vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Choose a narrow set of user goals first. If you start small and choose the goals with the highest impact first, you'll have room and time to grow the expertise of your assistant. After your assistant is live, the built-in metrics of active user conversations help you understand what your customers are asking about, how well your assistant is able to meet their needs, and what to focus on next.





 3. Choose the tone and language of your assistant 

Before you build your assistant, it can also be helpful to choose the tone with which your assistant communicates. Is your assistant an optimist or a pessimist, a shy intellectual type, or an upbeat sidekick? Write conversations that reflect your assistant's personality. Don't overdo it by sacrificing usability for the sake of keeping your assistant in character. Strive to present a consistent tone and attitude.

Never misrepresent the assistant as being a human. If users believe that the assistant is a person, then find out it's not, they are likely to distrust it. In fact, some US states have laws that require chatbots to identify themselves as chatbots.

Decide whether and how you want to handle more than one spoken language. For more information about ways to approach language support, see [Adding support for global audiences](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-admin-language-support).





 4. Connect to content sources 

The answer to common questions might already be documented somewhere in your organization's technical information. Plan whether you want to connect the assistant to existing content sources by taking an inventory of relevant help content (for example, product information, knowledge articles, FAQs) available to your customers.

You can give your assistant access to this information by adding a [search integration](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-search-add) to your assistant. The search integration uses IBM Watson® Discovery to return smart answers to natural language questions.





 5. Plan your handoff strategy
The users now have access to the new org, which will show up in the org selector on the Watson IoT Platform dashboard.





 Migrating devices 

Migrating devices may be accomplished in these steps:



1. Migrate the Device Types that you wish to preserve to the new org.
2. Migrate the devices themselves to the new org.
3. Update the hostname that the devices use to connect to IBM Cloud to start with your new org id.
4. Migrate any Data Management information you created in the Lite platform.
This might include Device Physical and Logical Interfaces, Things, and Rules.



Device Types and Devices can be migrated in bulk with the following APIs:



 API Documentation Export API Import API 

 [Device Type API ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://docs.internetofthings.ibmcloud.com/apis/swagger/v0002/org-admin.html) GET /device/types POST /device/types 
 [Device API ![External link icon](https://cloud.ibm.com/docs-content/v1/content/icons/launch-glyph.svg)](https://docs.internetofthings.ibmcloud.com/apis/swagger/v0002/org-admin.html!/Device_Bulk_Configuration) GET /bulk/devices POST /bulk/devices/add 



Important: Watson IoT Platform does not store device authentication tokens, only salted version of these. The GET /bulk/devices call is thus unable to return the authentication tokens for stored devices. If you have a record of the device authentication tokens you can add these to the JSON object before doing the bulk load. Otherwise, you must generate new device authentication tokens for each device and copy them to the devices.

If you are using device-side certificates to authenticate devices, rather than authentication tokens, you do not need to do this.
For information, see [Improving result relevance with the tooling](https://cloud.ibm.com/docs/discovery?topic=discovery-improving-result-relevance-with-the-tooling).
* How do you know that relevancy training is complete?

For answers to common questions about training a collection and explanations of common error and warning messages, see [Relevancy training tips](https://cloud.ibm.com/docs/discovery?topic=discovery-relevancy-tips).
* Where can I learn more about uploading documents to Watson Discovery?

You can upload documents using the API or the Discovery tooling. You can also connect to several different data sources (including Box, Salesforce, SharePoint Online, SharePoint 2016, and IBM Cloud Object Storage), or do a web crawl. For details, see [Adding content](https://cloud.ibm.com/docs/discovery?topic=discovery-addcontent).
* What document types are supported for ingestion?

Refer to [Supported document types and browsers](https://cloud.ibm.com/docs/discovery?topic=discovery-sdudoctypes) for a list of the available document types by plan.
* How do you use the Smart Document Understanding editor?

The SDU editor functions are available in the Discovery tooling. Read [Using the Smart Document Understanding editor](https://cloud.ibm.com/docs/discovery?topic=discovery-sduannotate) to get started.
* How much does Discovery cost?

Discovery has several options available. See [Discovery pricing plans](https://cloud.ibm.com/docs/discovery?topic=discovery-discovery-pricing-plans).
* How do I upgrade my existing Discovery plan?

For information on resizing your plan from Lite to Advanced, or switching from one Advanced tier to another, see [Upgrading your plan](https://cloud.ibm.com/docs/discovery?topic=discovery-upgrading-your-plan).
* How do I create a stopwords list?

