vania-janet/multiturn-rag-retrieval-data huggingface dataset:
You can use this service to secure your builds, detect and respond to runtime threats, and continuously manage cloud configurations, permissions, and compliance.

The IBM Cloud Security and Compliance Center Workload Protection service is a cloud workload protection tool (CWPT) that helps you monitor and protect your organization. Includes Cloud security posture management (CSPM), Kubernetes Security Posture Management (KSPM) and more. Use cloud security posture management features to secure the infrastructure where workloads are deployed. Use Kubernetes Security Posture Management features to secure Kubernetes clusters or Red Hat OpenShift clusters, and the workloads running within it.

IBM Cloud Security and Compliance Center Workload Protection main goal is to provide the tools that will help you keep your organization secure and able to resist threats and attacks, while making sure that workloads are deployed successfully.



 Cloud security posture management (CSPM) 

Cloud security posture management (CSPM) is a framework that includes the policies and controls, the practices, and the technologies to detect and remediate security issues, and compliance risks across multi-cloud environments in your organization. It is focused on securing cloud infrastructures where workloads are deployed. In addition, it addresses security and compliance of Infrastructure as a Service (IaaS), Software as a Service (SaaS), and Platform as a Service (PaaS).

The IBM Cloud Security and Compliance Center Workload Protection service includes CSPM functionality to continuously monitor infrastructures in your organization where workloads are deployed.

The IBM Cloud Security and Compliance Center Workload Protection service inspects cloud environments across multiple environments to continuously:



* Scan, detect and alert of misconfigurations, malware, secrets, and software configuration vulnerabilities. Empower DevOps to fix them fast.
* Provide fixes and guidance on how to remediate security findings.
* Map misconfigurations in production to infrastructure as code (IaC) manifests.
* Provide control over cloud infrastructure configurations and ensure consistent implementation of policies across multiple cloud providers.

Use out-of-the-box compliance controls.

Create customized security policies that align with your security mandates.

Duplicate an existing security policy and turn on/off controls, or create a new ones from scratch.
Guidelines for United Kingdom English only:



* You cannot use periods or dashes in sounds-like pronunciations for UK English.
* To pronounce a single letter, use the letter followed by a space. For example, use N C A A, notN. C. A. A., N.C.A.A., or NCAA.
* Use the spelling of numbers without dashes, for example, seventy five for 75.







 Guidelines for Dutch, French, German, Italian, Portuguese, and Spanish 

Guidelines for all supported dialects of Dutch, French, German, Italian, Portuguese, and Spanish:



* You cannot use dashes in sounds-like pronunciations.
* Use alphabetic characters that are valid for the language: a-z and A-Z including valid accented letters.
* To pronounce a single letter, use the letter followed by a period. If the period is followed by another character, be sure to use a space between the period and the next character. For example, use N. C. A. A., notN.C.A.A.
* Use real or made-up words that are pronounceable in the language for words that are difficult to pronounce.
* Use the spelling of numbers without dashes, for example, for 75 use



* Dutch (Netherlands):vijfenzeventig
* French:soixante quinze
* German:fünfundsiebzig
* Italian:settantacinque
* Portuguese (Brazilian):setenta e cinco
* Spanish:setenta y cinco



* You can include multiple words that are separated by spaces. The service enforces a maximum of 40 total characters, not including leading or trailing spaces.







 Guidelines for Japanese 



* Use only full-width Katakana characters by using the ― lengthen symbol (chou-on, or 長音, in Japanese). Do not use half-width characters.
* Use contracted sounds (yoh-on, or 拗音, in Japanese) only in the following syllable contexts:
Cleaning up your namespaces 

You can clean up your

namespaceby choosing to retain only the most recent images in each repository in that namespace in IBM Cloud® Container Registry.

You can also choose whether to delete or retain your untagged images.

You can detect and delete old images from all the repositories in a namespace by running a one-off command, ibmcloud cr retention-run, or by setting a scheduled policy by running the ibmcloud cr retention-policy-set command. You can choose the number of images that you want to keep in each repository in a namespace, all other images are automatically deleted. Both options keep the most recent images. The age of the image is determined by when the image was created, not when it was pushed to the

registry. The number of images that are kept is the same for each repository in that namespace.

When you run the [ibmcloud cr retention-run](https://cloud.ibm.com/docs/Registry?topic=Registry-registry_retentionretention_images) and [ibmcloud cr retention-policy-set](https://cloud.ibm.com/docs/Registry?topic=Registry-registry_retentionretention_policy_set) commands, a list of images to delete is shown, and you must confirm that you want to delete those images. After you run the ibmcloud cr retention-policy-set command the first time, the policy runs automatically and deletes any images that meet the criteria that are specified in the policy. Deleted images are stored in the trash for 30 days.

