vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Contextual entities 20 
 Contextual entity annotations 2,000 
 Dialog nodes 100,000 
 Entities 1,000 
 Entity synonyms 100,000 
 Entity values 100,000 
 Intents 2,000 
 Intent user examples 25,000 
 Integrations 100 
 Logs 30 days 
 Skills 50 



User-based Premium plan
: The Premium plan now bases its billing on the number of active unique users. If you choose to use this plan, design any custom applications that you build to properly identify the users who generate /message API calls. See [User-based plans](https://cloud.ibm.com/docs/assistant?topic=assistant-services-informationservices-information-user-based-plans) for more information.

Existing Premium plan service instances are not impacted by this change; they continue to use API-based billing methods. Only existing Premium plan users will see the API-based plan listed as the Premium (API) plan option.

See Watson Assistant [service plan options](https://www.ibm.com/cloud/watson-assistant/pricing/) for more information about all available service plans.





 20 November 2018 

**Recommendations are discontinued
: The Recomendations section on the Improve tab was removed. Recommendations was a beta feature available to Premium plan users only. It recommended actions that users could take to improve their training data. Instead of consolidating recommendations in one place, recommendations are now being made available from the parts of the tool where you make actual training data changes. For example, while adding entity synonyms, you can now opt to see a list of synonymous terms that are recommended by Watson. If you are looking for other ways to analyze your user conversation logs in more detail, consider using Jupyter notebooks. See [Advanced tasks](https://cloud.ibm.com/docs/assistant?topic=assistant-logs-resources) for more details.





 9 November 2018 

Major user interface revision
: The Watson Assistant service has a new look and added features.

This version of the tool was evaluated by beta program participants over the past several months.



* Skills: What you think of as a workspace is now called a skill.
On Apple devices, the Intelligent Tracking Prevention feature automatically deletes any client-side cookie after 7 days. This means that if an anonymous customer accesses your website and then visits again two weeks later, the two visits are treated as two different MAUs. For information about how to avoid this problem, see [Managing user identity information](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-web-chat-develop-userid).

For information about how to customize the handling of user identity information for billing purposes, see [Managing user identity information](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-web-chat-develop-userid).

The usage is measured differently depending on the plan type. For Lite plans, usage is measured by the number of /message calls (API) are sent to the assistant from the web chat integration. For all other plans, usage is measured by the number of monthly active users (MAU) that the web chat interacts with. The maximum number of allowed MAUs differs depending on your Watson Assistant plan type.



Plan details

 Plan Maximum usage 

 Enterprise Unlimited MAU 
 Premium (legacy) Unlimited MAU 
 Plus Unlimited MAU 
 Trial 5,000 MAU 
 Lite 10,000 API (approximately 1,000 MAU)
For more information, see [Changing the result content](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-query-resultsquery-results-content).
7. Choose Yes or No for each suggestion.

Click the Preview document icon if you want to see the example in context before you make a choice.
8. Continue highlighting examples and validating suggestions until a message is displayed to inform you that you identified enough examples.
9. Click the Review examples tab to review the lists of examples that were identified by you and Discovery.
10. If the examples are correct, click Save pattern.

If Discovery cannot discern a consistent and valid pattern based on the information you provided, the Save pattern button is never enabled. A pattern might not be created if you provide contradictory examples, for example. To start over, click the Reset button. The documents are returned to their original state and any examples that you identified previously are removed.
11. To apply the pattern immediately, choose the collection and field where you want to apply the enrichments from the model, and then click Apply.



When Discovery finds text in a document that matches a pattern that you defined, it is annotated in the enriched_{fieldname}.entities field. You can find it by checking the enriched_{fieldname}.entities.model_name field for your pattern name.



 Downloading a pattern 

To download a pattern, complete the following step:



1. In the Patterns view, click the download icon.

A pattern model is downloaded as a ZIP file.



You can import the downloaded ZIP file as the source for an advanced rules model resource. For more information, see [Advanced rules models](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-domain-mladvanced-rules).





 Pattern limits 

The number of patterns that you can define per service instance depends on your Discovery plan type.



