vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Additional clusters (optional) 

Optionally, you can order a separate cluster for workloads by toggling the Deploy separate workload cluster switch on.

Also, you can order a dedicated cluster for the network edge and the firewall components that are required for the Juniper® vSRX service by toggling the Deploy gateway cluster switch on. The gateway cluster is deployed in the same data center as the consolidated cluster.

The data center of the consolidated cluster must be available for gateway cluster deployment. Gateway cluster deployment is not supported for Dallas 09.



 Workload cluster configuration 

By default, the name of the separate workload cluster is set to the instance name-workload.

You can also specify a new cluster name that meets the requirements listed in [Cluster name](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-vc_orderinginstance-consold-clustervc_orderinginstance-consold-cluster-name).

The configuration options for the separate workload cluster are similar to the consolidated cluster, with the exception of the number of bare metal servers that you can order:



* If you are planning to use NFS storage, you can order 2-59 servers. However, for production workloads, a minimum of 3 servers is recommended. For more information, see [Is a two-node vCenter Server instance highly available?](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-faq-vmwaresolutionsis-a-two-node-vcenter-server-instance-highly-available)
* If you are planning to use vSAN™ storage, you can order 4-59 servers.
* All servers that you order have the same configuration.





 Reuse VLANs from the consolidated cluster 

This setting is specific to the separate workload cluster. In the VLANs section of the workload cluster configuration, you can select the Reuse VLANs from the consolidated cluster option to reuse the public and private VLANs of the consolidated cluster.







 Gateway cluster configuration 

By default, the name of the gateway cluster is set to the instance name-edge.
Understanding SSML 

The Speech Synthesis Markup Language (SSML) is an XML-based markup language that provides annotations of text for speech-synthesis applications. It is a recommendation of the W3C Voice-Browser Working Group that has been adopted as the standard markup language for speech synthesis by the VoiceXML 2.0 specification. SSML provides developers of speech applications with a standard way to control aspects of the synthesis process by enabling them to specify pronunciation, volume, pitch, speed, and other attributes via markup. You can use SSML to control the synthesis of your text with all supported languages.

The IBM Watson® Text to Speech service bases its support on SSML version 1.1, which was recommended by W3C on 7 September 2010. For more information about the W3C SSML recommendation, see [W3C Speech Synthesis Markup Language (SSML) Version 1.1](http://www.w3.org/TR/speech-synthesis/).



 Introduction to SSML 

SSML operates by augmenting the plain text that is passed to a synthesizer with a predefined set of elements, or tags. An XML parser first separates the plain input text from the markup specifications. The specifications are then processed and sent as a set of instructions in a form that can be understood by the synthesizer to produce the desired effects. For the XML parser to carry out this job, the markup needs to be well formed; for example, elements must be closed and multiple elements must be properly nested. For an introduction to basic XML concepts, see [w3schools.com/xml/xml_whatis.asp](http://www.w3schools.com/xml/xml_whatis.asp).

An SSML element is anything contained within, and including, an opening tag and its matching closing tag. As shown in the following example, an element can contain a combination of other elements (tags can be nested) and text. Additionally, elements can require or optionally accept attributes set to particular values.

<tag1>
<tag2 attributeName="attributeValue">
... some text ...
</tag2>
location The location of the date as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Contract amounts 

The contract_amounts array identifies the monetary amounts specified in the document.



Amount values

 contract_amounts Description 

 confidence_level The confidence level of the identification of the contract amount. Possible values include High, Medium, and Low. 
 text A contract amount, listed as a string. 
 normalized_text The normalized text, if applicable. 
 interpretation The details of the normalized text, if applicable. 
 value A string that lists the value that was found in the normalized text. 
 numeric_value An integer or float expressing the numeric value of the value key. 
 unit A string that lists the unit of the value that was found in the normalized text. 
 location The location of the contract amount as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Termination dates 

The termination_dates array identifies the document's termination dates.



Termination datae values

 termination_dates Description 

 confidence_level The confidence level of the identification of the termination date. Possible values include High, Medium, and Low. 
 text The termination date, listed as a string. 
 text_normalized The normalized text of the text if available, listed as a string. 
 location The location of the termination date as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Contract types 

The contract_types array identifies the document's contract type or types as declared in the document.



