vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Table 25: Tuples deleted count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_deleted_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 Tuples deleted rate 

Tuples deleted rate



Table 26: Tuples deleted rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_deleted_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Tuples fetched count 

Tuples fetched count



Table 27: Tuples fetched count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_fetched_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 Tuples fetched rate 

Tuples fetched rate



Table 28: Tuples fetched rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_fetched_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Tuples inserted count 

Tuples inserted count



Table 29: Tuples inserted count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_inserted_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance 







 Tuples inserted rate 

Tuples inserted rate



Table 30: Tuples inserted rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_inserted_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Tuples returned rate 

Tuples returned rate



Table 31: Tuples returned rate metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_returned_rate 
 Metric Type gauge 
 Value Type rate 
 Segment By Service instance 







 Tuples updated count 

Tuples updated count



Table 32: Tuples updated count metric metadata

 Metadata Description 

 Metric Name ibm_databases_for_mysql_tuples_updated_count 
 Metric Type gauge 
 Value Type none 
 Segment By Service instance
See [Digressions](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtimedialog-runtime-digressions) for more details.





 12 July 2018 

Rich response types
: You can now add rich responses that include elements such as images or buttons in addition to text, to your dialog. See [Rich responses](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-overviewdialog-overview-multimedia) for more information.

Contextual entities (Beta)
: Contextual entities are entities that you define by labeling mentions of the entity type that occur in intent user examples. These entity types teach your assistant not only terms of interest, but also the context in which terms of interest typically appear in user utterances, enabling your assistant to recognize never-seen-before entity mentions based solely on how they are referenced in user input. For example, if you annotate the intent user example, I want a flight to Boston by labeling Boston as a @destination entity, then your assistant can recognize Chicago as a @destination mention in a user input that says, I want a flight to Chicago. This feature is currently available for English only. See [Adding contextual entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-create-annotation-based) for more information.

When you access the tool with an Internet Explorer web browser, you cannot label entity mentions in intent user examples nor edit user example text.

Entity recommendations
: Watson can now recommend synonyms for your entity values. 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:
This might improve the confidence score for query results, and might change the passages returned because the title might be returned, as part of a passage. Existing collections must be reindexed to extract the title field. : The Discovery tooling does not yet use the current API version: 2019-03-25 (it currently uses 2018-12-03), so the title is not extracted when ingesting HTML documents by uploading or using the Web Crawl connector in the Discovery tooling.

New enrichment for arrays : Arrays can now be enriched (previously, arrays could not be enriched). When using Smart Document Understanding, this means that you can now enrich both default (for example: answer, author, footer, header, question, subtitle, table_of_contents, text, and title) and custom fields. Previously, you were limited to enriching only the text field.





 21 March 2019 

Update to visibility of user role credentials : From March 21, 2019, you can only see service credential information associated with the role that is assigned to your IBM Cloud account. For example, if you assign a reader role, any writer or higher levels of service credentials are not visible. : This change does not affect API access for users or applications with existing service key credentials. Only the viewing of credentials within IBM Cloud is affected. For more information about service keys and user roles, see [IAM service API keys](https://cloud.ibm.com/docs/watson?topic=watson-iam).





 10 February 2019 

New option to connect and sync with IBM Cloud Object storage : Added the option to connect to and sync with IBM Cloud Object Storage. This data source is not available in Dedicated environments. See [IBM Cloud Object Storage](https://cloud.ibm.com/docs/discovery?topic=discovery-sourcesconnectcos) for more information.





 4 February 2019 

Updates to Smart Document Understanding : [Smart Document Understanding (SDU)](https://cloud.ibm.com/docs/discovery?topic=discovery-sdu) moved from beta status to GA status.
The fieldname is the name of a String or Number field, and type is either a number, a string, or a JSON array of strings. The type part is optional, and defaults to number. Some examples are "foo", "-foo", "bar<string>", "-foo<number>", and ["-foo<number>","bar<string>"]. String fields that are used for sorting must not be analyzed fields. Fields that are used for sorting must be indexed by the same indexer that is used for the search query. Yes 
 stale Do not wait for the index to finish building to return results. Yes String OK Yes 



Do not combine the bookmark and stale options. These options constrain the choice of shard replicas to use for the response. When used together, the options might cause problems when you try to contact replicas that are slow or not available.

