vania-janet/multiturn-rag-retrieval-data huggingface dataset:
You can choose any of the following options to deploy your own code to Continuous Delivery:



* Create a toolchain by using one of the available templates (dependent on the deployment target and tool integrations). On the Create a Toolchain page, select the appropriate provider for your source repository, and then specify the link to your source code repo. After you create your toolchain, you might need to adjust the pipeline scripts for your deployment goals.
* Create an empty toolchain, and then add tool integrations to deploy your app. For more information about using this method to deploy your code to Continuous Delivery, see [Continuous Deployment to Kubernetes](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-continuous-deployment-to-kubernetes).



* How do I find the status of IBM Cloud and the Continuous Delivery service?

Check the IBM Cloud Status page to determine whether known issues are affecting the IBM Cloud platform and the major services in IBM Cloud.

You can find the Status page by choosing either of the following options:



* In the IBM Cloud console, go to Support. From the Today's notifications widget, click View all, and then click Status to view the status of resources in all IBM Cloud locations. You can view a list of events, in chronological order, for maintenance and incidents. You can search across all notifications, or filter by type, geographic locations, and individual resources. You can also view planned maintenance windows for which prior notice is provided and unplanned incidents or outages, which are posted as soon as the IBM Cloud team becomes aware of them. Incident notifications are regularly updated until they're resolved.
* Access it directly at [IBM Cloud - System Status](https://cloud.ibm.com/status).



For more information about the IBM Cloud Status page, see [Viewing IBM Cloud status](https://cloud.ibm.com/docs/get-support?topic=get-support-viewing-cloud-statusviewing-cloud-status).
* How do I remove authorized users from the Continuous Delivery service?

You can remove authorized users from the Continuous Delivery service and prevent them from being added again.
Performance 

IBM Cloud® Messages for RabbitMQ deployments can be both manually [scaled to your usage](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-resources-scaling), or configured to [autoscale](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-autoscaling) under certain resource conditions. When you are tuning the performance of your deployment, consider a few factors.



 Monitoring your deployment 

Messages for RabbitMQ deployments offer an integration with the [IBM Cloud® Monitoring service](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-monitoring) for basic monitoring of resource usage on your deployment. Many of the available metrics, like disk usage and IOPS, are presented to help you configure [autoscaling](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-autoscaling) on your deployment. Observing trends in your usage and configuring the autoscaling to respond to them can help alleviate performance problems before your databases become unstable due to resource exhaustion.





 RabbitMQ Memory Usage 

[RabbitMQ provides a robust breakdown of memory usage](https://www.rabbitmq.com/memory-use.htmlbreakdown), which can provide you information on how memory resources are allocated and being used in your deployment. Most notably, connections, queue mirrors, and accumulated messages all use memory. If your use-case calls for many open connections at a time, you might want to look into increasing memory. Likewise, if you have queues that contain only transient messages that don't need replication, you can bring down memory usage by adjusting their [mirroring policy](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-high-availabilityhigh-availability-queue-configuration).
This example illustrates the response that a prefix filter with the specified ID could not be found:

{
"errors": [
{
"code": "not_found",
"message": "Cannot find Prefix Filter",
"more_info": "https://cloud.ibm.com/apidocs/transit-gatewayerror-handling"
}
],
"trace": "request_id"
}

For more information (including Java, Node, Python and Go examples), see "Remove prefix filter from Transit Gateway Connection" in the [Transit Gateway API reference](https://cloud.ibm.com/apidocs/transit-gatewaydelete-transit-gateway-connection-prefix-filter).









 Working with prefix filters using Terraform 

Review the following argument references that you can specify for your resource when adding or deleting a prefix filter:



Table 9. Arguments when adding or deleting a prefix filter using Terraform

 Argument Details 

 gateway <br>Required <br>String The unique identifier of the gateway. 
 connection_id <br>Required <br>String The unique identifier of the gateway connection. 
 action <br>Required <br>String Whether to permit or deny any matching prefix. 
 prefix <br>Required <br>String The IP prefix. 
 before <br>Optional <br>String The identifier of the prefix filter to place this filter in front of. When a filter references another filter in it's before field, then the filter making the reference is applied before the referenced filter. <br>For example, if filter A references filter B in its before field, A is applied before B. 
 ge <br>Optional <br>Integer The IP prefix GE. The GE (greater than or equal to) value sets the minimum prefix length on which the filter action is applied. 
 le <br>Optional <br>Integer The IP prefix LE. The LE (less than or equal to) value sets the maximum prefix length on which the filter action is applied. 





