# paths./{feature}/{subfeature}/batch/{name}/.get.description
Return paginated response of requests with their status and their
responses if the request succeeded or errror if failed

# paths./{feature}/{subfeature}/batch/{name}/.get.parameters[].description
* `succeeded` - Status Succeeded
* `failed` - Status Failed
* `finished` - Status Finished
* `processing` - Status Processing

# paths./{feature}/{subfeature}/batch/{name}/.post.description
Launch a async Batch job, given a job name that will be used as its id.

Each request should have the same parameters as you would normally pass to a feature.


You can also pass an optional paramater `name` to help better identify each requests you send.


Example with `text`/`sentiment_analysis`:

```json
"requests": [
    {
        "text": "It's -25 outside and I am so hot.",
        "language": "en",
        "providers": "google,amazon"
    },
    {
        "name": "mixed",
        "text": "Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed.",
        "language": "en",
        "providers": "google"
    },
    ...
]
```


### Limitations:
You can have up to `5` concurrent running Jobs & input up to `500` requests per Batch
        

<details><summary><strong style='color: #0072a3; cursor: pointer'>Available Features</strong></summary>



|Feature Name|Subfeature Name|
|------------|---------------|
|`image`|`face_detection`|
|`text`|`chat`|
|`image`|`explicit_content`|
|`image`|`landmark_detection`|
|`image`|`logo_detection`|
|`text`|`topic_extraction`|
|`audio`|`text_to_speech`|
|`text`|`generation`|
|`translation`|`document_translation`|
|`text`|`question_answer`|
|`text`|`anonymization`|
|`text`|`code_generation`|
|`text`|`custom_named_entity_recognition`|
|`text`|`custom_classification`|
|`text`|`moderation`|
|`text`|`embeddings`|
|`text`|`spell_check`|
|`translation`|`language_detection`|
|`audio`|`speech_to_text_async`|
|`translation`|`automatic_translation`|
|`image`|`anonymization`|
|`image`|`generation`|
|`video`|`text_detection_async`|
|`text`|`sentiment_analysis`|
|`text`|`syntax_analysis`|
|`text`|`keyword_extraction`|
|`text`|`named_entity_recognition`|
|`text`|`search`|
|`text`|`summarize`|
|`ocr`|`invoice_parser`|
|`ocr`|`resume_parser`|
|`ocr`|`receipt_parser`|
|`ocr`|`identity_parser`|
|`image`|`object_detection`|

</details>

# paths./{feature}/{subfeature}/batch/{name}/.delete.description
Api view with custom pagination method to return paginated response from any queryset

# paths./aiproducts/askyoda/v2/.post.description
Allows you to create a new Ask YODA project with specified details.

# paths./aiproducts/askyoda/v2/{project_id}/add_file.post.description
This endpoint enables you to upload files (**CSV**, **AUDIO**, **PDF**, or **XML**) into your project. <br> Upon upload, the file will be processed and stored as text embeddings within the project's database. <br>

### Supported File Types
- **CSV**: Comma-separated values files.
- **Audio**: Supported audio formats for transcription to text.
- **PDF**: Portable Document Format files.
- **XML**: Extensible Markup Language files.

### OCR Processing and Speech-to-Text
If the uploaded file is a PDF, it will undergo Optical Character Recognition (OCR) processing using the default provider chosen during the project creation step. <br>
Similarly, audio files will be converted to text using Speech-to-Text, also utilizing the default provider configured during project setup.

### Accepted File Extensions
- **PDF**
- **CSV**
- **AMR**
- **FLAC**
- **WAV**
- **OGG**
- **MP3**
- **MP4**
- **WEBM**
- **XML**

# paths./aiproducts/askyoda/v2/{project_id}/add_text.post.description
Add text data in your project, which will be stored as embeddings
within your chosen database provider.

# paths./aiproducts/askyoda/v2/{project_id}/add_url.post.description
Add a list of URLs into your projects,
they will be processed and stored as text embeddings within your project.

# paths./aiproducts/askyoda/v2/{project_id}/ask_llm.post.description
Retrieve a list of search query responses and compare them to your
input. Provide a query, and in return, receive scores for the most relevant items from your project,
ranked by their proximity to your query.

# paths./aiproducts/askyoda/v2/{project_id}/delete_chunk.delete.description
Delete a query from your project by its ID.

# paths./aiproducts/askyoda/v2/{project_id}/info.get.description
Retrieve details about your project within your Ask YODA project,
including the total number of items stored in your project collection and default models

# paths./aiproducts/askyoda/v2/{project_id}/query.post.description
Interact with your data by selecting your preferred Language Model 
provider. The chosen provider will then respond to queries based on the data you have added
to your collection

# paths./aiproducts/askyoda/v2/{project_id}/update_project.patch.description
Update the default settings of the Yoda project.
It allows you to modify the project's default settings,
specifically changing the llm_provider (language model provider),
llm_model (language model), ocr_provider (upload pdf), speech_to_text provider (upload audio)
and the default chunks parameter associated with the default project.

# paths./aiproducts/translathor/.get.description
List all language pairs of all projects created by the user

# paths./aiproducts/translathor/.post.description
Create a new language pair, and associate it to an existing project. If the project does not exist, a new project will be
        created

# paths./aiproducts/translathor/{project_id}.get.description
List all language pairs withing a `project_name`. You should at leat provide one or both of `source_lang` and `target_lang`
        query parameters. 
        
**Example**: to retreive this language pair (Any, Enlish):  aiproducts/translathor/{project_name}?source_lang=`all`&target_lang=`en`

# paths./aiproducts/translathor/{project_id}.patch.description
Update a language pair withing a `project_name`. Both `source_lang` and `target_lang` query parameters should be provided.
       
**NB**: If  your source or target language references `any language`, use `all` as value for the query parameter

# paths./aiproducts/translathor/{project_id}.delete.description
Delete a language pair withing a `project_name`. Both `source_lang` and `target_lang` query parameters should be provided.
       
**NB**: If  your source or target language references `any language`, use `all` as value for the query parameter

# paths./audio/speech_to_text_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./audio/speech_to_text_async.get.responses.200.content.application/json.examples.ResponseExample.value.jobs[].providers
['ibm', 'symbl', 'gladia', 'microsoft', 'voci', 'neuralspace', 'assembly', 'deepgram', 'google', 'speechmatics', 'revai', 'amazon', 'openai']

# paths./audio/speech_to_text_async.get.responses.200.content.application/json.examples.ResponseExample.value.jobs[].providers
['ibm', 'symbl', 'gladia', 'microsoft', 'voci', 'neuralspace', 'assembly', 'deepgram', 'google', 'speechmatics', 'revai', 'amazon', 'openai']

# paths./audio/speech_to_text_async.get.responses.200.content.application/json.examples.ResponseExample.value.jobs[].providers
['ibm', 'symbl', 'gladia', 'microsoft', 'voci', 'neuralspace', 'assembly', 'deepgram', 'google', 'speechmatics', 'revai', 'amazon', 'openai']

# paths./audio/speech_to_text_async.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|0.024 (per 60 seconde)|15 seconde |**google**|-|`v1p1beta1`|0.024 (per 60 seconde)|1 seconde |**ibm**|-|`v1`|0.02 (per 60 seconde)|1 seconde |**microsoft**|-|`v1.0`|0.0168 (per 60 seconde)|1 seconde |**revai**|-|`v1`|0.02 (per 60 seconde)|15 seconde |**symbl**|-|`v1`|0.027 (per 60 seconde)|60 seconde |**voci**|-|`v1`|0.0162 (per 60 seconde)|1 seconde |**neuralspace**|-|`v1`|0.024 (per 60 seconde)|60 seconde |**assembly**|-|`v2`|0.011 (per 60 seconde)|1 seconde |**deepgram**|**enhanced**|`v1`|0.0189 (per 60 seconde)|1 seconde |**deepgram**|-|`v1`|0.0189 (per 60 seconde)|1 seconde |**deepgram**|**base**|`v1`|0.0169 (per 60 seconde)|1 seconde |**openai**|-|`boto3 (v1.15.18)`|0.006 (per 60 seconde)|1 seconde |**speechmatics**|**enhanced**|`v2`|0.022 (per 60 seconde)|1 seconde |**speechmatics**|**standard**|`v2`|0.017 (per 60 seconde)|1 seconde |**speechmatics**|-|`v2`|0.022 (per 60 seconde)|1 seconde |**gladia**|-|`v1`|0.0102 (per 60 seconde)|1 seconde

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Azerbaijani**|`az`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Hebrew**|`iw`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Indonesian**|`id`| |**Interlingua (International Auxiliary Language Association)**|`ia`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latvian**|`lv`| |**Lingala**|`ln`| |**Lithuanian**|`lt`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Mandarin Chinese**|`cmn`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Occitan (post 1500)**|`oc`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`mo`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Serbian**|`sr`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Wu Chinese**|`wuu`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`| |**at**|`at`| |**jp**|`jp`| |**mymr**|`mymr`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Afrikaans (South Africa)**|`af-ZA`| |**Albanian (Albania)**|`sq-AL`| |**Amharic (Ethiopia)**|`am-ET`| |**Arabic (Algeria)**|`ar-DZ`| |**Arabic (Bahrain)**|`ar-BH`| |**Arabic (Egypt)**|`ar-EG`| |**Arabic (Iraq)**|`ar-IQ`| |**Arabic (Israel)**|`ar-IL`| |**Arabic (Jordan)**|`ar-JO`| |**Arabic (Kuwait)**|`ar-KW`| |**Arabic (Lebanon)**|`ar-LB`| |**Arabic (Libya)**|`ar-LY`| |**Arabic (Mauritania)**|`ar-MR`| |**Arabic (Montserrat)**|`ar-MS`| |**Arabic (Morocco)**|`ar-MA`| |**Arabic (Oman)**|`ar-OM`| |**Arabic (Palestinian Territories)**|`ar-PS`| |**Arabic (Qatar)**|`ar-QA`| |**Arabic (Saudi Arabia)**|`ar-SA`| |**Arabic (Syria)**|`ar-SY`| |**Arabic (Tunisia)**|`ar-TN`| |**Arabic (United Arab Emirates)**|`ar-AE`| |**Arabic (Yemen)**|`ar-YE`| |**Armenian (Armenia)**|`hy-AM`| |**Azerbaijani (Azerbaijan)**|`az-AZ`| |**Bangla (Bangladesh)**|`bn-BD`| |**Bangla (India)**|`bn-IN`| |**Basque (Spain)**|`eu-ES`| |**Bosnian (Bosnia & Herzegovina)**|`bs-BA`| |**Bulgarian (Bulgaria)**|`bg-BG`| |**Burmese (Myanmar (Burma))**|`my-MM`| |**Cantonese (China)**|`yue-CN`| |**Cantonese (Traditional, Hong Kong SAR China)**|`yue-Hant-HK`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-CN`| |**Chinese (Hong Kong SAR China)**|`zh-HK`| |**Chinese (Taiwan)**|`zh-TW`| |**Croatian (Croatia)**|`hr-HR`| |**Czech (Czechia)**|`cs-CZ`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Belgium)**|`nl-BE`| |**Dutch (Netherlands)**|`nl-NL`| |**English (Australia)**|`en-AU`| |**English (Canada)**|`en-CA`| |**English (Ghana)**|`en-GH`| |**English (Hong Kong SAR China)**|`en-HK`| |**English (India)**|`en-IN`| |**English (Ireland)**|`en-IE`| |**English (Kenya)**|`en-KE`| |**English (New Zealand)**|`en-NZ`| |**English (Nigeria)**|`en-NG`| |**English (Pakistan)**|`en-PK`| |**English (Philippines)**|`en-PH`| |**English (Singapore)**|`en-SG`| |**English (South Africa)**|`en-ZA`| |**English (Tanzania)**|`en-TZ`| |**English (United Kingdom)**|`en-GB`| |**English (United Kingdom)**|`en-UK`| |**English (United States)**|`en-US`| |**Estonian (Estonia)**|`et-EE`| |**Filipino (Philippines)**|`fil-PH`| |**Finnish (Finland)**|`fi-FI`| |**French (Belgium)**|`fr-BE`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**French (Switzerland)**|`fr-CH`| |**Galician (Spain)**|`gl-ES`| |**Georgian (Georgia)**|`ka-GE`| |**German (Austria)**|`de-AT`| |**German (Germany)**|`de-DE`| |**German (Switzerland)**|`de-CH`| |**Greek (Greece)**|`el-GR`| |**Gujarati (India)**|`gu-IN`| |**Hebrew (Israel)**|`he-IL`| |**Hebrew (Israel)**|`iw-IL`| |**Hindi (India)**|`hi-IN`| |**Hindi (Latin)**|`hi-Latn`| |**Hungarian (Hungary)**|`hu-HU`| |**Icelandic (Iceland)**|`is-IS`| |**Indonesian (Indonesia)**|`id-ID`| |**Irish (Ireland)**|`ga-IE`| |**Italian (Italy)**|`it-IT`| |**Italian (Switzerland)**|`it-CH`| |**Japanese (Japan)**|`ja-JP`| |**Javanese (Indonesia)**|`jv-ID`| |**Kannada (India)**|`kn-IN`| |**Kazakh (Kazakhstan)**|`kk-KZ`| |**Khmer (Cambodia)**|`km-KH`| |**Korean (South Korea)**|`ko-KR`| |**Lao (Laos)**|`lo-LA`| |**Latvian (Latvia)**|`lv-LV`| |**Lithuanian (Lithuania)**|`lt-LT`| |**Macedonian (North Macedonia)**|`mk-MK`| |**Malay (Malaysia)**|`ms-MY`| |**Malayalam (India)**|`ml-IN`| |**Maltese (Malta)**|`mt-MT`| |**Marathi (India)**|`mr-IN`| |**Mongolian (Mongolia)**|`mn-MN`| |**Nepali (Nepal)**|`ne-NP`| |**Norwegian (Norway)**|`no-NO`| |**Norwegian Bokmål (Norway)**|`nb-NO`| |**Pashto (Afghanistan)**|`ps-AF`| |**Persian (Iran)**|`fa-IR`| |**Polish (Poland)**|`pl-PL`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`| |**Punjabi (India)**|`pa-Guru-IN`| |**Romanian (Romania)**|`ro-RO`| |**Russian (Russia)**|`ru-RU`| |**Serbian (Serbia)**|`sr-RS`| |**Sinhala (Sri Lanka)**|`si-LK`| |**Slovak (Slovakia)**|`sk-SK`| |**Slovenian (Slovenia)**|`sl-SI`| |**Somali (Somalia)**|`so-SO`| |**Spanish (Argentina)**|`es-AR`| |**Spanish (Bolivia)**|`es-BO`| |**Spanish (Chile)**|`es-CL`| |**Spanish (Colombia)**|`es-CO`| |**Spanish (Costa Rica)**|`es-CR`| |**Spanish (Cuba)**|`es-CU`| |**Spanish (Dominican Republic)**|`es-DO`| |**Spanish (Ecuador)**|`es-EC`| |**Spanish (El Salvador)**|`es-SV`| |**Spanish (Equatorial Guinea)**|`es-GQ`| |**Spanish (Guatemala)**|`es-GT`| |**Spanish (Honduras)**|`es-HN`| |**Spanish (Laos)**|`es-LA`| |**Spanish (Latin America)**|`es-419`| |**Spanish (Mexico)**|`es-MX`| |**Spanish (Nicaragua)**|`es-NI`| |**Spanish (Panama)**|`es-PA`| |**Spanish (Paraguay)**|`es-PY`| |**Spanish (Peru)**|`es-PE`| |**Spanish (Puerto Rico)**|`es-PR`| |**Spanish (Spain)**|`es-ES`| |**Spanish (United States)**|`es-US`| |**Spanish (Uruguay)**|`es-UY`| |**Spanish (Venezuela)**|`es-VE`| |**Sundanese (Indonesia)**|`su-ID`| |**Swahili (Kenya)**|`sw-KE`| |**Swahili (Tanzania)**|`sw-TZ`| |**Swedish (Sweden)**|`sv-SE`| |**Tamil (India)**|`ta-IN`| |**Tamil (Malaysia)**|`ta-MY`| |**Tamil (Singapore)**|`ta-SG`| |**Tamil (Sri Lanka)**|`ta-LK`| |**Telugu (India)**|`te-IN`| |**Thai (Thailand)**|`th-TH`| |**Turkish (Türkiye)**|`tr-TR`| |**Ukrainian (Ukraine)**|`uk-UA`| |**Urdu (India)**|`ur-IN`| |**Urdu (Pakistan)**|`ur-PK`| |**Uzbek (Uzbekistan)**|`uz-UZ`| |**Vietnamese (Vietnam)**|`vi-VN`| |**Welsh (United Kingdom)**|`cy-GB`| |**Wu Chinese (China)**|`wuu-CN`| |**Zulu (South Africa)**|`zu-ZA`|

Supported Models

deepgram
|Name|Value| |----|-----| |**deepgram**|`base`| ||`enhanced`|

speechmatics
|Name|Value| |----|-----| |**speechmatics**|`enhanced`| ||`standard`|

# paths./audio/speech_to_text_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./audio/speech_to_text_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.ibm.text
unit one page fourteen real conversations  hello hi my name's Cody %HESITATION what's your name I'm she hero nice to meet you she he ro yeah that's right %HESITATION we've from 

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.symbl.text
Unit one page. 14. Real conversations. Hello. Hi named Claudia. What's your name? I am Chihiro. Nice to meet you Chihiro. Yeah, that's right. Where you from.

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.gladia.text
Unit 1, page 14, real conversations. Hello. Hi. What's your name? Claudia. What's your name? I'm Chihiro. Nice to meet you. Chihiro? Yeah, that's right. Where are you from?

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.microsoft.text
Unit 1, page 14, real conversations. Hello. Hi. What's your name? Claudia. What's your name? I'm chihiro. Nice to meet you, Chihiro. Yeah, that's right. Where you from?

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.voci.text
Unit one page 14 real conversations. Hello. Hi. Unit one page 14 real conversations. Hello. Hi. What's your name me to she hi to get is right where you from. What's your name, let me to sheehy to get us right away from. 

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.neuralspace.text
Unit 1 page 14. Real conversations. Hello. I'm Chihiro nice to meet you.

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.assembly.text
Unit one, page 14. Real conversations. Hello. Hi. What's your name? Claudia. What's your name? I'm Chihiro. Nice to meet you, Chihiro. Yeah, that's right. Where are you from?

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.deepgram.text
Unit one, page fourteen, real conversations. Hello. Hi. Your name. Claudia. What's your name? I'm ji hero. Let's meet you. Chi. Yeah. This right. Away from

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.google.text
unit 1 page 14 real conversations, hello hi what's your name Claudia what's your name I'm Chihiro nice to meet you shehiro yeah that's right away from

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.speechmatics.text
 Unit one , page 14 . Real conversations . Hello . Hi . What's your name ? Claudia . What's your name ? I'm Chihiro . Nice to meet you . Shiro . Yeah . That's right . Uh , where are you from ?

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.revai.text
Unit one. Page 14, real conversations. Hello? Hi. What's your name? Claudia. What's your name? I'm Chihiro. Nice to meet you. Chihiro. Yeah, that's right. Uh, where you from?

# paths./audio/speech_to_text_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.text
Unit one page 14. Real Conversations. Hello? Hi. What's your name? Claudia. What's your name? I'm Chihiro. Nice to meet you, Chihiro. Yeah, that's right. Uh, where are you from?

# paths./audio/text_to_speech.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|4.0 (per 1000000 char)|1 char |**amazon**|**Neural**|`boto3 (v1.15.18)`|16.0 (per 1000000 char)|1 char |**google**|-|`v1`|4.0 (per 1000000 char)|1 char |**google**|**Standard**|`v1`|4.0 (per 1000000 char)|1 char |**google**|**Neural**|`v1`|16.0 (per 1000000 char)|1 char |**google**|**Wavenet**|`v1`|16.0 (per 1000000 char)|1 char |**google**|**Studio**|`v1`|0.16 (per 1000 char)|1 char |**ibm**|-|`v1`|20.0 (per 1000000 char)|1000 char |**microsoft**|-|`v1.0`|16.0 (per 1000000 char)|1 char |**lovoai**|-|`v1`|160.0 (per 1000000 char)|1000 char |**elevenlabs**|-|`v1`|0.3 (per 1000 char)|1 char |**openai**|-|`v1.0`|0.015 (per 1000 char)|1 char

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Azerbaijani**|`az`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Mandarin Chinese**|`cmn`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Serbian**|`sr`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Standard Arabic**|`arb`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Wu Chinese**|`wuu`| |**Xhosa**|`xh`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Afrikaans (South Africa)**|`af-ZA`| |**Albanian (Albania)**|`sq-AL`| |**Amharic (Ethiopia)**|`am-ET`| |**Arabic (Algeria)**|`ar-DZ`| |**Arabic (Bahrain)**|`ar-BH`| |**Arabic (Egypt)**|`ar-EG`| |**Arabic (Iraq)**|`ar-IQ`| |**Arabic (Jordan)**|`ar-JO`| |**Arabic (Kuwait)**|`ar-KW`| |**Arabic (Lebanon)**|`ar-LB`| |**Arabic (Libya)**|`ar-LY`| |**Arabic (Morocco)**|`ar-MA`| |**Arabic (Oman)**|`ar-OM`| |**Arabic (Pseudo-Accents)**|`ar-XA`| |**Arabic (Qatar)**|`ar-QA`| |**Arabic (Saudi Arabia)**|`ar-SA`| |**Arabic (Syria)**|`ar-SY`| |**Arabic (Tunisia)**|`ar-TN`| |**Arabic (United Arab Emirates)**|`ar-AE`| |**Arabic (Yemen)**|`ar-YE`| |**Armenian (Armenia)**|`hy-AM`| |**Azerbaijani (Azerbaijan)**|`az-AZ`| |**Bangla (Bangladesh)**|`bn-BD`| |**Bangla (India)**|`bn-IN`| |**Basque (Spain)**|`eu-ES`| |**Bosnian (Bosnia & Herzegovina)**|`bs-BA`| |**Bulgarian (Bulgaria)**|`bg-BG`| |**Burmese (Myanmar (Burma))**|`my-MM`| |**Cantonese (China)**|`yue-CN`| |**Cantonese (Hong Kong SAR China)**|`yue-HK`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-CN`| |**Chinese (China)**|`zh-CN-henan`| |**Chinese (China)**|`zh-CN-shandong`| |**Chinese (China)**|`zh-CN-sichuan`| |**Chinese (Hong Kong SAR China)**|`zh-HK`| |**Chinese (Taiwan)**|`zh-TW`| |**Croatian (Croatia)**|`hr-HR`| |**Czech (Czechia)**|`cs-CZ`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Belgium)**|`nl-BE`| |**Dutch (Netherlands)**|`nl-NL`| |**English (Australia)**|`en-AU`| |**English (Canada)**|`en-CA`| |**English (Curaçao)**|`en-AN`| |**English (Hong Kong SAR China)**|`en-HK`| |**English (India)**|`en-IN`| |**English (Ireland)**|`en-IE`| |**English (Kenya)**|`en-KE`| |**English (New Zealand)**|`en-NZ`| |**English (Nigeria)**|`en-NG`| |**English (Philippines)**|`en-PH`| |**English (Singapore)**|`en-SG`| |**English (South Africa)**|`en-ZA`| |**English (Tanzania)**|`en-TZ`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**Estonian (Estonia)**|`et-EE`| |**Filipino (Philippines)**|`fil-PH`| |**Finnish (Finland)**|`fi-FI`| |**French (Belgium)**|`fr-BE`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**French (Switzerland)**|`fr-CH`| |**Galician (Spain)**|`gl-ES`| |**Georgian (Georgia)**|`ka-GE`| |**German (Austria)**|`de-AT`| |**German (Germany)**|`de-DE`| |**German (Switzerland)**|`de-CH`| |**Greek (Greece)**|`el-GR`| |**Gujarati (India)**|`gu-IN`| |**Hebrew (Israel)**|`he-IL`| |**Hindi (India)**|`hi-IN`| |**Hungarian (Hungary)**|`hu-HU`| |**Icelandic (Iceland)**|`is-IS`| |**Indonesian (Indonesia)**|`id-ID`| |**Irish (Ireland)**|`ga-IE`| |**Italian (Italy)**|`it-IT`| |**Japanese (Japan)**|`ja-JP`| |**Javanese (Indonesia)**|`jv-ID`| |**Kannada (India)**|`kn-IN`| |**Kazakh (Kazakhstan)**|`kk-KZ`| |**Khmer (Cambodia)**|`km-KH`| |**Korean (South Korea)**|`ko-KR`| |**Lao (Laos)**|`lo-LA`| |**Latvian (Latvia)**|`lv-LV`| |**Lithuanian (Lithuania)**|`lt-LT`| |**Macedonian (North Macedonia)**|`mk-MK`| |**Malay (Malaysia)**|`ms-MY`| |**Malayalam (India)**|`ml-IN`| |**Maltese (Malta)**|`mt-MT`| |**Mandarin Chinese (China)**|`cmn-CN`| |**Mandarin Chinese (Taiwan)**|`cmn-TW`| |**Marathi (India)**|`mr-IN`| |**Mongolian (Mongolia)**|`mn-MN`| |**Nepali (Nepal)**|`ne-NP`| |**Norwegian Bokmål (Norway)**|`nb-NO`| |**Pashto (Afghanistan)**|`ps-AF`| |**Persian (Iran)**|`fa-IR`| |**Polish (Poland)**|`pl-PL`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`| |**Punjabi (India)**|`pa-IN`| |**Romanian (Romania)**|`ro-RO`| |**Russian (Russia)**|`ru-RU`| |**Serbian (Serbia)**|`sr-RS`| |**Sinhala (Sri Lanka)**|`si-LK`| |**Slovak (Slovakia)**|`sk-SK`| |**Slovenian (Slovenia)**|`sl-SI`| |**Somali (Somalia)**|`so-SO`| |**Spanish (Argentina)**|`es-AR`| |**Spanish (Bolivia)**|`es-BO`| |**Spanish (Chile)**|`es-CL`| |**Spanish (Colombia)**|`es-CO`| |**Spanish (Costa Rica)**|`es-CR`| |**Spanish (Cuba)**|`es-CU`| |**Spanish (Dominican Republic)**|`es-DO`| |**Spanish (Ecuador)**|`es-EC`| |**Spanish (El Salvador)**|`es-SV`| |**Spanish (Equatorial Guinea)**|`es-GQ`| |**Spanish (Guatemala)**|`es-GT`| |**Spanish (Honduras)**|`es-HN`| |**Spanish (Laos)**|`es-LA`| |**Spanish (Mexico)**|`es-MX`| |**Spanish (Nicaragua)**|`es-NI`| |**Spanish (Panama)**|`es-PA`| |**Spanish (Paraguay)**|`es-PY`| |**Spanish (Peru)**|`es-PE`| |**Spanish (Puerto Rico)**|`es-PR`| |**Spanish (Spain)**|`es-ES`| |**Spanish (United States)**|`es-US`| |**Spanish (Uruguay)**|`es-UY`| |**Spanish (Venezuela)**|`es-VE`| |**Sundanese (Indonesia)**|`su-ID`| |**Swahili (Kenya)**|`sw-KE`| |**Swahili (Tanzania)**|`sw-TZ`| |**Swedish (Sweden)**|`sv-SE`| |**Tamil (India)**|`ta-IN`| |**Tamil (Malaysia)**|`ta-MY`| |**Tamil (Singapore)**|`ta-SG`| |**Tamil (Sri Lanka)**|`ta-LK`| |**Telugu (India)**|`te-IN`| |**Thai (Thailand)**|`th-TH`| |**Turkish (Türkiye)**|`tr-TR`| |**Ukrainian (Ukraine)**|`uk-UA`| |**Urdu (India)**|`ur-IN`| |**Urdu (Pakistan)**|`ur-PK`| |**Uzbek (United Kingdom)**|`uz-UK`| |**Uzbek (Uzbekistan)**|`uz-UZ`| |**Vietnamese (Vietnam)**|`vi-VN`| |**Welsh (United Kingdom)**|`cy-GB`| |**Wu Chinese (China)**|`wuu-CN`| |**Xhosa (South Africa)**|`xh-ZA`| |**Zulu (South Africa)**|`zu-ZA`|

Supported Models

amazon
|Name|Value| |----|-----| |**amazon**|`Neural`|

google
|Name|Value| |----|-----| |**google**|`Neural`| ||`Standard`| ||`Studio`| ||`Wavenet`|

ibm
|Name|Value| |----|-----| |**ibm**|`de-DE_BirgitV3Voice`| ||`de-DE_DieterV3Voice`| ||`de-DE_ErikaV3Voice`| ||`en-AU_HeidiExpressive`| ||`en-AU_JackExpressive`| ||`en-GB_CharlotteV3Voice`| ||`en-GB_JamesV3Voice`| ||`en-GB_KateV3Voice`| ||`en-US_AllisonExpressive`| ||`en-US_AllisonV3Voice`| ||`en-US_EmilyV3Voice`| ||`en-US_EmmaExpressive`| ||`en-US_HenryV3Voice`| ||`en-US_KevinV3Voice`| ||`en-US_LisaExpressive`| ||`en-US_LisaV3Voice`| ||`en-US_MichaelExpressive`| ||`en-US_MichaelV3Voice`| ||`en-US_OliviaV3Voice`| ||`es-ES_EnriqueV3Voice`| ||`es-ES_LauraV3Voice`| ||`es-LA_SofiaV3Voice`| ||`es-US_SofiaV3Voice`| ||`fr-CA_LouiseV3Voice`| ||`fr-FR_NicolasV3Voice`| ||`fr-FR_ReneeV3Voice`| ||`it-IT_FrancescaV3Voice`| ||`ja-JP_EmiV3Voice`| ||`ko-KR_JinV3Voice`| ||`nl-NL_MerelV3Voice`| ||`pt-BR_IsabelaV3Voice`|

microsoft
|Name|Value| |----|-----| |**microsoft**|`af-ZA-AdriNeural`| ||`af-ZA-WillemNeural`| ||`am-ET-AmehaNeural`| ||`am-ET-MekdesNeural`| ||`ar-AE-FatimaNeural`| ||`ar-AE-HamdanNeural`| ||`ar-BH-AliNeural`| ||`ar-BH-LailaNeural`| ||`ar-DZ-AminaNeural`| ||`ar-DZ-IsmaelNeural`| ||`ar-EG-SalmaNeural`| ||`ar-EG-ShakirNeural`| ||`ar-IQ-BasselNeural`| ||`ar-IQ-RanaNeural`| ||`ar-JO-SanaNeural`| ||`ar-JO-TaimNeural`| ||`ar-KW-FahedNeural`| ||`ar-KW-NouraNeural`| ||`ar-LB-LaylaNeural`| ||`ar-LB-RamiNeural`| ||`ar-LY-ImanNeural`| ||`ar-LY-OmarNeural`| ||`ar-MA-JamalNeural`| ||`ar-MA-MounaNeural`| ||`ar-OM-AbdullahNeural`| ||`ar-OM-AyshaNeural`| ||`ar-QA-AmalNeural`| ||`ar-QA-MoazNeural`| ||`ar-SA-HamedNeural`| ||`ar-SA-ZariyahNeural`| ||`ar-SY-AmanyNeural`| ||`ar-SY-LaithNeural`| ||`ar-TN-HediNeural`| ||`ar-TN-ReemNeural`| ||`ar-YE-MaryamNeural`| ||`ar-YE-SalehNeural`| ||`az-AZ-BabekNeural`| ||`az-AZ-BanuNeural`| ||`bg-BG-BorislavNeural`| ||`bg-BG-KalinaNeural`| ||`bn-BD-NabanitaNeural`| ||`bn-BD-PradeepNeural`| ||`bn-IN-BashkarNeural`| ||`bn-IN-TanishaaNeural`| ||`bs-BA-GoranNeural`| ||`bs-BA-VesnaNeural`| ||`ca-ES-AlbaNeural`| ||`ca-ES-EnricNeural`| ||`ca-ES-JoanaNeural`| ||`cs-CZ-AntoninNeural`| ||`cs-CZ-VlastaNeural`| ||`cy-GB-AledNeural`| ||`cy-GB-NiaNeural`| ||`da-DK-ChristelNeural`| ||`da-DK-JeppeNeural`| ||`de-AT-IngridNeural`| ||`de-AT-JonasNeural`| ||`de-CH-JanNeural`| ||`de-CH-LeniNeural`| ||`de-DE-AmalaNeural`| ||`de-DE-BerndNeural`| ||`de-DE-ChristophNeural`| ||`de-DE-ConradNeural`| ||`de-DE-ElkeNeural`| ||`de-DE-GiselaNeural`| ||`de-DE-KasperNeural`| ||`de-DE-KatjaNeural`| ||`de-DE-KillianNeural`| ||`de-DE-KlarissaNeural`| ||`de-DE-KlausNeural`| ||`de-DE-LouisaNeural`| ||`de-DE-MajaNeural`| ||`de-DE-RalfNeural`| ||`de-DE-TanjaNeural`| ||`el-GR-AthinaNeural`| ||`el-GR-NestorasNeural`| ||`en-AU-AnnetteNeural`| ||`en-AU-CarlyNeural`| ||`en-AU-DarrenNeural`| ||`en-AU-DuncanNeural`| ||`en-AU-ElsieNeural`| ||`en-AU-FreyaNeural`| ||`en-AU-JoanneNeural`| ||`en-AU-KenNeural`| ||`en-AU-KimNeural`| ||`en-AU-NatashaNeural`| ||`en-AU-NeilNeural`| ||`en-AU-TimNeural`| ||`en-AU-TinaNeural`| ||`en-AU-WilliamNeural`| ||`en-CA-ClaraNeural`| ||`en-CA-LiamNeural`| ||`en-GB-AbbiNeural`| ||`en-GB-AlfieNeural`| ||`en-GB-BellaNeural`| ||`en-GB-ElliotNeural`| ||`en-GB-EthanNeural`| ||`en-GB-HollieNeural`| ||`en-GB-LibbyNeural`| ||`en-GB-MaisieNeural`| ||`en-GB-NoahNeural`| ||`en-GB-OliverNeural`| ||`en-GB-OliviaNeural`| ||`en-GB-RyanNeural`| ||`en-GB-SoniaNeural`| ||`en-GB-ThomasNeural`| ||`en-HK-SamNeural`| ||`en-HK-YanNeural`| ||`en-IE-ConnorNeural`| ||`en-IE-EmilyNeural`| ||`en-IN-NeerjaNeural`| ||`en-IN-PrabhatNeural`| ||`en-KE-AsiliaNeural`| ||`en-KE-ChilembaNeural`| ||`en-NG-AbeoNeural`| ||`en-NG-EzinneNeural`| ||`en-NZ-MitchellNeural`| ||`en-NZ-MollyNeural`| ||`en-PH-JamesNeural`| ||`en-PH-RosaNeural`| ||`en-SG-LunaNeural`| ||`en-SG-WayneNeural`| ||`en-TZ-ElimuNeural`| ||`en-TZ-ImaniNeural`| ||`en-US-AIGenerate1Neural`| ||`en-US-AIGenerate2Neural`| ||`en-US-AmberNeural`| ||`en-US-AnaNeural`| ||`en-US-AriaNeural`| ||`en-US-AshleyNeural`| ||`en-US-BrandonNeural`| ||`en-US-ChristopherNeural`| ||`en-US-CoraNeural`| ||`en-US-DavisNeural`| ||`en-US-ElizabethNeural`| ||`en-US-EricNeural`| ||`en-US-GuyNeural`| ||`en-US-JacobNeural`| ||`en-US-JaneNeural`| ||`en-US-JasonNeural`| ||`en-US-JennyMultilingualNeural`| ||`en-US-JennyNeural`| ||`en-US-MichelleNeural`| ||`en-US-MonicaNeural`| ||`en-US-NancyNeural`| ||`en-US-RogerNeural`| ||`en-US-SaraNeural`| ||`en-US-SteffanNeural`| ||`en-US-TonyNeural`| ||`en-ZA-LeahNeural`| ||`en-ZA-LukeNeural`| ||`es-AR-ElenaNeural`| ||`es-AR-TomasNeural`| ||`es-BO-MarceloNeural`| ||`es-BO-SofiaNeural`| ||`es-CL-CatalinaNeural`| ||`es-CL-LorenzoNeural`| ||`es-CO-GonzaloNeural`| ||`es-CO-SalomeNeural`| ||`es-CR-JuanNeural`| ||`es-CR-MariaNeural`| ||`es-CU-BelkysNeural`| ||`es-CU-ManuelNeural`| ||`es-DO-EmilioNeural`| ||`es-DO-RamonaNeural`| ||`es-EC-AndreaNeural`| ||`es-EC-LuisNeural`| ||`es-ES-AbrilNeural`| ||`es-ES-AlvaroNeural`| ||`es-ES-ArnauNeural`| ||`es-ES-DarioNeural`| ||`es-ES-EliasNeural`| ||`es-ES-ElviraNeural`| ||`es-ES-EstrellaNeural`| ||`es-ES-IreneNeural`| ||`es-ES-LaiaNeural`| ||`es-ES-LiaNeural`| ||`es-ES-NilNeural`| ||`es-ES-SaulNeural`| ||`es-ES-TeoNeural`| ||`es-ES-TrianaNeural`| ||`es-ES-VeraNeural`| ||`es-GQ-JavierNeural`| ||`es-GQ-TeresaNeural`| ||`es-GT-AndresNeural`| ||`es-GT-MartaNeural`| ||`es-HN-CarlosNeural`| ||`es-HN-KarlaNeural`| ||`es-MX-BeatrizNeural`| ||`es-MX-CandelaNeural`| ||`es-MX-CarlotaNeural`| ||`es-MX-CecilioNeural`| ||`es-MX-DaliaNeural`| ||`es-MX-GerardoNeural`| ||`es-MX-JorgeNeural`| ||`es-MX-LarissaNeural`| ||`es-MX-LibertoNeural`| ||`es-MX-LucianoNeural`| ||`es-MX-MarinaNeural`| ||`es-MX-NuriaNeural`| ||`es-MX-PelayoNeural`| ||`es-MX-RenataNeural`| ||`es-MX-YagoNeural`| ||`es-NI-FedericoNeural`| ||`es-NI-YolandaNeural`| ||`es-PA-MargaritaNeural`| ||`es-PA-RobertoNeural`| ||`es-PE-AlexNeural`| ||`es-PE-CamilaNeural`| ||`es-PR-KarinaNeural`| ||`es-PR-VictorNeural`| ||`es-PY-MarioNeural`| ||`es-PY-TaniaNeural`| ||`es-SV-LorenaNeural`| ||`es-SV-RodrigoNeural`| ||`es-US-AlonsoNeural`| ||`es-US-PalomaNeural`| ||`es-UY-MateoNeural`| ||`es-UY-ValentinaNeural`| ||`es-VE-PaolaNeural`| ||`es-VE-SebastianNeural`| ||`et-EE-AnuNeural`| ||`et-EE-KertNeural`| ||`eu-ES-AinhoaNeural`| ||`eu-ES-AnderNeural`| ||`fa-IR-DilaraNeural`| ||`fa-IR-FaridNeural`| ||`fi-FI-HarriNeural`| ||`fi-FI-NooraNeural`| ||`fi-FI-SelmaNeural`| ||`fil-PH-AngeloNeural`| ||`fil-PH-BlessicaNeural`| ||`fr-BE-CharlineNeural`| ||`fr-BE-GerardNeural`| ||`fr-CA-AntoineNeural`| ||`fr-CA-JeanNeural`| ||`fr-CA-SylvieNeural`| ||`fr-CH-ArianeNeural`| ||`fr-CH-FabriceNeural`| ||`fr-FR-AlainNeural`| ||`fr-FR-BrigitteNeural`| ||`fr-FR-CelesteNeural`| ||`fr-FR-ClaudeNeural`| ||`fr-FR-CoralieNeural`| ||`fr-FR-DeniseNeural`| ||`fr-FR-EloiseNeural`| ||`fr-FR-HenriNeural`| ||`fr-FR-JacquelineNeural`| ||`fr-FR-JeromeNeural`| ||`fr-FR-JosephineNeural`| ||`fr-FR-MauriceNeural`| ||`fr-FR-YvesNeural`| ||`fr-FR-YvetteNeural`| ||`ga-IE-ColmNeural`| ||`ga-IE-OrlaNeural`| ||`gl-ES-RoiNeural`| ||`gl-ES-SabelaNeural`| ||`gu-IN-DhwaniNeural`| ||`gu-IN-NiranjanNeural`| ||`he-IL-AvriNeural`| ||`he-IL-HilaNeural`| ||`hi-IN-MadhurNeural`| ||`hi-IN-SwaraNeural`| ||`hr-HR-GabrijelaNeural`| ||`hr-HR-SreckoNeural`| ||`hu-HU-NoemiNeural`| ||`hu-HU-TamasNeural`| ||`hy-AM-AnahitNeural`| ||`hy-AM-HaykNeural`| ||`id-ID-ArdiNeural`| ||`id-ID-GadisNeural`| ||`is-IS-GudrunNeural`| ||`is-IS-GunnarNeural`| ||`it-IT-BenignoNeural`| ||`it-IT-CalimeroNeural`| ||`it-IT-CataldoNeural`| ||`it-IT-DiegoNeural`| ||`it-IT-ElsaNeural`| ||`it-IT-FabiolaNeural`| ||`it-IT-FiammaNeural`| ||`it-IT-GianniNeural`| ||`it-IT-ImeldaNeural`| ||`it-IT-IrmaNeural`| ||`it-IT-IsabellaNeural`| ||`it-IT-LisandroNeural`| ||`it-IT-PalmiraNeural`| ||`it-IT-PierinaNeural`| ||`it-IT-RinaldoNeural`| ||`ja-JP-AoiNeural`| ||`ja-JP-DaichiNeural`| ||`ja-JP-KeitaNeural`| ||`ja-JP-MayuNeural`| ||`ja-JP-NanamiNeural`| ||`ja-JP-NaokiNeural`| ||`ja-JP-ShioriNeural`| ||`jv-ID-DimasNeural`| ||`jv-ID-SitiNeural`| ||`ka-GE-EkaNeural`| ||`ka-GE-GiorgiNeural`| ||`kk-KZ-AigulNeural`| ||`kk-KZ-DauletNeural`| ||`km-KH-PisethNeural`| ||`km-KH-SreymomNeural`| ||`kn-IN-GaganNeural`| ||`kn-IN-SapnaNeural`| ||`ko-KR-BongJinNeural`| ||`ko-KR-GookMinNeural`| ||`ko-KR-InJoonNeural`| ||`ko-KR-JiMinNeural`| ||`ko-KR-SeoHyeonNeural`| ||`ko-KR-SoonBokNeural`| ||`ko-KR-SunHiNeural`| ||`ko-KR-YuJinNeural`| ||`lo-LA-ChanthavongNeural`| ||`lo-LA-KeomanyNeural`| ||`lt-LT-LeonasNeural`| ||`lt-LT-OnaNeural`| ||`lv-LV-EveritaNeural`| ||`lv-LV-NilsNeural`| ||`mk-MK-AleksandarNeural`| ||`mk-MK-MarijaNeural`| ||`ml-IN-MidhunNeural`| ||`ml-IN-SobhanaNeural`| ||`mn-MN-BataaNeural`| ||`mn-MN-YesuiNeural`| ||`mr-IN-AarohiNeural`| ||`mr-IN-ManoharNeural`| ||`ms-MY-OsmanNeural`| ||`ms-MY-YasminNeural`| ||`mt-MT-GraceNeural`| ||`mt-MT-JosephNeural`| ||`my-MM-NilarNeural`| ||`my-MM-ThihaNeural`| ||`nb-NO-FinnNeural`| ||`nb-NO-IselinNeural`| ||`nb-NO-PernilleNeural`| ||`ne-NP-HemkalaNeural`| ||`ne-NP-SagarNeural`| ||`nl-BE-ArnaudNeural`| ||`nl-BE-DenaNeural`| ||`nl-NL-ColetteNeural`| ||`nl-NL-FennaNeural`| ||`nl-NL-MaartenNeural`| ||`pl-PL-AgnieszkaNeural`| ||`pl-PL-MarekNeural`| ||`pl-PL-ZofiaNeural`| ||`ps-AF-GulNawazNeural`| ||`ps-AF-LatifaNeural`| ||`pt-BR-AntonioNeural`| ||`pt-BR-BrendaNeural`| ||`pt-BR-DonatoNeural`| ||`pt-BR-ElzaNeural`| ||`pt-BR-FabioNeural`| ||`pt-BR-FranciscaNeural`| ||`pt-BR-GiovannaNeural`| ||`pt-BR-HumbertoNeural`| ||`pt-BR-JulioNeural`| ||`pt-BR-LeilaNeural`| ||`pt-BR-LeticiaNeural`| ||`pt-BR-ManuelaNeural`| ||`pt-BR-NicolauNeural`| ||`pt-BR-ValerioNeural`| ||`pt-BR-YaraNeural`| ||`pt-PT-DuarteNeural`| ||`pt-PT-FernandaNeural`| ||`pt-PT-RaquelNeural`| ||`ro-RO-AlinaNeural`| ||`ro-RO-EmilNeural`| ||`ru-RU-DariyaNeural`| ||`ru-RU-DmitryNeural`| ||`ru-RU-SvetlanaNeural`| ||`si-LK-SameeraNeural`| ||`si-LK-ThiliniNeural`| ||`sk-SK-LukasNeural`| ||`sk-SK-ViktoriaNeural`| ||`sl-SI-PetraNeural`| ||`sl-SI-RokNeural`| ||`so-SO-MuuseNeural`| ||`so-SO-UbaxNeural`| ||`sq-AL-AnilaNeural`| ||`sq-AL-IlirNeural`| ||`sr-RS-NicholasNeural`| ||`sr-RS-SophieNeural`| ||`su-ID-JajangNeural`| ||`su-ID-TutiNeural`| ||`sv-SE-HilleviNeural`| ||`sv-SE-MattiasNeural`| ||`sv-SE-SofieNeural`| ||`sw-KE-RafikiNeural`| ||`sw-KE-ZuriNeural`| ||`sw-TZ-DaudiNeural`| ||`sw-TZ-RehemaNeural`| ||`ta-IN-PallaviNeural`| ||`ta-IN-ValluvarNeural`| ||`ta-LK-KumarNeural`| ||`ta-LK-SaranyaNeural`| ||`ta-MY-KaniNeural`| ||`ta-MY-SuryaNeural`| ||`ta-SG-AnbuNeural`| ||`ta-SG-VenbaNeural`| ||`te-IN-MohanNeural`| ||`te-IN-ShrutiNeural`| ||`th-TH-AcharaNeural`| ||`th-TH-NiwatNeural`| ||`th-TH-PremwadeeNeural`| ||`tr-TR-AhmetNeural`| ||`tr-TR-EmelNeural`| ||`uk-UA-OstapNeural`| ||`uk-UA-PolinaNeural`| ||`ur-IN-GulNeural`| ||`ur-IN-SalmanNeural`| ||`ur-PK-AsadNeural`| ||`ur-PK-UzmaNeural`| ||`uz-UZ-MadinaNeural`| ||`uz-UZ-SardorNeural`| ||`vi-VN-HoaiMyNeural`| ||`vi-VN-NamMinhNeural`| ||`wuu-CN-XiaotongNeural`| ||`wuu-CN-YunzheNeural`| ||`yue-CN-XiaoMinNeural`| ||`yue-CN-YunSongNeural`| ||`zh-CN-XiaochenNeural`| ||`zh-CN-XiaohanNeural`| ||`zh-CN-XiaomengNeural`| ||`zh-CN-XiaomoNeural`| ||`zh-CN-XiaoqiuNeural`| ||`zh-CN-XiaoruiNeural`| ||`zh-CN-XiaoshuangNeural`| ||`zh-CN-XiaoxiaoNeural`| ||`zh-CN-XiaoxuanNeural`| ||`zh-CN-XiaoyanNeural`| ||`zh-CN-XiaoyiNeural`| ||`zh-CN-XiaoyouNeural`| ||`zh-CN-XiaozhenNeural`| ||`zh-CN-YunfengNeural`| ||`zh-CN-YunhaoNeural`| ||`zh-CN-YunjianNeural`| ||`zh-CN-YunxiNeural`| ||`zh-CN-YunxiaNeural`| ||`zh-CN-YunyangNeural`| ||`zh-CN-YunyeNeural`| ||`zh-CN-YunzeNeural`| ||`zh-CN-henan-YundengNeural`| ||`zh-CN-liaoning-XiaobeiNeural`| ||`zh-CN-shaanxi-XiaoniNeural`| ||`zh-CN-shandong-YunxiangNeural`| ||`zh-CN-sichuan-YunxiNeural`| ||`zh-HK-HiuGaaiNeural`| ||`zh-HK-HiuMaanNeural`| ||`zh-HK-WanLungNeural`| ||`zh-TW-HsiaoChenNeural`| ||`zh-TW-HsiaoYuNeural`| ||`zh-TW-YunJheNeural`| ||`zu-ZA-ThandoNeural`| ||`zu-ZA-ThembaNeural`|

