vania-janet/multiturn-rag-retrieval-data huggingface dataset:
You can filter the view by the zones and policies, types of findings and even by individual requirements or controls. The view can also be filtered to show failing controls by severity (high, medium, and low).





 Reviewing the control plane 

From the details page you can click on a requirement or control to see the sub-requirements and controls that make up the requirement or control. Each items indicates if it is passing or failing. If even one sub-requirement or control is failing, the overall requirement or control is marked as failing.

To see the details of a subsection, click on the row. The details for that control including the number and types of resources that are passing, failing, or have temporary accepted exceptions is displayed.

The resources can be filtered by the following:

Kubernetes identity
: Valid filters are cluster, name, and namespaceType. An example namespaceType would be a Group, ServiceAccount, or User.

Kubernetes resource
: Valid filters are cluster, labels, name, namespace, and Type. An example Type would be Deplyment, Daemonset, StatefulSet, ReplicaSet, Pod, or ChronJobHost.

Kubernetes, Linux, and Docker hosts
: Valid filters are cluster, name, OS (operating system in inventory), OSImage (operating system doesn't exist in inventory).

You can also filter the view by failing control severity (high, medium, and low)







 Next steps 

The next steps are to [evaluate and remediate](https://cloud.ibm.com/docs/workload-protection?topic=workload-protection-evaluate-remediate) the findings.
: Values appropriate to an [IBM Cloud service that generates metrics managed through [IBM Cloud Metrics Routing](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-cloud-services-mr)

For example, to define an inclusion filter that defines the condition where only metrics that are generated in the us-south region are routed, looks as follows:

{"operand": "location","operator": "is","values": "us-south"}





 Step 3: Configure the route 

Run the following command to route metrics from us-south to the target identified in previous steps.

ibmcloud metrics-router route create --name new-route --rules '[{"action": "send", "targets":{"id":"11111111-1111-1111-1111-1111111111111"}], "inclusion_filters":{"operand": "location","operator": "is","values": "us-south"}]}]'

Where targets represents the list of target IDs and inclusion_filters specifies the filters.
Kubernetes v1.14.9 v1.14.10 See the [Kubernetes release notes](https://github.com/kubernetes/kubernetes/releases/tag/v1.14.10). 
 Kubernetes Metrics Server N/A N/A Increased the metrics-server container's base CPU and memory to improve availability of the metrics server API service. 
 Load balancer and load balancer monitor for IBM Cloud Provider 159 169 Updated image for [CVE-2019-1551](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-1551). 
 OpenVPN server 2.4.6-r3-IKS-121 2.4.6-r3-IKS-131 Updated image for [CVE-2019-1551](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-1551).
{
"text": "Georgia",
"sentiment": {
"score": 0
},
"relevance": 0.306485
}
]
}
}
Show more

In the preceding example, you could query the keyword text by accessing enriched_text.keywords.text

sentiment is calculated for keywords even if the sentiment enrichment is not selected. To learn more about sentiment scoring, see [Sentiment analysis](https://cloud.ibm.com/docs/discovery?topic=discovery-configservicesentiment-analysis).

The relevance score ranges from 0.0 to 1.0. The higher the score, the more relevant the keyword.





 Category classification 

Categorizes input text, HTML, or web-based content into a hierarchical taxonomy up to five levels deep. Deeper levels allow you to classify content into more accurate and useful subsegments.

Example portion of a document enriched with Category Classification:

{
"text": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"enriched_text": {
"categories": [
{
"score": 0.361614,
"label": "/business and industrial"
},
{
"score": 0.329377,
"label": "/business and industrial/company/merger and acquisition"
},
{
"score": 0.154254,
"label": "/business and industrial/business operations/business plans"
}
]
Show more

In the preceding example, you could query the category label by accessing enriched_text.categories.label

The label is the detected category. The hierarchy levels are separated by forward slashes. The score for that category ranges from 0.0 to 1.0. The higher the score, the greater the confidence in that category.





 Concept tagging 

Identifies concepts with which the input text is associated, based on other concepts and entities that are present in that text.
(https://cloud.ibm.com/docs-content/v1/content/a6478671e8f845a84102d9cf49bf28ee7f4f3227/security-compliance/images/results-overview-tab.svg)<br><br>Figure 2. Example overview tab results<br><br>Success rate: The rate at which your configurations pass the evaluation that is conducted. Total controls: The total number of controls that were evaluated during this scan. Drift: The difference in results for your selected evaluation timeframe. 
 Controls On the Controls tab, you are provided with an overview of the controls that were evaluated. The controls and their compliance status are listed for the time that the scan was done. 
 Resources On the Resources tab, you are provided with the results for each specific resource that was evaluated.<br><br>Zoom<br><br>![A visual representation of detailed results that are returned when an evaluation is run.](https://cloud.ibm.com/docs-content/v1/content/a6478671e8f845a84102d9cf49bf28ee7f4f3227/security-compliance/images/results-resources-tab.svg)<br><br>Figure 3. Example results tab results<br><br>In the JSON tab, you can see the assessment definition. In the Parameters tab, you can see the parameters that are relevant to that resource. In the Non-compliant properties tab, you are able to view which properties are non-compliant to begin remediating any issues that are found. In the Controls tab, you are able to view which controls that the assessment is associated with. 