Discovery applies a default list of stopwords for several languages at query time.
For more information, see [Go Lite with Watson Knowledge Studio](https://www.ibm.com/cloud/blog/announcements/go-lite-watson-knowledge-studio?mhsrc=ibmsearch_a&mhq=go%20lite%20with%20Watson%20Knowledge%20Studio).







 March 2018 



 New features and changes 

New Deployed Models page
: A Deployed Models page is available where you can view all the Knowledge Studio models that are deployed to services in the spaces that you have access to. To view the Deployed Models page, from the Settings menu in the top right menu bar, click Manage deployed models. For more information about undeploying and viewing models on the Deployed Models page, see [Undeploying models](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-publish-mlpm-um) and [Undeploying rule-based models](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-wks_rule_publishramu-um).

French interface available
: A French translation of the Knowledge Studio interface is now available.

Alchemy no longer available
: IBM® AlchemyLanguage is no longer available as a pre-annotator. Instead of AlchemyLanguage, you can use Natural Language Understanding to pre-annotate your documents. For more information, see [Bootstrapping annotation](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-preannotation).







 December 2017 



 New features and changes 

Launch on Cloud
: Knowledge Studio launched on IBM Cloud. For more information about the migration process and schedule, see [Migrating to IBM Cloud](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-migrate).

Redesigned navigation
Log in as a Knowledge Studio administrator or project manager, and select your workspace.
2. Select Machine Learning Model > Versions.
3. Click Export current model.

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

The model is saved as a ZIP file, and you are prompted to download the file.
4. Download the file to your local system.
5. From the IBM Watson Explorer application, import the model.

You can then map the model to a machine learning model in Watson Explorer Content Analytics. After you perform the mapping step, when you crawl documents, the model finds instances of the entities and relations that your model understands. For more information about how to import and configure the model in IBM Watson Explorer, see the technical document that describes the integration: [Using machine-learning annotators from Knowledge Studio in Watson Explorer](https://www.ibm.com/support/pages/node/597611).





 Related tasks 

[Exporting analyzed documents from Watson Explorer Content Analytics](https://cloud.ibm.com/docs/watson-knowledge-studio?topic=watson-knowledge-studio-preannotationwks_uimawexca)
About Discovery v1 

IBM Watson™ Discovery v1 is deprecated. As of 1 October 2021, you cannot create new v1 instances. Lite or Advanced plan service instances are Discovery v1 instances, as are service instances that you created with a Premium plan before 16 July 2020. Existing v1 service instances are supported until 11 July 2023. Any v1 instances that still exist on that date will be deleted. Migrate your solutions to use Discovery v2 before 11 July 2023. For more information, see the [Discovery v2 documentation](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-version-choose).

Discovery makes it possible to rapidly build cognitive, cloud-based exploration applications that unlock actionable insights hidden in unstructured data — including your own proprietary data, as well as public and third-party data.

![Discovery architecture diagram](https://cloud.ibm.com/docs-content/v1/content/ded4adc3ea0bd2a81b113c579f2b1183926da211/discovery/images/about-discovery1.png)

With Discovery, it only takes a few steps to prepare your unstructured data, create a query that pinpoints the information you need, and then integrate those insights into your new application or existing solution.

How does Discovery do it? By using data analysis combined with cognitive intuition to take your unstructured data and enrich it so you can discover the information you need.

IBM Watson™ Discovery brings together a functionally rich set of integrated, automated Watson APIs to:



* Crawl, convert, enrich and normalize data.
* Securely explore your proprietary content as well as free and licensed public content.
* Apply additional enrichments such as concepts, relations, and sentiment through Natural Language Understanding (NLU).
* Simplify development while still providing direct access to APIs.



To try out Discovery, see the [IBM Watson™ Discovery Query Demo](https://www.ibm.com/demos/live/watson-discovery/self-service/home).
"resource_keys_url": "/v2/resource_instances/8d7af921-b136-4078-9666-081bd8470d94/resource_keys",
"plan_history":
{
"resource_plan_id": "2fdf0c08-2d32-4f46-84b5-32e0c92fffd8",
"start_date": "2018-04-19T00:18:53.302077457Z"
}
],
"migrated": false,
"controlled_by": ""
}
]
}
Show more
The spoke has egress routes that were initialized with one of the NLB appliance IPs. The spoke next hop will not be updated on NLB appliance fail over!

It will be required to maintain an ingress route in the transit VPC which will be rewritten by the NLB to reflect the active appliance. The spoke egress route will deliver packets to the correct zone of the transit VPC. Routing within the transit VPC zone will find the matching ingress rule which will contain the active appliance.

Below is the transit VPC ingress route table discussed earlier. The next hop will be kept up to date with the active NLB appliance. Note that Dallas 3 has a change written by the NLB route mode service to reflect the active appliance.



