vania-janet/multiturn-rag-retrieval-data huggingface dataset:
The ID can be obtained by running the [ibmcloud resource service-instance command.](https://cloud.ibm.com/docs/cli?topic=cli-ibmcloud_commands_resourceibmcloud_resource_service_instance) One of --instance-id or --instance-name must be specified. The --instance-id and --instance-name options cannot be specified together on the same command invocation.

--instance-name INSTANCE_NAME (required), exclusive with --instance-id
: The name of the IBM Cloud Security and Compliance Center Workload Protection instance. This is the [name](https://cloud.ibm.com/docs/workload-protection?topic=workload-protection-provision) you specified when creating the instance. One of --instance-id or --instance-name must be specified. The --instance-id and --instance-name options cannot be specified together on the same command invocation.

--region REGION | -r REGION
: Name of the region, for example, us-south or eu-gb. If not specified, the region logged into, or targeted, will be used.

--output FORMAT
: Available output formats are JSON, YAML, or TABLE. If not specified, output will be returned in a tabular format.

--quiet | -q
: Suppress verbose messages.

help | --help | -h
: List options available for the command.





 ibmcloud workload-protection policies 

This command list all the policies defined for your IBM Cloud Security and Compliance Center Workload Protection instance.

ibmcloud workload-protection policies (--instance-id ID | --instance-name INSTANCE_NAME) [--default] [--severity SEVERITY] [--filter FILTER] [--limit LIMIT] [--offset OFFSET]



 Policies command options 

--default
: Lists the default policies for your IBM Cloud Security and Compliance Center Workload Protection instance.

--filter FILTER
: A string to look for in the policy names or descriptions.

--limit LIMIT
: The number of items to be returned.
"pipeline_id": "b1cfb6a4-813d-4fec-bbf7-ce662e8ce4e0", // the id of the pipeline that generated this piece of evidence
"pipeline_run_id": 12345, // this is the id of the pipeline run that generated this piece of evidence
"id": "https://change_tool/1213", // link to the preproduction change request on the same baseline of the production one
"status": "auto-approved", "approved", "rejected"], // status of the change request
"approvers": "name1","name2", ..., "nameN"], // name of the approvers
"required": "yes" | "no" ] // yes: this is an auditable step; no: this is a non auditable step
}, {
// Partial: CM-2(1)(c) BASELINE CONFIGURATION | REVIEWS AND UPDATES
// As an integral part of information system component installations and upgrades.
// Partial: CM-2(2) BASELINE CONFIGURATION | AUTOMATION SUPPORT FOR ACCURACY / CURRENCY
// The organization employs automated mechanisms to maintain an up-to-date, complete, accurate, and readily available baseline
// configuration of the information system.
// CM-2(6) BASELINE CONFIGURATION | DEVELOPMENT AND TEST ENVIRONMENTS
// The organization maintains a baseline configuration for information system development and test environments that is managed
// separately from the operational baseline configuration.
// CM-3(1)(a) CONFIGURATION CHANGE CONTROL | AUTOMATED DOCUMENT / NOTIFICATION / PROHIBITION OF CHANGES
// The organization employs automated mechanisms to: Document proposed changes to the information system;
// CM-3(1)(e) CONFIGURATION CHANGE CONTROL | AUTOMATED DOCUMENT / NOTIFICATION / PROHIBITION OF CHANGES
// The organization employs automated mechanisms to: Document all changes to the information system;
// Control passed if:
// 1- CR contains baselines changes in the change log
// 2- contains a link to the to-be-baseline
// CM-3(2) CONFIGURATION CHANGE CONTROL | TEST / VALIDATE / DOCUMENT CHANGES
"enrichment_id": "701db916-fc83-57ab-0000-000000000002",
"fields":
"text"
]
},
{
"enrichment_id": "701db916-fc83-57ab-0000-00000000001e",
"fields":
"text"
]
},
{
"enrichment_id": "701db916-fc83-57ab-0000-000000000018",
"fields":
"text"
]
}
]
Show more
6. Submit the updated JSON request body with the [update collection](https://cloud.ibm.com/apidocs/discovery-dataupdatecollection) method to apply the enrichment to your collection.