 Understanding statuses 

When you view results in Security and Compliance Center, each evaluation produces a result of pass, fail, unable to perform, or user_evaluation_required. Check out the following table to learn more about what each result means.



Table 2. Understanding result statuses

 Result Description 

 Pass Your resource was compliant with the defined standard. 
 Fail Your resource was not compliant with the defined standard.
Norwegian (Bokmal) 

Please note that Natural Language Understanding considers Norwegian (standard language code no) as equivalent to Norwegian-Bokmal (standard language code nb), and uses the same Norwegian-Bokmal model for both language codes.



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Relevance ranking is not supported.





 Norwegian (Nyorsk) 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Relevance ranking is not supported.





 Polish 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X 



* Relevance ranking is not supported.





 Portuguese 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories X 
 Classifications X 
 Concepts X 
 Emotion 
 Entities X X 
 Keywords X 
 Metadata X 
 Relations X X 
 Semantic roles 
 Sentiment X 
 Syntax X 







 Romanian 



 Feature Standard Support [Custom model](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizing) support 

 Categories 
 Classifications 
 Concepts 
 Emotion 
 Entities X* 
 Keywords X 
 Metadata 
 Relations 
 Semantic roles 
 Sentiment X 
 Syntax X
* CLI
* API







 Defining routing rules 

To define a routing rule, you must specify 1 or more targets as the destinations for metrics. You can also define 1 or more inclusion filters that define the conditions of how those metrics are routed to those destinations.

For each route that you define in the account, you can configure up to 4 rules. The rules specify what metrics are routed in a region and where to route them. For more information, see [Understanding how routes work in your account](https://cloud.ibm.com/docs/metrics-router?topic=metrics-router-routes&interface=cliroute_behaviour).

A rule consists of 1 action, 1 or more targets, and 0 or more inclusion filters.



 Targets 

Targets define the list of target IDs where the metrics are routed.



* You can specify up to three target IDs per rule.
* You can define target IDs for resources that are available in the same region where you are configuring the route, in a different region, and in a different account.



For example, you can define a list of targets as follows:

"targets": [{"id":"11111111-1111-1111-1111-111111111111"},{"id":"22222222-2222-2222-2222-222222222222"}]

Targets must be IBM Cloud Monitoring instances.





 Action 

Action defines whether IBM Cloud® Metrics Routing includes or excludes metrics on the route. Two actions are supported: send and drop. If not specified, the default action is to send the metrics.

send
: Metrics are sent, based on the routing rule, on the defined route.

drop
: Metrics are excluded, based on the routing rule, when metrics are sent on the defined route.





 Inclusion filters 

Inclusion filters define the conditions that are used to determine which metrics are routed to the targets specified in the rule.

To route all metrics, exclude the inclusion_filters definition when you configure a route.

Inclusion filters are composed of an operand, operator, and value:
Now, enhanced irrelevant detection uses any provided counterexamples in training. This change does not affect workspaces without counterexamples. This update applies to the following languages: English, French, Spanish, Italian, and the universal language model. For more information, see [Defining what's irrelevant](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-irrelevance-detection).

Fuzzy matching updates
: Interactions between the stemming and misspelling fuzzy matching features are not allowed. Improve fuzzy matching behavior by limiting the interactions between different fuzzy matching features. This change applies to the following languages: English, French, German, and Czech. For more information, see [How fuzzy matching works](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-entitiesentities-fuzzy-matching).





 23 February 2022 

IBM Watson® Assistant Cartridge for IBM Cloud Pak® for Data 4.0.6 is available
: Watson Assistant for IBM Cloud Pak® for Data 4.0.6 is compatible with IBM Cloud Pak® for Data 4.0 on Red Hat OpenShift 4.8. This release of Watson Assistant for IBM Cloud Pak® for Data includes various fixes and features.

New API version
: The current API version is now 2021-11-27. This version introduces the following changes:



* The output.text object is no longer returned in message responses. All responses, including text responses, are returned only in the output.generic array.



