vania-janet/multiturn-rag-retrieval-data huggingface dataset:
After you create the action, the action editor opens.

Zoom

![Action editor showing newly created action](https://cloud.ibm.com/docs-content/v1/content/ca9232b3702768f24bb897f48f10778fcda28718/watson-assistant/images/action-editor-new-action.png)

Action editor

The editor window shows the parts of an action:



* The Customer starts with: tile shows the customer input that starts the action. You can click this tile to edit the example text or add more examples, but we'll leave it as is for now.
* Under Conversation steps, you can see the steps that make up the action. A step is an interaction between the assistant and the customer; steps are executed in order, from first to last. You can reorder the steps in an action by clicking and dragging steps in the list.
* The Preview button opens a pane that shows you how the assistant responds to customer input. You can preview the assistant at any time to see the effect of changes you make.
* The Action notes icon opens an area where you can add a description, documentation, comments, or any other annotations to help you track your work as you build an action.



The action editor supports basic Markdown syntax.





 Steps 

An action consists of one or more steps. The steps in an action define the conversation turns that follow the initial customer input that triggered the action. In a simple case, a step might consist of a direct answer to a question from the customer; for example, if the customer asks What are your business hours?, a one-step action might reply with We are open Monday through Friday from 9 AM to 5 PM..

More commonly, though, an action requires multiple steps to fully understand the customer's request. For our I want to withdraw money example, we need more information:



* Which account should the money come from?
* What is the amount to withdraw?



Each of these follow-up questions represents a step in the action.



 Editing a step 

Within a step, you define the following things:



* A step title that describes what the step does.
This documentation for the classic Watson Assistant experience has moved. For the most up-to-date version, see [Autocorrecting user input](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-autocorrection). To see all documentation for the new Watson Assistant, please go [here](https://cloud.ibm.com/docs/watson-assistant).



 Autocorrecting user input 

Autocorrection fixes misspellings that users make in their requests. The corrected words are used to match to an action or an intent.

Autocorrection corrects user input in the following way:



* Original input: letme applt for a memberdhip
* Corrected input: let me apply for a membership



When your assistant evaluates whether to correct the spelling of a word, it does not rely on a simple dictionary lookup process. Instead, it uses a combination of natural language processing and probabalistic models to assess whether a term is, in fact, misspelled and should be corrected.

Autocorrection is enabled automatically for all English-language assistants. It is also available in French-language assistants, but is disabled by default. Autocorrection isn't available for any other languages.



 Disabling autocorrection 

If necessary, you can disable autocorrection for your assistant.

If you find that a domain-specific term is being corrected that shouldn't be, you can prevent the correction from happening by adding the term or phrase to your training data. For more details, see [Autocorrection rules](https://cloud.ibm.com/docs/assistant?topic=assistant-autocorrectionautocorrection-rules).

If you are using actions in your assistant, follow these steps to disable autocorrection:



1. On the Actions page, click Global settings !![Gear icon](https://cloud.ibm.com/docs-content/v1/content/icons/settings.svg).
2. Click the Autocorrection tab.
3. Set the switch to Off, then click Save.
The US English short-form model, en-US_ShortForm_NarrowbandModel, can improve speech recognition for Interactive Voice Response (IVR) and Automated Customer Support solutions. The short-form model is trained to recognize the short utterances that are frequently expressed in customer support settings like automated support call centers. In addition to being tuned for short utterances in general, the model is also tuned for precise utterances such as digits, single-character word and name spellings, and yes-no responses.

The en-US_ShortForm_NarrowbandModel is optimal for the kinds of responses that are common to human-to-machine exchanges, such as the use case of IBM® Voice Agent with Watson™. The en-US_NarrowbandModel is generally optimal for human-to-human conversations. However, depending on the use case and the nature of the exchange, some users might find the short-form model suitable for human-to-human conversations as well. Given this flexibility and overlap, you might experiment with both models to determine which works best for your application. In either case, applying a custom language model with a grammar to the short-form model can further improve recognition results.

As with all models, noisy environments can adversely impact the results. For example, background acoustic noise from airports, moving vehicles, conference rooms, and multiple speakers can reduce transcription accuracy. Audio from speaker phones can also reduce accuracy due to the echo common to such devices. Using the parameters available for speech activity detection can counteract such effects and help improve speech transcription accuracy. Applying a custom acoustic model can further fine-tune the acoustics for speech recognition, but only as a final measure.



