vania-janet/multiturn-rag-retrieval-data huggingface dataset:
For more information, see the [Controlling access to resources by using tags](https://cloud.ibm.com/docs/account?topic=account-access-tags-tutorial) UI tutorial. 
 SSH key type Select a key type for the SSH key. SSH key type ed25519 can't be used for Windows® instances. 



3. Select Paste public key material.
4. Paste in the public key information.
5. Optionally, click Get sample API call to get an API code with all your SSH key information that you can copy.
6. Click Create.



When you copy an SSH key from a terminal to add the key to your VPC, sometimes extra line breaks are introduced which cause a parsing error. To avoid this issue, first paste your SSH key into a text editor and remove any extra line breaks. Then, copy the SSH key from text editor and paste it into the VPC UI, CLI, or API.

Your imported SSH key is now displayed in the list of SSH keys on the UI.





 Importing your SSH key by using the CLI 

To import an SSH key by using the CLI, use the ibmcloud is key-create command. You also must specify a KEY_NAME and the KEY. The SSH key file is either the id_rsa.pub or id_ed25519.pub depending on which key type you are importing. Optionally, you can specify the resource by using either the RESOURCE_GROUP_ID or RESOURCE_GROUP_NAME variable. See [ibmcloud is key-create](https://cloud.ibm.com/docs/vpc?topic=vpc-vpc-referencekey-create) in the VPC CLI reference guide.

ibmcloud is key-create KEY_NAME (KEY | @KEY_FILE) [--resource-group-id RESOURCE_GROUP_ID | --resource-group-name RESOURCE_GROUP_NAME]





 Importing your SSH key by using the API
The following attributes are part of the Input container and are required when creating a new Geographical Access Control rule:



* uniqueId - Provide a unique ID of the mapping to assign the rule.
* accessType - Specifies whether the rule will ALLOW or DENY traffic to the given region.
* regionType - The type of region to apply the Geographical Access Control rule to, either CONTINENT or COUNTRY_OR_REGION.
* regions - An array listing the locations that the accessType applies to.



See the [SoftLayer_Network_CdnMarketplace_Configuration_Behavior_Geoblocking](https://cloud.ibm.com/docs/CDN?topic=CDN-geoblocking-class) page to see a list of possible regions.
* Returns - An object of type SoftLayer_Network_CdnMarketplace_Configuration_Behavior_Geoblocking.

[Geo-blocking class](https://cloud.ibm.com/docs/CDN?topic=CDN-geoblocking-class)



--------------------





 updateGeoblocking 

Updates an existing Geographical Access Control rule for an existing domain mapping and returns the updated rule.



* Parameters: A collection of type SoftLayer_Container_Network_CdnMarketplace_Configuration_Input. You can view all of the attributes in the Input container here:

[Input container](https://cloud.ibm.com/docs/CDN?topic=CDN-input-container)

The following attributes are part of the Input container and can be provided when updating a GeographicalAccess Control rule (parameters are optional unless otherwise noted):



* uniqueId - (Required) Provide a unique ID of the mapping the rule to be updated belongs.
* accessType - Specifies whether the rule will ALLOW or DENY traffic to the given region.
* regionType - The type of region to apply the rule to, either CONTINENT or COUNTRY_OR_REGION.
* regions - An array listing the locations that the accessType applies to.
4. Select the Storage type.
5. Select the Version and click Next
6. If the Storage type that you selected accepts custom parameters, enter them on the Parameters tab.
7. If the Storage type that you selected requires secrets, enter them on the Secrets tab.
8. On the Storage classes tab, review the storage classes that are deployed by the configuration or create a custom storage class.
9. On the Assign to service tab, select the service that you want to assign your configuration to.
10. Click Complete to assign your storage configuration.







 Creating a configuration in the CLI 



1. Log in to the IBM Cloud CLI.

ibmcloud login
2. List your Satellite locations and note the Managed from column.

ibmcloud sat location ls
3. Target the Managed from region of your Satellite location. For example, for wdc target us-east. For more information, see [Satellite regions](https://cloud.ibm.com/docs/satellite?topic=satellite-sat-regions).

ibmcloud target -r us-east
4. If you use a resource group other than default, target it.

ibmcloud target -g <resource-group>
5. Copy one of the following example command for the template version that you want to use. For more information about the command, see ibmcloud sat storage config create in the [command reference](https://cloud.ibm.com/docs/satellite?topic=satellite-satellite-cli-referencecli-storage-config-create).