 Zone Destination Next hop 

 Dallas 1 0.0.0.0/0 10.1.0.196 
 Dallas 2 0.0.0.0/0 10.2.0.196 
 Dallas 3 0.0.0.0/0 10.3.0.197 







 Step 3: DNS 

The IBM Cloud DNS Services service is used to convert names to IP addresses. In this example a separate DNS service is created for the transit and each of the spokes. This approach provides isolation between teams and allows the architecture to spread across different accounts. If a single DNS service in a single account meets your isolation requirements it will be simpler to configure. All zones are configured similarly and below is a diagram for a two zone architecture:

Zoom

![DNS Layout](https://cloud.ibm.com/docs-content/v1/content/6a28e4ae2660b609a18e90e1239ed772f80373d6/transit-gateway/includes/solution-tutorials/includes/solution-tutorials/images/vpc-transit/vpc-transit-dns.svg)

DNS Layout



 DNS Resources 



1. Apply the dns_tf layer to create the DNS services and add a DNS zone for each VPC and an A record for each of the test instances:
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. A dialog skill is a container for the natural language processing training data and artifacts that enable your assistant to understand user questions, and respond to them.



Where are my workspaces? Any workspaces that you created previously are now listed in your service instance as skills. Click the Skills tab to see them.
Percentage rollout capability is available for Lite and Enterprise plans.

Following are some of the percentage rollout scenarios:



* If the feature flag is disabled, then SDK returns the default Disabled value.
* If the feature flag is enabled without any segment rules and no percentage rollout is set, then the SDK returns default Enabled value.
* If the feature flag is enabled without any segment rule and percentage rollout is set to 0%, then the SDK returns Disabled value for all users.
* If the feature flag is enabled without any segment rule and percentage rollout is set to a percent, say 50%, then the SDK checks if the user belongs to the configured percentage rollout size, if the user belongs to the rollout criteria, then the SDK returns the Enabled value. If the user doesn't belong to the rollout criteria, then the SDK returns the Disabled value.
* If the feature flag is enabled with segment rules, then SDK first evaluates the user against the configured rules. If user is part of the configured rule, then the SDK evaluates if the user is eligible for percentage rollout.



* If multiple rules are configured and the user is checked for rule match until the first match. If no match found, then the SDK evaluates against the default rollout.
* If the user is evaluated to be part of the segment and the segment percentage rollout is 0%, then even if the user is part of the segment, user will not receive the segment value. The SDK returns the default Disabled value.
* If the user is evaluated to be part of the segment and the segment percentage rollout is set to a percentage, say 50%, then the SDK checks if the user belongs to configured rollout size, if the user belongs to the rollout criteria, then the SDK returns the segment overriding value. If the user doesn't belongs to the rollout criteria, then the SDK returns Disabled value.







 View as table 



Table 1. Percentage rollout scenarios

 Feature Flag Is targeting configured? Percentage <br>rollout Is user <br>part of <br>configured segment Is user <br>part of <br>percentage rollout <br>criteria Value <br>returned <br>by SDK
If you have a Pay-As-You-Go or Subscription account, there's no limit to the number of resource groups, orgs, or spaces that you can create. However, if you have a Lite account, you're limited to one org and one resource group.





 Can I change which notifications I receive? 

Yes, you can update your email preferences for receiving notifications from the Email preferences page in the console. Click the Avatar icon ![Avatar icon](https://cloud.ibm.com/docs-content/v1/content/4d8c6eec891cff72b666dc24bd3211810c77fb42/icons/i-avatar-icon.svg) > Profile > Email preferences.

For more information, see [Setting email preferences](https://cloud.ibm.com/docs/account?topic=account-email-prefs).





 Can I reset my password? 

To reset your account password, click the Avatar icon ![Avatar icon](https://cloud.ibm.com/docs-content/v1/content/4d8c6eec891cff72b666dc24bd3211810c77fb42/icons/i-avatar-icon.svg) > Profile in the console. Then, click Edit in the Account user information widget.

To reset your VPN password, complete the following steps:



1. In the IBM Cloud console, go to Manage > Access (IAM), and select Users.
2. Select the user.
3. From the VPN subnets section, click the Edit icon ![Edit icon](https://cloud.ibm.com/docs-content/v1/content/4d8c6eec891cff72b666dc24bd3211810c77fb42/icons/icon_write.svg) to enter a new VPN password.
4. Click Apply.







 What can I do if I forget my password?
Adding content 

How do I decide which document upload method to use?

