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

</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.

Default Embedding Models
|Name|Value| |----|-----| |**openai**|`text-embedding-3-large`| |**cohere**|`embed-multilingual-v3.0`| |**google**|`text-multilingual-embedding-002`| |**mistral**|`mistral-embed`| |**jina**|`jina-embeddings-v3`|

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.description
Update the bot prompt for your RAG project using an existing prompt.
This endpoint allows you to change the current bot prompt to a different existing prompt.

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.parameters[].description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.parameters[].description
The model used to call the prompt. E.g. openai/gpt-4o

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.parameters[].description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.parameters[].description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# paths./aiproducts/askyoda/v2/{project_id}/{prompt_name}/.patch.parameters[].description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# 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_image/.post.description
This endpoint enables you to upload images into your project. <br> Upon upload, the file will be processed and stored as text embeddings within the project's database.

# 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}/add_video/.post.description
This endpoint enables you to upload videos into your project. <br> Upon upload, the file will be processed and stored as text embeddings within the project's database.

# 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}/create_bot_prompt/.post.description
Create a default bot prompt for your RAG project.
The bot prompt serves as the default system message or personality definition for your RAG-powered chatbot. This prompt helps define how your bot should interact with users and what context it should consider when processing queries.

# paths./aiproducts/askyoda/v2/{project_id}/create_bot_prompt/.post.parameters[].description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# paths./aiproducts/askyoda/v2/{project_id}/create_bot_prompt/.post.parameters[].description
The model used to call the prompt. E.g. openai/gpt-4o

# paths./aiproducts/askyoda/v2/{project_id}/create_bot_prompt/.post.parameters[].description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# paths./aiproducts/askyoda/v2/{project_id}/create_bot_prompt/.post.parameters[].description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# paths./aiproducts/askyoda/v2/{project_id}/create_bot_prompt/.post.parameters[].description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# paths./aiproducts/askyoda/v2/{project_id}/delete_all_chunks/.delete.description
Delete all chunks from your project.
This action is irreversible and will remove all data stored in your project.

# paths./aiproducts/askyoda/v2/{project_id}/delete_chunk/.delete.description
Delete one or multiple chunks from your project. 
You can delete a single chunk by providing it ID either as a query parameter or in the request body. 
For bulk deletion, provide a list of chunk IDs in the request body.

Examples:
- Single chunk deletion (query parameter): DELETE /chunks?id=chunk123
- Single chunk deletion (body): {"id": "chunk123"}
- Bulk deletion: {"ids": ["chunk123", "chunk456", "chunk789"]}

# paths./aiproducts/askyoda/v2/{project_id}/generate/.post.description
Interact with different LLM providers, this endpoint is based on the structure of the
LLM API.

# 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}/list_chunks_ids/.post.description
Retrieve a list of all chunk IDs stored in your project.

# 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}/remove_bot_prompt/.delete.description
Remove the bot prompt from your RAG project.
 This endpoint removes the custom bot prompt from your project, effectively resetting the bot's personality to system defaults. This action cannot be undone, but you can always set a new bot prompt later.

# 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./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/.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 |**microsoft**|-|`v1.0`|0.0168 (per 60 seconde)|1 seconde |**assembly**|-|`v2`|0.011 (per 60 seconde)|1 seconde |**deepgram**|**nova-3**|`v1`|0.0052 (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 |**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 (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 (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

Default Models
|Name|Value| |----|-----| |**deepgram**|`enhanced`|

# 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.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.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 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.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 |**google**|**gemini-2.5-flash-tts**|`v1`|10.0 (per 1000000 char)|1 char |**google**|**gemini-2.5-pro-tts**|`v1`|20.0 (per 1000000 char)|None 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 |**deepgram**|-|`v1`|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 (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

# 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

# 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./cost_management/.get.description
Returns aggregated consumption over time.

Only `feature`, `subfeature`, `provider`, and `phase` are accepted as row filters; any other unknown query parameter returns 400.

Results are scoped by the caller's role: an organization owner or a member with members:manage sees the whole organization and may narrow with `user`, `token` or `group_by=user`; a regular member always sees only their own consumption and receives 403 if it sends `user` or `group_by=user`.

`token` matches by key NAME, not by key identity. Names are unique per user and only among non-revoked keys, so a name shared by several members of the organization aggregates all of them, and a name reused after a rotation also includes the rotated-out key's history. Use `base_token` to select consumption not attributed to any key.

# paths./image/ai_detection/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**winstonai**|`v1`|0.0145 (per 1 image)|1 image |**resemble**|`v2`|0.04 (per 1 request)|1 request

# 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 |**photoroom**|`v1`|20.0 (per 1000 file)|1 file |**sentisight**|`v3.3.1`|0.75 (per 1000 file)|1 file |**stabilityai**|`v2Beta`|0.02 (per 1 request)|1 request |**clipdrop**|`v1Beta`|0.5 (per 1 request)|1 request |**picsart**|`1.0`|0.04 (per 1 image)|1 image

# paths./image/deepfake_detection/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**sightengine**|`v1beta`|0.0145 (per 1 image)|1 image |**resemble**|`v2`|0.04 (per 1 image)|1 image

# paths./image/embeddings/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`v1`|0.06 (per 1000 file)|1 file |**google**|**gemini-embedding-001**|`v1`|0.0 (per 1 token)|1 token

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

Supported Models

# paths./image/explicit_content/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|1.0 (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 |**openai**|**gpt-4o**|`v1`|24.0 (per 1000 file)|1 file |**openai**|-|`v1`|24.0 (per 1000 file)|1 file

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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 |**google**|`v1`|1.5 (per 1000 file)|1 file |**api4ai**|`v1.0.0`|0.75 (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 image)|1 image

# paths./image/face_recognition/delete_face/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|free|-

# paths./image/face_recognition/list_faces/.get.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 1.26.8`|free|-

# 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

# paths./image/generation/.post.description
Available Providers
|Provider|Model|Version|Resolution|Price|Billing unit| |----|----|-------|------|-----|------------| |**openai**|-|`v1Beta`|`1024x1024`|0.034 (per 1 image)|1 image |**openai**|**gpt-image-2**|`v1Beta`|`1024x1024`|0.053 (per 1 image)|1 image |**openai**|**gpt-image-1**|`v1Beta`|`1024x1024`|0.042 (per 1 image)|None image |**openai**|**gpt-image-1.5**|`v1Beta`|`1024x1024`|0.034 (per 1 image)|None image |**openai**|**gpt-image-1-mini**|`v1Beta`|`1024x1024`|0.011 (per 1 image)|None image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1024x1024`|15.0 (per 1000 image)|1 image |**stabilityai**|-|`v1Beta`|`1024x1024`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1152x896`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`896x1152`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1216x832`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1344x768`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`768x1344`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`1536x640`|15.0 (per 1000 image)|1 image |**stabilityai**|**stable-diffusion-xl-1024-v1-0**|`v1Beta`|`640x1536`|15.0 (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**|-|`v1`|-|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**classic**|`v1`|`512x512`|0.00115 (per 1 exec_time)|1 exec_time |**replicate**|**anime-style**|`v1`|`512x512`|0.000225 (per 1 exec_time)|1 exec_time |**replicate**|**vintedois-diffusion**|`v1`|`512x512`|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**|**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 |**leonardo**|**Leonardo Phoenix**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**Leonardo Phoenix**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**Leonardo Lightning XL**|`v1`|`512x512`|0.011 (per 1 image)|1 image |**leonardo**|**Leonardo Lightning XL**|`v1`|`1024x1024`|0.012 (per 1 image)|1 image |**leonardo**|**Leonardo Anime XL**|`v1`|`512x512`|0.011 (per 1 image)|1 image |**leonardo**|**Leonardo Anime XL**|`v1`|`1024x1024`|0.012 (per 1 image)|1 image |**leonardo**|**Leonardo Kino XL**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**Leonardo Kino XL**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**Leonardo Vision XL**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**Leonardo Vision XL**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**Leonardo Diffusion XL**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**Leonardo Diffusion XL**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**AlbedoBase XL**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**AlbedoBase XL**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**SDXL 0.9**|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|**SDXL 0.9**|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|-|`v1`|`512x512`|0.014 (per 1 image)|1 image |**leonardo**|-|`v1`|`1024x1024`|0.017 (per 1 image)|1 image |**leonardo**|**AlbedoBase XL**|`v1`|-|0.017 (per 1 image)|1 image |**minimax**|**image-01**|`v1`|-|0.0035 (per 1 image)|1 image |**minimax**|-|`v1`|-|0.0035 (per 1 image)|1 image |**bytedance**|**seedream-5-0-260128**|`v3`|-|0.035 (per 1 image)|1 image |**bytedance**|**seedream-4-0-250828**|`v3`|-|0.03 (per 1 request)|1 request |**bytedance**|**seedream-4-5-251128**|`v3`|-|0.03 (per 1 request)|1 request |**bytedance**|-|`v3`|-|0.03 (per 1 request)|1 request |**bytedance**|**seedream-3-0-t2i-250415**|`v3`|-|0.03 (per 1 request)|1 request |**google**|-|`v1`|-|0.04 (per 1 image)|1 image |**google**|**gemini-2.5-flash-image**|`v1`|-|0.039 (per 1 image)|1 image |**google**|**imagen-4.0-generate-001**|`v1`|-|0.04 (per 1 image)|1 image |**google**|**imagen-3.0-capability-001**|`v1`|-|0.04 (per 1 image)|1 image |**google**|**imagen-4.0-ultra-generate-001**|`v1`|-|0.06 (per 1 image)|1 image |**google**|**imagen-4.0-fast-generate-001**|`v1`|-|0.02 (per 1 image)|1 image |**google**|**imagen-3.0-generate-002**|`v1`|-|0.04 (per 1 image)|1 image |**google**|**imagen-3.0-generate-001**|`v1`|-|0.04 (per 1 image)|1 image |**google**|**imagen-3.0-fast-generate-001**|`v1`|-|0.02 (per 1 image)|1 image |**google**|**gemini-3-pro-image-preview**|`v1`|-|0.039 (per 1 image)|1 image

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-image-1.5`| |**stabilityai**|`stable-diffusion-xl-1024-v1-0`| |**replicate**|`classic`| |**leonardo**|`Leonardo Phoenix`| |**minimax**|`image-01`| |**bytedance**|`seedream-3-0-t2i-250415`| |**google**|`imagen-4.0-generate-001`|

# 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 |**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 |**openai**|-|`v1`|24.0 (per 1000 file)|1 file |**openai**|**gpt-4o**|`v1`|24.0 (per 1000 file)|1 file |**openai**|**gpt-4-turbo**|`v1`|48.0 (per 1000 file)|1 file

Supported Models

Default Models
|Name|Value| |----|-----| |**api4ai**|`v1`| |**openai**|`gpt-4o`|

# paths./image/object_detection/.post.description
Available Providers
|Provider|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 |**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

# paths./image/question_answer/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|**gpt-4-turbo**|`v1`|30.0 (per 1000000 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v1`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v1`|8.0 (per 1000000 token)|1 token |**openai**|**gpt-5**|`v1`|10.0 (per 1000000 token)|1 token |**openai**|**o1**|`v1`|60.0 (per 1000000 token)|1 token |**openai**|**o1-mini**|`v1`|12.0 (per 1000000 token)|1 token |**openai**|**gpt-4o**|`v1`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4o-mini**|`v1`|0.6 (per 1000000 token)|1 token |**google**|**gemini-1.5-pro**|`v1`|10.0 (per 1000000 token)|1 token |**google**|**gemini-2.5-flash**|`v1`|2.5 (per 1000000 token)|1 token |**google**|**gemini-2.5-pro**|`v1`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash**|`v1`|0.4 (per 1000000 token)|1 token |**google**|**gemini-1.5-flash**|`v1`|0.6 (per 1000000 token)|1 token

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

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**google**|`gemini-2.5-pro`|

# paths./image/question_answer/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.answers[]
Logos - Facebook, Apple, Amazon, Netflix, Google

# paths./image/question_answer/.post.responses.200.content.application/json.examples.ResponseExample.value.google.answers[]
Logo - Facebook, Apple, Amazon, Netflix, and Google