Contract type values

 contract_types Description 

 confidence_level The confidence level of the identification of the contract type. Possible values include High, Medium, and Low. 
 text The contract type, listed as a string. 
 location The location of the contract type as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Contract terms
* Last updated: Fri Mar 10 10:35:42 2023
* Last change: Fri Mar 10 09:52:08 2023 by root via cibadmin on cl-nfs-1
* 2 nodes configured
* 1 resource instance configured

Node List:
* Online: [ cl-nfs-1 cl-nfs-2 ]

Full List of Resources:
* res_fence_ibm_powervs	(stonith:fence_ibm_powervs):	 Started cl-nfs-1

Daemon Status:
corosync: active/disabled
pacemaker: active/disabled
pcsd: active/enabled



 Configuring general cluster properties 

To prevent the cluster from moving healthy resources to another node (for example when you restart the cluster on a previously failed node), you can set the default value for the resource-stickiness meta attribute to 1.

On NODE1, run the following command.

pcs resource defaults resource-stickiness=1





 Configuring NFS resource group and resources 

Use the following steps to configure the NFS resources in the cluster.



 Creating the LVM-activate resource 

To make sure that all NFS resources run on the same node, configure them as part of the resource group nfsgroup.

This resource group is created with the first resource. Resources start in the order in which they are added to the group. The resources stop in reverse order.

On NODE1, run the following command.

pcs resource create nfs_vg ocf:heartbeat:LVM-activate vgname=${NFS_vg} vg_access_mode=system_id --group nfsgroup

To avoid data corruption, don't configure more than one LVM-activate resource that uses the same LVM volume group in an active-passive HA configuration. Don't configure an LVM-activate resource as a clone resource in an active-passive HA configuration.

Check the status of the cluster and verify that resource nfs_vg is active.

On NODE1, run the following command.

pcs resource status

Sample output:
For a relation mention to exist, text must explicitly define the relation and bind mentions of the two entities together, and must do so within a single sentence. For example, the sentence Mary works for IBM is textual evidence of the employedBy relation type.

For some relation types, the order of entity mentions matters. For example, the employedBy relation type allows the entity type PERSON or PEOPLE as the first mention in the relationship, and ORGANIZATION or GPE as the second mention, but not the other way around. MaryemployedByIBM is a valid relationship. IBMemployedByMary is not. For some relation types, such as spouseOf, colleague, or sibling, order does not matter. When you define a relation type where order is not important, a best practice is to add information to the annotation guidelines to regularize how the relation type is used. A convention for noting such symmetrical relations is to say that the entity mention that occurs first in the text should be the first one in the relation.

Related concepts:

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







 Adding a type system to the workspace 

You must create or upload a type system before you begin any annotation tasks.



 About this task 

The following naming rules apply to type system entries:



* Names cannot contain spaces.
* Use only the following alphanumeric ASCII characters and the underscore character in values that you add to the type system: A through Z, a through z, 0 through 9.
* The first character in an entity or relation type name must be alphabetical.
* Entity type names cannot be longer than 64 characters.
* Relation type names cannot be longer than 128 characters.



By convention, entity type names are specified in uppercase characters (ORGANIZATION) and relation type names are specified in camel case (employedBy). But, this convention is optional.





 Procedure 



1. Log in as a Knowledge Studio administrator or project manager and open the Assets> Entity Types page.
2. Choose one of the following methods to create a type system:
About Speech to Text 

The IBM Watson® Speech to Text service provides speech transcription capabilities for your applications. The service leverages machine learning to combine knowledge of grammar, language structure, and the composition of audio and voice signals to accurately transcribe the human voice. It continuously updates and refines its transcription as it receives more speech.

The service provides APIs that make it suitable for any application where speech is the input and a textual transcript is the output. It can be used for applications such as voice-automated chatbots, analytic tools for customer-service call centers, and multimedia transcription. Voice control of embedded devices, transcribing meetings and conference calls, and dictating messages and notes are also possible applications, among many others.

The service is ideal for clients who need to extract high-quality speech transcripts from call center audio. Clients in industries such as financial services, healthcare, insurance, and telecommunication can develop cloud-native applications for customer care, customer voice, agent assistance, and other solutions.



 Product versions 

Speech to Text can be deployed as a managed cloud service or can be installed on premises. This documentation describes how to use both versions of the product. Information such as topics, paragraphs, and examples that applies exclusively to one version is clearly denoted:



IBM Cloud

for managed instances of Speech to Text that are hosted on IBM Cloud or for instances that are hosted on [IBM Cloud Pak for Data as a Service](https://dataplatform.cloud.ibm.com/docs/content/wsj/landings/wstt.html).



* For information about all service updates, see the [Release notes for Speech to Text for IBM Cloud](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-release-notes).
* For information about the latest service update, see the [2 May 2023](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-release-notesspeech-to-text-2may2023) service update in the release notes.