lovoai
|Name|Value| |----|-----| |**lovoai**|`af-ZA_Albertus Ruan`| ||`af-ZA_Danelle Wessel`| ||`am-ET_Abai Berhe`| ||`am-ET_Cherenet Tesfaye`| ||`ar-AE_Mansour Ahmed`| ||`ar-AE_Maryam Khan`| ||`ar-BH_Fatima Kumar`| ||`ar-BH_Omar Hassan`| ||`ar-DZ_Samia Touati`| ||`ar-DZ_Zuthimalin Brahimi`| ||`ar-EG_Ahmed Gamal`| ||`ar-EG_Reem Salah`| ||`ar-IQ_Aveen Majid`| ||`ar-IQ_Ismail Hashem`| ||`ar-JO_Fatima Jaber`| ||`ar-JO_Yousef Saleh`| ||`ar-KW_Areej Nair`| ||`ar-KW_Khaled Al Azmi`| ||`ar-LB_Abdel El Din`| ||`ar-LB_Eessa Kadifa`| ||`ar-LY_Abir Salem`| ||`ar-LY_Ahsan Omar`| ||`ar-MA_Hamid Bennani`| ||`ar-MA_Salma Naciri`| ||`ar-OM_Jabbar Singh`| ||`ar-OM_Zahra Sultan`| ||`ar-QA_Faizur Kumar`| ||`ar-QA_Noora Hussain`| ||`ar-SA_Majidah Khan`| ||`ar-SA_Omar Aziz`| ||`ar-SY_Oraida El-Assad`| ||`ar-SY_Rabah Ibrahim`| ||`ar-TN_Donia Cherif`| ||`ar-TN_Karim Dridi`| ||`ar-YE_Mansour Kasim`| ||`ar-YE_Mehdi Bawazeer`| ||`az-AZ_Ugur Quliyeva`| ||`az_AZ_Zeynab Cafarova`| ||`bg-BG_Damyan Ivanov`| ||`bg-BG_Fidanka Georgiev`| ||`bn-BD_Pranshu Akter`| ||`bn-BD_Vaagdevi Khatun`| ||`bn-IN_Gazi Mondal`| ||`bn-IN_Wali Ghosh`| ||`bs-BA_Esma Dodik`| ||`bs-BA_Ismet Rakic`| ||`ca-ES_Amada Fernando`| ||`ca-ES_Carmen Santiago`| ||`ca-ES_Miguel Torres`| ||`cs-CZ_Jana Rosicky`| ||`cs-CZ_Tomas Malecek`| ||`cy-GB_Branwen Jones`| ||`cy-GB_Elen Hughes`| ||`da-DK_Bjarne Jensen`| ||`da-DK_Helge Nielsen`| ||`de-AT_Lena Gruber`| ||`de-AT_Wilhelm Wagner`| ||`de-CH_Adolfus Meier`| ||`de-CH_Lara Keller`| ||`de-DE_Ada Weber`| ||`de-DE_Anna Schmidt`| ||`de-DE_Emma Muller`| ||`de-DE_Gerda Becker`| ||`de-DE_Hans Schulz`| ||`de-DE_Heidi Schneider`| ||`de-DE_Johanna Meyer`| ||`de-DE_Joshua Bauer`| ||`de-DE_Julian Koch`| ||`de-DE_Karl Hummels`| ||`de-DE_Maria Fischer`| ||`de-DE_Oliver Richter`| ||`de-DE_Otto Schaefer`| ||`de-DE_Petra Hoffman`| ||`de-DE_Walter Kimmich`| ||`el-GR_Petros Andino`| ||`el-GR_Thalia Klisiaris`| ||`en-AU_Amelia Taylor`| ||`en-AU_Charlotte Brown`| ||`en-AU_Darrell Robinson`| ||`en-AU_George Thompson`| ||`en-AU_Georgie Smith`| ||`en-AU_Isla Johnson`| ||`en-AU_Jake Nguyen`| ||`en-AU_Keegan Walker`| ||`en-AU_Kelly Opie`| ||`en-AU_Kevin Turner`| ||`en-AU_Mia White`| ||`en-AU_Nancy Jones`| ||`en-AU_Ryan Murphy`| ||`en-AU_Willow Martin`| ||`en-CA_Emily Salo`| ||`en-CA_Eric Fidyk`| ||`en-GB_Abigail Fraser`| ||`en-GB_Annie Smith`| ||`en-GB_Gertrude Baker`| ||`en-GB_Ian Kensington`| ||`en-GB_Kane Tooney`| ||`en-GB_Kelsey Michaels`| ||`en-GB_Kerrington Pacey`| ||`en-GB_Lizzy Wright`| ||`en-GB_Marcus O'Donell`| ||`en-GB_Nathaniel Jacobs`| ||`en-GB_Samuel Lee-Richards`| ||`en-GB_T.S. Cooper`| ||`en-GB_Theresa King`| ||`en-GB_William Tripp`| ||`en-HK_Heather Yiu`| ||`en-HK_Kevin Lau`| ||`en-IE_Aoife Byrne`| ||`en-IE_Bill Parkin`| ||`en-IN_Isha Singh`| ||`en-IN_Prabhdeep Patel`| ||`en-KE_Almasi Otieno`| ||`en-KE_Chitundu Mwangi`| ||`en-NG_Blessing Musa`| ||`en-NG_Kehinde Sade`| ||`en-NZ_Benson Duncan`| ||`en-NZ_Destiny Mitchell`| ||`en-PH_Angel dela Cruz`| ||`en-PH_Francis Reynaldo`| ||`en-SG_Chris Tan`| ||`en-SG_Rachel Ng`| ||`en-TZ_Busara Charles`| ||`en-TZ_Darweshi Juma`| ||`en-US_Alysha Imani`| ||`en-US_Betty Parker`| ||`en-US_Catherine Zania`| ||`en-US_Christopher Navarrez`| ||`en-US_Clara Ho`| ||`en-US_Eric Gonzalez`| ||`en-US_Heather Everett`| ||`en-US_Jamal Starke`| ||`en-US_Jasonna Johnson`| ||`en-US_Kaylee Montana`| ||`en-US_Ken hunter`| ||`en-US_Kim Howard`| ||`en-US_Kyle Moreno`| ||`en-US_Leroy Alshak`| ||`en-US_Micah Washington`| ||`en-US_Molly Richardson`| ||`en-US_Peter Lee`| ||`en-US_Phil Gough`| ||`en-US_Phoebe Nicholson`| ||`en-US_Samantha Hawthorne`| ||`en-US_Sean Orson`| ||`en-US_Serena Yang`| ||`en-US_Shannon Mechare`| ||`en-US_Thelma Browne`| ||`en-US_Tim Hardway`| ||`en-ZA_Cody Fergudson`| ||`en-ZA_Elna VanDijk`| ||`es-AR_Hyacinthe Castro`| ||`es-AR_Lautaro Gomez`| ||`es-BO_Elena Lopez`| ||`es-BO_Juan Perez`| ||`es-CL_Francisca Batistuta`| ||`es-CL_Gabriel Rodriguez`| ||`es-CO_Lorenzo Vazquez`| ||`es-CO_Sofia Garcia`| ||`es-CR_Guadalupe Suarez`| ||`es-CR_Sebastian Ramos`| ||`es-CU_Isabel Molina`| ||`es-CU_Luis Ortega`| ||`es-DO_Gabriela Serrano`| ||`es-DO_Raul Dominguez`| ||`es-EC_Angelina Romero`| ||`es-EC_Christian Diaz`| ||`es-EN_Carmen Martinela`| ||`es-ES_Andres Marin`| ||`es-ES_Emiliano Delgado`| ||`es-ES_Esmeralda Molina`| ||`es-ES_Hector Gavi`| ||`es-ES_Leo Gil`| ||`es-ES_Liliana Sanz`| ||`es-ES_Maria Ruiz`| ||`es-ES_Martin Enrique`| ||`es-ES_Miranda Navarro`| ||`es-ES_Pablo Iniesta`| ||`es-ES_Silvia Alvarez`| ||`es-ES_Teresa Iglesias`| ||`es-ES_Valentina Blanco`| ||`es-GQ_Elena Rubio`| ||`es-GQ_Teo Jimenez`| ||`es-GT_Ceciah Mendoza`| ||`es-GT_Paolo Ortiz`| ||`es-HN_Juana Flores`| ||`es-HN_Roberto Gutierrez`| ||`es-MX_Agata Albiol`| ||`es-MX_Darion Nunez`| ||`es-MX_Elias Lorenzo`| ||`es-MX_Elvira de Paul`| ||`es-MX_Enzo Paqueta`| ||`es-MX_Ezequiel Pacheco`| ||`es-MX_Iago Domingo`| ||`es-MX_Irene Vasquez`| ||`es-MX_Julieta Aguilar`| ||`es-MX_Lia Medina`| ||`es-MX_Nil Alvarez`| ||`es-MX_Pedro Rojas`| ||`es-MX_Rosa Valdoza`| ||`es-MX_Valencia Alba`| ||`es-MX_Veronica Mairal`| ||`es-NI_Abril Santacruz`| ||`es-NI_Lorenzo Llorente`| ||`es-PA_Liberto Marcos`| ||`es-PA_Yolanda Ezequiel`| ||`es-PE_Margarita de Fuentes`| ||`es-PE_Rey Sancho`| ||`es-PR_Alex de Santos`| ||`es-PR_Carlota Almiron`| ||`es-PY_Karina Diego`| ||`es-PY_Victor Mariela`| ||`es-SV_Jacinta Vela`| ||`es-SV_Marina Llorente`| ||`es-US_Jodrigo Alonso`| ||`es-US_Laia Paloma`| ||`es-US_Sergio Morata`| ||`es-UY_Lia Valentina`| ||`es-UY_Luis Ramon`| ||`es-VE_Manuel Rojos`| ||`es-VE_Sofia Vargas`| ||`et-EE_Barba Sepp`| ||`et-EE_Eduk Tamm`| ||`eu-ES_Markel Zubiri`| ||`eu-ES_Nahia Texpare`| ||`fa-IR_Bizhan Gilgamesh`| ||`fa-IR_Yasmina Hakimi`| ||`fi-FI_Anneli Niemnien`| ||`fi-FI_Kristiina Koskinen`| ||`fi-FI_Tapanni Korhonen`| ||`fil-PH_Amihan Reyes`| ||`fil-PH_Dennis de Saul`| ||`fr-BE_Louis Maes`| ||`fr-BE_Noah Peeters`| ||`fr-CA_Cherise DuPont`| ||`fr-CA_Olivier Varane`| ||`fr-CA_Raphael Jacques`| ||`fr-CH_Luca Dalot`| ||`fr-CH_Sylvie Gallace`| ||`fr-FR_Alain Hamel`| ||`fr-FR_Albertine Dubois`| ||`fr-FR_Antoine Petit`| ||`fr-FR_Brigitte Richard`| ||`fr-FR_Celeste Lefevre`| ||`fr-FR_Celine Bundchen`| ||`fr-FR_Damian Trezeguet`| ||`fr-FR_Diogo Pavard`| ||`fr-FR_Francoise LaFont`| ||`fr-FR_Gisele Guerin`| ||`fr-FR_Hugo Duval`| ||`fr-FR_Jacqueline Simon`| ||`fr-FR_Lois Allaire`| ||`fr-FR_Theo Bernard`| ||`ga-IE_Anja O'Brien`| ||`ga-IE_Liam Murphy`| ||`gl-ES_Clara Campos`| ||`gl-ES_Nicolas Montoya`| ||`gu-IN_Arzoo Chowdhury`| ||`gu-IN_Pramukh Barot`| ||`he-IL_Avi Goldmann`| ||`he-IL_Yael Haddad`| ||`hi-IN_Ashwin Nikhil`| ||`hi-IN_Uma Aravind`| ||`hr-HR_Krasna Perisic`| ||`hr-HR_Luka Horvat`| ||`hu-HU_Endre Szabo`| ||`hu-HU_Zoe Nagy`| ||`hy-AM_Arpi Hovhannisyan`| ||`hy-AM_Gor Grigoryan`| ||`id-ID_Bagaskoro Ulunjandi`| ||`id-ID_Diah Sukatendel`| ||`is-IS_Fridrika Sigurdsson`| ||`is-IS_Olafur Jonsdottir`| ||`it-IT_Alessandro Ferrari`| ||`it-IT_Alessia Ricci`| ||`it-IT_Allegra Greco`| ||`it-IT_Angelo Bianchi`| ||`it-IT_Antonio Colombo`| ||`it-IT_Eva De Luca`| ||`it-IT_Filomena Mancini`| ||`it-IT_Francesco Rossi`| ||`it-IT_Gaia Marino`| ||`it-IT_Gemma Conti`| ||`it-IT_Gianluigi Russo`| ||`it-IT_Greta Bruno`| ||`it-IT_Marco Romano`| ||`it-IT_Paul Esposito`| ||`it-IT_Serafina Gallo`| ||`ja-JP_Ayaka Musashi`| ||`ja-JP_Hiromi Tanaka`| ||`ja-JP_Hiroshi Maeda`| ||`ja-JP_Ichiro Sayaka`| ||`ja-JP_Naomi Sora`| ||`ja-JP_Sartoshi Juno`| ||`ja-JP_Sayuri Watanabe`| ||`jv-ID_Anom Zees`| ||`jv-ID_Bratawati Pulukadang`| ||`ka-GE_Ava Lomidze`| ||`ka-GE_Elijah Maisuradze`| ||`kk-KZ_Nurislam Omarov`| ||`kk-KZ_Rayana Kenes`| ||`km-KH_Chanthou Sok`| ||`km-KH_Kaliyanei Chea`| ||`kn-IN_Aadesh Madar`| ||`kn-IN_Rhyah Nayka`| ||`ko-KR_Eunjin Bae`| ||`ko-KR_Heechul Kim`| ||`ko-KR_Isu Choi`| ||`ko-KR_Jisoo Han`| ||`ko-KR_Meesun Kang`| ||`ko-KR_Minjoon Lee`| ||`ko-KR_Seung Hee Hwang`| ||`ko-KR_Yoosung Park`| ||`lo-LA_Lawan Vang`| ||`lo-LA_Sengphet Inthavong`| ||`lt-LT_Nojus Antanas`| ||`lt-LT_Ruta Bagdonas`| ||`lv-LV_Mozus Berzina`| ||`lv-LV_Santa Ozola`| ||`mk-MK_Berislav Stojanovski`| ||`mk-MK_Smaragda Jovanovska`| ||`ml-IN_Abha Nair`| ||`ml-IN_Akhil Kumar`| ||`mn-MN_Altan Batbayar`| ||`mn-MN_Enkhjargal Ganbold`| ||`mr-IN_Mihir Chitre`| ||`mr-IN_Vedvika Deo`| ||`ms-MY_Nur Tengku`| ||`ms-MY_Zikri Wan`| ||`mt-MT_Lola Farrugia`| ||`mt-MT_Milo Borg`| ||`my-MM_Dedan Khin`| ||`my-MM_Eindra Aung`| ||`nb-NO_Henrik Larsen`| ||`nb-NO_Vilde Hansen`| ||`nb_NO_Malin Pedersen`| ||`ne-NP_Batsal Khadka`| ||`ne-NP_Druhi Mahat`| ||`nl-BE_Arthur Mertens`| ||`nl-BE_Martine Claes`| ||`nl-NL_Adriana De Vries`| ||`nl-NL_Helena De Jong`| ||`nl-NL_Jan Visser`| ||`pl-PL_Ewa Grabowski`| ||`pl-PL_Piotr Duda`| ||`pl-PL_Zuzanna Kackz`| ||`ps-AF_Abdul-Alim Sayyid`| ||`ps-AF_Zahra Qurban`| ||`pt-BR_Adriana Rocha`| ||`pt-BR_Ana Bahiense`| ||`pt-BR_Anabella Teixeira`| ||`pt-BR_Antonia Macedo`| ||`pt-BR_Antonio Barros`| ||`pt-BR_Fernanda Pedreira`| ||`pt-BR_Francisco Guimaraes`| ||`pt-BR_Joao Azevedo`| ||`pt-BR_Jose Almeida`| ||`pt-BR_Juliana Costa`| ||`pt-BR_Marcia Ribeiro`| ||`pt-BR_Maria Cardoso`| ||`pt-BR_Paulo Correia`| ||`pt-BR_Pedro Magalhaes`| ||`pt-BR_Roberto Braga`| ||`pt-PT_Benedita Motta`| ||`pt-PT_Renato Matos`| ||`pt-PT_Rita Oliveira`| ||`ro-RO_Cristian Ionescu`| ||`ro-RO_Mirabela Gheata`| ||`ru-RU_Galina Ivanov`| ||`ru-RU_Nadezhda Smirnoff`| ||`ru-RU_Pyotr Semenov`| ||`si-LK_Kasun Perera`| ||`si-LK_Saanvi de Silva`| ||`sk-SK_Havel Varga`| ||`sk-SK_Olga Kovac`| ||`sl-SI_Neza Mlakar`| ||`sl-SI_Nik Krajnc`| ||`so-SO_Axado Ibrahim`| ||`so-SO_Taifa Mohamed`| ||`sq-AL_Bora Marku`| ||`sq-AL_Dren Kedare`| ||`sr-RS_Lara Markovic`| ||`sr-RS_Vlado Popovic`| ||`su-ID_Aarifa Bol`| ||`su-ID_Mustafa Deng`| ||`sv-SE_Agnes Lidstrom`| ||`sv-SE_Nicklas Forsberg`| ||`sv-SE_Wilma Sundin`| ||`sw-KE_Akeyo Njoroge`| ||`sw-KE_Chege Odhiambo`| ||`sw-TZ_Binti Ramadhani`| ||`sw-TZ_Darweshi Ally`| ||`ta-IN_Ashwin Kumar`| ||`ta-IN_Nila Suresh`| ||`ta-LK_Adya Pillai`| ||`ta-LK_Prahan Aachari`| ||`ta-MY_Aahna Konar`| ||`ta-MY_Kethan Nadar`| ||`ta-SG_Abilasha Naicker`| ||`ta-SG_Nemi Udayar`| ||`te-IN_Aarkash Reddy`| ||`te-IN_Tanvi Sharma`| ||`th-TH_Buppha Prasit`| ||`th-TH_Kanchana Sangthong`| ||`th-TH_Somchai Thongkham`| ||`tr-TR_Emre Ozdemir`| ||`tr-TR_Nehir Celik`| ||`uk-UA_Artem Shevchenko`| ||`uk-UA_Daryna Kovalenko`| ||`ur-IN_Farah Abbasi`| ||`ur-IN_Sharyar Alvi`| ||`ur-PK_Hamza Farooqi`| ||`ur-PK_Sana Dhanial`| ||`uz-UK_Javlonbek Yusupov`| ||`uz-UZ_Rustam Karimova`| ||`vi-VN_Huu Duong`| ||`vi-VN_Vi Ly`| ||`wuu-CN_Muchen Li`| ||`wuu-CN_Ruoxi Wang`| ||`yue-CN_Ah-lam Lei`| ||`yue-CN_Xiaoxiao Wong`| ||`zh-CN-henan_Genji Zhou`| ||`zh-CN-liaoning_Chu Zhang`| ||`zh-CN-shaanxi_Chunhua Lin`| ||`zh-CN-shandong_Jiayi Wu`| ||`zh-CN-sichuan_Juan Yang`| ||`zh-CN_An Liu`| ||`zh-CN_Bai Yang`| ||`zh-CN_Bao Huang`| ||`zh-CN_Chao Zhou`| ||`zh-CN_Chen Chen Huo`| ||`zh-CN_Cheng Sun`| ||`zh-CN_Chichi Wu`| ||`zh-CN_Chin Ma`| ||`zh-CN_Chun Hu`| ||`zh-CN_Cong Zhang`| ||`zh-CN_Da Xia Li`| ||`zh-CN_Daiyu Zhu`| ||`zh-CN_Diu Wang`| ||`zh-CN_Huan Luo`| ||`zh-CN_Huifen Chen`| ||`zh-CN_Huiqing Wang`| ||`zh-CN_Meng Li`| ||`zh-CN_Xuan Xu`| ||`zh-CN_Yifu Wu`| ||`zh-CN_Yihan Chen`| ||`zh-CN_Yinuo Zhang`| ||`zh-HK_Kun Lo`| ||`zh-HK_Lanying Lei`| ||`zh-HK_Lee Lam`| ||`zh-TW_Mingxia Luo`| ||`zh-TW_Mingzhu Gao`| ||`zh-TW_Susu Song`| ||`zu-ZA_Bonginkosi Masina`| ||`zu-ZA_Ulwazi Mangede`|

elevenlabs
|Name|Value| |----|-----| |**elevenlabs**|`de-DE_Multilingual_Callum`| ||`de-DE_Multilingual_Charlie`| ||`de-DE_Multilingual_Charlotte`| ||`de-DE_Multilingual_Clyde`| ||`de-DE_Multilingual_Daniel`| ||`de-DE_Multilingual_Dave`| ||`de-DE_Multilingual_Dorothy`| ||`de-DE_Multilingual_Emily`| ||`de-DE_Multilingual_Ethan`| ||`de-DE_Multilingual_Fin`| ||`de-DE_Multilingual_Freya`| ||`de-DE_Multilingual_Gigi`| ||`de-DE_Multilingual_Giovanni`| ||`de-DE_Multilingual_Grace`| ||`de-DE_Multilingual_Harry`| ||`de-DE_Multilingual_James`| ||`de-DE_Multilingual_Jeremy`| ||`de-DE_Multilingual_Jessie`| ||`de-DE_Multilingual_Joseph`| ||`de-DE_Multilingual_Liam`| ||`de-DE_Multilingual_Matilda`| ||`de-DE_Multilingual_Matthew`| ||`de-DE_Multilingual_Michael`| ||`de-DE_Multilingual_Mimi`| ||`de-DE_Multilingual_Nicole`| ||`de-DE_Multilingual_Patrick`| ||`de-DE_Multilingual_Ryan`| ||`de-DE_Multilingual_Serena`| ||`de-DE_Multilingual_Thomas`| ||`en-AU_Monolingual_Charlie`| ||`en-AU_Monolingual_James`| ||`en-GB_Monolingual_Daniel`| ||`en-GB_Monolingual_Dave`| ||`en-GB_Monolingual_Dorothy`| ||`en-GB_Monolingual_Joseph`| ||`en-GB_Monolingual_Matthew`| ||`en-IE_Monolingual_Fin`| ||`en-IT_Monolingual_Giovanni`| ||`en-SWE_Monolingual_Charlotte`| ||`en-SWE_Monolingual_Mimi`| ||`en-US_Monolingual_Adam`| ||`en-US_Monolingual_Antoni`| ||`en-US_Monolingual_Arnold`| ||`en-US_Monolingual_Bella`| ||`en-US_Monolingual_Callum`| ||`en-US_Monolingual_Clyde`| ||`en-US_Monolingual_Domi`| ||`en-US_Monolingual_Elli`| ||`en-US_Monolingual_Emily`| ||`en-US_Monolingual_Ethan`| ||`en-US_Monolingual_Freya`| ||`en-US_Monolingual_Gigi`| ||`en-US_Monolingual_Glinda`| ||`en-US_Monolingual_Grace`| ||`en-US_Monolingual_Harry`| ||`en-US_Monolingual_Jeremy`| ||`en-US_Monolingual_Jessie`| ||`en-US_Monolingual_Josh`| ||`en-US_Monolingual_Liam`| ||`en-US_Monolingual_Matilda`| ||`en-US_Monolingual_Michael`| ||`en-US_Monolingual_Nicole`| ||`en-US_Monolingual_Patrick`| ||`en-US_Monolingual_Rachel`| ||`en-US_Monolingual_Ryan`| ||`en-US_Monolingual_Sam`| ||`en-US_Monolingual_Sarah`| ||`en-US_Monolingual_Serena`| ||`en-US_Monolingual_Thomas`| ||`es-ES_Multilingual_Callum`| ||`es-ES_Multilingual_Charlie`| ||`es-ES_Multilingual_Charlotte`| ||`es-ES_Multilingual_Clyde`| ||`es-ES_Multilingual_Daniel`| ||`es-ES_Multilingual_Dave`| ||`es-ES_Multilingual_Dorothy`| ||`es-ES_Multilingual_Emily`| ||`es-ES_Multilingual_Ethan`| ||`es-ES_Multilingual_Fin`| ||`es-ES_Multilingual_Freya`| ||`es-ES_Multilingual_Gigi`| ||`es-ES_Multilingual_Giovanni`| ||`es-ES_Multilingual_Grace`| ||`es-ES_Multilingual_James`| ||`es-ES_Multilingual_Jeremy`| ||`es-ES_Multilingual_Jessie`| ||`es-ES_Multilingual_Joseph`| ||`es-ES_Multilingual_Liam`| ||`es-ES_Multilingual_Matilda`| ||`es-ES_Multilingual_Matthew`| ||`es-ES_Multilingual_Michael`| ||`es-ES_Multilingual_Mimi`| ||`es-ES_Multilingual_Nicole`| ||`es-ES_Multilingual_Patrick`| ||`es-ES_Multilingual_Ryan`| ||`es-ES_Multilingual_Serena`| ||`es-ES_Multilingual_Thomas`| ||`es-US_Multilingual_Harry`| ||`fr-FR_Multilingual_Callum`| ||`fr-FR_Multilingual_Charlie`| ||`fr-FR_Multilingual_Charlotte`| ||`fr-FR_Multilingual_Clyde`| ||`fr-FR_Multilingual_Daniel`| ||`fr-FR_Multilingual_Dave`| ||`fr-FR_Multilingual_Dorothy`| ||`fr-FR_Multilingual_Emily`| ||`fr-FR_Multilingual_Ethan`| ||`fr-FR_Multilingual_Fin`| ||`fr-FR_Multilingual_Freya`| ||`fr-FR_Multilingual_Gigi`| ||`fr-FR_Multilingual_Giovanni`| ||`fr-FR_Multilingual_Grace`| ||`fr-FR_Multilingual_Harry`| ||`fr-FR_Multilingual_James`| ||`fr-FR_Multilingual_Jeremy`| ||`fr-FR_Multilingual_Jessie`| ||`fr-FR_Multilingual_Joseph`| ||`fr-FR_Multilingual_Liam`| ||`fr-FR_Multilingual_Matilda`| ||`fr-FR_Multilingual_Matthew`| ||`fr-FR_Multilingual_Michael`| ||`fr-FR_Multilingual_Mimi`| ||`fr-FR_Multilingual_Nicole`| ||`fr-FR_Multilingual_Patrick`| ||`fr-FR_Multilingual_Ryan`| ||`fr-FR_Multilingual_Serena`| ||`fr-FR_Multilingual_Thomas`| ||`hi-IN_Multilingual_Callum`| ||`hi-IN_Multilingual_Charlie`| ||`hi-IN_Multilingual_Charlotte`| ||`hi-IN_Multilingual_Clyde`| ||`hi-IN_Multilingual_Daniel`| ||`hi-IN_Multilingual_Dave`| ||`hi-IN_Multilingual_Dorothy`| ||`hi-IN_Multilingual_Emily`| ||`hi-IN_Multilingual_Ethan`| ||`hi-IN_Multilingual_Fin`| ||`hi-IN_Multilingual_Freya`| ||`hi-IN_Multilingual_Gigi`| ||`hi-IN_Multilingual_Giovanni`| ||`hi-IN_Multilingual_Grace`| ||`hi-IN_Multilingual_Harry`| ||`hi-IN_Multilingual_James`| ||`hi-IN_Multilingual_Jeremy`| ||`hi-IN_Multilingual_Jessie`| ||`hi-IN_Multilingual_Joseph`| ||`hi-IN_Multilingual_Liam`| ||`hi-IN_Multilingual_Matilda`| ||`hi-IN_Multilingual_Matthew`| ||`hi-IN_Multilingual_Michael`| ||`hi-IN_Multilingual_Mimi`| ||`hi-IN_Multilingual_Nicole`| ||`hi-IN_Multilingual_Patrick`| ||`hi-IN_Multilingual_Ryan`| ||`hi-IN_Multilingual_Serena`| ||`hi-IN_Multilingual_Thomas`| ||`it-IT_Multilingual_Callum`| ||`it-IT_Multilingual_Charlie`| ||`it-IT_Multilingual_Charlotte`| ||`it-IT_Multilingual_Clyde`| ||`it-IT_Multilingual_Daniel`| ||`it-IT_Multilingual_Dave`| ||`it-IT_Multilingual_Dorothy`| ||`it-IT_Multilingual_Emily`| ||`it-IT_Multilingual_Ethan`| ||`it-IT_Multilingual_Fin`| ||`it-IT_Multilingual_Freya`| ||`it-IT_Multilingual_Gigi`| ||`it-IT_Multilingual_Giovanni`| ||`it-IT_Multilingual_Grace`| ||`it-IT_Multilingual_Harry`| ||`it-IT_Multilingual_James`| ||`it-IT_Multilingual_Jeremy`| ||`it-IT_Multilingual_Jessie`| ||`it-IT_Multilingual_Joseph`| ||`it-IT_Multilingual_Liam`| ||`it-IT_Multilingual_Matilda`| ||`it-IT_Multilingual_Matthew`| ||`it-IT_Multilingual_Michael`| ||`it-IT_Multilingual_Mimi`| ||`it-IT_Multilingual_Nicole`| ||`it-IT_Multilingual_Patrick`| ||`it-IT_Multilingual_Ryan`| ||`it-IT_Multilingual_Serena`| ||`it-IT_Multilingual_Thomas`| ||`pl-PL_Multilingual_Callum`| ||`pl-PL_Multilingual_Charlie`| ||`pl-PL_Multilingual_Charlotte`| ||`pl-PL_Multilingual_Clyde`| ||`pl-PL_Multilingual_Daniel`| ||`pl-PL_Multilingual_Dave`| ||`pl-PL_Multilingual_Dorothy`| ||`pl-PL_Multilingual_Emily`| ||`pl-PL_Multilingual_Ethan`| ||`pl-PL_Multilingual_Fin`| ||`pl-PL_Multilingual_Freya`| ||`pl-PL_Multilingual_Gigi`| ||`pl-PL_Multilingual_Giovanni`| ||`pl-PL_Multilingual_Grace`| ||`pl-PL_Multilingual_Harry`| ||`pl-PL_Multilingual_James`| ||`pl-PL_Multilingual_Jeremy`| ||`pl-PL_Multilingual_Jessie`| ||`pl-PL_Multilingual_Joseph`| ||`pl-PL_Multilingual_Liam`| ||`pl-PL_Multilingual_Matilda`| ||`pl-PL_Multilingual_Matthew`| ||`pl-PL_Multilingual_Michael`| ||`pl-PL_Multilingual_Mimi`| ||`pl-PL_Multilingual_Nicole`| ||`pl-PL_Multilingual_Patrick`| ||`pl-PL_Multilingual_Ryan`| ||`pl-PL_Multilingual_Serena`| ||`pl-PL_Multilingual_Thomas`| ||`pt-PT_Multilingual_Callum`| ||`pt-PT_Multilingual_Charlie`| ||`pt-PT_Multilingual_Charlotte`| ||`pt-PT_Multilingual_Clyde`| ||`pt-PT_Multilingual_Daniel`| ||`pt-PT_Multilingual_Dave`| ||`pt-PT_Multilingual_Dorothy`| ||`pt-PT_Multilingual_Emily`| ||`pt-PT_Multilingual_Ethan`| ||`pt-PT_Multilingual_Fin`| ||`pt-PT_Multilingual_Freya`| ||`pt-PT_Multilingual_Gigi`| ||`pt-PT_Multilingual_Giovanni`| ||`pt-PT_Multilingual_Grace`| ||`pt-PT_Multilingual_Harry`| ||`pt-PT_Multilingual_James`| ||`pt-PT_Multilingual_Jeremy`| ||`pt-PT_Multilingual_Jessie`| ||`pt-PT_Multilingual_Joseph`| ||`pt-PT_Multilingual_Liam`| ||`pt-PT_Multilingual_Matilda`| ||`pt-PT_Multilingual_Matthew`| ||`pt-PT_Multilingual_Michael`| ||`pt-PT_Multilingual_Mimi`| ||`pt-PT_Multilingual_Nicole`| ||`pt-PT_Multilingual_Patrick`| ||`pt-PT_Multilingual_Ryan`| ||`pt-PT_Multilingual_Serena`| ||`pt-PT_Multilingual_Thomas`|