Example command to create a version 1.9.0 configuration.
create_catalog(self, id=None, rev=None, label=None, short_description=None, catalog_icon_url=None, tags=None, url=None, crn=None, offerings_url=None, features=None, disabled=None, created=None, updated=None, resource_group_id=None, owning_account=None, catalog_filters=None, syndication_settings=None, kwargs)

Exapmle request

label = "{label}"
shortDesc = "{shortDesc}"
response = self.service.create_catalog(label=label, short_description=shortDesc)
print(response)

(catalogManagement CatalogManagementV1) CreateCatalog(createCatalogOptions CreateCatalogOptions) (result Catalog, response core.DetailedResponse, err error)

Example request

label := "{label}"
shortDesc := "{shortDesc}"
createOptions := service.NewCreateCatalogOptions()
createOptions.SetLabel(label)
createOptions.SetShortDescription(shortDesc)
_, response, _ := service.CreateCatalog(createOptions)
fmt.Println(response)

createCatalog(params, [callback()])

Example request

label = "{label}";
shortDesc = "{shortDesc}";
response = await service.createCatalog({ 'label': label, 'shortDescription': shortDesc });
console.log(response);

All the IBM Cloud public catalog offerings are visible by default when you create a new private catalog. See the [Catalog Management API](https://cloud.ibm.com/apidocs/resource-catalog/private-catalog?code=javacreate-catalog) for more information.





 Creating a private catalog with select products included by using the API
See the following example request that explicitly specifies the Accept header:

GET /recipes HTTP/1.1
Host: username.cloudant.com
Accept: application/json

See the following example of the headers returned in response, including the application/json content type:

Server: CouchDB/1.0.2 (Erlang OTP/R14B)
Date: Thu, 13 Jan 2011 13:40:11 GMT
Content-Type: application/json
Content-Length: 7
Cache-Control: must-revalidate





 Content-Type for request headers 

The Content-Type header specifies the content type of the information that is supplied within the request. The specification uses MIME type specifications. For most requests, the content type is JSON (application/json).

For some settings, the MIME type is plain text.

In particular, when you upload attachments the type must be the corresponding MIME type for the attachment or binary (application/octet-stream).

The use of the Content-Type on a request is highly recommended.





 Content-Encoding 

The Content-Encoding header specifies the encoding of the request body. The supported value is gzip. If the header is used, the request body must be encoded with the corresponding format.

See the following example of creating a compressed (gzipped) request body:

 create gzipped document
echo '{"foo":"bar"}' | gzip > doc.gzip

See the following example of sending a gzip-encoded request body to create a document by using HTTP:

PUT /db/doc HTTP/1.1
HOST: example.cloudant.com
Content-Encoding: gzip

See the following example of sending a gzip-encoded request body to create a document by using the command line:

curl "https://example.cloudant.com/db/doc" 	-X PUT 	-T doc.gzip 	-H "Content-Encoding: gzip"

By default, the SDKs compress the request body.





 If-None-Match
Use this response type when a customer's response must be either Yes or No. For more information, see [Confirmation](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-collect-infocustomer-response-type-confirmation).

Search integration highlights text in browser
: Search results in Watson Assistant include a link. Now, when a customer clicks the link, search results are highlighted in their browser so it’s easier for them to see the relevant content. This feature is supported on Chromium browsers, including Google Chrome and Microsoft Edge.





 24 February 2022 

Regex customer response type
: The regex customer response type is now available. Use this response type to capture a value that must conform to a particular pattern or format, such as an email address or telephone number. For more information, see [Regex](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-collect-infocustomer-response-type-regex).





 17 February 2022 

Adding users from the Manage menu
: If you want to collaborate with others on your assistants, you can now quickly add users with Administrator and Manager access from the Manage menu in your assistant. For more information, see [Adding users from the Manage menu](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-access-controlaccess-control-add-users).

Preview page share link
: When you use the Copy link to share button to share your assistant, the shared assistant now mirrors the Preview page. If you share the assistant with a colleague, they are able to see the assistant with any customizations that you made on the Preview page.





 10 February 2022 

Links in assistant responses can be configured to open in a new tab
: When you build an action, your assistant responses can include links. If you're using web chat, you can now control whether the link opens in a new tab. To enable a link to open in a new tab, select Open link in new tab from the Insert link configuration window.
Response<Case> response = service.createCase(createCaseOptions).execute();
Case xCase = response.getResult();

