vania-janet/multiturn-rag-retrieval-data huggingface dataset:
<-- </section "id="section-update-tls-settings-examples" "> --><-- </section "id="section-update-tls-settings" "> --><-- <section "id="section-list-cert" "> --> ibmcloud cis certificates List all certificates for a given DNS domain, including shared, dedicated and custom certificates. ibmcloud cis certificates DNS_DOMAIN_ID -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-list-cert-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-list-cert-options" "> --><-- <section "id="section-list-cert-examples" "> --> Examples List all certificates for domain 31984fea73a15b45779fa0df4ef62f9b. ibmcloud cis certificates 31984fea73a15b45779fa0df4ef62f9b -i "cis-demo"
<-- </section "id="section-list-cert-examples" "> --><-- </section "id="section-list-cert" "> --><-- <section "id="section-show-cert" "> --> ibmcloud cis certificate Get details of a given shared, dedicated, custom certificate. ibmcloud cis certificate DNS_DOMAIN_ID (--cert-id CERT_ID | --universal) -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- </section "id="section-show-range-app-examples" "> --><-- </section "id="section-show-range-app" "> --><-- <section "id="section-list-range-app" "> --> ibmcloud cis range-apps Retrieve a list of currently existing range applications for a given DNS domain (Enterprise plan only). ibmcloud cis range-apps DNS_DOMAIN_ID --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-list-range-app-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-list-range-app-options" "> --><-- <section "id="section-list-range-app-examples" "> --> Examples List all range applications in domain 31984fea73a15b45779fa0df4ef62f9b. ibmcloud cis range-apps 31984fea73a15b45779fa0df4ef62f9b -i "cis-demo"
<-- </section "id="section-list-range-app-examples" "> --><-- </section "id="section-list-range-app" "> --><-- <section "id="section-get-analytics-range-app" "> --> ibmcloud cis range-analytics Get analytics data for range applications (Enterprise plan only). ibmcloud cis range-analytics DNS_DOMAIN_ID --metrics METRICS] --dimensions DIMENSION] --filters FILTERS] --sort SORT] --since SINCE] --until UNTIL]
<-- </section "id="section-update-routing-examples" "> --><-- </section "id="section-update-routing" "> --><-- <section "id="section-display-routing-analytics" "> --> ibmcloud cis routing-analytics Get analytics of smart-routing latency. (Enterprise plans only) ibmcloud cis routing-analytics DNS_DOMAIN_ID --colos] -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! ! !
<-- <section "id="section-display-routing-analytics-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.--colos: Analytics of smart-routing latency colos.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-display-routing-analytics-options" "> --><-- <section "id="section-display-routing-analytics-examples" "> --> Examples Get analytics of smart-routing latency for domain 372e67954025e0ba6aaa6d586b9e0b60. ibmcloud cis routing-analytics 31984fea73a15b45779fa0df4ef62f9b -i "cis-demo"
<-- </section "id="section-display-routing-analytics-examples" "> --><-- </section "id="section-display-routing-analytics" "> --><-- </section "id="section-routing" "> --><-- <section "id="section-security-events" "> --> Security events (Deprecated) Security events will be deprecated on October 26, 2020. Use ibmcloud cis firewall-event-analytics]ibmcloud cis firewall-event-analytics] !
ibmcloud cis waf-package DNS_DOMAIN_ID WAF_PACKAGE_ID -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-show-waf-package-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.WAF_PACKAGE_ID: The ID of WAF package. Required.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-show-waf-package-options" "> --><-- <section "id="section-show-waf-package-examples" "> --> Examples Get detail of WAF package a25a9a7e9c00afc1fb2e0245519d725b. ibmcloud cis waf-package 31984fea73a15b45779fa0df4ef62f9b a25a9a7e9c00afc1fb2e0245519d725b -i "cis-demo"