openai
|Name|Value| |----|-----| |**openai**|`af_alloy`| ||`af_echo`| ||`af_fable`| ||`af_nova`| ||`af_onyx`| ||`af_shimmer`| ||`ar_alloy`| ||`ar_echo`| ||`ar_fable`| ||`ar_nova`| ||`ar_onyx`| ||`ar_shimmer`| ||`az_alloy`| ||`az_echo`| ||`az_fable`| ||`az_nova`| ||`az_onyx`| ||`az_shimmer`| ||`be_alloy`| ||`be_echo`| ||`be_fable`| ||`be_nova`| ||`be_onyx`| ||`be_shimmer`| ||`bg_alloy`| ||`bg_echo`| ||`bg_fable`| ||`bg_nova`| ||`bg_onyx`| ||`bg_shimmer`| ||`bs_alloy`| ||`bs_echo`| ||`bs_fable`| ||`bs_nova`| ||`bs_onyx`| ||`bs_shimmer`| ||`ca_alloy`| ||`ca_echo`| ||`ca_fable`| ||`ca_nova`| ||`ca_onyx`| ||`ca_shimmer`| ||`cs_alloy`| ||`cs_echo`| ||`cs_fable`| ||`cs_nova`| ||`cs_onyx`| ||`cs_shimmer`| ||`cy_alloy`| ||`cy_echo`| ||`cy_fable`| ||`cy_nova`| ||`cy_onyx`| ||`cy_shimmer`| ||`da_alloy`| ||`da_echo`| ||`da_fable`| ||`da_nova`| ||`da_onyx`| ||`da_shimmer`| ||`de_alloy`| ||`de_echo`| ||`de_fable`| ||`de_nova`| ||`de_onyx`| ||`de_shimmer`| ||`el_alloy`| ||`el_echo`| ||`el_fable`| ||`el_nova`| ||`el_onyx`| ||`el_shimmer`| ||`en_alloy`| ||`en_echo`| ||`en_fable`| ||`en_nova`| ||`en_onyx`| ||`en_shimmer`| ||`es_alloy`| ||`es_echo`| ||`es_fable`| ||`es_nova`| ||`es_onyx`| ||`es_shimmer`| ||`et_alloy`| ||`et_echo`| ||`et_fable`| ||`et_nova`| ||`et_onyx`| ||`et_shimmer`| ||`fa_alloy`| ||`fa_echo`| ||`fa_fable`| ||`fa_nova`| ||`fa_onyx`| ||`fa_shimmer`| ||`fi_alloy`| ||`fi_echo`| ||`fi_fable`| ||`fi_nova`| ||`fi_onyx`| ||`fi_shimmer`| ||`fr_alloy`| ||`fr_echo`| ||`fr_fable`| ||`fr_nova`| ||`fr_onyx`| ||`fr_shimmer`| ||`gl_alloy`| ||`gl_echo`| ||`gl_fable`| ||`gl_nova`| ||`gl_onyx`| ||`gl_shimmer`| ||`he_alloy`| ||`he_echo`| ||`he_fable`| ||`he_nova`| ||`he_onyx`| ||`he_shimmer`| ||`hi_alloy`| ||`hi_echo`| ||`hi_fable`| ||`hi_nova`| ||`hi_onyx`| ||`hi_shimmer`| ||`hr_alloy`| ||`hr_echo`| ||`hr_fable`| ||`hr_nova`| ||`hr_onyx`| ||`hr_shimmer`| ||`hu_alloy`| ||`hu_echo`| ||`hu_fable`| ||`hu_nova`| ||`hu_onyx`| ||`hu_shimmer`| ||`hy_alloy`| ||`hy_echo`| ||`hy_fable`| ||`hy_nova`| ||`hy_onyx`| ||`hy_shimmer`| ||`id_alloy`| ||`id_echo`| ||`id_fable`| ||`id_nova`| ||`id_onyx`| ||`id_shimmer`| ||`is_alloy`| ||`is_echo`| ||`is_fable`| ||`is_nova`| ||`is_onyx`| ||`is_shimmer`| ||`it_alloy`| ||`it_echo`| ||`it_fable`| ||`it_nova`| ||`it_onyx`| ||`it_shimmer`| ||`ja_alloy`| ||`ja_echo`| ||`ja_fable`| ||`ja_nova`| ||`ja_onyx`| ||`ja_shimmer`| ||`kk_alloy`| ||`kk_echo`| ||`kk_fable`| ||`kk_nova`| ||`kk_onyx`| ||`kk_shimmer`| ||`kn_alloy`| ||`kn_echo`| ||`kn_fable`| ||`kn_nova`| ||`kn_onyx`| ||`kn_shimmer`| ||`ko_alloy`| ||`ko_echo`| ||`ko_fable`| ||`ko_nova`| ||`ko_onyx`| ||`ko_shimmer`| ||`lt_alloy`| ||`lt_echo`| ||`lt_fable`| ||`lt_nova`| ||`lt_onyx`| ||`lt_shimmer`| ||`lv_alloy`| ||`lv_echo`| ||`lv_fable`| ||`lv_nova`| ||`lv_onyx`| ||`lv_shimmer`| ||`mi_alloy`| ||`mi_echo`| ||`mi_fable`| ||`mi_nova`| ||`mi_onyx`| ||`mi_shimmer`| ||`mk_alloy`| ||`mk_echo`| ||`mk_fable`| ||`mk_nova`| ||`mk_onyx`| ||`mk_shimmer`| ||`mr_alloy`| ||`mr_echo`| ||`mr_fable`| ||`mr_nova`| ||`mr_onyx`| ||`mr_shimmer`| ||`ms_alloy`| ||`ms_echo`| ||`ms_fable`| ||`ms_nova`| ||`ms_onyx`| ||`ms_shimmer`| ||`ne_alloy`| ||`ne_echo`| ||`ne_fable`| ||`ne_nova`| ||`ne_onyx`| ||`ne_shimmer`| ||`nl_alloy`| ||`nl_echo`| ||`nl_fable`| ||`nl_nova`| ||`nl_onyx`| ||`nl_shimmer`| ||`no_alloy`| ||`no_echo`| ||`no_fable`| ||`no_nova`| ||`no_onyx`| ||`no_shimmer`| ||`pl_alloy`| ||`pl_echo`| ||`pl_fable`| ||`pl_nova`| ||`pl_onyx`| ||`pl_shimmer`| ||`pt_alloy`| ||`pt_echo`| ||`pt_fable`| ||`pt_nova`| ||`pt_onyx`| ||`pt_shimmer`| ||`ro_alloy`| ||`ro_echo`| ||`ro_fable`| ||`ro_nova`| ||`ro_onyx`| ||`ro_shimmer`| ||`ru_alloy`| ||`ru_echo`| ||`ru_fable`| ||`ru_nova`| ||`ru_onyx`| ||`ru_shimmer`| ||`sk_alloy`| ||`sk_echo`| ||`sk_fable`| ||`sk_nova`| ||`sk_onyx`| ||`sk_shimmer`| ||`sl_alloy`| ||`sl_echo`| ||`sl_fable`| ||`sl_nova`| ||`sl_onyx`| ||`sl_shimmer`| ||`sr_alloy`| ||`sr_echo`| ||`sr_fable`| ||`sr_nova`| ||`sr_onyx`| ||`sr_shimmer`| ||`sv_alloy`| ||`sv_echo`| ||`sv_fable`| ||`sv_nova`| ||`sv_onyx`| ||`sv_shimmer`| ||`sw_alloy`| ||`sw_echo`| ||`sw_fable`| ||`sw_nova`| ||`sw_onyx`| ||`sw_shimmer`| ||`ta_alloy`| ||`ta_echo`| ||`ta_fable`| ||`ta_nova`| ||`ta_onyx`| ||`ta_shimmer`| ||`th_alloy`| ||`th_echo`| ||`th_fable`| ||`th_nova`| ||`th_onyx`| ||`th_shimmer`| ||`tl_alloy`| ||`tl_echo`| ||`tl_fable`| ||`tl_nova`| ||`tl_onyx`| ||`tl_shimmer`| ||`tr_alloy`| ||`tr_echo`| ||`tr_fable`| ||`tr_nova`| ||`tr_onyx`| ||`tr_shimmer`| ||`uk_alloy`| ||`uk_echo`| ||`uk_fable`| ||`uk_nova`| ||`uk_onyx`| ||`uk_shimmer`| ||`ur_alloy`| ||`ur_echo`| ||`ur_fable`| ||`ur_nova`| ||`ur_onyx`| ||`ur_shimmer`| ||`vi_alloy`| ||`vi_echo`| ||`vi_fable`| ||`vi_nova`| ||`vi_onyx`| ||`vi_shimmer`| ||`zh_alloy`| ||`zh_echo`| ||`zh_fable`| ||`zh_nova`| ||`zh_onyx`| ||`zh_shimmer`|

# paths./audio/text_to_speech_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./audio/text_to_speech_async.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**lovoai**|-|`v1`|0.16 (per 1000 char)|1 char |**amazon**|-|`boto3 (v1.15.18)`|4.0 (per 1000000 char)|1 char |**amazon**|**Neural**|`boto3 (v1.15.18)`|16.0 (per 1000000 char)|1 char

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Azerbaijani**|`az`| |**Basque**|`eu`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Mandarin Chinese**|`cmn`| |**Marathi**|`mr`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian Bokmål**|`nb`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Serbian**|`sr`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Standard Arabic**|`arb`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Wu Chinese**|`wuu`| |**Xhosa**|`xh`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Afrikaans (South Africa)**|`af-ZA`| |**Albanian (Albania)**|`sq-AL`| |**Amharic (Ethiopia)**|`am-ET`| |**Arabic (Algeria)**|`ar-DZ`| |**Arabic (Bahrain)**|`ar-BH`| |**Arabic (Egypt)**|`ar-EG`| |**Arabic (Iraq)**|`ar-IQ`| |**Arabic (Jordan)**|`ar-JO`| |**Arabic (Kuwait)**|`ar-KW`| |**Arabic (Lebanon)**|`ar-LB`| |**Arabic (Libya)**|`ar-LY`| |**Arabic (Morocco)**|`ar-MA`| |**Arabic (Oman)**|`ar-OM`| |**Arabic (Qatar)**|`ar-QA`| |**Arabic (Saudi Arabia)**|`ar-SA`| |**Arabic (Tunisia)**|`ar-TN`| |**Arabic (United Arab Emirates)**|`ar-AE`| |**Armenian (Armenia)**|`hy-AM`| |**Azerbaijani (Azerbaijan)**|`az-AZ`| |**Bangla (India)**|`bn-IN`| |**Basque (Spain)**|`eu-ES`| |**Bosnian (Bosnia & Herzegovina)**|`bs-BA`| |**Burmese (Myanmar (Burma))**|`my-MM`| |**Cantonese (China)**|`yue-CN`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-CN`| |**Chinese (China)**|`zh-CN-henan`| |**Chinese (China)**|`zh-CN-shandong`| |**Chinese (China)**|`zh-CN-sichuan`| |**Chinese (Hong Kong SAR China)**|`zh-HK`| |**Chinese (Taiwan)**|`zh-TW`| |**Croatian (Croatia)**|`hr-HR`| |**Czech (Czechia)**|`cs-CZ`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Belgium)**|`nl-BE`| |**Dutch (Netherlands)**|`nl-NL`| |**English (Australia)**|`en-AU`| |**English (Canada)**|`en-CA`| |**English (Curaçao)**|`en-AN`| |**English (Hong Kong SAR China)**|`en-HK`| |**English (India)**|`en-IN`| |**English (Ireland)**|`en-IE`| |**English (Kenya)**|`en-KE`| |**English (New Zealand)**|`en-NZ`| |**English (Nigeria)**|`en-NG`| |**English (Philippines)**|`en-PH`| |**English (Singapore)**|`en-SG`| |**English (South Africa)**|`en-ZA`| |**English (Tanzania)**|`en-TZ`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**Estonian (Estonia)**|`et-EE`| |**Filipino (Philippines)**|`fil-PH`| |**Finnish (Finland)**|`fi-FI`| |**French (Belgium)**|`fr-BE`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**French (Switzerland)**|`fr-CH`| |**Galician (Spain)**|`gl-ES`| |**Georgian (Georgia)**|`ka-GE`| |**German (Austria)**|`de-AT`| |**German (Germany)**|`de-DE`| |**German (Switzerland)**|`de-CH`| |**Hebrew (Israel)**|`he-IL`| |**Hindi (India)**|`hi-IN`| |**Hungarian (Hungary)**|`hu-HU`| |**Icelandic (Iceland)**|`is-IS`| |**Indonesian (Indonesia)**|`id-ID`| |**Irish (Ireland)**|`ga-IE`| |**Italian (Italy)**|`it-IT`| |**Japanese (Japan)**|`ja-JP`| |**Javanese (Indonesia)**|`jv-ID`| |**Kazakh (Kazakhstan)**|`kk-KZ`| |**Khmer (Cambodia)**|`km-KH`| |**Korean (South Korea)**|`ko-KR`| |**Lao (Laos)**|`lo-LA`| |**Latvian (Latvia)**|`lv-LV`| |**Lithuanian (Lithuania)**|`lt-LT`| |**Macedonian (North Macedonia)**|`mk-MK`| |**Malay (Malaysia)**|`ms-MY`| |**Malayalam (India)**|`ml-IN`| |**Maltese (Malta)**|`mt-MT`| |**Mandarin Chinese (China)**|`cmn-CN`| |**Marathi (India)**|`mr-IN`| |**Mongolian (Mongolia)**|`mn-MN`| |**Nepali (Nepal)**|`ne-NP`| |**Norwegian Bokmål (Norway)**|`nb-NO`| |**Pashto (Afghanistan)**|`ps-AF`| |**Persian (Iran)**|`fa-IR`| |**Polish (Poland)**|`pl-PL`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`| |**Romanian (Romania)**|`ro-RO`| |**Russian (Russia)**|`ru-RU`| |**Serbian (Serbia)**|`sr-RS`| |**Sinhala (Sri Lanka)**|`si-LK`| |**Slovak (Slovakia)**|`sk-SK`| |**Slovenian (Slovenia)**|`sl-SI`| |**Somali (Somalia)**|`so-SO`| |**Spanish (Argentina)**|`es-AR`| |**Spanish (Bolivia)**|`es-BO`| |**Spanish (Chile)**|`es-CL`| |**Spanish (Colombia)**|`es-CO`| |**Spanish (Costa Rica)**|`es-CR`| |**Spanish (Cuba)**|`es-CU`| |**Spanish (Ecuador)**|`es-EC`| |**Spanish (El Salvador)**|`es-SV`| |**Spanish (Equatorial Guinea)**|`es-GQ`| |**Spanish (Guatemala)**|`es-GT`| |**Spanish (Mexico)**|`es-MX`| |**Spanish (Nicaragua)**|`es-NI`| |**Spanish (Panama)**|`es-PA`| |**Spanish (Paraguay)**|`es-PY`| |**Spanish (Puerto Rico)**|`es-PR`| |**Spanish (Spain)**|`es-ES`| |**Spanish (United States)**|`es-US`| |**Spanish (Uruguay)**|`es-UY`| |**Spanish (Venezuela)**|`es-VE`| |**Sundanese (Indonesia)**|`su-ID`| |**Swahili (Kenya)**|`sw-KE`| |**Swahili (Tanzania)**|`sw-TZ`| |**Swedish (Sweden)**|`sv-SE`| |**Tamil (India)**|`ta-IN`| |**Tamil (Malaysia)**|`ta-MY`| |**Tamil (Singapore)**|`ta-SG`| |**Telugu (India)**|`te-IN`| |**Thai (Thailand)**|`th-TH`| |**Turkish (Türkiye)**|`tr-TR`| |**Ukrainian (Ukraine)**|`uk-UA`| |**Urdu (India)**|`ur-IN`| |**Urdu (Pakistan)**|`ur-PK`| |**Uzbek (United Kingdom)**|`uz-UK`| |**Vietnamese (Vietnam)**|`vi-VN`| |**Welsh (United Kingdom)**|`cy-GB`| |**Wu Chinese (China)**|`wuu-CN`| |**Xhosa (South Africa)**|`xh-ZA`| |**Zulu (South Africa)**|`zu-ZA`|

Supported Models

lovoai
|Name|Value| |----|-----| |**lovoai**|`af-ZA_Albertus Ruan`| ||`af-ZA_Danelle Wessel`| ||`am-ET_Abai Berhe`| ||`am-ET_Cherenet Tesfaye`| ||`ar-AE_Mansour Ahmed`| ||`ar-AE_Maryam Khan`| ||`ar-BH_Fatima Kumar`| ||`ar-BH_Omar Hassan`| ||`ar-DZ_Samia Touati`| ||`ar-DZ_Zuthimalin Brahimi`| ||`ar-EG_Ahmed Gamal`| ||`ar-EG_Reem Salah`| ||`ar-IQ_Aveen Majid`| ||`ar-IQ_Ismail Hashem`| ||`ar-JO_Fatima Jaber`| ||`ar-JO_Yousef Saleh`| ||`ar-KW_Areej Nair`| ||`ar-KW_Khaled Al Azmi`| ||`ar-LB_Abdel El Din`| ||`ar-LB_Eessa Kadifa`| ||`ar-LY_Abir Salem`| ||`ar-LY_Ahsan Omar`| ||`ar-MA_Hamid Bennani`| ||`ar-MA_Salma Naciri`| ||`ar-OM_Jabbar Singh`| ||`ar-OM_Zahra Sultan`| ||`ar-QA_Faizur Kumar`| ||`ar-QA_Noora Hussain`| ||`ar-SA_Majidah Khan`| ||`ar-SA_Omar Aziz`| ||`ar-SY_Oraida El-Assad`| ||`ar-SY_Rabah Ibrahim`| ||`ar-TN_Donia Cherif`| ||`ar-TN_Karim Dridi`| ||`ar-YE_Mansour Kasim`| ||`ar-YE_Mehdi Bawazeer`| ||`az-AZ_Ugur Quliyeva`| ||`az_AZ_Zeynab Cafarova`| ||`bg-BG_Damyan Ivanov`| ||`bg-BG_Fidanka Georgiev`| ||`bn-BD_Pranshu Akter`| ||`bn-BD_Vaagdevi Khatun`| ||`bn-IN_Gazi Mondal`| ||`bn-IN_Wali Ghosh`| ||`bs-BA_Esma Dodik`| ||`bs-BA_Ismet Rakic`| ||`ca-ES_Amada Fernando`| ||`ca-ES_Carmen Santiago`| ||`ca-ES_Miguel Torres`| ||`cs-CZ_Jana Rosicky`| ||`cs-CZ_Tomas Malecek`| ||`cy-GB_Branwen Jones`| ||`cy-GB_Elen Hughes`| ||`da-DK_Bjarne Jensen`| ||`da-DK_Helge Nielsen`| ||`de-AT_Lena Gruber`| ||`de-AT_Wilhelm Wagner`| ||`de-CH_Adolfus Meier`| ||`de-CH_Lara Keller`| ||`de-DE_Ada Weber`| ||`de-DE_Anna Schmidt`| ||`de-DE_Emma Muller`| ||`de-DE_Gerda Becker`| ||`de-DE_Hans Schulz`| ||`de-DE_Heidi Schneider`| ||`de-DE_Johanna Meyer`| ||`de-DE_Joshua Bauer`| ||`de-DE_Julian Koch`| ||`de-DE_Karl Hummels`| ||`de-DE_Maria Fischer`| ||`de-DE_Oliver Richter`| ||`de-DE_Otto Schaefer`| ||`de-DE_Petra Hoffman`| ||`de-DE_Walter Kimmich`| ||`el-GR_Petros Andino`| ||`el-GR_Thalia Klisiaris`| ||`en-AU_Amelia Taylor`| ||`en-AU_Charlotte Brown`| ||`en-AU_Darrell Robinson`| ||`en-AU_George Thompson`| ||`en-AU_Georgie Smith`| ||`en-AU_Isla Johnson`| ||`en-AU_Jake Nguyen`| ||`en-AU_Keegan Walker`| ||`en-AU_Kelly Opie`| ||`en-AU_Kevin Turner`| ||`en-AU_Mia White`| ||`en-AU_Nancy Jones`| ||`en-AU_Ryan Murphy`| ||`en-AU_Willow Martin`| ||`en-CA_Emily Salo`| ||`en-CA_Eric Fidyk`| ||`en-GB_Abigail Fraser`| ||`en-GB_Annie Smith`| ||`en-GB_Gertrude Baker`| ||`en-GB_Ian Kensington`| ||`en-GB_Kane Tooney`| ||`en-GB_Kelsey Michaels`| ||`en-GB_Kerrington Pacey`| ||`en-GB_Lizzy Wright`| ||`en-GB_Marcus O'Donell`| ||`en-GB_Nathaniel Jacobs`| ||`en-GB_Samuel Lee-Richards`| ||`en-GB_T.S. Cooper`| ||`en-GB_Theresa King`| ||`en-GB_William Tripp`| ||`en-HK_Heather Yiu`| ||`en-HK_Kevin Lau`| ||`en-IE_Aoife Byrne`| ||`en-IE_Bill Parkin`| ||`en-IN_Isha Singh`| ||`en-IN_Prabhdeep Patel`| ||`en-KE_Almasi Otieno`| ||`en-KE_Chitundu Mwangi`| ||`en-NG_Blessing Musa`| ||`en-NG_Kehinde Sade`| ||`en-NZ_Benson Duncan`| ||`en-NZ_Destiny Mitchell`| ||`en-PH_Angel dela Cruz`| ||`en-PH_Francis Reynaldo`| ||`en-SG_Chris Tan`| ||`en-SG_Rachel Ng`| ||`en-TZ_Busara Charles`| ||`en-TZ_Darweshi Juma`| ||`en-US_Alysha Imani`| ||`en-US_Betty Parker`| ||`en-US_Catherine Zania`| ||`en-US_Christopher Navarrez`| ||`en-US_Clara Ho`| ||`en-US_Eric Gonzalez`| ||`en-US_Heather Everett`| ||`en-US_Jamal Starke`| ||`en-US_Jasonna Johnson`| ||`en-US_Kaylee Montana`| ||`en-US_Ken hunter`| ||`en-US_Kim Howard`| ||`en-US_Kyle Moreno`| ||`en-US_Leroy Alshak`| ||`en-US_Micah Washington`| ||`en-US_Molly Richardson`| ||`en-US_Peter Lee`| ||`en-US_Phil Gough`| ||`en-US_Phoebe Nicholson`| ||`en-US_Samantha Hawthorne`| ||`en-US_Sean Orson`| ||`en-US_Serena Yang`| ||`en-US_Shannon Mechare`| ||`en-US_Thelma Browne`| ||`en-US_Tim Hardway`| ||`en-ZA_Cody Fergudson`| ||`en-ZA_Elna VanDijk`| ||`es-AR_Hyacinthe Castro`| ||`es-AR_Lautaro Gomez`| ||`es-BO_Elena Lopez`| ||`es-BO_Juan Perez`| ||`es-CL_Francisca Batistuta`| ||`es-CL_Gabriel Rodriguez`| ||`es-CO_Lorenzo Vazquez`| ||`es-CO_Sofia Garcia`| ||`es-CR_Guadalupe Suarez`| ||`es-CR_Sebastian Ramos`| ||`es-CU_Isabel Molina`| ||`es-CU_Luis Ortega`| ||`es-DO_Gabriela Serrano`| ||`es-DO_Raul Dominguez`| ||`es-EC_Angelina Romero`| ||`es-EC_Christian Diaz`| ||`es-EN_Carmen Martinela`| ||`es-ES_Andres Marin`| ||`es-ES_Emiliano Delgado`| ||`es-ES_Esmeralda Molina`| ||`es-ES_Hector Gavi`| ||`es-ES_Leo Gil`| ||`es-ES_Liliana Sanz`| ||`es-ES_Maria Ruiz`| ||`es-ES_Martin Enrique`| ||`es-ES_Miranda Navarro`| ||`es-ES_Pablo Iniesta`| ||`es-ES_Silvia Alvarez`| ||`es-ES_Teresa Iglesias`| ||`es-ES_Valentina Blanco`| ||`es-GQ_Elena Rubio`| ||`es-GQ_Teo Jimenez`| ||`es-GT_Ceciah Mendoza`| ||`es-GT_Paolo Ortiz`| ||`es-HN_Juana Flores`| ||`es-HN_Roberto Gutierrez`| ||`es-MX_Agata Albiol`| ||`es-MX_Darion Nunez`| ||`es-MX_Elias Lorenzo`| ||`es-MX_Elvira de Paul`| ||`es-MX_Enzo Paqueta`| ||`es-MX_Ezequiel Pacheco`| ||`es-MX_Iago Domingo`| ||`es-MX_Irene Vasquez`| ||`es-MX_Julieta Aguilar`| ||`es-MX_Lia Medina`| ||`es-MX_Nil Alvarez`| ||`es-MX_Pedro Rojas`| ||`es-MX_Rosa Valdoza`| ||`es-MX_Valencia Alba`| ||`es-MX_Veronica Mairal`| ||`es-NI_Abril Santacruz`| ||`es-NI_Lorenzo Llorente`| ||`es-PA_Liberto Marcos`| ||`es-PA_Yolanda Ezequiel`| ||`es-PE_Margarita de Fuentes`| ||`es-PE_Rey Sancho`| ||`es-PR_Alex de Santos`| ||`es-PR_Carlota Almiron`| ||`es-PY_Karina Diego`| ||`es-PY_Victor Mariela`| ||`es-SV_Jacinta Vela`| ||`es-SV_Marina Llorente`| ||`es-US_Jodrigo Alonso`| ||`es-US_Laia Paloma`| ||`es-US_Sergio Morata`| ||`es-UY_Lia Valentina`| ||`es-UY_Luis Ramon`| ||`es-VE_Manuel Rojos`| ||`es-VE_Sofia Vargas`| ||`et-EE_Barba Sepp`| ||`et-EE_Eduk Tamm`| ||`eu-ES_Markel Zubiri`| ||`eu-ES_Nahia Texpare`| ||`fa-IR_Bizhan Gilgamesh`| ||`fa-IR_Yasmina Hakimi`| ||`fi-FI_Anneli Niemnien`| ||`fi-FI_Kristiina Koskinen`| ||`fi-FI_Tapanni Korhonen`| ||`fil-PH_Amihan Reyes`| ||`fil-PH_Dennis de Saul`| ||`fr-BE_Louis Maes`| ||`fr-BE_Noah Peeters`| ||`fr-CA_Cherise DuPont`| ||`fr-CA_Olivier Varane`| ||`fr-CA_Raphael Jacques`| ||`fr-CH_Luca Dalot`| ||`fr-CH_Sylvie Gallace`| ||`fr-FR_Alain Hamel`| ||`fr-FR_Albertine Dubois`| ||`fr-FR_Antoine Petit`| ||`fr-FR_Brigitte Richard`| ||`fr-FR_Celeste Lefevre`| ||`fr-FR_Celine Bundchen`| ||`fr-FR_Damian Trezeguet`| ||`fr-FR_Diogo Pavard`| ||`fr-FR_Francoise LaFont`| ||`fr-FR_Gisele Guerin`| ||`fr-FR_Hugo Duval`| ||`fr-FR_Jacqueline Simon`| ||`fr-FR_Lois Allaire`| ||`fr-FR_Theo Bernard`| ||`ga-IE_Anja O'Brien`| ||`ga-IE_Liam Murphy`| ||`gl-ES_Clara Campos`| ||`gl-ES_Nicolas Montoya`| ||`gu-IN_Arzoo Chowdhury`| ||`gu-IN_Pramukh Barot`| ||`he-IL_Avi Goldmann`| ||`he-IL_Yael Haddad`| ||`hi-IN_Ashwin Nikhil`| ||`hi-IN_Uma Aravind`| ||`hr-HR_Krasna Perisic`| ||`hr-HR_Luka Horvat`| ||`hu-HU_Endre Szabo`| ||`hu-HU_Zoe Nagy`| ||`hy-AM_Arpi Hovhannisyan`| ||`hy-AM_Gor Grigoryan`| ||`id-ID_Bagaskoro Ulunjandi`| ||`id-ID_Diah Sukatendel`| ||`is-IS_Fridrika Sigurdsson`| ||`is-IS_Olafur Jonsdottir`| ||`it-IT_Alessandro Ferrari`| ||`it-IT_Alessia Ricci`| ||`it-IT_Allegra Greco`| ||`it-IT_Angelo Bianchi`| ||`it-IT_Antonio Colombo`| ||`it-IT_Eva De Luca`| ||`it-IT_Filomena Mancini`| ||`it-IT_Francesco Rossi`| ||`it-IT_Gaia Marino`| ||`it-IT_Gemma Conti`| ||`it-IT_Gianluigi Russo`| ||`it-IT_Greta Bruno`| ||`it-IT_Marco Romano`| ||`it-IT_Paul Esposito`| ||`it-IT_Serafina Gallo`| ||`ja-JP_Ayaka Musashi`| ||`ja-JP_Hiromi Tanaka`| ||`ja-JP_Hiroshi Maeda`| ||`ja-JP_Ichiro Sayaka`| ||`ja-JP_Naomi Sora`| ||`ja-JP_Sartoshi Juno`| ||`ja-JP_Sayuri Watanabe`| ||`jv-ID_Anom Zees`| ||`jv-ID_Bratawati Pulukadang`| ||`ka-GE_Ava Lomidze`| ||`ka-GE_Elijah Maisuradze`| ||`kk-KZ_Nurislam Omarov`| ||`kk-KZ_Rayana Kenes`| ||`km-KH_Chanthou Sok`| ||`km-KH_Kaliyanei Chea`| ||`kn-IN_Aadesh Madar`| ||`kn-IN_Rhyah Nayka`| ||`ko-KR_Eunjin Bae`| ||`ko-KR_Heechul Kim`| ||`ko-KR_Isu Choi`| ||`ko-KR_Jisoo Han`| ||`ko-KR_Meesun Kang`| ||`ko-KR_Minjoon Lee`| ||`ko-KR_Seung Hee Hwang`| ||`ko-KR_Yoosung Park`| ||`lo-LA_Lawan Vang`| ||`lo-LA_Sengphet Inthavong`| ||`lt-LT_Nojus Antanas`| ||`lt-LT_Ruta Bagdonas`| ||`lv-LV_Mozus Berzina`| ||`lv-LV_Santa Ozola`| ||`mk-MK_Berislav Stojanovski`| ||`mk-MK_Smaragda Jovanovska`| ||`ml-IN_Abha Nair`| ||`ml-IN_Akhil Kumar`| ||`mn-MN_Altan Batbayar`| ||`mn-MN_Enkhjargal Ganbold`| ||`mr-IN_Mihir Chitre`| ||`mr-IN_Vedvika Deo`| ||`ms-MY_Nur Tengku`| ||`ms-MY_Zikri Wan`| ||`mt-MT_Lola Farrugia`| ||`mt-MT_Milo Borg`| ||`my-MM_Dedan Khin`| ||`my-MM_Eindra Aung`| ||`nb-NO_Henrik Larsen`| ||`nb-NO_Vilde Hansen`| ||`nb_NO_Malin Pedersen`| ||`ne-NP_Batsal Khadka`| ||`ne-NP_Druhi Mahat`| ||`nl-BE_Arthur Mertens`| ||`nl-BE_Martine Claes`| ||`nl-NL_Adriana De Vries`| ||`nl-NL_Helena De Jong`| ||`nl-NL_Jan Visser`| ||`pl-PL_Ewa Grabowski`| ||`pl-PL_Piotr Duda`| ||`pl-PL_Zuzanna Kackz`| ||`ps-AF_Abdul-Alim Sayyid`| ||`ps-AF_Zahra Qurban`| ||`pt-BR_Adriana Rocha`| ||`pt-BR_Ana Bahiense`| ||`pt-BR_Anabella Teixeira`| ||`pt-BR_Antonia Macedo`| ||`pt-BR_Antonio Barros`| ||`pt-BR_Fernanda Pedreira`| ||`pt-BR_Francisco Guimaraes`| ||`pt-BR_Joao Azevedo`| ||`pt-BR_Jose Almeida`| ||`pt-BR_Juliana Costa`| ||`pt-BR_Marcia Ribeiro`| ||`pt-BR_Maria Cardoso`| ||`pt-BR_Paulo Correia`| ||`pt-BR_Pedro Magalhaes`| ||`pt-BR_Roberto Braga`| ||`pt-PT_Benedita Motta`| ||`pt-PT_Renato Matos`| ||`pt-PT_Rita Oliveira`| ||`ro-RO_Cristian Ionescu`| ||`ro-RO_Mirabela Gheata`| ||`ru-RU_Galina Ivanov`| ||`ru-RU_Nadezhda Smirnoff`| ||`ru-RU_Pyotr Semenov`| ||`si-LK_Kasun Perera`| ||`si-LK_Saanvi de Silva`| ||`sk-SK_Havel Varga`| ||`sk-SK_Olga Kovac`| ||`sl-SI_Neza Mlakar`| ||`sl-SI_Nik Krajnc`| ||`so-SO_Axado Ibrahim`| ||`so-SO_Taifa Mohamed`| ||`sq-AL_Bora Marku`| ||`sq-AL_Dren Kedare`| ||`sr-RS_Lara Markovic`| ||`sr-RS_Vlado Popovic`| ||`su-ID_Aarifa Bol`| ||`su-ID_Mustafa Deng`| ||`sv-SE_Agnes Lidstrom`| ||`sv-SE_Nicklas Forsberg`| ||`sv-SE_Wilma Sundin`| ||`sw-KE_Akeyo Njoroge`| ||`sw-KE_Chege Odhiambo`| ||`sw-TZ_Binti Ramadhani`| ||`sw-TZ_Darweshi Ally`| ||`ta-IN_Ashwin Kumar`| ||`ta-IN_Nila Suresh`| ||`ta-LK_Adya Pillai`| ||`ta-LK_Prahan Aachari`| ||`ta-MY_Aahna Konar`| ||`ta-MY_Kethan Nadar`| ||`ta-SG_Abilasha Naicker`| ||`ta-SG_Nemi Udayar`| ||`te-IN_Aarkash Reddy`| ||`te-IN_Tanvi Sharma`| ||`th-TH_Buppha Prasit`| ||`th-TH_Kanchana Sangthong`| ||`th-TH_Somchai Thongkham`| ||`tr-TR_Emre Ozdemir`| ||`tr-TR_Nehir Celik`| ||`uk-UA_Artem Shevchenko`| ||`uk-UA_Daryna Kovalenko`| ||`ur-IN_Farah Abbasi`| ||`ur-IN_Sharyar Alvi`| ||`ur-PK_Hamza Farooqi`| ||`ur-PK_Sana Dhanial`| ||`uz-UK_Javlonbek Yusupov`| ||`uz-UZ_Rustam Karimova`| ||`vi-VN_Huu Duong`| ||`vi-VN_Vi Ly`| ||`wuu-CN_Muchen Li`| ||`wuu-CN_Ruoxi Wang`| ||`yue-CN_Ah-lam Lei`| ||`yue-CN_Xiaoxiao Wong`| ||`zh-CN-henan_Genji Zhou`| ||`zh-CN-liaoning_Chu Zhang`| ||`zh-CN-shaanxi_Chunhua Lin`| ||`zh-CN-shandong_Jiayi Wu`| ||`zh-CN-sichuan_Juan Yang`| ||`zh-CN_An Liu`| ||`zh-CN_Bai Yang`| ||`zh-CN_Bao Huang`| ||`zh-CN_Chao Zhou`| ||`zh-CN_Chen Chen Huo`| ||`zh-CN_Cheng Sun`| ||`zh-CN_Chichi Wu`| ||`zh-CN_Chin Ma`| ||`zh-CN_Chun Hu`| ||`zh-CN_Cong Zhang`| ||`zh-CN_Da Xia Li`| ||`zh-CN_Daiyu Zhu`| ||`zh-CN_Diu Wang`| ||`zh-CN_Huan Luo`| ||`zh-CN_Huifen Chen`| ||`zh-CN_Huiqing Wang`| ||`zh-CN_Meng Li`| ||`zh-CN_Xuan Xu`| ||`zh-CN_Yifu Wu`| ||`zh-CN_Yihan Chen`| ||`zh-CN_Yinuo Zhang`| ||`zh-HK_Kun Lo`| ||`zh-HK_Lanying Lei`| ||`zh-HK_Lee Lam`| ||`zh-TW_Mingxia Luo`| ||`zh-TW_Mingzhu Gao`| ||`zh-TW_Susu Song`| ||`zu-ZA_Bonginkosi Masina`| ||`zu-ZA_Ulwazi Mangede`|

amazon
|Name|Value| |----|-----| |**amazon**|`Neural`|

# paths./audio/text_to_speech_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./audio/text_to_speech_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./image/anonymization.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**api4ai**|`v1.0.0`|25.0 (per 1000 file)|1 file

# paths./image/automl_classification/create_project.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nyckel**|`v1.0.0`|free|-

# paths./image/automl_classification/delete_project.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nyckel**|`v1.0.0`|free|-

# paths./image/automl_classification/predict_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./image/automl_classification/predict_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nyckel**|`v1.0.0`|0.005 (per 1 request)|1 request

# paths./image/automl_classification/predict_async.post.summary
Automl Classification Predict Launch Job

# paths./image/automl_classification/predict_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./image/automl_classification/predict_async/{public_id}.get.summary
Automl Classification - Predict Get Job Results

# paths./image/automl_classification/train_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./image/automl_classification/train_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nyckel**|`v1.0.0`|free|-

# paths./image/automl_classification/train_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./image/automl_classification/train_async/{public_id}.get.summary
Automl Classification - Train Get Job Results

# paths./image/automl_classification/upload_data_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./image/automl_classification/upload_data_async.get.summary
Automl Classification Upload Data List Job

# paths./image/automl_classification/upload_data_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nyckel**|`v1.0.0`|0.0005 (per 1 file)|1 file

# paths./image/automl_classification/upload_data_async.post.summary
Automl Classification Upload Data Launch Job

# paths./image/automl_classification/upload_data_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./image/automl_classification/upload_data_async/{public_id}.get.summary
Automl Classification - Upload Data Get Job Results

# paths./image/background_removal.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**api4ai**|`v1.0.0`|50.0 (per 1000 file)|1 file |**microsoft**|`v3.2`|1.0 (per 1000 file)|1 file |**photoroom**|`v1`|16.0 (per 1000 file)|1 file |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file

# paths./image/embeddings.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**alephalpha**|-|`1.12.0`|0.05 (per 1 file)|1 file |**alephalpha**|**luminous-base**|`1.12.0`|0.05 (per 1 file)|1 file

Supported Languages
|Name|Value| |----|-----| |**English**|`en`| |**French**|`fr`| |**German**|`de`| |**Italian**|`it`| |**Spanish**|`es`|

Supported Models

alephalpha
|Name|Value| |----|-----| |**alephalpha**|`luminous-base`|

# paths./image/explicit_content.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000 file)|1 file |**clarifai**|`8.0.0`|2.0 (per 1000 file)|1 file |**google**|`v1`|1.5 (per 1000 file)|1 file |**microsoft**|`v3.2`|1.0 (per 1000 file)|1 file |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file |**picpurify**|`v1.1`|2.0 (per 1000 file)|1 file |**api4ai**|`v1.0.0`|0.75 (per 1000 file)|1 file

# paths./image/face_compare.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**base64**|`latest`|0.25 (per 1 request)|1 request |**facepp**|`v3`|2.0 (per 1000 request)|1 request |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000 request)|1 request

# paths./image/face_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000 file)|1 file |**clarifai**|`8.0.0`|2.0 (per 1000 file)|1 file |**google**|`v1`|1.5 (per 1000 file)|1 file |**microsoft**|`v3.2`|1.0 (per 1000 file)|1 file |**api4ai**|`v1.0.0`|0.75 (per 1000 file)|1 file |**picpurify**|`v1.1`|1.2 (per 1000 file)|1 file |**skybiometry**|`v1`|0.7 (per 1000 file)|1 file

# paths./image/face_recognition/add_face.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|1.0 (per 1000 file)|1 file |**facepp**|`v3`|0.6 (per 1000 request)|1 request

# paths./image/face_recognition/delete_face.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|free|- |**facepp**|`v3`|0.1 (per 1000 request)|1 request

# paths./image/face_recognition/list_faces.get.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|free|- |**facepp**|`v3`|0.1 (per 1000 request)|1 request

# paths./image/face_recognition/list_faces.get.parameters[].description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# paths./image/face_recognition/list_faces.get.parameters[].description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# paths./image/face_recognition/list_faces.get.parameters[].description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# paths./image/face_recognition/list_faces.get.parameters[].description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# paths./image/face_recognition/list_faces.get.parameters[].description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# paths./image/face_recognition/list_faces.get.parameters[].description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# paths./image/face_recognition/recognize.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|1.0 (per 1000 file)|1 file |**facepp**|`v3`|2.0 (per 1000 request)|1 request

# paths./image/generation.post.description
Available Providers
|Provider|Model|Version|Resolution|Price|Billing unit| |----|----|-------|------|-----|------------| |**deepai**|-|`v1Beta`|`512x512`|0.05 (per 1 request)|1 request |**deepai**|-|`v1Beta`|`256x256`|0.05 (per 1 request)|1 request |**openai**|**dall-e-3**|`v1Beta`|`1024x1024`|0.04 (per 1 image)|1 image |**openai**|**dall-e-3**|`v1Beta`|`512x512`|0.04 (per 1 image)|1 image |**openai**|**dall-e-2**|`v1Beta`|`256x256`|0.016 (per 1 image)|1 image |**openai**|**dall-e-2**|`v1Beta`|`512x512`|0.018 (per 1 image)|1 image |**openai**|**dall-e-2**|`v1Beta`|`1024x1024`|0.02 (per 1 image)|1 image |**openai**|**dall-e-3**|`v1Beta`|`1024x1792`|0.08 (per 1 image)|1 image |**openai**|**dall-e-3**|`v1Beta`|`1792x1024`|0.08 (per 1 image)|1 image |**openai**|-|`v1Beta`|`1024x1024`|0.04 (per 1 image)|1 image |**openai**|-|`v1Beta`|`512x512`|0.018 (per 1 image)|1 image |**openai**|-|`v1Beta`|`1024x1792`|0.08 (per 1 image)|1 image |**openai**|-|`v1Beta`|`1792x1024`|0.08 (per 1 image)|1 image |**stabilityai**|-|`v1Beta`|`512x512`|0.25 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v0-9**|`v1Beta`|`1024x1024`|2.0 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-beta-v2-2-2**|`v1Beta`|`512x512`|0.83 (per 1 image)|1 image |**stabilityai**|-|`v1Beta`|`1024x1024`|0.25 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`512x512`|6.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-v1-6**|`v1Beta`|`512x512`|10.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-v1-6**|`v1Beta`|`256x256`|10.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`256x256`|6.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-beta-v2-2-2**|`v1Beta`|`1024x1024`|0.83 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-beta-v2-2-2**|`v1Beta`|`256x256`|0.83 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v0-9**|`v1Beta`|`512x512`|2.0 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v0-9**|`v1Beta`|`256x256`|2.0 (per 1 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1024x1024`|6.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-v1-6**|`v1Beta`|`1024x1024`|10.0 (per 1000 image)|1 image |**replicate**|**anime-style**|`v1`|`1024x1024`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**anime-style**|`v1`|`256x256`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**anime-style**|`v1`|`512x512`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**classic**|`v1`|`1024x1024`|0.00115 (per 1 exec_time)|1 exec_time |**replicate**|**classic**|`v1`|`256x256`|0.00115 (per 1 exec_time)|1 exec_time |**replicate**|**classic**|`v1`|`512x512`|0.00115 (per 1 exec_time)|1 exec_time |**replicate**|-|`v1`|-|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**vintedois-diffusion**|`v1`|`1024x1024`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**vintedois-diffusion**|`v1`|`256x256`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**vintedois-diffusion**|`v1`|`512x512`|0.000225 (per 1 exec_time)|1 exec_time |**amazon**|-|`boto3 (v1.29.6)`|`512x512`|0.01 (per 1 image)|1 image |**amazon**|-|`boto3 (v1.29.6)`|`1024x1024`|0.012 (per 1 image)|1 image |**amazon**|**titan-image-generator-v1_premium**|`boto3 (v1.29.6)`|`512x512`|0.01 (per 1 image)|1 image |**amazon**|**titan-image-generator-v1_premium**|`boto3 (v1.29.6)`|`1024x1024`|0.012 (per 1 image)|1 image |**amazon**|**titan-image-generator-v1_standard**|`boto3 (v1.29.6)`|`512x512`|0.008 (per 1 image)|1 image |**amazon**|**titan-image-generator-v1_standard**|`boto3 (v1.29.6)`|`1024x1024`|0.01 (per 1 image)|1 image

Supported Models

openai
|Name|Value| |----|-----| |**openai**|`dall-e-2`| ||`dall-e-3`|

stabilityai
|Name|Value| |----|-----| |**stabilityai**|`stable-diffusion-v1-6`| ||`stable-diffusion-xl-1024-v0-9`| ||`stable-diffusion-xl-1024-v1-0`| ||`stable-diffusion-xl-beta-v2-2-2`|

replicate
|Name|Value| |----|-----| |**replicate**|`anime-style`| ||`classic`| ||`vintedois-diffusion`|

amazon
|Name|Value| |----|-----| |**amazon**|`titan-image-generator-v1_premium`| ||`titan-image-generator-v1_standard`|

# paths./image/generation.post.requestBody.content.application/json.examples.RequestExample.value.text
A huge red ballon flying outside the city.

