vania-janet/multiturn-rag-retrieval-data huggingface dataset:
"path": ".wksrule.entities.GIVENNAME",
"text": "George",
"type": "GIVENNAME"
},
{
"path": ".wksrule.entities.SURNAME",
"text": "Washington",
"type": "SURNAME"
},
{
"path": ".wksrule.entities.POSITION",
"text": "politician",
"type": "POSITION"
},
{
"path": ".wksrule.entities.POSITION",
"text": "soldier",
"type": "POSITION"
},
{
"path": ".wksrule.entities.JOBTITLE",
"text": "President of the United States",
"type": "JOBTITLE"
}
],
"text":
"George Washington (February 22, 1732‚ December 14, 1799) was an American politician and soldier who served as the first President of the United States from 1789 to 1797 and was one of the Founding Fathers of the United States."
]
}
]
}
Show more

As a result, if someone [uses the API](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-concepts) to submit a Discovery Query Language query to look for occurrences of the enriched_{field_name}.entities.type:jobtitle enrichment, any passages that discuss a person's job title are returned.





 Machine learning model example 

In this example, a Machine learning model extracts entity types such as person, oranization, and date, and information about relationships between the entities. When this ML model is applied as an enrichment to a field, it uses machine learning to understand the linguistic nuances, meaning, and relationships that are mentioned in the document.

In the output, the information that is extracted by the Machine Learning enrichment in the enriched_{field_name} array, within the entities and the relations arrays. In this example, the field that is selected for enrichment is text.
For UDFs implemented in PMML, the machine learning model is stored inside the PMML XML file. Refer to the documentation of PMML for how to create these models: [[http://dmg.org/pmml/v4-1/GeneralStructure.html](http://dmg.org/pmml/v4-1/GeneralStructure.html)]

You can implement a Scalar UDF as a public method in a Java class. You can implement a table UDF as a public method in a Java class that extends the API com.ibm.avatar.api.udf.TableUDFBase. In addition, a table UDF can optionally override the method initState() of the superclass com.ibm.avatar.api.udf.TableUDFBase.

If the UDF has an input parameter of type Span, Text, or ScalarList, or the return type of the UDF is Span, Text, or ScalarList, the Java class must import the classes com.ibm.avatar.algebra.datamodel.Span, com.ibm.avatar.algebra.datamodel.Text, or com.ibm.avatar.algebra.datamodel.ScalarList to compile.

Table functions require extra APIs to provide output schema information. These APIs belong to the base class com.ibm.systemt.api.udf.TableUDFbase. If a subclass contains more than one table function, a separate Java object is created for each instance.

For the UDF code to retrieve non-class resources from its JAR file, the only supported method is getResourceAsStream(). Other methods for accessing resources (such as getResource(), getResources(), getSystemResource()) are not supported. For example, a UDF JAR file that contains the properties file my.properties in the package com.ibm.myproject.udfs, can access it with the following Java statement:

InputStream in = this.getClass().getClassLoader().
getResourceAsStream(“com/ibm/myproject/udfs/my.properties”);
A script and tool will assist you to simulate the transmission of web server log messages from a static file to Event Streams.

Object Storage Event Streams

+3

Analytics Engine,Data Engine,Key Protect



* 3 hours
* 2023-05-05



[Enhance cloud security by applying context-based restrictions](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-cbr-enhanced-security)Enhance cloud security by applying context-based restrictions

This tutorial walks you through the process of implementing context-based restrictions (CBRs) in your IBM Cloud account. CBRs help you to secure the cloud environment further and move towards a zero trust security model.

Kubernetes service Object Storage

+7

Activity Tracker hosted event search,Container Registry,Secrets Manager,App ID,Cloudant,Key Protect,Log Analysis



* 2 hours
* 2023-06-28



[Apply end to end security to a cloud application](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-cloud-e2e-security)Apply end to end security to a cloud application

This tutorial walks you through key security services available in the IBM Cloud® catalog and how to use them together. An application that provides file sharing will put security concepts into practice.

Kubernetes service Object Storage

+8

Activity Tracker hosted event search,Container Registry,Secrets Manager,App ID,Cloudant,Key Protect,Log Analysis,Cloud Internet Services (CIS)



* 2 hours
* 2023-05-05



[Build, deploy, test and monitor a predictive machine learning model](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-create-deploy-retrain-machine-learning-model)Build, deploy, test and monitor a predictive machine learning model

This tutorial walks you through the process of building a predictive machine learning model, deploying the generated model as an API to be used in your applications and testing the model all of this happening in an integrated and unified self-service experience on IBM Cloud. You will then monitor the deployed model with IBM Watson OpenScale.

Object Storage
The entity extractor that you want to export must be fully trained.

