vania-janet/multiturn-rag-retrieval-data huggingface dataset:
How many jobs can run in parallel on an agent? 



* Schematics Agent can perform three Git download jobs in parallel.
* Schematics Agent can run three Workspace jobs (Terraform commands), in parallel.
* Schematics Agent can run three Action jobs (Ansible playbook), in parallel.







 What is the default polling interval for Agents? 

Schematics maintains a queue of jobs that will be executed on an agent. The agent will poll for Schematics Jobs, every one minute by default.





 What is the difference between agent-location and location input variable flag in Agent service? 

The --agent-location parameter is a variable that specifies the region of the cluster where an agent service is deployed. For example, us-south. This must match the cluster region.

The --location parameter is a variable that specifies the region supported by Schematics service such as us-south, us-east, eu-de, eu-gb. The agent polls Schematics service instance from this location, for workspace or action jobs for processing.





 Can an agent run Workspace jobs belonging to different resource groups? 

Yes, an agent can run Schematics Jobs related to workspace or actions, from all or any resource group, in an account. Agent (assignment) policies are used to assign the execution of jobs, based on resource group, region and user tags to a specific agent.





 Can an agent run Jobs from multiple Schematics regions? 

Agents are associated with Schematics regions and geographies and can only execute jobs for the parent Schematics geography, e.g. North America or Europe.

The Agent periodically polls the regional endpoint of the Schematics service instance, to fetch and run jobs. It can connect to only one regional endpoint (home). For example, if an agent is deployed on a cluster in Sydney and has been configured to use the Schematics eu-de regional endpoint as it’s home location. The agent polls for jobs in eu-de region. Hence, the workspace or action to deploy resources using the Sydney agent must be created in the eu-de region.





 Can I register an agent with multiple accounts?
Hebrew [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2)
Use the sounds_like field to provide various pronunciations that reflect how a word can be spoken.

Information for the following languages is not yet available: Arabic, Chinese, Czech, Hindi, and Swedish. If you need this information for your custom language model, contact your IBM Support representative.



 General guidelines for all languages 

Follow these guidelines when specifying a sounds-like for any language:



* Do not use punctuation characters in sounds-likes. For example, do not use periods, dashes, underscores, commas, end of sentence punctuation characters, and special characters such as dollar and euro signs, parentheses, brackets, and braces.
* Use alphabetic characters that are valid for your language. For English, this includes a-z and A-Z. For other languages, valid characters can include accented letters or language-specific characters.
* In English, substitute equivalent English letters for non-English or accented letters. For example, s for ç, ny for ñ, or e for è.
* Use real or made-up words that are pronounceable for words that are difficult to pronounce. For example, in English you can use the sounds-like shuchesnie for the word Sczcesny.
* Use the spelling of numbers without dashes. For example, for the number 75, use seventy five in English, setenta y cinco in Spanish, and soixante quinze in French.
* To pronounce a single letter, use the letter followed by a space. For example, use N C A A, notN. C. A. A., N.C.A.A., or NCAA.
* You can include multiple words that are separated by spaces.



* For most languages, the service enforces a maximum of 40 total characters, not including leading or trailing spaces.
* For Japanese, the service enforces a maximum limit of 25 total characters, not including leading or trailing spaces. If you add a custom word or sounds-like that exceeds this limit, the service automatically treats the word as if it were added by a corpus. The word does not appear as a custom word for the model.
whitespace Divides text at white-space boundaries. 



See the following example analyzer document:

{
"_id": "_design/analyzer_example",
"indexes": {
"INDEX_NAME": {
"index": "function (doc) { ... }",
"analyzer": "$ANALYZER_NAME"
}
}
}



 Language-specific analyzers 

These analyzers omit common words in the specific language, and many also [remove prefixes and suffixes](https://en.wikipedia.org/wiki/Stemming). The name of the language is also the name of the analyzer.



* arabic
* armenian
* basque
* bulgarian
* brazilian
* catalan
* cjk (Chinese, Japanese, Korean)
* chinese ([smartcn](https://lucene.apache.org/core/4_2_1/analyzers-smartcn/org/apache/lucene/analysis/cn/smart/SmartChineseAnalyzer.html))
* czech
* danish
* dutch
* english
* finnish
* french
* german
* greek
* galician
* hindi
* hungarian
* indonesian
* irish
* italian
* japanese ([kuromoji](https://lucene.apache.org/core/4_2_1/analyzers-kuromoji/overview-summary.html))
* latvian
* norwegian
* persian
* polish ([stempel](https://lucene.apache.org/core/4_2_1/analyzers-stempel/overview-summary.html))
* portuguese
* romanian
* russian
* spanish
* swedish
* thai
* turkish



Language-specific analyzers are optimized for the specified language. You can't combine a generic analyzer with a language-specific analyzer.
: The new version string enables enrichments in German (de) or Spanish (es) if the language of a collection is set to one of those languages. Previously, all enrichments were performed in English regardless of a collection's language setting. : If you do not use enrichments in non-English languages, you can continue to use the 2016-12-01 version string. However, to avoid potential future conflicts, it is recommended that you update the version string as soon as possible.