# paths./image/landmark_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**google**|`v1`|1.5 (per 1000 file)|1 file |**microsoft**|`v3.2`|1.0 (per 1000 file)|1 file

# paths./image/logo_detection.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**google**|-|`v1`|1.5 (per 1000 file)|1 file |**microsoft**|-|`v3.2`|1.0 (per 1000 file)|1 file |**smartclick**|-|`v3.2`|0.5 (per 1000 file)|1 file |**api4ai**|-|`v1.0.0`|0.25 (per 1000 file)|1 file |**api4ai**|**v1**|`v1.0.0`|0.25 (per 1000 file)|1 file |**api4ai**|**v2**|`v1.0.0`|2.5 (per 1000 file)|1 file |**clarifai**|-|`8.0.0`|2.0 (per 1000 file)|1 file

Supported Models

api4ai
|Name|Value| |----|-----| |**api4ai**|`v1`| ||`v2`|

# paths./image/object_detection.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000 file)|1 file |**api4ai**|-|`1.9.2`|0.5 (per 1000 file)|1 file |**clarifai**|-|`8.0.0`|2.0 (per 1000 file)|1 file |**clarifai**|**general-image-detection**|`8.0.0`|2.0 (per 1000 file)|1 file |**google**|-|`v1`|2.25 (per 1000 file)|1 file |**microsoft**|-|`v3.2`|1.0 (per 1000 file)|1 file |**sentisight**|-|`v3.3.1`|0.75 (per 1000 file)|1 file

Supported Models

clarifai
|Name|Value| |----|-----| |**clarifai**|`general-image-detection`|

# paths./image/question_answer.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**alephalpha**|**luminous-base**|`1.12.0`|0.02 (per 1 request)|1 request |**alephalpha**|**luminous-extended**|`1.12.0`|0.03 (per 1 request)|1 request |**alephalpha**|-|`1.12.0`|0.03 (per 1 request)|1 request |**openai**|-|`v1`|0.02 (per 1 request)|1 request |**openai**|**gpt-4-vision-preview**|`v1`|0.02 (per 1 request)|1 request |**google**|-|`v1`|0.01 (per 1 request)|1 request |**google**|**gemini-pro-vision**|`v1`|0.01 (per 1 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**English**|`en`| |**French**|`fr`| |**German**|`de`| |**Italian**|`it`| |**Spanish**|`es`|

Supported Models

alephalpha
|Name|Value| |----|-----| |**alephalpha**|`luminous-base`| ||`luminous-extended`|

openai
|Name|Value| |----|-----| |**openai**|`gpt-4-vision-preview`|

google
|Name|Value| |----|-----| |**google**|`gemini-pro-vision`|

# paths./image/question_answer.post.responses.200.content.application/json.examples.ResponseExample.value.openai.answers[]
The image contains a combination of logos from four major technology companies, often referred to collectively as "FAANG" (Facebook, Apple, Amazon, Netflix, Google), but without the Netflix logo. From left to right, the logos are:

1. Facebook: The blue square with a lowercase 'f' is the logo

# paths./image/question_answer.post.responses.200.content.application/json.examples.ResponseExample.value.google.answers[]
 The image shows six logos of big tech companies, namely Facebook, Apple, Amazon, Netflix, and Google. The logos are all in their respective colors and are arranged in a row.

# paths./image/search/delete_image.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sentisight**|`v3.3.1`|free|- |**nyckel**|`v1.0.0`|free|-

# paths./image/search/get_image.get.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sentisight**|`v3.3.1`|free|- |**nyckel**|`v1.0.0`|free|-

# paths./image/search/get_image.get.parameters[].description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# paths./image/search/get_image.get.parameters[].description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# paths./image/search/get_image.get.parameters[].description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# paths./image/search/get_image.get.parameters[].description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# paths./image/search/get_image.get.parameters[].description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# paths./image/search/get_image.get.parameters[].description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# paths./image/search/get_images.get.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sentisight**|`v3.3.1`|free|- |**nyckel**|`v1.0.0`|free|-

# paths./image/search/get_images.get.parameters[].description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# paths./image/search/get_images.get.parameters[].description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# paths./image/search/get_images.get.parameters[].description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# paths./image/search/get_images.get.parameters[].description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# paths./image/search/get_images.get.parameters[].description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# paths./image/search/get_images.get.parameters[].description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# paths./image/search/launch_similarity.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file |**nyckel**|`v1.0.0`|1.0 (per 1000 file)|1 file

# paths./image/search/upload_image.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file |**nyckel**|`v1.0.0`|0.5 (per 1000 file)|1 file

# paths./info/provider_subfeatures.get.description
List Provider and features relations : You can get a list of *all providers* for a *feature* or *all features* for a *given provider*.

You can have the detailed information on the **pricing**, **supported languages** as well as the **models** for providers who propose different models (eg: voice models available for a text to speech provider).

Example : If you want the detailed list of all providers that can analyse the sentiment of a text written in french, you'd need to set `feature__name=text`,  `subfeature__name=sentiment_analysis` and `languages=fr`.

Which will look like the following :


```bash
curl --request GET  https://api.edenai.run/v2/info/provider_subfeatures?subfeature__name=sentiment_analysis&feature__name=text&languages=fr
```

# paths./info/provider_subfeatures.get.parameters[].description
Accepts two values: either 'male' or 'female'. Used to                              filter models voices for the text_to_speech subfeature

# paths./ocr/anonymization_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./ocr/anonymization_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**readyredact**|`v1`|0.05 (per 1 file)|1 file |**base64**|`v1`|0.25 (per 1 page)|1 page |**privateai**|`v3`|0.01 (per 1 page)|1 page

# paths./ocr/anonymization_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./ocr/anonymization_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./ocr/bank_check_parsing.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**base64**|`latest`|0.25 (per 1 page)|1 page |**veryfi**|`v8`|0.16 (per 1 request)|1 request |**mindee**|`v1`|0.1 (per 1 page)|1 page |**extracta**|`v1`|0.1 (per 1 page)|1 page

# paths./ocr/bank_check_parsing.post.responses.200.content.application/json.examples.ResponseExample.value.extracta.extracted_data[].bank_address
WASHINGTON'S OLDEST NATIONAL BANK THE FIRST NATIONAL BANK OF WASHINGTON WASHINGTON, D. C.

# paths./ocr/bank_check_parsing.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].bank_name
WASHINGTON'S OLDEST NATIONAL BANK
FIRST NATIONAL BANK

# paths./ocr/bank_check_parsing.post.responses.200.content.application/json.examples.ResponseExample.value.mindee.extracted_data[].payer_name
WASHINGTON'S OLDEST NATIONAL BANK FIRST NATIONAL BANK OF WASHINGTON

# paths./ocr/bank_check_parsing.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].bank_address
WASHINGTON'S OLDEST NATIONAL BANK THE FIRST NATIONAL BANK OF WASHINGTON WASHINGTON, D. C.

# paths./ocr/custom_document_parsing_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./ocr/custom_document_parsing_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|15.0 (per 1000 page)|1 page |**extracta**|`v1`|0.1 (per 1 page)|1 page

Supported Languages
|Name|Value| |----|-----| |**English**|`en`| |**French**|`fr`| |**German**|`de`| |**Italian**|`it`| |**Portuguese**|`pt`| |**Spanish**|`es`|

# paths./ocr/custom_document_parsing_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./ocr/custom_document_parsing_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./ocr/custom_document_parsing_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.extracta.items[].value
Determined work placement for 150 special needs adult clients.

# paths./ocr/custom_document_parsing_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.extracta.items[].query
What is the first Adult Care experience?

# paths./ocr/custom_document_parsing_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.items[].value
Determined work placement for 150 special needs adult clients.

# paths./ocr/custom_document_parsing_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.items[].query
What is the first Adult Care experience?

# paths./ocr/data_extraction.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|0.05 (per 1 page)|1 page |**base64**|`latest`|0.25 (per 1 page)|1 page

# paths./ocr/financial_parser.post.description
Available Providers
|Provider|Version|Document Type|Price|Billing unit| |----|-------|------|-----|------------| |**affinda**|`v3`|`invoice`|0.08 (per 1 page)|1 page |**affinda**|`v3`|`receipt`|0.07 (per 1 page)|1 page |**amazon**|`boto3 1.26.8`|-|0.01 (per 1 page)|1 page |**base64**|`latest`|-|0.25 (per 1 page)|1 page |**dataleon**|`v4.0.0`|-|0.05 (per 1 page)|1 page |**google**|`DocumentAI v1 beta3`|-|0.01 (per 1 page)|10 page |**klippa**|`v1`|-|0.1 (per 1 file)|1 file |**microsoft**|`v2.1-preview.3`|-|0.01 (per 1 page)|1 page |**mindee**|`v1.2`|-|0.1 (per 1 page)|1 page |**tabscanner**|`latest`|-|0.08 (per 1 page)|1 page |**veryfi**|`v8`|`receipt`|0.08 (per 1 file)|1 file |**veryfi**|`v8`|`invoice`|0.16 (per 1 file)|1 file

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malayalam**|`ml`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-tw`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Netherlands)**|`nl-NL`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**French (Switzerland)**|`fr-CH`| |**German (Germany)**|`de-DE`| |**German (Switzerland)**|`de-CH`| |**Italian (Italy)**|`it-IT`| |**Italian (Switzerland)**|`it-CH`| |**Portuguese (Portugal)**|`pt-PT`| |**Spanish (Spain)**|`es-ES`|

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.tabscanner.extracted_data[].merchant_information.address
344 Main Street Suite 200 Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.tabscanner.extracted_data[].item_lines[].description
Field inspection of conruction on 1/19/2003 deficiencies in house conruction,Garage drive way legal support to Attorney to

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].customer_information.billing_address
201 Stan Fey Dr
Upper Marlboro, MD 20774

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].merchant_information.address
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction, Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].customer_information.billing_address
201 Stan Fey Dr Upper Marlboro , MD 20774

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].merchant_information.address
344 Main Street Suite 200 Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house construction, Garage drive way & legal support to Attorney to

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].customer_information.remittance_address
SNG Engineering, Inc.
Consulting Engineers
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].merchant_information.address
SNG Engineering, Inc.
Consulting Engineers
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.base64.extracted_data[].merchant_information.address
344 Main Street Suite 200 Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.base64.extracted_data[].financial_document_information.invoice_due_date
 receipt of invoice by the owner/contractor.

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.base64.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house construction, Garage drive way & legal support to Attorney to 1/19/2003

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.dataleon.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house 
 construction Garage drive way & legal support to Attorney to 
 1/19/2003

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].customer_information.mailling_address
201 Stan Fey Dr
Upper Marlboro , MD 20774

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].merchant_information.address
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction, Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.mindee.extracted_data[].merchant_information.address
344 Main Street Suite 200 Gaithersburg.MD 20878

# paths./ocr/financial_parser.post.responses.200.content.application/json.examples.ResponseExample.value.mindee.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house

# paths./ocr/identity_parser.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|0.025 (per 1 page)|1 page |**base64**|`latest`|0.2 (per 1 page)|1 page |**microsoft**|`rest API 3.0`|0.01 (per 1 page)|1 page |**mindee**|`v2`|0.1 (per 1 page)|1 page |**klippa**|`v1`|0.1 (per 1 file)|1 file |**affinda**|`v3`|0.07 (per 1 file)|1 file

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malayalam**|`ml`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-tw`| |**English (United States)**|`en-US`| |**French (France)**|`fr-FR`| |**German (Germany)**|`de-DE`| |**Italian (Italy)**|`it-IT`| |**Portuguese (Portugal)**|`pt-PT`| |**Spanish (Spain)**|`es-ES`|

# paths./ocr/invoice_parser.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**affinda**|`v3`|0.08 (per 1 page)|1 page |**base64**|`latest`|0.25 (per 1 page)|1 page |**dataleon**|`v4.0.0`|0.05 (per 1 page)|1 page |**microsoft**|`v2.1-preview.3`|0.01 (per 1 page)|1 page |**mindee**|`v2`|0.1 (per 1 page)|1 page |**amazon**|`boto3 1.26.8`|0.01 (per 1 page)|1 page |**google**|`DocumentAI v1 beta3`|0.01 (per 1 page)|10 page |**klippa**|`v1`|0.1 (per 1 file)|1 file |**veryfi**|`v8`|0.16 (per 1 file)|1 file

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malayalam**|`ml`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-tw`| |**Danish (Denmark)**|`da-DK`| |**English (United States)**|`en-US`| |**French (France)**|`fr-FR`| |**German (Germany)**|`de-DE`| |**Italian (Italy)**|`it-IT`| |**Portuguese (Portugal)**|`pt-PT`| |**Spanish (Spain)**|`es-ES`|
**Note:** This feature is likely to be deprecated in future releases. It is recommended to use the `financial_parser` feature as an alternative.

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].customer_information.customer_address
201 Stan Fey Dr Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].customer_information.customer_billing_address
201 Stan Fey Dr Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].merchant_information.merchant_address
344 Main Street Suite 200 Gaithersburg, MD 20878

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house construction, Garage drive way & legal support to Attorney to 1/19/2003

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].customer_information.customer_address
Damita J Goldsmith
201 Stan Fey Dr
Upper Marlboro MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].merchant_information.merchant_address
SNG Engineering, Inc.
Consulting Engineers
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.base64.extracted_data[].due_date
 receipt of invoice by the owner/contractor.

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.base64.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house construction, Garage drive way & legal support to Attorney to 1/19/2003

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].customer_information.customer_address
201 Stan Fey Dr
Upper Marlboro, MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].customer_information.customer_billing_address
201 Stan Fey Dr
Upper Marlboro, MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].merchant_information.merchant_address
344 Main St, Gaithersburg, MD, 20878, US

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].item_lines[].description
Field inspection of construction on
1/19/2003
deficiencies in house
construction, Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.mindee.extracted_data[].merchant_information.merchant_address
344 Main Street Suite 200 Gaithersburg.MD 20878

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].customer_information.customer_address
201 Stan Fey Dr
Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].customer_information.customer_mailing_address
201 Stan Fey Dr
Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].merchant_information.merchant_address
344 Main Street Suite 200
Gaithersburg, MD 20878

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction, Garage drive way & legal support to Attorney to
1/19/2003

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.google.extracted_data[].customer_information.customer_address
201 Stan Fey Dr
Upper Marlboro, MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.google.extracted_data[].merchant_information.merchant_address
344 Main St #200 Gaithersburg, MD 20878 USA

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.google.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house
construction, Garage drive way & legal support to Attorney to

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].customer_information.customer_address
201 Stan Fey Dr Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].customer_information.customer_billing_address
201 Stan Fey Dr Upper Marlboro , MD 20774

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].merchant_information.merchant_address
344 Main Street Suite 200 Gaithersburg, MD 20878

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].due_date
 receipt of invoice by the owner/contractor.

# paths./ocr/invoice_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].item_lines[].description
Field inspection of construction on 1/19/2003 deficiencies in house construction, Garage drive way & legal support to Attorney to 1/19/2003

# paths./ocr/invoice_splitter_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./ocr/invoice_splitter_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**mindee**|`v1`|0.1 (per 1 page)|1 page

# paths./ocr/invoice_splitter_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./ocr/invoice_splitter_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./ocr/ocr.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.5 (per 1000 page)|1 page |**clarifai**|`8.0.0`|2.0 (per 1000 page)|1 page |**google**|`v1`|1.5 (per 1000 page)|1 page |**microsoft**|`v3.2`|1.0 (per 1000 page)|1 page |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file |**api4ai**|`v1.0.0`|3.0 (per 1000 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Abaza**|`abq`| |**Adyghe**|`ady`| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Angika**|`anp`| |**Arabic**|`ar`| |**Assamese**|`as`| |**Asturian**|`ast`| |**Avaric**|`av`| |**Awadhi**|`awa`| |**Azerbaijani**|`az`| |**Bagheli**|`bfy`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bhojpuri**|`bho`| |**Bihari languages**|`bh`| |**Bislama**|`bi`| |**Bodo (India)**|`brx`| |**Bosnian**|`bs`| |**Braj**|`bra`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Bundeli**|`bns`| |**Buriat**|`bua`| |**Camling**|`rab`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Chamorro**|`ch`| |**Chechen**|`ce`| |**Chhattisgarhi**|`hne`| |**Chinese**|`zh`| |**Cornish**|`kw`| |**Corsican**|`co`| |**Crimean Tatar**|`crh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dargwa**|`dar`| |**Dari**|`prs`| |**Dhimal**|`dhi`| |**Dogri (macrolanguage)**|`doi`| |**Dutch**|`nl`| |**English**|`en`| |**Erzya**|`myv`| |**Estonian**|`et`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Friulian**|`fur`| |**Gagauz**|`gag`| |**Galician**|`gl`| |**German**|`de`| |**Gilbertese**|`gil`| |**Goan Konkani**|`gom`| |**Gondi**|`gon`| |**Gurung**|`gvr`| |**Haitian**|`ht`| |**Halbi**|`hlb`| |**Hani**|`hni`| |**Haryanvi**|`bgc`| |**Hawaiian**|`haw`| |**Hindi**|`hi`| |**Hmong Daw**|`mww`| |**Ho**|`hoc`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Inari Sami**|`smn`| |**Indonesian**|`id`| |**Ingush**|`inh`| |**Interlingua (International Auxiliary Language Association)**|`ia`| |**Inuktitut**|`iu`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Jaunsari**|`jns`| |**Javanese**|`jv`| |**K'iche'**|`quc`| |**Kabardian**|`kbd`| |**Kabuverdianu**|`kea`| |**Kachin**|`kac`| |**Kalaallisut**|`kl`| |**Kangri**|`xnr`| |**Kara-Kalpak**|`kaa`| |**Karachay-Balkar**|`krc`| |**Kashubian**|`csb`| |**Kazakh**|`kk`| |**Khaling**|`klr`| |**Khasi**|`kha`| |**Kirghiz**|`ky`| |**Korean**|`ko`| |**Korku**|`kfq`| |**Koryak**|`kpy`| |**Kosraean**|`kos`| |**Kumarbhag Paharia**|`kmj`| |**Kumyk**|`kum`| |**Kurdish**|`ku`| |**Kurukh**|`kru`| |**Kölsch**|`ksh`| |**Lak**|`lbe`| |**Lakota**|`lkt`| |**Latin**|`la`| |**Latvian**|`lv`| |**Lezghian**|`lez`| |**Lithuanian**|`lt`| |**Lower Sorbian**|`dsb`| |**Lule Sami**|`smj`| |**Luxembourgish**|`lb`| |**Mahasu Pahari**|`bfz`| |**Maithili**|`mai`| |**Malay (macrolanguage)**|`ms`| |**Maltese**|`mt`| |**Manx**|`gv`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Marshallese**|`mh`| |**Mongolian**|`mn`| |**Montenegrin**|`cnr`| |**Neapolitan**|`nap`| |**Nepali (macrolanguage)**|`ne`| |**Newari**|`new`| |**Niuean**|`niu`| |**Nogai**|`nog`| |**Northern Sami**|`se`| |**Norwegian**|`no`| |**Occitan (post 1500)**|`oc`| |**Old English (ca. 450-1100)**|`ang`| |**Ossetian**|`os`| |**Pali**|`pi`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Romansh**|`rm`| |**Russian**|`ru`| |**Sadri**|`sck`| |**Samoan**|`sm`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Scots**|`sco`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Sherpa**|`xsr`| |**Sirmauri**|`srx`| |**Skolt Sami**|`sms`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Southern Sami**|`sma`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tabassaran**|`tab`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tatar**|`tt`| |**Tetum**|`tet`| |**Thangmi**|`thf`| |**Tonga (Tonga Islands)**|`to`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Tuvinian**|`tyv`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Upper Sorbian**|`hsb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Volapük**|`vo`| |**Walser**|`wae`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Yucateco**|`yua`| |**Zhuang**|`za`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Arabic (Pseudo-Accents)**|`ar-XA`| |**Belarusian**|`be-cyrl`| |**Belarusian (Latin)**|`be-latn`| |**Chinese (China)**|`zh-CN`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Netherlands)**|`nl-NL`| |**English (United States)**|`en-US`| |**Finnish (Finland)**|`fi-FI`| |**French (France)**|`fr-FR`| |**German (Germany)**|`de-DE`| |**Hungarian (Hungary)**|`hu-HU`| |**Italian (Italy)**|`it-IT`| |**Japanese (Japan)**|`ja-JP`| |**Kara-Kalpak (Cyrillic)**|`kaa-Cyrl`| |**Kazakh**|`kk-cyrl`| |**Kazakh (Latin)**|`kk-latn`| |**Korean (South Korea)**|`ko-KR`| |**Kurdish (Arabic)**|`ku-arab`| |**Kurdish (Latin)**|`ku-latn`| |**Polish**|`pl-PO`| |**Portuguese (Portugal)**|`pt-PT`| |**Region: Czechia**|`cz-CZ`| |**Region: Greece**|`gr-GR`| |**Russian (Russia)**|`ru-RU`| |**Serbian (Cyrillic, Montenegro)**|`sr-Cyrl-ME`| |**Serbian (Latin)**|`sr-latn`| |**Serbian (Latin, Montenegro)**|`sr-Latn-ME`| |**Spanish (Spain)**|`es-ES`| |**Swedish (Sweden)**|`sv-SE`| |**Turkish (Türkiye)**|`tr-TR`| |**Uzbek (Arabic)**|`uz-arab`| |**Uzbek (Cyrillic)**|`uz-cyrl`|

# paths./ocr/ocr.post.responses.200.content.application/json.examples.ResponseExample.value.google.text
I 福州科技馆 福州科技馆空调线路改造项目工程 施工招标公告 招标项目福州科技馆空调线路改造项目工程已批准建设, 招标人为福州科技馆,建设资金来源自筹。项目已具备 招标条件,现对该项目的施工进行公开招标。 1.项目概况与招标范围 建设地点: 福州市仓山区橘园洲桥下公园内 工程招标控 制价: 29135 元;计划工期: 15日历天质量要求:合 按招标范围:详见工程量清单为准, 2. 投标人资格要求 2.1 投标人须具备人员,设备,资金等方面具有相应的设 计经验及施工能力。 2. 2 本次招标不接受联合体投标。 3. 投标报名、开标时间 投标报名截止时间至2018年12月4日12时00分,开标

# paths./ocr/ocr.post.responses.200.content.application/json.examples.ResponseExample.value.api4ai.text
I
福州科技馆
福州科技馆空调线路改造项目工程
施工招标公告
招标项目福州科技馆空调线路改造项目工程已批准建设,
招标人为福州科技馆,建设资金来源自筹。项目已具备
招标条件,现对该项目的施工进行公开招标。
1.项目概况与招标范围
建设地点: 福州市仓山区橘园洲桥下公园内 工程招标控
制价: 29135 元;计划工期: 15日历天质量要求:合
按招标范围:详见工程量清单为准,
2. 投标人资格要求
2.1 投标人须具备人员,设备,资金等方面具有相应的设
计经验及施工能力。
2. 2 本次招标不接受联合体投标。
3. 投标报名、开标时间
投标报名截止时间至2018年12月4日12时00分,开标

# paths./ocr/ocr.post.responses.200.content.application/json.examples.ResponseExample.value.api4ai.bounding_boxes[].text
I
福州科技馆
福州科技馆空调线路改造项目工程
施工招标公告
招标项目福州科技馆空调线路改造项目工程已批准建设,
招标人为福州科技馆,建设资金来源自筹。项目已具备
招标条件,现对该项目的施工进行公开招标。
1.项目概况与招标范围
建设地点: 福州市仓山区橘园洲桥下公园内 工程招标控
制价: 29135 元;计划工期: 15日历天质量要求:合
按招标范围:详见工程量清单为准,
2. 投标人资格要求
2.1 投标人须具备人员,设备,资金等方面具有相应的设
计经验及施工能力。
2. 2 本次招标不接受联合体投标。
3. 投标报名、开标时间
投标报名截止时间至2018年12月4日12时00分,开标

# paths./ocr/ocr_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./ocr/ocr_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.5 (per 1000 page)|1 page |**microsoft**|`rest API 3.0`|10.0 (per 1000 page)|1 page

# paths./ocr/ocr_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./ocr/ocr_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.microsoft.raw_text
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
An Introduction to the Process of Optical Character Recognition
Umal Patel1 1 Department of Computer Engineering L D College Of Engineering, Gujarat Technological University, Gujarat, India
Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years. Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).
Keywords: OCR, online, offline, online, zoning, euler number.
1. Introduction
OCR is an approach that provides a full alphanumeric recognition of printed or handwritten characters at electronically by simply scanning them and generating into a form that can be scanned through a scanner and then the recognition engine of the OCR system interpret the images and turn images of handwritten or printed characters into ASCII data (machine-readable characters).Character recognition also popularly referred as optical character recognition (OCR) is a field of research that has immense potential in future where we want to track and locate every piece of information being exchanged. The problem with the hand written text is due to uncertainties such as variation in calligraphy over period of time, similarity in text, variation in styles of writing [3] The character recognition system helps in making the communication between a human and a computer easy.[4] The character recognition is basically classified into two types: offline handwritten text recognition, online handwritten text recognition. Offline means the text written on the plain paper or sheet and then the writing is usually captured optically by a scanner and the completed writing is available as an image. Online means the text written on any digital devices such as tablets using stylus i.e. the two dimensional coordinates of successive points are represented as a function of time and the order of strokes made by the writer are also available.[6]
2. Applications recognition
of optical character
The area of OCR is becoming an integral part of document scanners, and is used in many applications such as postal processing, script recognition, banking, security (i.e. passport authentication) and language identification, document reading, mail sorting, signature verification, writer identification., license plate recognition system, smart card processing system, automatic data entry, bank cheque /DD processing, money counting machine, postal automation, address and zip code recognition etc many organizations are depending on OCR systems to eliminate the human interactions for better performance and efficiency [2,4,6,7].
3. Potential problem areas for OCR
1. The same characters differ in sizes, shapes and styles from person to person and even from time to time with the same person. The source of confusion is the high level of abstraction: there are thousands styles of type in common use plus variations in calligraphy and a character recognition program must recognize most of these.
2. Like any image, visual characters are subject to spoilage due to noise. Some images containing characters are already blurred or not clear which makes them difficult to process. Noise consists of random changes to a pattern, particularly near the edges. A character with much noise may be interpreted as a completely different character by a computer program.
3. There are no hard-and-fast rules that define the appearance of a visual character. Hence rules need to be heuristically deduced from the samples.
4. Phases of OCR
Data Acquisition
Pre processing
Segmentation
Normalization
Feature Extraction
Classification
Post Processing
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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
1. Data Acquisition
Most Important initial phase in OCR is to gather the image from either device sensor like PDA or tablets in case on online recognition or getting the images containing characters directly for offline recognition.
In Image acquisition, the recognition system acquires a scanned image as an input image. The image should have a specific format such as JPEG, BMP etc. This image is acquired through a scanner, digital camera or any other suitable digital input device. Data samples for the experiment have been collected from different individuals [9].
2. Pre Processing
The goal of pre-processing is to simplify the pattern recognition problem without missing any vital information. It reduces the noises and inconsistent data. It enhances the image and prepares it for the next steps [3].
Preprocessing is the preliminary step which transforms the data into a format that will be more easily and effectively processed. Therefore, the main task in preprocessing the captured data is to decrease the variation that causes a reduction in the recognition rate and increases the complexities, as for example, preprocessing of the input raw stroke of characters is crucial for the success of efficient character recognition systems. Thus, preprocessing is an essential stage prior to feature extraction since it controls the suitability of the results for the successive stages [2].
Preprocessing can be done through various ways Binarization, Noise reduction, Stroke width normalization, Skew correction, Slant removal, Filtering, Morphological Operations, Noise Modelling, Skew Normalization, Size Normalization, Contour Smoothing, Compression, Thresholding, Thinning etc
start
start
Figure 1: Slant Removal
e :selected:
1 :selected: :selected: :selected:
Figure 2: Normalization of 'e' and 'l' as in [9]
3. Segmentation
Segmentation is an integral part of any text based recognition system. It assures efficiency of classification and
recognition. Accuracy of character recognition heavily depends upon segmentation phase.
payque
eighteen eighteen
eighteen
Figure 3: Segmentation [9]
4. Normalization
The results of segmentation process provides isolated characters which are ready to pass through feature extraction stage, thus the isolated characters are reduced to a specific size depending on the methods used. The segmentation process essentially renders the image in the form of m*n matrix. These matrices are then generally normalized by reducing the size and removing the redundant information from the image without losing any important information.
5. Feature Extraction
Feature extraction is the process of extracting the relevant features from objects/alphabets to form a feature vectors. These feature vectors is then used by classifiers to recognize the input unit with target output unit. It becomes easier for the classifier to classify between different classes by looking at these features as it allows fairly easy to distinguish.
Feature extraction is also defined as extracting the raw data the information which is most relevant for classification purposes in the sense of minimizing the pattern variability.[1]
Due to the nature of handwriting with its high degree of variability and imprecision obtaining these features, is a difficult task. Feature extraction methods are based on 3 types of features:
· Statistical
· Structural
. Global transformations and moments
Statistical Features includes:
1. Zoning
The character image is divided into NxM zones. From each zone features are extracted to form the feature vector. The
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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
goal of zoning is to obtain the local characteristics instead of global characteristics.
Figure 4: zoning
After dividing the character into different zones you can compare the density or direction features of it and classify each one of them.
2. Projection Histograms
The basic idea behind using projections is that character images, which are 2-D signals, can be represented as 1- D signal. These features, although independent to noise and deformation, depend on rotation. Projection histograms count the number of pixels in each column and row of a character image. Projection histograms can separate characters such as "m" and "n".
Figure 5: Projection Histogram
3. Profiles
The profile counts the number of pixels (distance) between the bounding box of the character image and the edge of the character. The profiles describe well the external shapes of characters and allow distinguishing between a great number of letters, such as "p" and "q".
a
Figure 6: Profiling
4. Structural features:
Structural features are based on topological and geometrical properties of the character, such as aspect ratio, cross points, loops, branch points, strokes and their directions, inflection between two points, horizontal curves at top or bottom, etc.
Global Transformations-Moments:
The Fourier Transform (FT) of the contour of the image is calculated. Since the first n coefficients of the FT can be used in order to reconstruct the contour, then these n coefficients are considered to be a n-dimensional feature vector that represents the character.
00855555 $5555555 Figure 7: Contouring
6 Classification
The results Classification is the last stage where we train the neural net using the feature vectors obtained during feature extraction method against the required targets. To optimize the whole recognition process, several combination methods of multilayer perceptron have been devised. E.g .: k-Nearest Neighbour (k-NN), Bayes Classifier, Neural Networks (NN), Hidden Markov Models (HMM), Support Vector Machines (SVM), etc there is no such thing as the "best classifier". The use of classifier depends on many factors, such as available training set, number of free parameters etc.
7. Post Processing
The goal of post processing is the incorporation of context and shape information in all the stages of OCR systems is necessary for meaningful improvements in recognition rates.
5. Conclusion
The character recognition methods have been introduced and developed over the years. In this paper, I have tried to explain the overview of the whole OCR process and the methods related to it. Many researchers try to hybrid two or more different methods and compare the results for efficiency but again this will be application specific and parameter specific. OCR has been implemented in various countries for recognizing different languages as well.
References
[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature Extraction Methods for Character Recognition-A Survey", July 1995
[2] Yasser Alginahi, Taibah University Kingdom of Saudi Arabia, "Preprocessing Techniques in Character Recognition"
[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, Benoy Kumar Thakur, "Recent Trends and Tools for Feature Extraction
[4] in OCR Technology", International Journal of Soft Computing and Engineering (IJSCE)
[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013
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[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok M. Sapkal, "Survey of Methods for Character Recognition", International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 5, May 2012
[7] Mohanad Alata, Mohammad Al-Shabi "TEXT DETECTION AND CHARACTER
[8] RECOGNITION USING FUZZY IMAGE PROCESSING", Journal of ELECTRICAL ENGINEERING, VOL. 57, NO. 5, 2006, 258-267
[9] Rejean Plamondon, Fellow, IEEE and Sargur N. Shrihari, Fellow, IEEE, "On-line and Off-line Handwriting Recognition: A comprehensive Survey", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 22, NO. 1 JANUARY 2000
[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram Shah, "An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler Number", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012
[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural Network Based Recognition System Integrating Feature
Extraction and Classification for English Handwritten", IJE TRANSACTIONS B: Applications Vol. 25, No. 2, (May 2012) 99-106
[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview of Character Recognition Focused on
[13] Off-Line Handwriting", IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART C: APPLICATIONS AND REVIEWS, VOL. 31, NO. 2, MAY 2001
Author Profile
Umal Patel, pursuing her Master Degree in Computer Science & Technology from Gujarat Technological University (L D College of Eng., Ahmedabad), received her Bachelor Degree in Computer Engg. from Gujarat University in 2008. Presently working as Assistant Professor in Department of MCA, L.J Institute of Technology. Earlier he has served as Software Test Engineer in Lodestone Software Services since June 2008. Her area of interest is Compilers and Image Processing.
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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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An Introduction to the Process of Optical Character

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L D College Of Engineering, Gujarat Technological University, Gujarat, India

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Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification

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of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years.

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Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely

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difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for

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researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).

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Keywords: OCR, online, offline, online, zoning, euler number.

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OCR is an approach that provides a full alphanumeric

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recognition of printed or handwritten characters at

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electronically by simply scanning them and generating into a

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form that can be scanned through a scanner and then the

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recognition engine of the OCR system interpret the images

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and turn images of handwritten or printed characters into

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recognition also popularly referred as optical character

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recognition (OCR) is a field of research that has immense

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potential in future where we want to track and locate every

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piece of information being exchanged. The problem with the

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hand written text is due to uncertainties such as variation in

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calligraphy over period of time, similarity in text, variation

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in styles of writing [3] The character recognition system

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helps in making the communication between a human and a

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computer easy.[4] The character recognition is basically

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classified into two types: offline handwritten text

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recognition, online handwritten text recognition. Offline

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means the text written on the plain paper or sheet and then

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the writing is usually captured optically by a scanner and the

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completed writing is available as an image. Online means the

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text written on any digital devices such as tablets using

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stylus i.e. the two dimensional coordinates of successive

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points are represented as a function of time and the order of

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strokes made by the writer are also available.[6]

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The area of OCR is becoming an integral part of document

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scanners, and is used in many applications such as postal

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processing, script recognition, banking, security (i.e.

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document reading, mail sorting, signature verification, writer

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identification., license plate recognition system, smart card

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processing system, automatic data entry, bank cheque /DD

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processing, money counting machine, postal automation,

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address and zip code recognition etc many organizations are

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depending on OCR systems to eliminate the human

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interactions for better performance and efficiency [2,4,6,7].

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1. The same characters differ in sizes, shapes and styles

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from person to person and even from time to time with

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the same person. The source of confusion is the high

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level of abstraction: there are thousands styles of type in

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common use plus variations in calligraphy and a

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character recognition program must recognize most of

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2. Like any image, visual characters are subject to spoilage

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due to noise. Some images containing characters are

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already blurred or not clear which makes them difficult to

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process. Noise consists of random changes to a pattern,

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particularly near the edges. A character with much noise

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may be interpreted as a completely different character by

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3. There are no hard-and-fast rules that define the

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appearance of a visual character. Hence rules need to be

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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Most Important initial phase in OCR is to gather the image

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from either device sensor like PDA or tablets in case on

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online recognition or getting the images containing

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characters directly for offline recognition.

