vania-janet/multiturn-rag-retrieval-data huggingface dataset:
* --type value: Type of the volume (use "ibmcloud pi storage-types" to see available storage types in the targeted region); required if --affinity-policy and --storage-pool are not provided
* --size value: Size of the volume (in GB)
* --shareable: Whether volume can be attached to multiple VMs
* --affinity-policy value: Affinity policy for data volume being created. Valid values are "affinity" and "anti-affinity". If --storage-pool is provided then this cannot be specified
* --affinity-instance value: PVM instance identifier or name to base volume affinity policy against; required if "--affinity-policy affinity" is specified and --affinity-volume is not provided
* --affinity-volume value: Volume identifier or name to base volume affinity policy against; required if "--affinity-policy affinity" is specified and --affinity-instance is not provided
* --anti-affinity-instances value: Space separated list of instance identifiers or names to base volume anit-affinity policy against; required if "--affinity-policy anti-affinity" is specified and --anti-affinity-volumes is not provided
* --anti-affinity-volumes value: Space separated list of volume identifiers or names to base volume anti-affinity policy against; required if "--affinity-policy anti-affinity" is specified and --anti-affinity-instances is not provided
* --replication-enabled: Enables storage replication on the volume. False by default. Cannot set to true if shareable is true.
* --json: Format output in JSON.



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







 ibmcloud pi volume-multi-create 



 Create multiple volume 

ibmcloud pi volume-multi-create VOLUME_BASE_NAME --type TYPE --volume-count COUNT --size SIZE [--shareable] [--json]

ibmcloud pi volume-multi-create VOLUME_BASE_NAME --storage-pool POOL --volume-count COUNT --size SIZE [--shareable] [--json]
: A recommended description of the new custom model.



* Use a localized description that matches the language of the custom model.
* Include a maximum of 128 characters in the description.



The following example creates a new custom language model named Example model. The model is created for the base model en-US-BroadbandModel and has the description Example custom language model. The required Content-Type header specifies that JSON data is being passed to the method.

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{"name": "Example model", "base_model_name": "en-US_BroadbandModel", "description": "Example custom language model"}" "{url}/v1/customizations"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{"name": "Example model", "base_model_name": "en-US_BroadbandModel", "description": "Example custom language model"}" "{url}/v1/customizations"

The example returns the customization ID of the new model. Each custom model is identified by a unique customization ID, which is a Globally Unique Identifier (GUID). You specify a custom model's GUID with the customization_id parameter of calls that are associated with the model.

{
"customization_id": "74f4807e-b5ff-4866-824e-6bba1a84fe96"
}





 Add a corpus to the custom language model 

Once you create your custom language model, the next step is to add domain-specific data to the model. The recommended means of populating a custom model is to add one or more corpora. A corpus is a plain text file that ideally contains sample sentences from your domain.



* For custom models that are based on previous-generation models, the service parses a corpus file and extracts any words that are not in its base vocabulary.
Japanese [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 Korean [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Brazilian Portuguese [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Russian [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Spanish [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Swedish [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 







 Entity type systems for version 2017-02-27 

The following entity type systems are used when you set the version parameter to 2017-02-27.



 Language Entity type system 

 English [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 French [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 German [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Italian [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Korean [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Brazilian Portuguese [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Russian [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1)
The following types are supported:<br><br><br><br> * adjective<br> * adposition<br> * adverb<br> * conjunction<br> * determiner<br> * interjection<br> * noun<br> * numeral<br> * pronoun<br> * residual<br> * verb<br><br><br> 
 Lemma Must have the same lemma as this token. 
 Character Type Must have the same character type as this token. The following types are supported:<br><br><br><br> * Arabic: Contains a sequence of Arabic characters<br> * ChineseNumeral: Contains only Chinese numerals<br> * ClauseEndingPunctuation: Punctuation characters that separate one clause or sentence from the next<br> * Han: Contains Han characters<br> * Hangul: Contains Korean Hangul syllabic characters<br> * Hebrew: Contains a sequence of Hebrew characters<br> * Hiragana: Contains Japanese Hiragana syllabic characters<br> * Ideographic: Contains an ideogram, or symbol representing an idea or thing<br> * Katakana: Contains Japanese Katakana syllabic characters<br> * Lowercase: Contains only lower case alphabetic characters<br> * Numeric: Contains only numeric characters<br> * Punctuation: One or more characters that provide punctuation in the text<br> * Syllabic: Contains syllabic characters<br> * Thai: Contains Thai characters<br> * Titlecase: Starts with a single upper case alphabetic character, followed by one or more lower case alphabetic characters<br> * Uppercase: A token containing only upper case alphabetic characters<br><br><br> 



* Rule Match:

Table 4. Rule matching



 Setting option Description 

 Rule Match Must match the named class. Remember, a class can be derived from a regex, a dictionary, or a rule. If the class specified here was derived from a regular expression, for example, then this token must match the search pattern of the expression. 