 Example 

This example shows how to add a prefix filter:
The application deployed in the previous steps allows you to log a message at a chosen log level. The available log levels are critical, error, warn, info and debug. The application's logging infrastructure is configured to allow only log entries on or above a set level to pass. Initially, the logger level is set to warn. Thus, a message logged at info with a server setting of warn would not show up in the diagnostic output.

Take a look at the code in the file [views.py](https://github.com/IBM-Cloud/application-log-analysis/blob/master/app/views.py). The code contains print statements as well as calls to logger functions. Printed messages are written to the stdout stream (regular output, application console / terminal), logger messages appear in the stderr stream (error log).



1. Open the web app at http://$MYINGRESSSUBDOMAIN/ and click on the Logging tab.
2. Generate several log entries by submitting messages at different levels. The UI allows to change the logger setting for the server log level as well. Change the server-side log level in-between to make it more interesting. For example, you can log a "500 internal server error" as an error or "This is my first log entry" as an info.







 Access application logs 

You can access the application specific log in the Log Analysis UI using the filters.



1. On the top bar, click on Apps.
2. Under containers, check app-log-analysis. A new unsaved view is shown with application logs of all levels. You can also type app:app-log-analysis in the Search... field.
3. To see logs of specific log level(s), Click on Levels and select multiple levels like Error, info, warning etc.,









 Step 5: Search and filter logs 

The Log Analysis UI, by default, shows all available log entries(Everything). Most recent entries are shown on the bottom through an automatic refresh.
Backups with Messages for RabbitMQ 

The IBM Cloud® Messages for RabbitMQ backups do not contain the actual messages. Your Messages for RabbitMQ deployment backups contain only configuration data.

For more information on backing up your Messages for RabbitMQ service configurations, see the [Managing backups](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=cloud-databases-dashboard-backups) section of the IBM Cloud® documentation.

Review the [management responsibilities and terms and conditions](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=cloud-databases-responsibilities-cloud-databases) that you have when you use Messages for RabbitMQ service.



  Concepts and suggestions 

The expected operation for a Messages for RabbitMQ deployment is to keep queues short: messages are written and read in a short cycle with focus on throughput. Messages for RabbitMQ deployments are not intended as a data store like other IBM Cloud® offerings.

For message delivery with durability and consistent quality of service, you must use [quorum queues](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-high-availabilityquorum-queues).

The [Shovel plug-in](https://www.rabbitmq.com/shovel.html) can also help move messages across instances. While not a backup mechanism, this plug-in can aid in message retention.

Remember, messages are not part of the Messages for RabbitMQ deployment backups regardless of the queue types or plug-ins implemented.

More information on IBM Cloud® can be found in the [high availability and disaster recovery](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=cloud-databases-ha-dr) section.
AU-10 - Non-repudiation 



  Control requirements 

AU-10 - 0
:   The information system protects against an individual (or process acting on behalf of an individual) falsely denying having performed [IBM Assignment: minimum actions including the addition, modification, deletion, approval, sending, or receiving of data].





  Implementation guidance 

See the resources that follow to learn more about how to implement this control.



*  [Audit logging of application provider events and SIEM](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-shared-logging-audit-provider)
*  [Audit logging of IBM Cloud events](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-shared-logging-audit)
*  [Connecting application provider to the management VPC](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-vpc-architecture-connectivity-management)
*  [IBM Cloud account setup](https://cloud.ibm.com/docs/framework-financial-services?topic=framework-financial-services-shared-account-setup)







  NIST supplemental guidance 

Types of individual actions covered by non-repudiation include, for example, creating information, sending and receiving messages, approving information (e.g., indicating concurrence or signing a contract). Non-repudiation protects individuals against later claims by: (i) authors of not having authored particular documents; (ii) senders of not having transmitted messages; (iii) receivers of not having received messages; or (iv) signatories of not having signed documents. Non-repudiation services can be used to determine if information originated from a particular individual, or if an individual took specific actions (e.g., sending an email, signing a contract, approving a procurement request) or received specific information. Organizations obtain non-repudiation services by employing various techniques or mechanisms (e.g., digital signatures, digital message receipts).
If your use-case calls for many open connections at a time, you might want to look into increasing memory. Likewise, if you have queues that contain only transient messages that don't need replication, you can bring down memory usage by adjusting their [mirroring policy](https://cloud.ibm.com/docs/messages-for-rabbitmq?topic=messages-for-rabbitmq-high-availabilityhigh-availability-queue-configuration). Changes to mirroring policy, where messages have fewer or no mirrors, can cause messages to be deleted on node restarts and those messages are gone forever.

