vania-janet/multiturn-rag-retrieval-data huggingface dataset:
OBJECT-TYPE not found with the identifier: IDENTIFIER.

Error type: Services

Response code: 404





 ST0020 error message 

The request parameter with name PARAMETER-NAME is not supported.

Error type: Services

Response code: 404





 ST0021 error message 

OBJECT-TYPE with the identifier OBJECT-IDENTIFIER already exists.

Error type: Services

Response code: 404





 ST0022 error message 

Failed to send credential audit event.

Error type: Services

Response code: 500





 ST0023 error message 

Unable to update configuration with name CONFIGURATION-NAME. NUMBER assignments depend on this configuration. Remove them before you update this configuration or create a new configuration.

Error type: Services

Response code: 500





 ST0024 error message 

The given parameter value VALUE does not match with regular expression REG-EX.

Error type: Services

Response code: 400





 ST0025 error message 

The length of given parameter value VALUE is less than minimum allowed length MININUM-LENGTH.

Error type: Services

Response code: 400





 ST0026 error message 

The length of given parameter value VALUE is greater than maximum allowed length MAXIMUM-LENGTH.

Error type: Services

Response code: 400





 ST0027 error message 

Unable to update OBJECT-TYPE with the identifier: IDENTIFIER. No OBJECT-TYPE parameter found in the request body to update.

Error type: Bad request

Response code: 404





 ST0028 error message 

Unable to add or update storage class as storage class template is not registered for TEMPLATE and VERSION.

Error type: Services

Response code: 404





 ST0030 error message 

Cluster not found corresponding to the given cluster ID: CLUSTER-ID

Error type: Bad request

How to fix it: To list all the Satellite clusters that you have access to, run ibmcloud oc cluster ls --provider=satellite.

Response code: 404





 ST0031 error message 

The required input query parameter PARAMETER-NAME is missing.

Error type: Bad request

Response code: 404





 ST0032 error message 

The PARAMETER and PARAMETER parameters are exclusive.
For more information, see [Protecting Cloud Databases resources](https://cloud.ibm.com/docs/cloud-databases?topic=cloud-databases-cbrcbr-overview-protect-services).
7. Click Continue.
8. Define the allowed endpoint types.



* Keep the toggle set to No to allow all endpoint types.
* Set the toggle to Yes to allow only specific endpoint types, then choose from the list.



9. Select a network zone or zones that you have already created, or create a new network zone by clicking Create.

Contexts define from where your resources can be accessed, effectively linking your network zone to your rule.
10. Click Add to add your configuration to the summary.
11. Click Next.
12. Name your rule.
13. Select how you want to enforce the rule.

Report-only is not available for Cloud Databases.







 Creating rules in the CLI 

To create rules in the CLI, [install the context-based restrictions CLI plug-in](https://cloud.ibm.com/docs/account?topic=account-cbr-plugin).

To create a rule in the CLI, you need the appropriate Cloud Databases service_name:



* databases-for-etcd
* databases-for-elasticsearch
* databases-for-mongodb
* databases-for-postgresql
* databases-for-redis
* messages-for-rabbitmq
* databases-for-cassandra
* databases-for-enterprisedb
* database-for-mysql



Create a rule by using a command like:

ibmcloud cbr rule-create --enforcement-mode enabled --context-attributes "networkZoneId=<ZONE-ID>" --resource-group-id <RESOURCE_GROUP_ID> --service-name <SERVICE-NAME> --service-instance <SERVICE-INSTANCE> --api-types crn:v1:bluemix:public:context-based-restrictions::::api-type:data-plane --description <DESCRIPTION>
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-data?topic=watson-knowledge-studio-data-glossarygloss_D) and [lemma](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-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-data?topic=watson-knowledge-studio-data-glossarygloss_B) and [training data](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-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.
App Configuration service allows you to configure feature flags and properties with the following data types: Boolean, Numeric, String. The String data type can be of the format of a TEXT string, JSON, or YAML. The SDK processes each format as shown in the table 1.