# 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./llm/chat/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|**amazon.nova-lite-v1:0**|`llmengine (v2)`|0.24 (per 1000000 token)|1 token |**amazon**|**amazon.nova-micro-v1:0**|`llmengine (v2)`|0.14 (per 1000000 token)|1 token |**amazon**|**amazon.nova-pro-v1:0**|`llmengine (v2)`|3.2 (per 1000000 token)|1 token |**anthropic**|**claude-opus-4-6**|`v1`|5e-06 (per 1 token)|1 token |**anthropic**|**claude-sonnet-4-20250514**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-opus-4-1-20250805**|`v1`|7.5e-05 (per 1 token)|1 token |**anthropic**|**claude-opus-4-5**|`v1`|2.5e-05 (per 1 token)|1 token |**anthropic**|**claude-3-7-sonnet-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-3-5-haiku-20241022**|`v1`|4e-06 (per 1 token)|1 token |**anthropic**|**claude-3-5-haiku-latest**|`v1`|5e-06 (per 1 token)|1 token |**anthropic**|**claude-haiku-4-5-20251001**|`v1`|5e-06 (per 1 token)|1 token |**anthropic**|**claude-haiku-4-5**|`v1`|5e-06 (per 1 token)|1 token |**anthropic**|**claude-3-7-sonnet-20250219**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-3-haiku-20240307**|`v1`|1.25e-06 (per 1 token)|1 token |**anthropic**|**claude-4-opus-20250514**|`v1`|7.5e-05 (per 1 token)|1 token |**anthropic**|**claude-4-sonnet-20250514**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-sonnet-4-5**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-sonnet-4-5-20250929**|`v1`|1.5e-05 (per 1 token)|1 token |**anthropic**|**claude-opus-4-1**|`v1`|7.5e-05 (per 1 token)|1 token |**anthropic**|**claude-opus-4-20250514**|`v1`|7.5e-05 (per 1 token)|1 token |**anthropic**|**claude-opus-4-5-20251101**|`v1`|2.5e-05 (per 1 token)|1 token |**cohere**|**command-r7b-12-2024**|`llmengine (v2)`|0.15 (per 1000000 token)|1 token |**cohere**|**command-r-08-2024**|`llmengine (v2)`|0.6 (per 1000000 token)|1 token |**deepseek**|**deepseek-chat**|`llmengine (v2)`|1.1e-06 (per 1 token)|1 token |**deepseek**|**deepseek-reasoner**|`llmengine (v2)`|2.19e-06 (per 1 token)|1 token |**deepseek**|**deepseek-coder**|`llmengine (v2)`|2.8e-07 (per 1 token)|1 token |**meta**|**meta.llama3-1-405b-instruct-v1:0**|`llmengine (v2)`|2.4 (per 1000000 token)|1 token |**meta**|**meta.llama3-1-70b-instruct-v1:0**|`llmengine (v2)`|0.72 (per 1000000 token)|1 token |**meta**|**meta.llama3-1-8b-instruct-v1:0**|`llmengine (v2)`|0.22 (per 1000000 token)|1 token |**mistral**|**magistral-medium-2506**|`llmengine (v2)`|5e-06 (per 1 token)|1 token |**mistral**|**magistral-small-2506**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**mistral**|**mistral-medium-latest**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**mistral**|**pixtral-large-latest**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**mistral**|**mistral-small-latest**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**codestral-latest**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**mistral**|**mistral-large-latest**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**mistral**|**codestral-2405**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**mistral**|**codestral-2508**|`llmengine (v2)`|9e-07 (per 1 token)|1 token |**mistral**|**devstral-medium-2507**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**mistral**|**devstral-small-2505**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**devstral-small-2507**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**labs-devstral-small-2512**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**devstral-2512**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**mistral**|**magistral-medium-2509**|`llmengine (v2)`|5e-06 (per 1 token)|1 token |**mistral**|**magistral-medium-latest**|`llmengine (v2)`|5e-06 (per 1 token)|1 token |**mistral**|**magistral-small-latest**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**mistral**|**mistral-large-2402**|`llmengine (v2)`|1.2e-05 (per 1 token)|1 token |**mistral**|**mistral-large-2407**|`llmengine (v2)`|9e-06 (per 1 token)|1 token |**mistral**|**mistral-large-2411**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**mistral**|**mistral-medium**|`llmengine (v2)`|8.1e-06 (per 1 token)|1 token |**mistral**|**mistral-medium-2312**|`llmengine (v2)`|8.1e-06 (per 1 token)|1 token |**mistral**|**mistral-medium-2505**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**mistral**|**mistral-small**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**mistral-tiny**|`llmengine (v2)`|2.5e-07 (per 1 token)|1 token |**mistral**|**open-mistral-7b**|`llmengine (v2)`|2.5e-07 (per 1 token)|1 token |**mistral**|**open-mistral-nemo**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**open-mistral-nemo-2407**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**mistral**|**open-mixtral-8x22b**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**mistral**|**open-mixtral-8x7b**|`llmengine (v2)`|7e-07 (per 1 token)|1 token |**mistral**|**pixtral-12b-2409**|`llmengine (v2)`|1.5e-07 (per 1 token)|1 token |**mistral**|**pixtral-large-2411**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`llmengine (v2)`|4.4e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`llmengine (v2)`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5.1**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-pro**|`llmengine (v2)`|100.0 (per 1000000 token)|1 token |**openai**|**o4-mini-2025-04-16**|`llmengine (v2)`|4.4e-06 (per 1 token)|1 token |**openai**|**o3**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4**|`llmengine (v2)`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`llmengine (v2)`|3e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`llmengine (v2)`|6e-05 (per 1 token)|1 token |**openai**|**o1**|`llmengine (v2)`|6e-05 (per 1 token)|1 token |**openai**|**o3-mini**|`llmengine (v2)`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`llmengine (v2)`|4.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-5.2**|`llmengine (v2)`|1.4e-05 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`llmengine (v2)`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`llmengine (v2)`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`llmengine (v2)`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`llmengine (v2)`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`llmengine (v2)`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`llmengine (v2)`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`llmengine (v2)`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`llmengine (v2)`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`llmengine (v2)`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`llmengine (v2)`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen2.5-72B-Instruct-Turbo**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo**|`llmengine (v2)`|1.8e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen2.5-72B-Instruct-Turbo**|`llmengine (v2)`|1.2 (per 1000000 token)|1 token |**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**together_ai**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**together_ai**|**openai/gpt-oss-120b**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-235B-A22B-Thinking-2507**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-235B-A22B-fp8-tput**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-R1**|`llmengine (v2)`|7e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-R1-0528-tput**|`llmengine (v2)`|2.19e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-V3**|`llmengine (v2)`|1.25e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-V3.1**|`llmengine (v2)`|1.7e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8**|`llmengine (v2)`|8.5e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-4-Scout-17B-16E-Instruct**|`llmengine (v2)`|5.9e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo**|`llmengine (v2)`|3.5e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo**|`llmengine (v2)`|1.8e-07 (per 1 token)|1 token |**together_ai**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**together_ai**|**moonshotai/Kimi-K2-Instruct**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**together_ai**|**openai/gpt-oss-20b**|`llmengine (v2)`|2e-07 (per 1 token)|1 token |**together_ai**|**zai-org/GLM-4.5-Air-FP8**|`llmengine (v2)`|1.1e-06 (per 1 token)|1 token |**together_ai**|**zai-org/GLM-4.6**|`llmengine (v2)`|2.2e-06 (per 1 token)|1 token |**together_ai**|**moonshotai/Kimi-K2-Instruct-0905**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Instruct**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Thinking**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-235B-A22B-Instruct-2507-tput**|`llmengine (v2)`|6e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo**|`llmengine (v2)`|3.5e-06 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-4-0709**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-0709**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`llmengine (v2)`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`llmengine (v2)`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`llmengine (v2)`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`llmengine (v2)`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`llmengine (v2)`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-latest**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**xai**|**grok-code-fast**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-3-pro-preview**|`llmengine (v2)`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-2.5-flash-image-preview**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-3.1-pro-preview**|`llmengine (v2)`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**google**|**gemini-3-pro-image-preview**|`llmengine (v2)`|12.0 (per 1000000 token)|1 token |**google**|**gemini-3-flash-preview**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-001**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-09-2025**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-09-2025**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-latest**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-lite-latest**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-pro**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**google**|**gemma-3-27b-it**|`llmengine (v2)`|0.0 (per 1 token)|1 token |**groq**|**llama-3.1-8b-instant**|`v1`|8e-08 (per 1 token)|1 token |**groq**|**openai/gpt-oss-120b**|`v1`|7.5e-07 (per 1 token)|1 token |**groq**|**llama-3.3-70b-versatile**|`v1`|7.9e-07 (per 1 token)|1 token |**groq**|**llama-3.3-70b-versatile**|`v1`|7.9e-07 (per 1 token)|1 token |**groq**|**llama-3.1-8b-instant**|`v1`|8e-08 (per 1 token)|1 token |**groq**|**meta-llama/llama-guard-4-12b**|`v1`|2e-07 (per 1 token)|1 token |**groq**|**meta-llama/llama-4-maverick-17b-128e-instruct**|`v1`|6e-07 (per 1 token)|1 token |**groq**|**meta-llama/llama-4-scout-17b-16e-instruct**|`v1`|3.4e-07 (per 1 token)|1 token |**groq**|**moonshotai/kimi-k2-instruct-0905**|`v1`|3e-06 (per 1 token)|1 token |**groq**|**openai/gpt-oss-20b**|`v1`|5e-07 (per 1 token)|1 token |**groq**|**qwen/qwen3-32b**|`v1`|5.9e-07 (per 1 token)|1 token |**microsoft**|**gpt-4o**|`Azure AI Foundry`|5.0 (per 1000000 token)|1 token |**microsoft**|**o3-mini**|`Azure AI Foundry`|4.4 (per 1000000 token)|1 token |**microsoft**|**o1-mini**|`Azure AI Foundry`|12.0 (per 1000000 token)|1 token |**microsoft**|**gpt-4o-mini**|`Azure AI Foundry`|0.66 (per 1000000 token)|1 token |**microsoft**|**gpt-4**|`Azure AI Foundry`|60.0 (per 1000000 token)|1 token |**microsoft**|**gpt-35-turbo-16k**|`Azure AI Foundry`|4.0 (per 1000000 token)|1 token |**microsoft**|**gpt-35-turbo**|`Azure AI Foundry`|1.5 (per 1000000 token)|1 token |**minimax**|**minimax-m1**|`v1`|2.2 (per 1000000 token)|1 token |**minimax**|**minimax-text-01**|`v1`|1.1 (per 1000000 token)|1 token |**minimax**|**MiniMax-M2.1**|`v1`|1.2e-06 (per 1 token)|1 token |**minimax**|**MiniMax-M2.1-lightning**|`v1`|2.4e-06 (per 1 token)|1 token |**minimax**|**MiniMax-M2**|`v1`|1.2e-06 (per 1 token)|1 token |**bytedance**|**seed-1-6-250915**|`llmengine (v2)`|2.0 (per 1000000 token)|1 token |**perplexityai**|**sonar**|`llmengine (v2)`|1.0 (per 1000000 token)|1 token |**perplexityai**|**sonar**|`llmengine (v2)`|1e-06 (per 1 token)|1 token |**perplexityai**|**sonar-pro**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**perplexityai**|**sonar-deep-research**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**perplexityai**|**sonar-pro**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**perplexityai**|**sonar-reasoning-pro**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**deepinfra**|**Gryphe/MythoMax-L2-13b**|`v1`|9e-08 (per 1 token)|1 token |**deepinfra**|**NousResearch/Hermes-3-Llama-3.1-405B**|`v1`|1e-06 (per 1 token)|1 token |**deepinfra**|**NousResearch/Hermes-3-Llama-3.1-70B**|`v1`|3e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/QwQ-32B**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen2.5-72B-Instruct**|`v1`|3.9e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen2.5-7B-Instruct**|`v1`|1e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen2.5-VL-32B-Instruct**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-14B**|`v1`|2.4e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-235B-A22B**|`v1`|5.4e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-235B-A22B-Instruct-2507**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-235B-A22B-Thinking-2507**|`v1`|2.9e-06 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-30B-A3B**|`v1`|2.9e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-32B**|`v1`|2.8e-07 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-Coder-480B-A35B-Instruct**|`v1`|1.6e-06 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo**|`v1`|1.2e-06 (per 1 token)|1 token |**deepinfra**|**Qwen/Qwen3-Next-80B-A3B-Instruct**|`v1`|1.4e-06 (per 1 token)|1 token |**deepinfra**|**Sao10K/L3-8B-Lunaris-v1-Turbo**|`v1`|5e-08 (per 1 token)|1 token |**deepinfra**|**Sao10K/L3.1-70B-Euryale-v2.2**|`v1`|7.5e-07 (per 1 token)|1 token |**deepinfra**|**Sao10K/L3.3-70B-Euryale-v2.3**|`v1`|7.5e-07 (per 1 token)|1 token |**deepinfra**|**anthropic/claude-3-7-sonnet-latest**|`v1`|1.65e-05 (per 1 token)|1 token |**deepinfra**|**anthropic/claude-4-opus**|`v1`|8.25e-05 (per 1 token)|1 token |**deepinfra**|**anthropic/claude-4-sonnet**|`v1`|1.65e-05 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1**|`v1`|2.4e-06 (per 1 token)|1 token |**deepinfra**|**nvidia/Llama-3.3-Nemotron-Super-49B-v1.5**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1-0528**|`v1`|2.15e-06 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1-Distill-Llama-70B**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1-Distill-Qwen-32B**|`v1`|2.7e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1-Turbo**|`v1`|3e-06 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-V3**|`v1`|8.9e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-V3-0324**|`v1`|8.8e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-V3.1**|`v1`|1e-06 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-V3.1-Terminus**|`v1`|1e-06 (per 1 token)|1 token |**deepinfra**|**google/gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**google/gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token |**deepinfra**|**google/gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token |**deepinfra**|**google/gemma-3-12b-it**|`v1`|1e-07 (per 1 token)|1 token |**deepinfra**|**google/gemma-3-27b-it**|`v1`|1.6e-07 (per 1 token)|1 token |**deepinfra**|**google/gemma-3-4b-it**|`v1`|8e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-3.2-11B-Vision-Instruct**|`v1`|4.9e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-3.2-3B-Instruct**|`v1`|2e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-3.3-70B-Instruct**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-3.3-70B-Instruct-Turbo**|`v1`|3.9e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-4-Scout-17B-16E-Instruct**|`v1`|3e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-Guard-3-8B**|`v1`|5.5e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-Guard-4-12B**|`v1`|1.8e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3-8B-Instruct**|`v1`|6e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3.1-70B-Instruct**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo**|`v1`|2.8e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3.1-8B-Instruct**|`v1`|5e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo**|`v1`|3e-08 (per 1 token)|1 token |**deepinfra**|**microsoft/WizardLM-2-8x22B**|`v1`|4.8e-07 (per 1 token)|1 token |**deepinfra**|**microsoft/phi-4**|`v1`|1.4e-07 (per 1 token)|1 token |**deepinfra**|**mistralai/Mistral-Nemo-Instruct-2407**|`v1`|4e-08 (per 1 token)|1 token |**deepinfra**|**mistralai/Mistral-Small-24B-Instruct-2501**|`v1`|8e-08 (per 1 token)|1 token |**deepinfra**|**mistralai/Mistral-Small-3.2-24B-Instruct-2506**|`v1`|2e-07 (per 1 token)|1 token |**deepinfra**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`v1`|4e-07 (per 1 token)|1 token |**deepinfra**|**moonshotai/Kimi-K2-Instruct**|`v1`|2e-06 (per 1 token)|1 token |**deepinfra**|**moonshotai/Kimi-K2-Instruct-0905**|`v1`|2e-06 (per 1 token)|1 token |**deepinfra**|**nvidia/Llama-3.1-Nemotron-70B-Instruct**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**nvidia/NVIDIA-Nemotron-Nano-9B-v2**|`v1`|1.6e-07 (per 1 token)|1 token |**deepinfra**|**openai/gpt-oss-120b**|`v1`|4.5e-07 (per 1 token)|1 token |**deepinfra**|**openai/gpt-oss-20b**|`v1`|1.5e-07 (per 1 token)|1 token |**deepinfra**|**zai-org/GLM-4.5**|`v1`|1.6e-06 (per 1 token)|1 token |**deepinfra**|**lizpreciatior/lzlv_70b_fp16_hf**|`v1`|9e-07 (per 1 token)|1 token |**deepinfra**|**Gryphe/MythoMax-L2-13b**|`v1`|2.2e-07 (per 1 token)|1 token |**deepinfra**|**mistralai/Mistral-7B-Instruct-v0.1**|`v1`|1.3e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-2-70b-chat-hf**|`v1`|9e-07 (per 1 token)|1 token |**deepinfra**|**cognitivecomputations/dolphin-2.6-mixtral-8x7b**|`v1`|2.7e-07 (per 1 token)|1 token |**deepinfra**|**Phind/Phind-CodeLlama-34B-v2**|`v1`|6e-07 (per 1 token)|1 token |**deepinfra**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`v1`|2.7e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Llama-2-13b-chat-hf**|`v1`|2.2e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3-8B-Instruct**|`v1`|8e-08 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3-70B-Instruct**|`v1`|7.9e-07 (per 1 token)|1 token |**deepinfra**|**meta-llama/Meta-Llama-3.1-405B-Instruct**|`v1`|9e-07 (per 1 token)|1 token |**deepinfra**|**openchat/openchat_3.5**|`v1`|1.3e-07 (per 1 token)|1 token |**deepinfra**|**deepseek-ai/DeepSeek-R1-0528-Turbo**|`v1`|3e-06 (per 1 token)|1 token |**cerebras**|**llama3.1-8b**|`llmengine (v2)`|1e-07 (per 1 token)|1 token |**cerebras**|**gpt-oss-120b**|`llmengine (v2)`|6.9e-07 (per 1 token)|1 token |**cloudflare**|**@cf/meta/llama-2-7b-chat-fp16**|`llmengine (v2)`|1.923e-06 (per 1 token)|1 token |**cloudflare**|**@cf/meta/llama-2-7b-chat-int8**|`llmengine (v2)`|1.923e-06 (per 1 token)|1 token |**cloudflare**|**@cf/mistral/mistral-7b-instruct-v0.1**|`llmengine (v2)`|1.923e-06 (per 1 token)|1 token |**cloudflare**|**@hf/thebloke/codellama-7b-instruct-awq**|`llmengine (v2)`|1.923e-06 (per 1 token)|1 token |**databricks**|**databricks-claude-3-7-sonnet**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**databricks**|**databricks-claude-haiku-4-5**|`llmengine (v2)`|5e-06 (per 1 token)|1 token |**databricks**|**databricks-claude-opus-4-1**|`llmengine (v2)`|7.5e-05 (per 1 token)|1 token |**databricks**|**databricks-claude-opus-4-5**|`llmengine (v2)`|2.5e-05 (per 1 token)|1 token |**databricks**|**databricks-claude-sonnet-4**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**databricks**|**databricks-claude-sonnet-4-5**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**databricks**|**databricks-gemini-2-5-flash**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**databricks**|**databricks-gemini-2-5-pro**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**databricks**|**databricks-gemma-3-12b**|`llmengine (v2)`|5e-07 (per 1 token)|1 token |**databricks**|**databricks-gpt-5**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**databricks**|**databricks-gpt-5-1**|`llmengine (v2)`|1e-05 (per 1 token)|1 token |**databricks**|**databricks-gpt-5-mini**|`llmengine (v2)`|2e-06 (per 1 token)|1 token |**databricks**|**databricks-gpt-5-nano**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**databricks**|**databricks-gpt-oss-120b**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**databricks**|**databricks-gpt-oss-20b**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**databricks**|**databricks-llama-4-maverick**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**databricks**|**databricks-meta-llama-3-1-405b-instruct**|`llmengine (v2)`|1.5e-05 (per 1 token)|1 token |**databricks**|**databricks-meta-llama-3-1-8b-instruct**|`llmengine (v2)`|4.5e-07 (per 1 token)|1 token |**databricks**|**databricks-meta-llama-3-3-70b-instruct**|`llmengine (v2)`|1.5e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen2p5-vl-32b-instruct**|`llmengine (v2)`|9e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-235b-a22b**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-235b-a22b-instruct-2507**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-235b-a22b-thinking-2507**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-8b**|`llmengine (v2)`|2e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-vl-235b-a22b-instruct**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-vl-235b-a22b-thinking**|`llmengine (v2)`|8.8e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-vl-30b-a3b-instruct**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-vl-30b-a3b-thinking**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/deepseek-r1-0528**|`llmengine (v2)`|8e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/deepseek-v3-0324**|`llmengine (v2)`|9e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/deepseek-v3p1**|`llmengine (v2)`|1.68e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/deepseek-v3p1-terminus**|`llmengine (v2)`|1.68e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/deepseek-v3p2**|`llmengine (v2)`|1.68e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/glm-4p6**|`llmengine (v2)`|2.19e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/gpt-oss-120b**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/gpt-oss-20b**|`llmengine (v2)`|2e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/kimi-k2-instruct-0905**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/kimi-k2-thinking**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/qwen3-coder-480b-a35b-instruct**|`llmengine (v2)`|1.8e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/llama-v3p3-70b-instruct**|`llmengine (v2)`|9e-07 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/minimax-m2**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**fireworks_ai**|**accounts/fireworks/models/mixtral-8x22b-instruct**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**nebius**|**MiniMaxAI/MiniMax-M2.1**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**nebius**|**moonshotai/Kimi-K2-Instruct**|`llmengine (v2)`|2.4e-06 (per 1 token)|1 token |**nebius**|**moonshotai/Kimi-K2-Thinking**|`llmengine (v2)`|2.5e-06 (per 1 token)|1 token |**nebius**|**NousResearch/Hermes-4-405B**|`llmengine (v2)`|3e-06 (per 1 token)|1 token |**nebius**|**NousResearch/Hermes-4-70B**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**nebius**|**nvidia/Llama-3_1-Nemotron-Ultra-253B-v1**|`llmengine (v2)`|1.8e-06 (per 1 token)|1 token |**nebius**|**nvidia/Nemotron-Nano-V2-12b**|`llmengine (v2)`|2e-07 (per 1 token)|1 token |**nebius**|**openai/gpt-oss-120b**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**nebius**|**openai/gpt-oss-20b**|`llmengine (v2)`|2e-07 (per 1 token)|1 token |**nebius**|**PrimeIntellect/INTELLECT-3**|`llmengine (v2)`|1.1e-06 (per 1 token)|1 token |**nebius**|**Qwen/Qwen2.5-VL-72B-Instruct**|`llmengine (v2)`|7.5e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-235B-A22B-Instruct-2507**|`llmengine (v2)`|6e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-235B-A22B-Thinking-2507**|`llmengine (v2)`|8e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-30B-A3B-Instruct-2507**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-30B-A3B-Thinking-2507**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-32B**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-Coder-30B-A3B-Instruct**|`llmengine (v2)`|3e-07 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-Coder-480B-A35B-Instruct**|`llmengine (v2)`|1.8e-06 (per 1 token)|1 token |**nebius**|**Qwen/Qwen3-Next-80B-A3B-Thinking**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**nebius**|**zai-org/GLM-4.5**|`llmengine (v2)`|2.2e-06 (per 1 token)|1 token |**nebius**|**zai-org/GLM-4.5-Air**|`llmengine (v2)`|1.2e-06 (per 1 token)|1 token |**ovhcloud**|**DeepSeek-R1-Distill-Llama-70B**|`llmengine (v2)`|6.7e-07 (per 1 token)|1 token |**ovhcloud**|**Llama-3.1-8B-Instruct**|`llmengine (v2)`|1e-07 (per 1 token)|1 token |**ovhcloud**|**Meta-Llama-3_3-70B-Instruct**|`llmengine (v2)`|6.7e-07 (per 1 token)|1 token |**ovhcloud**|**Mistral-7B-Instruct-v0.3**|`llmengine (v2)`|1e-07 (per 1 token)|1 token |**ovhcloud**|**Mistral-Nemo-Instruct-2407**|`llmengine (v2)`|1.3e-07 (per 1 token)|1 token |**ovhcloud**|**Mistral-Small-3.2-24B-Instruct-2506**|`llmengine (v2)`|2.8e-07 (per 1 token)|1 token |**ovhcloud**|**Mixtral-8x7B-Instruct-v0.1**|`llmengine (v2)`|6.3e-07 (per 1 token)|1 token |**ovhcloud**|**Qwen2.5-Coder-32B-Instruct**|`llmengine (v2)`|8.7e-07 (per 1 token)|1 token |**ovhcloud**|**Qwen2.5-VL-72B-Instruct**|`llmengine (v2)`|9.1e-07 (per 1 token)|1 token |**ovhcloud**|**Qwen3-32B**|`llmengine (v2)`|2.3e-07 (per 1 token)|1 token |**ovhcloud**|**gpt-oss-120b**|`llmengine (v2)`|4e-07 (per 1 token)|1 token |**ovhcloud**|**gpt-oss-20b**|`llmengine (v2)`|1.5e-07 (per 1 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**amazon**|`amazon.nova-pro-v1:0`| |**anthropic**|`claude-3-7-sonnet-latest`| |**cohere**|`command-r`| |**deepseek**|`deepseek-chat`| |**meta**|`meta.llama3-1-70b-instruct-v1:0`| |**mistral**|`mistral-large-latest`| |**openai**|`gpt-4o`| |**together_ai**|`Qwen/Qwen2.5-72B-Instruct-Turbo`| |**xai**|`grok-2-latest`| |**google**|`gemini-2.0-flash`| |**groq**|`llama-3.3-70b-versatile`| |**microsoft**|`gpt-4o`| |**minimax**|`MiniMax-M1`| |**bytedance**|`seed-1-6-250915`| |**perplexityai**|`sonar`| |**deepinfra**|`nvidia/Llama-3.3-Nemotron-Super-49B-v1.5`| |**cerebras**|`gpt-oss-120b`|