New anomaly detection availability : Anomaly detection is now available as part of timeslice aggregations as a GA capability.

New beta improvement to relevancy tooling : Added the beta ability to improve the relevancy of query results using the Discovery tooling (relevancy tooling). See [Improving the relevance of your query results with the Discovery tooling](https://cloud.ibm.com/docs/discovery?topic=discovery-improving-result-relevance-with-the-tooling).





 19 June 2017 

New select language of documents : Added option to specify the language of the documents in a new collection as English, Spanish, or German. To use it, choose Select the language of your documents on the Name your new collection dialog.

Added a Summary tab to the Build queries screen : The Summary tab displays an overview of the full query results provided in the existing JSON tab. The Summary display varies, based on your query and enrichments. Information that might be displayed includes: document name or ID, aggregation statistics, document passages in order of relevance, and results by enrichment.

Added a Natural Language Query option to the Build queries screen : To use it, click Ask a question in plain language in the Search for documents section, and a field displays where you can enter your question. You can now access the original query field, formerly titled Enter a query or keyword, by clicking the Use the Discovery Query Language button.

The Build queries screen was redesigned, but all fields and options remain. : Following are the old and new names for the fields.

 Old field name New field or section name 

 Write and run a query Search for documents 
 Narrow your query results (Filter) Limit which documents you query 
 Group query results (Aggregation) Include analysis of your results
They appear in the pronunciation for a word only if you include them in the word's translation.



For more information about working with SPR, see [Understanding phonetic symbols](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-symbols).





 Working with Japanese entries 

Extra rules and a part_of_speech field apply to the creation of entries for words in a Japanese custom model:



* A sounds-like translation can contain only Katakana characters. Kanji and Hiragana characters are not allowed.
* When you create a translation (sounds-like or phonetic) for a word, you can also specify an optional part_of_speech field to identify the word's part of speech. The service uses the part of speech to produce the correct intonation for the word. For a complete list, see [Japanese parts of speech](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-rulespartsOfSpeech).
* You can create only a single entry for any word, and you can specify only a single part of speech for any word. You cannot create multiple entries with different parts of speech (for instance, noun and verb) for the same word. Adding a translation for a word that exists in a model overwrites the word's existing translation, including its part of speech.

For improved naturalness of synthesized speech, do not create custom entries for long phrases. Create translations for single words or short phrases only. Note that other languages limit translation to single words only.
* The service applies the longest matching word from the word/translation pairs that are defined for a custom model. For example, consider the following three entries for a custom model.

{
"words": [
{
"word": "ＮＹ",
"translation": "ニューヨーク",
"part_of_speech": "Mesi"
},
{
"word": "ＮＹＣ",
"translation": "ニューヨークシティ",
"part_of_speech": "Mesi"
},
{
"word": "ＹＣ",
"language": "ba",
"name": "Bashkir"
}
. . .
]
}
Show more





 List of identifiable languages 

The following table list the identifiable languages. The table is sorted by language name and provides the language code for each language.

The service uses ISO two-character codes for most languages. It uses an ISO three-character code (cnr) for Montenegrin. In some cases, it uses a two-character language code and a two-character country code separated by a hyphen, such as pa-PK for Punjabi spoken in Pakistan and zh-TW for traditional (Mandarin) Chinese spoken in Taiwan.



Table 1. Identifiable languages

 Language name Language code Language name Language code 

 Afrikaans af Kirghiz ky 
 Albanian sq Korean ko 
 Arabic ar Kurdish ku 
 Armenian hy Lao lo 
 Azerbaijani az Latvian lv 
 Bashkir ba Lithuanian lt 
 Basque eu Malay ms 
 Belarusian be Malayalam ml 
 Bengali bn Maltese mt 
 Bulgarian bg Marathi mr 
 Burmese my Mongolian mn 
 Catalan ca Nepali ne 
 Central Khmer km Norwegian Bokmål nb 
 Chinese (Simplified) zh Norwegian Nynorsk nn 
 Chinese (Traditional) zh-TW Persian fa 
 Chuvash cv Polish pl 
 Croatian hr Portuguese pt 
 Czech cs Punjabi (Indian) pa 
 Danish da Punjabi (Pakistani) <br>(Shahmukhi script) pa-PK 
 Dutch nl Pushto ps 
 English en Romanian ro 
 Esperanto eo Russian ru 
 Estonian et Serbian sr 
 Finnish fi Sinhala si 
 French fr Slovakian sk 
 Georgian ka Slovenian sl 
 German de Somali so 
 Greek el Spanish es 
 Gujarati gu Swedish sv 
 Haitian ht Tagalog tl 
 Hebrew he Tamil ta 
 Hindi hi Telugu te 
 Hungarian hu Thai th 
 Icelandic is Turkish tr 
 Irish ga Ukrainian uk 
 Italian it Urdu ur 
 Japanese ja Vietnamese vi 
 Kazakh kk Welsh cy
{"word":"ＮＹ", "translation":"ニューヨーク", "part_of_speech":"Mesi"},
{"word":"ＮＹＣ", "translation":"ニューヨークシティ", "part_of_speech":"Mesi"}
]
}

