluded from the KNP, but you can add them manually by clicking the ![Map IP icon](https://cloud.ibm.com/docs-content/v1/content/667adc0b8a7fdb0b349b1158d8e128640b5bc72b/workload-protection//images/map_ip.png) and them mapping the IP addresses to an alias.

You can filter the list of unresolved IP addresses to show only Internal, External, Aliased, Unknown IP addresses or a combination of these.

Internal
: IP addresses that are found within the cluster.

External
: IP addresses that are not within the cluster.

Aliased
: IP addresses that have aliases associated with them.

Unknown
: IP addresses that cannot be determined to be Internal or External.





 Reviewing the topology
Or, you can choose Do not train on intent, which does not save this message as an example for training.



![Select intent](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/select_intent.png)
3. Select Save.

![Save intent](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/save_intent.png)

The Watson Assistant service supports adding user input as an example to an intent as-is. If you are using @entity references as examples in your intent training data, and a user message that you want to save contains an entity value or synonym from your training data, then you must edit the message later. After you save it, edit the message from the Intents page to replace the entity that it references. For more information, see [Directly referencing an @Entity as an intent example](https://cloud.ibm.com/docs/assistant?topic=assistant-intentsintents-entity-as-example).







 Adding an entity value or synonym 



1. To add an entity value or synonym, select the ![Edit](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/edit_icon.png) edit icon for the chosen @entity.
2. Select Add entity.

![Add entity](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/add_entity.png)
3.
Filtering documents with your facet 



1. Click the collection tile to open your collection in the data analysis page.
2. Do one of the following things:



* Your custom facets are listed in the Facets view. Scroll and click Load more repeatedly until your facets are displayed.
* Submit an empty search to return all documents. In the Facet analysis pane, select the facet that you created.
* To access your custom facets more quickly, add them to a custom view. Select Custom as the view, and then click Edit. Select one or more facets to add to the view, and then click Save.

Zoom

![Custom view](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-custom-view.png)

Figure 2. Collection menu
Identifying words to ignore 

To ignore meaningless terms during searches, add a list of custom stop words. Stop words are words that are not useful in distinguishing the semantic meaning of the content.

In English, the, is and and are examples of stop words.

The stop words that you define are filtered out of queries and improve the relevance of natural language query results.

For example, a company has three tiers of service. The documents in one of the collections pertain to only one tier, the Silver tier. You might want to add "silver" to the stop words list because the term doesn't help to distinguish the significance of one document over another, given that all of the documents relate to the Silver service tier. When a customer mentions the Silver tier in a query string, it is ignored. Other terms in the query that are more significant are used to search the data instead. Or maybe the document collection consists of car accident reports only. You might want to add "car" to the stop words list to prevent mentions of car in queries from adding noise to the search.

Discovery applies a list of default stop words for many of the supported languages automatically. These stop words are applied both at indexing time and at query time. The predefined stop words are ignored when content is indexed and they are filtered out of queries. However, stop words that you define are used at query time only. Your list doesn't replace the default list; it augments the default list. You can add stop words, but you cannot remove stop words.

Example custom stop word list:

{
"stopwords": [
"a", "an", "the", "ibm", "what", "how", "when", "can", "should", ...
]
}



 Default stop word lists 

You can access the default stop words list for English from the [Watson Developer Cloud GitHub repository](https://github.com/watson-developer-cloud/doc-tutorial-downloads/tree/master/discovery-data/custom_stopwords_en.json).

For the following languages, Discovery uses the default stop words list that is defined by Apache Lucene.
-d '{"id":"string","hide_IBM_cloud_catalog":true,"account_filters":{"include_all":true,"category_filters":{"additionalProp1":{"include":true,"filter":{"filter_terms":["string"]}},"additionalProp2":{"include":true,"filter":{"filter_terms":["string"]}},"additionalProp3":{"include":true,"filter":{"filter_terms":["string"]}}},"id_filters":{"include":{"filter_terms":["string"]},"exclude":{"filter_terms":["string"]}}}}'

The options for {accountFilters} are: include_all, category_filters, and id_filters.

See the [Catalog Management API](https://cloud.ibm.com/apidocs/resource-catalog/private-catalog?code=curlupdate-catalog-account) for more information.





 Managing access to products for specific users by using the API 

Set filters at a private catalog level for fine-grained control of which products in the public catalog are available only to the users you choose. The following example applies the filter id_filters where AdvancedMobileAccess-d6aece47-d840-45b0-8ab9-ad15354deeea is a product ID.

curl -X 'PUT' 'https://cm.globalcatalog.cloud.ibm.com/api/v1-beta/catalogs/<catalog-id>'
From alice's command shell, run the sample program to send a message to the target queue.

amqsputc DEV.QUEUE.1 <your Queue manager name>



1. Enter the application API key for alice when prompted for a password(this is your 'apiKey' value in the file "applicationApiKeyalice.json)
2. Type the text of the message, then press Enter key twice to send only a single message.