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.together_ai/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.qwen/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.bytedance/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.minimax/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.cohere/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr matey! In this fine image, ye be seein' a wooden pathway leadin' through a grand expanse of green grass and wild foliage. The sky above be painted in shades of blue, with fluffy clouds driftin' lazily. The boardwalk be settin' sail through this scenic wonder, invitin' ye to wander and explore the treasures of nature that lie ahead. Aye, 'tis a sight to behold for any landlubber or sea dog!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.openai/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.xai/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.databricks/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.perplexityai/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.azure/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.deepinfra/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.nebius/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.ovhcloud/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.fireworks_ai/fireworks_ai/accounts/fireworks/models/minimax-m2p1.choices[].message.content
Hello! I'm MiniMax-M2.1, nice to meet you! 

I'm here and ready to help with whatever you need - whether that's coding, answering questions, brainstorming ideas, writing, analyzing information, or just having a conversation.

What can I assist you with today?

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.google/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.groq/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.cloudflare/cloudflare/@cf/meta/llama-2-7b-chat-fp16.choices[].message.content
  Hello! It's nice to meet you. Is there something I can help you with or would you like to chat?

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.meta/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.deepseek/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.vertex/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.mistral/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.amazon/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.xiaomi/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.zai/gpt-4o-mini-2024-07-18.choices[].message.content
Arrr, matey! What ye be seein’ in this here image is a grand pathway, made of wooden planks, weavin' its way through a lush and green landscape. The verdant grass sways in the gentle breeze, and the sky above be a brilliant blue, decorated with fluffy white clouds. Ye can spot trees and bushes on either side, makin' it a perfect setting for a stroll amongst nature. A peaceful place for a pirate at heart, aye!

# paths./llm/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.tensorx/moonshotai/kimi-k3.choices[].message.content
Hello! I’m doing well, thank you. How are you today?

# paths./multimodal/chat/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**anthropic**|**claude-3-5-haiku-latest**|`bedrock-2023-05-31`|4.0 (per 1000000 token)|1 token |**anthropic**|-|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token |**anthropic**|**claude-3-7-sonnet-20250219**|`bedrock-2023-05-31`|15.0 (per 1000000 token)|1 token |**google**|**gemini-1.5-flash**|`v1`|0.6 (per 1000000 token)|1 token |**google**|**gemini-1.5-pro**|`v1`|10.0 (per 1000000 token)|1 token |**google**|**gemini-1.5-flash-latest**|`v1`|0.6 (per 1000000 token)|1 token |**google**|-|`v1`|0.6 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1`|0.3 (per 1000000 token)|1 token |**google**|**gemini-2.5-flash**|`v1`|2.5 (per 1000000 token)|1 token |**google**|**gemini-3-pro-preview**|`v1`|18.0 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash**|`v1`|0.4 (per 1000000 token)|1 token |**google**|**gemini-2.5-pro**|`v1`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.5-pro-preview-03-25**|`v1`|15.0 (per 1000000 token)|1 token |**google**|**gemini-1.5-pro-latest**|`v1`|10.0 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-exp**|`v1`|10.0 (per 1000000 token)|1 token |**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1000000 token)|1 token |**openai**|**o1**|`v1Beta`|60.0 (per 1000000 token)|1 token |**openai**|**gpt-4o**|`v1Beta`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4-turbo**|`v1Beta`|30.0 (per 1000000 token)|1 token |**openai**|-|`v1Beta`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v1Beta`|8.0 (per 1000000 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v1Beta`|1.6 (per 1000000 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v1Beta`|0.4 (per 1000000 token)|1 token |**openai**|**gpt-4o-mini**|`v1Beta`|0.6 (per 1000000 token)|1 token |**openai**|**gpt-5.2**|`v1Beta`|14.0 (per 1000000 token)|1 token |**openai**|**gpt-5**|`v1Beta`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-5-mini**|`v1Beta`|2.0 (per 1000000 token)|1 token |**openai**|**gpt-5-nano**|`v1Beta`|0.4 (per 1000000 token)|1 token |**openai**|**gpt-5-chat-latest**|`v1Beta`|10.0 (per 1000000 token)|1 token |**openai**|**o1-mini**|`v1Beta`|12.0 (per 1000000 token)|1 token |**openai**|**o1-2024-12-17**|`v1Beta`|60.0 (per 1000000 token)|1 token |**xai**|-|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-vision**|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|10.0 (per 1000000 token)|1 token |**amazon**|-|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token |**amazon**|**amazon.nova-pro-v1:0**|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token |**mistral**|-|`v0.0.1`|6.0 (per 1000000 token)|1 token |**mistral**|**pixtral-large-latest**|`v0.0.1`|6.0 (per 1000000 token)|1 token |**microsoft**|**gpt-4o**|`Azure AI Foundry`|15.0 (per 1000000 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**anthropic**|`claude-3-5-sonnet-latest`| |**google**|`gemini-2.0-flash`| |**openai**|`gpt-4o`| |**xai**|`grok-2-vision-latest`| |**amazon**|`amazon.nova-pro-v1:0`| |**mistral**|`pixtral-large-latest`| |**microsoft**|`gpt-4o`|

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.google.generated_text
Ahoy, matey!  Shiver me timbers, that there's a picture of a fine wooden boardwalk cuttin' through a field o' tall, green grass under a sky as blue as a mermaid's eye!  The sun be shinin' bright, and there ain't a cloud in sight, except for a few fluffy ones driftin' lazily about.  Looks like a peaceful spot fer a stroll, aye?  Avast ye, and enjoy the view!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.google.messages[].content[].content.text
Ahoy, matey!  Shiver me timbers, that there's a picture of a fine wooden boardwalk cuttin' through a field o' tall, green grass under a sky as blue as a mermaid's eye!  The sun be shinin' bright, and there ain't a cloud in sight, except for a few fluffy ones driftin' lazily about.  Looks like a peaceful spot fer a stroll, aye?  Avast ye, and enjoy the view!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.generated_text
Arrr, matey! What we have here be a fine scene of nature's bounty! A wooden plank path stretches straight ahead, leadin' through a lush green meadow filled with tall grass swayin' in the gentle breeze. The sky above be painted with a brilliant blue, streaked with soft, wispy clouds, as if the heavens themselves be smilin' down on this tranquil land. In the distance, ye can spy a line of trees and bushes, addin' depth to this peaceful landscape. Aye, 'tis a place where a pirate might rest his weary bones and dream of treasure!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.microsoft.messages[].content[].content.text
Arrr, matey! What we have here be a fine scene of nature's bounty! A wooden plank path stretches straight ahead, leadin' through a lush green meadow filled with tall grass swayin' in the gentle breeze. The sky above be painted with a brilliant blue, streaked with soft, wispy clouds, as if the heavens themselves be smilin' down on this tranquil land. In the distance, ye can spy a line of trees and bushes, addin' depth to this peaceful landscape. Aye, 'tis a place where a pirate might rest his weary bones and dream of treasure!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.generated_text
Ahoy! What we have here be a splendid vista, me hearty! A wooden plank path cuts through a sea of lush green grass, leading ye towards the horizon under a sky painted with soft clouds. The land be flanked by wild shrubbery and trees, a true haven for any landlubber or pirate lookin' for a bit o' peace away from the roarin' seas. It's a fine day with the sun shinin' bright, makin' the greens look vibrant and the path invitin' for a leisurely stroll or a treasure hunt, should ye be so inclined!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.messages[].content[].content.text
Ahoy! What we have here be a splendid vista, me hearty! A wooden plank path cuts through a sea of lush green grass, leading ye towards the horizon under a sky painted with soft clouds. The land be flanked by wild shrubbery and trees, a true haven for any landlubber or pirate lookin' for a bit o' peace away from the roarin' seas. It's a fine day with the sun shinin' bright, makin' the greens look vibrant and the path invitin' for a leisurely stroll or a treasure hunt, should ye be so inclined!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.generated_text
Arrr matey! Feast yer eyes on this magnificent sight! 

'Tis a wooden boardwalk, straight as an arrow, cuttin' through a sea of emerald grass that be swayin' in the breeze like waves on the ocean! The planks be weathered and gray, just like me old ship's deck, stretching far into the distance.

Above, the sky be a brilliant blue, with wispy white clouds sailin' across it like ghost ships on the horizon. The sunlight be makin' the grass glow like treasure, and off in the distance, ye can spot trees standin' guard like silent sentinels.

This here path would make for a fine walk for any landlubber, though it ain't quite as excitin' as walkin' the plank on me ship! The marsh grass be tall and thick on both sides, remindin' me of the reeds

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.anthropic.messages[].content[].content.text
Arrr matey! Feast yer eyes on this magnificent sight! 

'Tis a wooden boardwalk, straight as an arrow, cuttin' through a sea of emerald grass that be swayin' in the breeze like waves on the ocean! The planks be weathered and gray, just like me old ship's deck, stretching far into the distance.

Above, the sky be a brilliant blue, with wispy white clouds sailin' across it like ghost ships on the horizon. The sunlight be makin' the grass glow like treasure, and off in the distance, ye can spot trees standin' guard like silent sentinels.

This here path would make for a fine walk for any landlubber, though it ain't quite as excitin' as walkin' the plank on me ship! The marsh grass be tall and thick on both sides, remindin' me of the reeds

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.xai.generated_text
Arrr, matey! This here be a fine image o' a wooden boardwalk stretchin' out into a vast sea o' green grass, leadin' ye towards the horizon. The sky above be a brilliant blue, dotted with fluffy white clouds, like sails on the high seas. The path be straight and true, invitin' ye to walk the plank into the heart o' this lush meadow. Aye, 'tis a peaceful sight, perfect fer a pirate lookin' fer a bit o' tranquility on land!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.xai.messages[].content[].content.text
Arrr, matey! This here be a fine image o' a wooden boardwalk stretchin' out into a vast sea o' green grass, leadin' ye towards the horizon. The sky above be a brilliant blue, dotted with fluffy white clouds, like sails on the high seas. The path be straight and true, invitin' ye to walk the plank into the heart o' this lush meadow. Aye, 'tis a peaceful sight, perfect fer a pirate lookin' fer a bit o' tranquility on land!

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.generated_text
Ahoy, matey! Shiver me timbers, what a sight for these ol' pirate eyes!

Ye be lookin' at a right fine stretch o' land, as green as the emerald seas! There be a wooden walkway, straight as an arrow, cuttin' through a sea o' tall grass that'd tickle yer chin if ye were standin' in it.

The sky above, she's a beauty! Blue as a sapphire with wispy white clouds stretchin' across like sails on the horizon. Ye can almost feel the warm breeze blowin' through yer whiskers.

Off in the distance, ye can spy some trees standin' tall, like a crew of landlubbers waitin' to greet ye at the end of yer journey. The whole scene be as peaceful as a calm day at sea, with nary a storm cloud in sight.