11.
For example, FURNITURE_PATIO, FURNITURE_LIVING, and FURNITURE_DINING.
* Append the word NONE to the parent root-level entity label to identify it as the parent. For example, FURNITURE_NONE.
* Leave the labels of entity types that don't have subtypes unchanged. For example, the label APPLIANCES doesn't change.



To retain entity subtype information, complete the following steps:



1. Ensure that the annotation and training of the Knowledge Studio model is completed and the model is ready to be deployed.
2. Export the type system that was used to annotate the documents in your corpus from Knowledge Studio as a .json file.

Follow the appropriate steps for exporting based on your Knowledge Studio deployment type:



IBM Cloud

[Uploading resources from another workspace](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-exportimportwks_exportimport_expimp_type)
IBM Cloud Pak for Data

[Uploading resources from another workspace](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-exportimportwks_exportimport_expimp_type)



3. Modify the type system JSON file. For each subtype, add a new root-level entity type.

For example, the original type system might contain the following types:

{
"id":"b9d6caa2-90ac-47ff-91f6-2149b8ffcf20",
"label":"FURNITURE",
"sireProp":{
"mentionType":null,
"subtypes":["PATIO","LIVING","DINING"],
"roles":["b9d6caa2-90ac-47ff-91f6-2149b8ffcf20","93ba1f27-173f-4714-b31e-77bdd8cb9932"],
"clazz":null,
* POLY [google.golang.org/api] (No Copyright Found) (https://proxy.golang.org/google.golang.org/api/@v/v0.13.0.zip)
* ABOUTIVY.HTML (No Copyright Found) (https://reneefrench.blogspot.com)







 CREATIVE COMMONS ATTRIBUTION SHARE ALIKE 4.0 GENERIC 

The Program includes some or all of the following works licensed under the Creative Commons. The URL to the license is https://creativecommons.org/licenses/by-sa/4.0/legalcode.

GLOB LOGO [Glob] (Not Required) (http://registry.npmjs.org/glob/-/glob-7.1.7.tgz)





 SIL OPEN FONT LICENSE 1.1 

The Program includes some or all of the following works licensed under the SIL OPEN FONT LICENSE 1.1.



* VARELA_ROUND FONTS
* PLEX TYPEFACE[@carbon_charts]
* FONTAWESOME[smartystreets/goconvey]



PREAMBLE

The goals of the Open Font License (OFL) are to stimulate worldwide development of collaborative font projects, to support the font creation efforts of academic and linguistic communities, and to provide a free and open framework in which fonts may be shared and improved in partnership with others.

The OFL allows the licensed fonts to be used, studied, modified and redistributed freely as long as they are not sold by themselves. The fonts, including any derivative works, can be bundled, embedded, redistributed and/or sold with any software provided that any reserved names are not used by derivative works. The fonts and derivatives, however, cannot be released under any other type of license. The requirement for fonts to remain under this license does not apply to any document created using the fonts or their derivatives.

DEFINITIONS

"Font Software" refers to the set of files released by the Copyright Holder(s) under this license and clearly marked as such. This may include source files, build scripts and documentation.
Categories type system (Version 2) 



 Categories returned for version date 2021-08-15 and later 

Categories stock models including and later than Natural Language Understanding version 2021-08-15 use the [IAB Tech Lab 2.0 taxonomy ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://iabtechlab.com/standards/content-taxonomy/).



 LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 

 Automotive 
 Automotive Auto Body Styles 
 Automotive Auto Body Styles Commercial Trucks 
 Automotive Auto Body Styles Sedan 
 Automotive Auto Body Styles Station Wagon 
 Automotive Auto Body Styles SUV 
 Automotive Auto Body Styles Van 
 Automotive Auto Body Styles Convertible 
 Automotive Auto Body Styles Coupe 
 Automotive Auto Body Styles Crossover 
 Automotive Auto Body Styles Hatchback 
 Automotive Auto Body Styles Microcar 
 Automotive Auto Body Styles Minivan 
 Automotive Auto Body Styles Off-Road Vehicles 
 Automotive Auto Body Styles Pickup Trucks 
 Automotive Auto Type 
 Automotive Auto Type Budget Cars 
 Automotive Auto Type Certified Pre-Owned Cars 
 Automotive Auto Type Classic Cars 
 Automotive Auto Type Concept Cars 
 Automotive Auto Type Driverless Cars 
 Automotive Auto Type Green Vehicles 
 Automotive Auto Type Luxury Cars 
 Automotive Auto Type Performance Cars 
 Automotive Car Culture 
 Automotive Dash Cam Videos 
 Automotive Motorcycles 
 Automotive Road-Side Assistance 
 Automotive Scooters 
 Automotive Auto Buying and Selling 
 Automotive Auto Insurance 
 Automotive Auto Parts 
 Automotive Auto Recalls 
 Automotive Auto Repair 
 Automotive Auto Safety 
 Automotive Auto Shows 
 Automotive Auto Technology 
 Automotive Auto Technology Auto Infotainment Technologies 
 Automotive Auto Technology Auto Navigation Systems 
 Automotive Auto Technology Auto Safety Technologies 
 Automotive Auto Rentals 
 Books and Literature 
 Books and Literature Art and Photography Books 
 Books and Literature Biographies 
 Books and Literature Children's Literature 
 Books and Literature Comics and Graphic Novels 
 Books and Literature Cookbooks 
 Books and Literature Fiction 
 Books and Literature Poetry 
 Books and Literature Travel Books 
 Books and Literature Young Adult Literature 
 Business and Finance 
 Business and Finance Business 
 Business and Finance Business Business Accounting & Finance 
 Business and Finance Business Human Resources 
 Business and Finance Business Large Business
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.
You can also upload a [CSV event payload file](https://cloud.ibm.com/docs/IoT?topic=IoT-sim_device_datacsv) to set up the payload.



4. Click New Event Type to add and configure more event types for the device type.
5. Enable Save events as historical data to make your simulated device state data available for later access.
With historical data enabled, the Watson IoT Platform [data management ![External link icon](https://cloud.ibm.com/docs-content/v1/content/dc2670e9228a1dbe562d3d2c7ee63f7a48ba27c3/icons/launch-glyph.svg)](https://www.ibm.com/support/knowledgecenter/SSQP8H/iot/platform/GA_information_management/ga_im_definitions.html) components are created and the simulated device state can be accessed from the associated logical interface.
Important: After you enable the device type to save historical data, you cannot add or remove event types, nor can you modify the fields present in the event payloads.
6. Click Save to create the device type.
You are now ready to create one or more simulated devices.
7. Click Create Simulated Device to create a device and start sending data.
Tip: To create more than one device, click 1 x and select the number of devices to create.
8. To create more simulations, click New Simulation and repeat the configuration steps for each device that you want to apply custom settings to.
The message in the lower-right corner of the screen shows the number of active simulations.
9.
Japanese [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 Korean [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Brazilian Portuguese [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 Russian [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Spanish [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Swedish [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 







 Entity type systems for version 2018-09-21 

The following entity type systems are used when you set the version parameter to 2018-09-21.



 Language Entity type system 

 English [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 French [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 German [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 Italian [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Japanese [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2) 
 Korean [Version 1](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-1) 
 Brazilian Portuguese [Version 2](https://cloud.ibm.com/docs/discovery?topic=discovery-entity-types-version-2)
The following table shows the possible customer response types and the parameter data type compatible with each.



Compatible response types for parameters

 Customer response type Compatible data types Notes 

 options string A selected option is always treated as a string, even if it is a numeric value. 
 number number <br>integer A floating-point number passed as the value for an integer parameter might cause an error, depending on the behavior of the REST API. 
 date string Dates are rendered as YYYY-MM-DD. 
 time string Times are rendered as HH:MM:SS in 24-hour format, converted to the user's time zone. 
 Currency number <br>integer 
 Percent number <br>integer A percent value is passed as an integer (so 75% becomes 75). 
 Free text string 
 Regex string 





 Arrays 

In addition to the supported customer response types, a variable can also contain an array value. If you need to pass an array parameter to an operation, you must create an array session variable:



1. Create a new session variable, either using Set variable values in the step editor or from the Variables > Created by you page. (For more information about how to create a session variable, see [Creating a session variable](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-manage-infocreate-session-variable).)
2. In the Type field, select Any.
3. In the Initial value field, click the Use expression toggle to enable it. Enter an expression that defines an array value (such as ["New York", "London", "Tokyo"], [123, 456, 789], or []).



Because this variable contains an array value, your actions can use expressions with array methods to access or modify the array values. For example, you might want to create a variable that initially contains an empty array ([]) and then use the add() method to build a list one element at a time.
Not all rule types support all comparison types. For example, if you are using FILE_TYPE, it is best to use comparison types REGEX and ENDS_WITH.



 Layer 7 rule properties 



Rule properties

 Property Description 

 Type Specifies the type of rule. Rule types can be HOST_NAME, FILE_TYPE, HEADER, COOKIE, or PATH. 
 Comparison Type Comparison types are used in association with the rule type, key, and value to define a rule and classify traffic. Comparison types can be: REGEX, STARTS_WITH, ENDS_WITH, CONTAINS, and EQUAL_TO. 
 Key The description key for the rule types HEADER and COOKIE. 
 Value For the rule types HEADER and COOKIE, the value is compared against the key. 
 Invert If you set the value to 1, the value of this L7 rule comparison is set to true whenever the specified rule is not matched. 
 Layer 7 Policy ID The unique identifier of the policy to which the rules are attached.
* Array types describe a sequence of elements that can have an arbitrary length, but must all have the same type.
* Map types describe a mapping from keys to values.