Configure webhook timeout
: From the Pre-message webhook and Post-message webhook configuration pages, you can configure the webhook timeout length from a minimum of 1 second to a maximum of 30 seconds. For more information, see [Webhook overview](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-webhook-overview).

Rich response types
: Your assistant can now send responses that include elements such as audio, video, or embedded iframe content.
If the model contains a mix of valid and invalid audio resources, training of the model fails unless you set the strict query parameter to false. For more information, see [Training failures](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticfailedTraining-acoustic).
* training indicates that the model is being trained on audio data.
* available indicates that the model is trained and ready to use with recognition requests.
* upgrading indicates that the model is being upgraded.
* failed indicates that training of the model failed. Examine the model's audio resources to determine what prevented the model from being trained. Possible errors include not enough audio, too much audio, or an invalid audio resource.



Additionally, the output includes a progress field that indicates the current progress of the custom model's training. If you used the POST /v1/acoustic_customizations/{customization_id}/train method to start training the model, this field indicates the current progress of that request as a percentage complete. At this time, the value of the field is 100 if the status is available; otherwise, it is 0.

When you monitor the training or upgrading of a custom model, poll the value of the status field, not the value of the progress field. If the operation fails for any reason, the value of the status field changes to reflect the failure; the value of the progress field remains 0.



 List all custom acoustic models example 

The following example includes the language query parameter to list all US English custom acoustic models that are owned by the specified credentials:

IBM Cloud

curl -X GET -u "apikey:{apikey}" "{url}/v1/acoustic_customizations?language=en-US"

IBM Cloud Pak for Data

curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/acoustic_customizations?language=en-US"

The credentials own two such models. The first model is awaiting data or is being processed by the service. The second model is fully trained and ready for use.

{
"customizations": [
{
A measure of the correctness of the decisions and predictions that are made by machine learning models, which are often part of AI systems. See also [precision](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2003831), [recall](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2154357).





 accuracy analysis 

Analyzing machine learning model scores to determine whether changes are needed to improve accuracy.





 action 



* A code snippet that can be explicitly invoked, or run in response to an event. See also [feed](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx3129185), [invoke](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx2057232).
* A task that is performed in the context of a service.







 adjudication 

An iterative process for resolving annotation conflicts by comparing the annotations added to the same document by different human annotators.





 affinity 

Two or more container group instances running on the same network node. See also [anti-affinity](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx8888040).





 allowlist 

A list of items, such as usernames, email addresses, or IP addresses, that are granted access to a certain system or function. When an allowlist is used for access control, all entities are denied access, except for those that are included in the allowlist. See also [blocklist](https://cloud.ibm.com/docs/overview?topic=overview-glossaryx3954055).





 analysis engine 

A program that analyzes artifacts, such as documents, and infers information about them, and which implements the UIMA Analysis Engine interface specification. Analysis engines are constructed from building blocks called annotators. An analysis engine can contain a single annotator, which is referred to as a primitive analysis engine, or multiple annotators, which is referred to as an aggregate analysis engine.
In this example, the eCommerce_Cancel_Product_Order intent has a close confidence score of 46%.As a result, when the user input is i must cancel it today, both dialog nodes are likely to be considered viable candidates to respond. To determine which dialog node to process, the assistant asks the user to pick one. And to help the user choose between them, the assistant provides a short summary of what each node does. The summary text is extracted directly from the external node name information that is specified for each node.!]Notice that your assistant recognizes the term today in the user input as a date, a mention of the @sys-date entity. If your dialog tree contains a node that conditions on the @sys-date entity, then it is also included in the list of disambiguation choices. This image shows it included in the list as the Capture date information option.!]The following video explains the benefits of using disambiguation. A few things have changed since the video was created:<-- <ul> --> * You enable dismabiguation from the Options page instead of a Settings link from the Dialog page. * You can also set a maximum number of options to display in the disambiguation list.<-- </ul> --><-- </section "id="section-dialog-runtime-disambig-example" "> --><-- <section "id="section-dialog-runtime-disambig-enable" "> --> Enabling disambiguation To enable disambiguation, complete the following steps:<-- <ol> -->1. From the Skills menu of the dialog skill where you want to enable disabmiguation, click Options.2. On the Disambiguation page, switch the toggle to On.3. In the Disambiguation message field, add text to show before the list of dialog node options. For example, What do you want to do?4. In the Anything else field, add text to display as an additional option that users can pick if none of the other dialog node options reflect what the user wants to do.
: Performance optimization [Akamai CNAME](https://cloud.ibm.com/docs/CDN?topic=CDN-next-steps-after-orderingakamai-cname) support

DNS based challenge domain validation method added
: Added a DNS based [challenge domain](https://cloud.ibm.com/docs/CDN?topic=CDN-completing-domain-control-validation-for-https-with-dv-sanchallenge-domain) validation method for SAN HTTPS.