IBM Cloud Pak for Data

for installed or on-premises instances of Speech to Text for IBM Cloud Pak for Data.
INCIDENT impactPoint The PERSON, PART_OF_CAR, STRUCTURE, VEHICLE, MANUFACTURER, MODEL, or MODEL_YEAR ^1^ that is hit or involved in the accident. <br> <br>impactPoint for STRUCTURE does not include mere specifying the location of an impact that does not involve that STRUCTURE, so it does not apply to two vehicles colliding in an [intersection]STRUCTURE, but does apply to a vehicle striking an [embankment]STRUCTURE. 







 Table notes 



1. The notation VEHICLE/MODEL/MANUFACTURER/MODEL_YEAR refers to a mention of a vehicle. The last three are respectively for cases in which the text says something like "the Accord", "the Honda", or, probably rarely, "the '99". The four entity types are in priority order, so in "the driver of the '99 Honda Accord", the relation would be driver (as PERSON) occupantOf Accord (as MODEL), in which case Accord would have the hasProperty relation with both Honda and '99.
2. MODEL and MANUFACTURER can only be the first argument of hasProperty, only when they appear as nouns (references to a vehicle). MODEL can have the relation hasProperty to MANUFACTURER and MODEL_YEAR, as in "the '99 Honda Accord drove". MANUFACTURER can only have the hasProperty relation to MODEL_YEAR, as in "the '99 Honda drove".
In some cases, for example, you might want to change specific aspects of the audio such as which words are emphasized and the location or duration of pauses. In other cases, you might want to change more subtle aspects of the synthesis. For instance, you might feel that the synthesized audio sounds robotic, unnatural, or that the tone is unsatisfactory. These are the types of problems that Tune by Example can address.

To use the Tune by Example feature, you record a user who reads your text as you would like to hear it spoken by one of the service's voices. You then add your text and audio to a custom model in the form of a custom prompt. The service learns the preferred prosody of the text from the audio sample. When you include the prompt in a speech synthesis request by using the SSML extension <ibm:prompt id="{prompt_id}"/>, the voice that speaks the text of the prompt emulates the prosody of the prompt.

You can create an optional but recommended speaker model for a user who records spoken prompts. You then associate the speaker model with prompts that are spoken by that user. The addition of a speaker model can make an appreciable difference in the quality of a synthesized prompt. For example, the service can produce short prompts with more confidence if it knows the speaker's regular intonation. Because the lack of a defined speaker can potentially compromise the quality of a prompt, you are strongly encouraged to associate a speaker model with each custom prompt.

Because custom prompts are added to custom models, you must specify a prompt's custom model with a speech synthesis request to use that prompt. Speaker models, however, are independent from custom models. The relationship of speaker models to both custom models and custom prompts is one-to-many. The same speaker model can be associated with multiple prompts that are defined in different custom models. Moreover, you do not specify a speaker model with a synthesis request.



 Tune by Example and word pronunciation 

The service’s default pronunciation and tokenization rules might cause an unusual word that occurs in a prompt to be pronounced in an unsatisfactory way. However, Tune by Example is not intended to change a word's pronunciation.
(https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/tut-sdu-typical-labels.png)

Figure 17. Subtitle, footnote, and text labels are being applied

As you label and submit pages, the model learns from the annotations that you make. Gradually, the labels that are applied automatically become accurate and don't require any adjustments.

If the tool becomes overzealous in the application of labels, apply the text label to sections of standard text to correct it.
12. For tables, select the table caption and the entire table and label them with the table label.

Zoom

![Shows how to label a table in the SDU tool.](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/tut-sdu-table.png)

Figure 18. A table is being labeled
13. When a page contains an image, the image is not displayed in the representation of the page.

Images are never replicated. However, you can capture the text from an image so that the image text can be searched. To do so, enable the Optical Character Recognition (OCR) feature when you create a collection. OCR is helpful in cases where you want to extract text from images, such as from a scanned PDF, where the text is embedded in an image. For more information, see [Optical character recognition](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-collectionsocr).

After you enable OCR, if you want to remove annotated image text from the collection index, you can label the image so that you can exclude the associated text. You will learn about how to configure the index in the next procedure.

Zoom

!
The following names are contained in the external dictionary last.dict:

Dictionary for surnames
Anthony
Lehman
Radcliff







 The consolidate on clause 

The optional consolidate on clause specifies how overlapping spans are resolved across tuples that are output by a select or extract statement. Tuples with non-overlapping spans are not affected when this clause is used.