Occasionally, RabbitMQ can experience memory spikes. Specifically, with Messages for RabbitMQ deployments, updates and maintenance where we restart or delete a node causes memory usage to increase to resync the restarted or new node. If your RabbitMQ consistently uses a high percentage of its available memory, one of these spikes can run your deployment out of memory and cause it to crash. It is a good idea to scale your memory so that it can accommodate resyncing a node.



 RabbitMQ Memory Alarms 

By default, when the RabbitMQ server uses above 40% of the available RAM, it raises a memory alarm and blocks incoming messages from publishers. The memory alarm is cleared if consumers use enough of the messages or the messages are moved to disk. Once the memory alarm is cleared normal service resumes, and the publishers are unblocked. Note that this does not prevent the RabbitMQ server from using more than 40% of the allocated memory, it is merely the point at which publishers are throttled. For more information, see [RabbitMQ documentation](https://www.rabbitmq.com/memory.html).







 RabbitMQ Disk Alarms 

By default, when the RabbitMQ server detects that free disk space has dropped below a certain threshold, it raises a disk alarm. The threshold for Messages for RabbitMQ is 80% of your deployment's disk size. The alarm blocks incoming messages from publishers and prevents messages in memory from being written to disk. The alarm is cluster-wide so if disk space on one node gets too low, the alarm blocks on all nodes.
If you use the @sys-currency entity as a node condition and the user specifies $0 as the value, the value is recognized as a currency properly, but the condition is evaluated to the number zero, not the currency zero. As a result, the null in the condition is evaluated to false and the node is not processed. To check for currency values in a way that handles zeros properly, use the expression @sys-currency OR @sys-currency == 0 in the node condition instead.

For more information about currencies that are recognized per language, see [Currency support](https://cloud.ibm.com/docs/assistant?topic=assistant-sys-currency-details).







 @sys-date 

The @sys-date system entity detects mentions of dates in user input. The date value is stored as a string in the format yyyy-MM-dd. For example, the mention May 8 is stored as "2020-05-08". The system augments missing elements of a date (such as the year for "May 8") with the current date values.

If you select English as the skill language, the system uses US English (en-us) as the locale. For the US English locale only, the format of the date is MM/DD/YYYY. The format of the date changes to DD/MM/YYYY only if the first two numbers are greater than 12. The value that is stored has the format yyyy-MM-dd.



 Recognized formats 



* Friday
* today
* May 8







 Attributes 



* .alternatives: Saves alternative date values when the input is open to interpretation. Typically the future date is returned as the @sys-date value. If there is ambiguity, the past date is saved as an alternative date. For example, for the input in January, a future date is returned. However, if it's February (January passed for this year), then the past January is returned also as an alternative date. An alternative date is also provided for weekdays when the weekday is specified without a modifier.
[More icon](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/kabob.png) icon on the folder, and then select Add node to folder.

The dialog node is added to the end of the dialog tree within the folder.









 Deleting a folder 

You can delete either a folder alone or the folder and all of the dialog nodes in it.

To delete a folder, complete the following steps:



1. From the tree view of the Dialog tab, find the folder that you want to delete.
2. Click the More![More icon](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/kabob.png) icon on the folder, and then select Delete.
3. Do one of the following things:



* To delete the folder only, and keep the dialog nodes that are in the folder, deselect the Delete the nodes inside the folder checkbox, and then click Yes, delete it.
* To delete the folder and all of the dialog nodes in it, click Yes, delete it.





If you deleted the folder only, then the nodes that were in the folder are displayed in the dialog tree in the spot where the folder used to be.







 Dialog node limits 

The number of dialog nodes you can create per skill is 100,000.

The welcome and anything_else dialog nodes that are prepopulated in the tree do count toward the total.

Tree depth limit: The dialog supports 2,000 dialog node descendants; the dialog performs best with 20 or fewer.

You can see the number of dialog nodes in a dialog skill from the assistant tile. If the skill is not associated with an assistant already, add the dialog skill to an assistant, and then view the skill tile from the main page of the assistant.
If you use the @sys-currency entity as a node condition and the user specifies $0 as the value, the value is recognized as a currency properly, but the condition is evaluated to the number zero, not the currency zero. As a result, the null in the condition is evaluated to false and the node is not processed. To check for currency values in a way that handles zeros properly, use the expression @sys-currency OR @sys-currency == 0 in the node condition instead.