For example:

POST $authentication -d '$requestBody' $url/v2/projects/$project_id/collections/$collection_id?version=2019-11-22









 Default enrichments per project type 

Some prebuilt enrichments are applied automatically to collections in a project based on the project type. The following table shows the default enrichments that are applied to each project type.



Default enrichments per project type
This table has row and column headers. The row headers identify project types. The column headers identify different enrichments. To understand which enrichments are applied to a project type by default, go to the row that describes the enrichments, and find the columns for the project type that you are interested in.

 Enrichment Document Retrieval Document Retrieval for Contracts Conversational Search Content Mining 

 Contracts ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) 
 Entities !
"project_id" : "db268db0-160b-4911-8f93-89659000a927",
"is_draft" : true,
"version" : 2,
"state" : "ACTIVE",
"pipeline_state" : "PIPELINE_SUCCEEDED",
"update_available" : true,
"created_at" : "2023-02-22T19:51:23.253Z",
"updated_at" : "2023-02-22T19:51:23.253Z"
}
<-- </section "id="section-project-config-update-cli-output" "> --><-- </section "id="section-project-cli-config-update-command" "> --><-- <section "id="section-project-cli-config-delete-command" "> --> ibmcloud project config-delete Delete a configuration in a project. Deleting the configuration will also destroy all the resources deployed by the configuration if the query parameter destroy is specified. ibmcloud project config-delete --project-id PROJECT-ID --id ID --draft-only DRAFT-ONLY] !
<-- <section "id="section-project-config-delete-cli-options" "> --> Command options --project-id (string): The unique project ID. Required.
The maximum length is 128 characters. The value must match regular expression /^\.\-0-9a-zA-Z] +$/.
--id (string): The unique config ID. Required.
The maximum length is 128 characters. The value must match regular expression /^\.\-0-9a-zA-Z] ! ! ! ! ! ! ! +$/.
--draft-only (bool): The flag to determine if only the draft version should be deleted.
The default value is false.
From the response section of the node, click the More![More options](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/kebab.png) icon, and then select Open context editor.

![Shows the Open context editor menu option from the node edit view.](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/gs-ass-open-context-editor.png)
6. Enter the following context variable name and value pair:



Order number context variable details

 Variable Value 

 $ordernumber <? @order_number.literal ?> 



The context variable value (<? @order_number.literal ?>) is a SpEL expression that captures the number that the user specifies that matches the pattern defined by the @order_number pattern entity. It saves it to the $ordernumber variable.

![Shows the $ordernumber context variable definition.](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/gs-ass-ordernumber-context-var.png)
7. Click ![Close](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/close.png) to close the edit view.

Now, add child nodes that either ask for the order number or get confirmation from the user that she wants to cancel an order with the detected order number.
8. Click the More!
You can specify the parameter when you train a custom language model or when you use it with a speech recognition request.



* For a training request, the following example specifies a customization weight of 0.5 with the POST /v1/customizations/{customization_id}/train method:

IBM Cloud

curl -X POST -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/train?customization_weight=0.5"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/train?customization_weight=0.5"

Setting a customization weight during training saves the weight with the custom language model. You do not need to pass the weight with each recognition request that uses the custom model.
* For a recognition request, the following example specifies a customization weight of 0.7 with the POST /v1/recognize method:

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: audio/flac" --data-binary @audio-file1.flac "{url}/v1/recognize?language_customization_id={customization_id}&customization_weight=0.7"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: audio/flac" --data-binary @audio-file1.flac "{url}/v1/recognize?language_customization_id={customization_id}&customization_weight=0.7"

Setting a customization weight during speech recognition overrides a weight that was saved with the model during training.