Using include_docs=true might have [performance implications](https://cloud.ibm.com/docs/Cloudant?topic=Cloudant-using-viewsmulti-document-fetching).





 Relevance 

When more than one result might be returned, it is possible for them to be sorted. By default, the sorting order is determined by 'relevance'.

Relevance is measured according to [Apache Lucene Scoring](https://lucene.apache.org/core/3_6_0/scoring.html). As an example, if you search a simple database for the word example, two documents might contain the word. If one document mentions the word example 10 times, but the second document mentions it only twice, then the first document is considered to be more 'relevant'.

If you don't provide a sort parameter, relevance is used by default. The highest scoring matches are returned first.

If you provide a sort parameter, then matches are returned in that order, ignoring relevance.

If you want to use a sort parameter, and also include ordering by relevance in your search results, use the special fields -<score> or <score> within the sort parameter.





 POSTing search queries 

Instead of using the GET HTTP method, you can also use POST.
You can also type app:app-log-analysis in the Search... field.
3. To see logs of specific log level(s), Click on Levels and select multiple levels like Error, info, warning etc.,









 Step 5: Search and filter logs 

The Log Analysis UI, by default, shows all available log entries(Everything). Most recent entries are shown on the bottom through an automatic refresh. In this section, you will modify what and how much is displayed and save this as a View for future use.



 Search logs 



1. In the Search input box located at the bottom of the page in the Log Analysis UI,



* you can search for lines that contain a specific text like This is my first log entry or 500 internal server error.
* or a specific log level by entering level:info where level is a field that accepts string value.



For more search fields and help, click the syntax help icon next to the search input box
2. To jump to a specific timeframe, enter 5 mins ago in the Jump to timeframe input box. Click the icon next to the input box to find the other time formats within your retention period.
3. To highlight the terms, click on Toggle Viewer Tools icon.
4. Type error as your highlight term in the first input box and hit Enter on your keyboard. Check the highlighted lines with the terms.
5. Type container as your highlight term in the second input box and hit Enter on your keyboard. Check the highlighted lines with the terms.
6. Click on Toggle Timeline icon to see lines with logs at a specific time of a day.







 Filter logs 

You can filter logs by tags, sources, apps or levels.



1. On the top bar, click Sources and select the name of the host (worker node) you are interested in checking the logs. Works well if you have multiple worker nodes in your cluster.
2. To check other container or file logs, click app-log-analysis or Apps and select the checkbox(s) you are interested in seeing the logs.







 Create a view 

Views are saved shortcuts to a specific set of filters and search queries.
Add the length of time for the pause to last as a number of milliseconds (ms) to the Duration field.

The value cannot exceed 10,000 ms. Users are typically willing to wait about 8 seconds (8,000 ms) for someone to enter a response. To prevent a typing indicator from being displayed during the pause, choose Off.

Add another response type, such as a text response type, after the pause to clearly denote that the pause is over.
* Text. Add the text to return to the user in the text field. Optionally, choose a variation setting for the text response. See [Simple text response](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-overviewdialog-overview-simple-text) for more details.
* Search skill. Indicates that you want to search an external data source for a relevant response.

To edit the search query to pass to the Discovery service, click Customize, and then fill in the following fields:



* Query: Optional. You can specify a specific query in natural language to pass to Discovery. If you do not add a query, then the customer's exact input text is passed as the query.

For example, you can specify What cities do you fly to?. This query value is passed to Discovery as a search query. Discovery uses natural language understanding to understand the query and to find an answer or relevant information about the subject in the data collection that is configured for the search skill.

You can include specific information provided by the user by referencing entities that were detected in the user's input as part of the query. For example, Tell me about @product. Or you can reference a context variable, such as Do you have flights to $destination?. Just be sure to design your dialog such that the search is not triggered unless any entities or context variables that you reference in the query have been set to valid values.

This field is equivalent to the Discovery natural_language_query parameter. For more information, see [Query parameters](https://cloud.ibm.com/docs/discovery?topic=discovery-query-parametersnlq).
* Filter: Optional.
: A new search feature has been added that allows you to [search intents](https://cloud.ibm.com/docs/assistant?topic=assistant-intentsintents-search) for user examples, intent names, or descriptions, or to [search entity](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-search) values and synonyms.