Pattern plan limits

 Plan Patterns per service instance 

 Premium 100 
 Enterprise 100 
 Plus (includes Trial) 20
Let's update the myliteralconfigmap configmap to change the value of the TARGET key from Sunshine to Stranger.

ibmcloud ce configmap update --name myliteralconfigmap --from-literal "TARGET=Stranger"
2. Now that your configmap is updated, use the configmap get command to display details about a specific configmap. For example,

ibmcloud ce configmap get --name myliteralconfigmap

Example output

Getting configmap 'myliteralconfigmap'...
OK

Name: myliteralconfigmap
ID: abcdefgh-abcd-abcd-abcd-c88e2775388e
Project Name: myproject
Project ID: 01234567-abcd-abcd-abcd-abcdabcd1111
Age: 21m
Created: 2021-05-14T07:57:11-04:00

Data:
---
TARGET: Stranger









 Referencing configmaps 

Your apps or jobs can consume and use the information that is stored in a configmap by using environment variables.



 Referencing configmaps from the console 

You can use the console to create environment variables for your apps and jobs that fully reference a configmap or reference individual keys in a configmap.

Before you can reference a configmap, it must exist. See [create a configmap](https://cloud.ibm.com/docs/codeengine?topic=codeengine-configmap-create-ui).

From the console, you can reference only one individual key of a defined configmap per environment variable. If you need to reference more than one key of a configmap, then repeat the steps to define another environment variable that references a different key.



1.
The Basic deployment strategy is not recommended for critical apps that must be highly available.

Rolling update
: Similar to the Basic strategy, this deployment strategy is simple, fast, and has low runtime resource requirements. However, because each running instance is taken down and updated individually, avoiding downtime, rollback requires you to deploy the previous release again. This time consuming approach might cause issues if the current version of the app in production is broken.

Blue-Green deployment
: Creates two separate, permanent production environments (blue and green), and only one of those environments receives traffic at a time. The current release is always deployed to the idle environment and traffic is switched to it after deployment completes, with no downtime. Because you need to switch only the traffic to the unchanged environment, rollback does not cause downtime. Because this strategy requires two full production environments, the resource requirements are higher. However, this strategy enables powerful Developer flows, such as the ability to test new app versions in the production environment before it allows customer traffic. Blue-Green deployment also supports fast rollback.

Canary release
: Deploys a new release in parallel with the original production environment (similar to Blue-Green), with no downtime. The amount of traffic that is sent to both the updated and original instances is managed so that the new version is available to a controlled subset of users while the deployment proceeds. Over time, the traffic that is sent to the new version is increased until all of the traffic is sent there, at which point you can stop the old production environment. For rapid rollback while deployment is in progress, you can route all of the traffic to the original production environment. Since this strategy requires two full production environments only during deployment, the overall resource usage is lower than for the Blue-Green deployment. The Canary release deployment strategy is the slowest to move from a previous release to a current release of the software that is being deployed. Canary deployments allow organizations to test two different software versions side by side in production.



 Before you begin 

Before you start this tutorial, make sure that you have the following resources in place:



* An [IBM Cloud account](https://cloud.ibm.com/registration), with a Standard plan.
: See [Choosing a build strategy](https://cloud.ibm.com/docs/codeengine?topic=codeengine-plan-buildbuild-strategy).





 06 October 2022 

CLI version 1.39.5 released
: See [CLI version history](https://cloud.ibm.com/docs/codeengine?topic=codeengine-cli_versions).

Updated versions for buildpacks
: See [Choosing a build strategy](https://cloud.ibm.com/docs/codeengine?topic=codeengine-plan-buildbuild-strategy).







 September 2022 

Review the release notes for September 2022.



 21 September 2022 

New tutorial
: Added a tutorial that demonstrates how to secure your application with an open source tool called Guard. See [Securing your application with Guard](https://cloud.ibm.com/docs/codeengine?topic=codeengine-getting-started-with-guard).

Updated versions for buildpacks
: See [Choosing a build strategy](https://cloud.ibm.com/docs/codeengine?topic=codeengine-plan-buildbuild-strategy).





 15 September 2022 

CLI version 1.39.3 released
: See [CLI version history](https://cloud.ibm.com/docs/codeengine?topic=codeengine-cli_versions).

Updated versions for buildpacks
: See [Choosing a build strategy](https://cloud.ibm.com/docs/codeengine?topic=codeengine-plan-buildbuild-strategy).







 August 2022 

Review the release notes for August 2022.



 31 August 2022 

CLI version 1.39.2 released
: See [CLI version history](https://cloud.ibm.com/docs/codeengine?topic=codeengine-cli_versions).

Updated versions for buildpacks
When you send calls to the service with earlier API version dates, they will receive properly formatted and valid responses for a time, so you can gracefully transition to using the later API versions. However, the confidence scores and other results that are sent in the response will reflect those generated by a more recent version of the API.

Action: Do some testing of calls with the latest version to verify that things work as expected. Some functionality has changed over the last few years. After testing, change the version date on any API calls that you make from your applications.







