vania-janet/multiturn-rag-retrieval-data huggingface dataset:
* Update Node.js version to 6.17.0







 18 March 2019 

Updated Node.js buildpack v3.26
: The SDK for Node.js buildpack v3.26 provides IBM SDK for Node.js versions 4.8.5, 4.8.7 and Node.js community versions 6.16.0, 6.17.0, 8.15.0, 8.15.1, 10.15.0 and 10.15.3. The default is latest 6.x, so it is currently 6.17.0.





 1 March 2019 

Updated Liberty buildpack v3.29-20190223-2128
: The default Liberty runtime GA version is the [18.0.0.4](https://openliberty.io/blog/2018/12/14/microprofile21-18004.html) release.



* The alternate Liberty runtime GA version [19.0.0.2](https://openliberty.io/blog/2019/03/01/sharding-keys-jdbc43-19002.html) was added.
* The Cloudant client libraries were updated to 2.14.0.







 1 February 2019 

Updated Liberty buildpack v3.28-20190127-1723
: The default Liberty runtime GA version is the [18.0.0.4](https://openliberty.io/blog/2018/12/14/microprofile21-18004.html) release.



* The alternate Liberty runtime GA version [19.0.0.1](https://openliberty.io/blog/2019/02/01/open-liberty-19001.html) was added.
* The monthly Liberty beta release has been removed.
* The IBM JRE version was updated to 8 SR5 FP27.
* The MQ client was updated to the 9.1.0.0 release.
* The auto-scaling agent was updated.







 23 January 2019 

Updated Node.js buildpack v3.25.1
Managing custom acoustic models 

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

The customization interface includes the POST /v1/acoustic_customizations method for creating a custom acoustic model. The interface also includes the POST /v1/acoustic_customizations/train method for training a custom model on its latest audio resources. For more information, see



* [Create a custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acousticcreateModel-acoustic)
* [Train the custom acoustic model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-acoustictrainModel-acoustic)



In addition, the interface includes methods for listing information about custom acoustic models, resetting a custom model to its initial state, upgrading a custom model, and deleting a custom model. You cannot train, reset, upgrade, or delete a custom model while the service is handling another operation on that model, including adding audio resources to the model.



 Listing custom acoustic models 

The customization interface provides two methods for listing information about the custom acoustic models that are owned by the specified credentials:



* The GET /v1/acoustic_customizations method lists information about all custom acoustic models or about all custom acoustic models for a specified language.
* The GET /v1/acoustic_customizations/{customization_id} method lists information about a specified custom acoustic model. Use this method to poll the service about the status of a training request.



Both methods return the following information about a custom acoustic model:



* customization_id identifies the custom model's Globally Unique Identifier (GUID). The GUID is used to identify the model in methods of the interface.
* created is the date and time in Coordinated Universal Time (UTC) at which the custom model was created.
* updated is the date and time in Coordinated Universal Time (UTC) at which the custom model was last modified.
* language is the language of the custom model.
* owner identifies the credentials of the service instance that owns the custom model.
You need to use the command line to [pull](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referencestate-pull) the state file and [create workspace](https://cloud.ibm.com/docs/schematics?topic=schematics-schematics-cli-referenceschematics-workspace-new).

Copy the downloaded state file as terraform.tfstate into the Terraform execution folder.
7. Run the state replace provider command in on the command-line to update the IBM Cloud provider version in the state file.

terraform state replace-provider registry.terraform.io/-/ibm registry.terraform.io/ibm-cloud/ibm.
8. Verify that the updates are made to the terraform.tfstate file with Terraform version update from 0.12 to >= 0.13 and the provider that is updated as registry.terraform.io/ibm-cloud/ibm.
9. Push the upgraded TF config files and version.tf back to your Git repository.
10. Copy the content of modified terraform.tfstate file to state.json file.
11. Create or update a workspace.json file as shown in the code block.

 {: codeblock}
{

"name": "gb", "type": [ "terraform_v0.13" ], "description": "migration workspace", "template_repo": { "url": "Provide your Git repository link" }, "workspace_status" : { "frozen": false }, "template_data": [{ "folder": ".", "type": "terraform_v0.13" }] } ```
12. Run these commands through command-line to create a new Terraform v0.13 workspace



* ibmcloud schematics workspace new --file workspace.json --state state.json.
From the list of deployed models, find the model you want to view or undeploy.
4. To undeploy the model, from the last column of that row, click Undeploy model.
5. To find the model ID, see the Model ID column.



Alternatively, you can undeploy models from the Versions pages for rule-based models and machine learning models.