 Troubleshooting the use of custom language models 

If you apply a custom language model to speech recognition but find that the service does not appear to be using words that the model contains, check for the following possible problems:



* Make sure that you are correctly passing the customization ID to the recognition request as shown in the previous examples.
For more information, see [Retrieving an instance ID](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-retrieve-instance-ID). 
 key_ring_ID Optional. The unique identifier of the key ring that the key belongs to. If unspecified, Hyper Protect Crypto Services will search for the key in every key ring that is associated with the specified instance. Therefore, it is suggested to specify the key ring ID for a more optimized request.<br><br>Note: The key ring ID of keys that are created without an x-kms-key-ring header is: default. For more information, see [Managing key rings](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-managing-key-rings). 
 correlation_ID The unique identifier that is used to track and correlate transactions. 



A successful GET api/v2/keys/<key_ID_or_alias>/metadata response returns details about your key. The following JSON object shows an example returned value for a standard key.

{
"metadata": {
"collectionType": "application/vnd.ibm.kms.key+json",
"collectionTotal": 1
},
"resources": [
{
"type": "application/vnd.ibm.kms.key+json",
"id": "02fd6835-6001-4482-a892-13bd2085f75d",
"name": "test-standard-key",
"state": 1,
"extractable": true,
* The word was modified in some way with the POST /v1/customizations/{customization_id}/words or PUT /v1/customizations/{customization_id}/words/{word_name} method.



Removing a grammar does not affect the custom model until you train the model on its updated data by using the POST /v1/customizations/{customization_id}/train method. If you successfully trained the model on the grammar, the grammar continues to be available for speech recognition until you retrain the model.



 Delete a grammar example 

The following example deletes the grammar named list-abnf from the custom model with the specified customization ID.

IBM Cloud

curl -X DELETE -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"

IBM Cloud Pak for Data

curl -X DELETE --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/grammars/list-abnf"
The customization_id parameter will no longer be available from the equivalent methods of the SDKs as of their next major release. For more information about the speech recognition methods, see the [API & SDK reference](https://cloud.ibm.com/apidocs/speech-to-text/speech-to-textservice-endpoint).





 30 March 2022 (Version 4.0.7) 

Version 4.0.7 is now available
: Speech to Text for IBM Cloud Pak for Data version 4.0.7 is now available. This version supports IBM Cloud Pak for Data version 4.x and Red Hat OpenShift versions 4.6 and 4.8. For more information about installing and managing the service, see [Installing Watson Speech to Text](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.0?topic=text-installing-watson-speech).

Custom resource property for specifying a default model
: The default voice for speech recognition requests is en-US_BroadbandModel. If you do not install the en-US_BroadbandModel, you must either



* Use the model parameter to pass the voice that is to be used with each request.
* Specify a new default model for your installation of Speech to Text for IBM Cloud Pak for Data by using the defaultSTTModel property in the Speech services custom resource. For more information, see [Installing Watson Speech to Text](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.0?topic=text-installing-watson-speech) and [Using the default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-usemodels-use-default).



Updates to English and French next-generation multimedia models to support low latency
: The following multimedia models have been updated to support low latency:



* Australian English: en-AU_Multimedia
* UK English: en-GB_Multimedia
* US English: en-US_Multimedia
* French: fr-FR_Multimedia
Using the Login Widget 

With IBM Cloud® App ID you can use a default UI, called a Login Widget, to allow application users the ability to choose the identity provider that they want to sign in with. If you're using Cloud Directory, the Login Widget also provides additional UI's for extra functionalities such as sign-up, forgot password, multi-factor authentication, and more.



 Understanding the Login Widget 

One of the best parts of the Login Widget is that you can start using App ID before you implement any of your own authentication UIs - which makes the developer onboarding experience that much easier.



 What is the default Login Widget behavior? 

By default, the Login Widget is enabled to use Facebook, Google, and Cloud Directory. You can change the behavior at any time by choosing which identity providers that you want to configure as an option. When more than one identity provider is enabled, the Login Widget presents a screen where the user can make their identity provider selection. But, if you have a single provider that is enabled, users do not see the previously mentioned selection screen. They are taken directly to the identity provider to begin the sign-in process.