System.out.println(xCase);

offering_type = OfferingType(
group='crn_service_name',
key='cloud-object-storage'
)
offering_payload = Offering(
name='Cloud Object Storage',
type=offering_type
)

case = case_management_service.create_case(
type='technical',
subject='Example technical case',
description='This is an example case description. This is where the problem would be described.',
offering=offering_payload,
severity=4,
).get_result()

print(json.dumps(case, indent=2))

offeringType, _ := caseManagementService.NewOfferingType(
casemanagementv1.OfferingTypeGroupCRNServiceNameConst,
"cloud-object-storage",
)
offeringPayload, _ := caseManagementService.NewOffering(
"Cloud Object Storage",
offeringType,
)

createCaseOptions := caseManagementService.NewCreateCaseOptions(
"technical",
"Example technical case",
"This is an example case description. This is where the problem would be described.",
)
createCaseOptions.SetSeverity(4)
createCaseOptions.SetOffering(offeringPayload)

caseVar, response, err := caseManagementService.CreateCase(createCaseOptions)
if err != nil {
panic(err)
}
b, _ := json.MarshalIndent(caseVar, "", " ")
fmt.Println(string(b))

const offeringType = {
group: 'crn_service_name',
type = string
default = "Enter a default target name"
}
variable "atracker_route_receive_global_events" {
description = "Whether or not all global events should be forwarded to this region."
type = bool
default = false
}
variable "atracker_route_rules" {
description = "Routing rules that will be evaluated in their order of the array."
type = list(object({ example=string }))
default = [ { example: "object" } ]
}

Show more

To see the list of valid regions, see [Locations](https://cloud.ibm.com/docs/atracker?topic=atracker-regionsregions-atracker).





 Step 6. Create a Terraform configuration file 

Next, create a Terraform configuration file that is named main.tf. In this file, you configure Activity Tracker Event Routing by using HashiCorp Configuration Language (HCL). For more information, see the [Terraform documentation](https://www.terraform.io/docs/language/index.html).

The following code shows a sample configuration file to define the account setting configuration:

resource "ibm_atracker_settings" "atracker_settings" {
metadata_region_primary = var.atracker_metadata_region_primary
metadata_region_backup = var.atracker_metadata_region_backup
default_targets = ["comma-separated-list-of-target-ids"]
permitted_target_regions = ["comma-separated-list-of-locations"]
private_api_endpoint_only = false

 Optional but recommended lifecycle flag
lifecycle {
create_before_destroy = true
}
}

The following code shows sample configurations to define a target:

 Target type: cloud-object-storage
 Using service to service authorization
resource "ibm_atracker_target" "atracker_target" {
cos_endpoint {
endpoint = var.cos_endpoint
For more information about adding entity types, see [Defining entity types](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-entity-extractorentity-extractor-add-entities).
2. First, click the entity type from the Entity types panel.
3. In the document body, select the word or phrase that represents the entity example.

The term is selected and a color label is applied to the term. The first two characters of the entity type name are shown in uppercase superscript within the label boundary. Both the 2-character ID and the label color help you to associate the example with the entity type it represents.

Zoom

![Shows that a label is applied to the word wife in a sentence.](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/wife-label.png)

Figure 4. A label is applied to an entity example

The example text is also added to the Entity types panel. If you click the chevron to view details, you can see that the example is listed. The example text is saved in lowercase, regardless of the capitalization that is used in the original text.
4. If bulk labeling is enabled, a notification is displayed to show the number of occurrences of the term that were found and labeled in the current document.
5. If you want to label occurrences of the term in all of the documents in the collection, click Apply to all documents.

When you enable this option, occurrences of the term are labeled in all of the documents in the collection, including documents that you already reviewed and marked complete.

Zoom

![Shows the notification that asks whether to apply bulk labeling to all documents.]
[From the Locations console](https://cloud.ibm.com/satellite/locations), select the location where you want to create a storage configuration.
2. Select Storage > Create storage configuration
3. Enter a name for your configuration.
4. Select the Storage type.
5. Select the Version and click Next
6. If the Storage type that you selected accepts custom parameters, enter them on the Parameters tab.
7. If the Storage type that you selected requires secrets, enter them on the Secrets tab.
8. On the Storage classes tab, review the storage classes that are deployed by the configuration or create a custom storage class.
9. On the Assign to service tab, select the service that you want to assign your configuration to.
10. Click Complete to assign your storage configuration.







 Creating a configuration in the CLI 



1. Log in to the IBM Cloud CLI.

ibmcloud login
2. List your Satellite locations and note the Managed from column.

ibmcloud sat location ls
3. Target the Managed from region of your Satellite location. For example, for wdc target us-east. For more information, see [Satellite regions](https://cloud.ibm.com/docs/satellite?topic=satellite-sat-regions).

ibmcloud target -r us-east
4. If you use a resource group other than default, target it.

ibmcloud target -g <resource-group>
5. Copy one of the following example command for the template version that you want to use. For more information about the command, see ibmcloud sat storage config create in the [command reference](https://cloud.ibm.com/docs/satellite?topic=satellite-satellite-cli-referencecli-storage-config-create).

Example command to create a version 1.0.4 configuration.
Creating the rule-based model 

After defining rules, you can create a rule-based model.