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In Image acquisition, the recognition system acquires a

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scanned image as an input image. The image should have a

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specific format such as JPEG, BMP etc. This image is

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acquired through a scanner, digital camera or any other

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suitable digital input device. Data samples for the

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experiment have been collected from different individuals

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The goal of pre-processing is to simplify the pattern

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recognition problem without missing any vital information.

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It reduces the noises and inconsistent data. It enhances the

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image and prepares it for the next steps [3].

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Preprocessing is the preliminary step which transforms the

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data into a format that will be more easily and effectively

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processed. Therefore, the main task in preprocessing the

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captured data is to decrease the variation that causes a

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reduction in the recognition rate and increases the

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complexities, as for example, preprocessing of the input raw

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stroke of characters is crucial for the success of efficient

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character recognition systems. Thus, preprocessing is an

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essential stage prior to feature extraction since it controls the

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suitability of the results for the successive stages [2].

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Preprocessing can be done through various

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Binarization, Noise reduction, Stroke width normalization,

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Skew correction, Slant removal, Filtering, Morphological

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Operations, Noise Modelling, Skew Normalization, Size

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Figure 2: Normalization of 'e' and 'l' as in [9]

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Segmentation is an integral part of any text based

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recognition system. It assures efficiency of classification and

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recognition. Accuracy of character recognition heavily

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The results of segmentation process provides isolated

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characters which are ready to pass through feature extraction

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stage, thus the isolated characters are reduced to a specific

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size depending on the methods used. The segmentation

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process essentially renders the image in the form of m*n

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matrix. These matrices are then generally normalized by

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reducing the size and removing the redundant information

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from the image without losing any important information.

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Feature extraction is the process of extracting the relevant

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features from objects/alphabets to form a feature vectors.

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These feature vectors is then used by classifiers to recognize

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the input unit with target output unit. It becomes easier for

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the classifier to classify between different classes by looking

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at these features as it allows fairly easy to distinguish.

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Feature extraction is also defined as extracting the raw data

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the information which is most relevant for classification

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purposes in the sense of minimizing the pattern

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Due to the nature of handwriting with its high degree of

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variability and imprecision obtaining these features, is a

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difficult task. Feature extraction methods are based on 3

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The character image is divided into NxM zones. From each

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zone features are extracted to form the feature vector. The

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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goal of zoning is to obtain the local characteristics instead of

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After dividing the character into different zones you can

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compare the density or direction features of it and

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The basic idea behind using projections is that character

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images, which are 2-D signals, can be represented as 1-

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D signal. These features, although independent to noise

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and deformation, depend on rotation. Projection

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histograms count the number of pixels in each column

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and row of a character image. Projection histograms can

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separate characters such as "m" and "n".

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The profile counts the number of pixels (distance)

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between the bounding box of the character image and

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the edge of the character. The profiles describe well the

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external shapes of characters and allow distinguishing

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between a great number of letters, such as "p" and "q".

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Structural features are based on topological and geometrical

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properties of the character, such as aspect ratio, cross points,

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loops, branch points, strokes and their directions, inflection

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between two points, horizontal curves at top or bottom, etc.

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The Fourier Transform (FT) of the contour of the image is

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calculated. Since the first n coefficients of the FT can be

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used in order to reconstruct the contour, then these n

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coefficients are considered to be a n-dimensional feature

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The results Classification is the last stage where we train the

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neural net using the feature vectors obtained during feature

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extraction method against the required targets. To optimize

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the whole recognition process, several combination methods

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of multilayer perceptron have been devised. E.g .: k-Nearest

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Neighbour (k-NN), Bayes Classifier, Neural Networks (NN),

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Hidden Markov Models (HMM), Support Vector Machines

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(SVM), etc there is no such thing as the "best classifier". The

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use of classifier depends on many factors, such as available

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training set, number of free parameters etc.

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The goal of post processing is the incorporation of context

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and shape information in all the stages of OCR systems is

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necessary for meaningful improvements in recognition rates.

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The character recognition methods have been introduced and

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developed over the years. In this paper, I have tried to

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explain the overview of the whole OCR process and the

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methods related to it. Many researchers try to hybrid two or

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more different methods and compare the results for

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efficiency but again this will be application specific and

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parameter specific. OCR has been implemented in various

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countries for recognizing different languages as well.

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[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature

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Extraction Methods for Character Recognition-A

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[2] Yasser Alginahi, Taibah University Kingdom of Saudi

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[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram

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Shah, Benoy Kumar Thakur, "Recent Trends and Tools

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[4] in OCR Technology", International Journal of Soft

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok

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M. Sapkal, "Survey of Methods for Character

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Recognition", International Journal of Engineering and

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Innovative Technology (IJEIT) Volume 1, Issue 5, May

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[9] Rejean Plamondon, Fellow, IEEE and Sargur N.

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Shrihari, Fellow, IEEE, "On-line and Off-line

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[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram

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Shah, "An Improved Zone Based Hybrid Feature

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Recognition Using Euler Number", International Journal

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of Soft Computing and Engineering (IJSCE) ISSN:

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[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural

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Network Based Recognition System Integrating Feature

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Extraction and Classification for English Handwritten",

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IJE TRANSACTIONS B: Applications Vol. 25, No. 2,

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[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview

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[13] Off-Line Handwriting", IEEE TRANSACTIONS ON

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APPLICATIONS AND REVIEWS, VOL. 31, NO. 2,

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Umal Patel, pursuing her Master Degree in Computer

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Science & Technology from Gujarat Technological

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University (L D College of Eng., Ahmedabad), received her

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Bachelor Degree in Computer Engg. from Gujarat

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University in 2008. Presently working as Assistant Professor

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in Department of MCA, L.J Institute of Technology. Earlier

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he has served as Software Test Engineer in Lodestone

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Software Services since June 2008. Her area of interest is

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
An Introduction to the Process of Optical Character
Recognition
Umal Patel1
1
Department of Computer Engineering
LD College Of Engineering, Gujarat Technological University, Gujarat, India
Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification
of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years.
Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely
difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for
researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).
Keywords: OCR, online, offline, online, zoning, euler number.
1. Introduction
3. Potential problem areas for OCR
OCR is an approach that provides a full alphanumeric
1. The same characters differ in sizes, shapes and styles
recognition of printed or handwritten characters at
from person to person and even from time to time with
electronically by simply scanning them and generating into a
the same person. The source of confusion is the high
form that can be scanned through a scanner and then the
level of abstraction: there are thousands styles of type in
recognition engine of the OCR system interpret the images
common use plus variations in calligraphy and a
and turn images of handwritten or printed characters into
character recognition program must recognize most of
ASCII data (machine-readable characters). Character
these.
recognition also popularly referred as optical character
2. Like any image, visual characters are subject to spoilage
recognition (OCR) is a field of research that has immense
due to noise. Some images containing characters are
potential in future where we want to track and locate every
already blurred or not clear which makes them difficult to
piece of information being exchanged. The problem with the
process. Noise consists of random changes to a pattern,
hand written text is due to uncertainties such as variation in
particularly near the edges. A character with much noise
calligraphy over period of time, similarity in text, variation
may be interpreted as a completely different character by
in styles of writing [3] The character recognition system
a computer program.
helps in making the communication between a human and a
3. There are no hard-and-fast rules that define the
computer easy.[4] The character recognition is basically
appearance of a visual character. Hence rules need to be
classified into two types: offline handwritten text
heuristically deduced from the samples.
recognition, online handwritten text recognition. Offline
means the text written on the plain paper or sheet and then
4. Phases of OCR
the writing is usually captured optically by a scanner and the
completed writing is available as an image. Online means the
Data Acquisition
text written on any digital devices such as tablets using
stylus i.e. the two dimensional coordinates of successive
points are represented as a function of time and the order of
Pre processing
strokes made by the writer are also available.[6]
Segmentation
2. Applications
of
optical
character
recognition
Normalization
The area of OCR is becoming an integral part of document
Feature Extraction
scanners, and is used in many applications such as postal
processing, script recognition, banking, security (i.e.
Classification
passport authentication) and language identification,
document reading, mail sorting, signature verification, writer
identification., license plate recognition system, smart card
Post Processing
processing system, automatic data entry, bank cheque /DD
processing, money counting machine, postal automation,
address and zip code recognition etc many organizations are
depending on OCR systems to eliminate the human
interactions for better performance and efficiency [2,4,6,7].
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155
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
1. Data Acquisition
recognition. Accuracy of character recognition heavily
depends upon segmentation phase.
Most Important initial phase in OCR is to gather the image
from either device sensor like PDA or tablets in case on
online recognition or getting the images containing
characters directly for offline recognition.
radyape
In Image acquisition, the recognition system acquires a
scanned image as an input image. The image should have a
specific format such as JPEG, BMP etc. This image is
acquired through a scanner, digital camera or any other
suitable digital input device. Data samples for the
experiment have been collected from different individuals
[9].
2. Pre Processing
eighteen
eighteen
The goal of pre-processing is to simplify the pattern
recognition problem without missing any vital information.
It reduces the noises and inconsistent data. It enhances the
image and prepares it for the next steps [3].
ei gliteen
Figure 3: Segmentation [9]
Preprocessing is the preliminary step which transforms the
data into a format that will be more easily and effectively
4. Normalization
processed. Therefore, the main task in preprocessing the
captured data is to decrease the variation that causes a
reduction in the recognition rate and increases the
The results of segmentation process provides isolated
characters which are ready to pass through feature extraction
complexities, as for example, preprocessing of the input raw
stroke of characters is crucial for the success of efficient
stage, thus the isolated characters are reduced to a specific
size depending on the methods used. The segmentation
character recognition systems. Thus, preprocessing is an
process essentially renders the image in the form of m*n
essential stage prior to feature extraction since it controls the
suitability of the results for the successive stages [2].
matrix. These matrices are then generally normalized by
reducing the size and removing the redundant information
from the image without losing any important information.
Preprocessing can be done through various ways
Binarization, Noise reduction, Stroke width normalization,
5. Feature Extraction
Skew correction, Slant removal, Filtering, Morphological
Operations, Noise Modelling, Skew Normalization, Size
Feature extraction is the process of extracting the relevant
Normalization, Contour Smoothing, Compression,
features from objects/alphabets to form a feature vectors.
Thresholding, Thinning etc
These feature vectors is then used by classifiers to recognize
start
the input unit with target output unit. It becomes easier for
the classifier to classify between different classes by looking
at these features as it allows fairly easy to distinguish.
start
Feature extraction is also defined as extracting the raw data
the information which is most relevant for classification
Figure 1: Slant Removal
purposes in the sense of minimizing the pattern
variability.[1]
Due to the nature of handwriting with its high degree of
variability and imprecision obtaining these features, is a
difficult task. Feature extraction methods are based on 3
types of features:
Statistical
Structural
Global transformations and moments
Figure 2: Normalization of 'e' and 'l' as in [9]
Statistical Features includes:
3. Segmentation
1. Zoning
Segmentation is an integral part of any text based
recognition system. It assures efficiency of classification and
The character image is divided into NxM zones. From each
zone features are extracted to form the feature vector. The
Volume 2 Issue 5, May 2013
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156
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
goal of zoning is to obtain the local characteristics instead of
global characteristics.
Global Transformations-Moments:
The Fourier Transform (FT) of the contour of the image is
calculated. Since the first n coefficients of the FT can be
used in order to reconstruct the contour, then these n
coefficients are considered to be a n-dimensional feature
vector that represents the character.
Figure 4: zoning
OOSSESSS
After dividing the character into different zones you can
compare the density or direction features of it and
classify each one of them.
55555555
2. Projection Histograms
Figure 7: Contouring
The basic idea behind using projections is that character
6 Classification
images, which are 2-D signals, can be represented as 1-
D signal. These features, although independent to noise
The results Classification is the last stage where we train the
and deformation, depend on rotation. Projection
neural net using the feature vectors obtained during feature
histograms count the number of pixels in each column
extraction method against the required targets. To optimize
and row of a character image. Projection histograms can
the whole recognition process, several combination methods
separate characters such as "m" and "n".
of multilayer perceptron have been devised. E.g.: k-Nearest
Neighbour (k-NN), Bayes Classifier, Neural Networks (NN),
Hidden Markov Models (HMM), Support Vector Machines
(SVM), etc there is no such thing as the "best classifier". The
use of classifier depends on many factors, such as available
training set, number of free parameters etc.
7. Post Processing
The goal of post processing is the incorporation of context
and shape information in all the stages of OCR systems is
Figure 5: Projection Histogram
necessary for meaningful improvements in recognition rates.
3. Profiles
5. Conclusion
The profile counts the number of pixels (distance)
between the bounding box of the character image and
The character recognition methods have been introduced and
the edge of the character. The profiles describe well the
developed over the years. In this paper, I have tried to
external shapes of characters and allow distinguishing
explain the overview of the whole OCR process and the
between a great number of letters, such as "p" and "q".
methods related to it. Many researchers try to hybrid two or
more different methods and compare the results for
efficiency but again this will be application specific and
parameter specific. OCR has been implemented in various
countries for recognizing different languages as well.
References
[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature
Extraction Methods for Character Recognition-A
Survey", July 1995
Figure 6: Profiling
[2] Yasser Alginahi, Taibah University Kingdom of Saudi
Arabia, "Preprocessing Techniques in Character
4. Structural features:
Recognition"
[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram
Structural features are based on topological and geometrical
Shah, Benoy Kumar Thakur, "Recent Trends and Tools
properties of the character, such as aspect ratio, cross points,
for Feature Extraction
loops, branch points, strokes and their directions, inflection
[4] in OCR Technology", International Journal of Soft
between two points, horizontal curves at top or bottom, etc.
Computing and Engineering (IJSCE)
[5] ISSN: 2231-2307, Volume-2, Issue-6, January 2013
Volume 2 Issue 5, May 2013
www.ijsr.net
157
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064
[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok
Extraction and Classification for English Handwritten",
M. Sapkal, "Survey of Methods for Character
IJE TRANSACTIONS B: Applications Vol. 25, No. 2,
Recognition", International Journal of Engineering and
(May 2012) 99-106
Innovative Technology (IJEIT) Volume 1, Issue 5, May
[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview
2012
of Character Recognition Focused on
[7] Mohanad Alata, Mohammad Al-Shabi "TEXT [13] Off-Line Handwriting", IEEE TRANSACTIONS ON
DETECTION AND CHARACTER
SYSTEMS, MAN, AND CYBERNETICS-PART C:
[8] RECOGNITION USING FUZZY IMAGE
APPLICATIONS AND REVIEWS, VOL. 31, NO. 2,
PROCESSING",
Journal
of
ELECTRICAL
MAY 2001
ENGINEERING, VOL. 57, NO. 5, 2006, 258-267
[9] Rejean Plamondon, Fellow, IEEE and Sargur N.
Author Profile
Shrihari, Fellow, IEEE, "On-line and Off-line
Handwriting Recognition: A comprehensive Survey",
Umal Patel, pursuing her Master Degree in Computer
IEEE TRANSACTIONS ON PATTERN ANALYSIS
Science & Technology from Gujarat Technological
AND MACHINE INTELLIGENCE, VOL 22, NO. 1
University (L D College of Eng., Ahmedabad), received her
JANUARY 2000
Bachelor Degree in Computer Engg. from Gujarat
[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram
University in 2008. Presently working as Assistant Professor
Shah, "An Improved Zone Based Hybrid Feature
in Department of MCA, L.J Institute of Technology. Earlier
Extraction Model for Handwritten Alphabets
he has served as Software Test Engineer in Lodestone
Recognition Using Euler Number", International Journal
Software Services since June 2008. Her area of interest is
of Soft Computing and Engineering (IJSCE) ISSN:
Compilers and Image Processing.
2231-2307, Volume-2, Issue-2, May 2012
[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural
Network Based Recognition System Integrating Feature
Volume 2 Issue 5, May 2013
www.ijsr.net
158

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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An Introduction to the Process of Optical Character

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LD College Of Engineering, Gujarat Technological University, Gujarat, India

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Abstract: This paper presents an overview of methods and techniques used for feature extraction that helps in efficient classification

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
of the alphabets and numbers of English language. Character recognition has long been a essential area for research since years.

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Recognition of character is a minor work for humans, but to make a computer program that does character recognition is extremely

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
difficult. Hence to make a machine recognize the characters and efficiently determine a pattern has been the primary concern for

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
researchers now days This paper discusses various offline and online Optical Character Recognition Techniques (OCR).

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Keywords: OCR, online, offline, online, zoning, euler number.

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OCR is an approach that provides a full alphanumeric

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1. The same characters differ in sizes, shapes and styles

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recognition of printed or handwritten characters at

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from person to person and even from time to time with

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electronically by simply scanning them and generating into a

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the same person. The source of confusion is the high

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form that can be scanned through a scanner and then the

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level of abstraction: there are thousands styles of type in

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recognition engine of the OCR system interpret the images

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common use plus variations in calligraphy and a

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and turn images of handwritten or printed characters into

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character recognition program must recognize most of

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recognition also popularly referred as optical character

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2. Like any image, visual characters are subject to spoilage

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recognition (OCR) is a field of research that has immense

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due to noise. Some images containing characters are

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potential in future where we want to track and locate every

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already blurred or not clear which makes them difficult to

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piece of information being exchanged. The problem with the

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process. Noise consists of random changes to a pattern,

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hand written text is due to uncertainties such as variation in

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particularly near the edges. A character with much noise

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calligraphy over period of time, similarity in text, variation

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may be interpreted as a completely different character by

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in styles of writing [3] The character recognition system

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helps in making the communication between a human and a

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3. There are no hard-and-fast rules that define the

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computer easy.[4] The character recognition is basically

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appearance of a visual character. Hence rules need to be

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classified into two types: offline handwritten text

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recognition, online handwritten text recognition. Offline

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means the text written on the plain paper or sheet and then

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the writing is usually captured optically by a scanner and the

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completed writing is available as an image. Online means the

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text written on any digital devices such as tablets using

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stylus i.e. the two dimensional coordinates of successive

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points are represented as a function of time and the order of

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strokes made by the writer are also available.[6]

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The area of OCR is becoming an integral part of document

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scanners, and is used in many applications such as postal

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processing, script recognition, banking, security (i.e.

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document reading, mail sorting, signature verification, writer

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identification., license plate recognition system, smart card

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processing system, automatic data entry, bank cheque /DD

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processing, money counting machine, postal automation,

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address and zip code recognition etc many organizations are

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depending on OCR systems to eliminate the human

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interactions for better performance and efficiency [2,4,6,7].

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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recognition. Accuracy of character recognition heavily

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Most Important initial phase in OCR is to gather the image

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from either device sensor like PDA or tablets in case on

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online recognition or getting the images containing

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characters directly for offline recognition.

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In Image acquisition, the recognition system acquires a

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scanned image as an input image. The image should have a

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specific format such as JPEG, BMP etc. This image is

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acquired through a scanner, digital camera or any other

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suitable digital input device. Data samples for the

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experiment have been collected from different individuals

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The goal of pre-processing is to simplify the pattern

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recognition problem without missing any vital information.

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It reduces the noises and inconsistent data. It enhances the

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image and prepares it for the next steps [3].

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Preprocessing is the preliminary step which transforms the

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data into a format that will be more easily and effectively

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processed. Therefore, the main task in preprocessing the

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captured data is to decrease the variation that causes a

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reduction in the recognition rate and increases the

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The results of segmentation process provides isolated

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characters which are ready to pass through feature extraction

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complexities, as for example, preprocessing of the input raw

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stroke of characters is crucial for the success of efficient

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stage, thus the isolated characters are reduced to a specific

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size depending on the methods used. The segmentation

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character recognition systems. Thus, preprocessing is an

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process essentially renders the image in the form of m*n

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essential stage prior to feature extraction since it controls the

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suitability of the results for the successive stages [2].

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matrix. These matrices are then generally normalized by

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reducing the size and removing the redundant information

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from the image without losing any important information.

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Preprocessing can be done through various ways

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Binarization, Noise reduction, Stroke width normalization,

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Skew correction, Slant removal, Filtering, Morphological

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Operations, Noise Modelling, Skew Normalization, Size

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Feature extraction is the process of extracting the relevant

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features from objects/alphabets to form a feature vectors.

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These feature vectors is then used by classifiers to recognize

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the input unit with target output unit. It becomes easier for

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the classifier to classify between different classes by looking

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at these features as it allows fairly easy to distinguish.

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Feature extraction is also defined as extracting the raw data

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the information which is most relevant for classification

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purposes in the sense of minimizing the pattern

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Due to the nature of handwriting with its high degree of

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variability and imprecision obtaining these features, is a

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difficult task. Feature extraction methods are based on 3

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Figure 2: Normalization of 'e' and 'l' as in [9]

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Segmentation is an integral part of any text based

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recognition system. It assures efficiency of classification and

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The character image is divided into NxM zones. From each

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zone features are extracted to form the feature vector. The

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International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

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goal of zoning is to obtain the local characteristics instead of

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The Fourier Transform (FT) of the contour of the image is

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calculated. Since the first n coefficients of the FT can be

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used in order to reconstruct the contour, then these n

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coefficients are considered to be a n-dimensional feature

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After dividing the character into different zones you can

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compare the density or direction features of it and

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The basic idea behind using projections is that character

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images, which are 2-D signals, can be represented as 1-

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D signal. These features, although independent to noise

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The results Classification is the last stage where we train the

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and deformation, depend on rotation. Projection

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neural net using the feature vectors obtained during feature

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histograms count the number of pixels in each column

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extraction method against the required targets. To optimize

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and row of a character image. Projection histograms can

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the whole recognition process, several combination methods

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separate characters such as "m" and "n".

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of multilayer perceptron have been devised. E.g.: k-Nearest

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Neighbour (k-NN), Bayes Classifier, Neural Networks (NN),

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Hidden Markov Models (HMM), Support Vector Machines

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(SVM), etc there is no such thing as the "best classifier". The

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use of classifier depends on many factors, such as available

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training set, number of free parameters etc.

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The goal of post processing is the incorporation of context

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and shape information in all the stages of OCR systems is

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necessary for meaningful improvements in recognition rates.

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The profile counts the number of pixels (distance)

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between the bounding box of the character image and

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The character recognition methods have been introduced and

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the edge of the character. The profiles describe well the

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developed over the years. In this paper, I have tried to

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external shapes of characters and allow distinguishing

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explain the overview of the whole OCR process and the

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
between a great number of letters, such as "p" and "q".

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
methods related to it. Many researchers try to hybrid two or

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
more different methods and compare the results for

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
efficiency but again this will be application specific and

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
parameter specific. OCR has been implemented in various

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
countries for recognizing different languages as well.

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[1] Oivind Due Trier, Anil K. Jain, Torfinn Taxt, "Feature

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Extraction Methods for Character Recognition-A

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[2] Yasser Alginahi, Taibah University Kingdom of Saudi

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Arabia, "Preprocessing Techniques in Character

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[3] Om Prakash Sharma, M. K. Ghose, Krishna Bikram

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Structural features are based on topological and geometrical

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Shah, Benoy Kumar Thakur, "Recent Trends and Tools

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
properties of the character, such as aspect ratio, cross points,

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
loops, branch points, strokes and their directions, inflection

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[4] in OCR Technology", International Journal of Soft

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
between two points, horizontal curves at top or bottom, etc.

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
International Journal of Science and Research (IJSR), India Online ISSN: 2319-7064

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[6] Suruchi G. Dedgaonkar, Anjali A. Chandavale, Ashok

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Extraction and Classification for English Handwritten",

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
M. Sapkal, "Survey of Methods for Character

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
IJE TRANSACTIONS B: Applications Vol. 25, No. 2,

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Recognition", International Journal of Engineering and

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Innovative Technology (IJEIT) Volume 1, Issue 5, May

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[12] Nafiz Arica and Fatos T. Yarman-Vural, "An Overview

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[7] Mohanad Alata, Mohammad Al-Shabi "TEXT [13] Off-Line Handwriting", IEEE TRANSACTIONS ON

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
APPLICATIONS AND REVIEWS, VOL. 31, NO. 2,

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[9] Rejean Plamondon, Fellow, IEEE and Sargur N.

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Shrihari, Fellow, IEEE, "On-line and Off-line

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Umal Patel, pursuing her Master Degree in Computer

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Science & Technology from Gujarat Technological

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
University (L D College of Eng., Ahmedabad), received her

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Bachelor Degree in Computer Engg. from Gujarat

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[10] Om Prakash Sharma, M. K. Ghose, Krishna Bikram

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
University in 2008. Presently working as Assistant Professor

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Shah, "An Improved Zone Based Hybrid Feature

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
in Department of MCA, L.J Institute of Technology. Earlier

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Extraction Model for Handwritten Alphabets

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
he has served as Software Test Engineer in Lodestone

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Recognition Using Euler Number", International Journal

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Software Services since June 2008. Her area of interest is

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
of Soft Computing and Engineering (IJSCE) ISSN:

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
[11]J. Pradeepa,, E. Srinivasan, S. Himavathi, "Neural

# paths./ocr/ocr_async/{public_id}.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.pages[].lines[].text
Network Based Recognition System Integrating Feature

# paths./ocr/ocr_tables_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./ocr/ocr_tables_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|15.0 (per 1000 page)|1 page |**google**|`DocumentAI v1 beta3`|65.0 (per 1000 page)|1 page |**microsoft**|`rest API 3.0`|10.0 (per 1000 page)|1 page

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Angika**|`anp`| |**Arabic**|`ar`| |**Asturian**|`ast`| |**Awadhi**|`awa`| |**Azerbaijani**|`az`| |**Bagheli**|`bfy`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bhojpuri**|`bho`| |**Bislama**|`bi`| |**Bodo (India)**|`brx`| |**Bosnian**|`bs`| |**Braj**|`bra`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Bundeli**|`bns`| |**Buriat**|`bua`| |**Camling**|`rab`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Chamorro**|`ch`| |**Chhattisgarhi**|`hne`| |**Chinese**|`zh`| |**Cornish**|`kw`| |**Corsican**|`co`| |**Crimean Tatar**|`crh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dari**|`prs`| |**Dhimal**|`dhi`| |**Dogri (macrolanguage)**|`doi`| |**Dutch**|`nl`| |**English**|`en`| |**Erzya**|`myv`| |**Estonian**|`et`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Friulian**|`fur`| |**Gagauz**|`gag`| |**Galician**|`gl`| |**German**|`de`| |**Gilbertese**|`gil`| |**Gondi**|`gon`| |**Gurung**|`gvr`| |**Haitian**|`ht`| |**Halbi**|`hlb`| |**Hani**|`hni`| |**Haryanvi**|`bgc`| |**Hawaiian**|`haw`| |**Hindi**|`hi`| |**Hmong Daw**|`mww`| |**Ho**|`hoc`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Inari Sami**|`smn`| |**Indonesian**|`id`| |**Interlingua (International Auxiliary Language Association)**|`ia`| |**Inuktitut**|`iu`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Jaunsari**|`jns`| |**Javanese**|`jv`| |**K'iche'**|`quc`| |**Kabuverdianu**|`kea`| |**Kachin**|`kac`| |**Kalaallisut**|`kl`| |**Kangri**|`xnr`| |**Kara-Kalpak**|`kaa`| |**Karachay-Balkar**|`krc`| |**Kashubian**|`csb`| |**Kazakh**|`kk`| |**Khaling**|`klr`| |**Khasi**|`kha`| |**Kirghiz**|`ky`| |**Korean**|`ko`| |**Korku**|`kfq`| |**Koryak**|`kpy`| |**Kosraean**|`kos`| |**Kumarbhag Paharia**|`kmj`| |**Kumyk**|`kum`| |**Kurdish**|`ku`| |**Kurukh**|`kru`| |**Kölsch**|`ksh`| |**Lakota**|`lkt`| |**Latin**|`la`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Lower Sorbian**|`dsb`| |**Lule Sami**|`smj`| |**Luxembourgish**|`lb`| |**Mahasu Pahari**|`bfz`| |**Malay (macrolanguage)**|`ms`| |**Maltese**|`mt`| |**Manx**|`gv`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Mongolian**|`mn`| |**Montenegrin**|`cnr`| |**Neapolitan**|`nap`| |**Nepali (macrolanguage)**|`ne`| |**Niuean**|`niu`| |**Nogai**|`nog`| |**Northern Sami**|`se`| |**Norwegian**|`no`| |**Occitan (post 1500)**|`oc`| |**Ossetian**|`os`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Romansh**|`rm`| |**Russian**|`ru`| |**Sadri**|`sck`| |**Samoan**|`sm`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Scots**|`sco`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Sherpa**|`xsr`| |**Sirmauri**|`srx`| |**Skolt Sami**|`sms`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Southern Sami**|`sma`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tatar**|`tt`| |**Tetum**|`tet`| |**Thangmi**|`thf`| |**Tonga (Tonga Islands)**|`to`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Tuvinian**|`tyv`| |**Uighur**|`ug`| |**Upper Sorbian**|`hsb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Volapük**|`vo`| |**Walser**|`wae`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Yucateco**|`yua`| |**Zhuang**|`za`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Belarusian**|`be-Cyrl`| |**Belarusian (Latin)**|`be-Latn`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Traditional)**|`zh-Hant`| |**Kara-Kalpak (Cyrillic)**|`kaa-Cyrl`| |**Kazakh**|`kk-Cyrl`| |**Kazakh (Latin)**|`kk-Latn`| |**Kurdish (Arabic)**|`ku-Arab`| |**Kurdish (Latin)**|`ku-Latn`| |**Serbian (Cyrillic)**|`sr-Cyrl`| |**Serbian (Cyrillic, Montenegro)**|`sr-Cyrl-ME`| |**Serbian (Latin)**|`sr-Latn`| |**Serbian (Latin, Montenegro)**|`sr-Latn-ME`| |**Uzbek (Arabic)**|`uz-Arab`| |**Uzbek (Cyrillic)**|`uz-cyrl`|

# paths./ocr/ocr_tables_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./ocr/ocr_tables_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./ocr/receipt_parser.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**base64**|`latest`|0.25 (per 1 page)|1 page |**dataleon**|`v4.0.0`|0.05 (per 1 page)|1 page |**microsoft**|`rest API 3.0`|0.01 (per 1 page)|1 page |**mindee**|`v2`|0.1 (per 1 page)|1 page |**tabscanner**|`latest`|0.08 (per 1 page)|1 page |**google**|`DocumentAI v1 beta3`|0.01 (per 1 page)|10 page |**klippa**|`v1`|0.1 (per 1 file)|1 file |**veryfi**|`v8`|0.08 (per 1 file)|1 file |**amazon**|`boto3 1.26.8`|0.01 (per 1 page)|1 page |**affinda**|`v3`|0.07 (per 1 file)|1 file

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malayalam**|`ml`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-tw`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Netherlands)**|`nl-NL`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**French (Switzerland)**|`fr-CH`| |**German (Germany)**|`de-DE`| |**German (Switzerland)**|`de-CH`| |**Italian (Italy)**|`it-IT`| |**Italian (Switzerland)**|`it-CH`| |**Portuguese (Portugal)**|`pt-PT`| |**Spanish (Spain)**|`es-ES`|
**Note:** This feature is likely to be deprecated in future releases. It is recommended to use the `financial_parser` feature as an alternative.

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.extracted_data[].merchant_information.merchant_address
STR. ZAHARIA STANCU, NR. 1 BRASOV, JUD. BRASOV

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.veryfi.extracted_data[].merchant_information.merchant_address
Str. Zaharia Stancu nr. 1, Brașov, Brașov, 500167, RO

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.extracted_data[].merchant_information.merchant_address
SC AUCHAN ROMANIA SA
HYPERMARCHE AUCHAN
STR. ZAHARIA STANCU, NR. 1
BRASOV, JUD. BRASOV

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.mindee.extracted_data[].merchant_information.merchant_address
hypermarche auchan str zaharia stancu nr 1 brasov jud brasov

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.google.extracted_data[].merchant_information.merchant_address
Str. Zaharia Stancu 1
Brașov 500167
România

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data[].merchant_information.merchant_address
HYPERMARCHE AUCHAN STR. ZAHARIA STANCU, NR. 1 BRASOV, JUD. BRASOV

# paths./ocr/receipt_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data[].merchant_information.merchant_address
STR. ZAHARIA STANCU, NR. 1 BRASOV, JUD. BRASOV

# paths./ocr/resume_parser.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**affinda**|`v3`|0.07 (per 1 file)|1 file |**hireability**|`hireability 1.0.0`|0.05 (per 1 file)|1 file |**klippa**|`v1`|0.1 (per 1 file)|1 file |**senseloaf**|`v3`|0.045 (per 1 file)|1 file |**extracta**|`v1`|0.1 (per 1 page)|1 page

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Gujarati**|`gu`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Macedonian**|`mk`| |**Malayalam**|`ml`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-tw`|

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.senseloaf.extracted_data.personal_infos.address.formatted_location
2002 Front Range Way Fort Collins CO 80525

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.senseloaf.extracted_data.personal_infos.self_summary
Career Summary Four years experience in early childhood development with a diverse background in the care of special needs children and adults . Adult Care Experience • Determined work placement for 150 special needs adult clients . Maintained client databases and records . Coordinated client contact with local health care professionals on a monthly basis . Managed 25 volunteer workers .

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.senseloaf.extracted_data.skills[].name
Data Base User Interface And Query Software

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data.personal_infos.address.formatted_location
2002 Caribou Dr Suite 101, Fort Collins, CO 80525, USA

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data.personal_infos.address.raw_input_location
2002 Front Range Way Fort Collins, CO 80525

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data.personal_infos.self_summary
Four years experience in early childhood development with a diverse background in the care of special needs children and adults. Adult Care Experience • Determined work placement for 150 special needs adult clients. • Maintained client databases and records. • Coordinated client contact with local health care professionals on a monthly basis. • Managed 25 volunteer workers.

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.extracta.extracted_data.personal_infos.address.formatted_location
2002 Front Range Way Fort Collins, CO 80525

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.extracta.extracted_data.personal_infos.self_summary
Four years experience in early childhood development with a diverse background in the care of special needs children and adults.

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.extracta.extracted_data.work_experience.entries[].description
Client Specialist, Rainbow Special Care Center, Little Rock, Arkansas

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data.personal_infos.address.formatted_location
2002 Front Range Way Fort Collins CO 80525

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data.personal_infos.self_summary
Career Summary Four years experience in early childhood development with a diverse background in the care of special needs children and adults . Adult Care Experience • Determined work placement for 150 special needs adult clients . Maintained client databases and records . Coordinated client contact with local health care professionals on a monthly basis . Managed 25 volunteer workers .

# paths./ocr/resume_parser.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.extracted_data.skills[].name
Data Base User Interface And Query Software

# paths./pipeline/.post.description
Workflow let you create a pipeline of subfeature by inputing an entry data, letting it pass through all the subfeatures and retreiving the result


**Note**: You can try Workflow on our [developer portal](https://app.edenai.run/bricks/workflows) and a `description` snippet will automatically get generated as you build your workflow

**Example:**

Schema: ocr --> automatic_translation --> summarize
In this workflow we pass a file as input, ocr parse the text, pass it to translate, and summarize will return a summary of the translated text

**Inputs:**

Depending on the first subfeature you have three choice for the inital input data: `file`, `text` or `texts`.

To create a workflow you have to pass an object to the `description` parameter, the object should look like this:


```
[
  {
    "feature": "ocr",
    "subfeature": "ocr",
    "params": {
      "language": "auto-detect",
      "providers": "google"
    }
  },
  {
    "feature": "translation",
    "subfeature": "automatic_translation",
    "params": {
      "source_language": "auto-detect",
      "target_language": "fr",
      "providers": "google"
    }
  },
  {
    "feature": "text",
    "subfeature": "summarize",
    "params": {
      "providers": "openai"
    }
  }
]
```

- `description` should be a list of *nodes* each node representing a subfeature.Inside each node, enter the desired feature and subfeature and its params. `params` are the parameters you should normally send to the subfeature as if you were doing a direct call but with a few constraints:
    + `providers` should take only one provider name, if you input multiple providers, the response of all the other providers will be ignored
    + `file`, `text`, `texts` shouldn't be present in `params` as these are initial inputs

**Important!**: description should be sent as a string since the content-type is a form-data

- `return_only_last` allows you to chose wether you want a list of all the response returned by each providers or just getting the last response. If set to `true` the response will be the last subfeature result, if set to `false` the reponse will be a list of all subfeature results.
    

# paths./text/ai_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**originalityai**|`v1`|0.01 (per 400 char)|400 char |**sapling**|`v1`|5.0 (per 1000000 char)|1000 char |**winstonai**|`v2`|14.0 (per 1000000 char)|1 char

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`|

# paths./text/ai_detection.post.requestBody.content.application/json.examples.RequestExample.value.text
The panther, also known as the black panther, is a magnificent and enigmatic creature that captivates the imagination of many. It is not a distinct species itself, but rather a melanistic variant of leopards and jaguars. The mesmerizing black coat of the panther is a result of a genetic mutation that increases the production of dark pigment, melanin.         Panthers are highly adaptable predators, found primarily in dense forests and jungles across Africa, Asia, and the Americas. Their stealthy nature and exceptional agility make them formidable hunters. They are solitary creatures, preferring to roam alone in their vast territories, which can span over a hundred square miles.         Equipped with incredible strength and sharp retractable claws, panthers are skilled climbers and swimmers. Their keen senses, including sharp vision and acute hearing, aid them in locating prey, often stalking their victims from the cover of trees or thick underbrush before launching a precise and powerful attack.         The diet of a panther consists mainly of deer, wild boar, and smaller mammals. However, they are opportunistic hunters and can also target livestock and domestic animals in areas where their habitats overlap with human settlements. Unfortunately, this sometimes leads to conflicts with humans, resulting in the panther being perceived as a threat.         Despite their association with darkness and mystery, panthers play a vital role in maintaining the balance of ecosystems. As apex predators, they help control populations of herbivores, preventing overgrazing and maintaining healthy prey dynamics.         Conservation efforts are crucial to the survival of panther populations worldwide. Habitat loss, poaching, and illegal wildlife trade pose significant threats to their existence. Various organizations and governments are working tirelessly to protect these magnificent creatures through initiatives such as establishing protected areas, promoting sustainable land use practices, and raising awareness about their importance in the natural world.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
The panther, also known as the black panther, is a magnificent and enigmatic creature that captivates the imagination of many.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
It is not a distinct species itself, but rather a melanistic variant of leopards and jaguars.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
The mesmerizing black coat of the panther is a result of a genetic mutation that increases the production of dark pigment, melanin.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
Panthers are highly adaptable predators, found primarily in dense forests and jungles across Africa, Asia, and the Americas.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
Their stealthy nature and exceptional agility make them formidable hunters.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
They are solitary creatures, preferring to roam alone in their vast territories, which can span over a hundred square miles.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
Equipped with incredible strength and sharp retractable claws, panthers are skilled climbers and swimmers.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
Their keen senses, including sharp vision and acute hearing, aid them in locating prey, often stalking their victims from the cover of trees or thick underbrush before launching a precise and powerful attack.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
The diet of a panther consists mainly of deer, wild boar, and smaller mammals.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].text
However, they are opportunistic hunters and can also target livestock and domestic animals in areas where their habitats overlap with human settlements.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
The panther, also known as the black panther, is a magnificent and enigmatic creature that captivates the imagination of many. It is not a distinct species itself, but rather a melanistic variant of leopards and jaguars. The mesmerizing black coat of the panther is a result of a genetic mutation that increases the production of dark pigment, melanin.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Panthers are highly adaptable predators, found primarily in dense forests and jungles across Africa, Asia, and the Americas. Their stealthy nature and exceptional agility make them formidable hunters. They are solitary creatures, preferring to roam alone in their vast territories, which can span over a hundred square miles.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Equipped with incredible strength and sharp retractable claws, panthers are skilled climbers and swimmers. Their keen senses, including sharp vision and acute hearing, aid them in locating prey, often stalking their victims from the cover of trees or thick underbrush before launching a precise and powerful attack. The diet of a panther consists mainly of deer, wild boar, and smaller mammals.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
However, they are opportunistic hunters and can also target livestock and domestic animals in areas where their habitats overlap with human settlements. Unfortunately, this sometimes leads to conflicts with humans, resulting in the panther being perceived as a threat.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Despite their association with darkness and mystery, panthers play a vital role in maintaining the balance of ecosystems. As apex predators, they help control populations of herbivores, preventing overgrazing and maintaining healthy prey dynamics. Conservation efforts are crucial to the survival of panther populations worldwide.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Habitat loss, poaching, and illegal wildlife trade pose significant threats to their existence. Various organizations and governments are working tirelessly to protect these magnificent creatures through initiatives such as establishing protected areas, promoting sustainable land use practices, and raising awareness about their importance in the natural world.