If you want to check what's in the trash, run the [ibmcloud cr trash-list](https://cloud.ibm.com/docs/Registry?topic=Registry-registry_images_registry_images_list_trash) command. You can restore images from the trash by running the [ibmcloud cr image-restore](https://cloud.ibm.com/docs/Registry?topic=Registry-registry_images_registry_images_restore) command.
* [Resource allocation](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-adv-deploymentibp-console-adv-deployment-orderer-sizing-creation) - Configure the CPU, memory, and storage for the node.
* [Hardware Security Module](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-adv-deploymentibp-console-adv-deployment-cfg-hsm) - Configure the ordering node to use an HSM to generate and store private keys.
* [Orderer configuration override](https://cloud.ibm.com/docs/blockchain?topic=blockchain-ibp-console-adv-deploymentibp-console-adv-deployment-orderer-create-json) - Choose this option when you want to override ordering node configuration.



You also have the ability to choose the version of Fabric that will be used to deploy your ordering nodes. It is recommended to always choose the latest version, as this version will have the latest fixes and improvements. Note that it is currently not possible to enable any v2.0 [Fabric capabilities](https://hyperledger-fabric.readthedocs.io/en/release-2.0/capabilities_concept.html).



 Number of ordering nodes 

In Raft, a majority of the total number of nodes must be available for the ordering service to function (this is known as achieving a "quorum" of nodes). In other words, if you have one node, you need that node available to have a quorum, because the majority of one is one. While satisfying the quorum makes sure that the ordering service is functioning, production networks also have to think about deployment configurations that are highly available (in other words, configurations in which the loss of a certain number of nodes can be tolerated by the system). Typically, this means tolerating the loss of two nodes: one node going down during a normal maintenance cycle, and another going down for any other reason (such as a power outage or error).
Logging and Monitoring 

IBM® Cloud Foundry is deprecated and no longer supported as of 1 June 2023. For more information, see the [deprecation details](https://cloud.ibm.com/docs/cloud-foundry-public?topic=cloud-foundry-public-deprecationdep_details).

IBM Cloud® has a built-in logging mechanism to produce log files for your apps as they are running. In the logs, you can view the errors, warnings, and informational messages that are produced for your app. In addition, you can also configure your app to write log messages to the log file. For more information about log formats and how to view logs, see [Collecting and analyzing logs from IBM Cloud Foundry resources](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-monitor_cfapp_logs).

Monitoring your app enables you to see and control your app deployment. With monitoring, you can accomplish the following tasks:



*  Collect and monitor performance information for app instances, and check whether they are functional.
*  Gain insight on app operations, for example, detect the potential bottlenecks or when upgrades are required.
*  Estimate resource usage and charges.



For stable operations of your deployments on IBM Cloud platform, you want to detect problems promptly and determine causes efficiently. To accomplish this objective, keep troubleshooting in mind when you design your apps, and use services or tools for monitoring and logging when your app is deployed to IBM Cloud.

To retain logs for a longer period and advanced analysis, services like [Object Storage](https://cloud.ibm.com/docs/log-analysis?topic=log-analysis-archiving) can be used.

For more information about how to monitor IBM Cloud Foundry apps, see [Monitoring Cloud Foundry metrics](https://cloud.ibm.com/docs/monitoring?topic=monitoring-monitor-cf-sysdig).

IBM Cloud Foundry apps can configure and expose metrics that can be monitored using IBM Cloud® Monitoring. For more information, see [Monitoring Cloud Foundry metrics](https://cloud.ibm.com/docs/monitoring?topic=monitoring-monitor-cf-sysdig).
For more information, see [Validating a words resource for previous-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordsvalidateModel).



 Monitoring the add corpus request 

The service returns a 201 response code if the corpus is valid. It then asynchronously processes the contents of the corpus. You cannot submit requests to add data to the custom model or to train the model until the service's analysis of the corpus for the current request completes.

To determine the status of the analysis, use the GET /v1/customizations/{customization_id}/corpora/{corpus_name} method to poll the status of the corpus. The method accepts the ID of the model and the name of the corpus, as shown in the following example:

IBM Cloud

curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"

IBM Cloud Pak for Data

curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}/corpora/corpus1"

The response includes the status of the corpus. Because the custom model is based on a previous-generation model, the response shows the number of OOV words.