This documentation applies to Discovery service instances that you create with a Lite or an Advanced plan, or that you created with a Premium plan before 16 July 2020. For more information about features in Premium plan instances created on or after 16 July 2020, and in Plus (including Plus Trial) plan instances, see [these docs](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-upload-data).



* Use the [API](https://cloud.ibm.com/docs/discovery?topic=discovery-gs-api) if you are integrating the upload of content with an existing application or creating your own custom upload mechanism.
* Use the [Discovery tooling](https://cloud.ibm.com/docs/discovery?topic=discovery-getting-started) if you want to quickly upload locally accessible files. When uploading documents using the Discovery tooling, all documents must have a unique file name. If two files have the same name, the original is overwritten when the newer version is uploaded. If you would prefer that documents with the same file name coexist in your collection, the Document ID needs to be specified. You can specify the Document ID if you upload documents using the API.
* Use the Discovery tooling or API to connect to Box, Salesforce, Microsoft SharePoint Online, IBM Cloud Object Storage, and Microsoft SharePoint 2016 data sources, or do a web crawl. See [Connecting to data sources](https://cloud.ibm.com/docs/discovery?topic=discovery-sources) for more information.



You can use an IBM App Connect default connector to send data from a large set of popular data sources to Discovery by creating flows within the App Connect tooling. Note that creating a separate App Connect instance is required to use this App Connect default connector and that any costs that you incur when you use a paid App Connect instance are not included with the cost for using Discovery. Additionally, except for indexing, Discovery does not support any integration with App Connect that you perform on your own.
You could end up reversing any gains you made by doing a thorough job with the first 5 documents.
* Consistent annotation is at least as important as correct annotation.

Some decisions regarding annotation guidelines are arbitrary, like whether car trim lines should be considered part of model names, such as Camry or Camry LX. Which of the policies is chosen is far less important than that the project team agrees on one or the other and annotates in accordance with that policy consistently.
* Label entity mentions on word-token boundaries only, because mention-detection search works at the word-token level of granularity.
* Label entity mentions that are limited to one or two adjacent words whenever possible.

Doing so is not always possible or easy. Consider these examples:



* The source document contains a sentence from which, for the purposes of the application that will use the type system, we want to annotate the problem and its cause.

The electronic module was burnt because the wrong voltage was applied.

One might be tempted to annotate the problem and cause like this:

[PROBLEM] [CAUSE]

[The electronic module was burnt] [because the wrong voltage was applied].

However, it is not a good practice to annotate such long phrases as entity types. Instead, find the entities of importance and identify how they are associated with one another by defining a relation mention.

[LOCATION] [SYMPTOM] [CAUSE]

The [electronic module] was [burnt] because the [wrong voltage] was applied.

^---isStatusOf--| |------causedBy-------^
* The source document contains a split verb that you want to annotate. How do you annotate noncontiguous text as a single entity type? You can annotate each entity mention, and identify them as being related to each other by using a relation mention.

[EVENT_ANSWER] [EVENT_ANSWER]

All of the phones were ringing, but he knew he should [pick] the red phone [up] first.

^----splitType-----^
Managing the app lifecycle 

Use Kubernetes-native and third-party tools to perform rolling updates and rollbacks of apps without downtime for your users.



 Update strategies 

To update your app, you can choose from various strategies such as the following. You might start with a rolling deployment or instantaneous switch before you progress to a more complicated canary deployment.

Rolling deployment
: You can use Kubernetes-native functionality to create a v2 deployment and to gradually replace your previous v1 deployment. This approach requires that apps are backwards-compatible so that users who are served the v2 app version don't experience any breaking changes. For more information, see [Managing rolling deployments to update your apps](https://cloud.ibm.com/docs/openshift?topic=openshift-update_appapp_rolling).

Instantaneous switch
: Also referred to as a blue-green deployment, an instantaneous switch requires double the compute resources to have two versions of an app running at once. With this approach, you can switch your users to the newer version in near real time. Make sure that you use service label selectors (such as version: green and version: blue) to make sure that requests are sent to the correct app version. You can create the new version: green deployment, wait until it is ready, and then delete the version: blue deployment. Or you can perform a [rolling update](https://cloud.ibm.com/docs/openshift?topic=openshift-update_appapp_rolling), but set the maxUnavailable parameter to 0% and the maxSurge parameter to 100%.

Canary or A/B deployment
: A more complex update strategy, a canary deployment is when you pick a percentage of users such as 5% and send them to the new app version. You collect metrics in your logging and monitoring tools on how the new app version performs, do A/B testing, and then roll out the update to more users. As with all deployments, labeling the app (such as version: stable and version: canary) is critical.