This

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.messages[].content[].content.text
Ahoy, matey! Shiver me timbers, what a sight for these ol' pirate eyes!

Ye be lookin' at a right fine stretch o' land, as green as the emerald seas! There be a wooden walkway, straight as an arrow, cuttin' through a sea o' tall grass that'd tickle yer chin if ye were standin' in it.

The sky above, she's a beauty! Blue as a sapphire with wispy white clouds stretchin' across like sails on the horizon. Ye can almost feel the warm breeze blowin' through yer whiskers.

Off in the distance, ye can spy some trees standin' tall, like a crew of landlubbers waitin' to greet ye at the end of yer journey. The whole scene be as peaceful as a calm day at sea, with nary a storm cloud in sight.

This

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.generated_text
Arr matey, this here image be a sight for sore eyes! It shows a wooden plank walkway, stretchin' into the horizon, invitin' us to explore what lies beyond. The path be flanked by tall, green grasses, swayin' gently in the breeze. The sky above be a clear blue, with wisps of clouds scattered about, like a hidden treasure map. In the distance, ye can see a line of trees standin' tall and proud, guardin' the secrets of the land. The sun be castin' a warm glow over the scene, makin' it feel like a true paradise. It be a perfect place to bury some treasure, savvy?

# paths./multimodal/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.messages[].content[].content.text
Arr matey, this here image be a sight for sore eyes! It shows a wooden plank walkway, stretchin' into the horizon, invitin' us to explore what lies beyond. The path be flanked by tall, green grasses, swayin' gently in the breeze. The sky above be a clear blue, with wisps of clouds scattered about, like a hidden treasure map. In the distance, ye can see a line of trees standin' tall and proud, guardin' the secrets of the land. The sun be castin' a warm glow over the scene, makin' it feel like a true paradise. It be a perfect place to bury some treasure, savvy?

# 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/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|Model|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 |**google**|-|`DocumentAI v1 beta3`|`invoice`|0.01 (per 1 page)|10 page |**google**|-|`DocumentAI v1 beta3`|`receipt`|0.01 (per 1 page)|10 page |**klippa**|-|`v1`|-|0.1 (per 1 file)|1 file |**microsoft**|-|`rest API 4.0 (2024-02-29-preview)`|-|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 |**eagledoc**|-|`v1`|-|0.03 (per 1 page)|1 page |**extracta**|-|`v1`|-|0.1 (per 1 page)|1 page |**openai**|**gpt-4o**|`v1.0`|-|0.04 (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`| |**Catalan (Spain)**|`ca-ES`| |**Chinese (China)**|`zh-CN`| |**Chinese (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-TW`| |**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`|

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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.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.extracta.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.extracta.extracted_data[].merchant_information.name
SNG Engineering, Inc. Consulting Engineers

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

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

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

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

# paths./ocr/identity_parser/.post.description
Available Providers
|Provider|Model|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 4.0 (2024-02-29-preview)`|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 |**openai**|**gpt-4o**|`v1`|0.02 (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`| |**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`|

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# paths./ocr/ocr/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**amazon**|`boto3 (v1.15.18)`|1.5 (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 |**mistral**|`v1`|1.0 (per 1000 page)|1 page

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.mistral.text
# 福州科技馆空调线路改造项目工程 

## 施工招标公告

招标项日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——互惠。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：福州市仓山区横溢洲桥下公园内 工程招标控制价：29135元/ 计划工期：15日历天 质量要求：企络 㫮标范围：涉及工程量请来为准。

## 2. 授标人资格要求

2.1 授标人须具备人员、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授标。

## 3. 授标报名、开标时间

授标报名截止时间至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 4.0 (2024-11-30)`|10.0 (per 1000 page)|1 page |**mistral**|`v1`|1.0 (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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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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# An Introduction to the Process of Optical Character Recognition 

Umal Patel ${ }^{1}$<br>${ }^{1}$ Department of Computer Engineering<br>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 of optical character recognition

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 |## 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
![img-0.jpeg](img-0.jpeg)

Figure 1: Slant Removal
![img-1.jpeg](img-1.jpeg)

Figure 2: Normalization of ' e ' and ' 1 ' 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.
![img-2.jpeg](img-2.jpeg)

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 $\mathrm{m} * \mathrm{n}$ 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. Thegoal of zoning is to obtain the local characteristics instead of global characteristics.
![img-3.jpeg](img-3.jpeg)

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 1D 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".
![img-4.jpeg](img-4.jpeg)

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".
![img-5.jpeg](img-5.jpeg)

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.
![img-6.jpeg](img-6.jpeg)

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[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.

# paths./ocr/ocr_async/{public_id}/.get.responses.200.content.application/json.examples.ResponseExample.value.results.amazon.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
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].
Volume 2 Issue 5, May 2013
www.ijsr.net
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
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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
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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
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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

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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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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

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

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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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Arabia, "Preprocessing Techniques in Character

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

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Structural features are based on topological and geometrical

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Shah, Benoy Kumar Thakur, "Recent Trends and Tools

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

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

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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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Extraction and Classification for English Handwritten",

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M. Sapkal, "Survey of Methods for Character

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

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

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[7] Mohanad Alata, Mohammad Al-Shabi "TEXT [13] Off-Line Handwriting", IEEE TRANSACTIONS ON

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

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

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University in 2008. Presently working as Assistant Professor

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Shah, "An Improved Zone Based Hybrid Feature

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

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Extraction Model for Handwritten Alphabets

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he has served as Software Test Engineer in Lodestone

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Recognition Using Euler Number", International Journal

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Software Services since June 2008. Her area of interest is

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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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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.

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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 4.0 (2024-02-29-preview)`|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/resume_parser/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**affinda**|-|`v3`|0.07 (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 |**openai**|-|`v1.0`|0.04 (per 1 page)|1 page |**openai**|**gpt-4o**|`v1.0`|0.04 (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 (China)**|`zh-cn`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Taiwan)**|`zh-tw`|

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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.openai.extracted_data.personal_infos.address.formatted_location
123 Business Rd, Mumbai, MH 400001, India

# paths./ocr/resume_parser/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.extracted_data.personal_infos.self_summary
Dynamic marketing professional with a robust background in digital marketing, SEO, and branding strategies. Over 8 years of experience in driving growth and managing online presence for top-tier companies.

# paths./ocr/resume_parser/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.extracted_data.personal_infos.objective
Seeking a senior role in digital marketing to leverage my skills in a growth-focused environment.

# paths./ocr/resume_parser/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.extracted_data.work_experience.entries[].description
Led a team of marketers to enhance online presence and drive growth. Managed SEO, PPC campaigns and social media strategies.

# paths./ocr/resume_parser/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.extracted_data.work_experience.entries[].description
Implemented SEO and content marketing strategies for clients. Analyzed performance metrics and improved website rankings.

# paths./ocr/resume_parser/.post.responses.200.content.application/json.examples.ResponseExample.value.affinda.extracted_data.personal_infos.address.formatted_location
2716 S College Ave Suite A, Fort Collins, CO 80525, USA

# 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

# 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./prompts/.get.parameters[].description
A page number within the paginated result set.

# paths./prompts/{name}/.post.description
Call prompt with your variables in prompt_context. You can override params saved in the prompt and specifiy file urls

# paths./prompts/{name}/history/.get.parameters[].description
A page number within the paginated result set.

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

Supported Languages
|Name|Value| |----|-----| |**Chinese**|`zh`| |**Dutch**|`nl`| |**English**|`en`| |**French**|`fr`| |**German**|`de`| |**Spanish**|`es`|

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

# 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/anonymization/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token |**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde |**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**microsoft**|-|`v3.1`|0.25 (per 1000000 char)|1000 char |**privateai**|-|`v3`|5.0 (per 1000000 char)|100 char |**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Arabic**|`ar`| |**Bambara**|`bm`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bulgarian**|`bg`| |**Burmese**|`my`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**Georgian**|`ka`| |**German**|`de`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Khmer**|`km`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Luxembourgish**|`lb`| |**Malay (macrolanguage)**|`ms`| |**Modern Greek (1453-)**|`el`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**Tagalog**|`tl`| |**Tamil**|`ta`| |**Thai**|`th`| |**Turkish**|`tr`| |**Ukrainian**|`uk`| |**Vietnamese**|`vi`|

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`|

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**xai**|`grok-2-latest`|

# 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.openai.result
The phone number of *** is the ** ** ** ** **.

# paths./text/chat/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|**o3-mini**|`v1Beta`|4.4e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v1Beta`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v1Beta`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|-|`v1Beta`|10.0 (per 1000000 token)|1 token |**openai**|**o1-preview**|`v1Beta`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v1Beta`|4.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v1Beta`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v1Beta`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v1Beta`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v1Beta`|6e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v1Beta`|1.4e-05 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v1Beta`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v1Beta`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v1Beta`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v1Beta`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v1Beta`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v1Beta`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v1Beta`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v1Beta`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4**|`v1Beta`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v1Beta`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v1Beta`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v1Beta`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v1Beta`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v1Beta`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v1Beta`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v1Beta`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v1Beta`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v1Beta`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v1Beta`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v1Beta`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v1Beta`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v1Beta`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v1Beta`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v1Beta`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v1Beta`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v1Beta`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v1Beta`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v1Beta`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v1Beta`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v1Beta`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v1Beta`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v1Beta`|1e-05 (per 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token)|1 token |**anthropic**|**claude-sonnet-4-20250514**|`bedrock-2023-05-31`|1.5e-05 (per 1 token)|1 token |**xai**|-|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**deepseek**|**deepseek-v3**|`v1`|1.1e-06 (per 1 token)|1 token |**deepseek**|-|`v1`|1.25 (per 1000000 token)|1 token |**deepseek**|**deepseek-reasoner**|`v1`|2.19e-06 (per 1 token)|1 token |**deepseek**|**deepseek-chat**|`v1`|1.1e-06 (per 1 token)|1 token |**deepseek**|**deepseek-coder**|`v1`|2.8e-07 (per 1 token)|1 token |**deepseek**|**deepseek-r1**|`v1`|2.19e-06 (per 1 token)|1 token |**deepseek**|**deepseek-v3.2**|`v1`|4e-07 (per 1 token)|1 token |**amazon**|-|`boto3 (v1.29.6)`|0.24 (per 1000000 token)|1 token |**amazon**|**amazon.nova-lite-v1:0**|`boto3 (v1.29.6)`|0.24 (per 1000000 token)|1 token |**amazon**|**amazon.nova-pro-v1:0**|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token |**amazon**|**amazon.nova-micro-v1:0**|`boto3 (v1.29.6)`|0.14 (per 1000000 token)|1 token |**together_ai**|-|`v1`|1.2 (per 1000000 token)|1 token |**together_ai**|**Qwen/Qwen2.5-72B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo**|`v1`|8.8e-07 (per 1 token)|1 token |**together_ai**|**together-ai-21.1b-41b**|`v1`|8e-07 (per 1 token)|1 token |**together_ai**|**together-ai-4.1b-8b**|`v1`|2e-07 (per 1 token)|1 token |**together_ai**|**together-ai-41.1b-80b**|`v1`|9e-07 (per 1 token)|1 token |**together_ai**|**together-ai-8.1b-21b**|`v1`|3e-07 (per 1 token)|1 token |**together_ai**|**together-ai-81.1b-110b**|`v1`|1.8e-06 (per 1 token)|1 token |**together_ai**|**together-ai-up-to-4b**|`v1`|1e-07 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen2.5-7B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**Qwen/Qwen3-235B-A22B-Instruct-2507-tput**|`v1`|6e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-235B-A22B-Thinking-2507**|`v1`|3e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-235B-A22B-fp8-tput**|`v1`|6e-07 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8**|`v1`|2e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-R1**|`v1`|7e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-R1-0528-tput**|`v1`|2.19e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-V3**|`v1`|1.25e-06 (per 1 token)|1 token |**together_ai**|**deepseek-ai/DeepSeek-V3.1**|`v1`|1.7e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-3.2-3B-Instruct-Turbo**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**meta-llama/Llama-3.3-70B-Instruct-Turbo-Free**|`v1`|0.0 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8**|`v1`|8.5e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Llama-4-Scout-17B-16E-Instruct**|`v1`|5.9e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo**|`v1`|3.5e-06 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo**|`v1`|8.8e-07 (per 1 token)|1 token |**together_ai**|**meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo**|`v1`|1.8e-07 (per 1 token)|1 token |**together_ai**|**mistralai/Mistral-7B-Instruct-v0.1**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**mistralai/Mistral-Small-24B-Instruct-2501**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**mistralai/Mixtral-8x7B-Instruct-v0.1**|`v1`|6e-07 (per 1 token)|1 token |**together_ai**|**moonshotai/Kimi-K2-Instruct**|`v1`|3e-06 (per 1 token)|1 token |**together_ai**|**openai/gpt-oss-120b**|`v1`|6e-07 (per 1 token)|1 token |**together_ai**|**openai/gpt-oss-20b**|`v1`|2e-07 (per 1 token)|1 token |**together_ai**|**togethercomputer/CodeLlama-34b-Instruct**|`v1`|0.0 (per 1 seconde)|1 seconde |**together_ai**|**zai-org/GLM-4.5-Air-FP8**|`v1`|1.1e-06 (per 1 token)|1 token |**together_ai**|**zai-org/GLM-4.6**|`v1`|2.2e-06 (per 1 token)|1 token |**together_ai**|**moonshotai/Kimi-K2-Instruct-0905**|`v1`|3e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Instruct**|`v1`|1.5e-06 (per 1 token)|1 token |**together_ai**|**Qwen/Qwen3-Next-80B-A3B-Thinking**|`v1`|1.5e-06 (per 1 token)|1 token |**microsoft**|**gpt-4o**|`Azure AI Foundry`|15.0 (per 1000000 token)|1 token |**groq**|**llama3-70b-8192**|`v1`|0.59 (per 1000000 token)|1 token |**groq**|**llama-3.1-8b-instant**|`v1`|8e-08 (per 1 token)|1 token |**groq**|**llama-3.3-70b-versatile**|`v1`|7.9e-07 (per 1 token)|1 token |**groq**|**gemma-7b-it**|`v1`|8e-08 (per 1 token)|1 token |**groq**|**meta-llama/llama-guard-4-12b**|`v1`|2e-07 (per 1 token)|1 token |**groq**|**meta-llama/llama-4-maverick-17b-128e-instruct**|`v1`|6e-07 (per 1 token)|1 token |**groq**|**meta-llama/llama-4-scout-17b-16e-instruct**|`v1`|3.4e-07 (per 1 token)|1 token |**groq**|**moonshotai/kimi-k2-instruct-0905**|`v1`|3e-06 (per 1 token)|1 token |**groq**|**openai/gpt-oss-120b**|`v1`|7.5e-07 (per 1 token)|1 token |**groq**|**openai/gpt-oss-20b**|`v1`|5e-07 (per 1 token)|1 token |**groq**|**qwen/qwen3-32b**|`v1`|5.9e-07 (per 1 token)|1 token

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

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**google**|`gemini-2.5-flash`| |**cohere**|`command`| |**meta**|`llama3-1-8b-instruct-v1:0`| |**mistral**|`mistral-large-latest`| |**perplexityai**|`sonar`| |**anthropic**|`claude-3-5-sonnet-latest`| |**xai**|`grok-2-latest`| |**deepseek**|`deepseek-chat`| |**amazon**|`amazon.nova-lite-v1:0`| |**together_ai**|`Qwen/Qwen2.5-72B-Instruct-Turbo`| |**microsoft**|`gpt-4o`| |**groq**|`llama3-70b-8192`|

# 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.xai.generated_text
Barack Hussein Obama, American, politician, 44th president, United States, 2009, 2017, Democratic Party, African-American, U.S. senator, Illinois, 2005, 2008, state senator, 1997, 2004

# paths./text/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.xai.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.xai.message[].message
Barack Hussein Obama, American, politician, 44th president, United States, 2009, 2017, Democratic Party, African-American, U.S. senator, Illinois, 2005, 2008, state senator, 1997, 2004

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

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

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

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

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

# 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 obama, president, united states, democratic party, african-american, senator, illinois

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

# 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 obama, president, united states, democratic party, illinois, senator, african-american

# paths./text/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.openai.generated_text
barack hussein obama, american, politician, 44th president, united states, democratic party, african-american, u.s. senator, illinois, state senator

# 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, 44th president, united states, democratic party, african-american, u.s. senator, illinois, state senator

# paths./text/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.meta.generated_text
Barack Obama, Democratic Party, United States, 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, Democratic Party, United States, Illinois

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

- Steve Jobs
- Apple Inc.
- Co-founder
- Multinational technology company
- Cupertino
- California
- CEO
- Pixar Animation Studios
- Walt Disney Company
- Visionary entrepreneur
- Pioneer
- Personal computer industry
- Charismatic personality
- Black turtleneck
- Keynote presentations
- Barack Hussein Obama
- American politician
- 44th President of the United States
- Democratic Party
- African-American President
- U.S. Senator
- Illinois
- State Senator.