Table 1. Example outputs

 Feature or Property value Data type Data format Type of data returned by GetCurrentValue() Example output 

 true BOOLEAN not applicable bool true 
 25 NUMERIC not applicable float64 25 
 a string text STRING TEXT string a string text 
 {"firefox": { <br>"name": "Firefox", <br>"pref_url": "about:config" <br>}} STRING JSON map[string]interface{} map[browsers:mapfirefox:mapname:Firefox pref_url:about:config]]] 
 men: <br>- John Smith <br>- Bill Jones <br>women: <br>- Mary Smith <br>- Susan Williams STRING YAML java.lang.String `"men:<br><br><br><br> * John Smith<br> * Bill Jones\women:<br> * Mary Smith<br> * Susan Williams"`<br><br><br> 





 Feature flag 

Feature feature = appConfigClient.getFeature("json-feature");
if (feature != null) {
feature.getFeatureDataType(); // STRING
feature.getFeatureDataFormat(); // JSON
feature.getCurrentValue(entityId, entityAttributes); // JSONObject or JSONArray is returned
}

// Example Below
// input json :- [{"role": "developer", "description": "do coding"},{"role": "tester", "description": "do testing"}]
// expected output :- "do coding"
Type Example 

 number 4.56 
 string example value 
 bool false 
 map(string) {key1 = "value1", key2 = "value2"} 
 set(string) ["hello", "he"] 
 map(number) {internal = 8080, external = 2020} 
 list(string) ["us-south", "eu-gb"] 
 list ["value", 30] 
 list(list(string)) :[{internal = 8300 external = 8300 protocol = "tcp"},{internal = 8301 external = 8301 protocol = "ldp" } ] : list(object({internal = number external = number protocol = string})) : [{internal = 8300 external = 8300 protocol = "tcp"} {internal = 8301 external = 8301 protocol = "ldp"}] 









 Using Terraform templates in IBM Cloud 

You can [Manage IBM Cloud resources with Schematics](https://cloud.ibm.com/docs/schematics?topic=schematics-manage-lifecycle) to start creating, updating, or deleting IBM Cloud resources with Terraform.







 Creating a workspace using the CLI 



1. Create a JSON file on your local workstation and add your workspace configuration. For more configuration options when creating the workspace, see the [ibmcloud schematics workspace new command](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referenceschematics-workspace-new).

To create a workspace with Agent, refer to [ibmcloud schematics workspace new with Agent](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referenceschematics-agent-new) command.

{
"name": "<workspace_name>",
"type": [
"<terraform_version>"
],
"location": "<location>",
"description": "<workspace_description>",
About 

Use IBM Watson® Knowledge Studio to create a machine learning model that understands the linguistic nuances, meaning, and relationships specific to your industry or to create a rule-based model that finds entities in documents based on rules that you define.



 Identify custom entities and relations 

An entities and relations workspace enables you to create your own entity type system and train a custom model that can recognize custom entities in text. With machine learning models, you can also define custom relation types and train the model to recognize when two entities are related. Consider the following example sentence.

ABC Motors has received great reviews for its new 2020 Lightning.

A custom entities and relations model could be trained to recognize "2020 Lightning" as a Vehicle entity, and "ABC Motors" as a Manufacturer entity. The model could also be trained to recognize that the two entities are connected by a isManufacturedBy relation.



 Build a machine learning model 

Knowledge Studio provides easy-to-use tools for annotating unstructured domain literature, and uses those annotations to create a custom machine learning model that understands the language of the domain. The accuracy of the model improves through iterative testing, ultimately resulting in an algorithm that can learn from the patterns that it sees and recognize those patterns in large collections of new documents. You can deploy the finished machine learning model to other Watson cloud-based offerings and cognitive solutions to find and extract mentions of relations and entities, including entity coreferences.

![Overview of the process to build a machine learning model](https://cloud.ibm.com/docs-content/v1/content/50eddb8fd81f33092880335ff107a78ff5cd0f65/watson-knowledge-studio/images/wks-ovw-anno.svg) Figure 1. Overview of the process to build a machine learning model



1. Based on a set of domain-specific source documents, the team creates a type system that defines entity types and relation types for the information of interest to the application that will use the model.
2.
By using the device simulator, you can create simulated devices for existing device types or create new device types and devices. Each simulated device can use unique event details, but you can also create a default configuration that is applied to all devices.



 Before you begin 

Enable device simulation for your user ID.



1. Log in to Watson IoT Platform.
2. From the main navigation pane, select Settings.
3. In the Data and Devices section, activate the device simulator.



If device simulation is enabled for your user ID, all enabled simulated devices automatically start when you log in to the Watson IoT Platform dashboard.





 Simulating devices 

When activated, the device simulator is available on all dashboard pages. A message in the lower-right corner of the screen indicates the number of simulations that are running.

Important: The simulated devices and device types that you define for your user ID are available for other users of the Watson IoT Platform organization. However, to generate simulated data for your devices, you must be logged in to the Watson IoT Platform dashboard in an active user session.