<-- </section "id="section-show-waf-package-examples" "> --><-- </section "id="section-show-waf-package" "> --><-- <section "id="section-update-waf-owasp-package" "> --> ibmcloud cis waf-package-set Update OWASP Package setting. ibmcloud cis waf-package-set DNS_DOMAIN_ID OWASP_PACKAGE_ID --sensitivity SENSITIVITY] --action-mode ACTION_MODE] -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- </section "id="section-list-cert-examples" "> --><-- </section "id="section-list-cert" "> --><-- <section "id="section-show-cert" "> --> ibmcloud cis certificate Get details of a given shared, dedicated, custom certificate. ibmcloud cis certificate DNS_DOMAIN_ID (--cert-id CERT_ID | --universal) -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-show-cert-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.--cert-id: ID of dedicated or custom certificate.--universal: Show universal certificate details.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-show-cert-options" "> --><-- <section "id="section-show-cert-examples" "> --> Examples Show details of a given certificate details. ibmcloud cis certificate 31984fea73a15b45779fa0df4ef62f9b --universal -i "cis-demo"
ibmcloud cis certificate 31984fea73a15b45779fa0df4ef62f9b --cert-id 5a7805061c76ada191ed06f989cc3dac -i "cis-demo"
<-- </section "id="section-update-waf-override-examples" "> --><-- </section "id="section-update-waf-override" "> --><-- <section "id="section-list-waf-overrides" "> --> ibmcloud cis cis waf-overrides List all URL based Web Application Firewall (WAF) rules. ibmcloud cis waf-overrides DNS_DOMAIN_ID (--json @JSON_FILE | JSON_STRING) -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-list-waf-overrides-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.--page value: Page number of paginated results. The default value is 1.--per-page value: Number of rules per page. The default value is 50.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-list-waf-overrides-options" "> --><-- <section "id="section-list-waf-override-examples" "> --> Examples List WAF overide rules under instance cis-demo. ibmcloud cis waf-overrides 31984fea73a15b45779fa0df4ef62f9b -i "cis-demo"
ibmcloud cis certificate 31984fea73a15b45779fa0df4ef62f9b --universal -i "cis-demo"
ibmcloud cis certificate 31984fea73a15b45779fa0df4ef62f9b --cert-id 5a7805061c76ada191ed06f989cc3dac -i "cis-demo"
<-- </section "id="section-show-cert-examples" "> --><-- </section "id="section-show-cert" "> --><-- <section "id="section-order-dedicated-cert" "> --> ibmcloud cis certificate-order Order a certificate pack with an optional list of hostnames for a given DNS domain. ibmcloud cis certificate-order DNS_DOMAIN_ID --hostnames host1 --hostnames host2 ...] -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
<-- <section "id="section-order-dedicated-cert-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.--hostnames: valid host names for the certificate packs. Add up to 50 custom hostnames - May affect price.-i, --instance: Instance name or ID. If not set, the context instance specified by ibmcloud cis instance-set INSTANCE is used.--output: Specify output format, only JSON is supported.<-- </section "id="section-order-dedicated-cert-options" "> --><-- <section "id="section-order-dedicated-cert-examples" "> --> Examples Order a certificate pack for domain 31984fea73a15b45779fa0df4ef62f9b.
* [RStudio Server](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/rstudio.html): Use an integrated development environment for working with R in Watson Studio to create R Shiny applications.
* [Scheduling](https://www.ibm.com/docs/SSQNUZ_4.6.x/fixlist/sched-fixlist.html): The scheduling service offers enhancements over the default Kubernetes scheduler including Quota enforcement, Co-scheduling of pods and GPU sharing.
* [Watson Knowledge Catalog](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/wkc.html): Know your data inside and out. Ensure that your data is high quality, aligns with business objectives, and complies with regulations.
* [Watson Machine Learning](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/wml.html): Build analytical models and neural networks that are trained with your data. Then, deploy them into production at scale.
* [Watson Machine Learning Accelerator](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/wmlaccelerator.html): Deep learning platform that data scientists can use to build, train, and deploy deep learning models.
* [Watson OpenScale](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/aiopenscale.html): Infuse your AI with trust and transparency. Understand how your AI models make decisions to detect and mitigate bias.
* [Watson Query](https://www.ibm.com/docs/SSQNUZ_4.6.x/svc-welcome/dv.html): Create data sets from disparate data sources so that you can query and use the data as if it came from a single source.
You can import [character patterns](https://www.ibm.com/support/knowledgecenter/en/SS8NLW_12.0.0/com.ibm.watson.wex.ee.doc/c_ce_adm_char_ann.html) from IBM Watson Explorer oneWEX.