Content catalogs
: The new [content catalogs](https://cloud.ibm.com/docs/assistant?topic=assistant-catalogcatalog-add) contain a single category of prebuilt common intents and entities that you can add to your application. For example, most applications require a general greeting-type intent that starts a dialog with the user. You can add it from the content catalog rather than building your own.

Enhanced user metrics
: The Improve component has been enhanced with additional user metrics and logging statistics. For example, the Overview page includes several new, detailed graphs that summarize interactions between users and your application, the amount of traffic for a given time period, and the intents and entities that were recognized most often in user conversations.





 12 March 2018 

New date and time methods
: Methods were added that make it easier to perform date calculations from the dialog. See [Date and time calculations](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-methodsdialog-methods-date-time-calculations) for more details.





 16 February 2018 

Dialog node tracing
: When you use the "Try it out" pane to test a dialog, a location icon is displayed next to each response. You can click the icon to highlight the path that your assistant traversed through the dialog tree to arrive at the response. See [Building a dialog](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-tasksdialog-tasks-test) for details.

New API version
: The current API version is now 2018-02-16. This version introduces the following changes:



* A new include_audit parameter is now supported on most GET requests.
"started_on": 1363274083
},
{
"user": "acceptly",
"updated_on": 1363273779,
"type": "indexer",
"node": "dbcore@db11.julep.cloudant.net",
"pid": "<0.20723.4070>",
"changes_done": 189,
"database": "shards/00000000-3fffffff/acceptly/acceptly_my_chances_logs_live.1321035717",
"design_document": "_design/MyChancesLogCohortReport",
"started_on": 1363273094,
"total_changes": 26389
},
{
"user": "username",
"updated_on": 1371118433,
"type": "search_indexer",
"total_changes": 5466,
"node": "dbcore@db7.meritage.cloudant.net",
"pid": "<0.29569.7037>",
"changes_done": 4611,
"database": "shards/40000000-7fffffff/username/database_name",
"design_document": "_design/lucene",
"index": "search1",
"started_on": 1371118426
},
{
"view": 1,
"user": "acceptly",
"updated_on": 1363273504,
"type": "view_compaction",
"total_changes": 26095,
"node": "dbcore@db11.julep.cloudant.net",
"pid": "<0.21218.4070>",
"changes_done": 20000,
If you see this happening, consider choosing a different collection field from which to extract search results.

The Discovery document highlight field stores values in an array.







 Next steps 

After you create the skill, it appears as a tile on the Skills page.

The search skill cannot interact with customers until it is added to an assistant and the assistant is deployed. See [Creating assistants](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-add).



 Adding the skill to an assistant 

Open the assistant tile and add the skill to the assistant from there. You cannot choose the assistant that will use the skill from within the skill configuration page.

One search skill can be used by more than one assistant.



1. From the Assistants page, click to open the tile for the assistant to which you want to add the skill.
2. Click Add search skill.
3. Click Add existing skill, then click the skill that you want to add from the available skills that are displayed.









 Search triggers 

The search skill is triggered in the following ways:



* From a specific dialog node or action step: This approach is useful if you want to narrow down a user query before you trigger a search.

For example, the conversational flow might collect information about the type of device a customer wants to buy. When you know the device model, you can then send a model keyword in the query that is submitted to the search skill to get better results.

Trigger the search only at a specific point in a conversation in one of the following ways:



* Dialog skill: Add a search skill response type to a dialog node. When the node is processed, your assistant retrieves a passage from an external data source and returns it as the response to a particular question. This type of search occurs only when the individual dialog node is processed. For more information, see [Adding a search skill response type](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-overviewdialog-overview-add-search-skill).
* Actions skill: In the And then field of the step where you want the search to be triggered, choose Search for the answer.
* Use the GET /v1/customizations/{customization_id}/words/{word} method to list information about a specified word/translation pair from a custom model. For more information, see [Querying a single word from a custom model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customWordscuWordQueryModel).