# 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
Here are the keywords extracted from the text:

- Steve Jobs
- Apple Inc.
- Co-founder
- Multinational technology company
- Cupertino
- California
- CEO
- Pixar Animation Studios
- Walt Disney Company
- Visionary entrepreneur
- Pioneer
- Personal computer industry
- Charismatic personality
- Black turtleneck
- Keynote presentations
- Barack Hussein Obama
- American politician
- 44th President of the United States
- Democratic Party
- African-American President
- U.S. Senator
- Illinois
- State Senator.

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

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

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

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

# 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.groq.generated_text
barack obama, american, politician, democratic, african, united states, senator, illinois

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

# paths./text/chat/.post.responses.200.content.application/json.examples.ResponseExample.value.mistral.generated_text
Obama
American
president
United States
Democratic
African
Illinois
senator

# 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
Obama
American
president
United States
Democratic
African
Illinois
senator

# 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|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|-|`v1`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v1`|1.5e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**o1**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**o3-mini**|`v1`|4.4e-06 (per 1 token)|1 token |**openai**|**gpt-4**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**o1-preview**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v1`|4.4e-06 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v1`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v1`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v1`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v1`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v1`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v1`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v1`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v1`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v1`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v1`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v1`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v1`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v1`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v1`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v1`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v1`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v1`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v1`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v1`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v1`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v1`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v1`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v1`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v1`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v1`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v1`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v1`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v1`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v1`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v1`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v1`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v1`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v1`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v1`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v1`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v1`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v1`|1.6e-05 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v1`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v1`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v1`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v1`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v1`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v1`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v1`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v1`|0.0 (per 1 seconde)|1 seconde |**google**|-|`v1`|0.6 (per 1000000 token)|1 token |**google**|**gemini-1.5-flash-8b**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-latest**|`v1`|0.3 (per 1000000 token)|1 token |**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token |**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token |**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token |**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token |**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token |**xai**|-|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**google**|`gemini-1.5-flash`| |**xai**|`grok-2-latest`|

# 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.xai.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.openai.generated_text
 a leap year, it must be divisible by 4. However, if the year is divisible by 100, it is not a leap year unless it is also divisible by 400.

Here's a Python function to check if a year is a leap year:

```python
def is_leap_year(year):
    if year % 4 == 0:
        if year % 100 == 0:
            if year % 400 == 0:
                return True
            else:
                return False
        else:
            return True
    else:
        return False
```

You can use this function by passing a year as an argument, and it will return `True` if the year is a leap year and `False` otherwise. For example:

```python
print(is_leap_year(2020))  # Output: True
print(is_leap_year(1900))  # Output: False
print(is_leap_year(2000))  # Output: True
```

# 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/embeddings/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|-|`v3.0.0`|0.1 (per 1000000 token)|1 token |**openai**|**1536__text-embedding-ada-002**|`v3.0.0`|0.1 (per 1000000 token)|1 token |**openai**|**text-embedding-3-large**|`v3.0.0`|0.0 (per 1 token)|1 token |**openai**|**text-embedding-3-small**|`v3.0.0`|0.0 (per 1 token)|1 token |**openai**|**text-embedding-ada-002**|`v3.0.0`|0.0 (per 1 token)|1 token |**openai**|**text-embedding-ada-002-v2**|`v3.0.0`|0.0 (per 1 token)|1 token |**google**|**gemini-embedding-001**|`v1`|1.5e-07 (per 1 token)|1 token |**cohere**|**embed-english-v3.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-english-light-v3.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-multilingual-v3.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-english-v2.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-english-light-v2.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-multilingual-v2.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-v4.0**|`v1`|0.0 (per 1 token)|1 token |**cohere**|**embed-multilingual-light-v3.0**|`v1`|0.0 (per 1 token)|1 token |**mistral**|**1024__mistral-embed**|`v0.0.1`|0.1 (per 1000000 token)|1 token |**mistral**|-|`v0.0.1`|0.1 (per 1000000 token)|1 token |**mistral**|**mistral/mistral-embed**|`v0.0.1`|0.1 (per 1000000 seconde)|1 seconde |**mistral**|**mistral-embed**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde |**mistral**|**codestral-embed**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde |**mistral**|**codestral-embed-2505**|`v0.0.1`|0.0 (per 1 seconde)|1 seconde |**jina**|-|`v1`|0.018 (per 1000000 token)|1 token |**jina**|**jina-embeddings-v2-base-en**|`v1`|0.018 (per 1000000 token)|1 token |**jina**|**jina-embeddings-v3**|`v1`|0.02 (per 1000000 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`1536__text-embedding-ada-002`| |**google**|`text-multilingual-embedding-002`| |**cohere**|`4096__embed-english-v2.0`| |**mistral**|`1024__mistral-embed`| |**jina**|`jina-embeddings-v3`|

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

# paths./text/entity_sentiment/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 1.26.8`|1.0 (per 1000000 char)|300 char |**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token |**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token |**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token |**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token |**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token

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

Supported Models

Default Models
|Name|Value| |----|-----| |**google**|`gemini-1.5-flash`|

# 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/keyword_extraction/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char |**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token |**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde |**tenstorrent**|-|`v1.0.0`|0.7 (per 1000000 char)|1000 char |**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Arabic**|`ar`| |**Bulgarian**|`bg`| |**Catalan**|`ca`| |**Chinese**|`zh`| |**Croatian**|`hr`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Indonesian**|`id`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Modern Greek (1453-)**|`el`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Spanish**|`es`| |**Swedish**|`sv`| |**Turkish**|`tr`|

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

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**xai**|`grok-2-latest`|

# 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/moderation/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**microsoft**|-|`v1.0`|1.0 (per 1000 request)|1 request |**openai**|-|`v3.0.0`|free|- |**openai**|**text-moderation-stable**|`v3.0.0`|0.0 (per 1 token)|1 token |**openai**|**text-moderation-007**|`v3.0.0`|0.0 (per 1 token)|1 token |**openai**|**text-moderation-latest**|`v3.0.0`|0.0 (per 1 token)|1 token |**google**|-|`v1`|5.0 (per 1000000 char)|100 char

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Azerbaijani**|`az`| |**Basque**|`eu`| |**Belarusian**|`be`| |**Bengali**|`bn`| |**Bosnian**|`bs`| |**Breton**|`br`| |**Bulgarian**|`bg`| |**Catalan**|`ca`| |**Central Kurdish**|`ckb`| |**Cherokee**|`chr`| |**Chinese**|`zh`| |**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`| |**Hausa**|`ha`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Indonesian**|`id`| |**Inuktitut**|`iu`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Konkani (macrolanguage)**|`kok`| |**Korean**|`ko`| |**Lao**|`lo`| |**Latvian**|`lv`| |**Lithuanian**|`lt`| |**Luxembourgish**|`lb`| |**Macedonian**|`mk`| |**Malay (macrolanguage)**|`ms`| |**Malayalam**|`ml`| |**Maltese**|`mt`| |**Maori**|`mi`| |**Marathi**|`mr`| |**Modern Greek (1453-)**|`el`| |**Mongolian**|`mn`| |**Nepali (macrolanguage)**|`ne`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Oriya (macrolanguage)**|`or`| |**Panjabi**|`pa`| |**Pedi**|`nso`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Quechua**|`qu`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Scottish Gaelic**|`gd`| |**Serbian**|`sr`| |**Sindhi**|`sd`| |**Sinhala**|`si`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Southern Sotho**|`st`| |**Spanish**|`es`| |**Swahili (macrolanguage)**|`sw`| |**Swedish**|`sv`| |**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`| |**Welsh**|`cy`| |**Wolof**|`wo`| |**Xhosa**|`xh`| |**Yoruba**|`yo`| |**Zulu**|`zu`|

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

Supported Models

# paths./text/named_entity_recognition/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char |**openai**|-|`v3.0.0`|10.0 (per 1000000 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|10.0 (per 1000000 token)|1 token |**tenstorrent**|-|`v1.0.0`|1.0 (per 1000000 char)|1000 char

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

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

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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| |----|-------|-----|------------| |**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/prompt_optimization/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde

Supported Models

# 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/sentiment_analysis/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|-|`boto3 (v1.15.18)`|1.0 (per 1000000 char)|300 char |**google**|**gemini-live-2.5-flash-preview-native-audio-09-2025**|`v1`|2e-06 (per 1 token)|1 token |**google**|**gemini-1.5-flash-001**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-002**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-flash-latest**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-1.5-pro**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-latest**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-8b-exp-0924**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-flash-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-1.5-pro-001**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-002**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0801**|`v1`|1.05e-05 (per 1 token)|1 token |**google**|**gemini-1.5-pro-exp-0827**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-001**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-lite-preview-02-05**|`v1`|3e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-live-001**|`v1`|1.5e-06 (per 1 token)|1 token |**google**|**gemini-2.0-flash-preview-image-generation**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-flash-thinking-exp-01-21**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.0-pro-exp-02-05**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-flash**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-09-2025**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-09-2025**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-latest**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-flash-lite-latest**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite-preview-06-17**|`v1`|4e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-04-17**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-05-20**|`v1`|2.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-flash-preview-tts**|`v1`|6e-07 (per 1 token)|1 token |**google**|**gemini-2.5-pro**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-computer-use-preview-10-2025**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-3-pro-preview**|`v1`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-3-flash-preview**|`v1`|3e-06 (per 1 token)|1 token |**google**|**gemini-2.5-pro-exp-03-25**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-03-25**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-05-06**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-06-05**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-tts**|`v1`|1e-05 (per 1 token)|1 token |**google**|**gemini-exp-1114**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-exp-1206**|`v1`|0.0 (per 1 token)|1 token |**google**|**gemini-gemma-2-27b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-gemma-2-9b-it**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemini-pro-vision**|`v1`|1.05e-06 (per 1 token)|1 token |**google**|**gemma-3-27b-it**|`v1`|0.0 (per 1 token)|1 token |**google**|**learnlm-1.5-pro-experimental**|`v1`|0.0 (per 1 token)|1 token |**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char |**openai**|-|`v3.0.0`|20.0 (per 1000000 token)|1 token |**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde |**tenstorrent**|-|`v1.1.0`|0.7 (per 1000000 char)|1000 char |**sapling**|-|`v1`|20.0 (per 1000000 char)|1000 char |**xai**|**grok-2-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v1`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v1`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v1`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v1`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v1`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v1`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v1`|1.5e-05 (per 1 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Arabic**|`ar`| |**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`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Russian**|`ru`| |**Spanish**|`es`| |**Swedish**|`sv`| |**Thai**|`th`| |**Turkish**|`tr`| |**Vietnamese**|`vi`|

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

Supported Models

Default Models
|Name|Value| |----|-----| |**google**|`gemini-2.5-flash`| |**openai**|`gpt-4o`| |**xai**|`grok-2-latest`|

# 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.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.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/spell_check/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**prowritingaid**|`v2`|10.0 (per 1000 request)|1 request |**sapling**|`v1`|2.0 (per 1000000 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`| |**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`| |**Norwegian Bokmål**|`nb`| |**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`| |**jp**|`jp`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-CN`| |**Chinese (Simplified)**|`zh-hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-hant`| |**English (United Kingdom)**|`en-gb`| |**Portuguese (Brazil)**|`pt-br`| |**Portuguese (Portugal)**|`pt-pt`|

# paths./text/summarize/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**microsoft**|-|`v3.1`|2.0 (per 1000000 char)|1000 char |**openai**|-|`v3.0.0`|60.0 (per 1000000 token)|1 token |**openai**|**gpt-4**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-1106**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-32k-0314**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-2024-04-09**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0125**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4-turbo**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**chatgpt-4o-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0301**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-0613**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-3.5-turbo-16k-0613**|`v3.0.0`|4e-06 (per 1 token)|1 token |**openai**|**gpt-4-0125-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-0314**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-0613**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-1106-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-32k**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-32k-0613**|`v3.0.0`|0.00012 (per 1 token)|1 token |**openai**|**gpt-4-turbo-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4-vision-preview**|`v3.0.0`|3e-05 (per 1 token)|1 token |**openai**|**gpt-4.1**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-2025-04-14**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-mini-2025-04-14**|`v3.0.0`|1.6e-06 (per 1 token)|1 token |**openai**|**gpt-4.1-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.1-nano-2025-04-14**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-4.5-preview**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4.5-preview-2025-02-27**|`v3.0.0`|0.00015 (per 1 token)|1 token |**openai**|**gpt-4o-2024-05-13**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-08-06**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-2024-11-20**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-10-01**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2024-12-17**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-audio-preview-2025-06-03**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-mini-2024-07-18**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-audio-preview-2024-12-17**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-realtime-preview-2024-12-17**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-mini-search-preview-2025-03-11**|`v3.0.0`|6e-07 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-10-01**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2024-12-17**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-realtime-preview-2025-06-03**|`v3.0.0`|2e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-4o-search-preview-2025-03-11**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-2025-11-13**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.1-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5.2**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-2025-12-11**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5.2-chat-latest**|`v3.0.0`|1.4e-05 (per 1 token)|1 token |**openai**|**gpt-5-2025-08-07**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-chat-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**openai**|**gpt-5-mini**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-mini-2025-08-07**|`v3.0.0`|2e-06 (per 1 token)|1 token |**openai**|**gpt-5-nano**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-5-nano-2025-08-07**|`v3.0.0`|4e-07 (per 1 token)|1 token |**openai**|**gpt-realtime**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**gpt-realtime-mini**|`v3.0.0`|2.4e-06 (per 1 token)|1 token |**openai**|**gpt-realtime-2025-08-28**|`v3.0.0`|1.6e-05 (per 1 token)|1 token |**openai**|**o1**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-2024-12-17**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o1-mini-2024-09-12**|`v3.0.0`|1.2e-05 (per 1 token)|1 token |**openai**|**o1-preview**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o1-preview-2024-09-12**|`v3.0.0`|6e-05 (per 1 token)|1 token |**openai**|**o3**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-2025-04-16**|`v3.0.0`|8e-06 (per 1 token)|1 token |**openai**|**o3-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o3-mini-2025-01-31**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**o4-mini-2025-04-16**|`v3.0.0`|4.4e-06 (per 1 token)|1 token |**openai**|**container**|`v3.0.0`|0.0 (per 1 seconde)|1 seconde |**cohere**|-|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**summarize-xlarge**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**cohere**|**command-nightly**|`2022-12-06`|2.0 (per 1000000 token)|1 token |**xai**|**grok-2-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-2**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-1212**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-1212**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-2-vision-latest**|`v3.0.0`|1e-05 (per 1 token)|1 token |**xai**|**grok-3**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-beta**|`v3.0.0`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-fast-latest**|`v3.0.0`|2.5e-05 (per 1 token)|1 token |**xai**|**grok-3-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-3-mini**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-beta**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-3-mini-fast**|`v3.0.0`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-beta**|`v3.0.0`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-fast-latest**|`v3.0.0`|4e-06 (per 1 token)|1 token |**xai**|**grok-3-mini-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-fast-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-fast-non-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-0709**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-latest**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-4-1-fast**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-reasoning-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-4-1-fast-non-reasoning-latest**|`v3.0.0`|5e-07 (per 1 token)|1 token |**xai**|**grok-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token |**xai**|**grok-code-fast**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-code-fast-1-0825**|`v3.0.0`|1.5e-06 (per 1 token)|1 token |**xai**|**grok-vision-beta**|`v3.0.0`|1.5e-05 (per 1 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Bulgarian**|`bg`| |**Chinese**|`zh`| |**Czech**|`cs`| |**Danish**|`da`| |**Dutch**|`nl`| |**English**|`en`| |**Estonian**|`et`| |**Finnish**|`fi`| |**French**|`fr`| |**German**|`de`| |**Hungarian**|`hu`| |**Italian**|`it`| |**Japanese**|`ja`| |**Korean**|`ko`| |**Latvian**|`lv`| |**Modern Greek (1453-)**|`el`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Romanian**|`ro`| |**Russian**|`ru`| |**Slovak**|`sk`| |**Slovenian**|`sl`| |**Spanish**|`es`| |**Swedish**|`sv`|

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

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4`| |**cohere**|`summarize-xlarge`| |**xai**|`grok-2-latest`|

# 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.openai.result
Barack Hussein Obama, an American politician, served as the 44th president of the United States from 2009 to 2017. A Democrat, Obama was the first African-American U.S. president. He was also 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.xai.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.cohere.result
"You have to have a big-picture vision for where you want to go.