For example, if you're using the default - Facebook, Google, and Cloud Directory - users see the screen. If you enable Facebook only, user's are taken directly to Facebook for authentication.





 Which screens can be displayed for each provider? 

When you use Cloud Directory, App ID is able to provide you with the extended functionality of user management. The extended functionality also applies to the Login Widget's capabilities. User's that are stored in Cloud Directory can take advantage of the functionality such as signing up or resetting their password directly in the Login Widget. Check out the following table to see which screens you can display for each type of identity provider.



Table 1. The Login Widget screens that each identity provider can display

 Login Widget screen Social identity provider Enterprise identity provider Cloud Directory 

 Sign in ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/icons/checkmark-icon.svg) !
! !
<-- <section "id="section-show-waf-rule-options" "> --> Command options DNS_DOMAIN_ID: The ID of DNS domain. Required.WAF_PACKAGE_ID: The ID of WAF package. Required.WAF_RULE_ID: The ID of WAF rule. 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-rule-options" "> --><-- <section "id="section-show-waf-rule-examples" "> --> Examples Get details of WAF rule f939de3be84e66e757adcdcb87908023 in WAF package a25a9a7e9c00afc1fb2e0245519d725b. ibmcloud cis waf-rule 31984fea73a15b45779fa0df4ef62f9b a25a9a7e9c00afc1fb2e0245519d725b f939de3be84e66e757adcdcb87908023 -i "cis-demo"
<-- </section "id="section-show-waf-rule-examples" "> --><-- </section "id="section-show-waf-rule" "> --><-- <section "id="section-update-waf-rule" "> --> ibmcloud cis waf-rule-mode-set Set mode of a WAF rule. ibmcloud cis waf-rule-mode-set DNS_DOMAIN_ID WAF_PACKAGE_ID WAF_RULE_ID WAF_RULE_MODE -i, --instance INSTANCE] --output FORMAT] ! ! ! ! ! !
Customization overview 

You can customize a serverless instance specifically to suit your application needs, over and above what is provisioned on a default basis.

For example, you might want to install custom analytics third-party libraries or you might want to fine-tune some cluster configurations, for example, the Spark default configurations.

You can customize an instance at any point of its lifecycle. The customizations are applied only to those applications that are submitted after you added the customization. They are not applied to an currently running applications.



  Customization options 

You can customize your instance by:



*  Specifying configuration values that are inherited by all Spark applications that run in the instance
*  Making Python, R, Scala or custom libraries available to your Spark applications



When you create an instance you can:



*  Specify default values for configuration properties and environment variables supported by the Apache Spark configuration. You can specify configuration properties and environment variables as name-value pairs that are saved at the instance level and passed to all Spark applications that run in the instance. These default configuration parameters can simplify the payload that is passed when submitting a Spark application. You can also override these values at the time a Spark application is submitted.

For a list of the default Spark configurations and environment variables, see [Spark configurations](https://spark.apache.org/docs/latest/configuration.html).
*  Customize the instance with libraries required by your Spark applications after instance creation. You can create a library set that packages all libraries to be made available to all Spark applications that run in the instance, and then refer to this defined library set at the time the Spark application is submitted.

To create a library set, see [Creating a library set](https://cloud.ibm.com/docs/AnalyticsEngine?topic=AnalyticsEngine-create-lib-set).