 Leveraging a rule-based model in IBM Watson Explorer 

Export the PEAR file that is produced when the rule-based model is created so it can be used in IBM Watson Explorer.



 Procedure 

To leverage a rule-based model in IBM Watson Explorer, 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 Export current model.

If you have a Lite plan subscription, no export option is available.

The model is saved as a PEAR file, and you are prompted to download the file. A PEAR (Processing Engine ARchive) file is the UIMA standard packaging format for UIMA components. The model is saved in PEAR format so it can be distributed and reused within UIMA applications.
4. Download the file to your local system.
5. From the IBM Watson Explorer application, import the PEAR file.
[train-acoustic-model](https://cloud.ibm.com/apidocs/speech-to-texttrain-a-custom-acoustic-model) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, custom_language_model_id Train a custom acoustic model. 
 [upgrade-acoustic-model](https://cloud.ibm.com/apidocs/speech-to-textupgrade-a-custom-acoustic-model) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, custom_language_model_id Upgrade a custom acoustic model. 
 [add-audio](https://cloud.ibm.com/apidocs/speech-to-textadd-an-audio-resource) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, audio_name, audio_resource, content_type, contained_content_type, allow_overwrite Add an audio resource. 
 [delete-audio](https://cloud.ibm.com/apidocs/speech-to-textdelete-an-audio-resource) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, audio_name Delete an audio resource. 
 [get-audio](https://cloud.ibm.com/apidocs/speech-to-textget-an-audio-resource) Action username, password, iam_access_token, iam_apikey, iam_url, headers, headers[X-Watson-Learning-Opt-Out], url, customization_id, audio_name Get an audio resource.
Reloading a software version 

You can reload a version of software in your private catalog to update the version based on your current repository. When you reload a version, you revert it to its default state when you first imported the software.



  Reloading an unpublished version 

To update an unpublished version of software, you must reload the version from your source repository and validate it.



1.  In the IBM Cloud® console, click Manage > Catalogs > Private catalogs.
2.  Select the private catalog where your product is located.
3.  Select your product.
4.  Select the version of the software that you want to reload.
5.  Open the Actions menu, and select Reload version.
6.  Click Reload version.
7.  Configure and validate your software.

The configuration and validation steps vary based on the deployment method.







  Reloading a published version 

To update a published version of your software, you must create a draft, reload the version from your source repository, validate, and merge the changes.



1.  In the IBM Cloud console, click Manage > Catalogs > Private catalogs.
2.  Select the private catalog where your product is located.
3.  Select your product.
4.  Select the version of the software that you want to reload.
5.  Open the Actions menu, and select Edit to create a draft of your original version.
6.  In the draft version, open the Actions menu, and select Reload version.
7.  In the Reload version panel, click Reload version.
8.  Configure and validate your software.

The configuration and validation steps vary based on the deployment method.
9.  Open the Actions menu, and select Merge changes.
10. Click Merge to update your original version.
* For more information about using the custom resource to install models, see [Installing Watson Speech to Text](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.0?topic=text-installing-watson-speech).
* For more information about all available next-generation models, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).
* For more information about customization support for next-generation models, see [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng).



Next-generation US English models are now installed by default
: The next-generation US English models, en-US_Multimedia and en-US_Telephony, are now installed by default with Speech to Text for IBM Cloud Pak for Data. These models join en-US_BroadbandModel, en-US_NarrowbandModel, en-US_ShortForm_NarrowbandModel as the models that are installed by default. The models now have the following entries in the Speech services custom resource:


 Speech to Text next-generation models

enUsMultimedia: US English (en-US) Multimedia model
enabled: true
enUsTelephony: US English (en-US) Telephony model
enabled: true

For more information about using the custom resource to install models, see [Installing Watson Speech to Text](https://www.ibm.com/docs/en/cloud-paks/cp-data/4.0?topic=text-installing-watson-speech).