1. From your Discovery Content mining project, open the Content Mining application.
2. From the analysis view of your collection, click the Collections link in the breadcrumb to open the Create a collection for your analytics solutions page of the Content Mining application.
3. To create an annotator, click collection, and then select custom annotator from the list.

Zoom

![Shows the collection menu](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-collection-menu.png)

Figure 3. Collection menu
4. Click Create custom annotator.
5. Name your annotator, and then optionally add a description.
6. From the Annotator Type menu, select Regular expression, and then click Next.
7. Click the Import button.
8. Select the JSON file that you want to import, and then click Save.







 Importing facets from Watson Explorer Content Analytics Studio IBM Cloud Pak for Data 

You can import a PEAR file to use as the machine learning source file from IBM Cloud Pak for Data deployments only.

You can show Content Analytics Studio facets in the Content Mining application. Only facets with a UIMA Feature of type Literal Value are displayed.

For more information about how to import Content Analytics Studio machine learning models for use in other project types, see [Use imported ML models to find custom terms](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-ml).



1. From the Watson Explorer Content Analytics Studio, export the machine learning model that defines the facets that you want to use.
If you did not [retain the document IDs](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-migrate-to-v2migrate-to-v2-keep-docid) between the v1 and v2 collections, then you must find the v2 document ID that corresponds to the v1 document ID that is referenced in the downloaded query example.







 Transferring models 

You can reuse some of the models that you created in v1 with your v2 project.

Smart Document Understading (SDU) models
: You can import an SDU model that was built with Discovery v1 into Discovery v2. However, the performance of the model might differ between versions. Compare the results of the v1 SDU model in v2 to verify that the behavior is the same. You cannot edit the imported v1 SDU model. If the imported model can't recognize document elements that it recognized in v1 and that are important to your use case, you must re-create the SDU model in the Discovery v2 product user interface. For more information, see [Exporting SDU models](https://cloud.ibm.com/docs/discovery?topic=discovery-sduimport) in the v1 documentation and [importing the SDU model](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-configuring-fieldsimport) in the v2 documentation.

Machine learning models
: You cannot deploy models directly to Discovery v2 service instances from Knowledge Studio. Instead, you must export the machine learning models from Knowledge Studio, and then import them into Discovery. The model must have been exported from Knowledge Studio after 16 July 2020. If you have a model that was exported before that date, you must reexport the model from Knowledge Studio. Only paid Knowledge Studio plans support exporting models.

For more information, see one of the following topics:
Only facets with a UIMA Feature of type Literal Value are displayed.

For more information about how to import Content Analytics Studio machine learning models for use in other project types, see [Use imported ML models to find custom terms](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-ml).



1. From the Watson Explorer Content Analytics Studio, export the machine learning model that defines the facets that you want to use. The model file must have a .pear extension.
2. In the export configuration, remove the facet path, but keep the subfacet value. Set the Index Field name to the Facet Tree Path in Content Analytics Studio.

For more information, see [Creating Custom PEAR Files for Use with Lexical Analysis Streams](https://www.ibm.com/docs/en/watson-explorer/12.0.x?topic=las-creating-custom-pear-files-use-lexical-analysis-streams).
3. From your Discovery Content mining project, open the Content Mining application.
4. From the analysis view of your collection, click the Collections link in the breadcrumb to open the Create a collection for your analytics solutions page of the Content Mining application.
5. To create an annotator, click collection, and then select custom annotator from the list.

Zoom

![Shows the collection menu](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/cm-collection-menu.png)

Figure 4. Collection menu
6. Click Create custom annotator.
7. Name your annotator, and then optionally add a description.
8. From the Annotator Type menu, select PEAR File, and then click Next.
9. Click Select file to find the .pear file that you exported.
10. Specify a facet path, and then click Save.
A typical example is the processing of image data in a Object Storage bucket or serving machine learning models. Use [applications](https://cloud.ibm.com/docs/codeengine?topic=codeengine-application-workloads) or [jobs](https://cloud.ibm.com/docs/codeengine?topic=codeengine-job-plan).

Does your computation run for a long time?
: If the computation runs for longer durations, jobs are the better choice because of their asynchronous nature. The maximum duration of applications is always limited because maintaining an open connection at scale is expensive. Typical workloads are training machine learning models or hyper-parameter optimization. Use [jobs](https://cloud.ibm.com/docs/codeengine?topic=codeengine-job-plan).

Can you specify the concurrency of your computation upfront?
: If you know how much computation you need to perform, you can run a job with the exact number of instances until it completes. Typical examples are hyper-parameter tuning or training a neural network. Use [jobs](https://cloud.ibm.com/docs/codeengine?topic=codeengine-job-plan).

Does your workload react to some event?
: If your workload is required to react to an event, such as a Git commit that is pushed to your repository, an object that is uploaded into a Object Storage bucket, or a document that is modified within your database, use applications. Applications provide an endpoint that can be configured to receive events from the event source. Use [applications](https://cloud.ibm.com/docs/codeengine?topic=codeengine-application-workloads).

Do you need to process a large amount of data in a short time in response to events or requests?
: If your workload requires a fast response to unpredicted requests or events, then applications are typically a better fit because applications are scaled dynamically, even from zero. Use [applications](https://cloud.ibm.com/docs/codeengine?topic=codeengine-application-workloads).
Knowledge Studio model build and deployment paths 

In IBM Watson® Knowledge Studio, there are three types of models that can be produced and deployed: machine Learning, rule-based, and advanced rules.

The table below shows supported model deployment paths From Knowledge Studio source To target services.