* For more information about language model and acoustic model customization, see [Understanding customization](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-customization).
* For more information about grammars, see [Using grammars with custom language models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammars).
* Issues with crashes of the expense reimbursement application
* Requests for help or assistance with the expense reimbursement application



These similarities can help you decide to add one or more actions that correspond to what you want to cover with your assistant, rather than adding all requests to a single action.
3. You can click to select unrecognized requests that you want to use as example phrases in a new action.

Zoom

![Select requests](https://cloud.ibm.com/docs-content/v1/content/ca9232b3702768f24bb897f48f10778fcda28718/watson-assistant/images/analytics-unrecognized-create-action.png)

Select requests
4. After you make your selections, click Create new action.
5. Enter a name for the action, or use the default, and then click Apply.
6. The action editor opens with each of the unrecognized requests that are included as an example phrase. You can now build an action to address these questions or issues.

Zoom

![Customer starts with](https://cloud.ibm.com/docs-content/v1/content/ca9232b3702768f24bb897f48f10778fcda28718/watson-assistant/images/analytics-unrecognized-new-action-phrases.png)

Customer starts with





 Modifying existing actions 

You can also use the unrecognized request groups to identify existing actions that you might want to modify. You can:



* Add unrecognized requests as example phrases to existing actions
* Move example phrases from existing actions and add them to any new actions you create based on unrecognized requests



If a group lists related actions, you might focus on modifying them to address the unrecognized request.

Zoom

!
* [What attributes of Workspaces or Actions are used to dynamically select a target agent for execution](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentagent-dynamic-attribute)
* [How can I enable debug mode in an agent?](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentfaqs-agent-debugmode)
* [Can I upgrade an agent beta-0 to agent beta-1?](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentfaqs-agent-upgrade)
* [Does agent beta-1 support backward compatibility?](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentfaqs-agent-compatibility)
* [Are Schematics Agents the same as Terraform Cloud Agents?](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentfaqs-agent-terraform-agent)
* [Do the agents run on IBM Cloud cloud resources?](https://cloud.ibm.com/docs/schematics?topic=schematics-faqs-agentfaqs-agent-run)



[Blueprints](https://cloud.ibm.com/docs/schematics?topic=schematics-blueprints-faqblueprints-faq)



* [What are the Git repositories that are supported by Blueprints?](https://cloud.ibm.com/docs/schematics?topic=schematics-blueprints-faqfaqs-bp-repos)
* [Are variable operators and functions supported in blueprint templates?](https://cloud.ibm.com/docs/schematics?topic=schematics-blueprints-faqfaqs-bp-values)
In this event, we recommend you contact IBM and provide details of your test so that that IBM can support you in the steps to resolve the problem.

It's also recommended that you try the Beta version in one of your test systems after you upgrade. This gives you early visibility to changes that are likely to be delivered in a future version, and will reduce the probability of negative impacts to your production systems. IBM values both positive and negative feedback from customers who use Beta. You will have the opportunity to shape how the algorithms function before the version is promoted to Latest in a future version. If you choose Beta, your assistant always trains on the most current beta version.
So, any user of a Watson Assistant instance can switch between the new and classic experiences, and other users of that Watson Assistant instance are not affected.





 27 November 2021 

New API version
: The current API version is now 2021-11-27. This version introduces the following changes:



* The output.text object is no longer returned in message responses. All responses, including text responses, are returned only in the output.generic array.







 12 November 2021 

Completion analytic information
: On the Analyze page, the How often chart can now also show the percentage of complete actions. Use the icon in the upper right of the chart to toggle between a line chart that shows the percentage of complete actions and a bar chart that shows the number of complete and incomplete actions. For more information, see [Improving completion](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-analytics-action-completionanalytics-improving-completion).

Preview page update
: The Test integrations panel no longer exists on the Preview page. You can manage your draft web chat channel from the Preview page. However, all other draft environment integrations are managed from the Draft environment page. For more information, see [Previewing and sharing your assistant](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-preview-share).





 4 November 2021 

Draft and Live Environment pages
: Two pages, Draft environment and Live environment, help you to see how your channels and resolution methods are connected, both for testing/preview and for live deployment. The Draft environment page is new as of this release. The Live environment page was previously named Connect. For more information, see [Overview: Publishing and deploying your assistant](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-publish-overview).

Add variables to links
: When including a link in an assistant response, you can now access and use variables. In the URL field for a link, type a dollar sign ($) character to see a list of variables to choose from.





 25 October 2021 

Facebook and Slack integrations now available
The Latest (20 Dec 2022) version includes a new irrelevance detection implementation to improve off-topic detection. For details, see [Algorithm version and training](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-algorithm-version).