Security vulnerabilities addressed
: The following security vulnerabilities associated with Apache Log4j have been fixed:



* [Security Bulletin: Vulnerability in Apache Log4j may affect IBM Watson Speech Services Cartridge for IBM Cloud Pak for Data (CVE-2021-4104)](https://www.ibm.com/support/pages/node/6551170)
Ubuntu 18.04 packages N/A N/A Updated worker node images with package updates for [CVE-2019-11481](https://nvd.nist.gov/vuln/detail/CVE-2019-11481), [CVE-2019-11482](https://nvd.nist.gov/vuln/detail/CVE-2019-11482), [CVE-2019-11483](https://nvd.nist.gov/vuln/detail/CVE-2019-11483), [CVE-2019-11485](https://nvd.nist.gov/vuln/detail/CVE-2019-11485), [CVE-2019-12290](https://nvd.nist.gov/vuln/detail/CVE-2019-12290), [CVE-2019-14866](https://nvd.nist.gov/vuln/detail/CVE-2019-14866), [CVE-2019-15790](https://nvd.nist.gov/vuln/detail/CVE-2019-15790), [CVE-2019-18218](https://nvd.nist.gov/vuln/detail/CVE-2019-18218), [CVE-2019-18224](https://nvd.nist.gov/vuln/detail/CVE-2019-18224), and [CVE-2019-18277](https://nvd.nist.gov/vuln/detail/CVE-2019-18277). 







 Change log for 1.16.2_1514, released 4 November 2019 

The following tables show the changes that are in the patch 1.16.2_1514. If you update to this version from an earlier version, you choose when to update your cluster master and worker nodes.
Migrating to next-generation models 

The IBM Watson® Speech to Text service supports speech recognition with both previous-generation and next-generation models. Effective 31 July 2023, all previous-generation models will reach their end of service date. On that date, they will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022.

You must migrate your use of any deprecated previous-generation models to the equivalent next-generation models by the 31 July 2023 end of service date. Next-generation models provide appreciably better transcription accuracy and throughput. But they currently provide slightly fewer features than previous-generation models.

This topic provides an overview of the steps that you need to take to migrate from previous- to next-generation models. For more information about migrating, you can also see [Watson Speech to Text: How to Plan Your Migration to the Next-Generation Models](https://medium.com/ibm-data-ai/watson-speech-to-text-how-to-plan-your-migration-to-the-next-generation-models-6b10605b3bc5).



 Step 1: Identify the next-generation model to which to migrate 

The following topics describe all previous- and next-generation models:



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



The tables in [Supported previous-generation language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-modelsmodel-pg-supported) list the recommended next-generation model to which to migrate from a previous generation model. Use the indicated next-generation model in your speech recognition requests.

The service continues to make new next-generation models available. All new models are identified in the release notes and in the tables that describe the available models.
Previous-generation languages and models 

Effective 31 July 2023, all previous-generation models will be removed from the service and the documentation. Most previous-generation models were deprecated on 15 March 2022. You must migrate to the equivalent next-generation model by 31 July 2023. For more information, see [Migrating to next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-migrate).

The IBM Watson® Speech to Text service supports speech recognition with previous-generation models in many languages. The model indicates the language in which the audio is spoken and the rate at which it is sampled.

The models described on this page are referred to as previous-generation models. The service also offers next-generation models with enhanced qualities for improved speech recognition. For more information, see [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng).



 Previous-generation model types 

For most languages, the service makes available two types of previous-generation models:



* Narrowband models are intended for audio that has a minimum sampling rate of 8 kHz. Use narrowband models for offline decoding of telephone speech, which is the typical use for this sampling rate.
* Broadband models are for intended audio that has a minimum sampling rate of 16 kHz. Use broadband models for responsive, real-time applications, for example, for live-speech applications.



Choosing the correct model for your application is important. Use the model that matches the sampling rate (and language) of your audio. The service automatically adjusts the sampling rate of your audio to match the model that you specify. To achieve the best recognition accuracy, you also need to consider the frequency content of your audio.
![rep-api-list.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/rep-api-list.png)
6. Change the Version to 3.0.0.
7. Click the disk icon to save the API changes.
![sup-change-version.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/sup-change-version.png)
8. Click All APIs.
![rep-all-apis.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/rep-all-apis.png)
9. Click Products.
![sup-prods.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/sup-prods.png)
10. Select Weather Provider API Product 2.0.0.
![sup-draft-prod-list.png](https://cloud.ibm.com/docs-content/v1/content/78bb71851d95d7580503eb9ba10cf3ae31490ade/apiconnect/tutorials/images/sup-draft-prod-list.png)
11. Change the Version to 3.0.0. Click the disk icon to save the changes. Click the Stage icon.
Click Save and continue



2. Click the edit icon on the Training data tile and select



1. Storage type: Database or cloud storage
2. Location: Cloud Object Storage
3. For Resource instance ID and API key, run the below command in the Cloud Shell. Make sure to change the value after --instance-name to match the name of the Object Storage instance you have been using for this tutorial.