To export an entity extractor, complete the following steps:



1. Open the project with the entity extractor that you want to export.
2. From the Improvement tools panel of the Improve and customize page, expand Teach domain concepts, and then click Extract entities.
3. From the Entity extractors list, find the entity extractor that you want to export.
4. Click the Actions icon for your extractor, and then choose Download model to save the model to your system.

The Download model option is not available unless the model is trained.



The entity extractor model is saved as a .ent file. You can import it into a project in another service instance as a machine learning model, and then apply it to your collections. For more information about importing the model, see [Use imported ML models to find custom terms](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-ml).





 Applying an entity extractor enrichment 

When you publish the extractor, you specify the field where you want the extractor to be applied. If you decide to apply the enrichment to different or more fields later, you can follow these steps to do so.



1. From the navigation panel, click Manage collections.
2. Click to open the collection where you want to apply the enrichment.
3. Click Enrichments.
4. Find the entity extractor name in the list, and then choose a field to apply the enrichment to.
5. Click Apply changes and reprocess.



For more information about how to remove an entity extractor enrichment from a collection, see [Managing enrichments](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-manage-enrichments).



 Entity extractor output 

When the enrichment recognizes one of your custom entities in a document, an entry is added to the enriched_text.entities section of the JSON representation of the document. The section contains occurrences of entities that are recognized by your custom model along with entities that are recognized by the built-in Entities enrichment.
You cannot back up certain data types and must manually re-create them. There are several Content Mining custom user resources that the application does not automatically back up. If data loss occurs, you must either manually re-create the following custom user resources in the Content Mining application or upload a locally saved file that contains the resource:



* Custom map
* Searched document export: You can export a searched document in the Documents view in the Content Mining application, but you cannot reupload it in the application.
* Facet analysis result export: You can download the results of your facet analysis by clicking the Export icon, then Export results, and Export in the Analysis export options dialog box.
* Collection: You can restore a Content Mining collection if you stored the collection locally as a .csv file and then upload it in the application. Otherwise, you must manually re-create the collection.
* Document classifier: You can restore a document classifier if you stored the document classifier locally as a .csv file and then upload it in the application. Otherwise, you must manually re-create the document classifier.
* Custom annotators



* Dictionary: You can restore a dictionary in the application if you stored the dictionary locally as a .csv file and upload it in the application.
* Regular expressions: You can restore a regular expression in the application if you stored the regular expression locally as a .csv file and upload it in the application.
* Machine learning models: You can restore a machine learning model if you stored the model locally as a .zip file and then upload it in the application.
* PEAR File: You can upload a .pear file if you stored the file locally and then upload it in the application.





You cannot back up the following resources locally and must recreate them in the Content Mining application.



* Saved analysis
* Report
* Dashboard







 Restoring your data to a new Watson Discovery instance 

Consider using your backups to restore to a new Discovery instance in a different data center, also known as a region or location.

To begin restoration, first start by reviewing your list of collections and associated data sources, as well as your file backups.



* Create your projects and collections. Use the Discovery tooling, or the API.
Use cognitive APIs to gain insight into tech trends on Hacker News with a twist.



* [Learn more ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://developer.ibm.com/patterns/use-swift-interpret-unstructured-data-hacker-news/)
* [Get the Code ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://github.com/IBM/Hackernews-NLU?cm_sp=IBMCode-_-use-swift-interpret-unstructured-data-hacker-news-_-Get-the-Code)
* [View the Demo ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://youtu.be/sFbI6nu31ss?cm_sp=IBMCode-_-use-swift-interpret-unstructured-data-hacker-news-_-View-the-Demo)







 Accelerate training of machine learning algorithms 

Achieve faster training of machine learning algorithms using Google TensorFlow on IBM PowerAI.



* [Learn more](https://developer.ibm.com/patterns/accelerate-training-of-machine-learning-algorithms/)
* [Get the Code ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://github.com/IBM/powerai-notebook?cm_sp=IBMCode-_-accelerate-training-of-machine-learning-algorithms-_-Get-the-Code)
* [View the Demo !
These updates are required for hosts that are running RHEL. If your hosts are running Red Hat CoreOS images, you do not need to update the packages.

For RHEL 7

Repository 'rhel-server-rhscl-7-rpms' is enabled for this system.
Repository 'rhel-7-server-optional-rpms' is enabled for this system.
Repository 'rhel-7-server-rh-common-rpms' is enabled for this system.
Repository 'rhel-7-server-supplementary-rpms' is enabled for this system.
Repository 'rhel-7-server-extras-rpms' is enabled for this system.

For RHEL 8

Repository 'rhel-8-for-x86_64-appstream-rpms' is enabled for this system.
Repository 'rhel-8-for-x86_64-baseos-rpms' is enabled for this system.

