vania-janet/multiturn-rag-retrieval-data huggingface dataset:
To see all of the mentions you annotated for a particular entity, from the entity's configuration page, click the Annotation tab.

![Annotation view selector highlighted](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/oe-annotated.png)

Contextual entities understand values that you have not explicitly defined. The system makes predictions about additional entity values based on how your user examples are annotated, and uses those values to train other entities. Any similar user examples are added to the Annotation view, so you can see how this option impacts training.

If you do not want your contextual entities to use this expanded understanding of entity values, select all the user examples in the Annotation view for that entity, and then click Delete.



Tutorial: To walk through a tutorial that shows you how to define contextual entities before you add your own, go to [Tutorial: Defining contextual entities](https://www.ibm.com/cloud/architecture/demo/try-watson-assistant-contextual-entities).

The contextual entities tutorial shows a slightly older version of the user interface. But the concepts it covers are the same, so it is still a worthwhile exercise.



 What you don't annotate matters 

If you have an intent example with an annotation, and another word in that example matches the value or a synonym of the same entity, but the value is not annotated, that omission has impact. The model also learns from the context of the term you did not annotate. Therefore, if you label one term as a mention of an entity in a user example, be sure to label any other applicable mentions also.



1. The Customer_Care_Appointments intent includes two intent examples with the word visit.

!
* [List custom prompts](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechlistcustomprompts): GET /v1/customizations/{customization_id}/prompts
* [Get a custom prompt](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechgetcustomprompt): GET /v1/customizations/{customization_id}/prompts/{prompt_id}
* [Delete a custom prompt](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechdeletecustomprompt): DELETE /v1/customizations/{customization_id}/prompts/{prompt_id}



* The service includes four methods for working with speaker models:



* [Create a speaker model](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechcreatespeakermodel): POST /v1/speakers
* [List speaker models](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechlistspeakermodels): GET /v1/speakers
* [Get a speaker model](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechgetspeakermodel): GET /v1/speakers/{speaker_id}
* [Delete a speaker model](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechdeletespeakermodel): DELETE /v1/speakers/{speaker_id}





Unified Text to Speech documentation
: The documentation for IBM Watson Text to Speech for IBM Cloud Pak for Data is now combined with the documentation for managed instances of the Text to Speech service that are hosted on IBM Cloud. This is true of both the guide and reference documentation for the two forms of the service. Links to the formerly separate version of the IBM Cloud Pak for Data documentation for the service redirect to the unified documentation.
For more information, see the following sections:



* For more information, see [Creating a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreate) and [Using a custom language model for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUse).
* For general and Japanese-specific considerations for adding a corpus text file, see [Preparing a corpus text file](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordsprepareCorpus) and [What happens when I add a corpus file?](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordsparseCorpus)
* For Japanese-specific considerations when specifying the sounds_like field for a custom word, see [Guidelines for Japanese](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-corporaWordswordLanguages-jaJP).
* For more information about all methods of the customization interface, see the [API & SDK reference](https://cloud.ibm.com/apidocs/speech-to-text).



New method for listing information about a corpus
: The language model customization interface now includes a GET /v1/customizations/{customization_id}/corpora/{corpus_name} method that lists information about a specified corpus. The method is useful for monitoring the status of a request to add a corpus to a custom model. For more information, see [Listing corpora for a custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageCorporalistCorpora).

New count field for methods that list words for custom language models
To understand which enrichments are applied to a project type by default, go to the row that describes the enrichments, and find the columns for the project type that you are interested in.

 Enrichment Document Retrieval Document Retrieval for Contracts Conversational Search Content Mining 

 Contracts ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) 
 Entities ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) 
 Keywords 
 Part of Speech ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) ![checkmark icon](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/icons/checkmark-icon.svg) !
* [Understanding Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-intro)
* [Rules for creating custom prompts and speaker models](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-rules)
* [Creating a custom prompt](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-create)
* [Using a custom prompt for speech synthesis](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-use)
* [Managing custom prompts](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-custom-prompts)
* [Managing speaker models](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-speaker-models)



The service includes eight new methods for working with the Tune by Example feature. The descriptions of the new methods that follow provide links to their entries in the API & SDK reference.



* The service includes four methods for working with custom prompts:



* [Add a custom prompt](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechaddcustomprompt): POST /v1/customizations/{customization_id}/prompts/{prompt_id}
* [List custom prompts](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechlistcustomprompts): GET /v1/customizations/{customization_id}/prompts
* [Get a custom prompt](https://cloud.ibm.com/apidocs/text-to-speech/text-to-speechgetcustomprompt): GET /v1/customizations/{customization_id}/prompts/{prompt_id}
For more information about language models, see [Languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models) in the Speech to Text documentation.

Click Next.
10. On the Text to Speech page, select the instance of the Text to Speech service you want to use for the phone integration.



* If you have existing Text to Speech instances, select the instance you want to use from the list.
* If you do not have any existing Text to Speech instances, click Create new instance to create a new Standard instance.



