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NLU / NLP Methods
    Analyze textpostList available languagesgetText clean-uppostNamed entity comparisonpostSemantic similaritypostDetect languagepostTranslate textpost
Language Model Direct Access
    List inflected formsget
Schemas
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Overview
Overview

NLU / NLP Methods

Endpointhttps://api2.tisane.ai

These are the methods that actually perform analysis and transformation of input text.


Analyze text

POST
https://api2.tisane.ai
/parse

The method analyzes the input and detects problematic content, sentiment snippets, entities, topics, phrase structure, parts of speech, stopwords, and more.

Analyze text › Headers

Content-Type
string

Analyze text › Request Body

language
string · required
content
string · required
settings
object

Analyze text › Responses

Successful response

text
string · required

Input text.

language
string

Language code, when the language identification is used.

object

The state after the analysis. Can be used to set the state of the future analyses. (Set state to true to show.)

array
object[]
object[]
object[]
object[]
POST/parse
curl --request POST \
  --url https://api2.tisane.ai/parse \
  --header 'Content-Type: application/json' \
  --data '
{
  "language": "en",
  "content": "Hello Tisane API!!"
}
'
shell
Example Request Body
{
  "language": "en",
  "content": "Hello Tisane API!!"
}
json
application/json
Example Responses
{
  "text": "Hello Tisane API!",
  "entities_summary": [
    {
      "type": "software",
      "name": "Tisane API",
      "mentions": [
        {
          "sentence_index": 0,
          "offset": 6,
          "length": 10
        }
      ]
    }
  ]
}
json
application/json

List available languages

GET
https://api2.tisane.ai
/languages

Retrieve a list of supported languages.

The response includes the following attributes:

  • id - Language ID.
  • name - Native name.
  • englishName - English name.
  • nativeEncoding - Encoding of the language.
  • preferredFont - Recommended font for display.
  • latin - Whether the language uses Latin script.
  • rightToLeft - Whether the language uses right-to-left script (e.g., Arabic, Hebrew, Persian).

List available languages › Headers

Content-Type
string

List available languages › Responses

200

Successful response

object[]
isoCode
string
name
string
englishName
string
nativeEncoding
string
fontFace
string
latin
boolean
rightToLeft
boolean
GET/languages
curl --request GET \
  --url https://api2.tisane.ai/languages
shell
Example Responses
[
  {
    "isoCode": "ar",
    "name": "العربية",
    "englishName": "Arabic",
    "nativeEncoding": "iso-8859-6",
    "fontFace": "Arial",
    "latin": false,
    "rightToLeft": true
  },
  {
    "isoCode": "en",
    "name": "English",
    "englishName": "English",
    "nativeEncoding": "iso-8859-1",
    "fontFace": "Arial",
    "latin": true
  },
  {
    "isoCode": "zh-CN",
    "name": "简体中文",
    "englishName": "Chinese, Simplified",
    "nativeEncoding": "gb18030",
    "fontFace": "NSimSun",
    "latin": false
  },
  {
    "isoCode": "zh-TW",
    "name": "繁體中文",
    "englishName": "Chinese, Traditional",
    "nativeEncoding": "iso-8859-1",
    "fontFace": "Arial Unicode MS",
    "latin": false
  },
  {
    "isoCode": "ja",
    "name": "日本語",
    "englishName": "Japanese",
    "nativeEncoding": "ISO-2022",
    "fontFace": "MS Mincho",
    "latin": false
  },
  {
    "isoCode": "ru",
    "name": "русский",
    "englishName": "Russian",
    "nativeEncoding": "iso-8859-1",
    "fontFace": "Arial",
    "latin": false
  }
]
json
application/json

Text clean-up

POST
https://api2.tisane.ai
/helper/extract_text

A service method to remove JavaScript, CSS tags, JSON, and other markup, returning pure decoded text. The request body includes the markup content from which text will be extracted.

Note: This method does not process binary content.

Text clean-up › Headers

Content-Type
string

Text clean-up › Request Body

string

Text clean-up › Responses

200

Successful response

string
POST/helper/extract_text
curl --request POST \
  --url https://api2.tisane.ai/helper/extract_text \
  --header 'Content-Type: text/plain' \
  --data '"<p>Clean me up</p> <style>/* Styles are unnecessary for text analysis */</style>"'
shell
Example Request Body
<p>Clean me up</p> <style>/* Styles are unnecessary for text analysis */</style>
plain
text/plain
Example Responses
Clean me up
plain
text/plain

Named entity comparison

POST
https://api2.tisane.ai
/compare/entities

Compares two compound named entities and identifies differences. The request body should include:

  • language1 (string) - IETF tag for the first entity's language.
  • entity1 (string) - The first entity.
  • language2 (string) - IETF tag for the second entity's language.
  • entity2 (string) - The second entity.
  • type (string) - The entity type (currently only person is supported).