After the host is created, do not install any additional packages, configuration, or other customizations to your hosts. For more information, see [Why can't I install extra software like vulnerability scanning tools on my host?](https://cloud.ibm.com/docs/satellite?topic=satellite-faqshost-software).

You might need to refresh your packages on the host machine. For example, in IBM Cloud infrastructure you can run the following commands to add the required packages.



1. Refresh the Red Hat packages on your machine.

subscription-manager refresh
2. Enable the package repositories on your machine.

RHEL 7:

subscription-manager repos --enable rhel-server-rhscl-7-rpms
subscription-manager repos --enable rhel-7-server-optional-rpms
subscription-manager repos --enable rhel-7-server-rh-common-rpms
subscription-manager repos --enable rhel-7-server-supplementary-rpms
subscription-manager repos --enable rhel-7-server-extras-rpms

RHEL 8:
For more information, see [Upgrading custom models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-upgrade).

Update to Japanese ja-JP_Multimedia next-generation model to support low latency
: The next-generation Japanese model ja-JP_Multimedia now supports low latency. You can use the low_latency parameter with speech recognition requests that use the model. You do not need to upgrade custom models that are based on the updated Japanese base model. For more information about the next-generation models and low latency, see



* [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng)
* [Low latency](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-interimlow-latency)







 3 December 2021 

New Latin American Spanish next-generation telephony model
: The service now offers a next-generation telephony model for Latin American Spanish: es-LA_Telephony. The new model supports low latency and is generally available.

The es-LA_Telephony model applies to all Latin American dialects. It is the equivalent of the previous-generation models that are available for the Argentinian, Chilean, Colombian, Mexican, and Peruvian dialects. If you used a previous-generation model for any of these specific dialects, use the es-LA_Telephony model to migrate to the equivalent next-generation model.

For more information about all available next-generation models, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).

Important: Custom language models based on certain next-generation models must be re-created
: Important: If you created custom language models based on certain next-generation models, you must re-create the custom models. Until you re-create the custom language models, speech recognition requests that attempt to use the custom models fail with HTTP error code 400.
* [Mounting File Storage for Classic on Ubuntu](https://cloud.ibm.com/docs/FileStorage?topic=FileStorage-mountingUbuntu)



To prevent this situation from recurring, the customer might consider the following actions:



* Increasing disk timeout values. For more information, see [VMware® KB - Increasing the disk timeout values for a Linux® 2.6 virtual machine](https://kb.vmware.com/s/article/1009465).
* Adding guest OS tunings. For more information, see [NetApp's recommendations for guest OS tunings for a VMware® vSphere deployment](https://kb.netapp.com/Advice_and_Troubleshooting/Data_Storage_Software/Virtual_Storage_Console_for_VMware_vSphere/What_are_the_guest_OS_tunings_needed_for_a_VMware_vSphere_deployment%3F).
* Reconfiguring Host systems that use NFSv4.1 for NFSv3 for increased resilience during maintenance operations.
* Discontinuing session trunking on host systems that run VMware® ESXi. Session trunking is not supported and is known to cause disruptions.







 I expanded the volume size of my File Storage for Classic by using the Cloud console, but the size on my server is still the same. How do I fix it? 

To see the expanded volume size, mount and remount your existing File Storage for Classic disk on your server. In a VMware® implementation, rescan storage to refresh the VMware® datastore and show the new volume size.





 How do I reconnect storage after a chassis swap? 

Complete the following tasks to connect storage after a swap.



1. Remove the authorization (revoke access) from the storage devices, and then authorize the host again.
2. Discover the storage devices again, with the new credentials that were gained from the reauthorization.
This new field specifies whether an SDU model is enabled for the collection and indicates the model type.
* A similar parameter is available from the Query method. Use it to find documents that are similar to documents of interest to you.



The suggested_refinements parameter of the Query method is deprecated. The suggested_refinements parameter was used to identify dynamic facets from Premium plan data.





 Discovery v1 deprecation announcement 

Watson Discovery v1 is being deprecated. Existing clients who use Watson Discovery v1 are asked to migrate to Watson Discovery v2 before the end-of-support date of 11 July 2023. End of Support means that no v1 instance will work on or after 11 July 2023. For more information about migration, see [Getting the most from Discovery](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-version-choose).





 11 July 2022 

The advanced document view highlights even more enrichments
: In addition to the built-in Entities and Keywords enrichments that are recognized by Watson Natural Language Processing models, the advanced document view now highlights the following types of enrichments:



* Custom dictionary terms
* Terms or numbers that match regular expression patterns that you define
* Custom entities and relationships that are defined by Watson Knowledge Studio machine learning and rules-based models
* Custom entities that are defined by using the entity extractor tool that is available as a beta feature



For more information about enrichments that you can add to your documents, see [Adding domain-specific resources](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain).