{
"name": "corpus1",
"total_words": 5037,
"out_of_vocabulary_words": 401,
"status": "analyzed"
}

The status field has one of the following values:



* analyzed indicates that the service has successfully analyzed the corpus.
* being_processed indicates that the service is still analyzing the corpus.
* undetermined indicates that the service encountered an error while processing the corpus.



Use a loop to check the status of the corpus every 10 seconds until it becomes analyzed.
Extra considerations and a part_of_speech field apply when creating entries for words in a Japanese custom model. For more information, see [Working with Japanese entries](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-rulesjaNotes).

New voice transformation SSML feature
: The service now offers SSML voice transformation via the new <voice-transformation> element. You can expand the range of possible voices by creating custom transformations that modify the pitch, pitch range, glottal tension, breathiness, rate, and timbre of a voice. The service also offers two built-in virtual voices, Young and Soft. The service currently supports voice transformation only for the US English Allison voice.

Word timings now available with WebSocket interface
: The service can now return word timing information for all strings of the input text that you pass to the WebSocket interface. To receive the start and end time of every string in the input, specify an array that includes the string words for the optional timings parameter of the JSON object that you pass to the service. The feature is not currently available for Japanese input text. For more information, see [Generating word timings](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-timing).

New support for SSML validation
: The service now validates all SSML elements that you submit in any context. If it finds an invalid tag, the service reports an HTTP 400 response code with a descriptive message, and the method fails. In previous releases, the service handled errors inconsistently; specifying an invalid word pronunciation, for example, could lead to unpredictable or inconsistent behavior. For more information, see [SSML validation](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-ssmlssml-errors).

IBM SPR format now specified with ibm instead of spr
: The use of spr is deprecated as an argument to the format option of the GET /v1/pronunciation method and for use with the alphabet attribute of an SSML <phoneme> element.
* For narrowband models, the sampling rate must be at least 8 kHz (8000 samples per second).



If the sampling rate of the audio is higher than the minimum required sampling rate, the service downsamples the audio to the appropriate rate. If the sampling rate of the audio is lower than the minimum required rate, the service labels the audio file as invalid. If any audio file that is contained in an archive file is invalid, the service considers the entire archive invalid.
* Create a custom language model to use with your custom acoustic model in the following cases:



* If your audio is less than an hour long, create a custom language model based on transcriptions of the audio to achieve the best results.
* If your audio is domain-specific and contains unique words that are not found in the service's base vocabulary, use language model customization to expand the service's base vocabulary. Acoustic model customization alone cannot produce those words during transcription.



For more information, see [Using custom acoustic and custom language models together](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-useBoth).







 Maximum hours of audio 

The maximum hour of audio that you can add to a custom acoustic model depend on the version of the service that you are using:



IBM Cloud Pak for Data

Add a maximum of 200 hours of audio data.
IBM Cloud

Add a maximum of approximately 50 hours of audio data. You might be able to add 10 or 20 hours of additional audio data per model. However, it is safer to assume a maximum of 50 hours of audio data per custom acoustic model and plan accordingly.

The maximum amount of audio per custom acoustic model was previously 200 hours. The limit is being reduced by location according to the schedule in Table 1.



Table 1. Schedule of 50-hour limit per location

 Location Planned date of new limit Actual date of new limit 

 eu-gb (London) 11 August 2022 11 August 2022 
 au-syd (Sydney) 19 October 2022 19 October 2022 
 us-east (Washington, DC) First-quarter 2023 TBD 
 us-south (Dallas) First-quarter 2023 TBD
The method returns a JSON array with the following data. For Japanese custom models, the output includes the part of speech for individual words.

{
"words": [
{
"word": "IEEE",
"translation": "I triple E"
},
{
"word": "NCAA",
"translation": "N C double A"
},
{
"word": "iPhone",
"translation": "I phone"
}
]
}
Show more

As described in [Querying a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customModelscuModelsQuery), you can also use the GET /v1/customizations/{customization_id} method to see both the metadata and the words for a custom model:

IBM Cloud

curl -X GET -u "apikey:{apikey}" "{url}/v1/customizations/{customization_id}"

IBM Cloud Pak for Data

curl -X GET --header "Authorization: Bearer {token}" "{url}/v1/customizations/{customization_id}"

The method returns JSON output of the following form. Because this and the previous example specify the same custom model, the array of words in their output is identical.

{
"customization_id": "64f4807f-a5f1-5867-924f-7bba1a84fe97",
"owner": "297cfd08-330a-22ba-93ce-1a73f454dd98",
"created": "2016-07-15T19:15:17.926Z",
"name": "Test",
"language": "en-US",
"description": "Customization test",
"last_modified": "2016-07-15T19:46:01.387Z",
"words": [
{
Creating a custom acoustic model 

Acoustic model customization is available only for previous-generation models. It is not available for next-generation models.