Composite types can be nested, as in the following example:

STRUCT<
firstName: STRING,
lastName: STRING,
age: INTEGER,
addresses: ARRAY<
STRUCT<
streetAddress: STRING,
city: STRING,
postalCode: STRING,
country: STRING
>
>
>

Some data formats, particularly CSV, do not support composite types. When your query result contains data with a composite type, use an [INTO clause](https://cloud.ibm.com/docs/sql-query?topic=sql-query-sql-referenceintoClause) to specify an appropriate target format, like JSON.



 Primitive types overview 

The following primitive data types are supported in Data Engine:



* Numeric types
* Strings
* Date and timestamp
* Boolean
* Binary







 Numeric types 

Numeric data types are summarized in the following table.



Table 55. Numeric data types.

 Identifier Type Bytes Minimum value Maximum value 

 TINYINT signed integer 1 -128 127 
 SMALLINT signed integer 2 -32768 32767 
 INT or INTEGER signed integer 4 -2147483648 2147483647 
 INTEGER signed integer 4 -2147483648 2147483647 
 BIGINT or LONG signed integer 8 -9223372036854775808 9223372036854775807 
 FLOAT single precision floating point number 4 n/a n/a 
 DOUBLE double precision floating point number 8 n/a n/a 
 DECIMAL precision of 38 digits n/a -10e37+1 10e37-1 



Numeric literals with fractional digits are parsed as DECIMAL by default. For example, 123.45 is treated as DECIMAL(5,2), and 1.234e-3 is treated as DECIMAL(6,6) with value 0.001234, and not as a floating point number.
types=['management', 'data']
).get_result()

print(response)

postActivityTrackerEventsOptions := service.NewPostActivityTrackerEventsOptions(
[]string{"management", "data"},
)

activityTrackerEvents, response, err := service.PostActivityTrackerEvents(postActivityTrackerEventsOptions)
if err != nil {
panic(err)
}

b, _ := json.MarshalIndent(activityTrackerEvents, "", " ")
fmt.Println(string(b))

The previous Go example requires the following import block:

import (
"encoding/json"
"fmt"
"github.com/IBM/cloudant-go-sdk/cloudantv1"
)

All Go examples require the service object to be initialized. For more information, see the API documentation's [Authentication section](https://cloud.ibm.com/apidocs/cloudant?code=goauthentication-with-external-configuration) for examples.

The following example response shows that the update was accepted:

{
"ok":true
}

If the types field includes invalid event types, then a response similar to the following one is returned:

{
"code":400,
"error":"Unknown event types: <unrecognized events>"
}

If the types field is missing, then a similar response to the following one is returned:

{
"code":400,
"error":"Missing required events: "management""
}

It can take up to 5 minutes for the change to be reflected in the events seen in IBM Cloud Activity Tracker.









 List of events 



 Management events 



Table 1. Management actions

 Action Description 

 cloudantnosqldb.account-status.configure Set the status of an instance. 
 cloudantnosqldb.account-status.read Get the status of an instance.
[Shows picking the Patterns type when creating an entity](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/define-entity.png)
2. Add a regular expression that defines the pattern you want to look for.



* For each entity value, there can be a maximum of up to 5 patterns.
* Each pattern (regular expression) is limited to 512 characters.



![Screen capture of defining a pattern entity](https://cloud.ibm.com/docs-content/v1/content/a2b18c153837bce00a5b4de496e4b4ca64bbf56e/assistant-data/images/patternents1.png)

Follow these syntax rules:



* Entity patterns may not contain:



* Positive repetitions (for example x+)
* Backreferences (for example g1)
* Conditional branches (for example (?(cond)true))



* When a pattern entity starts or ends with a Unicode character, and includes word boundaries, for example bšb, the pattern match does not match the word boundary correctly. In this example, for input š zkouška, the match returns Group 0: 6-7 š (š zkouška), instead of the correct Group 0: 0-1 š (šzkouška).

The regular expression engine is loosely based on the Java regular expression engine. You will see an error if you try to upload an unsupported pattern, either by using the API or from within the Watson Assistant user interface.



For example, for entity ContactInfo, the patterns for phone, email, and website values can be defined as follows:



* Phone



* localPhone: (d{3})-(d{4}), e.g. 426-4968
* fullUSphone: (d{3})-(d{3})-(d{4}), e.g. 800-426-4968