Pressing the Enter key twice will exit the sample application.

![Image showing alice sending plain-text message](https://cloud.ibm.com/docs-content/v1/content/3a120b16db5fdc3e70d071af8a63ce5515c1b096/mqcloud/images/mqoc_app_ams_pt_putpq.png)
5. Now, check that the user bob is able to retrieve the messages from the target queue.



1. From bob's command shell, run the sample program to receive the message.

amqsgetc DEV.QUEUE.1 <your Queue manager name>
2. Enter the application API key of bob when prompted for a password(This is your 'apiKey' value in the file "applicationApiKeybob.json).
3. You can see that the message received- message <Hello> confirms that user bob is able to read the message from the target queue.
![Image showing bob receiving plain-text message](https://cloud.ibm.com/docs-content/v1/content/3a120b16db5fdc3e70d071af8a63ce5515c1b096/mqcloud/images/mqoc_app_ams_pt_getpq.png)





* Note that the message comes back in plain text while reading the queue, as this is how it is stored on the target queue.





This test confirms that alice and bob can send and receive messages on the target queue. Next step is to create the configuration required by AMS for end to end security.
For prefix filtering considerations and step-by-step instructions, see [Adding and deleting prefix filters](https://cloud.ibm.com/docs/transit-gateway?topic=transit-gateway-adding-prefix-filters).

To begin, expand the drop-down arrow in the upper right of the Prefix filtering section, and complete the following information:



1. Select the default filter that you want to use. You can either permit (default) or deny all prefixes.

The default filter is applied only if you don't create prefix filters that bypass this default setting.
2. Click Create prefix filter to open the side panel and start creating prefix filters. In turn, your prefix filters are added to an ordered list that is processed sequentially.
3. Click Save to save your changes.



9. View Terms on the right of the page.
10. Click Create to complete your order.







 Creating a transit gateway from the CLI 



 Before you begin 

Complete these prerequisites to use the Transit Gateway CLI, which is implemented as an IBM Cloud CLI plug-in.



1. Install the [IBM Cloud CLI](https://cloud.ibm.com/docs/cli?topic=cli-install-ibmcloud-cliinstall-ibmcloud-cli).
2. Install the tg-cli/tg CLI plug-in to the IBM Cloud CLI.

To install:

ibmcloud plugin install tg



If you are going to use the CLI with a Virtual Private Endpoint (VPE), you must set the following variable:

export IBMCLOUD_TG_API_ENDPOINT=private.transit.cloud.ibm.com

For more information, see [Integrating with Virtual Private Endpoint for VPC](https://cloud.ibm.com/docs/transit-gateway?topic=transit-gateway-vpe-for-ibm-cloud-transit-gateway).





 Creating a transit gateway from the CLI 

To create a transit gateway from the CLI, enter the following command:
[Log Format](https://cloud.ibm.com/docs-content/v1/content/f84e2238288b7f0355715a4c355609ce7ac48fb0/solution-tutorials/images/solution55-openshift-microservices/views-img-19.png)

Log Format







 Create a custom view to monitor logs 

You can select the events that are displayed through a view by applying a search query in the search bar, selecting values in the search area, or a combination of both. You can save that view for reuse later.



1. In the Log Analysis web UI, filter out the logs for the sample app that you have deployed in the cluster in previous steps. Click in the search bar at the bottom and enter the following query: app:patient-health-frontend.
2. Filter out log lines to display only lines that are tagged as debug lines. Add in the search bar the following query: level:debug and hit enter. The view will show lines that meet the filter and search criteria.

Zoom

![View Debug Logs](https://cloud.ibm.com/docs-content/v1/content/f84e2238288b7f0355715a4c355609ce7ac48fb0/solution-tutorials/images/solution55-openshift-microservices/views-img3.png)

View Debug Logs
3. Click Unsaved view. Select Save as new view.

Zoom

![Save View](https://cloud.ibm.com/docs-content/v1/content/f84e2238288b7f0355715a4c355609ce7ac48fb0/solution-tutorials/images/solution55-openshift-microservices/views-img-6.png)

Save View



1. Enter the name of the view. Use the following format: <Enter your user name> patientUI.
From the logging Web UI, you can view your logs as they pass through the system. You view logs by using log tailing.