# paths./text/topic_extraction/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**google**|-|`v1`|0.6 (per 1000000 char)|1 char |**openai**|**gpt-4o**|`v1`|10.0 (per 1000000 token)|1 token |**openai**|-|`v1`|10.0 (per 1000000 token)|1 token |**tenstorrent**|-|`v1.0.0`|2.0 (per 1000000 char)|1000 char

Supported Languages
|Name|Value| |----|-----| |**English**|`en`|

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

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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./translation/automatic_translation/.post.description
Available Providers
|Provider|Model|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 |**microsoft**|-|`v3.0`|10.0 (per 1000000 char)|1 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 |**openai**|**gpt-4o**|`v1`|20.0 (per 1000000 token)|1 token

Supported Languages
|Name|Value| |----|-----| |**Afrikaans**|`af`| |**Albanian**|`sq`| |**Amharic**|`am`| |**Arabic**|`ar`| |**Armenian**|`hy`| |**Assamese**|`as`| |**Azerbaijani**|`az`| |**Bashkir**|`ba`| |**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`| |**Dari**|`prs`| |**Dhivehi**|`dv`| |**Dutch**|`nl`| |**English**|`en`| |**Esperanto**|`eo`| |**Estonian**|`et`| |**Faroese**|`fo`| |**Fijian**|`fj`| |**Filipino**|`fil`| |**Finnish**|`fi`| |**French**|`fr`| |**Galician**|`gl`| |**Georgian**|`ka`| |**German**|`de`| |**Gujarati**|`gu`| |**Haitian**|`ht`| |**Hausa**|`ha`| |**Hawaiian**|`haw`| |**Hebrew**|`he`| |**Hindi**|`hi`| |**Hmong**|`hmn`| |**Hmong Daw**|`mww`| |**Hungarian**|`hu`| |**Icelandic**|`is`| |**Igbo**|`ig`| |**Indonesian**|`id`| |**Inuinnaqtun**|`ikt`| |**Inuktitut**|`iu`| |**Irish**|`ga`| |**Italian**|`it`| |**Japanese**|`ja`| |**Javanese**|`jv`| |**Kannada**|`kn`| |**Kazakh**|`kk`| |**Khmer**|`km`| |**Kinyarwanda**|`rw`| |**Kirghiz**|`ky`| |**Klingon**|`tlh`| |**Korean**|`ko`| |**Kurdish**|`ku`| |**Lao**|`lo`| |**Latin**|`la`| |**Latvian**|`lv`| |**Literary Chinese**|`lzh`| |**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`| |**Northern Kurdish**|`kmr`| |**Norwegian**|`no`| |**Norwegian Bokmål**|`nb`| |**Norwegian Nynorsk**|`nn`| |**Nyanja**|`ny`| |**Oriya (macrolanguage)**|`or`| |**Panjabi**|`pa`| |**Persian**|`fa`| |**Polish**|`pl`| |**Portuguese**|`pt`| |**Pushto**|`ps`| |**Querétaro Otomi**|`otq`| |**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`| |**Tahitian**|`ty`| |**Tajik**|`tg`| |**Tamil**|`ta`| |**Tatar**|`tt`| |**Telugu**|`te`| |**Thai**|`th`| |**Tibetan**|`bo`| |**Tigrinya**|`ti`| |**Tonga (Tonga Islands)**|`to`| |**Turkish**|`tr`| |**Turkmen**|`tk`| |**Uighur**|`ug`| |**Ukrainian**|`uk`| |**Upper Sorbian**|`hsb`| |**Urdu**|`ur`| |**Uzbek**|`uz`| |**Vietnamese**|`vi`| |**Welsh**|`cy`| |**Western Frisian**|`fy`| |**Xhosa**|`xh`| |**Yiddish**|`yi`| |**Yoruba**|`yo`| |**Yucateco**|`yua`| |**Yue Chinese**|`yue`| |**Zulu**|`zu`|

Supported Detailed Languages
|Name|Value| |----|-----| |**Auto detection**|`auto-detect`| |**Chinese (China)**|`zh-CN`| |**Chinese (Simplified)**|`zh-Hans`| |**Chinese (Taiwan)**|`zh-TW`| |**Chinese (Traditional)**|`zh-Hant`| |**English (United Kingdom)**|`en-GB`| |**English (United States)**|`en-US`| |**French (Canada)**|`fr-CA`| |**Inuktitut (Latin)**|`iu-Latn`| |**Klingon (Klingon (KLI pIqaD))**|`tlh-Piqd`| |**Klingon (Latin)**|`tlh-Latn`| |**Mongolian (Cyrillic)**|`mn-Cyrl`| |**Mongolian (Mongolian)**|`mn-Mong`| |**Persian (Afghanistan)**|`fa-AF`| |**Portuguese (Brazil)**|`pt-BR`| |**Portuguese (Portugal)**|`pt-PT`| |**Serbian (Cyrillic)**|`sr-Cyrl`| |**Serbian (Latin)**|`sr-Latn`| |**Spanish (Latin America)**|`es-419`| |**Spanish (Mexico)**|`es-MX`| |**Spanish (Spain)**|`es-ES`|

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`|

# 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 intelligent machines and machine intelligence, refers to the wisdom demonstrated by machines created by humans. Typically, artificial intelligence refers to the technology that presents human intelligence through ordinary computer programs. The term also points to the study of whether and how such intelligent systems can be implemented. At the same time, through advances in medicine, neuroscience, robotics, and statistics, the common prediction is that many human occupations are gradually being replaced by them.

# 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.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|Model|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 |**microsoft**|-|`v3.1`|1.0 (per 1000000 char)|1000 char |**modernmt**|-|`1.1.0`|8.0 (per 1000000 char)|1 char |**openai**|-|`v1`|20.0 (per 1000000 token)|1 token |**openai**|**gpt-4o**|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2-latest**|`v1`|10.0 (per 1000000 token)|1 token |**xai**|**grok-2**|`v1`|10.0 (per 1000000 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**openai**|`gpt-4o`| |**xai**|`grok-2-latest`|

# 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./user/custom_token/{name}/rotate/.post.description
Rotate a key's secret in place: same key (name, id, budget, guardrail),
brand-new ``sk-eden`` value. The old value stops working immediately; the new
one is returned exactly once. Operates on real ``Token`` rows only — a legacy
primary/sandbox key (a JWT in a ``User`` column) is regenerated via
MigrateLegacyKey instead. Mutating, so the session access token is required —
no API key can rotate itself or others.

# paths./user/custom_token/legacy/{legacy_type}/regenerate/.post.description
Regenerate a legacy primary/sandbox key — a JWT stored in a ``User`` column,
not a ``Token`` row — as a real hashed ``sk-eden`` custom key.

Nulls the ``User`` column, which revokes the old JWT immediately in BOTH
services (edenai-back matches the column in ``auth.py``; aifeatures re-checks
it against the column on every request), and mints a new key returned exactly
once. The new key's name defaults to "Production"/"Sandbox" (auto-suffixed to
"Production 2" etc. if that name is already taken) and may be overridden in the
body. Session-only and NOT quota-gated — migrating an existing key is never
blocked, even at the custom-key cap. The old key can't authorize its own
replacement.

# paths./user/custom_token/me/.get.description
Return info about the API key used to authenticate *this* request — its
label, masking, budget and remaining balance. The introspection counterpart
to OpenRouter's ``GET /key``: authenticated BY the key itself (FeatureAuth),
never by a session, and it can only ever see its own row. Key management
(list/create/rotate/delete) lives on the session-only endpoints.

# paths./video/deepfake_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/deepfake_detection_async/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**resemble**|`v2`|0.07 (per 1 seconde)|1 seconde

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

# 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/generation_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/generation_async/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**amazon**|**amazon.nova-reel-v1:1**|`boto3`|0.08 (per 1 seconde)|1 seconde |**amazon**|**amazon.nova-reel-v1:0**|`boto3`|0.08 (per 1 seconde)|1 seconde |**amazon**|-|`boto3`|0.5 (per 1 request)|1 request |**minimax**|-|`v1`|0.56 (per 1 request)|1 request |**minimax**|**S2V-01**|`v1`|0.65 (per 1 request)|1 request |**minimax**|**T2V/I2V-01-Director**|`v1`|0.43 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-02**|`v1`|`912x512`|0.084 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-02**|`v1`|`1280x720`|0.28 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-02**|`v1`|`1920x1080`|0.56 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-2.3**|`v1`|`912x512`|0.084 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-2.3**|`v1`|`1280x720`|0.28 (per 1 request)|1 request |**minimax**|**MiniMax-Hailuo-2.3**|`v1`|`1920x1080`|0.56 (per 1 request)|1 request |**bytedance**|-|`v3`|2.5 (per 1000000 token)|1 token |**bytedance**|**seedance-1-0-pro-250528**|`v3`|2.5 (per 1000000 token)|1 token |**bytedance**|**seedance-1-0-pro-fast-251015**|`v3`|1.0 (per 1000000 token)|1 token |**bytedance**|**seedance-1-5-pro-251215**|`v3`|1.2 (per 1000000 token)|1 token |**bytedance**|**seedance-2-0-260128**|`v3`|6.79 (per 1000000 token)|1 token |**google**|**veo-3.1-generate-preview**|`v1Beta`|`3840x2160`|0.6 (per 1 seconde)|1 seconde |**google**|**veo-3.1-fast-generate-preview**|`v1Beta`|`1280x720`|0.1 (per 1 seconde)|1 seconde |**google**|**veo-3.1-fast-generate-preview**|`v1Beta`|`1920x1080`|0.12 (per 1 seconde)|1 seconde |**google**|**veo-3.1-fast-generate-preview**|`v1Beta`|`3840x2160`|0.3 (per 1 seconde)|1 seconde |**google**|**veo-3.1-fast-generate-preview**|`v1Beta`|0.1 (per 1 seconde)|1 seconde |**google**|**veo-3.1-generate-preview**|`v1Beta`|0.4 (per 1 seconde)|1 seconde |**google**|**veo-3.1-lite-generate-preview**|`v1Beta`|`1280x720`|0.05 (per 1 seconde)|1 seconde |**google**|**veo-3.1-lite-generate-preview**|`v1Beta`|`1920x1080`|0.08 (per 1 seconde)|1 seconde |**google**|**veo-3.0-generate-001**|`v1Beta`|0.4 (per 1 seconde)|1 seconde |**google**|**veo-3.1-lite-generate-preview**|`v1Beta`|0.05 (per 1 seconde)|1 seconde |**google**|-|`v1Beta`|3.6 (per 1 request)|1 request |**google**|**veo-3.0-fast-generate-001**|`v1Beta`|0.1 (per 1 seconde)|1 seconde |**google**|**veo-3.0-fast-generate-001**|`v1Beta`|`1280x720`|0.1 (per 1 seconde)|1 seconde |**google**|**veo-3.0-fast-generate-001**|`v1Beta`|`1920x1080`|0.12 (per 1 seconde)|1 seconde |**google**|**veo-3.0-fast-generate-001**|`v1Beta`|`3840x2160`|0.3 (per 1 seconde)|1 seconde |**google**|**veo-3.1-generate-preview**|`v1Beta`|`1280x720`|0.4 (per 1 seconde)|1 seconde |**google**|**veo-3.1-generate-preview**|`v1Beta`|`1920x1080`|0.4 (per 1 seconde)|1 seconde |**openai**|-|`v1`|5.0 (per 1 request)|1 request |**openai**|**sora-2**|`v1`|`1280x720`|0.1 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`720x1280`|0.3 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`1280x720`|0.3 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`1024x1792`|0.5 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`1792x1024`|0.5 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`1080x1920`|0.7 (per 1 seconde)|1 seconde |**openai**|**sora-2-pro**|`v1`|`1920x1080`|0.7 (per 1 seconde)|1 seconde |**openai**|**sora-2**|`v1`|`720x1280`|0.1 (per 1 seconde)|1 seconde |**pixverse**|-|`v2`|0.004 (per 1 token)|None token |**pixverse**|**v6**|`v2`|0.004 (per 1 token)|None token |**pixverse**|**c1**|`v2`|0.004 (per 1 token)|None token |**pixverse**|**v5.5**|`v2`|0.004 (per 1 token)|None token |**pixverse**|**v5**|`v2`|0.004 (per 1 token)|None token

Supported Models

Default Models
|Name|Value| |----|-----| |**amazon**|`amazon.nova-reel-v1:1`| |**minimax**|`MiniMax-Hailuo-2.3`| |**bytedance**|`seedance-1-0-pro-250528`| |**google**|`veo-3.1-generate-preview`| |**openai**|`sora-2`| |**pixverse**|`v6`|

# paths./video/generation_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/generation_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/question_answer/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**google**|**gemini-3-flash-preview**|`v1Beta`|3e-06 (per 1 token)|1 token |**google**|**gemini-3-pro-preview**|`v1Beta`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-2.0-flash**|`v1Beta`|0.4 (per 1000000 token)|1 token |**google**|-|`v1Beta`|0.6 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-lite-preview**|`v1Beta`|0.3 (per 1000000 token)|1 token |**google**|**gemini-3.1-pro-preview**|`v1Beta`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-2.5-flash-lite**|`v1Beta`|0.4 (per 1000000 token)|1 token |**google**|**gemini-2.5-pro**|`v1Beta`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.5-flash**|`v1Beta`|2.5 (per 1000000 token)|1 token |**google**|**gemini-3.1-flash-lite-preview**|`v1Beta`|1.5e-06 (per 1 token)|1 token |**google**|**gemini-2.5-pro-preview-03-25**|`v1Beta`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1Beta`|0.3 (per 1000000 token)|1 token |**amazon**|-|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token |**amazon**|**amazon.nova-pro-v1:0**|`boto3 (v1.29.6)`|3.2 (per 1000000 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**google**|`gemini-2.5-flash`| |**amazon**|`amazon.nova-pro-v1:0`|

# paths./video/question_answer/.post.responses.200.content.application/json.examples.ResponseExample.value.amazon.answer
The video starts with a person holding a smartphone and showing a picture of a room with a table, a chair, and a potted plant. The table has a glass and a skull-shaped object on it. The room has a checkered floor and a window with a curtain. The person then shows a picture of a building with a tower and a statue on top. The building has a clock tower and a tower with a statue of a person on top. The building is surrounded by trees and has a fence around it. The person then shows a picture of a man sitting in front of a desk with two monitors, a keyboard, and a mouse. He is holding a bottle and a pen in his hands. He is looking at the monitor on the right. The desk has a keyboard, a mouse, and a bottle. Behind him is a chair. The room is illuminated by a neon light.

# paths./video/question_answer/.post.responses.200.content.application/json.examples.ResponseExample.value.google.answer
Here is a description of the video.

The video is a tutorial on how to create AI hyperlapses. The video opens with a shot of a phone displaying a short video of a table with a glass of water and a small skull on it. The narrator then explains that AI hyperlapses are currently going viral on YouTube and Instagram. The video then shows several examples of AI hyperlapses, including a statue of a woman and a lion, a church dome, and a statue of a woman and child. The narrator then shows how to create these videos using a phone and computer.