# paths./text/ai_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].text
The panther, also known as the black panther, is a magnificent and enigmatic creature that captivates the imagination of many. It is not a distinct species itself, but rather a melanistic variant of leopards and jaguars. The mesmerizing black coat of the panther is a result of a genetic mutation that increases the production of dark pigment, melanin.         Panthers are highly adaptable predators, found primarily in dense forests and jungles across Africa, Asia, and the Americas. Their stealthy nature and exceptional agility make them formidable hunters. They are solitary creatures, preferring to roam alone in their vast territories, which can span over a hundred square miles.         Equipped with incredible strength and sharp retractable claws, panthers are skilled climbers and swimmers. Their keen senses, including sharp vision and acute hearing, aid them in locating prey, often stalking their victims from the cover of trees or thick underbrush before launching a precise and powerful attack.         The diet of a panther consists mainly of deer, wild boar, and smaller mammals. However, they are opportunistic hunters and can also target livestock and domestic animals in areas where their habitats overlap with human settlements. Unfortunately, this sometimes leads to conflicts with humans, resulting in the panther being perceived as a threat.         Despite their association with darkness and mystery, panthers play a vital role in maintaining the balance of ecosystems. As apex predators, they help control populations of herbivores, preventing overgrazing and maintaining healthy prey dynamics.         Conservation efforts are crucial to the survival of panther populations worldwide. Habitat loss, poaching, and illegal wildlife trade pose significant threats to their existence. Various organizations and governments are working tirelessly to protect these magnificent creatures through initiatives such as establishing protected areas, promoting sustainable land use practices, and raising awareness about their importance in the natural world.

# paths./text/anonymization.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**emvista**|`v1.0`|3.0 (per 1000000 char)|1 char |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**microsoft**|`v3.1`|0.25 (per 1000000 char)|1000 char

Supported Languages
|Name|Value| |----|-----| |**Arabic**|`ar`| |**Chinese**|`zh`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Hindi**|`hi`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Modern Greek (1453-)**|`el`| |**Norwegian**|`no`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Russian**|`ru`| |**Spanish**|`es`| |**Swedish**|`sv`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Traditional)**|`zh-Hant`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`|

# paths./text/anonymization.post.requestBody.content.application/json.examples.RequestExample.value.text
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/anonymization.post.responses.200.content.application/json.examples.ResponseExample.value.emvista.result
The phone number of *** is the 06 21 ** 43 54.

# paths./text/chat.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|**gpt-3.5-turbo**|`v1Beta`|0.002 (per 1000 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v1Beta`|0.002 (per 1000 token)|1 token |**openai**|-|`v1Beta`|0.002 (per 1000 token)|1 token |**openai**|**gpt-4**|`v1Beta`|0.06 (per 1000 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v1Beta`|0.002 (per 1000 token)|1 token |**openai**|**gpt-4-0314**|`v1Beta`|0.06 (per 1000 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v1Beta`|0.004 (per 1000 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v1Beta`|0.03 (per 1000 token)|1 token |**openai**|**gpt-4-1106-preview**|`v1Beta`|0.03 (per 1000 token)|1 token |**openai**|**gpt-4-vision-preview**|`v1Beta`|0.03 (per 1000 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v1Beta`|0.0015 (per 1000 token)|1 token |**openai**|**gpt-4-32k-0314**|`v1Beta`|0.12 (per 1000 token)|1 token |**google**|**chat-bison**|`v1`|0.5 (per 1000000 char)|1000 char |**google**|-|`v1`|0.5 (per 1000000 char)|1000 char |**replicate**|-|`v1`|0.0032 (per 1 exec_time)|1 exec_time |**replicate**|**llama-2-70b-chat**|`v1`|0.0032 (per 1 exec_time)|1 exec_time |**cohere**|**command**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**command-light**|`2022-12-06`|0.6 (per 1000000 token)|1 token |**cohere**|**command-light-nightly**|`2022-12-06`|0.6 (per 1000000 token)|1 token |**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**command-r**|`2022-12-06`|1.5 (per 1000000 token)|1 token |**cohere**|**command-r-plus**|`2022-12-06`|15.0 (per 1000000 token)|1 token |**cohere**|-|`2022-12-06`|2.0 (per 1000000 token)|1 token |**meta**|-|`v1`|2.56 (per 1000000 token)|1 token |**meta**|**llama2-13b-chat-v1**|`v1`|1.0 (per 1000000 token)|1 token |**meta**|**llama2-70b-chat-v1**|`v1`|2.56 (per 1000000 token)|1 token |**mistral**|**small**|`v0.0.1`|6.0 (per 1000000 token)|1 token |**mistral**|**medium**|`v0.0.1`|8.1 (per 1000000 token)|1 token |**mistral**|**tiny**|`v0.0.1`|0.25 (per 1000000 token)|1 token |**mistral**|**large-latest**|`v0.0.1`|24.0 (per 1000000 token)|1 token |**mistral**|-|`v0.0.1`|24.0 (per 1000000 token)|1 token |**perplexityai**|**mistral-7b-instruct**|`v1.0`|0.28 (per 1000000 token)|1 token |**perplexityai**|**mixtral-8x7b-instruct**|`v1.0`|0.28 (per 1000000 token)|1 token |**perplexityai**|**pplx-7b-chat**|`v1.0`|0.28 (per 1000000 token)|1 token |**perplexityai**|**pplx-7b-online**|`v1.0`|0.28 (per 1000000 token)|1 token |**perplexityai**|**codellama-34b-instruct**|`v1.0`|1.4 (per 1000000 token)|1 token |**perplexityai**|**llama-2-70b-chat**|`v1.0`|2.8 (per 1000000 token)|1 token |**perplexityai**|**pplx-70b-chat**|`v1.0`|2.8 (per 1000000 token)|1 token |**perplexityai**|**pplx-70b-online**|`v1.0`|2.8 (per 1000000 token)|1 token |**perplexityai**|-|`v1.0`|2.8 (per 1000000 token)|1 token |**perplexityai**|**sonar-small-chat**|`v1.0`|0.2 (per 1000000 token)|1 token |**perplexityai**|**sonar-medium-chat**|`v1.0`|0.6 (per 1000000 token)|1 token |**anthropic**|**claude-3-haiku-20240307-v1:0**|`bedrock-2023-05-31`|1.25 (per 1000000 token)|1 token |**anthropic**|**claude-3-sonnet-20240229-v1:0**|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token |**anthropic**|**claude-instant-v1**|`bedrock-2023-05-31`|2.4 (per 1000000 token)|1 token |**anthropic**|**claude-v2**|`bedrock-2023-05-31`|24.0 (per 1000000 token)|1 token |**anthropic**|-|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`|

Supported Models

openai
|Name|Value| |----|-----| |**openai**|`gpt-3.5-turbo`| ||`gpt-3.5-turbo-0125`| ||`gpt-3.5-turbo-0301`| ||`gpt-3.5-turbo-1106`| ||`gpt-3.5-turbo-16k`| ||`gpt-4`| ||`gpt-4-0314`| ||`gpt-4-1106-preview`| ||`gpt-4-32k-0314`| ||`gpt-4-turbo-2024-04-09`| ||`gpt-4-vision-preview`|

google
|Name|Value| |----|-----| |**google**|`chat-bison`|

replicate
|Name|Value| |----|-----| |**replicate**|`llama-2-70b-chat`|

cohere
|Name|Value| |----|-----| |**cohere**|`command`| ||`command-light`| ||`command-light-nightly`| ||`command-nightly`| ||`command-r`| ||`command-r-plus`|

meta
|Name|Value| |----|-----| |**meta**|`llama2-13b-chat-v1`| ||`llama2-70b-chat-v1`|

mistral
|Name|Value| |----|-----| |**mistral**|`large-latest`| ||`medium`| ||`small`| ||`tiny`|

perplexityai
|Name|Value| |----|-----| |**perplexityai**|`codellama-34b-instruct`| ||`llama-2-70b-chat`| ||`mistral-7b-instruct`| ||`mixtral-8x7b-instruct`| ||`pplx-70b-chat`| ||`pplx-70b-online`| ||`pplx-7b-chat`| ||`pplx-7b-online`| ||`sonar-medium-chat`| ||`sonar-small-chat`|

anthropic
|Name|Value| |----|-----| |**anthropic**|`claude-3-haiku-20240307-v1:0`| ||`claude-3-sonnet-20240229-v1:0`| ||`claude-instant-v1`| ||`claude-v2`|

# paths./text/chat.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.requestBody.content.application/json.examples.RequestExample.value.chatbot_global_action
You are a keyword extractor. Extract Only the word from the text provided.

# paths./text/chat.post.requestBody.content.application/json.examples.RequestExample.value.previous_history[].message
Steve Jobs was a co-founder of Apple Inc., a multinational technology company headquartered in Cupertino, California. He was also the CEO and a major shareholder of Pixar Animation Studios, which was later acquired by The Walt Disney Company. Jobs was widely recognized as a visionary entrepreneur and a pioneer in the personal computer industry. In addition to his business ventures, he was also known for his charismatic personality, his signature black turtleneck, and his famous keynote presentations at Apple's product launches.

# paths./text/chat.post.requestBody.content.application/json.examples.RequestExample.value.previous_history[].message
steve jobs, apple inc, pixar, california

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.replicate.generated_text
 Sure! Here are the keywords extracted from the given text:

* Barack Obama
* President
* United States
* African-American
* Democratic Party
* Illinois
* Senator

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.replicate.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.replicate.message[].message
 Sure! Here are the keywords extracted from the given text:

* Barack Obama
* President
* United States
* African-American
* Democratic Party
* Illinois
* Senator

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.perplexityai.generated_text
Barack Hussein Obama II is an American politician who served as the 44th President of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president in U.S. history. Before becoming President, Obama held various political positions, including:

- U.S. Senator from Illinois (2005-2008)
- Illinois State Senator (1997-2004)

In addition to his political career, Obama worked as a civil rights lawyer and university lecturer. He was born on August 4, 1961, in Honolulu, Hawaii, and graduated from Columbia University in 1983 with a B.A. in political science. Obama's story is the American story, with values from the heartland, a middle-class upbringing in a strong family, hard work and education as the means of getting ahead, and the conviction that a life so blessed should be lived in service to others.

During his presidency, Obama implemented significant healthcare reforms and worked on various domestic and foreign policy issues. He was awarded the Nobel Peace Prize in 2009 for his extraordinary efforts to strengthen international diplomacy, human rights, and peace. After serving two terms as President, Obama is currently serving as a private citizen.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.perplexityai.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.perplexityai.message[].message
Barack Hussein Obama II is an American politician who served as the 44th President of the United States from 2009 to 2017. A member of the Democratic Party, he was the first African-American president in U.S. history. Before becoming President, Obama held various political positions, including:

- U.S. Senator from Illinois (2005-2008)
- Illinois State Senator (1997-2004)

In addition to his political career, Obama worked as a civil rights lawyer and university lecturer. He was born on August 4, 1961, in Honolulu, Hawaii, and graduated from Columbia University in 1983 with a B.A. in political science. Obama's story is the American story, with values from the heartland, a middle-class upbringing in a strong family, hard work and education as the means of getting ahead, and the conviction that a life so blessed should be lived in service to others.

During his presidency, Obama implemented significant healthcare reforms and worked on various domestic and foreign policy issues. He was awarded the Nobel Peace Prize in 2009 for his extraordinary efforts to strengthen international diplomacy, human rights, and peace. After serving two terms as President, Obama is currently serving as a private citizen.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.generated_text
barack hussein obama, democratic party, african-american, united states, illinois, senator

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.message[].message
barack hussein obama, democratic party, african-american, united states, illinois, senator

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.openai.generated_text
Barack Hussein Obama, American, politician, president, United States, Democratic Party, African-American, senator, Illinois, state

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.openai.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.openai.message[].message
Barack Hussein Obama, American, politician, president, United States, Democratic Party, African-American, senator, Illinois, state

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.meta.generated_text
barack obama, united states, democratic party, illinois

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.meta.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.meta.message[].message
barack obama, united states, democratic party, illinois

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.generated_text
Barack Hussein Obama was an American politician who served as the 44th president of the United States from 2009 to 2017. He was a member of the Democratic Party and previously served as a U.S. senator</co:0,1,5,8> from <co: 0,5,8>Illinois from 2005 to 2008. 

Obama was born in Honolulu, Hawaii in 1961 and was raised in Kenya. He has a degree from Columbia University and Harvard Law School. After serving as an Illinois state senator, he was elected as a U.S. senator</co:1,5,8> in <co: 1,4>2004. 

During his presidency, Obama took several actions to raise vehicle fuel efficiency, including increasing corporate average fuel economy, signing the "cash-for-clunkers" bill, and lifting the ban on federal funding for embryonic stem cell research. 

Barack Obama's personal information I could find includes his birthday, place of birth, family diversity, and personal charisma. Would you like me to continue extracting information or provide specific details on any of these categories?

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.message[].message
Barack Hussein Obama was an American politician who served as the 44th president of the United States from 2009 to 2017. He was a member of the Democratic Party and previously served as a U.S. senator</co:0,1,5,8> from <co: 0,5,8>Illinois from 2005 to 2008. 

Obama was born in Honolulu, Hawaii in 1961 and was raised in Kenya. He has a degree from Columbia University and Harvard Law School. After serving as an Illinois state senator, he was elected as a U.S. senator</co:1,5,8> in <co: 1,4>2004. 

During his presidency, Obama took several actions to raise vehicle fuel efficiency, including increasing corporate average fuel economy, signing the "cash-for-clunkers" bill, and lifting the ban on federal funding for embryonic stem cell research. 

Barack Obama's personal information I could find includes his birthday, place of birth, family diversity, and personal charisma. Would you like me to continue extracting information or provide specific details on any of these categories?

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.google.generated_text
Barack Obama, president, united states, african american, democrat, senator, illinois

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.google.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.google.message[].message
Barack Obama, president, united states, african american, democrat, senator, illinois

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.generated_text
barack, hussein, obama, president, united, states, democratic, party, illinois, senator

(Note: I've included "president" as a keyword even though it's part of the sentence "president of the United States" because it is a key role and title associated with Barack Obama.)

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.message[].message
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.message[].message
barack, hussein, obama, president, united, states, democratic, party, illinois, senator

(Note: I've included "president" as a keyword even though it's part of the sentence "president of the United States" because it is a key role and title associated with Barack Obama.)

# paths./text/chat/stream.post.description
Streamed version of Chat feature, the raw text will be streamed chunk by chunk.

NOTE: For this feature, you an only request one provider at a time.

# paths./text/chat/stream.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/chat/stream.post.requestBody.content.application/json.examples.RequestExample.value.chatbot_global_action
You are a keyword extractor. Extract Only the word from the text provided.

# paths./text/chat/stream.post.requestBody.content.application/json.examples.RequestExample.value.previous_history[].message
Steve Jobs was a co-founder of Apple Inc., a multinational technology company headquartered in Cupertino, California. He was also the CEO and a major shareholder of Pixar Animation Studios, which was later acquired by The Walt Disney Company. Jobs was widely recognized as a visionary entrepreneur and a pioneer in the personal computer industry. In addition to his business ventures, he was also known for his charismatic personality, his signature black turtleneck, and his famous keynote presentations at Apple's product launches.

# paths./text/chat/stream.post.requestBody.content.application/json.examples.RequestExample.value.previous_history[].message
steve jobs, apple inc, pixar, california

# paths./text/code_generation.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**openai**|`v1`|2.0 (per 1000000 token)|1 token |**google**|`v1`|0.5 (per 1000000 char)|1 char |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Achinese**|`ace`| |**Afrikaans**|`af`| |**Akan**|`ak`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Asturian**|`ast`| |**Awadhi**|`awa`| |**Ayacucho Quechua**|`quy`| |**Balinese**|`ban`| |**Bambara**|`bm`| |**Banjar**|`bjn`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bemba (Zambia)**|`bem`| |**Bengali**|`bn`| |**Bhojpuri**|`bho`| |**Bosnian**|`bs`| |**Buginese**|`bug`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Central Atlas Tamazight**|`tzm`| |**Central Aymara**|`ayr`| |**Central Kanuri**|`knc`| |**Central Kurdish**|`ckb`| |**Chhattisgarhi**|`hne`| |**Chinese**|`zh`| |**Chokwe**|`cjk`| |**Crimean Tatar**|`crh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**Dyula**|`dyu`| |**Dzongkha**|`dz`| |**Eastern Yiddish**|`ydd`| |**Egyptian Arabic**|`arz`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Ewe**|`ee`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Finnish**|`fi`| |**Fon**|`fon`| |**French**|`fr`| |**Friulian**|`fur`| |**Galician**|`gl`| |**Ganda**|`lg`| |**Georgian**|`ka`| |**German**|`de`| |**Guarani**|`gn`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Halh Mongolian**|`khk`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Iloko**|`ilo`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kabiyè**|`kbp`| |**Kabuverdianu**|`kea`| |**Kabyle**|`kab`| |**Kachin**|`kac`| |**Kamba (Kenya)**|`kam`| |**Kannada**|`kn`| |**Kashmiri**|`ks`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kikuyu**|`ki`| |**Kimbundu**|`kmb`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Kongo**|`kg`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latgalian**|`ltg`| |**Ligurian**|`lij`| |**Limburgan**|`li`| |**Lingala**|`ln`| |**Lithuanian**|`lt`| |**Lombard**|`lmo`| |**Luba-Katanga**|`lu`| |**Luo (Kenya and Tanzania)**|`luo`| |**Lushai**|`lus`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Magahi**|`mag`| |**Maithili**|`mai`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Manipuri**|`mni`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Mesopotamian Arabic**|`acm`| |**Minangkabau**|`min`| |**Modern Greek (1453-)**|`el`| |**Moroccan Arabic**|`ary`| |**Mossi**|`mos`| |**Najdi Arabic**|`ars`| |**Nepali (macrolanguage)**|`ne`| |**Nigerian Fulfulde**|`fuv`| |**North Azerbaijani**|`azj`| |**North Levantine Arabic**|`apc`| |**Northern Kurdish**|`kmr`| |**Northern Uzbek**|`uzn`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nuer**|`nus`| |**Nyanja**|`ny`| |**Occitan (post 1500)**|`oc`| |**Oriya (macrolanguage)**|`or`| |**Pangasinan**|`pag`| |**Panjabi**|`pa`| |**Papiamento**|`pap`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Plateau Malagasy**|`plt`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Rundi**|`rn`| |**Russian**|`ru`| |**Samoan**|`sm`| |**Sango**|`sg`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Sardinian**|`sc`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Shan**|`shn`| |**Shona**|`sn`| |**Sicilian**|`scn`| |**Silesian**|`szl`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**South Azerbaijani**|`azb`| |**South Levantine Arabic**|`ajp`| |**Southern Pashto**|`pbt`| |**Southern Sotho**|`st`| |**Southwestern Dinka**|`dik`| |**Spanish**|`es`| |**Standard Latvian**|`lvs`| |**Standard Malay**|`zsm`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swati**|`ss`| |**Swedish**|`sv`| |**Ta'izzi-Adeni Arabic**|`acq`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamashek**|`tmh`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tok Pisin**|`tpi`| |**Tosk Albanian**|`als`| |**Tsonga**|`ts`| |**Tswana**|`tn`| |**Tumbuka**|`tum`| |**Tunisian Arabic**|`aeb`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Twi**|`tw`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Umbundu**|`umb`| |**Urdu**|`ur`| |**Venetian**|`vec`| |**Vietnamese**|`vi`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**West Central Oromo**|`gaz`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yoruba**|`yo`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Arabic (world)**|`ar-001`| |**Chinese (Traditional)**|`zh-Hant`| |**Persian (Afghanistan)**|`fa-AF`|

# paths./text/code_generation.post.requestBody.content.application/json.examples.RequestExample.value.instruction
Write a function in python that calculates fibonacci

# paths./text/code_generation.post.responses.200.content.application/json.examples.ResponseExample.value.openai.generated_text
Sure, here's a Python function that checks if a year is a leap year:

```python
def is_leap_year(year):
    """
    Returns True if the given year is a leap year, False otherwise.
    """
    if year % 4 == 0:
        if year % 100 == 0:
            if year % 400 == 0:
                return True
            else:
                return False
        else:
            return True
    else:
        return False
```

This function takes a year as input and returns True if it is a leap year, and False otherwise. It uses the standard rules for determining leap years: a year is a leap year if it is divisible by 4, unless it is also divisible by 100, in which case it is only a leap year if it is also divisible by 400.

# paths./text/code_generation.post.responses.200.content.application/json.examples.ResponseExample.value.google.generated_text
```python
def is_leap_year(year):
  """
  Determines whether a year is a leap year.

  Args:
    year: The year to check.

  Returns:
    True if the year is a leap year, False otherwise.
  """

  # A year is a leap year if it is divisible by 4, unless it is divisible by 100
  # unless it is also divisible by 400.

  if year % 4 == 0:
    if year % 100 == 0:
      return year % 400 == 0
    else:
      return True
  else:
    return False
```

# paths./text/code_generation.post.responses.200.content.application/json.examples.ResponseExample.value.nlpcloud.generated_text
def is_leap_year(year):
    if (year%4 == 0 and year%100 != 0) or (year%400 == 0):
            return True
    return False

# paths./text/custom_classification.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**cohere**|`2022-12-06`|0.5 (per 1000 request)|1 request |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token

# paths./text/custom_classification.post.requestBody.content.application/json.examples.RequestExample.value.examples[][]
I need help please wire me $1000 right now

# paths./text/custom_classification.post.requestBody.content.application/json.examples.RequestExample.value.examples[][]
Review changes to our Terms and Conditions

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Congratulations! You've won a free vacation! Click the link to claim your prize now!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Make money fast! Join our exclusive program and start earning thousands in just a few days!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Get rich quick with this amazing investment opportunity. Guaranteed returns!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Unlock special discounts on luxury goods. Limited-time offer! Click here to shop now!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
You've been selected for a special promotion. Act now to secure your spot!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Meet hot singles in your area! Chat now and find your perfect match!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Eliminate debt effortlessly! Our program can erase your financial worries.

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.openai.classifications[].input
Claim your inheritance! Just provide your bank details for a seamless transfer.

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
Congratulations! You've won a free vacation! Click the link to claim your prize now!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
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# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
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# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
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# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
You've been selected for a special promotion. Act now to secure your spot!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
Meet hot singles in your area! Chat now and find your perfect match!

# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
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# paths./text/custom_classification.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.classifications[].input
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# paths./text/custom_named_entity_recognition.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**cohere**|`2022-12-06`|2.0 (per 1000000 token)|1 token

# paths./text/custom_named_entity_recognition.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/custom_named_entity_recognition.post.requestBody.content.application/json.examples.RequestExample.value.examples[].text
Steve Jobs was the co-founder of Apple Inc., a multinational technology company based in Cupertino, California. He was born in San Francisco, California in 1955, and studied at Reed College before dropping out to start Apple with Steve Wozniak in 1976

# paths./text/embeddings.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|**1536__text-embedding-ada-002**|`v3.0.0`|0.1 (per 1000000 token)|1 token |**openai**|-|`v3.0.0`|0.1 (per 1000000 token)|1 token |**google**|-|`v1`|0.1 (per 1000000 char)|1 char |**google**|**768__textembedding-gecko**|`v1`|0.1 (per 1000000 char)|1 char |**cohere**|-|`v1`|0.1 (per 1000000 char)|1 char |**cohere**|**4096embed-english-v2.0**|`v1`|0.1 (per 1000000 char)|1 char |**cohere**|**1024embed-english-light-v2.0**|`v1`|0.1 (per 1000000 char)|1 char |**cohere**|**768__embed-multilingual-v2.0**|`v1`|0.1 (per 1000000 char)|1 char |**cohere**|**4096__embed-english-v2.0**|`v1`|0.1 (per 1000000 char)|1 char |**mistral**|**1024__mistral-embed**|`v0.0.1`|0.1 (per 1000000 token)|1 token |**mistral**|-|`v0.0.1`|0.1 (per 10000001 token)|1 token |**jina**|-|`v1`|0.018 (per 1000000 token)|1 token |**jina**|**jina-embeddings-v2-base-en**|`v1`|0.018 (per 1000000 token)|1 token

Supported Models

openai
|Name|Value| |----|-----| |**openai**|`1536__text-embedding-ada-002`|

google
|Name|Value| |----|-----| |**google**|`768__textembedding-gecko`|

cohere
|Name|Value| |----|-----| |**cohere**|`1024embed-english-light-v2.0`| ||`4096__embed-english-v2.0`| ||`4096embed-english-v2.0`| ||`768__embed-multilingual-v2.0`|

mistral
|Name|Value| |----|-----| |**mistral**|`1024__mistral-embed`|

jina
|Name|Value| |----|-----| |**jina**|`jina-embeddings-v2-base-en`|

# paths./text/emotion_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request |**vernai**|`v1`|2.0 (per 1000 request)|1 request

# paths./text/entity_sentiment.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|1.0 (per 1000000 char)|300 char |**google**|`v1`|2.0 (per 1000000 char)|1000 char

Supported Languages
|Name|Value| |----|-----| |**English**|`en`| |**Japanese**|`ja`| |**Spanish**|`es`|

# paths./text/entity_sentiment.post.requestBody.content.application/json.examples.RequestExample.value.text
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/generation.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**cohere**|**command**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**command-light**|`2022-12-06`|0.6 (per 1000000 token)|1 token |**cohere**|**command-light-nightly**|`2022-12-06`|0.6 (per 1000000 token)|1 token |**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|-|`2022-12-06`|2.0 (per 1000000 token)|1 token |**openai**|-|`v1`|2.0 (per 1000000 token)|1 token |**openai**|**gpt-3.5-turbo-instruct**|`v1`|2.0 (per 1000000 token)|1 token |**openai**|**text-babbage-002**|`v1`|0.4 (per 1000000 token)|1 token |**openai**|**text-davinci-002**|`v1`|2.0 (per 1000000 token)|1 token |**google**|-|`v1`|0.375 (per 1000000 char)|1000 char |**google**|**gemini-pro**|`v1`|0.375 (per 1000000 char)|1000 char |**google**|**text-bison**|`v1`|0.5 (per 1000000 char)|1000 char |**ai21labs**|-|`v1`|0.0188 (per 1000 token)|1 token |**ai21labs**|**j2-mid**|`v1`|0.0125 (per 1000 token)|1 token |**ai21labs**|**j2-ultra**|`v1`|0.0188 (per 1000 token)|1 token |**anthropic**|**claude-instant-v1**|`bedrock-2023-05-31`|2.4 (per 1000000 token)|1 token |**anthropic**|**claude-v1**|`bedrock-2023-05-31`|24.0 (per 1000000 token)|1 token |**anthropic**|**claude-v2**|`bedrock-2023-05-31`|24.0 (per 1000000 token)|1 token |**anthropic**|-|`bedrock-2023-05-31`|24.0 (per 1000000 token)|1 token |**mistral**|-|`v0.0.1`|24.0 (per 1000000 token)|1 token |**mistral**|**large-latest**|`v0.0.1`|24.0 (per 1000000 token)|1 token |**mistral**|**medium**|`v0.0.1`|8.1 (per 1000000 token)|1 token |**mistral**|**small**|`v0.0.1`|6.0 (per 1000000 token)|1 token |**mistral**|**tiny**|`v0.0.1`|0.42 (per 1000000 token)|1 token |**amazon**|-|`v1`|1.6 (per 1000000 token)|1 token |**amazon**|**titan-text-express-v1**|`v1`|1.6 (per 1000000 token)|1 token |**amazon**|**titan-text-lite-v1**|`v1`|0.4 (per 1000000 token)|1 token |**amazon**|**titan-tg1-large**|`v1`|1.6 (per 1000000 token)|1 token |**meta**|-|`v1`|2.56 (per 1000000 token)|1 token |**meta**|**llama2-13b-chat-v1**|`v1`|1.0 (per 1000000 token)|1 token |**meta**|**llama2-70b-chat-v1**|`v1`|2.56 (per 1000000 token)|1 token

Supported Models

cohere
|Name|Value| |----|-----| |**cohere**|`command`| ||`command-light`| ||`command-light-nightly`| ||`command-nightly`|

openai
|Name|Value| |----|-----| |**openai**|`gpt-3.5-turbo-instruct`| ||`text-babbage-002`| ||`text-davinci-002`|

google
|Name|Value| |----|-----| |**google**|`gemini-pro`| ||`text-bison`|

ai21labs
|Name|Value| |----|-----| |**ai21labs**|`j2-mid`| ||`j2-ultra`|

anthropic
|Name|Value| |----|-----| |**anthropic**|`claude-instant-v1`| ||`claude-v1`| ||`claude-v2`|

mistral
|Name|Value| |----|-----| |**mistral**|`large-latest`| ||`medium`| ||`small`| ||`tiny`|

amazon
|Name|Value| |----|-----| |**amazon**|`titan-text-express-v1`| ||`titan-text-lite-v1`| ||`titan-tg1-large`|

meta
|Name|Value| |----|-----| |**meta**|`llama2-13b-chat-v1`| ||`llama2-70b-chat-v1`|

# paths./text/generation.post.requestBody.content.application/json.examples.RequestExample.value.text
The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.

Human: Hello, who are you?

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.generated_text
AI Assistant: Hello! I'm an AI assistant, a helpful, creative, clever, and very friendly entity designed to

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.generated_text
 I'm Claude, an AI assistant created by Anthropic.

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.ai21labs.generated_text
I am an AI assistant. I am here to help with a variety of tasks, such as answering questions, playing music, and providing general information.

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.openai.generated_text
AI Assistant: Hello! I am your AI assistant. I am here to help you with anything you need. What can

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.generated_text
Sorry - this model is unable to respond to this request.

# paths./text/generation.post.responses.200.content.application/json.examples.ResponseExample.value.google.generated_text
AI: Greetings, I am Gemini, a versatile and advanced conversational AI designed to assist and engage with you in a comprehensive manner

# paths./text/keyword_extraction.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**ibm**|`v1 (2021-08-01)`|0.3 (per 1000000 char)|10000 char |**microsoft**|`v3.1`|1.0 (per 1000000 char)|1000 char |**emvista**|`v1.0`|1.0 (per 1000000 char)|1000 char |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**tenstorrent**|`v1.0.0`|0.7 (per 1000000 char)|1000 char |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request |**corticalio**|`v1.3.0`|0.97 (per 1000 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Achinese**|`ace`| |**Afrikaans**|`af`| |**Akan**|`ak`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Asturian**|`ast`| |**Awadhi**|`awa`| |**Ayacucho Quechua**|`quy`| |**Balinese**|`ban`| |**Bambara**|`bm`| |**Banjar**|`bjn`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bemba (Zambia)**|`bem`| |**Bengali**|`bn`| |**Bhojpuri**|`bho`| |**Bosnian**|`bs`| |**Buginese**|`bug`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Central Atlas Tamazight**|`tzm`| |**Central Aymara**|`ayr`| |**Central Kanuri**|`knc`| |**Central Kurdish**|`ckb`| |**Chhattisgarhi**|`hne`| |**Chinese**|`zh`| |**Chokwe**|`cjk`| |**Crimean Tatar**|`crh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**Dyula**|`dyu`| |**Dzongkha**|`dz`| |**Eastern Yiddish**|`ydd`| |**Egyptian Arabic**|`arz`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Ewe**|`ee`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Finnish**|`fi`| |**Fon**|`fon`| |**French**|`fr`| |**Friulian**|`fur`| |**Galician**|`gl`| |**Ganda**|`lg`| |**Georgian**|`ka`| |**German**|`de`| |**Guarani**|`gn`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Halh Mongolian**|`khk`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Iloko**|`ilo`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kabiyè**|`kbp`| |**Kabuverdianu**|`kea`| |**Kabyle**|`kab`| |**Kachin**|`kac`| |**Kamba (Kenya)**|`kam`| |**Kannada**|`kn`| |**Kashmiri**|`ks`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kikuyu**|`ki`| |**Kimbundu**|`kmb`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Kongo**|`kg`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latgalian**|`ltg`| |**Latvian**|`lv`| |**Ligurian**|`lij`| |**Limburgan**|`li`| |**Lingala**|`ln`| |**Lithuanian**|`lt`| |**Lombard**|`lmo`| |**Luba-Katanga**|`lu`| |**Luo (Kenya and Tanzania)**|`luo`| |**Lushai**|`lus`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Magahi**|`mag`| |**Maithili**|`mai`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Manipuri**|`mni`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Mesopotamian Arabic**|`acm`| |**Minangkabau**|`min`| |**Modern Greek (1453-)**|`el`| |**Moroccan Arabic**|`ary`| |**Mossi**|`mos`| |**Najdi Arabic**|`ars`| |**Nepali (macrolanguage)**|`ne`| |**Nigerian Fulfulde**|`fuv`| |**North Azerbaijani**|`azj`| |**North Levantine Arabic**|`apc`| |**Northern Kurdish**|`kmr`| |**Northern Uzbek**|`uzn`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nuer**|`nus`| |**Nyanja**|`ny`| |**Occitan (post 1500)**|`oc`| |**Oriya (macrolanguage)**|`or`| |**Pangasinan**|`pag`| |**Panjabi**|`pa`| |**Papiamento**|`pap`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Plateau Malagasy**|`plt`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Rundi**|`rn`| |**Russian**|`ru`| |**Samoan**|`sm`| |**Sango**|`sg`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Sardinian**|`sc`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Shan**|`shn`| |**Shona**|`sn`| |**Sicilian**|`scn`| |**Silesian**|`szl`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**South Azerbaijani**|`azb`| |**South Levantine Arabic**|`ajp`| |**Southern Pashto**|`pbt`| |**Southern Sotho**|`st`| |**Southwestern Dinka**|`dik`| |**Spanish**|`es`| |**Standard Latvian**|`lvs`| |**Standard Malay**|`zsm`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swati**|`ss`| |**Swedish**|`sv`| |**Ta'izzi-Adeni Arabic**|`acq`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamashek**|`tmh`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tok Pisin**|`tpi`| |**Tosk Albanian**|`als`| |**Tsonga**|`ts`| |**Tswana**|`tn`| |**Tumbuka**|`tum`| |**Tunisian Arabic**|`aeb`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Twi**|`tw`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Umbundu**|`umb`| |**Urdu**|`ur`| |**Venetian**|`vec`| |**Vietnamese**|`vi`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**West Central Oromo**|`gaz`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yoruba**|`yo`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Arabic (world)**|`ar-001`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**Persian (Afghanistan)**|`fa-AF`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`|

# paths./text/keyword_extraction.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/keyword_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.ibm.items[].keyword
first African-American president of the United States

# paths./text/moderation.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**microsoft**|`v1.0`|1.0 (per 1000 request)|1 request |**openai**|`v3.0.0`|free|- |**clarifai**|`8.0.0`|1.2 (per 1000 request)|1 request |**google**|`v1`|5.0 (per 1000000 char)|100 char

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Aragonese**|`an`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Asturian**|`ast`| |**Azerbaijani**|`az`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Bavarian**|`bar`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bishnupriya**|`bpy`| |**Bosnian**|`bs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Central Kurdish**|`ckb`| |**Chechen**|`ce`| |**Cherokee**|`chr`| |**Chinese**|`zh`| |**Chuvash**|`cv`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Fulah**|`ff`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Ido**|`io`| |**Igbo**|`ig`| |**Indonesian**|`id`| |**Inuktitut**|`iu`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Konkani (macrolanguage)**|`kok`| |**Korean**|`ko`| |**Lahnda**|`lah`| |**Lao**|`lo`| |**Latin**|`la`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Lombard**|`lmo`| |**Low German**|`nds`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Minangkabau**|`min`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Occitan (post 1500)**|`oc`| |**Oriya (macrolanguage)**|`or`| |**Panjabi**|`pa`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Piemontese**|`pms`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Quechua**|`qu`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Scots**|`sco`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Serbo-Croatian**|`sh`| |**Sicilian**|`scn`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**South Azerbaijani**|`azb`| |**Southern Sotho**|`st`| |**Spanish**|`es`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Tigrinya**|`ti`| |**Tswana**|`tn`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Volapük**|`vo`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yoruba**|`yo`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Traditional)**|`zh-Hant`| |**Low German (Netherlands)**|`nds-NL`|

# paths./text/named_entity_recognition.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**google**|`v1`|1.0 (per 1000000 char)|1000 char |**ibm**|`v1 (2021-08-01)`|0.3 (per 1000000 char)|10000 char |**lettria**|`v5.5.2`|2.0 (per 1000000 char)|1000 char |**microsoft**|`v3.1`|1.0 (per 1000000 char)|1000 char |**neuralspace**|`v1`|0.007 (per 1 request)|1 request |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**tenstorrent**|`v1.0.0`|1.0 (per 1000000 char)|1000 char |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Arabic**|`ar`| |**Aragonese**|`an`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Azerbaijani**|`az`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Chechen**|`ce`| |**Chinese**|`zh`| |**Chuvash**|`cv`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Kirghiz**|`ky`| |**Korean**|`ko`| |**Latin**|`la`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Occitan (post 1500)**|`oc`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Serbian**|`sr`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Spanish**|`es`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Turkish**|`tr`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Yoruba**|`yo`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**English (United States)**|`en-US`| |**French (France)**|`fr-FR`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`|

# paths./text/named_entity_recognition.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/plagia_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**originalityai**|`v1`|0.01 (per 400 char)|400 char |**winstonai**|`v2`|14.0 (per 1000000 char)|1 char

# paths./text/plagia_detection.post.requestBody.content.application/json.examples.RequestExample.value.text
The Galaxy S23 launch may be far behind us, but Samsung likely has plenty more to announce in 2023.             That's if history repeats itself. Should Samsung stick to its annual routine, we can expect to see new             foldable phones and wearable devices in August. The company also previewed new designs for bendable phones and tablets             earlier this year, hinting that the company may be planning to expand beyond the Z Fold and Z Flip in the near future.             Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors,             I'm most interested in where its mobile devices are headed. Samsung is one of the world's largest smartphone manufacturers             by market share, meaning it has more influence than most other tech companies on the devices we carry in our pockets each day.             Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers.             It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Rumored Samsung Gadgets For 2023: Galaxy Z Flip 5, Z Fold 5 and More - MSN

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers. It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Should Samsung stick to its annual routine, we can expect to see new foldable phones and wearable devices in August. The company also previewed new designs for ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... The company also previewed new designs for bendable phones and tablets earlier this year, hinting that the company may be planning to expand beyond the Z ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in where its mobile ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers. It's a strategy to create ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.", "matches": [ { "website": "https://www.cnet.com ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Rumored Samsung Gadgets For 2023: Galaxy Z Flip 5, Z Fold 5 and More - CNET

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers. It's a ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers. It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
The Biggest Rumored Samsung Gadgets to Expect in 2023 - YouTube

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in where its mobile ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... influence than most other tech companies on the devices we carry in our pockets each day. Wearables have also become a large part of how Samsung intends to ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices. Here are the rumored Samsung products I'm most ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Galaxy Z Fold 5 and Other Samsung Gadgets to Look for in 2023 IT ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... the company may be planning to expand beyond the Z Fold and Z Flip in the near future. Though Samsung regularly releases new products across many categories ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Galaxy S23, Z Fold 5 and more Samsung gadgets rumored for 2023 - US Today News

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
... the company may be planning to expand beyond the Z Fold and Z Flip in the near future. Although Samsung regularly releases new products in ...

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].text
Galaxy Z Fold 5 and Other Samsung Gadgets to Look for in 2023

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.winstonai.items[].candidates[].plagiarized_text
The Galaxy S23 launch may be far behind us, but Samsung likely has plenty more to announce in 2023. That's if history repeats itself.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].text
Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in where its mobile devices are headed.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].candidates[].plagiarized_text
Though Samsung regularly releases new products across many categories, including TVs, home appliances and monitors, I'm most interested in where its mobile devices are headed.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].text
Samsung is one of the world's largest smartphone manufacturers by market share, meaning it has more influence than most other tech companies on the devices we carry in our pockets each day.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].candidates[].plagiarized_text
Samsung is one of the world's largest smartphone manufacturers by market share, meaning it has more influence than most other tech companies on the devices we carry in our pockets each day.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].candidates[].plagiarized_text
Wearables have also become a large part of how Samsung intends to differentiate its phones from those of other Android device makers.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].text
It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.

# paths./text/plagia_detection.post.responses.200.content.application/json.examples.ResponseExample.value.originalityai.items[].candidates[].plagiarized_text
It's a strategy to create a web of products that keep people hooked, much like Apple's range of devices.