 26 February 2020 

Slot Save it as field retains your edits
: When you edit what gets saved for a slot by using the JSON editor to edit the value of the context variable to be something other than what is specified in the Check for field, your changes are kept even if someone subsequently clicks the Save it as field.





 20 February 2020 

Access control changes are coming
: Notifications are displayed in the user interface for anyone with Reader and Writer level access to a service instance. The notification explains that access control is going to change soon, and that what they can do in the instance will change unless they are given Manager service access beforehand. For more information, see [Preventing loss of access](https://cloud.ibm.com/docs/assistant?topic=assistant-access-control).





 14 February 2020 

More web chat color settings
: You can now specify the color of more elements of the web chat integration. For example, you can define one color for the web chat window header. You can define a different color for the user message bubble. And another color for interactive components, such as the launcher button for the chat.





 13 February 2020 

Track API events
: Premium plan users can now use the Activity Tracker service to track how users and applications interact with IBM Watson® Assistant in IBM Cloud®. See [Activity Tracker events](https://cloud.ibm.com/docs/assistant?topic=assistant-at-events).





 5 February 2020 

New API version
: The current API version is now 2020-02-05. The following changes were made with this version:
[Preview button](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/preview-button.png)
4. Copy the URL from Share this link and use it in a new tab. You can start submitting message to see how your assistant responds.

With a Lite plan, you can use the service for free. With other plans, you are charged for messages that you submit from the preview link. You can review metrics about the test user conversations from the Analytics page. You are not charged for messages that you submit from the "Try it out" pane, and the exchanges you have there are not logged.
5. Type hello into the text field, and watch your assistant respond.

![The widget in the preview link integration showing a single dialog exchange.](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/gs-test-from-preview-link.png)

You can share the URL with others who might want to try out your assistant.
6. After testing, close the web page. Click the X to close the preview link integration page.







 Next steps 

This tutorial is built around a simple example. For a real application, you need to define some more interesting intents, some entities, and a more complex dialog that uses them both. When you have a polished version of the assistant, you can integrate it with web sites or channels, such as Slack, that your customers already use. As traffic increases between the assistant and your customers, you can use the tools that are provided in the Analytics page to analyze real conversations, and identify areas for improvement.



* Complete follow-on tutorials that build more advanced dialogs:



* Add more dialog nodes to design complex conversational exchanges.
So this first one might have [Writing] current location, and [Writing] zip code.

Attributes, this allows users who are either currently in the location or have already stipulated that they live nearby to view this feature, but before we do that we want to test the feature out on our own employees. [Writing] So we would have segment Bof our testers because we want them to be our employees the attribute might be [Writing] email ID.

Now we can effectively test our feature in production by {Draw] flipping this toggle on. So now this feature is on for our testers, and say maybe something went wrong so we're actually going to flip it off fix it, and then we'll turn it back on for segment B.

Once we're satisfied that everything is working well. Then we can flip this on for our [Draw] segment A.

Now all this is done without a deployment because our feature is already in production. All we're doing is making it visible or not visible to certain users.

Once it is in production. [Draw] We can actually add a little bit of automation to this

with our testers, we did it manually, we flipped it on and off manually, but with feature flags we can actually add a time element. So let's say we only want this to be viewable for [Writing] three weeks,

two weeks before grand opening, one week after grand opening of our new shop. This will be flipped on and then automatically turned off or not visible for this segment of users after three weeks.

Okay, so now we're getting really good at using feature flags so our [Draw] feature flags are potentially starting to stack up we might have a couple apps and a couple websites with a couple different types of features that are flagged.

So we've got maybe [Writing] app one here, [Writing] app two, and say a [Writing] web page.

With a feature flagging service we can actually group these in collections so that we're a little bit more organized with which feature flags are tied to, which apps are web pages.

So now today we've learned about returning feature flags on and off without deployment testing directly in production, and then segmenting those features based on the user attributes.

Thank you for watching.
In the Explore section, you can view and troubleshoot key metrics and entities of your infrastructure stack through any of the following tabs:



* Metrics: Use this section to view and analyze individual metrics.

When you analyze metrics, you can drill down into layers of your infrastructure hierarchy and view granular-level data. You can use the Grouping selection to control how entities are organized in Explore such as All Workloads, Containerized Apps, DaemonSets, Deployments, and more.

In the metrics tab, you can view and analyze individual metrics. You can also view troubleshooting views and topology views.
* PromQL Query: Use the PromQL Metric Explorer to build PromQL queries.
* PromQL library: Try predefined PromQL queries. Then, create an alert based on a query that you try or create a dashboard panel from a predefined query.