To simulate device data:



1. From the Watson IoT Platform dashboard, click the "0 Simulations running" message.
2. In the Simulations configuration window that opens, click Create Simulation.
3. Configure the simulation details.
Click Import/Export Simulation to import an [existing configuration](https://cloud.ibm.com/docs/services/IoT?topic=IoT-sim_device_datareuse) or manually configure the details:



1. Select an existing device type that you want to simulate data for or create a new device type by typing the device type name.
2. Modify the default name for the event type.
3. Under Schedule, set the frequency of the event.
4. Edit the event payload.
Edit the sample JSON payload by adding properties and their corresponding values or use [functions](https://cloud.ibm.com/docs/services/IoT?topic=IoT-sim_device_datafunctions) to add dynamically generated data points.
* Requires an [engine configuration](https://cloud.ibm.com/docs/secrets-manager?topic=secrets-manager-secrets-engines) before secrets can be created in the service.



 Supported features by secret type 

The following table compares and contrasts some common characteristics between the secret types.



Table 2. Feature comparison between secret types

 Arbitrary secrets IAM credentials User credentials Key-value secrets Imported certificates Public certificates Private certificates 

 [Automatic rotation](https://cloud.ibm.com/docs/secrets-manager?topic=secrets-manager-automatic-rotation) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/checkmark-icon.svg)<br><br>[1] ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/991f60ddbe3ab0a9aa69481a07a0a73b359fa329/icons/checkmark-icon.svg) 
 [Manual rotation](https://cloud.ibm.com/docs/secrets-manager?topic=secrets-manager-manual-rotation) !
By using the device simulator, you can create simulated devices for existing device types or create new device types and devices. Each simulated device can use unique event details, but you can also create a default configuration that is applied to all devices.



 Before you begin 

Enable device simulation for your user ID.



1. Log in to Watson IoT Platform.
2. From the main navigation pane, select Settings.
3. In the Data and Devices section, activate the device simulator.



If device simulation is enabled for your user ID, all enabled simulated devices automatically start when you log in to the Watson IoT Platform dashboard.





 Simulating devices 

When activated, the device simulator is available on all dashboard pages. A message in the lower-right corner of the screen indicates the number of simulations that are running.

Important: The simulated devices and device types that you define for your user ID are available for other users of the Watson IoT Platform organization. However, to generate simulated data for your devices, you must be logged in to the Watson IoT Platform dashboard in an active user session.

To simulate device data:



1. From the Watson IoT Platform dashboard, click the "0 Simulations running" message.
2. In the Simulations configuration window that opens, click Create Simulation.
3. Configure the simulation details.
Click Import/Export Simulation to import an [existing configuration](https://cloud.ibm.com/docs/IoT?topic=IoT-sim_device_datareuse) or manually configure the details:



1. Select an existing device type that you want to simulate data for or create a new device type by typing the device type name.
2. Modify the default name for the event type.
3. Under Schedule, set the frequency of the event.
4. Edit the event payload.
Edit the sample JSON payload by adding properties and their corresponding values or use [functions](https://cloud.ibm.com/docs/IoT?topic=IoT-sim_device_datafunctions) to add dynamically generated data points.
can't get credentials: Wrong Secret Type.Provided secret of type XXXX.Expected type ibm/ibmc-s3fs The Kubernetes secret that you created for your IBM Cloud Object Storage service instance does not include the type: ibm/ibmc-s3fs. Edit the Kubernetes secret that holds your IBM Cloud Object Storage credentials to add or change the type to ibm/ibmc-s3fs. 
 Bad value for ibm.io/object-store-endpoint XXXX: scheme is missing. Must be of the form http://<hostname> or https://<hostname>Bad value for ibm.io/iam-endpoint XXXX: scheme is missing. Must be of the form http://<hostname> or https://<hostname> The s3fs API or IAM API endpoint has the wrong format, or the s3fs API endpoint could not be retrieved based on your cluster location. See [PVC creation fails due to wrong s3fs API endpoint](https://cloud.ibm.com/docs/openshift?topic=openshift-cos_api_endpoint_failure). 
 object-path can't be set when auto create is enabled You specified an existing subdirectory in your bucket that you want to mount to your PVC by using the ibm.io/object-path annotation. If you set a subdirectory, you must disable the bucket auto create feature. In your PVC, set ibm.io/auto-create-bucket: "false" and provide the name of the existing bucket in ibm.io/bucket. 
 bucket auto-create must be enabled when bucket auto-delete is enabled In your PVC, you set ibm.io/auto-delete-bucket: true to automatically delete your data, the bucket, and the PV when you remove the PVC. This option requires ibm.io/auto-create-bucket to be set to true, and ibm.io/bucket to be set to "" at the same time.
From the classic Watson Assistant experience, see [Creating a search skill](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-skill-search-add).