# paths./text/prompt_optimization.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**openai**|`v3.0.0`|0.08 (per 1 request)|1 request

# paths./text/prompt_optimization.post.requestBody.content.application/json.examples.RequestExample.value.text
Entity extractor, i give you an entity or multiple entities and a text and i want the entitites extracted from the text

# paths./text/prompt_optimization.post.responses.200.content.application/json.examples.ResponseExample.value.openai.missing_information
What type of entities are you specifically looking to extract (names, locations, organizations, etc.)? Do you have any specific language or data format preferences for the text? Do you need this tool to handle any particular size, complexity, or other special characteristics of the texts?

# paths./text/prompt_optimization.post.responses.200.content.application/json.examples.ResponseExample.value.openai.items[].text
Given the following text and a list of entities, please extract and list all occurrences of the specified entities in the text. The entities to be extracted are [entities]. The text for entity extraction is [text].

# paths./text/prompt_optimization.post.responses.200.content.application/json.examples.ResponseExample.value.openai.items[].text
Given the following text, please identify and extract all instances of the specified entities. The entities to look for are [Entity1, Entity2, Entity3]. Here is the text: [Insert Text Here]. Please list the extracted entities in a clear and organized manner.

# paths./text/prompt_optimization.post.responses.200.content.application/json.examples.ResponseExample.value.openai.items[].text
Given the following text and a list of entities, please extract and list all instances of the provided entities within the text. The entities are [list of entities]. The text is as follows: [text]. Please format your response as a bulleted list, with each bullet representing an instance of an entity found in the text.

# paths./text/question_answer.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**tenstorrent**|`v1.0.0`|10.0 (per 1000000 char)|1000 char

# paths./text/question_answer.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
The bar-shouldered dove (Geopelia humeralis) is a species of dove, in the family Columbidae, native to Australia and southern New Guinea. Its typical habitat consists of areas of thick vegetation where water is present, damp gullies, forests and gorges, mangroves, plantations, swamps, eucalyptus woodland, tropical and sub-tropical shrubland, and river margins. It can be found in both inland and coastal regions.

# paths./text/question_answer.post.requestBody.content.application/json.examples.RequestExample.value.question
What is the scientific name of bar-shouldered dove?

# paths./text/question_answer.post.requestBody.content.application/json.examples.RequestExample.value.examples_context
In 2017, U.S. life expectancy was 78.6 years.

# paths./text/question_answer.post.requestBody.content.application/json.examples.RequestExample.value.examples[][]
What is human life expectancy in the United States?

# paths./text/question_answer.post.responses.200.content.application/json.examples.ResponseExample.value.openai.answers[]
The scientific name of bar-shouldered dove is Geopelia humeralis.

# paths./text/question_answer.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.answers[]
The scientific name of bar-shouldered dove is Geopelia humeralis.

# paths./text/search.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**cohere**|`v1`|0.1 (per 1000000 char)|1 char |**google**|`v1`|0.1 (per 1000000 char)|1 char

# paths./text/search.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
In Roman mythology, Romulus and Remus (Latin: [ˈroːmʊlʊs], [ˈrɛmʊs]) are twin brothers whose story tells of the events that led to the founding of the city of Rome and the Roman Kingdom by Romulus.

# paths./text/search.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
In ancient Roman religion and myth, Mars (Latin: Mārs, pronounced [maːrs]) was the god of war and also an agricultural guardian, a combination characteristic of early Rome.

# paths./text/search.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
Proto-Indo-European mythology is the body of myths and deities associated with the Proto-Indo-Europeans, the hypothetical speakers of the reconstructed Proto-Indo-European language

# paths./text/search.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
The Ashvamedha (Sanskrit: अश्वमेध, romanized: aśvamedha) was a horse sacrifice ritual followed by the Śrauta tradition of Vedic religion.

# paths./text/search.post.requestBody.content.application/json.examples.RequestExample.value.texts[]
Purusha (puruṣa or Sanskrit: पुरुष) is a complex concept whose meaning evolved in Vedic and Upanishadic times. Depending on source and historical timeline, it means the cosmic being or self, consciousness, and universal principle.

# paths./text/sentiment_analysis.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**connexun**|`v1.0`|2.0 (per 1000 request)|1 request |**google**|`v1`|1.0 (per 1000000 char)|1000 char |**ibm**|`v1 (2021-08-01)`|0.3 (per 1000000 char)|10000 char |**lettria**|`v5.5.2`|2.0 (per 1000000 char)|1000 char |**microsoft**|`v3.1`|1.0 (per 1000000 char)|1000 char |**emvista**|`v1.0`|3.0 (per 1000000 char)|1000 char |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**tenstorrent**|`v1.1.0`|0.7 (per 1000000 char)|1000 char |**sapling**|`v1`|20.0 (per 1000000 char)|1000 char |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Arabic**|`ar`| |**Bengali**|`bn`| |**Chinese**|`zh`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Hindi**|`hi`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Modern Greek (1453-)**|`el`| |**Norwegian**|`no`| |**Panjabi**|`pa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Spanish**|`es`| |**Swedish**|`sv`| |**Tamil**|`ta`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Vietnamese**|`vi`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**English (United States)**|`en-US`| |**French (France)**|`fr-FR`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`|

# paths./text/sentiment_analysis.post.requestBody.content.application/json.examples.RequestExample.value.text
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. First is the product reviews and pricing. There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].segment
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].segment
First is the product reviews and pricing.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].segment
There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].segment
There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc).

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.sapling.items[].segment
The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].segment
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].segment
First is the product reviews and pricing.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].segment
There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].segment
There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc).

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].segment
The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.items[].segment
Overall I am satisfied with my experience at Amazon, but two areas of major improvement needed. 

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.items[].segment
First is the product reviews and pricing. 

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.items[].segment
There are thousands of positive reviews for so many items, and it's clear that the reviews are bogus or not really associated with that product. 

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.items[].segment
There needs to be a way to only view products sold by Amazon directly, because many market sellers way overprice items that can be purchased cheaper elsewhere (like Walmart, Target, etc). 

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.items[].segment
The second issue is they make it too difficult to get help when there's an issue with an order.

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.lettria.items[].segment
overall i am satisfied with my experience at Amazon , but two areas of major improvement needed .

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.lettria.items[].segment
first is the product reviews and pricing .

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.lettria.items[].segment
there are thousands of positive reviews for so many items , and it is clear

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.lettria.items[].segment
there needs to be a way to only view products sold by Amazon directly , because many market sellers way overprice items that can be purchased cheaper elsewhere ( like Walmart , Target , etc ) .

# paths./text/sentiment_analysis.post.responses.200.content.application/json.examples.ResponseExample.value.lettria.items[].segment
the second issue is they make it too difficult to get help when there is an issue with an order .

# paths./text/spell_check.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**microsoft**|`v7`|0.3 (per 1000 request)|1 request |**openai**|`v3.0.0`|20.0 (per 1000000 token)|1 token |**prowritingaid**|`v2`|10.0 (per 1000 request)|1 request |**cohere**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**sapling**|`v1`|2.0 (per 1000000 char)|1 char |**nlpcloud**|`v1`|25.0 (per 1000 request)|1 request |**ai21labs**|`v1`|0.0005 (per 1 request)|1 request

Supported Languages
|Name|Value| |----|-----| |**Achinese**|`ace`| |**Afrikaans**|`af`| |**Akan**|`ak`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Asturian**|`ast`| |**Awadhi**|`awa`| |**Ayacucho Quechua**|`quy`| |**Azerbaijani**|`az`| |**Balinese**|`ban`| |**Bambara**|`bm`| |**Banjar**|`bjn`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bemba (Zambia)**|`bem`| |**Bengali**|`bn`| |**Bhojpuri**|`bho`| |**Bosnian**|`bs`| |**Buginese**|`bug`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Central Atlas Tamazight**|`tzm`| |**Central Aymara**|`ayr`| |**Central Kanuri**|`knc`| |**Central Kurdish**|`ckb`| |**Chhattisgarhi**|`hne`| |**Chinese**|`zh`| |**Chokwe**|`cjk`| |**Corsican**|`co`| |**Crimean Tatar**|`crh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**Dyula**|`dyu`| |**Dzongkha**|`dz`| |**Eastern Yiddish**|`ydd`| |**Egyptian Arabic**|`arz`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Ewe**|`ee`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Finnish**|`fi`| |**Fon**|`fon`| |**French**|`fr`| |**Friulian**|`fur`| |**Galician**|`gl`| |**Ganda**|`lg`| |**Georgian**|`ka`| |**German**|`de`| |**Guarani**|`gn`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Halh Mongolian**|`khk`| |**Hausa**|`ha`| |**Hawaiian**|`haw`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hmong**|`hmn`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Iloko**|`ilo`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kabiyè**|`kbp`| |**Kabuverdianu**|`kea`| |**Kabyle**|`kab`| |**Kachin**|`kac`| |**Kamba (Kenya)**|`kam`| |**Kannada**|`kn`| |**Kashmiri**|`ks`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kikuyu**|`ki`| |**Kimbundu**|`kmb`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Kongo**|`kg`| |**Korean**|`ko`| |**Kurdish**|`ku`| |**Lao**|`lo`| |**Latgalian**|`ltg`| |**Latin**|`la`| |**Latvian**|`lv`| |**Ligurian**|`lij`| |**Limburgan**|`li`| |**Lingala**|`ln`| |**Lithuanian**|`lt`| |**Lombard**|`lmo`| |**Luba-Katanga**|`lu`| |**Luo (Kenya and Tanzania)**|`luo`| |**Lushai**|`lus`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Magahi**|`mag`| |**Maithili**|`mai`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Manipuri**|`mni`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Mesopotamian Arabic**|`acm`| |**Minangkabau**|`min`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Moroccan Arabic**|`ary`| |**Mossi**|`mos`| |**Najdi Arabic**|`ars`| |**Nepali (macrolanguage)**|`ne`| |**Nigerian Fulfulde**|`fuv`| |**North Azerbaijani**|`azj`| |**North Levantine Arabic**|`apc`| |**Northern Kurdish**|`kmr`| |**Northern Uzbek**|`uzn`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nuer**|`nus`| |**Nyanja**|`ny`| |**Occitan (post 1500)**|`oc`| |**Oriya (macrolanguage)**|`or`| |**Pangasinan**|`pag`| |**Panjabi**|`pa`| |**Papiamento**|`pap`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Plateau Malagasy**|`plt`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Rundi**|`rn`| |**Russian**|`ru`| |**Samoan**|`sm`| |**Sango**|`sg`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Sardinian**|`sc`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Shan**|`shn`| |**Shona**|`sn`| |**Sicilian**|`scn`| |**Silesian**|`szl`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**South Azerbaijani**|`azb`| |**South Levantine Arabic**|`ajp`| |**Southern Pashto**|`pbt`| |**Southern Sotho**|`st`| |**Southwestern Dinka**|`dik`| |**Spanish**|`es`| |**Standard Latvian**|`lvs`| |**Standard Malay**|`zsm`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swati**|`ss`| |**Swedish**|`sv`| |**Ta'izzi-Adeni Arabic**|`acq`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamashek**|`tmh`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tok Pisin**|`tpi`| |**Tosk Albanian**|`als`| |**Tsonga**|`ts`| |**Tswana**|`tn`| |**Tumbuka**|`tum`| |**Tunisian Arabic**|`aeb`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Twi**|`tw`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Umbundu**|`umb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Venetian**|`vec`| |**Vietnamese**|`vi`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**West Central Oromo**|`gaz`| |**Western Frisian**|`fy`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`| |**jp**|`jp`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Arabic (world)**|`ar-001`| |**Chinese (China)**|`zh-CN`| |**Chinese (Simplified)**|`zh-hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**Chinese (Traditional)**|`zh-hant`| |**English (United Kingdom)**|`en-gb`| |**Persian (Afghanistan)**|`fa-AF`| |**Portuguese (Brazil)**|`pt-br`| |**Portuguese (Portugal)**|`pt-pt`|

# paths./text/summarize.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**connexun**|-|`v1.0`|2.0 (per 1000 request)|1 request |**microsoft**|-|`v3.1`|2.0 (per 1000000 char)|1000 char |**openai**|-|`v3.0.0`|60.0 (per 1000000 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|2.0 (per 1000000 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5 (per 1000000 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2.0 (per 1000000 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4.0 (per 1000000 token)|1 token |**openai**|**gpt-4**|`v3.0.0`|60.0 (per 1000000 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|120.0 (per 1000000 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|30.0 (per 1000000 token)|1 token |**emvista**|-|`v1.0`|1.0 (per 1000000 char)|1000 char |**cohere**|-|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**summarize-xlarge**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**alephalpha**|-|`1.12.0`|50.0 (per 1000000 char)|1 char |**alephalpha**|**luminous-base**|`1.12.0`|1.0 (per 1000000 char)|1 char |**alephalpha**|**luminous-extended**|`1.12.0`|1.5 (per 1000000 char)|1 char |**alephalpha**|**luminous-supreme**|`1.12.0`|50.0 (per 1000000 char)|1 char |**alephalpha**|**luminous-base-control**|`1.12.0`|1.25 (per 1000000 char)|1 char |**alephalpha**|**luminous-extended-control**|`1.12.0`|1.8 (per 1000000 char)|1 char |**alephalpha**|**luminous-supreme-control**|`1.12.0`|62.5 (per 1000000 char)|1 char |**nlpcloud**|-|`v1`|25.0 (per 1000 request)|1 request |**nlpcloud**|**bart-large-cnn**|`v1`|25.0 (per 1000 request)|1 request |**nlpcloud**|**fast-gpt-j**|`v1`|25.0 (per 1000 request)|1 request |**nlpcloud**|**finetuned-llama-2-70b**|`v1`|25.0 (per 1000 request)|1 request |**nlpcloud**|**chatdolphin**|`v1`|25.0 (per 1000 request)|1 request |**ai21labs**|-|`v1`|0.005 (per 1000 char)|1000 char |**anthropic**|**claude-3-haiku-20240307-v1:0**|`bedrock-2023-05-31`|1.25 (per 1000000 token)|1 token |**anthropic**|**claude-3-sonnet-20240229-v1:0**|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token |**anthropic**|**claude-instant-v1**|`bedrock-2023-05-31`|2.4 (per 1000000 token)|1 token |**anthropic**|**claude-v2**|`bedrock-2023-05-31`|24.0 (per 1000000 token)|1 token |**anthropic**|-|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Abkhazian**|`ab`| |**Acoli**|`ach`| |**Afar**|`aa`| |**Afrikaans**|`af`| |**Akan**|`ak`| |**Albanian**|`sq`| |**American Sign Language**|`ase`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Aragonese**|`an`| |**Argentine Sign Language**|`aed`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Avaric**|`av`| |**Avestan**|`ae`| |**Awadhi**|`awa`| |**Aymara**|`ay`| |**Azerbaijani**|`az`| |**Bambara**|`bm`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bemba (Zambia)**|`bem`| |**Bengali**|`bn`| |**Berber languages**|`ber`| |**Bhojpuri**|`bho`| |**Bihari languages**|`bh`| |**Bislama**|`bi`| |**Bosnian**|`bs`| |**Brazilian Sign Language**|`bzs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Celtic languages**|`cel`| |**Central Bikol**|`bcl`| |**Chamorro**|`ch`| |**Chechen**|`ce`| |**Chilean Sign Language**|`csg`| |**Chinese**|`zh`| |**Church Slavic**|`cu`| |**Chuukese**|`chk`| |**Chuvash**|`cv`| |**Colombian Sign Language**|`csn`| |**Congo Swahili**|`swc`| |**Cornish**|`kw`| |**Corsican**|`co`| |**Cree**|`cr`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dhivehi**|`dv`| |**Dogri (macrolanguage)**|`doi`| |**Dutch**|`nl`| |**Dzongkha**|`dz`| |**Efik**|`efi`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Ewe**|`ee`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Finnish**|`fi`| |**Finnish Sign Language**|`fse`| |**Fon**|`fon`| |**French**|`fr`| |**Fulah**|`ff`| |**Ga**|`gaa`| |**Galician**|`gl`| |**Ganda**|`lg`| |**Georgian**|`ka`| |**German**|`de`| |**Gilbertese**|`gil`| |**Guarani**|`gn`| |**Gujarati**|`gu`| |**Gun**|`guw`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Herero**|`hz`| |**Hiligaynon**|`hil`| |**Hindi**|`hi`| |**Hiri Motu**|`ho`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Ido**|`io`| |**Igbo**|`ig`| |**Iloko**|`ilo`| |**Indonesian**|`id`| |**Interlingua (International Auxiliary Language Association)**|`ia`| |**Interlingue**|`ie`| |**Inuktitut**|`iu`| |**Inupiaq**|`ik`| |**Irish**|`ga`| |**Isoko**|`iso`| |**Isthmus Zapotec**|`zai`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kabyle**|`kab`| |**Kalaallisut**|`kl`| |**Kannada**|`kn`| |**Kanuri**|`kr`| |**Kaonde**|`kqn`| |**Kashmiri**|`ks`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kikuyu**|`ki`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Komi**|`kv`| |**Kongo**|`kg`| |**Konkani (macrolanguage)**|`kok`| |**Korean**|`ko`| |**Kuanyama**|`kj`| |**Kurdish**|`ku`| |**Kwangali**|`kwn`| |**Lao**|`lo`| |**Latin**|`la`| |**Latvian**|`lv`| |**Limburgan**|`li`| |**Lingala**|`ln`| |**Lithuanian**|`lt`| |**Lozi**|`loz`| |**Luba-Katanga**|`lu`| |**Luba-Lulua**|`lua`| |**Lunda**|`lun`| |**Luo (Kenya and Tanzania)**|`luo`| |**Lushai**|`lus`| |**Luvale**|`lue`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Maithili**|`mai`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Manx**|`gv`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Marshallese**|`mh`| |**Mexican Sign Language**|`mfs`| |**Min Nan Chinese**|`nan`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Morisyen**|`mfe`| |**Mossi**|`mos`| |**Nauru**|`na`| |**Navajo**|`nv`| |**Ndonga**|`ng`| |**Nepali (macrolanguage)**|`ne`| |**Niuean**|`niu`| |**North Ndebele**|`nd`| |**Northern Sami**|`se`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nyaneka**|`nyk`| |**Nyanja**|`ny`| |**Occitan (post 1500)**|`oc`| |**Ojibwa**|`oj`| |**Oriya (macrolanguage)**|`or`| |**Oromo**|`om`| |**Ossetian**|`os`| |**Pali**|`pi`| |**Pangasinan**|`pag`| |**Panjabi**|`pa`| |**Papiamento**|`pap`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Peruvian Sign Language**|`prl`| |**Pijin**|`pis`| |**Pohnpeian**|`pon`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Quechua**|`qu`| |**Romance languages**|`roa`| |**Romanian**|`mo`| |**Romanian**|`ro`| |**Romansh**|`rm`| |**Rundi**|`rn`| |**Russian**|`ru`| |**Ruund**|`rnd`| |**Samoan**|`sm`| |**San Salvador Kongo**|`kwy`| |**Sango**|`sg`| |**Sanskrit**|`sa`| |**Santali**|`sat`| |**Sardinian**|`sc`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Seselwa Creole French**|`crs`| |**Shan**|`shn`| |**Shona**|`sn`| |**Sichuan Yi**|`ii`| |**Sicilian**|`scn`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**South Ndebele**|`nr`| |**Southern Sotho**|`st`| |**Spanish**|`es`| |**Spanish Sign Language**|`ssp`| |**Sranan Tongo**|`srn`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swati**|`ss`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tahitian**|`ty`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Tetela**|`tll`| |**Tetun Dili**|`tdt`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tiv**|`tiv`| |**Tok Pisin**|`tpi`| |**Tonga (Tonga Islands)**|`to`| |**Tonga (Zambia)**|`toi`| |**Tsonga**|`ts`| |**Tswana**|`tn`| |**Tumbuka**|`tum`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Tuvalu**|`tvl`| |**Twi**|`tw`| |**Tzotzil**|`tzo`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Umbundu**|`umb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Venda**|`ve`| |**Venezuelan Sign Language**|`vsl`| |**Vietnamese**|`vi`| |**Volapük**|`vo`| |**Wallisian**|`wls`| |**Walloon**|`wa`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Wolaytta**|`wal`| |**Wolof**|`wo`| |**Wu Chinese**|`wuu`| |**Xhosa**|`xh`| |**Yapese**|`yap`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Yucateco**|`yua`| |**Yue Chinese**|`yue`| |**Zande (individual language)**|`zne`| |**Zhuang**|`za`| |**Zulu**|`zu`| |**me**|`me`| |**ra**|`ra`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Simplified)**|`zh-Hans`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Brazil)**|`pt-br`| |**Portuguese (Portugal)**|`pt-PT`| |**Portuguese (Portugal)**|`pt-pt`|

Supported Models

openai
|Name|Value| |----|-----| |**openai**|`gpt-3.5-turbo`| ||`gpt-3.5-turbo-0125`| ||`gpt-3.5-turbo-1106`| ||`gpt-3.5-turbo-16k`| ||`gpt-4`| ||`gpt-4-32k-0314`| ||`gpt-4-turbo-2024-04-09`|

cohere
|Name|Value| |----|-----| |**cohere**|`summarize-xlarge`|

alephalpha
|Name|Value| |----|-----| |**alephalpha**|`luminous-base`| ||`luminous-base-control`| ||`luminous-extended`| ||`luminous-extended-control`| ||`luminous-supreme`| ||`luminous-supreme-control`|

nlpcloud
|Name|Value| |----|-----| |**nlpcloud**|`bart-large-cnn`| ||`chatdolphin`| ||`fast-gpt-j`| ||`finetuned-llama-2-70b`|

anthropic
|Name|Value| |----|-----| |**anthropic**|`claude-3-haiku-20240307-v1:0`| ||`claude-3-sonnet-20240229-v1:0`| ||`claude-instant-v1`| ||`claude-v2`|

# paths./text/summarize.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.result
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.emvista.result
Barack Hussein Obama is an politician who . A member of the Democratic Party . He served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004 . 

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.openai.result
Barack Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. He was the first African-American president and had previously served as a U.S. senator and an Illinois state senator.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.ai21labs.result
Barack Hussein Obama was the 44th president of the United States from 2009 to 2017. He was the first African-American president.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.result
 Barack Obama is a former U.S. president who served from 2009 to 2017. He was the first African-American president and a member of the Democratic Party. Prior to becoming president, he served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.alephalpha.result
Barack Obama was the 44th president of the United States.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.nlpcloud.result
Barack Obama was the 44th president of the United States from 2009 to 2017. He was the first African-American president of the United States, and a member of the Democratic Party. Prior to his presidency, Obama served as a U.S. senator from Illinois from 2005 to 2008, and an Illinois state senator from 1997 to 2004.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.connexun.result
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States.

# paths./text/summarize.post.responses.200.content.application/json.examples.ResponseExample.value.cohere.result
"You have to have a big-picture vision for where you want to go.

# paths./text/syntax_analysis.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|0.5 (per 1000000 char)|300 char |**google**|`v1`|0.5 (per 1000000 char)|1000 char |**ibm**|`v1 (2021-08-01)`|0.3 (per 1000000 char)|10000 char |**lettria**|`v5.5.2`|2.0 (per 1000000 char)|1000 char |**emvista**|`v1.0`|1.0 (per 1000000 char)|1 char

Supported Languages
|Name|Value| |----|-----| |**Arabic**|`ar`| |**Chinese**|`zh`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Norwegian**|`no`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Spanish**|`es`| |**Swedish**|`sv`| |**Turkish**|`tr`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**English (United States)**|`en-US`| |**French (France)**|`fr-FR`|

# paths./text/syntax_analysis.post.requestBody.content.application/json.examples.RequestExample.value.text
Barack Hussein Obama is an American politician who served as the 44th president of the United States from 2009 to 2017. A member of the Democratic Party, Obama was the first African-American president of the United States. He previously served as a U.S. senator from Illinois from 2005 to 2008 and as an Illinois state senator from 1997 to 2004.

# paths./text/topic_extraction.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**google**|`v1`|2.0 (per 1000000 char)|1000 char |**ibm**|`v1 (2021-08-01)`|0.3 (per 1000000 char)|10000 char |**openai**|`v1`|20.0 (per 1000000 token)|1 token |**tenstorrent**|`v1.0.0`|2.0 (per 1000000 char)|1000 char

Supported Languages
|Name|Value| |----|-----| |**Arabic**|`ar`| |**Chinese**|`zh`| |**Dutch**|`nl`| |**English**|`en`| |**French**|`fr`| |**German**|`de`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Portuguese**|`pt`| |**Russian**|`ru`| |**Spanish**|`es`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`|

# paths./text/topic_extraction.post.requestBody.content.application/json.examples.RequestExample.value.text
That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows.

# paths./text/topic_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.google.items[].category
/Arts & Entertainment/Tv & Video/Tv Shows & Programs

# paths./text/topic_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.ibm.items[].category
/Art And Entertainment/Movies And Tv/Movies

# paths./text/topic_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.ibm.items[].category
/Art And Entertainment/Movies And Tv/Television

# paths./text/topic_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.items[].category
/Art And Entertainment/Movies And Tv/Television

# paths./text/topic_extraction.post.responses.200.content.application/json.examples.ResponseExample.value.eden-ai.items[].category
/Art And Entertainment/Movies And Tv/Movies

# paths./translation/automatic_translation.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|15.0 (per 1000000 char)|1 char |**google**|`v3`|20.0 (per 1000000 char)|1 char |**ibm**|`v3 (2018-05-01)`|20.0 (per 1000000 char)|1000 char |**microsoft**|`v3.0`|10.0 (per 1000000 char)|1 char |**neuralspace**|`v1`|7.0 (per 1000 request)|1 request |**phedone**|`-`|4.0 (per 1000000 char)|1000 char |**deepl**|`v2`|20.0 (per 1000000 char)|1 char |**modernmt**|`1.2.8`|8.0 (per 1000000 char)|1 char |**openai**|`v1`|20.0 (per 1000000 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Abkhazian**|`ab`| |**Acoli**|`ach`| |**Afar**|`aa`| |**Afrikaans**|`af`| |**Akan**|`ak`| |**Albanian**|`sq`| |**American Sign Language**|`ase`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Aragonese**|`an`| |**Argentine Sign Language**|`aed`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Avaric**|`av`| |**Avestan**|`ae`| |**Aymara**|`ay`| |**Azerbaijani**|`az`| |**Bambara**|`bm`| |**Bashkir**|`ba`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bemba (Zambia)**|`bem`| |**Bengali**|`bn`| |**Berber languages**|`ber`| |**Bihari languages**|`bh`| |**Bislama**|`bi`| |**Bosnian**|`bs`| |**Brazilian Sign Language**|`bzs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Celtic languages**|`cel`| |**Central Bikol**|`bcl`| |**Chamorro**|`ch`| |**Chechen**|`ce`| |**Chilean Sign Language**|`csg`| |**Chinese**|`zh`| |**Church Slavic**|`cu`| |**Chuukese**|`chk`| |**Chuvash**|`cv`| |**Colombian Sign Language**|`csn`| |**Congo Swahili**|`swc`| |**Cornish**|`kw`| |**Corsican**|`co`| |**Cree**|`cr`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dari**|`prs`| |**Dhivehi**|`dv`| |**Dutch**|`nl`| |**Dzongkha**|`dz`| |**Efik**|`efi`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Ewe**|`ee`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**Finnish Sign Language**|`fse`| |**Fon**|`fon`| |**French**|`fr`| |**Fulah**|`ff`| |**Ga**|`gaa`| |**Galician**|`gl`| |**Ganda**|`lg`| |**Georgian**|`ka`| |**German**|`de`| |**Gilbertese**|`gil`| |**Guarani**|`gn`| |**Gujarati**|`gu`| |**Gun**|`guw`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hawaiian**|`haw`| |**Hebrew**|`he`| |**Herero**|`hz`| |**Hiligaynon**|`hil`| |**Hindi**|`hi`| |**Hiri Motu**|`ho`| |**Hmong**|`hmn`| |**Hmong Daw**|`mww`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Ido**|`io`| |**Igbo**|`ig`| |**Iloko**|`ilo`| |**Indonesian**|`id`| |**Interlingua (International Auxiliary Language Association)**|`ia`| |**Interlingue**|`ie`| |**Inuinnaqtun**|`ikt`| |**Inuktitut**|`iu`| |**Inupiaq**|`ik`| |**Irish**|`ga`| |**Isoko**|`iso`| |**Isthmus Zapotec**|`zai`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kabyle**|`kab`| |**Kalaallisut**|`kl`| |**Kannada**|`kn`| |**Kanuri**|`kr`| |**Kaonde**|`kqn`| |**Kashmiri**|`ks`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kikuyu**|`ki`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Klingon**|`tlh`| |**Komi**|`kv`| |**Kongo**|`kg`| |**Korean**|`ko`| |**Kuanyama**|`kj`| |**Kurdish**|`ku`| |**Kwangali**|`kwn`| |**Lao**|`lo`| |**Latin**|`la`| |**Latvian**|`lv`| |**Limburgan**|`li`| |**Lingala**|`ln`| |**Literary Chinese**|`lzh`| |**Lithuanian**|`lt`| |**Lozi**|`loz`| |**Luba-Katanga**|`lu`| |**Luba-Lulua**|`lua`| |**Lunda**|`lun`| |**Luo (Kenya and Tanzania)**|`luo`| |**Lushai**|`lus`| |**Luvale**|`lue`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Manx**|`gv`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Marshallese**|`mh`| |**Mexican Sign Language**|`mfs`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Montenegrin**|`cnr`| |**Morisyen**|`mfe`| |**Mossi**|`mos`| |**Nauru**|`na`| |**Navajo**|`nv`| |**Ndonga**|`ng`| |**Nepali (macrolanguage)**|`ne`| |**Niuean**|`niu`| |**North Ndebele**|`nd`| |**Northern Kurdish**|`kmr`| |**Northern Sami**|`se`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nyaneka**|`nyk`| |**Nyanja**|`ny`| |**Occitan (post 1500)**|`oc`| |**Ojibwa**|`oj`| |**Oriya (macrolanguage)**|`or`| |**Oromo**|`om`| |**Ossetian**|`os`| |**Pali**|`pi`| |**Pangasinan**|`pag`| |**Panjabi**|`pa`| |**Papiamento**|`pap`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Peruvian Sign Language**|`prl`| |**Pijin**|`pis`| |**Pohnpeian**|`pon`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Quechua**|`qu`| |**Querétaro Otomi**|`otq`| |**Romance languages**|`roa`| |**Romanian**|`ro`| |**Romansh**|`rm`| |**Rundi**|`rn`| |**Russian**|`ru`| |**Ruund**|`rnd`| |**Samoan**|`sm`| |**San Salvador Kongo**|`kwy`| |**Sango**|`sg`| |**Sanskrit**|`sa`| |**Sardinian**|`sc`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Serbo-Croatian**|`sh`| |**Seselwa Creole French**|`crs`| |**Shona**|`sn`| |**Sichuan Yi**|`ii`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**South Ndebele**|`nr`| |**Southern Sotho**|`st`| |**Spanish**|`es`| |**Spanish Sign Language**|`ssp`| |**Sranan Tongo**|`srn`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swati**|`ss`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tahitian**|`ty`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Tetela**|`tll`| |**Tetun Dili**|`tdt`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tiv**|`tiv`| |**Tok Pisin**|`tpi`| |**Tonga (Tonga Islands)**|`to`| |**Tonga (Zambia)**|`toi`| |**Tsonga**|`ts`| |**Tswana**|`tn`| |**Tumbuka**|`tum`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Tuvalu**|`tvl`| |**Twi**|`tw`| |**Tzotzil**|`tzo`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Umbundu**|`umb`| |**Upper Sorbian**|`hsb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Venda**|`ve`| |**Venezuelan Sign Language**|`vsl`| |**Vietnamese**|`vi`| |**Volapük**|`vo`| |**Wallisian**|`wls`| |**Walloon**|`wa`| |**Waray (Philippines)**|`war`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Wolaytta**|`wal`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yapese**|`yap`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Yucateco**|`yua`| |**Yue Chinese**|`yue`| |**Zande (individual language)**|`zne`| |**Zhuang**|`za`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Arabic (Argentina)**|`ar-AR`| |**Bangla (Bangladesh)**|`bn-BD`| |**Basque (Spain)**|`eu-ES`| |**Bosnian (Bosnia & Herzegovina)**|`bs-BA`| |**Bulgarian (Bulgaria)**|`bg-BG`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-CN`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**Croatian (Croatia)**|`hr-HR`| |**Czech (Czechia)**|`cs-CZ`| |**Danish (Denmark)**|`da-DK`| |**Dutch (Netherlands)**|`nl-NL`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**Estonian (Estonia)**|`et-EE`| |**Finnish (Finland)**|`fi-FI`| |**French (Canada)**|`fr-CA`| |**French (France)**|`fr-FR`| |**German (Germany)**|`de-DE`| |**Greek (Greece)**|`el-GR`| |**Gujarati (India)**|`gu-IN`| |**Hebrew (Israel)**|`he-IL`| |**Hindi (India)**|`hi-IN`| |**Hungarian (Hungary)**|`hu-HU`| |**Indonesian (Indonesia)**|`id-ID`| |**Inuktitut (Latin)**|`iu-Latn`| |**Irish (Ireland)**|`ga-IE`| |**Italian (Italy)**|`it-IT`| |**Japanese (Japan)**|`ja-JP`| |**Kannada (India)**|`kn-IN`| |**Klingon (Klingon (KLI pIqaD))**|`tlh-Piqd`| |**Klingon (Latin)**|`tlh-Latn`| |**Korean (South Korea)**|`ko-KR`| |**Latvian (Latvia)**|`lv-LV`| |**Lithuanian (Lithuania)**|`lt-LT`| |**Malay (Malaysia)**|`ms-MY`| |**Malayalam (India)**|`ml-IN`| |**Maltese (Malta)**|`mt-MT`| |**Marathi (India)**|`mr-IN`| |**Mongolian (Cyrillic)**|`mn-Cyrl`| |**Mongolian (Mongolian)**|`mn-Mong`| |**Nepali (Nepal)**|`ne-NP`| |**Norwegian Bokmål (Norway)**|`nb-NO`| |**Persian (Afghanistan)**|`fa-AF`| |**Polish (Poland)**|`pl-PL`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`| |**Punjabi (India)**|`pa-IN`| |**Romanian (Romania)**|`ro-RO`| |**Russian (Russia)**|`ru-RU`| |**Serbian (Cyrillic)**|`sr-Cyrl`| |**Serbian (Latin)**|`sr-Latn`| |**Serbian (Montenegro)**|`sr-ME`| |**Serbian (Serbia)**|`sr-RS`| |**Sinhala (Sri Lanka)**|`si-LK`| |**Slovak (Slovakia)**|`sk-SK`| |**Slovenian (Slovenia)**|`sl-SI`| |**Spanish (Latin America)**|`es-419`| |**Spanish (Mexico)**|`es-MX`| |**Spanish (Spain)**|`es-ES`| |**Swedish (Sweden)**|`sv-SE`| |**Tamil (India)**|`ta-IN`| |**Telugu (India)**|`te-IN`| |**Thai (Thailand)**|`th-TH`| |**Turkish (Türkiye)**|`tr-TR`| |**Ukrainian (Ukraine)**|`uk-UA`| |**Urdu (Pakistan)**|`ur-PK`| |**Vietnamese (Vietnam)**|`vi-VN`| |**Welsh (United Kingdom)**|`cy-GB`|

# paths./translation/automatic_translation.post.requestBody.content.application/json.examples.RequestExample.value.text
人工智能 亦稱智械、機器智能，指由人製造出來的機器所表現出來的智慧。通常人工智能是指通过普通電腦程式來呈現人類智能的技術。該詞也指出研究這樣的智能系統是否能夠實現，以及如何實現。同时，通過醫學、神經科學、機器人學及統計學等的進步，常態預測則認為人類的很多職業也逐漸被其取代。

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.text
Artificial intelligence, also known as intelligent weapons, machine intelligence, refers to the wisdom expressed by machines made by people. Usually artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also points to the study of whether such intelligent systems can be achieved, and how. At the same time, through advances in medicine, neuroscience, robotics, and statistics, normal forecasts believe that many human occupations are gradually being replaced by them