More information: Named Entities in the Tisane API Response Guide The response contains:

  • result (string) - Comparison result:

    • no_single_entity - One or both entities are invalid.
    • same - Entities are identical (name order might differ).
    • different - Entities differ, with details in differences.
  • differences (array of strings) - Lists detected differences:

    • given_name
    • surname
    • title (e.g., Mr., Mrs.)
    • social_role (e.g., academic degrees)
    • suffix
    • variation (e.g., spelling or style differences)

Named entity comparison › Headers

Content-Type
string

Named entity comparison › Request Body

Named entity comparison › Responses

200

Successful response

POST/compare/entities
curl --request POST \
  --url https://api2.tisane.ai/compare/entities \
  --header 'Content-Type: application/json' \
  --data '
{
  "language1": "en",
  "entity1": "Gary Youngman MD",
  "language2": "en",
  "entity2": "Gary Oldman",
  "type": "person"
}
'
shell
Example Request Body
{
  "language1": "en",
  "entity1": "Gary Youngman MD",
  "language2": "en",
  "entity2": "Gary Oldman",
  "type": "person"
}
json
application/json
Example Responses
{
  "result": "different",
  "differences": [
    "surname",
    "social_role"
  ]
}
json
application/json

Semantic similarity

POST
https://api2.tisane.ai
/similarity

Calculate the semantic similarity between two text fragments, either in the same language or in different languages.

The request body should contain:

  • content1 (string) - The first text fragment.
  • language1 (string) - The IETF language code for content1.
  • content2 (string) - The second text fragment to compare.
  • language2 (string) - The IETF language code for content2.
  • settings (object) - Additional settings as per specifications. More information: Tisane API Configuration And Customization Guide.

The response is a number between 0 and 1, representing the similarity of the submitted text fragments.

Semantic similarity › Headers

Content-Type
string

Semantic similarity › Request Body

Semantic similarity › Responses

200

Successful response

number
POST/similarity
curl --request POST \
  --url https://api2.tisane.ai/similarity \
  --header 'Content-Type: application/json' \
  --data '
{
  "language1": "en",
  "content1": "Franz Ferdinand was assassinated by a Serbian revolutionary",
  "language2": "en",
  "content2": "A Yugoslav radical assassinated Franz Ferdinand",
  "settings": {}
}
'
shell
Example Request Body
{
  "language1": "en",
  "content1": "Franz Ferdinand was assassinated by a Serbian revolutionary",
  "language2": "en",
  "content2": "A Yugoslav radical assassinated Franz Ferdinand",
  "settings": {}
}
json
application/json
Example Responses
0.6190476190476191
json
application/json

Detect language

POST
https://api2.tisane.ai
/detectLanguage

Detects the languages used in the provided text fragment and returns the breakdown by offsets.

The request body should include:

  • content (string) - the text fragment to analyze.
  • languages (string, optional) - a vertical bar-delimited list of language codes to use as cues.
  • delimiter (string, optional) - a regular expression for segmenting the fragment; by default, the fragment is not segmented. The response is a JSON structure containing languages, which provides a breakdown of language codes.

Detect language › Headers

Content-Type
string

Detect language › Request Body

content
string

The text fragment to analyze.

languages
string

A vertical bar-delimited list of language codes to use as cues.

delimiter
string

A regular expression for segmenting the fragment; by default, the fragment is not segmented

Detect language › Responses

200

Successful response

POST/detectLanguage
curl --request POST \
  --url https://api2.tisane.ai/detectLanguage \
  --header 'Content-Type: application/json' \
  --data @- <<EOF
{
  "content": "c'est la vie"
}
EOF
shell
Example Request Body
{
  "content": "c'est la vie"
}
json
application/json
Example Responses
{
  "languages": [
    {
      "offset": 0,
      "length": 12,
      "language": "fr",
      "score": 1181
    }
  ]
}
json
application/json

Translate text

POST
https://api2.tisane.ai
/transform

This method translates the input text. The request body should include the following elements:

  • from (string) - A standard IETF tag for the source language. Use * or a vertical bar-delimited set of language codes to invoke autodetect.
  • to (string) - A standard IETF tag for the target language. If the source and target languages are the same, paraphrasing will be applied.
  • content (string) - The text content to translate.
  • settings (object) - Additional translation settings.

More information: Tisane API Configuration And Customization Guide

The response is the transformed text.

Translate text › Headers

Content-Type
string

Translate text › Request Body

from
string
to
string
content
string
object

Translate text › Responses

Successful translation response

string
POST/transform
curl --request POST \
  --url https://api2.tisane.ai/transform \
  --header 'Content-Type: application/json' \
  --data '
{
  "from": "ja",
  "to": "en",
  "content": "彼女はりんごを食べます",
  "settings": {}
}
'
shell
Example Request Body
{
  "from": "ja",
  "to": "en",
  "content": "彼女はりんごを食べます",
  "settings": {}
}
json
application/json
Example Responses
She is eating an apple
plain
text/plain

Language Model Direct Access