 Content Mining 

Use this project type to discover hidden insights, trends, and relationships in your data.

Only users of installed deployments (IBM Cloud Pak for Data) or Premium or Enterprise plan managed deployments can create this type of project.

This project type is especially useful for analyzing structured data, such as data that you add by uploading a CSV file or by connecting to a database data source. You can add only one collection to a project of this type from the Discovery user interface.

Documents that you add as part of the initial collection are automatically enriched in the following way:



* Parts of speech are identified and tagged.



After you add a collection and optionally apply more enrichments to the data, a full-featured application is available for you to deploy. You can use the application to research your data in depth. For more information about using the application, see [Analyzing your data with the deployed Content Mining application](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-contentminerapp).

From the Content Mining application, you can create the following enrichment types which are not available in other project types:



* [Document classifier](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-cm-doc-classifier)
* [Phrase sentiment](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-cm-phrase-sentiment)



You can create a collection from the deployed Content Mining application. The collection that you create is not added to your existing Content Mining project. A new Content Mining project is created to store the collection. The collection can contain an uploaded CSV file only. The project that is generated is given the name that you specify for the collection.

Because the data that you add to this type of project is often structured, consider using the API to submit queries in the Discovery Query Language (DQL).
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?topic=watson-knowledge-studio-wks_rule_publish) or use it to [pre-annotate documents](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-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.
In the downloaded corpus, for all mentions with a subtype defined, update the type information for the mention to specify the new root-level entity type.

For example, the original type system might include the PATIO subtype mention:

{
"id" : "Blogs_shopper.com_dc5cf4764d91f87575b17ac8a5268462.en-M92",
"source" : "IMPORT",
"properties" : {
"SIRE_ENTITY_CLASS" : "SPC",
"SIRE_MENTION_CLASS" : "SPC",
"SIRE_ENTITY_LEVEL" : "NONE",
"SIRE_ENTITY_SUBTYPE" : "PATIO",
"SIRE_MENTION_ROLE" : "FURNITURE",
"SIRE_MENTION_TYPE" : "NONE"
},
"type" : "FURNITURE",
"begin" : 3221,
"end" : 3234,
"inCoref" : false
},
Show more

Replace the value of the SIRE_MENTION_ROLE and type for the mention with the new root-level entity label, such as FURNITURE_PATIO. Specify NONE as the SIRE_ENTITY_SUBTYPE value.

{
"id" : "Blogs_shopper.com_dc5cf4764d91f87575b17ac8a5268462.en-M92",
"source" : "IMPORT",
"properties" : {
"SIRE_ENTITY_CLASS" : "SPC",
"SIRE_MENTION_CLASS" : "SPC",
"SIRE_ENTITY_LEVEL" : "NONE",
"SIRE_ENTITY_SUBTYPE" : "NONE",
"SIRE_MENTION_ROLE" : "FURNITURE_PATIO",
"SIRE_MENTION_TYPE" : "NONE"
},
"type" : "FURNITURE_PATIO",
"begin" : 3221,
"end" : 3234,
"inCoref" : false
},
Show more
Click Join channel.



In these tutorials, we create all of our peers using default options, which means that every peer uses CouchDB. As a result, you don't have to worry about a conflict between the database type used by your peer and any other peers on the channel. However, in a production scenario, a best practice will be to ensure that the peer you are joining to this channel uses the same database type as other peers on the channel. For more information, see [LevelDB vs CouchDB](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-adv-deploymentibp-console-adv-deployment-level-couch).





 Step four: Creating a channel 

In this tutorial, we will presume that users will not be attempting to edit any of the advanced options. For information about editing advanced options both before and after a channel has been created, see [Advanced channel deployment and management](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-governibp-console-govern).



 Join the consortium hosted by the ordering service 

A peer organization must be known to the ordering service before it can create a channel (this is also known as joining the "consortium", the list of organizations known to the ordering service). This is because channels are, at a technical level, messaging paths between peers through the ordering service. Just as a peer can be joined to multiple channels without information passing from one channel to another, so too can an ordering service have multiple channels running through it without exposing data to organizations on other channels.

Because only ordering service admins can add peer organizations to the consortium, you need to either:



* Be the ordering service admin. In which case you can add the peer organization you created to the consortium directly.
* Send MSP information to the ordering service admin, who will add your organization to their consortium.



In the next step, we will show how to add your peer organization to a consortium hosted by an ordering service in another IBM Blockchain Platform service instance. The tutorial assumes that you have created the ordering service and can follow every step yourself.