Table 1. Model deployment paths

 From: Public Cloud - Knowledge Studio   To: IBM Cloud Pak for Data - IBM Watson® Discovery   To: Public Cloud - IBM Watson® Discovery   To: Public Cloud - IBM Watson® Natural Language Understanding  

 Machine learning                        Upload via Discovery UI                              Upload via UI                              Deploy via Knowledge Studio UI                                 
 Rule-based                              Upload via Discovery UI                              Upload via UI                              Deploy via Knowledge Studio UI                                 
 Advanced rules                          Upload via Discovery UI                              Upload via UI                              Deploy via API ^1^                                             



^1^ Deploying an advanced rules model to Natural Language Understanding is deprecated. As of June 10, 2021, you cannot deploy advanced rules models to Natural Language Understanding.
Targeting definition rules per feature <br><br> * <br><br><br> <br><br> * <br><br><br> 50 
 Delivery mode Websocket (server)pull or get (client) Websocket (server) pull or get(client) Websocket(server)pull or get (client) 
 Role-based access Env-level Env-level Env-level 
 Locations London, Dallas, Washington DC, Sydney London, Dallas, Washington DC, Sydney London, Dallas, Washington DC, Sydney 
 HA Regional Regional Regional 
 Security End-to-end encryption RBAC End-to-end encryption RBAC End-to-end encryption RBAC 
 Monitoring IBM Cloud Monitoring IBM Cloud Monitoring IBM Cloud Monitoring 
 Audit IBM Cloud Activity Tracker IBM Cloud Activity Tracker IBM Cloud Activity Tracker 
 Support per your IBM Cloud support plan per your IBM Cloud support plan per your IBM Cloud support plan 
 Percentage rollout Supported Not Supported Supported 
 Snapshots Not Supported Not Supported Supported 
 KMS integration (BYOK and KYOK) Not Supported Not Supported Supported 



See the App Configuration catalog page for current pricing.
* How do I audit App Configuration activity?

If you need strict governance and accountability within your App Configuration instance, create an instance of IBM Cloud Activity Tracker from the [Observability](https://cloud.ibm.com/observe) section of the IBM Cloud console. Use that to record and audit App Configuration activity.
* How do I archive App Configuration activity data?

If you would like to retain a long-term record of activity within your App Configuration instance, either for audit purposes or for post-processing and data analysis, including application of machine learning models, create an instance of IBM Cloud Activity Tracker from the [Observability](https://cloud.ibm.com/observe) section of the IBM Cloud console. Then archive events from an IBM Cloud Activity Tracker instance into a bucket in an IBM Cloud Object Storage (COS) instance. [Learn more](https://cloud.ibm.com/docs/activity-tracker?topic=activity-tracker-archiving).
* In what regions is App Configuration available?
See also [block](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2000384).





 globally unique identifier (GUID) 

An algorithmically determined number that uniquely identifies an entity within a system.





 gossip 

A method of sharing network information among peers in which each peer forwards messages to a random selection of the current peers in the network.





 Gossip Data Dissemination Protocol 

A protocol for secure, reliable, and scalable communication of information in an network by passing messages among peers.





 ground truth 

The set of vetted data, consisting of annotations added by human annotators, that is used to adapt a machine learning model to a particular domain. Ground truth is used to train machine learning models, measure model performance (precision and recall), and calculate headroom to decide where to focus development efforts for improving performance. Accuracy of ground truth is essential since inaccuracies in the ground truth will correlate to inaccuracies in the components that use it.





 GUID 

See [globally unique identifier](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2390455).







 H 



 HA 

See [high availability](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2284708).





 hardware security module (HSM) 

A physical appliance that provides on-demand encryption, key management, and key storage as a managed service.





 hardware virtual machine mode (HVM) 

Hardware-assisted full virtualization. A virtual machine uses resources from the host computer to operate as a complete hardware environment. The host operating system is unaware of the virtual client.





 Hash-Based Message Authentication Code (HMAC) 

A cryptographic code that uses a cryptic hash function and a secret key.





 headroom analysis 

The process of determining how much improvement in accuracy, precision, or recall can be expected by addressing some class of problems that are identified while performing accuracy analysis.





 health check 

A process that monitors system resources and conditions to determine whether the system is running efficiently.
Knowledge Studio model build and deployment paths 

In IBM Watson™ Knowledge Studio, there are three types of models that can be produced and deployed: machine Learning, rule-based, and advanced rules.

The table below shows supported model deployment paths From Knowledge Studio source To target services.



Table 1. Model deployment paths

 From: IBM Cloud Pak for Data - Knowledge Studio   To: IBM Cloud Pak for Data - IBM Watson™ Discovery   To: Public Cloud - IBM Watson™ Discovery   To: Public Cloud - IBM Watson™ Natural Language Understanding  

 Machine learning                                  Upload via Discovery UI                              N/A                                        N/A                                                            
 Rule-based                                        Upload via Discovery UI                              N/A                                        N/A                                                            
 Advanced rules                                    Upload via Discovery UI                              N/A                                        Deploy via Knowledge Studio ^1^                                



^1^ See [Deploy advanced rules model to Natural Language Understanding](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-managing-projects-and-extractorsdeploy-adv-rule-model-to-nlu-cloud)