  Procedure 

To create a rule-based model. complete the following steps:



1.  Log in as a Knowledge Studio administrator or project manager, and select your workspace.
2.  Select the Rule-based Model > Versions > Rule-based Model tab.
3.  Click Map entity types and classes.
4.  Map the entity types from your type system to one or more classes that you used to define rules.

After the entity types are mapped, you can [deploy the rule-based model](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-wks_rule_publish) or use it to [pre-annotate documents](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-preannotationwks_preannotrule) in the process of creating a machine learning model.







  Deleting a rule-based model 

You cannot delete a rule-based model.

You can delete the workspace that was used to develop the rule-based model, but you cannot delete the model itself. Deleting a model is not the best approach. Instead, recreate the rules that are defined for it. Editing the rules directly changes the behavior of the rule-based model.
Creating sequences 

You can create a special type of IBM Cloud® Functions action called a sequence, that chains together several actions. The result of one action is passed as an argument to the next action. Sequences can use standard actions or web actions.

Parameters that are passed between actions in the sequence are explicit, except for default parameters. Therefore, parameters that are passed to the action sequence are only available to the first action in the sequence. The result of the first action in the sequence becomes the input JSON object to the second action in the sequence, and so on. This object does not include any of the parameters that are originally passed to the sequence unless the first action includes them in its result. Input parameters to an action are merged with the action's default parameters, with the former taking precedence and overriding any matching default parameters.

A sequence does not have limits that are separate from those limits of each action that is contained within the sequence. While you can set limits for timeout, memory, and logsize when you create a sequence, those limits are ignored. Note that the limits configured for each action in the sequence are individually enforced. Because a sequence is a pipeline of actions, a failure in one action breaks the pipeline. For example, if one action times out, the entire sequence is exited with that failure.

After you create a sequence, you can use the name of the sequence when you create a rule or invoke the actions.



 Creating a sequence from the console 

Create a sequence from the console that uses actions that are available in your namespace or public actions.

Before you begin

Select a namespace to contain your Cloud Functions entities from the [console](https://cloud.ibm.com/functions). For more information about namespaces, see [Managing namespaces](https://cloud.ibm.com/docs/openwhisk?topic=openwhisk-namespaces).



1. Go to the [Create page](https://cloud.ibm.com/functions/create) in the Cloud Functions console.
2. Click Create Sequence.
3. Specify a name, package, and initial action for your sequence.
: The URL of the Git repository that contains your source code; for example https://github.com/IBM/CodeEngine. The source option is required if the --build-type option is git and not allowed if the --build-type option is local. This value is optional.

--strategy, --str
: The strategy to use for building the image. Valid values are dockerfile and buildpacks. This value is optional. The default value is dockerfile.

--timeout, --to
: The amount of time, in seconds, that can pass before the build must succeed or fail. This value is optional. The default value is 600.





 Example 

The following example creates a build configuration file called helloworld-build from a source Dockerfile, that is located in https://github.com/IBM/CodeEngine within the hello directory in the main branch, with dockerfile strategy and medium size. When this build is submitted, the container image that is built is stored in a Container Registry instance at us.icr.io/mynamespace/codeengine-helloworld that is accessed by using an image registry secret called myregistry.

ibmcloud ce build create --name helloworld-build --source https://github.com/IBM/CodeEngine --context-dir /hello --commit main --strategy dockerfile --size medium --image us.icr.io/mynamespace/codeengine-helloworld --registry-secret myregistry





 Example output 

Creating build helloworld-build...
OK







 ibmcloud ce build delete 

Delete a build.

ibmcloud ce build delete --name BUILD_NAME [--force] [--ignore-not-found] [--quiet]



 Command Options 

--name, -n
: The name of the build. This value is required.

--force, -f
: Force deletion without confirmation. This value is optional. The default value is false.
A rule typically specifies the condition that must match, an optional quantifier, a list of additional constraints that the matched text must fulfill, and the actions to be taken when a match occurs, such as creating a new annotation or modifying an existing annotation.







 S 



* subtype

A type that extends or implements another type; the supertype.
* surface form

The form of a word or multiword unit as it is found in the corpus. For example, some surface forms of the lemma 'organize' are the terms 'organizing' and 'organized'. See also [dictionary](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_D) and [lemma](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_L).







 T 



* testing data

A set of annotated documents that can be used to evaluate system metrics after ingestion and training. See also [blind data](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_B) and [training data](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_T).
* train

The process of setting up a Watson instance with components that enable the system to function in a particular domain (for example: corpus content, training data that generates machine learning models, programmatic algorithms, or other ground truth components) and then making improvements and updates to these components based on accuracy analysis.
* training data

A set of annotated documents that can be used to train machine learning models. See also [blind data](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_B) and [testing data](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-glossarygloss_T).
* true negative