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.openai.text
```Artificial intelligence, also known as machine intelligence, refers to the intelligence exhibited by machines created by humans. Usually, artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also refers to the research on whether such intelligent systems can be realized and how to achieve them. At the same time, with advancements in fields such as medicine, neuroscience, robotics, and statistics, it is commonly predicted that many human professions will gradually be replaced by artificial intelligence.```

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.google.text
Artificial intelligence, also known as omniscient, machine intelligence, refers to the wisdom expressed by machines made by humans. Usually artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also points to research into whether and how such intelligent systems can be realized. At the same time, through advances in medicine, neuroscience, robotics, and statistics, the norm predicts that many human occupations are gradually being replaced by them.

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.modernmt.text
Artificial intelligence, also known as intelligent machines, refers to the intelligence exhibited by machines manufactured by humans. Generally, artificial intelligence refers to technology that presents human intelligence through ordinary computer programs. The term also points to the study of whether and how such an intelligent system can be achieved. At the same time, through advances in medicine, neuroscience, robotics and statistics, it is usually predicted that many human professions will gradually be replaced by them.

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.ibm.text
Artificial intelligence also refers to intellectual machinery, machine intelligence, which refers to the wisdom shown by the machines manufactured by people.Usually artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs.The word also points to the question of whether a smart system like this can be realized, and how it can be achieved.At the same time, through advances in medicine, neuroscience, robotics, and statistics, the norm predicts that many occupations in humans are also gradually being replaced by them

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.phedone.text
Artificial intelligence, also known as omniscient, machine intelligence, refers to the wisdom expressed by machines made by humans. Usually artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also points to research into whether and how such intelligent systems can be realized. At the same time, through advances in medicine, neuroscience, robotics, and statistics, the norm predicts that many human occupations are gradually being replaced by them.

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.neuralspace.text
Artificial intelligence, also known as omniscient and machine intelligence, refers to the wisdom expressed by machines made by humans. Usually artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also points to research into whether and how such intelligent systems can be realized. At the same time, through advances in medicine, neuroscience, robotics, and statistics, the norm predicts that many human occupations are gradually being replaced by them.

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.text
Artificial intelligence, also known as intelligent devices and equipment intelligence, refers to the wisdom expressed by devices manufactured by humans. Generally, artificial intelligence refers to technology that presents human intelligence through ordinary computer programs. This phrase also indicates whether research such an intelligent system can be realized and how it can be implemented. At the same time, through advances in medicine, neuroscience, robot science, statistics, etc., many occupations recognized as human by the Normal Predictive Rules have gradually been replaced by them

# paths./translation/automatic_translation.post.responses.200.content.application/json.examples.ResponseExample.value.deepl.text
Artificial intelligence, also known as robotics and machine intelligence, refers to the intelligence expressed by human-made machines. Usually artificial intelligence refers to the technology of presenting human intelligence through ordinary computer programs. The term also refers to the study of whether and how such an intelligent system can be realized. At the same time, through advances in medicine, neuroscience, robotics and statistics, the norm is predicted that many human occupations are gradually being replaced by them.

# paths./translation/document_translation.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**deepl**|`v2`|2.0 (per 20 page)|20 page |**google**|`v3`|0.08 (per 1 page)|1 page

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Azerbaijani**|`az`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Cebuano**|`ceb`| |**Chinese**|`zh`| |**Corsican**|`co`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hawaiian**|`haw`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hmong**|`hmn`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Indonesian**|`id`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Korean**|`ko`| |**Kurdish**|`ku`| |**Lao**|`lo`| |**Latin**|`la`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malagasy**|`mg`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian**|`no`| |**Nyanja**|`ny`| |**Oriya (macrolanguage)**|`or`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Samoan**|`sm`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Shona**|`sn`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Somali**|`so`| |**Southern Sotho**|`st`| |**Spanish**|`es`| |**Sundanese**|`su`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Xhosa**|`xh`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-CN`| |**Chinese (Taiwan)**|`zh-TW`|

# paths./translation/language_detection.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**google**|`v1`|20.0 (per 1000000 char)|1 char |**ibm**|`v1 (2021-08-01)`|20.0 (per 1000000 char)|1000 char |**microsoft**|`v3.1`|1.0 (per 1000000 char)|1000 char |**neuralspace**|`v1`|7.0 (per 1000 request)|1 request |**modernmt**|`1.1.0`|8.0 (per 1000000 char)|1 char |**openai**|`v1`|20.0 (per 1000000 token)|1 token

# paths./translation/language_detection.post.requestBody.content.application/json.examples.RequestExample.value.text
Ogni individuo ha diritto all'istruzione. L'istruzione deve essere gratuita almeno per quanto riguarda le classi elementari e fondamentali. L'istruzione elementare deve essere obbligatoria. L'istruzione tecnica e professionale deve essere messa alla portata di tutti e l'istruzione superiore deve essere egualmente accessibile a tutti sulla base del merito.
L'istruzione deve essere indirizzata al pieno sviluppo della personalità umana ed al rafforzamento del rispetto dei diritti umani e delle libertà fondamentali. Essa deve promuovere la comprensione, la tolleranza, l'amicizia fra tutte le Nazioni, i gruppi razziali e religiosi, e deve favorire l'opera delle Nazioni Unite per il mantenimento della pace.
I genitori hanno diritto di priorità nella scelta del genere di istruzione da impartire ai loro figli.

# paths./video/explicit_content_detection_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/explicit_content_detection_async.get.summary
Video Explicit Content Detection List Jobs

# paths./video/explicit_content_detection_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3`|0.1 (per 60 seconde)|60 seconde |**google**|`Video intelligence v1`|0.1 (per 60 seconde)|1 seconde

# paths./video/explicit_content_detection_async.post.summary
Video Explicit Content Detection Launch Job

# paths./video/explicit_content_detection_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/explicit_content_detection_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/explicit_content_detection_async/{public_id}.get.summary
Video Explicit Content Detection Get Job Results

# paths./video/face_detection_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/face_detection_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3`|0.1 (per 60 seconde)|60 seconde |**google**|`Video intelligence v1`|0.1 (per 60 seconde)|1 seconde

# paths./video/face_detection_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/face_detection_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/label_detection_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/label_detection_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3`|0.1 (per 60 seconde)|60 seconde |**google**|`Video intelligence v1`|0.1 (per 60 seconde)|1 seconde

# paths./video/label_detection_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/label_detection_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/logo_detection_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/logo_detection_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**google**|`Video intelligence v1`|0.15 (per 60 seconde)|1 seconde |**twelvelabs**|`v1.1.2`|0.1 (per 60 seconde)|60 seconde

# paths./video/logo_detection_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/logo_detection_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/object_tracking_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/object_tracking_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**google**|`Video intelligence v1`|0.15 (per 60 seconde)|1 seconde

# paths./video/object_tracking_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/object_tracking_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/person_tracking_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/person_tracking_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3`|0.1 (per 60 seconde)|60 seconde |**google**|`Video intelligence v1`|0.1 (per 60 seconde)|1 seconde

# paths./video/person_tracking_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/person_tracking_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./video/text_detection_async.get.description
Get a list of all jobs launched for this feature. You'll then be able to use the ID of each one to get its status and results.<br>
                            Please note that a **job status doesn't get updated until a get request** is sent.

# paths./video/text_detection_async.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3`|0.1 (per 60 seconde)|60 seconde |**google**|`Video intelligence v1`|0.15 (per 60 seconde)|1 seconde |**twelvelabs**|`v1.1.2`|0.067 (per 60 seconde)|60 seconde

# paths./video/text_detection_async.delete.description
Generic class to handle method GET all async job for user

Attributes:
    feature (str): EdenAI feature
    subfeature (str): EdenAI subfeature

# paths./video/text_detection_async/{public_id}.get.description
Get the status and results of an async job given its ID.

# paths./workflow/.get.description
List all workflows or create a new workflow.

# paths./workflow/.post.description
List all workflows or create a new workflow.

# paths./workflow/{workflow_id}/execution/.get.parameters[].description
A page number within the paginated result set.

# components.schemas.AIProjectProjectTypeEnum.description
* `AskYoDa` - Askyoda
* `Translathor` - Translathor
* `X-Merge` - X Merge

# components.schemas.AddFileRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AddFileRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.AddFileRequest.properties.metadata.description
Optional parameter: Attach metadata to the uploaded file data in your database. Provide a stringified JSON with key-value pairs. Useful in `filter_document` when querying the language model, it allows you to filter data with your Chatbot by considering only documents that have the specified metadata.

# components.schemas.AddFileRequest.properties.provider.description
Select a provider to use, only for audio (speech-to-text) & pdf (ocr-async) files.

# components.schemas.AddUrlRequest.properties.urls.description
Add multiple urls into the database, it loads all the text from HTML webpages into a document format.

# components.schemas.AnonymizationAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AnonymizationAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AnonymizationAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AnonymizationAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AnonymizationAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AnonymizationAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AnonymizationAsyncRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AnonymizationAsyncRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.AnonymizationAsyncRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.AnonymizationEntity.description
This model represents an entity extracted from the text.

Attributes:
    offset (int): The offset of the entity in the text.
    length (int): The lenght of the entity in the text.
    category (CategoryType): The category of the entity.
    subcategory (SubCategoryType): The subcategory of the entity.
    original_label (str): The original label of the entity.
    content (str): The content of the entity.

# components.schemas.AskLLMRequest.properties.query.description
Enter your question or query about the data. The large language model (LLM) will provide a response.

# components.schemas.AskLLMRequest.properties.llm_provider.description
Select a provider for the large language model for processing. Leave empty for default.

# components.schemas.AskLLMRequest.properties.llm_model.description
Specify the model to use for language processing. Leave empty for default.

# components.schemas.AskLLMRequest.properties.k.description
How many results chunk you want to return

# components.schemas.AskLLMRequest.properties.history.items.description
A dictionary representing a single conversation in the previous history. Must contain 'user' and 'assistant' fields.

# components.schemas.AskLLMRequest.properties.history.description
A list containing all the previous conversations between the user and the chatbot AI. Each item in the list should be a dictionary with two keys: 'user' and 'assistant'.

# components.schemas.AskLLMRequest.properties.chatbot_global_action.description
A system message that helps set the behavior of the assistant.

# components.schemas.AskLLMRequest.properties.filter_documents.description
Filter uploaded documents based on their metadata. Specify key-value pairs where the key represents the metadata field and the value is the desired metadata value. Please ensure that the provided metadata keys are available in your database.

# components.schemas.AskLLMRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.AskLLMRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.AskYourDataProjectRequest.properties.llm_provider.description
Select a default LLM provider to use in your project.

# components.schemas.AskYourDataProjectRequest.properties.llm_model.description
Select a default Model for LLM provider to use in your project

# components.schemas.AskYourDataProjectRequest.properties.db_provider.description
Database Provider

* `qdrant` - qdrant
* `supabase` - supabase

# components.schemas.AskYourDataProjectRequest.properties.embeddings_provider.description
Select an embedding provider to use in your search database. Leave empty for default.

# components.schemas.AsyncOcrRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AsyncOcrRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AsyncOcrRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AsyncOcrRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AsyncOcrRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AsyncOcrRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AsyncOcrRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AsyncOcrRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.AsyncOcrRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.AsyncVideoAnalysisRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AsyncVideoAnalysisRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AsyncVideoAnalysisRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AsyncVideoAnalysisRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AsyncVideoAnalysisRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AsyncVideoAnalysisRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AsyncVideoAnalysisRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AsyncVideoAnalysisRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.AutomlClassificationPredictRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AutomlClassificationPredictRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AutomlClassificationPredictRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AutomlClassificationPredictRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AutomlClassificationPredictRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AutomlClassificationPredictRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AutomlClassificationPredictRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AutomlClassificationPredictRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.AutomlClassificationTrainRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AutomlClassificationTrainRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AutomlClassificationTrainRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AutomlClassificationTrainRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AutomlClassificationTrainRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AutomlClassificationTrainRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AutomlClassificationUploadDataRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.AutomlClassificationUploadDataRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.AutomlClassificationUploadDataRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.AutomlClassificationUploadDataRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.AutomlClassificationUploadDataRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.AutomlClassificationUploadDataRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.AutomlClassificationUploadDataRequest.properties.type_of_data.description
Type of data : TRAINING or TEST

* `TRAINING` - TRAINING
* `TEST` - TEST

# components.schemas.AutomlClassificationUploadDataRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.AutomlClassificationUploadDataRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.BatchLaunchFailedRequest.properties.errors.description
Error received from the request validator

# components.schemas.BatchLaunchResponse.properties.nb_launched.description
Number of successfully launched requests

# components.schemas.BatchLaunchResponse.properties.failed_requests.description
if any requests failed, they will be shown in this list

# components.schemas.BoundingBox.description
Bounding box of a word in the image

Attributes:
    left (float): Left coordinate of the bounding box
    top (float): Top coordinate of the bounding box
    width (float): Width of the bounding box
    height (float): Height of the bounding box
    text (str): Text detected in the bounding box

Constructor:
    from_json (classmethod): Create a new instance of BoundingBox from a JSON object
    from_normalized_vertices (classmethod): Create a new instance of BoundingBox from normalized vertices
    unknown (classmethod): Return a invalid bouding_box with all field filled with `-1`

# components.schemas.CategoryType.description
This enum are used to categorize the explicit content extracted from the text

# components.schemas.CustomDocumentParsingAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.CustomDocumentParsingAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.CustomDocumentParsingAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.CustomDocumentParsingAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.CustomDocumentParsingAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.CustomDocumentParsingAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.CustomDocumentParsingAsyncRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.CustomDocumentParsingAsyncRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.CustomDocumentParsingAsyncRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.CustomDocumentParsingAsyncRequest.properties.queries.description
Your queries need to be a list of dict containing the questions you want answered and the page to look for the information in :  '[{'query':'your query','pages':'your pages'},{'query':'your query','pages':'your pages'}]'

# components.schemas.CustomDocumentParsingAsyncRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.DataTypeEnum.description
* `pdf` - pdf
* `audio` - audio
* `csv` - csv
* `xml` - xml

# components.schemas.DbProviderEnum.description
* `qdrant` - qdrant
* `supabase` - supabase

# components.schemas.DetailTypeEnum.description
* `resolution` - Resolution
* `document_type` - Document Type

# components.schemas.DocParserCallParametersRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.DocParserCallParametersRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.DocParserCallParametersRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.DocumentTypeEnum.description
* `auto-detect` - auto-detect
* `invoice` - invoice
* `receipt` - receipt

# components.schemas.EmotionItem.description
This class is used in EmotionAnalysisDataClass to list emotion analysed.
Args:
    - emotion (EmotionEnum): emotion of the text
    - emotion_score (float): score of the emotion

# components.schemas.FallbackTypeEnum.description
* `rerun` - Rerun
* `continue` - Continue

# components.schemas.FinancialBankInformation.properties.swift.description
Society for Worldwide Interbank Financial Telecommunication code.

# components.schemas.FinancialBankInformation.properties.routing_number.description
Routing number for banks in the United States.

# components.schemas.FinancialCustomerInformation.properties.billing_address.description
The explicit billing address for the customer.

# components.schemas.FinancialCustomerInformation.properties.service_address.description
The service address associated with the customer.

# components.schemas.FinancialCustomerInformation.properties.remittance_address.description
The address to which payments should be remitted.

# components.schemas.FinancialCustomerInformation.properties.phone.description
The phone number associated with the customer.

# components.schemas.FinancialCustomerInformation.properties.vat_number.description
VAT (Value Added Tax) number of the customer.

# components.schemas.FinancialCustomerInformation.properties.abn_number.description
ABN (Australian Business Number) of the customer.

# components.schemas.FinancialCustomerInformation.properties.gst_number.description
GST (Goods and Services Tax) number of the customer.

# components.schemas.FinancialCustomerInformation.properties.pan_number.description
PAN (Permanent Account Number) of the customer.

# components.schemas.FinancialCustomerInformation.properties.business_number.description
Business registration number of the customer.

# components.schemas.FinancialCustomerInformation.properties.siret_number.description
SIRET (Système d'Identification du Répertoire des Entreprises et de leurs Établissements) number of the customer.

# components.schemas.FinancialCustomerInformation.properties.siren_number.description
SIREN (Système d'Identification du Répertoire des Entreprises) number of the customer.

# components.schemas.FinancialCustomerInformation.properties.coc_number.description
Chamber of Commerce registration number.

# components.schemas.FinancialCustomerInformation.properties.fiscal_number.description
Fiscal identification number of the customer.

# components.schemas.FinancialCustomerInformation.properties.registration_number.description
Official registration number of the customer.

# components.schemas.FinancialCustomerInformation.properties.tax_id.description
Tax identification number of the customer.

# components.schemas.FinancialCustomerInformation.properties.website.description
The website associated with the customer.

# components.schemas.FinancialCustomerInformation.properties.remit_to_name.description
The name associated with the customer's remittance address.

# components.schemas.FinancialCustomerInformation.properties.city.description
The city associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.country.description
The country associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.house_number.description
The house number associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.province.description
The province associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.street_name.description
The street name associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.zip_code.description
The ZIP code associated with the customer's address.

# components.schemas.FinancialCustomerInformation.properties.municipality.description
The municipality associated with the customer's address.

# components.schemas.FinancialDocumentInformation.properties.service_start_date.description
Start date of the service associated with the document.

# components.schemas.FinancialDocumentInformation.properties.service_end_date.description
End date of the service associated with the document.

# components.schemas.FinancialDocumentInformation.properties.reference.description
Reference number associated with the document.

# components.schemas.FinancialDocumentInformation.properties.biller_code.description
Biller code associated with the document.

# components.schemas.FinancialDocumentInformation.properties.order_date.description
Date of the order associated with the document.

# components.schemas.FinancialDocumentInformation.properties.tracking_number.description
Tracking number associated with the document.

# components.schemas.FinancialDocumentInformation.properties.barcodes.description
List of barcodes associated with the document.

# components.schemas.FinancialLineItem.properties.unit_price.description
Unit price for each unit in the line item.

# components.schemas.FinancialLineItem.properties.product_code.description
Product code or identifier for the line item.

# components.schemas.FinancialLineItem.properties.purchase_order.description
Purchase order related to the line item.

# components.schemas.FinancialLineItem.properties.base_total.description
Base total amount before any discounts or taxes.

# components.schemas.FinancialLineItem.properties.discount_amount.description
Amount of discount applied to the line item.

# components.schemas.FinancialLineItem.properties.discount_rate.description
Rate of discount applied to the line item.

# components.schemas.FinancialLineItem.properties.discount_code.description
Code associated with any discount applied to the line item.

# components.schemas.FinancialLineItem.properties.order_number.description
Order number associated with the line item.

# components.schemas.FinancialLocalInformation.properties.currency.description
Currency used in financial transactions.

# components.schemas.FinancialLocalInformation.properties.currency_exchange_rate.description
Exchange rate for the specified currency.

# components.schemas.FinancialLocalInformation.properties.country.description
Country associated with the local financial information.

# components.schemas.FinancialLocalInformation.properties.language.description
Language used in financial transactions.

# components.schemas.FinancialMerchantInformation.properties.tax_id.description
Tax identification number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.vat_number.description
VAT (Value Added Tax) number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.abn_number.description
ABN (Australian Business Number) of the merchant.

# components.schemas.FinancialMerchantInformation.properties.gst_number.description
GST (Goods and Services Tax) number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.business_number.description
Business registration number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.siret_number.description
SIRET (Système d'Identification du Répertoire des Entreprises et de leurs Établissements) number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.siren_number.description
SIREN (Système d'Identification du Répertoire des Entreprises) number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.pan_number.description
PAN (Permanent Account Number) of the merchant.

# components.schemas.FinancialMerchantInformation.properties.coc_number.description
Chamber of Commerce registration number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.fiscal_number.description
Fiscal identification number of the merchant.

# components.schemas.FinancialMerchantInformation.properties.registration.description
Official registration information of the merchant.

# components.schemas.FinancialMerchantInformation.properties.city.description
City associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.country.description
Country associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.house_number.description
House number associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.province.description
Province associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.street_name.description
Street name associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.zip_code.description
ZIP code associated with the merchant's address.

# components.schemas.FinancialMerchantInformation.properties.country_code.description
Country code associated with the merchant's location.

# components.schemas.FinancialParserObjectDataClass.properties.item_lines.description
List of line items associated with the document.

# components.schemas.FinancialPaymentInformation.properties.amount_shipping.description
Shipping cost in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.amount_change.description
Change amount in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.amount_paid.description
Amount already paid in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.subtotal.description
Subtotal amount in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.total_tax.description
Total tax amount in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.tax_rate.description
Tax rate applied in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.discount.description
Discount amount applied in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.gratuity.description
Gratuity amount in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.service_charge.description
Service charge in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.previous_unpaid_balance.description
Previous unpaid balance in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.prior_balance.description
Prior balance before the current financial transaction.

# components.schemas.FinancialPaymentInformation.properties.payment_method.description
Payment method used in the financial transaction.

# components.schemas.FinancialPaymentInformation.properties.shipping_handling_charge.description
Charge for shipping and handling in a financial transaction.

# components.schemas.FinancialPaymentInformation.properties.transaction_number.description
Unique identifier for the financial transaction.

# components.schemas.FinancialPaymentInformation.properties.transaction_reference.description
Reference number for the financial transaction.

# components.schemas.InvoiceSplitterAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.InvoiceSplitterAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.InvoiceSplitterAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.InvoiceSplitterAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.InvoiceSplitterAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.InvoiceSplitterAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.InvoiceSplitterAsyncRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.InvoiceSplitterAsyncRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.InvoiceSplitterAsyncRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.Line.description
Line of a document

Attributes:
    text (str): Text detected in the line
    bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the line
    words (Sequence[Word]): List of words of the line
    confidence (float): Confidence of the line

# components.schemas.OcrTablesAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.OcrTablesAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.OcrTablesAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.OcrTablesAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.OcrTablesAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.OcrTablesAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.OcrTablesAsyncRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.OcrTablesAsyncRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.OcrTablesAsyncRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.OcrTablesAsyncRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.Page.description
Page of a document

Attributes:
    lines (Sequence[Line]): List of lines of the page

# components.schemas.PatchedAskYodaProjectUpdateRequest.properties.llm_provider.description
Select a default LLM provider to use in your project.

# components.schemas.PatchedAskYodaProjectUpdateRequest.properties.llm_model.description
Select a default Model for LLM provider to use in your project

# components.schemas.PatchedUniversalTranslatorCreatetRequest.properties.source_language.description
The language code from which the text will be translated

# components.schemas.PatchedUniversalTranslatorCreatetRequest.properties.target_language.description
The language code in which the text will be translated

# components.schemas.PipelineRequest.properties.text.description
The input text for the first feature of the pipeline

# components.schemas.PipelineRequest.properties.texts.description
List of texts for the first feature of the pipeline

# components.schemas.PipelineRequest.properties.file.description
The input file for the first feature of the pipeline

# components.schemas.PipelineRequest.properties.return_only_last.description
This parameter allows user to choose to output only the final result or all the intermediate results.

# components.schemas.PriceUnitTypeEnum.description
* `file` - File
* `image` - Image
* `page` - Page
* `size` - Size
* `request` - Request
* `seconde` - Second
* `minute` - Minute
* `free` - Free
* `hour` - Hour
* `char` - Characters
* `token` - Token
* `exec_time` - Execution Time
* `unknown` - Unknown

# components.schemas.PricingSerialzier.properties.model_name.description
Model name, default to 'default' if no models to chose from

# components.schemas.PricingSerialzier.properties.detail_type.description
(Optional) type of extra value, MUST be the same name as the feature parameter name. eg: resolution

* `resolution` - Resolution
* `document_type` - Document Type

# components.schemas.PricingSerialzier.properties.detail_value.description
(Optional) extra value for detailed pricing, eg: 250x250 for resolution

# components.schemas.RepresentationEnum.description
* `document` - document
* `query` - query
* `symetric` - symetric

# components.schemas.SegmentSentimentAnalysisDataClass.description
This class is used in SentimentAnalysisDataClass to describe each segment analyzed.

Args:
    - segment (str): The segment analyzed
    - sentiment (Literal['Positve', 'Negative', 'Neutral']) (Case is ignore): Sentiment of segment
    - sentiment_rate (float between 0 and 1): Rate of sentiment

# components.schemas.SimilarityMetricEnum.description
* `cosine` - cosine
* `manhattan` - manhattan
* `euclidean` - euclidean

# components.schemas.SpeechToTextAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.SpeechToTextAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.SpeechToTextAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.SpeechToTextAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.SpeechToTextAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.SpeechToTextAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.SpeechToTextAsyncRequest.properties.provider_params.description
Parameters specific to the provider that you want to send along the request.

it should take a *provider* name as key and an object of parameters as value.

Example:

    {
      "deepgram": {
        "filler_words": true,
        "smart_format": true,
        "callback": "https://webhook.site/0000"
      },
      "assembly": {
        "webhook_url": "https://webhook.site/0000"
      }
    }

Please refer to the documentation of each provider to see which parameters to send.

# components.schemas.SpeechToTextAsyncRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.SpeechToTextAsyncRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.SpeechToTextAsyncRequest.properties.profanity_filter.description
Boolean value to specify weather or not the service will filter profanity and replace inappropriate words with a series of asterisks

# components.schemas.SpeechToTextAsyncRequest.properties.custom_vocabulary.description
List of words or composed words to be detected by the speech to text engine. (Ex: Word, Mike, Draw, Los Angeles,...)

# components.schemas.SpeechToTextAsyncRequest.properties.convert_to_wav.description
Boolean value to specify weather to convert the audio/video file to wav format to be accepted by a majority of the providers

# components.schemas.SpellCheckItem.description
Represents a spell check item with suggestions.

Args:
    text (str): The text to spell check.
    type (str, optional): The type of the text.
    offset (int): The offset of the text.
    length (int): The length of the text.
    suggestions (Sequence[SuggestionItem], optional): The list of suggestions for the misspelled text.

Raises:
    ValueError: If the offset or length is not positive.

Returns:
    SpellCheckItem: An instance of the SpellCheckItem class.

# components.schemas.StateEnum.description
* `finished` - finished
* `failed` - failed
* `Timeout error` - Timeout error
* `processing` - processing

# components.schemas.Status889Enum.description
* `succeeded` - Status Succeeded
* `failed` - Status Failed
* `finished` - Status Finished
* `processing` - Status Processing

# components.schemas.SuggestionItem.description
Represents a suggestion for a misspelled word.

Args:
    suggestion (str): The suggested text.
    score (float, optional): The score of the suggested text (between 0 and 1).

Raises:
    ValueError: If the score is not between 0 and 1.

Returns:
    SuggestionItem: An instance of the SuggestionItem class.

# components.schemas.TextToSpeechAsyncRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.TextToSpeechAsyncRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.TextToSpeechAsyncRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.TextToSpeechAsyncRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.TextToSpeechAsyncRequest.properties.webhook_receiver.description
Webhook receiver should be a valid https URL (ex : https://your.listner.com/endpoint).             After the processing is done, the webhook endpoint will receive a POST request with the result.

# components.schemas.TextToSpeechAsyncRequest.properties.users_webhook_parameters.description
Json data that contains of additional parameters that will be sent back to the webhook receiver             (ex: api key for security or client's data ID to link the result internally).             Will only be used when webhook_receiver is set.

# components.schemas.TextToSpeechAsyncRequest.properties.rate.description
Increase or decrease the speaking rate by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.TextToSpeechAsyncRequest.properties.pitch.description
Increase or decrease the speaking pitch by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.TextToSpeechAsyncRequest.properties.volume.description
Increase or decrease the audio volume by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.TextToSpeechAsyncRequest.properties.audio_format.description
Optional parameter to specify the audio format in which the audio will be generated. By default,             audios are encoded in MP3, except for lovoai which use the wav container.

# components.schemas.TextToSpeechAsyncRequest.properties.sampling_rate.description
Optional. The synthesis sample rate (in hertz) for this audio. When this is specified, the audio will be converted             either to the right passed value, or to a the nearest value acceptable by the provider

# components.schemas.TypeEnum.description
* `db` - Db
* `bucket` - Bucket
* `db_vector` - Db Vector
* `ai` - Ai

# components.schemas.UniversalTranslatorCreatet.properties.source_language.description
The language code from which the text will be translated

# components.schemas.UniversalTranslatorCreatet.properties.target_language.description
The language code in which the text will be translated

# components.schemas.UniversalTranslatorCreatetRequest.properties.source_language.description
The language code from which the text will be translated

# components.schemas.UniversalTranslatorCreatetRequest.properties.target_language.description
The language code in which the text will be translated

# components.schemas.UniversalTranslatorList.properties.source_language.description
The language code from which the text will be translated

# components.schemas.UniversalTranslatorList.properties.target_language.description
The language code in which the text will be translated

# components.schemas.Word.description
Word of a document

Attributes:
    text (str): Text detected in the word
    bounding_boxes (Sequence[BoundingBox]): Bounding boxes of the words in the word
    confidence (float): Confidence score of the word

# components.schemas.XMergeListProjectTypeEnum.description
* `DOC_PARSER` - Doc Parser
* `TEXT` - Text

# components.schemas.audiospeech_to_text_asyncSpeechToTextAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.audiotext_to_speechTextToSpeechDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.rate.description
Increase or decrease the speaking rate by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.pitch.description
Increase or decrease the speaking pitch by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.volume.description
Increase or decrease the audio volume by expressing a positif or negatif number ranging between             100 and -100 (a relative value as percentage varying from -100% to 100%)

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.audio_format.description
Optional parameter to specify the audio format in which the audio will be generated. By default,             audios are encoded in MP3, except for lovoai which use the wav container.

# components.schemas.audiotext_to_speechTextToSpeechRequest.properties.sampling_rate.description
Optional. The synthesis sample rate (in hertz) for this audio. When this is specified, the audio will be converted             either to the right passed value, or to a the nearest value acceptable by the provider

# components.schemas.audiotext_to_speech_asyncTextToSpeechAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageanonymizationAnonymizationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageanonymizationimagelandmark_detectionimageexplicit_contentIm.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageautoml_classificationAutomlClassificationCreateProjectDataC.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageautoml_classificationcreate_projectAutomlClassificationCrea.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageautoml_classificationdelete_projectAutomlClassificationDele.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagebackground_removalBackgroundRemovalDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imagebackground_removalBackgroundRemovalRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageembeddingsEmbeddingsDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageembeddingsEmbeddingsRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageembeddingsEmbeddingsRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageembeddingsEmbeddingsRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageembeddingsEmbeddingsRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageembeddingsEmbeddingsRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageembeddingsEmbeddingsRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageembeddingsEmbeddingsRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageembeddingsEmbeddingsRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageembeddingsEmbeddingsRequest.properties.representation.description
The type of embedding representation to embed the image with

* `document` - document
* `query` - query
* `symetric` - symetric

# components.schemas.imageexplicit_contentExplicitContentDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageface_compareFaceCompareDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageface_compareFaceCompareRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageface_compareFaceCompareRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageface_compareFaceCompareRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageface_compareFaceCompareRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageface_compareFaceCompareRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageface_compareFaceCompareRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageface_compareFaceCompareRequest.properties.file1.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageface_compareFaceCompareRequest.properties.file1_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageface_compareFaceCompareRequest.properties.file2.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageface_compareFaceCompareRequest.properties.file2_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageface_detectionFaceDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageface_detectionFaceDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageface_detectionFaceDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageface_detectionFaceDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageface_detectionFaceDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageface_detectionFaceDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageface_detectionFaceDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageface_detectionFaceDetectionRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageface_detectionFaceDetectionRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageface_recognitionFaceRecognitionAddFaceDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageface_recognitiondelete_faceFaceRecognitionDeleteFaceRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageface_recognitionrecognizeimageface_recognitionadd_faceFaceR.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imagegenerationGenerationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagegenerationGenerationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagegenerationGenerationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagegenerationGenerationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagegenerationGenerationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagegenerationGenerationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagegenerationGenerationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagegenerationGenerationRequest.properties.text.description
Description of the desired image(s). The maximum length is 1000 characters

# components.schemas.imagegenerationGenerationRequest.properties.num_images.description
The number of images to generate. Must be between 1 and 10.

# components.schemas.imagelandmark_detectionLandmarkDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagelogo_detectionLogoDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imagelogo_detectionLogoDetectionRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imageobject_detectionObjectDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imageobject_detectionObjectDetectionRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imagequestion_answerQuestionAnswerDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.imagequestion_answerQuestionAnswerRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.imagesearchSearchDeleteImageDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagesearchdelete_imageDeleteImageRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imagesearchlaunch_similaritySearchImageRequest.properties.n.description
The `n` parameter specifies the number of images you want to be returned in the response.             It determines the count of the most similar images to the input image that will be included in the response.             By default, it is set to 10.

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.imagesearchupload_imageUploadImageRequest.properties.image_name.description
The image name need to have the extension of the file.

# components.schemas.ocranonymization_asyncAnonymizationAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrbank_check_parsingBankCheckParsingDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrbank_check_parsingBankCheckParsingRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocrcustom_document_parsing_asyncCustomDocumentParsingAsyncDataCl.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrdata_extractionDataExtractionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrdata_extractionDataExtractionRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocrfinancial_parserFinancialParserDataClass.properties.extracted_data.description
List of parsed financial data objects (per page).

# components.schemas.ocrfinancial_parserFinancialParserDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.document_type.description
Specify the type of your document. Can be Set to 'auto-detect' for automatic detection if the provider supports it. Otherwise, the default is 'invoice'.

* `auto-detect` - auto-detect
* `invoice` - invoice
* `receipt` - receipt

# components.schemas.ocrfinancial_parserFinancialParserRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocridentity_parserIdentityParserDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocridentity_parserIdentityParserRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocridentity_parserIdentityParserRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocridentity_parserIdentityParserRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocridentity_parserIdentityParserRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocridentity_parserIdentityParserRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocridentity_parserIdentityParserRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocridentity_parserIdentityParserRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocridentity_parserIdentityParserRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocridentity_parserIdentityParserRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocridentity_parserIdentityParserRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocrinvoice_parserInvoiceParserDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.ocrinvoice_parserInvoiceParserRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocrinvoice_splitter_asyncInvoiceSplitterAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrocrOcrDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrocrOcrRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrocrOcrRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrocrOcrRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrocrOcrRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrocrOcrRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrocrOcrRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrocrOcrRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrocrOcrRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrocrOcrRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrocrOcrRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.ocrocr_asyncOcrAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrocr_tables_asyncOcrTablesAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrreceipt_parserReceiptParserDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.language.description
Language code of the language the document is written in (ex: fr (French), en (English), es (Spanish))

# components.schemas.ocrreceipt_parserReceiptParserRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.ocrresume_parserResumeParserDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.ocrresume_parserResumeParserRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.ocrresume_parserResumeParserRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.ocrresume_parserResumeParserRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.ocrresume_parserResumeParserRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.ocrresume_parserResumeParserRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.ocrresume_parserResumeParserRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.ocrresume_parserResumeParserRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.ocrresume_parserResumeParserRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.ocrresume_parserResumeParserRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.ocrresume_parserResumeParserRequest.properties.convert_to_pdf.description
Boolean value to specify weather to convert the doc/docx files to pdf format to be accepted by a majority of the providers

# components.schemas.textai_detectionAiDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textai_detectionAiDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textai_detectionAiDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textai_detectionAiDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textai_detectionAiDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textai_detectionAiDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textai_detectionAiDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textai_detectionAiDetectionRequest.properties.language.description
Language code for the language the input text is written in (eg: en, fr).

# components.schemas.textai_detectionAiDetectionRequest.properties.provider_params.description
Parameters specific to the provider that you want to send along the request.

it should take a *provider* name as key and an object of parameters as value.

Example:

    {
      "deepgram": {
        "filler_words": true,
        "smart_format": true,
        "callback": "https://webhook.site/0000"
      },
      "assembly": {
        "webhook_url": "https://webhook.site/0000"
      }
    }

Please refer to the documentation of each provider to see which parameters to send.

# components.schemas.textanonymizationAnonymizationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textchatChatDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textchatChatRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textchatChatRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textchatChatRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textchatChatRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textchatChatRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textchatChatRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textchatChatRequest.properties.chatbot_global_action.description
A system message that helps set the behavior of the assistant. For example, 'You are a helpful assistant'.

# components.schemas.textchatChatRequest.properties.previous_history.items.description
A dictionary representing a single conversation in the previous history. Must contain 'role' and 'message' fields.

# components.schemas.textchatChatRequest.properties.previous_history.description
A list containing all the previous conversations between the user and the chatbot AI. Each item in the list should be a dictionary with two keys: 'role' and 'message'. The 'role' key specifies the role of the speaker and can have the values 'user' or 'assistant'. The 'message' key contains the text of the conversation from the respective role. For example: [{'role': 'user', 'message': 'Hello'}, {'role': 'assistant', 'message': 'Hi, how can I help you?'}, ...]. This format allows easy identification of the speaker's role and their corresponding message.

# components.schemas.textchatChatRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.textchatChatRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.textchatChatStreamRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textchatChatStreamRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textchatChatStreamRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textchatChatStreamRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textchatChatStreamRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textchatChatStreamRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textchatChatStreamRequest.properties.chatbot_global_action.description
A system message that helps set the behavior of the assistant. For example, 'You are a helpful assistant'.

# components.schemas.textchatChatStreamRequest.properties.previous_history.items.description
A dictionary representing a single conversation in the previous history. Must contain 'role' and 'message' fields.

# components.schemas.textchatChatStreamRequest.properties.previous_history.description
A list containing all the previous conversations between the user and the chatbot AI. Each item in the list should be a dictionary with two keys: 'role' and 'message'. The 'role' key specifies the role of the speaker and can have the values 'user' or 'assistant'. The 'message' key contains the text of the conversation from the respective role. For example: [{'role': 'user', 'message': 'Hello'}, {'role': 'assistant', 'message': 'Hi, how can I help you?'}, ...]. This format allows easy identification of the speaker's role and their corresponding message.

# components.schemas.textchatChatStreamRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.textchatChatStreamRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.textcode_generationCodeGenerationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textcode_generationCodeGenerationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textcode_generationCodeGenerationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textcode_generationCodeGenerationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textcode_generationCodeGenerationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textcode_generationCodeGenerationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textcode_generationCodeGenerationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textcode_generationCodeGenerationRequest.properties.prompt.description
Entrer the source code that will be used as a context.

# components.schemas.textcode_generationCodeGenerationRequest.properties.instruction.description
Entrer the instruction you want to be followed.

# components.schemas.textcode_generationCodeGenerationRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.textcode_generationCodeGenerationRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.textcustom_classificationCustomClassificationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.labels.description
List of the labels (classes) you want the texts to be classified as.

# components.schemas.textcustom_classificationCustomClassificationRequest.properties.examples.description
List of text/label pairs (eg: [['I need help please wire me $1000 right now', 'spam'],]

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionD.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.entities.description
List of entities (at least two) to extract from your text eg : ['job', 'country'].

# components.schemas.textcustom_named_entity_recognitionCustomNamedEntityRecognitionR.properties.examples.description
List of examples eg : [{'text': 'an input text', 'entities' : [{'entity':'entity_1', 'category':'category_1'}, ...]}, ...]

# components.schemas.textembeddingsEmbeddingsDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textembeddingsEmbeddingsRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textembeddingsEmbeddingsRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textembeddingsEmbeddingsRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textembeddingsEmbeddingsRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textembeddingsEmbeddingsRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textembeddingsEmbeddingsRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textembeddingsEmbeddingsRequest.properties.texts.description
List of texts to transform into embeddings.

# components.schemas.textemotion_detectionEmotionDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textemotion_detectionEmotionDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textentity_sentimentEntitySentimentDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textentity_sentimentEntitySentimentRequest.properties.language.description
Language code for the language the input text is written in (eg: en, fr).

# components.schemas.textgenerationGenerationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textgenerationGenerationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textgenerationGenerationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textgenerationGenerationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textgenerationGenerationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textgenerationGenerationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textgenerationGenerationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textgenerationGenerationRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.textgenerationGenerationRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length.

# components.schemas.textkeyword_extractionKeywordExtractionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textmoderationModerationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textnamed_entity_recognitionNamedEntityRecognitionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textplagia_detectionPlagiaDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textplagia_detectionPlagiaDetectionRequest.properties.text.description
A text content on which a plagiarism detection analysis will be run

# components.schemas.textprompt_optimizationPromptOptimizationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textprompt_optimizationPromptOptimizationRequest.properties.target_provider.description
Select the provider for the prompt optimization

# components.schemas.textquestion_answerQuestionAnswerDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.temperature.description
Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

# components.schemas.textquestion_answerQuestionAnswerRequest.properties.examples.description
List of question/answer pairs (eg: [['When was Barack Obama elected president?', 'in 2009.'],]

# components.schemas.textsearchSearchDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textsearchSearchRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textsearchSearchRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textsearchSearchRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textsearchSearchRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textsearchSearchRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textsearchSearchRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textsearchSearchRequest.properties.similarity_metric.description
Is used to specify the similarity function that calculates the similarity between embedding vectors when performing text searches. This parameter allows you to customize how text similarity is measured, which can be crucial in various natural language processing tasks such as document retrieval, recommendation systems, and clustering.

* `cosine` - cosine
* `manhattan` - manhattan
* `euclidean` - euclidean

# components.schemas.textsentiment_analysisSentimentAnalysisDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textspell_checkSpellCheckDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textspell_checkSpellCheckRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textspell_checkSpellCheckRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textspell_checkSpellCheckRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textspell_checkSpellCheckRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textspell_checkSpellCheckRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textspell_checkSpellCheckRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textspell_checkSpellCheckRequest.properties.language.description
Language code for the language the input text is written in (eg: en, fr).

# components.schemas.textsummarizeSummarizeDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.textsummarizeSummarizeRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.textsummarizeSummarizeRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.textsummarizeSummarizeRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.textsummarizeSummarizeRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.textsummarizeSummarizeRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.textsummarizeSummarizeRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.textsummarizeSummarizeRequest.properties.language.description
Language code for the language the input text is written in (eg: en, fr).

# components.schemas.textsyntax_analysisSyntaxAnalysisDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.texttopic_extractionTopicExtractionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.texttopic_extractiontextanonymizationtextmoderationtextnamed_ent.properties.language.description
Language code for the language the input text is written in (eg: en, fr).

# components.schemas.translationautomatic_translationAutomaticTranslationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.translationautomatic_translationAutomaticTranslationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.translationdocument_translationDocumentTranslationDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.file.description
File to analyse in binary format to be used with *content-type*: **multipart/form-data** <br> **Does not work with application/json !**

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.translationdocument_translationDocumentTranslationRequest.properties.file_password.description
If your PDF file has a password, you can pass it here!

# components.schemas.translationlanguage_detectionLanguageDetectionDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.providers.description
It can be one (ex: **'amazon'** or **'google'**) or multiple provider(s) (ex: **'amazon,microsoft,google'**)             that the data will be redirected to in order to get the processed results.<br>             Providers can also be invoked with specific models (ex: providers: **'amazon/model1, amazon/model2, google/model3'**)

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.fallback_providers.description
Providers in this list will be used as fallback if the call to provider in `providers` parameter fails.
    To use this feature, you must input **only one** provider in the `providers` parameter. but you can put up to 5 fallbacks.

They will be tried in the same order they are input, and it will stop to the first provider who doesn't fail.


*Doesn't work with async subfeatures.*
    

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.settings.description
A dictionnary or a json object to specify specific models to use for some providers. <br>                     It can be in the following format: {"google" : "google_model", "ibm": "ibm_model"...}.
                     

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.response_as_dict.description
Optional : When set to **true** (default), the response is an object of responses with providers names as keys :
``` {"google" : { "status": "success", ... }, } ```
When set to **false** the response structure is a list of response objects :
``` [{"status": "success", "provider": "google" ... }, ] ```.

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.attributes_as_list.description
Optional : When set to **false** (default) the structure of the extracted items is list of objects having different attributes :
```{'items': [{"attribute_1": "x1","attribute_2": "y2"}, ... ]}```
When it is set to **true**, the response contains an object with each attribute as a list :
```{ "attribute_1": ["x1","x2", ...], "attribute_2": [y1, y2, ...]}```

# components.schemas.translationlanguage_detectionLanguageDetectionRequest.properties.show_original_response.description
Optional : Shows the original response of the provider.<br>
        When set to **true**, a new attribute *original_response* will appear in the response object.

# components.schemas.videoexplicit_content_detection_asyncExplicitContentDetectionAsy.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videoface_detection_asyncFaceDetectionAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videolabel_detection_asyncLabelDetectionAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videologo_detection_asyncLogoDetectionAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videoobject_tracking_asyncObjectTrackingAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videoperson_tracking_asyncPersonTrackingAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request

# components.schemas.videotext_detection_asyncTextDetectionAsyncDataClass.properties.original_response.description
original response sent by the provider, hidden by default, show it by passing the `show_original_response` field to `true` in your request