Actions templates updated with new design and new choices
: The actions template catalog has a new design. Now you can select multiple templates at the same time. The catalog also has new and updated templates, including starter kits that you can use with external services such as Google and HubSpot. For details, see [Building actions from a template](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-actions-templates).

Organize actions into collections
: Now you can put actions into collections, which are folder-style groups based on whatever categorization you need at your organization, such as by use case, internal team, or status. For details, see [Organizing actions in collections](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-collections).

Display iframe inline in the conversation
: In the web chat, an assistant can now include an iframe response within the conversation. This new option is useful for smaller pieces of iframe content. For details, see [Adding an iframe response](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-respondrespond-add-iframe).

New validation choices for date, time, and numeric customer responses
: For Number, Date, Time, Currency, and Percentage customer responses, you can now customize the validation to check for a specific answer, such as a range of dates or a limited currency amount. For details, see [Customizing validation for a response](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-handle-errorscustomize-validation).

Confirmation to return to previous action
This difference is a result of how writes were applied to the shard. For example, they might be applied in a different order. To be sure that all changes are reported for your specified sequence, it might be necessary to go further back into the shard's history to find a suitable starting point. The changes are then reported from that starting point. This "rolling back" might give the appearance of duplicate updates, or updates that are apparently before the specified since value.
* _changes reported by a shard are always presented in order. But the ordering between all the contributing shards might appear to be different. For more information, see [A Changes Feed Example](https://gist.github.com/smithsz/30fb97662c549061e581).
* Sequence values are unique for a shard, but might vary between shards. This variation means that, if you have sequence values from different shards, you can't assume that the same sequence value refers to the same document within the different shards.







 Using POST to get changes 

Instead of GET, you can also use POST to query the changes feed. The only difference, if you're using POST and you're using either of the docs_ids or selector filters, is that it's possible to include the "doc_ids" : [...] or "selector": {...} parts in the request body. All other parameters are expected to be in the query string, the same as using GET.

See the following example that uses HTTP to POST to the _changes endpoint:

POST /$DATABASE/_changes?filter=_selector HTTP/1.1
Host: $ACCOUNT.cloudant.com
Content-Type: application/json

See the following example to POST to the _changes endpoint:



* Curl
* Java
* Python
* Go
* Node



curl -H "Authorization: Bearer $API_BEARER_TOKEN" -X POST "$SERVICE_URL/orders/_changes" -H "Content-Type: application/json"'

import com.ibm.cloud.cloudant.v1.Cloudant;
API version comparison 

For most API methods, the request parameters and response bodies differ between v1 and v2. Learn about the equivalent or alternative v2 methods that you can use to do actions that are supported by the v1 API.

The comparison information assumes you are using the latest version of the v1 API (version 2019-04-30) and compares it to the latest version of the v2 API (version 2020-08-30).



 Environments 

There is no concept of an environment in v2. The deployment details such as size and index capacity are managed based on the service plan type. In v2, collections are organized in projects. You can create different types of projects to apply default configuration settings to the collections that you add to the projects.

There are no equivalent methods in v2 for the v1 environment methods. However, the following table shows v2 methods that serve similar functions to the corresponding v1 methods. The supported parameters and response bodies that are returned for each method differ also.



Environment API action support details

 Action v1 API Related v2 API 

 Create an environment [POST /v1/environments](https://cloud.ibm.com/apidocs/discoverycreateenvironment) [POST /v2/projects](https://cloud.ibm.com/apidocs/discovery-datacreateproject) 
 List environments [GET /v1/environments](https://cloud.ibm.com/apidocs/discoverylistenvironments) [GET /v2/projects](https://cloud.ibm.com/apidocs/discovery-datalistprojects) 
 Get environment info [GET /v1/environments/{environment_id}](https://cloud.ibm.com/apidocs/discoverygetenvironment) [GET /v2/projects/{project_id}](https://cloud.ibm.com/apidocs/discovery-datagetproject)
[Optional aggregated evidence summary captured in a comment on promotion pull and merge request](https://cloud.ibm.com/docs-content/v1/content/5eb88a2575e30e316dbb83829c7f4c8b1ddbc67c/devsecops/images/promotion-pull-request-summary.png)

Figure 3. Aggregated evidence summary in promotion pull and merge request





 Next step 

After the Promotion Pipeline finishes successfully, you can [proceed to the CD Pipeline](https://cloud.ibm.com/docs/devsecops?topic=devsecops-cd-devsecops-cd-pipeline).
Autocorrection fixes misspellings that users make in their requests. The corrected words are used to match to an action.