11. In the Choose your Text to Speech voice field, select the voice you want to use.

The list of voices is automatically filtered to use the same language as your assistant. To see all voices, toggle the Filter voices based on assistant language switch to Off.

For more information about voice options, and to listen to audio samples, see [Languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices) in the Text to Speech documentation.

Click Next.



Any speech service charges incurred by the phone integration are billed with the Watson Assistant service plan as voice add-on charges. After the instances are created, you can access them directly from the IBM Cloud dashboard. Any use of speech instances that occurs outside of your assistant is charged separately as speech service usage costs.

The phone integration setup is now complete. On the Phone page, you can click the tabs to view or edit the phone integration.

If you chose to generate a free telephone number, your new number is displayed on the Phone number tab immediately. However, provisioning the new number might take several minutes.





 Adding more phone numbers 

If you are using existing phone numbers you configured via a SIP trunk provider, you can add multiple numbers to the same phone integration.

If you generated a free phone number, you cannot add more numbers.

To add more phone numbers:



1. In the phone integration settings, go to the Phone number tab.
2. Use one of the following methods:
The recommender finds related synonyms based on contextual similarity extracted from a vast body of existing information, including large sources of written text, and uses natural language processing techniques to identify words similar to the existing synonyms in your entity value. For more information see [Synonyms](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-create-dictionary-based).

New API version
: The current API version is now 2018-07-10. This version introduces the following changes:



* The content of the /message output object changed from being a text JSON object to being a generic array that supports multiple rich response types, including image, option, pause, and text.
* Support for contextual entities was added.
* You can no longer add user-defined properties in context.metadata. However, you can add them directly to context.



Overview page date filter
: Use the new date filters to choose the period for which data is displayed. These filters affect all data shown on the page: not just the number of conversations displayed in the graph, but also the statistics displayed along with the graph, and the lists of top intents and entities. See [Controls](https://cloud.ibm.com/docs/assistant?topic=assistant-logs-overviewlogs-overview-controls) for more information.

Pattern limit expanded
: When using the Patterns field to [define specific patterns for an entity value](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-patterns), the pattern (regular expression) is now limited to 512 characters.





 2 July 2018 

Jump-tos from conditional responses
: You can now configure a conditional response to jump directly to another node. See [Conditional responses](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-overviewdialog-overview-multiple) for more details.





 21 June 2018 

Language updates for system entities
: Dutch and Simplified Chinese language support are now generally available. Dutch language support includes fuzzy matching for misspellings.
See [Defining what to do next](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-overviewdialog-overview-jump-to).
* Before you enable digressions away from a node, test the most common user scenarios. And be sure that likely digressed-to nodes are configured to return. See [Digressions](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-runtimedialog-runtime-digressions).







 Adding responses 



* Keep answers short and useful.
* Reflect the user's intent in the response.

Doing so assures users that the bot is understanding them, or if it is not, gives users a chance to correct a misunderstanding.
* Only include links to external sites in responses if the answer depends on data that changes frequently.
* Avoid overusing buttons. Encouraging users to pick predefined options from a set of buttons is less like a real conversation, and decreases your ability to learn what users really want to do. When you let real users ask for things in their own words, you can use the input to train the system and derive better intents.
* Avoid using a bunch of nodes when one node will do. For example, add multiple conditional responses to a single node to return different responses depending on details provided by the user. See [Conditional responses](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-overviewdialog-overview-multiple).
* Word your responses carefully. You can change how someone reacts to your system based simply on how you phrase a response. Changing one line of text can prevent you from having to write multiple lines of code to implement a complex programmatic solution.
* Back up your skill frequently. See [Downloading a skill](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-skill-dialog-addskill-dialog-add-download).







 Tips for capturing information from user input
The chat window indicates what intents and entities were recognized in the input.

![Screen capture of test dialog output](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/test_dialog_output.png)
4. To see the top intents that were recognized in the test input along with their confidence scores, hover over the eye icon that is displayed next to the intent with the highest confidence score.

![Screen capture of the eye icon that is shown next to the top-most recognizes intent](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/tryit-intent-details.png)
5. If the response is not what you expected it to be, you can take the following actions from the "Try it out" pane:



* If you want to edit an entity that is recognized in the input, click the entity name to open it in the Entities page.
* If the wrong intent is recognized, you can click the arrow next to the intent name to correct it or mark the topic as irrelevant. For more information, see [Making training data improvements](https://cloud.ibm.com/docs/assistant?topic=assistant-logslogs-fix-data).



6. If you want to know which node in the dialog tree triggered a response, click the Location![Location](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/location.png) icon next to it.

If you are not already on the Dialog page, open it.

The source node is given focus and the route that your assistant traversed through the tree to get to it is highlighted.
This documentation for the classic Watson Assistant experience has moved. For the most up-to-date version, see [Accessing context data in dialog](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-api-client-get-context). To see all documentation for the new Watson Assistant, please go [here](https://cloud.ibm.com/docs/watson-assistant).