* A list of all of your speaker models and their definitions. To list information about your speaker models:



* Use the GET /v1/speakers method to list information about all of your speaker models. For more information, see [Listing all speaker models](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-speaker-modelstbe-speaker-models-list).
* Use the GET /v1/speakers/{speaker_id} method to list information about a specified speaker model and the custom prompts that are associated with that speaker model. For more information, see [Listing the custom prompts for a speaker model](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-speaker-modelstbe-speaker-models-list).



* Information about all custom prompts in your custom models:



* Use the GET /v1/customizations/{customization_id}/prompts method to list information about all custom prompts from a custom model. For more information, see [Listing custom prompts](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-custom-promptstbe-custom-prompts-list).
* Use the GET /v1/customizations/{customization_id}/prompts/{prompt_id} method to list information about a specified custom prompt from a custom model. For more information, see [Listing custom prompts](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-custom-promptstbe-custom-prompts-list).
* To keep the page where you edit a slot simple, you now select menu options to a.) add a condition that must be met for the slot to be processed, and b.) add conditional responses for the Found and Not found conditions for a slot. Unless you choose to add this extra functionality, the slot condition and multiple responses fields are not displayed, which declutters the page and makes it easier to use.







 25 October 2017 

Updates to Simplified Chinese
: Language support has been enhanced for Simplified Chinese. This includes intent classification improvements using character-level word embeddings, and the availability of system entities. Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.

Updates to Spanish
: Improvements have been made to Spanish intent classification, for very large datasets.





 11 October 2017 

Updates to Korean
: Language support has been enhanced for Korean. Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.





 3 October 2017 

Pattern-defined entities (Beta)
: You can now define specific patterns for an entity, using regular expressions. This can help you identify entities that follow a defined pattern, for example SKU or part numbers, phone numbers, or email addresses. See [Pattern-defined entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-patterns) for additional details.



* You can add either synonyms or patterns for a single entity value; you cannot add both.
* For each entity value, there can be a maximum of up to 5 patterns.
* Each pattern (regular expression) is limited to 128 characters.
* Importing or exporting via a CSV file does not currently support patterns.
* The REST API does not support direct access to patterns, but you can retrieve or modify patterns using the /values endpoint.



Fuzzy matching filtered by dictionary (English only)
: An improved version of fuzzy matching for entities is now available, for English. This improvement prevents the capturing of some common, valid English words as fuzzy matches for a given entity.
6. Scroll through the document to label every valid example of every entity type that you want your extractor to recognize.

The machine learning model learns as much from the terms that you don't label as the terms that you do.

If you miss labeling a valid example, the model learns that when the term is used in that context, it is not a valid mention of the entity type. In some cases, an omission is appropriate. For example, some terms have different meanings in different contexts. You don't want to label the term when it is used in the wrong context. However, if the term is used in the right context and you don't label it, you are teaching the model to ignore it. You decrease the model's effectiveness when your training data is inconsistent.

After you label many examples, entity example suggestions are displayed. You can accept or reject entity example suggestions.

Zoom

![Shows the prompt that is shown to ask whether you want to accept a suggestion.](https://cloud.ibm.com/docs-content/v1/content/f16b1f78a92498247223b337bd9303c2758b9ba8/discovery-data/images/ee-suggestion.png)

Figure 7. Decide whether to accept a suggestion

Accepting example suggestions is another way to speed up the labeling process. For more information, see [Entity example suggestions](https://cloud.ibm.com/docs/discovery-data?topic=discovery-data-entity-extractorentity-extractor-suggestions). After you accept a suggestion, you can bulk label the term.
7. If you make a mistake and label the wrong word or a word was labeled incorrectly by the bulk labeling process, you can delete the label.

Hover over the labeled word until the Delete this example option is displayed, and then click it. You can choose to delete only this mention or all of the mentions in the document. Make a choice, and then click Delete.
8. After you label all of the entity examples in the current document, change the document status from In progress to Complete.
fmt.Println(string(b))

The previous Go example requires the following import block:

import (
"encoding/json"
"fmt"
"github.com/IBM/cloudant-go-sdk/cloudantv1"
)

The response includes information about your index, such as the number of documents in the index and the size of the index on disk.

See the following example response after you request search index metadata:

{
"name": "_design/DDOC/INDEX",
"search_index": {
"pending_seq": 7125496,
"doc_del_count": 129180,
"doc_count": 1066173,
"disk_size": 728305827,
"committed_seq": 7125496
}
}