For more information, see [Explore](https://docs.sysdig.com/en/docs/sysdig-monitor/explore/).
Python 

Given its ease of use and adoption for data science applications, [Python](https://www.python.org/) support is imperative to increase adoption of your IBM Cloud service. Supporting Python applications using [Flask](https://github.com/pallets/flask), [Django](https://www.djangoproject.com/), [Jupyter](https://jupyter.org/), and functional programming paradigms introduces special considerations that need to be observed by your SDK.



 Environment support 



* Your Python SDK should be written to support all [Python >=3.5](https://www.python.org/downloads/) releases.
* Ensure your SDK is compatible with data science usecases, such as [Jupyter Notebooks](https://jupyter.org/).







 Publishing 

All Python SDKs should be publicly available on an [IBM GitHub organization](https://cloud.ibm.com/docs/sdk-handbook?topic=sdk-handbook-distributionopen-source). The releases of these SDKs should be published on [PyPI](https://pypi.org/).

Your SDK should follow the [semantic versioning best practices](https://cloud.ibm.com/docs/sdk-handbook?topic=sdk-handbook-distributionsemantic-versioning).





 Community support 

Allow your users to find answers to their questions. Users should be able to report problems on GitHub by raising issues on your SDK repository. Having a public Slack channel is a great way to engage with users who have questions specific to their use cases.





 Style guidelines 

You should follow the [PEP8 style guide for Python](https://www.python.org/dev/peps/pep-0008/), with a few modifications, like four spaces instead of tabs for indentation.

You should use the standard [development tools](https://cloud.ibm.com/docs/sdk-handbook?topic=sdk-handbook-developer-tools) for Python to check style and code coverage.
The SDK simplifies connecting to the Hive metastore and IBM Cloud Object Storage buckets referenced by tables or views.

In case of using Python download both, the Scala and the Python SDK, and place them in a folder that is in the classpath of your Apache Spark cluster. When using Scala, the Scala SDK is enough.



* [spark-dataengine-scala](https://us.sql-query.cloud.ibm.com/download/catalog/dataengine-spark-integration-1.4.51.jar)
* [spark-dataengine-python](https://us.sql-query.cloud.ibm.com/download/catalog/dataengine_spark-1.4.51-py3-none-any.whl)



An example of how to use the Python helper can be found in the [Watson Studio Notebooks section](https://cloud.ibm.com/docs/sql-query?topic=sql-query-hive_metastoreusage_watson_notebooks).

Use the following example to get started with IBM® Analytics Engine (IAE) or Spark runtimes in Watson Studio when using Scala. Submit the following application using a notebook or the spark-submit command:

package com.ibm.cloud.dataengine
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.SparkSession.{Builder => SessionBuilder}
import SparkSessionBuilderAddOn._
object SparkSessionBuilderHMSConfigTest {
def main(args: Array[String]) = {
val spark = SparkSession
.builder()
.appName("Spark DataEngine integration")
.enableDataengine(args(0), args(1), "public")
.config("fs.cos.impl", "com.ibm.stocator.fs.ObjectStoreFileSystem")
.config("fs.stocator.scheme.list", "cos")
In the interests of making your SDK easy to consume and cloud native, you should provide the ability to read in environment variables. Abstracting the application logic from the environment logic allows your users to focus on using your service capabilities their applications.

If you build this capability into your SDK, you must document this mechanism clearly with examples.





 Using python-sdk-core 

[IBM python-sdk-core](https://github.com/IBM/python-sdk-core) provides configuration and authentication support. You can use the existing functionality provided by this dependency in your SDK.





 Documentation 

Your SDK is not useful if your audience cannot understand how to consume it in order to do the basic operations for your service. Your SDK needs to contain the following resources to help your users:



* README.md
* [Contributor guidelines](https://cloud.ibm.com/docs/sdk-handbook?topic=sdk-handbook-documentationsdk-contributor-docs)
* Code Samples
* [Service documentation](https://cloud.ibm.com/docs/sdk-handbook?topic=sdk-handbook-documentation)







 Samples 

For your Python SDK, you will want some simple code samples and explanations of what each does. Linking out to the API reference documentation for more advanced use is strongly encouraged.

At minimum, the samples should be included in the README.md file. They should communicate how to install the library, and complete the basic operations provided by your API.

The samples should include simple installation and initialization instructions for the popular frameworks and data science tools. Additional examples should be available in an /examples directory for more advanced operations which can be copied and pasted in to user applications.