If the service finds the string ＮＹＣ in the input text, it matches that word because it is a longer match than ＮＹ. Previously, the service would have matched the string ＮＹ. For more information about working with Japanese entries for a custom model, see [Working with Japanese entries](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-rulesjaNotes).





 22 September 2016 

Customization now available for all languages
: The customization interface, which includes the customization and GET /v1/pronunciation methods, is now available for all languages that are supported by the service. The interface remains a beta release. For more information, see [Understanding customization](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-customIntro).

New Japanese support for SSML
: The service now supports SSML for Japanese. For general information about SSML support, see [Understanding SSML](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-ssml). For information about Japanese SPR and IPA symbols, see [Japanese symbols](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-jaSymbols). Extra considerations and a part_of_speech field apply when creating entries for words in a Japanese custom model. For more information, see [Working with Japanese entries](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-rulesjaNotes).

New voice transformation SSML feature
: The service now offers SSML voice transformation via the new <voice-transformation> element.
* polish ([stempel](https://lucene.apache.org/core/4_2_1/analyzers-stempel/overview-summary.html))
* portuguese
* romanian
* russian
* spanish
* swedish
* thai
* turkish



Language-specific analyzers are optimized for the specified language. You can't combine a generic analyzer with a language-specific analyzer. Instead, you might use a [perfield analyzer](https://cloud.ibm.com/docs/Cloudant?topic=Cloudant-cloudant-searchper-field-analyzers) to select different analyzers for different fields within the documents.





 Per-field analyzers 

The perfield analyzer configures many analyzers for different fields.

See the following example that defines different analyzers for different fields:

{
"_id": "_design/analyzer_example",
"indexes": {
"INDEX_NAME": {
"analyzer": {
"name": "perfield",
"default": "english",
"fields": {
"spanish": "spanish",
"german": "german"
}
},
"index": "function (doc) { ... }"
}
}
}
Show more





 Stop words 

Stop words are words that don't get indexed. You define them within a design document by turning the analyzer string into an object.

The keyword, simple, and whitespace analyzers don't support stop words.

The default stop words for the standard analyzer are included in the following list:

"a", "an", "and", "are", "as", "at", "be", "but", "by", "for", "if",
"in", "into", "is", "it", "no", "not", "of", "on", "or", "such",
* Does not remove a tilde () symbol.
* Does not remove or otherwise process Unicode wide-character symbols, for example, … (triple dot or ellipsis).



In general, IBM recommends that you remove punctuation, special characters, and Unicode wide-characters before you process a corpus file.
* Does not remove or ignore phrases that are enclosed in ( ) (parentheses), < > (angle brackets), [ ] (square brackets), or { } (curly braces).
* Converts tokens that include certain symbols to meaningful string representations, for example:



* 24% becomes 이십사퍼센트.
* $10 becomes 십달러.



This list is not exhaustive. The service makes similar adjustments for other characters as needed.
* For phrases that consist of Latin (English) characters or a mix of Hangul and Latin characters, the service uses the phrases exactly as they appear in the corpus file.











 Working with custom words for next-generation models 

You can use the POST /v1/customizations/{customization_id}/words and PUT /v1/customizations/{customization_id}/words/{word_name} methods to add new words to a custom model. You can also use the methods to modify or augment a custom word.

You might, for instance, need to use the methods to correct a typographical error or other mistake that is made when a word is added to a custom model. If you modify an existing word, the new data that you provide overwrites the word's existing definition in the words resource. The rules for adding a word also apply to modifying an existing word.

You are likely to add most custom words from corpora. Make sure that you know the character encoding of your corpus text files. The service preserves the encoding that it finds in the text files. You must use that same encoding when working with custom words in the custom model. For more information, see [Character encoding for custom words](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-manageWordscharEncoding).



 Using the sounds_like field
Specifies a comma-delimited list of two-letter language codes, such as en (English) or zh (Chinese) for the languages, or for the document languages for external dictionaries, on which to evaluate the dictionary. The dictionary produces no results on documents whose language code is not contained in this string.