# paths./video/question_answer_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/question_answer_async/.post.description
Available Providers
|Provider|Model|Version|Price|Billing unit| |----|----|-------|-----|------------| |**google**|**gemini-3-pro-preview**|`v1Beta`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-3-flash-preview**|`v1Beta`|3e-06 (per 1 token)|1 token |**google**|-|`v1Beta`|0.6 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash**|`v1Beta`|0.4 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-lite-preview**|`v1Beta`|0.3 (per 1000000 token)|1 token |**google**|**gemini-2.5-flash-lite**|`v1Beta`|0.4 (per 1000000 token)|1 token |**google**|**gemini-3.1-pro-preview**|`v1Beta`|1.2e-05 (per 1 token)|1 token |**google**|**gemini-2.5-pro**|`v1Beta`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.5-flash**|`v1Beta`|2.5 (per 1000000 token)|1 token |**google**|**gemini-3.1-flash-lite-preview**|`v1Beta`|1.5e-06 (per 1 token)|None token |**google**|**gemini-2.5-pro-preview-03-25**|`v1Beta`|15.0 (per 1000000 token)|1 token |**google**|**gemini-2.0-flash-lite**|`v1Beta`|0.3 (per 1000000 token)|1 token

Supported Models

Default Models
|Name|Value| |----|-----| |**google**|`gemini-2.5-flash`|

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

# paths./video/question_answer_async/{public_id}/.get.responses.200.content.application/json.examples.ResponseExample.value.results.google.answer
Here is a description of the video.

The video is a tutorial on how to create AI hyperlapses. The video opens with a shot of a phone displaying a short video of a table with a glass of water and a small skull on it. The narrator then explains that AI hyperlapses are currently going viral on YouTube and Instagram. The video then shows several examples of AI hyperlapses, including a statue of a woman and a lion, a church dome, and a statue of a woman and child. The narrator then shows how to create these videos using a phone and computer.

# paths./video/shot_change_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/shot_change_detection_async/.post.description
Available Providers
|Provider|Version|Price|Billing unit| |----|-------|-----|------------| |**google**|`Video intelligence v1`|0.05 (per 1 minute)|1 minute

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

# paths./video/shot_change_detection_async/{public_id}/.get.summary
Video Shot Change Detection Get Job Results

# 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/{workflow_id}/.get.description
Get workflow Content (JSON representation)

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

# paths./workflow/{workflow_id}/execution/{execution_id}/.get.description
An Execution represents the process of running a workflow with specific input data. It tracks the workflow's progress, providing a unique id, a status (e.g., pending, running, completed), and a result once the execution is finished. You can retrieve an Execution by its id to monitor its status or access its result after completion

# 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.AddImageRequest.properties.metadata.description
Optional parameter: Attach metadata to the uploaded 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. Key 'collection_name' is not allowed.

# components.schemas.AddImageRequest.properties.model.description
Optional. Select the Language Model provider (e.g., `google/gemini-2.0-flash`, `openai/gpt-4-vision-preview`) to use for describing the uploaded content.  This model will analyze the content and generate a text description, which will be stored in the RAG database. If left blank, a default model is used.

# components.schemas.AddImageRequest.properties.prompt.description
Optional. Specify a custom prompt to guide the Language Model in generating the description for the content.  If no prompt is provided, a default prompt will be used to describe what's in the image/video.  This allows for more specific or targeted analysis.

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

# 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.AddUrlRequest.properties.js_render.description
Enable JavaScript rendering for the provided URLs.

# components.schemas.AddVideoRequest.properties.metadata.description
Optional parameter: Attach metadata to the uploaded 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. Key 'collection_name' is not allowed.

# components.schemas.AddVideoRequest.properties.model.description
Optional. Select the Language Model provider (e.g., `google/gemini-2.0-flash`, `openai/gpt-4-vision-preview`) to use for describing the uploaded content.  This model will analyze the content and generate a text description, which will be stored in the RAG database. If left blank, a default model is used.

# components.schemas.AddVideoRequest.properties.prompt.description
Optional. Specify a custom prompt to guide the Language Model in generating the description for the content.  If no prompt is provided, a default prompt will be used to describe what's in the image/video.  This allows for more specific or targeted analysis.

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

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# components.schemas.AnonymizationAsyncRequest.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.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.min_score.description
A minimum score threshold for the model to consider a chunk as a valid response. Higher values mean the model will be more conservative and only return chunks that are more similar to the query. Lower values mean the model will be more open to returning chunks that are less similar to the query.

# 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.AskLLMRequest.properties.use_reranking.description
Activate/deactivate the reranking. This is experimental.

# components.schemas.AskLLMRequest.properties.top_n.description
The number of documents returned by the reranker (experimental)

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

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

# 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.

* `openai` - openai
* `cohere` - cohere
* `google` - google
* `mistral` - mistral
* `jina` - jina

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.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.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.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.AutomlClassificationTrainRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.BalanceResetPeriodEnum.description
* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# 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.BatchRequest.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.BatchRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.ChatAvailableToolsRequest.properties.parameters.description
The tool's parameters are specified using a JSON Schema object. Detailed format documentation is available in the [JSON Schema reference](https://json-schema.org/understanding-json-schema/).

**Make sure to well describe each parameter for best results.**


Example for a weather tool:

    {
      "type": "object",
      "properties": {
        "location": {
          "type": "string"
          "description": "The geographical location for which weather data is requested."
        },
        "unit": {
          "type": "string", "enum": ["Celsius", "Fahrenheit"]
          "description": "The unit of measurement for temperature."
        }
      },
      "required": ["location"]
    }
    

# components.schemas.ChatCompletionChoice.properties.finish_reason.description
The reason the completion finished: 'stop', 'length', 'tool_calls', 'content_filter', or 'function_call'

# components.schemas.ChatCompletionUsage.properties.completion_tokens_details.description
Number of tokens in the generated completion

# components.schemas.ChatCompletionUsage.properties.total_tokens.description
Total number of tokens used (prompt + completion)

# components.schemas.ChatToolResultRequest.properties.id.description
the id of the `tool_call` used to generate result

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.CustomTokenUpdate.properties.balance.description
Optional remaining credits balance for this Token, if `active_balance` is set to True and the balance reaches 0, this token will become unusable

# components.schemas.CustomTokenUpdate.properties.active_balance.description
Weither to use the balance field or not.

# components.schemas.CustomTokenUpdate.properties.balance_reset_amount.description
The amount this token's balance is reset to at the start of each balance_reset_period. Null when balance_reset_period is 'none'.

# components.schemas.CustomTokenUpdate.properties.balance_reset_period.description
How often this token's balance is reinitialized to balance_reset_amount. 'none' = one-time balance (default, current behaviour).

* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# components.schemas.CustomTokensCreate.properties.balance.description
Optional remaining credits balance for this Token, if `active_balance` is set to True and the balance reaches 0, this token will become unusable

# components.schemas.CustomTokensCreate.properties.active_balance.description
Weither to use the balance field or not.

# components.schemas.CustomTokensCreate.properties.balance_reset_amount.description
The amount this token's balance is reset to at the start of each balance_reset_period. Null when balance_reset_period is 'none'.

# components.schemas.CustomTokensCreate.properties.balance_reset_period.description
How often this token's balance is reinitialized to balance_reset_amount. 'none' = one-time balance (default, current behaviour).

* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# components.schemas.CustomTokensCreateRequest.properties.balance.description
Optional remaining credits balance for this Token, if `active_balance` is set to True and the balance reaches 0, this token will become unusable

# components.schemas.CustomTokensCreateRequest.properties.active_balance.description
Weither to use the balance field or not.

# components.schemas.CustomTokensCreateRequest.properties.balance_reset_amount.description
The amount this token's balance is reset to at the start of each balance_reset_period. Null when balance_reset_period is 'none'.

# components.schemas.CustomTokensCreateRequest.properties.balance_reset_period.description
How often this token's balance is reinitialized to balance_reset_amount. 'none' = one-time balance (default, current behaviour).

* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# components.schemas.CustomTokensList.properties.masked.description
Non-secret display value, e.g. ``sk-eden-live…c5d6``. Owns the
legacy-JWT vs hash-row duality so every surface (API, admin, shell) masks
the same way. Falls back to the last 4 of a legacy row's stored token.

# components.schemas.CustomTokensList.properties.balance.description
Optional remaining credits balance for this Token, if `active_balance` is set to True and the balance reaches 0, this token will become unusable

# components.schemas.CustomTokensList.properties.active_balance.description
Weither to use the balance field or not.

# components.schemas.CustomTokensList.properties.balance_reset_amount.description
The amount this token's balance is reset to at the start of each balance_reset_period. Null when balance_reset_period is 'none'.

# components.schemas.CustomTokensList.properties.balance_reset_period.description
How often this token's balance is reinitialized to balance_reset_amount. 'none' = one-time balance (default, current behaviour).

* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# 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
* `depth` - Depth
* `reasoning_depth` - Reasoning Depth

# components.schemas.DocumentTypeEnum.description
* `auto-detect` - auto-detect
* `invoice` - invoice
* `receipt` - receipt

# components.schemas.EmbeddingsProviderEnum.description
* `openai` - openai
* `cohere` - cohere
* `google` - google
* `mistral` - mistral
* `jina` - jina

# 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.ExecutionContentCreateStatusEnum.description
* `success` - Success
* `failed` - Failed
* `processing` - Processing

# components.schemas.ExecutionExampleSuccessCreate.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.ExecutionExampleSuccessCreate.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.ExecutionExampleSuccessCreate.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# components.schemas.ExplicitItem.properties.category.description
The category of the detected content. Possible values include: 'Toxic', 'Content', 'Sexual', 'Violence', 'DrugAndAlcohol', 'Finance', 'HateAndExtremism', 'Safe', 'Other'.

# components.schemas.ExplicitItem.properties.subcategory.description
The subcategory of content. Possible values:

Toxic Subcategories:
- Insult
- Obscene
- Derogatory
- Profanity
- Threat
- Toxic

Content Subcategories:
- MiddleFinger
- PublicSafety
- Health
- Explicit
- QRCode
- Medical
- Politics
- Legal

Sexual Subcategories:
- SexualActivity
- SexualSituations
- Nudity
- PartialNudity
- Suggestive
- AdultToys
- RevealingClothes
- Sexual

Violence Subcategories:
- GraphicViolenceOrGore
- PhysicalViolence
- WeaponViolence
- Violence

Drug and Alcohol Subcategories:
- DrugProducts
- DrugUse
- Tobacco
- Smoking
- Alcohol
- Drinking

Finance Subcategories:
- Gambling
- Finance
- MoneyContent

Hate and Extremism Subcategories:
- Hate
- Harassment
- Threatening
- Extremist
- Racy

Safe Subcategories:
- Safe
- NotSafe

Other Subcategories:
- Spoof
- Religion
- Offensive
- Other

# 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.GenerateRequest.properties.messages.description
A list containing all the conversations between the user and the assistant.
Each item in the list should be a dictionary with two keys: 'role' and 'message'.

**role**: Specifies the role of the speaker and can have the values 'user', 'system', 'assistant' or 'tool'.
The system role instructs the way the model should answer, e.g. 'You are a helpful assistant'. The user
role specifies the user query and assistant is the model's response. The tool role is for external tools that
can be used in the conversation.

**message**: A list of dictionaries. Each dictionary in the 'message' list must contain the keys 'type' and 'content'.

#### Structure
- **type**: Specifies the type of content and can be 'image_url' or 'text'.
- **content**: A dictionary with the actual content based on the 'type':
    - If 'type' is 'image_url', 'content' must contain 'image_url' and must not contain 'text'.
    - If 'type' is 'text', 'content' must contain 'text' and must not contain 'image_url'.

#### Example
```json
[
  {
    "role": "user",
    "content": [
        {
          "type": "text",
          "text": "Describe this image"
        },
        {
        "type": "image_url",
        "image_url": {
          "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
          }
        }
    ]
  }
]
```

# components.schemas.GenerateRequest.properties.model.description
The OpenAI model to use for the chat completion. 
This field is required and specifies which language model will process the conversation. 

**Example values**: 'gpt-3.5-turbo', 'gpt-4', 'gpt-4-turbo'

# components.schemas.GenerateRequest.properties.reasoning_effort.description
Optional parameter to control the model's reasoning depth. 
Allows specifying the level of analytical effort in generating responses. 

**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# components.schemas.GenerateRequest.properties.metadata.description
Optional list of metadata associated with the chat request. 
Can be used to provide additional context or tracking information. 

**Example**:
```json
{
  "metadata": [
    {"key": "conversation_id", "value": "chat_12345"},
    {"key": "source", "value": "customer_support"}
  ]
}
```

# components.schemas.GenerateRequest.properties.frequency_penalty.description
Controls repetitiveness of model responses by penalizing frequent tokens. 
Ranges from -2.0 to 2.0. 

**Values**:
- Positive values: Reduce token repetition
- Negative values: Encourage repetition
- 0.0: Default behavior

**Example**: 1.5 to significantly reduce repeated phrases

# components.schemas.GenerateRequest.properties.logit_bias.description
Modify the likelihood of specific tokens appearing in the response. 
A dictionary where keys are token IDs and values are bias scores. 

**Example**:
```json
{
  "logit_bias": {
    "50256": -100,  # Reduce probability of end-of-text token
    "15": 5  # Slightly increase probability of a specific token
  }
}
```

# components.schemas.GenerateRequest.properties.logprobs.description
If set to True, returns log probabilities of the most likely tokens. 
Useful for advanced token probability analysis. 

**Example**: True to get detailed token likelihood information

# components.schemas.GenerateRequest.properties.top_logprobs.description
Number of top log probabilities to return with each token. 
Must be between 0 and 20. 

**Example**: 5 to get top 5 most likely tokens for each position

# components.schemas.GenerateRequest.properties.max_completion_tokens.description
Maximum number of tokens to generate in the completion. 
Must be at least 1. 

**Example**: 150 to limit response to approximately 100-150 words

# components.schemas.GenerateRequest.properties.n.description
Number of chat completion choices to generate.

    **Example**: 3 to generate multiple alternative responses

# components.schemas.GenerateRequest.properties.modalities.description
List of supported input/output modalities for the chat. 

**Example**:
```json
{
  "modalities": ["text", "image", "audio"]
}
```

# components.schemas.GenerateRequest.properties.prediction.description
Optional field for storing prediction-related information. 
Flexible dictionary to capture model's predictive metadata. 

**Example**:
```json
{
  "prediction": {
    "confidence_score": 0.85,
    "top_prediction": "response_category"
  }
}
```

# components.schemas.GenerateRequest.properties.audio.description
Optional dictionary for audio-related parameters or metadata. 

**Example**:
```json
{
  "audio": {
    "language": "en-US",
    "transcription_format": "srt"
  }
}
```

# components.schemas.GenerateRequest.properties.presence_penalty.description
Adjusts likelihood of discussing new topics by penalizing existing tokens. 
Ranges from -2.0 to 2.0. 

**Values**:
- Positive values: Encourage more diverse topics
- Negative values: Keep discussion more focused
- 0.0: Default behavior

**Example**: 1.0 to promote topic diversity

# components.schemas.GenerateRequest.properties.response_format.description
Specify the desired response format for the completion. 

**Example**:
```json
{
  "response_format": {
    "type": "json_object",
    "schema": {...}
  }
}
```

# components.schemas.GenerateRequest.properties.seed.description
Set a seed for deterministic sampling to reproduce consistent results. 

**Example**: 42 for a reproducible random generation process

# components.schemas.GenerateRequest.properties.service_tier.description
Select the service tier for the API request. 

**Choices**:
- 'auto': Automatically select appropriate tier
- 'default': Use default service configuration

* `auto` - auto
* `default` - default

# components.schemas.GenerateRequest.properties.stop.description
List of strings that will cause the model to stop generating. 

**Example**:
```json
{
  "stop": ["\n", "Human:", "AI:"]
}
```

# components.schemas.GenerateRequest.properties.stream.description
If True, returns tokens as they are generated in a streaming format. 
Default is False. 

**Example**: True for real-time token streaming

# components.schemas.GenerateRequest.properties.stream_options.description
Additional configuration for streaming responses. 

**Example**:
```json
{
  "stream_options": {
    "include_usage": true
  }
}
```

# components.schemas.GenerateRequest.properties.temperature.description
Controls randomness in token selection. 
Ranges from 0.0 to 2.0. 

**Values**:
- 0.0: Most deterministic, focused responses
- 1.0: Balanced randomness
- 2.0: Most creative, unpredictable responses

**Example**: 0.7 for a good balance of creativity and focus

# components.schemas.GenerateRequest.properties.top_p.description
Nucleus sampling threshold for token selection. 
Ranges from 0.0 to 1.0. Default is 1.0. 

**Values**:
- 1.0: Consider all tokens
- Lower values: More focused, deterministic sampling

**Example**: 0.9 to select from top 90% most probable tokens

# components.schemas.GenerateRequest.properties.tools.description
List of tools or function definitions available to the model. 