While the setting is new, the autocorrection feature was already used automatically by all English-language assistants. Autocorrection is also available in French-language assistants, but is disabled by default. The autocorrection setting isn't available for any other languages. The new setting lets you disable or enable autocorrection if necessary.

For more information, see [Autocorrecting user input](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-autocorrection).

Improved experience when setting a variable value
: The dropdown list for setting a variable value within an action step has a new organization. The new list is intended to provide an improved experience.





 11 January 2023 

Algorithm version 01-Jun-2022 uses enhanced intent detection by default
: As of this date, the algorithm version Latest (01-Jun-2022) now uses enhanced intent detection by default. Before this change, some skills that did not include a specific algorithm version selection inadvertently used Previous (01-Jan-2022). You can notice small changes in intent detection behavior when changes are made to an assistant that previously didn't have enhanced intent detection enabled. For more information, see [Algorithm version and training](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-algorithm-version).





 6 December 2022 

Updated expression methods
: The following new and updated methods are available in expressions:



* The [Array.joinToArray()](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-expression-methods-actionsexpression-methods-actions-arrays-join-to-array) method now supports a new boolean parameter you can use to specify that the data type of values from the input array should be preserved in the returned array.
In this example, a Machine learning model extracts entity types such as person, oranization, and date, and information about relationships between the entities. When this ML model is applied as an enrichment to a field, it uses machine learning to understand the linguistic nuances, meaning, and relationships that are mentioned in the document.

In the output, the information that is extracted by the Machine Learning enrichment in the enriched_{field_name} array, within the entities and the relations arrays. In this example, the field that is selected for enrichment is text.

{
"enriched_text": [
{
"entities":
{
"count": 1,
"text": "Democratic Party",
"type": "ORGANIZATION"
},
{
"count": 1,
"text": "March 15, 1767",
"type": "DATE"
},
{
"count": 1,
"text": "President",
"type": "POSITION"
},
{
"count": 1,
"text": "Andrew Jackson",
"type": "PERSON"
}
],
"relations":
{
"sentence": "Andrew Jackson (March 15, 1767‚ June 8, 1845) was an American soldier and statesman who served as the seventh President of the United States from 1829 to 1837 and was the founder of the Democratic Party."
}
]
}
]
}
Show more





 Machine learning model limits 

The number of Machine Learning (ML) models you can create per service instance depends on your Discovery plan type.



ML model plan limits

 Plan ML models per service instance 

 Cloud Pak for Data Unlimited 
 Premium 10 
 Enterprise 10 
 Plus (includes Trial) 3 



For each Knowledge Studio machine learning model, the maximum number of entities that can be detected is 50.





 Advanced rules models 

Add an advanced rules model to apply a text extraction model that was created and exported from the Advanced Rule editor of IBM Watson® Knowledge Studio to your collection.

Your model must be created with the appropriate Knowledge Studio deployment:



IBM Cloud Pak for Data
CIS WAF contains rulesets to mitigate non-volumetric attacks, including cross-site forgery, cross-site-scripting (XSS), file inclusion, and SQL injection. For additional information about WAF, see [Web Application Firewall concepts](https://cloud.ibm.com/docs/cis?topic=cis-waf-q-and-awhat-types-of-attacks-can-waf-prevent).





 Cost protection 

CIS does not meter or bill for traffic that is blocked as part of DDoS mitigation, firewall, or rate limiting. Only requests that are passed through the CIS network to the origin destination incur charges or usage.

CIS also helps keep egress bandwidth charges from your origin under control by only passing along good requests that the origin needs to respond to. All CIS plans offer unlimited and unmetered mitigation of DDoS attacks. You are never charged for attack traffic. There’s no penalty for spikes due to attack traffic, so there's no chargeback by the customer.







 Reliability features 

Zoom

![reliability graphic](https://cloud.ibm.com/docs-content/v1/content/cb1eb27836421578019401fa7556779109430b29/cis/images/reliability-graphic.png)

Figure 2. Reliability features



 Global load balancing features 

The global load balancing service distributes your traffic across multiple servers with a combination of origin pools, health checks, and a load balancer. Global load balancing has the following features:



* Proxy and non-proxy options for load balancing
* Origin pools and health checks





 Global anycast network 

The available health check regions are based on the [Cloudflare Global Anycast Network](https://www.cloudflare.com/network/).







 DNS features 

DNS within CIS has the following features:



* DNS management - Manage your DNS records, control proxying, and enable DNS security.