Note:



*  The maximum size limit of a customization library set is 2 GB.
*  The start time of your Spark application or the time taken for additional executors to get added in an autoscaling scenario, is proportional to the size of the custom library set. Therefore it is a best practice to limit a library set to only the files that are needed for a specific application. If other applications require different sets of files, it is better to use different library sets.
"service_id":"iam-ServiceId-c0c7cfa4-b24e-4917-ad74-278f2fee5ba0",
"secret_type": "iam_credentials",
"secret_group_id": "bfc0a4a9-3d58-4fda-945b-76756af516aa",
"labels": [
"dev",
"us-south"
],
"ttl": "30m",
"service_id": "ServiceId-c0c7cfa4-b24e-4917-ad74-278f2fee5ba0,
"reuse_api_key": false,
"custom_metadata": {
"metadata_custom_key": "metadata_custom_value"
},
"version_custom_metadata": {
"custom_version_key": "custom_version_value"
}
}' "https://{instance_ID}.{region}.secrets-manager.appdomain.cloud/api/v2/secrets"

A successful request returns the ID value of the secret, along with other metadata. For more information, check out the [API reference](https://cloud.ibm.com/apidocs/secrets-manager/secrets-manager-v2).







 Creating IAM credentials with Terraform 

You can create IAM credentials programmatically by using Terraform for Secrets Manager.

You must add a depends_on Terraform meta-argument and refer it to your IAM configuration resource. The depends_on meta-argument instructs Terraform to complete all actions on the IAM configuration before you perform actions on the IAM credentials secrets.

The following example shows a configuration that you can use to create IAM credentials.

resource "ibm_sm_iam_credentials_secret" "test_iam_credentials_secret" {
instance_id = local.instance_id
region = local.region
For more information, see [Retrieving an instance ID](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-retrieve-instance-ID). 
 key_ring_ID Optional. The unique identifier of the key ring that the key belongs to. If unspecified, Hyper Protect Crypto Services will search for the key in every key ring that is associated with the specified instance. Therefore, it is suggested to specify the key ring ID for a more optimized request.<br><br>Note: The key ring ID of keys that are created without an x-kms-key-ring header is: default. For more information, see [Managing key rings](https://cloud.ibm.com/docs/hs-crypto?topic=hs-crypto-managing-key-rings). 
 correlation_ID The unique identifier that is used to track and correlate transactions. 



A successful GET api/v2/keys/<key_ID_or_alias> response returns details about your key and the key material. The following JSON object shows an example returned value for a standard key.

{
"metadata": {
"collectionType": "application/vnd.ibm.kms.key+json",
"collectionTotal": 1
},
"resources": [
{
"type": "application/vnd.ibm.kms.key+json",
"id": "02fd6835-6001-4482-a892-13bd2085f75d",
"name": "test-standard-key",
"aliases":
"alias-1",
"alias-2"
],
"state": 1,
"expirationDate": "2020-03-15T03:50:12Z",
"extractable": true,
In your customization_script.py, implement a function called customize(<install_path>, <params>), where <install_path> is the predesignated location where the libraries are downloaded to, namely /home/spark/shared/user-libs/<libraryset_name>/custom/<subdir_if_applicable>. You can't change this path. <params> is a list of the parameters required by the customization script.
3. Store the customization_script.py file in IBM Cloud Object Storage or in GitHub.
4. Pass the location of the customization_script.py file to the customization Spark application through the "application_details" > "conf" > "spark.submit.pyFiles" parameter.

The "arguments" section in the Spark application submission payload must contain a "library_set" section with details, like "action" and "name" as shown in the following sample payload.

Example of the payload:

{
"application_details": {
"application": "/opt/ibm/customization-scripts/customize_instance_app.py",
"arguments": ["{"library_set":{"action":"add","name":"customize_integration_custom_lib","script":{"source":"py_files","params":"https://s3.direct.<CHANGEME_REGION>.cloud-object-storage.appdomain.cloud","<CHANGEME_BUCKET_NAME>","libcalc.so","<CHANGEME_ACCESS_KEY>","<CHANGEME_SECRET_KEY>"]}}}"],
"conf": {
"spark.hadoop.fs.cos.dev-cos.endpoint":"https://s3.direct.<CHANGEME_REGION>.cloud-object-storage.appdomain.cloud",
"spark.hadoop.fs.cos.dev-cos.access.key":"<CHANGEME_ACCESS_KEY>",