Follow these steps to create a custom acoustic model for the IBM Watson® Speech to Text service:



1. [Create a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticcreateModel-acoustic). You can create multiple custom models for the same or different domains or environments. The process is the same for any model that you create. Acoustic model customization is available for all languages that are supported by previous-generation models. For more information about which languages are generally available and beta, see [Language support for customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-support).
2. [Add audio to the custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticaddAudio). The service accepts the same audio file formats for acoustic modeling that it accepts for speech recognition. It also accepts archive files that contain multiple audio files. Archive files are the preferred means of adding audio resources. You can repeat the method to add more audio or archive files to a custom model.
3. [Train the custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acoustictrainModel-acoustic). Once you add audio resources to the custom model, you must train the model. Training prepares the custom acoustic model for use in speech recognition. The training time depends on the cumulative amount of audio data that the model contains.

You can specify a helper custom language model during training of your custom acoustic model. A custom language model that includes transcriptions of your audio files or OOV words from the domain of your audio files can improve the quality of the custom acoustic model.
The following table shows examples of how some words are stemmed versus lemmatized.



Stemmer versus Lemmatizer comparison

 Surface form Lemmatized form Stemmed form 

 running run run 
 ran run ran 
 instructor instructor instruct 
 instruction instruction instruct 



As you can see from the examples, the lemmatizer captures the word meanings better than the stemmer. Both running and ran are recognized as different forms of the same root verb run. And the difference in meaning between the two nouns instructor and instruction is preserved. However, if the data contains misspellings such as instructer and instructoin, the normalized form that is generated by stemming (instruct) will return better matches.

Discovery normalizes words when it ingests and stores data in the index and at run time when it analyzes queries that are submitted by users. The same normalization method is used for both operations, even though one operation occurs at the collection-level and the other occurs at the project-level. When a query is submitted, it is federated to each collection within the project, where the query is normalized based on that collection's configuration. Collections that are configured to use the stemmer normalize the query by using stemming. The collections that are not, normalize the query by using lemmatization.

To enable the stemmer instead of the lemmatizer when you create the collection, expand More processing options, and then set the Use stemming instead of lemmatization when indexing switcher to On.

If you configure Discovery to use the stemmer, consider also designing the queries that extract information from the collection to allow for character differences during matching. For more information, see the [String variation operator](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-operatorsvariation).

For more information about the languages for which the stemmer is supported, see [Language support](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-language-support).





 Collection limits 

The number of collections that you can create per project differs by project type.



Collections per project limits

 Project type Collections per project 

 Document Retrieval 5
The toolchain consists of resources such as IBM Cloud® Secrets Manager vault, IBM Cloud Kubernetes Service cluster, IBM Cloud® Container Registry namespace, IBM Cloud API key, IBM Cloud CLI, IBM Cloud Object Storage instance and bucket, IBM Cloud Satellite cluster group and application, and inventory repositories. It helps to keep the resource information from the CI and CD toolchains handy.





 Continuous Compliance (CC) toolchain introduction 

DevSecOpsintegrates a set of security and compliance controls into the [DevOps](https://www.ibm.com/cloud/learn/devops-a-complete-guide) processes. This integration allows organizations to deliver rapidly and often while maintaining a strong security posture and continuous state of audit-readiness. Additionally, DevSecOps makes application and infrastructure security a shared responsibility of development, security, and IT operations teams, rather than the sole responsibility of a security silo.

This tutorial provides information and hands-on guidance on Continuous Compliance reference implementation by using IBM Cloud Continuous Delivery with Tekton Pipelines. This tutorial steps you through the creation of Continuous Compliance (CC) toolchain by using a toolchain template.

The Continuous [Compliance (CC) pipeline](https://cloud.ibm.com/docs/devsecops?topic=devsecops-devsecops-cc-pipeline) from this reference implementation can be triggered manually or periodically by using triggers. This pipeline is useful for a continuous scanning of existing deployed artifacts and their source repositories independent of your deployment schedule. It runs the static scans and dynamic scans on the Application Source Code, detect secrets in Git repos, Bill Of Materials (BOM) check, CIS check, and Vulnerability Advisor scan. After scanning and running checks on artifacts and source repositories, the pipeline creates a new incident issue or updates the existing incident issues in the incident repository. Finally, using these issues and the results, the pipeline collects evidence and summarizes the evidence, so the Security and Compliance Center Center can update the compliance status of the found artifacts.