 30 June 2022 

Watson SDK support change
: Support for the following SDKs is provided by the Watson community of developers instead of IBM:



* Go
* Ruby
* Swift
* Unity



For more information, see [Watson SDKs](https://cloud.ibm.com/docs/discovery-data?topic=watson-using-sdks).





 1 June 2022 

The entity extractor tool is now easier to use
Click the checkbox next to the user that you want to delete. A box opens.
3. In the box, click Delete. A screen opens.
4. Confirm that you understand that deleting a user cannot be undone by clicking Delete. If the action was a mistake, you can add the user to your directory again, but any information about that user is no longer available.







 Deleting a single user with the API 



1. Obtain your tenant ID.
2. Obtain an IBM Cloud IAM token.

curl -X GET "https://iam.cloud.ibm.com/oidc/token" -H "accept: application/x-www-form-urlencoded"
3. By using the email that is attached to the user, search your directory to find the user's ID.

curl -X GET "https://<region>.appid.cloud.ibm.com/management/v4/<tenantID>/users?email=<user@domain.com>" -H "accept: application/json"
4. Delete the user.

curl -X DELETE "https://<region>.appid.cloud.ibm.com/management/v4/<tenantID>/cloud_directory/remove/<userID>" -H "accept: application/json" -H "authorization: Bearer <token>"







 Deleting multiple users with the API 

You can also bulk delete cloud directory users and their corresponding profiles by using the bulk delete API.

You can delete up to 100 users per request.



1. Obtain your tenant ID.
2. Obtain an IBM Cloud IAM token.

curl -X GET "https://iam.cloud.ibm.com/oidc/token" -H "accept: application/x-www-form-urlencoded"
3. Delete the users by running the following command with a list of user ID's.
* A primary private range of 64 IP addresses



In addition, the following management subnets are also reserved for IBM Cloud for VMware Solutions:



* Two portable private subnets of 64 IP addresses on the first VLAN - one for management and the other one for VTEPS
* Two portable private subnets of 64 IP addresses on the second VLAN - one for VMotion and one for vSAN
* A public portable subnet of 16 IP addresses on the public VLAN

Do not use these components for other purposes, do not change their names, and do not delete them, or the stability of your environment is severely compromised.



If you need more subnets to use, you can obtain IP addresses to use in one of the following ways.



* Option 1 (recommended) - Use VMware NSX® virtual network overlays. A sample VXLAN template is provided upon order. This VXLAN can be used as a starting point for building software-defined networking (SDN). For more information, see [Configuring your network to use the customer-managed NSX Edge](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-vc_esg_config).
* Option 2 - Order your own portable public or private subnets to obtain IP addresses. To distinguish the subnets that you order from the management subnets, you can add notes to all the subnets that you are ordering.





VMware as a Service



* What user accounts do I need for VMware as a Service?

See [User accounts](https://cloud.ibm.com/docs/vmware-service?topic=vmware-service-getting-startedgetting-started-user-accts).
* What if I have issues with VMware as a Service?

If you need assistance with VMware as a Service, contact IBM Support through one of the support channels. For more information, see [Contacting IBM Support](https://cloud.ibm.com/docs/vmware-service?topic=vmware-service-support).



VMware for Classic
Connecting to ESXi with Bare Metal Servers on VPC 

After your bare metal server is running, you can connect to the ESXi direct console user interface (DCUI) and the ESXi web client. You can use the VNC or serial console to access ESXi DCUI. You can access the ESXi web client through a floating IP.

For more information about VMWare deployments, see [Deploying Roll Your Own VMware on IBM Cloud Bare Metal Servers for Virtual Private Cloud](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-vpc-bm-vmware).



 Obtaining your ESXi account name and password 

You need to enter the account name and password to access both ESXi DCUI and the ESXi web client. You can retrieve the account information by using the CLI.

The password is automatically generated and encrypted by using the first SSH key that was provided when the bare metal server was created. You need to decrypt the password by using this SSH key.

For Windows or VMware images, you must use the RSA SSH key type. The Ed25519 SSH key type can't be used to with Windows or VMware images. For more information, see [Getting started with SSH keys](https://cloud.ibm.com/docs/vpc?topic=vpc-ssh-keys).



1. Use the following command to retrieve the account name and identify the SSH key that you use to decrypt the password.

ibmcloud is bare-metal-server-initialization-values $bare_metal_server_id
2. In the response, locate the User accounts and SSH keys fields. The value of “User accounts” is your account name. You need to use the first SSH key to decrypt the password.
3. After you have the SSH key, run the following command to obtain your password:

ibmcloud is bare-metal-server-initialization-values $bare_metal_server_id [--private-key (KEY | @KEY_FILE)]
4.