 Syntax 

The following code is an example of the general structure of this clause:

consolidate on <target>
[using '<policy>' with priority from <priority_column>
<priority_order> ]]]





 Description 



* <target>

Specifies a column in a view in the from clause, or an expression that is composed of scalar function calls involving columns of views that are specified in the from clause as arguments.
* '<policy>'

Specifies one of the following consolidation policies that is supported by Text Analytics:



* ContainedWithin

This policy is the default. If spans A and B overlap, and A completely contains B, this policy then removes the tuple that contains span B from the output. If A and B are the same, then it removes one of them. The choice of which tuple to remove is arbitrary.
* NotContainedWithin

If spans A and B overlap, and A completely contains B, this policy then removes the span A from the output. If A and B are the same, then it removes one of them. The choice of which tuple to remove is arbitrary.
* ContainsButNotEqual

This policy is the same as ContainedWithin, except that the spans that are exactly equal are retained.
* ExactMatch

If a set of spans covers the same region of text, this policy returns exactly one of them. All other spans are left untouched.
* LeftToRight

This policy processes the spans in order from left to right. When overlap occurs, it retains the leftmost, longest, non-overlapping span. This policy emulates the overlap-handling policy of most regular expression engines.



* <priority_column>

Specifies a column of type Text, String, Integer, or Float. Can be specified only with the LeftToRight consolidation policy.
Contract type values

 contract_types Description 

 confidence_level The confidence level of the identification of the contract type. Possible values include High, Medium, and Low. 
 text The contract type, listed as a string. 
 location The location of the contract type as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Contract terms 

The contract_terms array identifies the duration or durations of the contract as declared in the document.



Contract term values

 contract_terms Description 

 confidence_level The confidence level of the identification of the contract term. Possible values include High, Medium, and Low. 
 text The contract term, listed as a string. 
 normalized_text The normalized text, if applicable. 
 interpretation The details of the normalized text, if applicable. 
 value A string that lists the value that was found in the normalized text. 
 numeric_value An integer or float expressing the numeric value of the value key. 
 unit A string that lists the unit of the value that was found in the normalized text. 
 location The location of the contract term as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support. 







 Payment terms 

The payment_terms array identifies the payment duration or durations as declared in the document.



Payment term values

 payment_terms Description 

 confidence_level The confidence level of the identification of the payment term. Possible values include High, Medium, and Low. 
 text The payment term, listed as a string. 
 normalized_text The normalized text, if applicable. 
 interpretation The details of the normalized text, if applicable. 
 value A string that lists the value that was found in the normalized text. 
 numeric_value An integer or float expressing the numeric value of the value key. 
 unit A string that lists the unit of the value that was found in the normalized text. 
 location The location of the payment term as defined by its begin and end indexes. 
 provenance_ids An array of hashed values that you can send to IBM to provide feedback or receive support.
On an object PUT, HEAD, or GET request, the If-Unmodified-Since header will check to see if the object's Last-Modified value (for example Sat, 14 March 2020 19:43:31 GMT) is equal to or earlier than a provided value. If the object has not been modified, the operation will proceed. If the Last-Modified value is more recent, the system will return a 412 Precondition Failed error on a PUT and a 304 Not Modified on GET or HEAD.

> If-Unmodified-Since is most often used with state-changing methods (e.g., POST, PUT, DELETE) to prevent accidental overwrites when multiple user agents might be acting in parallel on a resource that does not supply entity-tags with its representations (i.e., to prevent the "lost update" problem). It can also be used with safe methods to abort a request if the selected representation does not match one already stored (or partially stored) from a prior request.







 Retry strategy 

While most libraries and SDKs will automatically handle retry logic, care must be taken when writing software that uses the API directly to properly handle transient errors. Most importantly, it is critical to provide appropriate retry logic that implements exponential back-off when receiving 503 errors.





 Cypher tuning 

IBM COS supports a variety of Cipher settings to encrypt data in transit. Not all cipher settings yield the same level performance and using TLS in general leads to small performance degradation. The following cipher settings are recommended (in descending order of priority):



* TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA384
* TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA256
* TLS_ECDHE_RSA_WITH_AES_256_CBC_SHA
* TLS_ECDHE_RSA_WITH_AES_128_CBC_SHA
* TLS_RSA_WITH_AES_256_CBC_SHA256
* TLS_RSA_WITH_AES_128_CBC_SHA256
* TLS_RSA_WITH_AES_256_CBC_SHA