 @sys-date 

The @sys-date system entity detects mentions of dates in user input. The date value is stored as a string in the format yyyy-MM-dd. For example, the mention May 8 is stored as "2020-05-08". The system augments missing elements of a date (such as the year for "May 8") with the current date values.

If you select English as the skill language, the system uses US English (en-us) as the locale. For the US English locale only, the format of the date is MM/DD/YYYY. The format of the date changes to DD/MM/YYYY only if the first two numbers are greater than 12. The value that is stored has the format yyyy-MM-dd.



 Recognized formats 



* Friday
* today
* May 8







 Attributes 



* .alternatives: Saves alternative date values when the input is open to interpretation. Typically the future date is returned as the @sys-date value. If there is ambiguity, the past date is saved as an alternative date. For example, for the input in January, a future date is returned. However, if it's February (January passed for this year), then the past January is returned also as an alternative date. An alternative date is also provided for weekdays when the weekday is specified without a modifier. For example, for the input Are you open Wednesday?, the future date is stored, but an alternative date with the date of the previous Wednesday is created also. Alternative dates are created for phrases that contain modifiers such as next, this, or last, which can have different meanings in different locales.
Numeric system entities upgrade available in Dallas
: The new system entities are now also available as a beta feature for instances that are hosted in Dallas. See [New system entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entities)





 12 June 2019 

Numeric system entities upgrade
: New system entities are available as a beta feature that you can enable in dialog skills that are written in English or German. The revised system entities offer better date and time understanding. They can recognize date and number spans, national holiday references, and classify mentions with more precision. For example, a date such as May 15 is recognized as a date mention(@sys-date:2019-05-15), and is not also identified as a number mention (@sys-number:15). See [New system entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entities)

A Plus Trial plan is available
: You can use the free Plus Trial plan to try out the features of the Plus plan as you make a purchasing decision. The trial lasts for 30 days. After the trial period ends, if you do not upgrade to a Plus plan, your Plus Trial instance is converted to a Lite plan instance.





 23 May 2019 

Updated navigation
: The home page was removed, and the order of the Assistants and Skills tabs was reversed. The new tab order encourages you to start your development work by creating an assistant, and then a skill.

Disambiguation settings have moved
: The toggle to enable disamibugation, which is a feature that is available to Plus and Premium plan users only, has moved. The Settings button was removed from the Dialog page. You can now enable disambiguation and configure it from the skill's Options tab.

An introductory tour is now available
: A short product tour is now displayed when a new service instance is created. Brand new users are also given help as they start development. A new assistant is created for them automatically.
: If you integrate with Watson Knowledge Studio, you must still use the AlchemyLanguage enrichment configuration. For details, see [Integrating with IBM Watson™ Knowledge Studio](https://cloud.ibm.com/docs/discovery?topic=discovery-integrating-with-wks).

Update to version string : The version string for all API calls changed to 2017-07-19 from 2017-06-25. This version enables an NLU default config on collection creation. You should still be able to enrich with AlchemyLanguage in previous versions. : The default configuration is updated to use Natural Language Understanding. To avoid conflicts and possible errors, it is recommended that you update the version string as soon as possible.

Known issue for Insight Cards : The Insight Cards for collections with AlchemyLanguage enrichments do not update automatically any longer. You must migrate your collection to Natural Language Understanding Enrichments for the insight cards to update. : If you created a collection prior to 18 July, 2017 and applied the Default Configuration, that collection was enriched with the AlchemyLanguage enrichments. If you apply the Default Configuration to a collection after this date, the Natural Language Understanding enrichments are used. The configuration name switches to Default Configuration with NLU in the tooling. Because AlchemyLanguage enrichments are being deprecated, it is recommended that they not be used with new collections.





 30 June 2017 

Update to entity normalization : The entity normalization capability introduced as a beta feature on 5 May 2017 moved to GA status.





 23 June 2017 

Update to version string : The version string for all API calls changed to 2017-06-25 from 2016-12-01. : The new version string enables enrichments in German (de) or Spanish (es) if the language of a collection is set to one of those languages. Previously, all enrichments were performed in English regardless of a collection's language setting. : If you do not use enrichments in non-English languages, you can continue to use the 2016-12-01 version string. However, to avoid potential future conflicts, it is recommended that you update the version string as soon as possible.