 Accessing context data 

The context is an object containing variables that persist throughout a conversation and can be shared by the dialog and the client application. Both the dialog and the client application can read and write context variables.

You can choose whether you want the context to be maintained by your application or by the Watson Assistant service:



* If you use the stateful v2 message API, the context is automatically maintained by the assistant on a per-session basis. Your application must explicitly create a session at the beginning of each conversation; the context is stored by the service as part of the session and is not returned in message responses unless you request it. For more information, see the [v2 API Reference](https://cloud.ibm.com/apidocs/assistant/assistant-v2message).
* If you use the stateless v2 message API (or the legacy v1 message API) your application is responsible for storing the context after each conversation turn and sending it back to the service with the next message. For a complex application, or an application that needs to store personally identifiable information, you might choose to store the context in a database.

A session ID is automatically generated at the beginning of the conversation, but no session data is stored by the service. With the stateless message API, the context is always included with each message response. For more information, see the [v2 API Reference](https://cloud.ibm.com/apidocs/assistant/assistant-v2messagestateless).



Important: One use of the context is to specify a unique user ID for each end user who interacts with the assistant. For user-based plans, this ID is used for billing purposes.
* Conversation: A set of messages consisting of the messages that an individual user sends to your assistant, and the messages your assistant sends back.
* Conversation ID: Unique identifier that is added to individual message calls to link related message exchanges together. App developers using the V1 version of the Watson Assistant API add this value to the message calls in a conversation by including the ID in the metadata of the context object.
* Customer ID: A unique ID that can be used to label customer data such that it can be subsequently deleted if the customer requests the removal of their data.
* Deployment ID: A unique label that app developers using the V1 version of the Watson Assistant API pass with each user message to help identify the deployment environment that produced the message.
* Instance: Your deployment of Watson Assistant, accessible with unique credentials. A Watson Assistant instance might contain multiple assistants.
* Message: A message is a single utterance a user sends to the assistant.
* Skill ID: The unique identifier of a skill.
* User: A user is anyone who interacts with your assistant; often these are your customers.
* User ID: A unique label that is used to track the level of service usage of a specific user.
* Workspace ID: The unique identifier of a workspace. Although any workspaces that you created before November 9 are shown as skills in the product user interface, a skill and a workspace are not the same thing. A skill is effectively a wrapper for a V1 workspace.



Important: The User ID property is not equivalent to the Customer ID property, though both can be passed with the message. The User ID field is used to track levels of usage for billing purposes, whereas the Customer ID field is used to support the labeling and subsequent deletion of messages that are associated with end users. Customer ID is used consistently across all Watson services and is specified in the X-Watson-Metadata header. User ID is used exclusively by the Watson Assistant service and is passed in the context object of each /message API call.





 Enabling user metrics
* [Pricing](https://www.ibm.com/cloud/watson-text-to-speech/pricing)
* [Blogs](https://developer.ibm.com/blogs/)
* [Webinars](https://www.ibm.com/watson/webinars/)





* How to



* Using languages and voices



* [Languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices)
* [Migrating from neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices-migrate)
* [Using a voice for speech synthesis](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices-use)
* [Listing information about voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voices-list)



* [Using audio formats](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-audio-formats)
* Synthesizing speech with the service



* [The HTTP interface](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingHTTP)
* [The WebSocket interface](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-usingWebSocket)
* [Modifying speech synthesis characteristics](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-params)
* [Modifying speech synthesis with expressive neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressive)
* [Generating word timings](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-timing)
"type": "locatedAt",
"sentence": "The stockholders were pleased that Acme Corporation plans to build a new factory in Atlanta, Georgia.",
"score": 0.989245,
"arguments":
{
"text": "Atlanta",
"location":
94,
101
],
"entities":
{
"type": "GeopoliticalEntity",
"text": "Atlanta"
}
]
},
{
"text": "Georgia",
"location":
103,
110
],
"entities":
{
"type": "GeopoliticalEntity",
"text": "Georgia"
}
]
}
]
}
]
}
}
Show more

In the preceding example, you could query the relation type by accessing enriched_text.relations.type.

The related entities are listed in the arguments.

The score ranges from 0.0 to 1.0. The higher the score, the more relevant the relation.



 Using a custom relation model 

To create a custom enrichment model, you can use IBM Watson™ Knowledge Studio and import the model into Discovery by adding the ID in the Custom Model ID box of the Discovery tooling. For more information about integrating with IBM Watson™ Knowledge Studio, see [Integrating with IBM Watson™ Knowledge Studio](https://cloud.ibm.com/docs/discovery?topic=discovery-integrating-with-wks). The custom IBM Watson™ Knowledge Studio model overrides the default Relation extraction enrichment.

Only one IBM Watson™ Knowledge Studio model can be assigned to an enrichment.







 Keyword extraction 

Important topics in your content that are typically used when indexing data, generating tag clouds, or when searching. Discovery automatically identifies supported languages in your input content, and then identifies and ranks keywords in that content.

Example portion of a document enriched with Keyword Extraction:

{