With the Free service plan, you can only tail your latest logs.

For more information, see [Viewing logs](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-view_logsview_logs).





 Next steps 

The following additional features are available:



* [Filtering logs](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-view_logsview_logs_step5)
* [Searching logs](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-view_logsview_logs_step6)
* [Defining views](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-view_logsview_logs_step7)
* [Configuring alerts](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-alerts).



To use any of these features, you must upgrade the IBM Log Analysis plan to a paid plan.
ibmcloud ks logging filter create --cluster <cluster_name_or_ID> --type <log_type> --logging-configs <configs> --namespace <kubernetes_namespace> --container <container_name> --level <logging_level> --regex-message <message>
2. View the log filter that you created.

ibmcloud ks logging filter get --cluster <cluster_name_or_ID> --id <filter_ID> --show-matching-configs
3. Update the log filter that you created.

ibmcloud ks logging filter update --cluster <cluster_name_or_ID> --id <filter_ID> --type <server_type> --logging-configs <configs> --namespace <kubernetes_namespace --container <container_name> --level <logging_level> --regex-message <message>
4. Delete a log filter that you created.

ibmcloud ks logging filter rm --cluster <cluster_name_or_ID> --id <filter_ID> [--all]







 Verifying, updating, and deleting log forwarding 



 Verifying log forwarding 

You can verify that your configuration is set up correctly in 1 of 2 ways:



* To list all the logging configurations in a cluster:

ibmcloud ks logging config get --cluster <cluster_name_or_ID>
* To list the logging configurations for one type of log source:

ibmcloud ks logging config get --cluster <cluster_name_or_ID> --logsource <source>







 Updating log forwarding 

You can update a logging configuration that you already created:
Any document that exceeds the limit is truncated to 5,000 characters.



4. Click Next.
5. Select example words or phrases that fit the pattern that you want to define.

For example, if you want to define a date pattern, start highlighting date mentions in each document. If you make a mistake, hover over the selection and click the x to delete it.

Be sure to select every mention of the pattern. The model learns from what you omit as much as what you select.
6. Continue selecting examples. After you identify enough examples, Discovery shows a list of suggested examples for you to validate. Choose Yes or No for each one.

Click the Preview document icon if you want to confirm the example in context.
7. Continue highlighting examples and validating suggestions until a message is displayed to indicate that you provided enough examples.
8. Click the Review examples tab to review the lists of examples.
9. If the examples are correct, click Save pattern.



If the system cannot determine a valid pattern, the Save pattern button is never enabled. The system cannot determine a valid pattern if you choose contradictory examples to illustrate the pattern. If you cannot save your work, click the Reset button to start over. The documents are returned to their original state and any examples that were identified previously are no longer selected.

After you save the pattern, the name that you specified for the facet label is shown in the list of facets.



 When to use a pattern facet instead of a regular expression field 

Creating a pattern-based facet is similar to creating a pattern from a field that is derived by applying the Regular expression enrichment to a collection.



* If the information you want to capture follows strict formatting rules, you can use the Regular expression enrichment to find it. Apply the enrichment, and then you can use the resulting field as the source for the facet.
* If the information you want to capture can occur in different formatting styles, the pattern facet is a better choice. For example, dates or currencies can be formatted in various ways. No single regex rule can capture all of the variations.
If a value of Some data is posted with isBinaryValue set to true, the trigger payload might look something like the following example.

{
"messages": [
{
"partition": 0,
"key": null,
"offset": 421760,
"topic": "mytopic",
"value": "U29tZSBkYXRh"
}
]
}

If the same message is posted without isBinaryData set to true, the trigger payload would resemble the following example.

{
"messages": [
{
"partition": 0,
"key": null,
"offset": 421760,
"topic": "mytopic",
"value": "Some data"
}
]
}





 Messages are batched 

Notice that the trigger payload contains an array of messages. If these messages are produced to your messaging system quickly, the feed attempts to batch up the posted messages into a single firing of your trigger. Batch processing allows the messages to be posted to your trigger more rapidly and efficiently.

Keep in mind when you are coding actions that are fired by your trigger, that the number of messages in the payload is technically unbounded, but is always greater than 0. See the following example of a batched message (note the change in the offset value).

{
"messages": [
{
"partition": 0,
"key": null,
"offset": 100,
"topic": "mytopic",
"value": {
"amount": 5
}
},
{
"partition": 0,
"key": null,
"offset": 101,
"topic": "mytopic",
"value": {
"amount": 1
}
},
{
"partition": 0,
"key": null,
"offset": 102,
"topic": "mytopic",
"value": {
"amount": 999