If the language parameter is omitted, the dictionary language defaults to one of the following language sets:



* The language sets that are specified through the set default language statement, if it is declared, in the containing module.
* The language sets that contain German (de), Spanish (es), English (en), French (fr), Italian (it), and the unspecified language (x_unspecified).



* lemma_match

Use lemmatization to find matches for similar words to a dictionary term in your documents.

Lemmatization is the process of determining the lemma for a given word. A lemma is a word that can be used as a match for a single given term. For example, the term “go” can be matched to the terms “goes”, “going”, “gone”, or “went”. This process involves complex tasks such as understanding context and determining the part of speech of a word in a sentence. Lemmatization is available for all languages for which IBM Multilingual tokenizer provides part-of-speech support.

Lemma match is performed only for dictionaries declared with the lemma match clause.

The semantics for dictionary extraction with the lemma_match clause are as follows:



* The lemmatized form for each token of the input document is computed.
* The dictionary is evaluated against the lemmatized document. You cannot use the lemma_match option with the case exact option. If both are used, a compiler error is returned.



* case (exact | insensitive)

Specifies the type of case-folding that the dictionary performs when it determines whether a specific region of the document matches.



* exact

Specifies an exact case-sensitive match.
* insensitive

Specifies a match that is not case-sensitive match. This option is the default value.



* '<entry 1>', '<entry 2>', ...
Overriding language detection 

To override automatic language detection in /analyze requests, specify a language code in the language attribute of the parameters JSON object.

Example parameters.json file

{
"text": "...X, Y, Z, now I know my A, B, Cs",
"features": {
"semantic_roles": {}
},
"language": "en"
}

Example curl request

curl --user "apikey:{apikey}" "{url}/v1/analyze?version=2018-09-21" --request POST --header "Content-Type: application/json" --data @parameters.json
Identifiable languages 

IBM is announcing the deprecation of the IBM Watson® Language Translator service for IBM Cloud® in all regions. As of 10 June 2023, the Language Translator tile will be removed from the IBM Cloud Platform for new customers; only existing customers will be able to access the product. As of 10 June 2024, the service will reach its End of Support date. As of 10 December 2024, the service will be withdrawn entirely and will no longer be available to any customers.

IBM Watson® Language Translator can identify a variety of languages.



 Listing identifiable languages 

You can use the [List identifiable languages](https://cloud.ibm.com/apidocs/language-translatorlistidentifiablelanguages) method to retrieve the list of identifiable languages. The following example calls the method:

curl -X GET --user "apikey:{apikey}" "{url}/v3/identifiable_languages?version=2018-05-01"

The method returns a complete list of all identifiable languages, sorted by language code (for example, af, ar). The output includes the name of the language.

{
"languages": [
{
"language": "af",
"name": "Afrikaans"
},
{
"language": "ar",
"name": "Arabic"
},
{
"language": "az",
"name": "Azerbaijani"
},
{
"language": "ba",
"name": "Bashkir"
}
. . .
]
}
Show more





 List of identifiable languages 

The following table list the identifiable languages. The table is sorted by language name and provides the language code for each language.

The service uses ISO two-character codes for most languages. It uses an ISO three-character code (cnr) for Montenegrin.
* Converts numbers to their equivalent words, for example, 10 becomes 십.
* Removes the following punctuation and special characters: - ( ) : . , ' ". However, not all punctuation and special characters that are removed for other languages are removed for Korean, for example:



* Removes a period (.) symbol only when it occurs at the end of a line of input.
* Does not remove a tilde () symbol.
* Does not remove or otherwise process Unicode wide-character symbols, for example, … (triple dot or ellipsis).



In general, IBM recommends that you remove punctuation, special characters, and Unicode wide-characters before you process a corpus file.
* Does not remove or ignore phrases that are enclosed in ( ) (parentheses), < > (angle brackets), [ ] (square brackets), or { } (curly braces).
* Converts tokens that include certain symbols to meaningful string representations, for example:



* 24% becomes 이십사퍼센트.
* $10 becomes 십달러.



This list is not exhaustive. The service makes similar adjustments for other characters as needed.
* For phrases that consist of Latin (English) characters or a mix of Hangul and Latin characters, the service creates OOV words for the phrases exactly as they appear in the corpus file. And it creates sounds-like pronunciations for the words based on Hangul transcriptions.



* It gives the OOV word London a sounds-like of 런던.
* It gives the OOV word IBM홈페이지 a sounds-like of 아이 비 엠 홈페이지.













 Working with custom words for previous-generation models 

You can use the POST /v1/customizations/{customization_id}/words and PUT /v1/customizations/{customization_id}/words/{word_name} methods to add new words to a custom model's words resource. You can also use the methods to modify or augment a word in a words resource.