 August 2020 

Token authentication support added
: Token authentication support [feature description](https://cloud.ibm.com/docs/CDN?topic=CDN-about-content-delivery-networks-cdn-token-authentication) (available through API).





 July 2020 

Integrated CDN mapping metrics
: Integrated CDN mapping metrics API support [API reference](https://cloud.ibm.com/docs/CDN?topic=CDN-cdn-api-referencegetmappingintegratedmetrics).





 March 2020 

Added multiple files purge
: Multiple files purge [feature description](https://cloud.ibm.com/docs/CDN?topic=CDN-about-content-delivery-networks-cdn-purge-cached-content) (available through UI and API).

Added real-time metrics
: Real-time metrics API [feature description](https://cloud.ibm.com/docs/CDN?topic=CDN-metrics) (available through UI and API).

Added activity tracker events for audit log
: Activity Tracker Events for Audit log [feature description](https://cloud.ibm.com/docs/CDN?topic=CDN-at_events) (available through UI).





 October 2019 

Dynamic content acceleration and metrics report added
: Dynamic Content Acceleration [feature description](https://cloud.ibm.com/docs/CDN?topic=CDN-about-content-delivery-networks-cdn-dynamic-content-acceleration-description) (available through UI and API).

Metrics reports for overall CDN mappings added
* Increased control: The frequency and depth of disambiguation can now be controlled by using the sensitivity parameter in the [workspace API](https://cloud.ibm.com/apidocs/assistant-data/assistant-data-v1updateworkspace). There are 5 levels of sensitivity:



* high
* medium_high
* medium
* medium_low
* low



The default (auto) is medium_high if this option is not set.
* More predictable: The new disambiguation feature is more stable and predictable. The choices shown may sometimes vary slightly to enable learning and analytics, but the order and depth of disambiguation is largely stable.



These new features may affect various metrics, such as disambiguation rate and click rates, as well as influence conversation-level key performance indicators such as containment.

If the new disambiguation algorithm works differently than expected for your assistant, you can adjust it using the sensitivity parameter in the update workspace API. For more information, see [Update workspace](https://cloud.ibm.com/apidocs/assistant-data/assistant-data-v1updateworkspace).





 26 January 2022 

IBM Watson® Assistant Cartridge for IBM Cloud Pak® for Data 4.0.5 is available
: Watson Assistant for IBM Cloud Pak® for Data 4.0.5 is compatible with IBM Cloud Pak® for Data 4.0 on Red Hat OpenShift 4.8. This release of Watson Assistant for IBM Cloud Pak® for Data includes various fixes. Watson Assistant for IBM Cloud Pak® for Data 4.0.5 is compatible with FIPS-enabled clusters.





 20 December 2021 

IBM Watson® Assistant Cartridge for IBM Cloud Pak® for Data 4.0.4 is available
: Watson Assistant for IBM Cloud Pak® for Data 4.0.4 is compatible with IBM Cloud Pak® for Data 4.0 on Red Hat OpenShift 4.8. This release of Watson Assistant for IBM Cloud Pak® for Data includes critical security fixes. Watson Assistant for IBM Cloud Pak® for Data 4.0.4 is not compatible with FIPS-enabled clusters.
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. Informational popups are displayed to introduce the product user interface features, and guide the new user toward taking the key first step of creating a dialog skill.





 10 April 2019 

Autocorrection is now available
: Autocorrection is a beta feature that helps your assistant understand what your customers want. It corrects misspellings in the input that customers submit before the input is evaluated. With more precise input, your assistant can more easily recognize entity mentions and understand the customer's intent. See [Correcting user input](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtime-spell-check) for more details.





 22 March 2019 

Introducing search skill
: A search skill helps you to make your assistant useful to customers faster. Customer inquiries that you did not anticipate and so have not built dialog logic to handle can be met with useful responses. Instead of saying it can't help, the assistant can query an external data source to find relevant information to share in its response. Over time, you can build dialog responses to answer customer queries that require follow-up questions to clarify the user's meaning or for which a short and clear response is suitable. And you can use search skill responses to address more open-ended customer queries that require a longer explanation. This beta feature is available to users of Premium and Plus service plans only.

See [Building a search skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-search-add) for more details.





 4 March 2019 

Simplified navigation
: The sidebar navigation with separate Build, Improve, and Deploy tabs has been removed. Now, you can get to all the tools you need to build a dialog skill from the main skill page.

Improve page is now called Analytics