ibmcloud resource service-key $(ibmcloud resource service-keys --instance-name "cloud-object-storage-tutorial" | awk '/WDP-Project-Management/ {print $1}')
4. Copy and paste the credentials without any trailing spaces and click Connect
5. Select the Bucket that starts with irisproject-donotdelete-
6. Select iris_initial.csv from the Data set dropdown and click Next



3. Select species as your label column and click Next.
4. Select all the four training features and click Next.
5. Select Automatic logging as the Scoring method and back on your cloud shell, [repeat the cURL code to generate the prediction results for logging](https://cloud.ibm.com/docs/solution-tutorials?topic=solution-tutorials-create-deploy-retrain-machine-learning-modelcreate-deploy-retrain-machine-learning-model-try_api). Click Check now to see Logging is active Click Next.
6. Check whether both Prediction and Probability are checked and click Save to complete the model details.
7. On the left pane, click on Quality under Evaluations and click the edit icon on the Quality threshold tile



1. Threshold value: Accuracy - 0.98 and click Next
2. Minimum sample size (number of transactions) - 10 and click Save
3. On the left pane, Click on Go to model summary



The quality monitor (previously known as the accuracy monitor) reveals how well your model predicts outcomes.



As the tutorial uses a small dataset, configuring Fairness and Drift won't have any impact.





 Evaluate the deployed model
As mentioned in [Updating a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customModelscuModelsUpdate), you can also use the POST /v1/customizations/{customization_id} method to add words to a custom model. The following example uses this method to add the same two words as the previous example; it makes no changes to the model's metadata. With the exception of the URL, the two methods are identical.

IBM Cloud

curl -X POST -u "apikey:{apikey}" --header "Content-Type: application/json" --data "{"words": [{"word":"NCAA", "translation":"N C double A"}, {"word":"iPhone", "translation":"I phone"}]}" "{url}/v1/customizations/{customization_id}"

IBM Cloud Pak for Data

curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --data "{"words": [{"word":"NCAA", "translation":"N C double A"}, {"word":"iPhone", "translation":"I phone"}]}" "{url}/v1/customizations/{customization_id}"





 Adding words to a Japanese custom model 

Additional considerations and an additional 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). Specify a part of speech for a Japanese custom entry as follows:



* For the PUT /v1/customizations/{customization_id}/words/{word} method, pass a JSON object of the following form:

{
"translation": "value",
"part_of_speech": "value"
}
This documentation is for the classic Watson Assistant experience. To see the documentation for the new Watson Assistant, please go [here](https://cloud.ibm.com/docs/watson-assistant).



 Upgrading 

Learn how to upgrade your service plan.



 Upgrading your plan 

You can explore the Watson Assistant [service plan options](https://www.ibm.com/cloud/watson-assistant/pricing/) to decide which plan is best for you.

The page header shows the plan you are using today.

![Shows that the Trial plan is displayed in the page header](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/plan-in-header.png)

To upgrade your plan, complete these steps:



1. Do one of the following things:



* Trial plan only: The number of days that are left in your trial is displayed in the page header. To upgrade your plan, click Upgrade from the page header before your trial period ends.
* For all other plan types, click the User icon ![user icon](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/user-icon.png) from the page header, and then choose Upgrade Plan from the menu.



2. From here, you can see other available plan options. For most plan types, you can step through the upgrade process yourself.



* If you upgrade to an Enterprise with Data Isolation plan, you cannot do an in-place upgrade of your service instance. An Enterprise with Data Isolation plan instance must be provisioned for you first. After the service instance is provisioned, you must export your dialog skills from the old plan instance, and import them into the new plan instance. For more information, see [Backing up and restoring data](https://cloud.ibm.com/docs/assistant?topic=assistant-backup).
[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. A group of two or more human annotators annotates a small set of source documents to label words that represent entity types, to identify relation types where the text identifies relationships between entity mentions, and to define coreferences, which identify different mentions that refer to the same thing, that is, the same entity. Any inconsistencies in annotation are resolved, and one set of optimally annotated documents is built, which forms the ground truth.
3. Knowledge Studio uses the ground truth to train a model.
4. The trained model is used to find entities, relations, and coreferences in new, never-seen-before documents.



See [Creating a machine learning model](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-ml_annotator) for more details.





 Build a rule-based model 

Knowledge Studio provides a rules editor that simplifies the process of finding and capturing common patterns in your documents as rules. You can then create a model that recognizes the rule patterns, and deploy it for use in other services.

See [Creating a rule-based model](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-rule-annotator) for more details.







 Analyze text with advanced rules 

The advanced rules feature is Beta. The feature is in a trial stage of development and is not intended for use in production environments.