**Example**:
```json
{
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Retrieve current weather"
      }
    }
  ]
}
```

# components.schemas.GenerateRequest.properties.tool_choice.description
Specify how tools should be used in the completion. 

**Example values**:
- 'auto': Model decides when to use tools
- 'none': Disable tool usage
- Specific tool name to always use a particular tool

# components.schemas.GenerateRequest.properties.parallel_tool_calls.description
Allow the model to make multiple tool calls in parallel. 

**Example**: True to enable concurrent tool invocations

# components.schemas.GenerateRequest.properties.user.description
Optional identifier for the end-user to help track and monitor API usage. 

**Example**: 'user_123456'

# components.schemas.GenerateRequest.properties.function_call.description
Control how function calls are handled. 

**Example values**:
- 'auto': Default behavior
- 'none': Disable function calls
- Specific function name to force its execution

# components.schemas.GenerateRequest.properties.functions.description
List of function definitions available to the model. 

**Example**:
```json
{
  "functions": [
    {
      "name": "get_current_weather",
      "description": "Get the current weather for a location",
      "parameters": {...}
    }
  ]
}
```

# components.schemas.GenerateRequest.properties.thinking.description
Configuration for enabling Claude's extended thinking. When enabled, responses include thinking content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your max_tokens limit.

**Example**:
```json
{
  'thinking': {
    'type': 'enabled'
    'budget_tokens': '1024'  }
}
```

# components.schemas.GenerateRequest.properties.web_search_options.description
Options for web search integration. 
        **Example**:
        ```json
        web_search_options={
            "search_context_size": "medium"  # Options: "low", "medium", "high"
        }
        ```

# components.schemas.GenerateRequest.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.GenerateRequest.properties.min_score.description
A minimum score threshold for the model to consider a chunk as a valid response. Higher values mean the model will be more conservative and only return chunks that are more similar to the query. Lower values mean the model will be more open to returning chunks that are less similar to the query.

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

# components.schemas.GenerateRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.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.GenerationAsyncRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# components.schemas.GenerationAsyncRequest.properties.text.description
The text prompt that describes the content and style of the video you want to generate. Be specific and detailed to guide the AI in creating your desired video.

# components.schemas.GenerationAsyncRequest.properties.file.description
Initial keyframe image for the video. Upload a single JPEG or PNG file that will serve as the starting point for video generation. This image will influence the style, composition, and initial scene of the generated video.

# components.schemas.GenerationAsyncRequest.properties.duration.description
Length of the generated video in seconds. Minimum duration is 1 second. If not specified, the video will default to 6 seconds.

# components.schemas.GenerationAsyncRequest.properties.fps.description
Frames per second (FPS) of the generated video. Determines the smoothness of the video motion. Default is 24 FPS, which is standard for most video content. Minimum value is 1 FPS.

# components.schemas.GenerationAsyncRequest.properties.dimension.description
Video resolution in the format 'width x height' (e.g., '1280x720'). Default resolution is 1280x720 (720p). Ensure the format is exactly 'width x height' with a lowercase 'x'.

# components.schemas.GenerationAsyncRequest.properties.seed.description
Random seed value that initializes the noise for the generation process. Use this to create consistent or unique variations of your video:
- Range: 0 to 2,147,483,646
- Default: 12
- Changing the seed while keeping other parameters constant will produce a different video that still matches your prompt
- Useful for exploring multiple creative variations of the same concept

# 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.ListChunkRequest.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.ListChunkRequest.properties.limit.description
Specifies the maximum number of chunk IDs to return. Defaults to 10 if not provided.

# components.schemas.ListChunkRequest.properties.with_payload.description
If set to True, includes the full payload of each chunk in the response. By default, only chunk IDs are returned.

# components.schemas.LogoItem.properties.score.description
Confidence score how sure it's this is a real logo.

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.PatchedCustomTokenUpdateRequest.properties.balance.description
Optional remaining credits balance for this Token, if `active_balance` is set to True and the balance reaches 0, this token will become unusable

# components.schemas.PatchedCustomTokenUpdateRequest.properties.active_balance.description
Weither to use the balance field or not.

# components.schemas.PatchedCustomTokenUpdateRequest.properties.balance_reset_amount.description
The amount this token's balance is reset to at the start of each balance_reset_period. Null when balance_reset_period is 'none'.

# components.schemas.PatchedCustomTokenUpdateRequest.properties.balance_reset_period.description
How often this token's balance is reinitialized to balance_reset_amount. 'none' = one-time balance (default, current behaviour).

* `none` - None
* `daily` - Daily
* `weekly` - Weekly
* `monthly` - Monthly

# components.schemas.PatchedPromptHistoryRequest.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PatchedPromptHistoryRequest.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PatchedPromptHistoryRequest.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PatchedPromptHistoryRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PatchedPromptUpdateRequest.properties.name.description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# components.schemas.PatchedPromptUpdateRequest.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PatchedPromptUpdateRequest.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PatchedPromptUpdateRequest.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PatchedPromptUpdateRequest.properties.current_version.description
The production version of the prompt, that is used by default when you call the prompt

# components.schemas.PatchedPromptUpdateRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PatchedWebhookParametersRequest.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.PatchedWebhookParametersRequest.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.PatchedWebhookParametersRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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
* `depth` - Depth
* `reasoning_depth` - Reasoning Depth

# components.schemas.PricingSerialzier.properties.detail_value.description
(Optional) extra value for detailed pricing, eg: 250x250 for resolution

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

# components.schemas.PromptCallRequest.properties.model.description
Optional model override (e.g., 'openai/gpt-4o'). If not provided, the model specified in the prompt will be used.

# components.schemas.PromptCallRequest.properties.prompt_context.description
Variables to inject into the prompt template.
#### Example
```json
{
    "my_variable": "some_value",
}
```

# components.schemas.PromptCallRequest.properties.params.description
Optional params override. If not provided, the default params in the prompt will be used

Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptCallRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models. Overrides the file urls set on the prompt.

# components.schemas.PromptCreate.properties.name.description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# components.schemas.PromptCreateRequest.properties.name.description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# components.schemas.PromptCreateRequest.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PromptCreateRequest.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PromptCreateRequest.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptCreateRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PromptHistory.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PromptHistory.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PromptHistory.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptHistory.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PromptHistoryRequest.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PromptHistoryRequest.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PromptHistoryRequest.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptHistoryRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PromptUpdate.properties.name.description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# components.schemas.PromptUpdate.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PromptUpdate.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PromptUpdate.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptUpdate.properties.current_version.description
The production version of the prompt, that is used by default when you call the prompt

# components.schemas.PromptUpdate.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.PromptUpdateRequest.properties.name.description
The unique identifier for the prompt. Must contain only alphanumeric characters, underscores (_) and hyphens (-). No spaces allowed.

# components.schemas.PromptUpdateRequest.properties.text.description
The text used for the prompt

You can include prompt variables with {{ my_variable }}. These variables are injected when you later call your prompt.

The template language is compatible with jinja2

#### Example
```bash
Translate this word to {{ language }}: {{ word }}
```

# components.schemas.PromptUpdateRequest.properties.model.description
The model used to call the prompt. E.g. openai/gpt-4o

# components.schemas.PromptUpdateRequest.properties.params.description
Params that are passed on to the llm request. See llm chat docs for more details.

#### Example
```json
{
  "temperature": 0.7,
  "max_tokens": 100
}
```

# components.schemas.PromptUpdateRequest.properties.current_version.description
The production version of the prompt, that is used by default when you call the prompt

# components.schemas.PromptUpdateRequest.properties.file_urls.description
Optional list of URLs to images or other files that should be included with the prompt for multimodal models. Files are not supported by all models.

# components.schemas.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

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

# components.schemas.QuestionAnswerAsyncRequest.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.QuestionAnswerAsyncRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. This value, combined with the token count of your prompt, cannot exceed the model's context length.

# components.schemas.ReasoningEffortEnum.description
**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# 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.ServiceTierEnum.description
**Choices**:
- 'auto': Automatically select appropriate tier
- 'default': Use default service configuration

* `auto` - auto
* `default` - default

# 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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# 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.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.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.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.TextToSpeechAsyncRequest.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.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.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.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.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# components.schemas.ThinkingTypeEnum.description
* `enabled` - enabled
* `disabled` - disabled

# components.schemas.TokenTypeEnum.description
* `sandbox_api_token` - Sandbox
* `api_token` - Back

# components.schemas.ToolChoiceEnum.description
* `auto` - auto
* `required` - required
* `none` - none

# components.schemas.TypeDe8Enum.description
* `db` - Db
* `bucket` - Bucket
* `db_vector` - Db Vector
* `ai` - Ai

# components.schemas.UsageTokensDetails.properties.completion_tokens.description
Number of tokens in the generated completion

# components.schemas.UsageTokensDetails.properties.total_tokens.description
Total number of tokens used (prompt + completion)

# components.schemas.UsageTokensDetails.properties.reasoning_tokens.description
Number of reasoning tokens in the prompt

# components.schemas.WebhookParameters.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.WebhookParameters.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.WebhookParameters.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

# components.schemas.WebhookParametersRequest.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.WebhookParametersRequest.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.WebhookParametersRequest.properties.send_webhook_data.description
If set to false the webhook will not contain the result data. Use if your webhook receiver has a request size limit.

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

# 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.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.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.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.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.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.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.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.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.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.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.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.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.imagedeepfake_detectionDeepfakeDetectionDataClass.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.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.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.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.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.nsfw_likelihood.description
An integer representing the likelihood of NSFW content. Higher values indicate a higher likelihood.

# components.schemas.imageexplicit_contentExplicitContentDataClass.properties.nsfw_likelihood_score.description
A floating-point score representing the confidence level of the NSFW likelihood assessment. This is typically a value between 0.0 and 1.0.

# components.schemas.imageexplicit_contentExplicitContentDataClass.properties.items.description
A list of items identified as potentially explicit. Each item contains details of the explicit content detected.

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

# 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.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.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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_recognitionrecognizeFaceRecognitionDetectFaceRequest.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.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.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.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.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.items.description
List of the detected brands logo from the image.

# 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.llmchatChatDataClass.properties.created.description
Unix timestamp for when the completion was created

# components.schemas.llmchatChatDataClass.properties.choices.description
List of chat completion choices generated by the model

# components.schemas.llmchatChatDataClass.properties.usage.description
Usage statistics for the completion request

# components.schemas.llmchatChatDataClass.properties.system_fingerprint.description
Identifier for the system version that processed the request

# components.schemas.llmchatChatDataClass.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.llmchatllmchatChatRequest.properties.messages.description
A list containing all the conversations between the user and the assistant.
Each item in the list should be a dictionary with two keys: 'role' and 'message'.

**role**: Specifies the role of the speaker and can have the values 'user', 'system', 'assistant' or 'tool'.
The system role instructs the way the model should answer, e.g. 'You are a helpful assistant'. The user
role specifies the user query and assistant is the model's response. The tool role is for external tools that
can be used in the conversation.

**message**: A list of dictionaries. Each dictionary in the 'message' list must contain the keys 'type' and 'content'.

#### Structure
- **type**: Specifies the type of content and can be 'image_url' or 'text'.
- **content**: A dictionary with the actual content based on the 'type':
    - If 'type' is 'image_url', 'content' must contain 'image_url' and must not contain 'text'.
    - If 'type' is 'text', 'content' must contain 'text' and must not contain 'image_url'.

#### Example
```json
[
  {
    "role": "user",
    "content": [
        {
          "type": "text",
          "text": "Describe this image"
        },
        {
        "type": "image_url",
        "image_url": {
          "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
          }
        }
    ]
  }
]
```

# components.schemas.llmchatllmchatChatRequest.properties.model.description
The OpenAI model to use for the chat completion. 
This field is required and specifies which language model will process the conversation. 

**Example values**: 'gpt-3.5-turbo', 'gpt-4', 'gpt-4-turbo'

# components.schemas.llmchatllmchatChatRequest.properties.reasoning_effort.description
Optional parameter to control the model's reasoning depth. 
Allows specifying the level of analytical effort in generating responses. 

**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# components.schemas.llmchatllmchatChatRequest.properties.metadata.description
Optional list of metadata associated with the chat request. 
Can be used to provide additional context or tracking information. 

**Example**:
```json
{
  "metadata": [
    {"key": "conversation_id", "value": "chat_12345"},
    {"key": "source", "value": "customer_support"}
  ]
}
```

# components.schemas.llmchatllmchatChatRequest.properties.frequency_penalty.description
Controls repetitiveness of model responses by penalizing frequent tokens. 
Ranges from -2.0 to 2.0. 

**Values**:
- Positive values: Reduce token repetition
- Negative values: Encourage repetition
- 0.0: Default behavior

**Example**: 1.5 to significantly reduce repeated phrases

# components.schemas.llmchatllmchatChatRequest.properties.logit_bias.description
Modify the likelihood of specific tokens appearing in the response. 
A dictionary where keys are token IDs and values are bias scores. 

**Example**:
```json
{
  "logit_bias": {
    "50256": -100,  # Reduce probability of end-of-text token
    "15": 5  # Slightly increase probability of a specific token
  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.logprobs.description
If set to True, returns log probabilities of the most likely tokens. 
Useful for advanced token probability analysis. 

**Example**: True to get detailed token likelihood information

# components.schemas.llmchatllmchatChatRequest.properties.top_logprobs.description
Number of top log probabilities to return with each token. 
Must be between 0 and 20. 

**Example**: 5 to get top 5 most likely tokens for each position

# components.schemas.llmchatllmchatChatRequest.properties.max_completion_tokens.description
Maximum number of tokens to generate in the completion. 
Must be at least 1. 

**Example**: 150 to limit response to approximately 100-150 words

# components.schemas.llmchatllmchatChatRequest.properties.n.description
Number of chat completion choices to generate.

    **Example**: 3 to generate multiple alternative responses

# components.schemas.llmchatllmchatChatRequest.properties.modalities.description
List of supported input/output modalities for the chat. 

**Example**:
```json
{
  "modalities": ["text", "image", "audio"]
}
```

# components.schemas.llmchatllmchatChatRequest.properties.prediction.description
Optional field for storing prediction-related information. 
Flexible dictionary to capture model's predictive metadata. 

**Example**:
```json
{
  "prediction": {
    "confidence_score": 0.85,
    "top_prediction": "response_category"
  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.audio.description
Optional dictionary for audio-related parameters or metadata. 

**Example**:
```json
{
  "audio": {
    "language": "en-US",
    "transcription_format": "srt"
  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.presence_penalty.description
Adjusts likelihood of discussing new topics by penalizing existing tokens. 
Ranges from -2.0 to 2.0. 

**Values**:
- Positive values: Encourage more diverse topics
- Negative values: Keep discussion more focused
- 0.0: Default behavior

**Example**: 1.0 to promote topic diversity

# components.schemas.llmchatllmchatChatRequest.properties.response_format.description
Specify the desired response format for the completion. 

**Example**:
```json
{
  "response_format": {
    "type": "json_object",
    "schema": {...}
  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.seed.description
Set a seed for deterministic sampling to reproduce consistent results. 

**Example**: 42 for a reproducible random generation process

# components.schemas.llmchatllmchatChatRequest.properties.service_tier.description
Select the service tier for the API request. 

**Choices**:
- 'auto': Automatically select appropriate tier
- 'default': Use default service configuration

* `auto` - auto
* `default` - default

# components.schemas.llmchatllmchatChatRequest.properties.stop.description
List of strings that will cause the model to stop generating. 

**Example**:
```json
{
  "stop": ["\n", "Human:", "AI:"]
}
```

# components.schemas.llmchatllmchatChatRequest.properties.stream.description
If True, returns tokens as they are generated in a streaming format. 
Default is False. 

**Example**: True for real-time token streaming

# components.schemas.llmchatllmchatChatRequest.properties.stream_options.description
Additional configuration for streaming responses. 

**Example**:
```json
{
  "stream_options": {
    "include_usage": true
  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.temperature.description
Controls randomness in token selection. 
Ranges from 0.0 to 2.0. 

**Values**:
- 0.0: Most deterministic, focused responses
- 1.0: Balanced randomness
- 2.0: Most creative, unpredictable responses

**Example**: 0.7 for a good balance of creativity and focus

# components.schemas.llmchatllmchatChatRequest.properties.top_p.description
Nucleus sampling threshold for token selection. 
Ranges from 0.0 to 1.0. Default is 1.0. 

**Values**:
- 1.0: Consider all tokens
- Lower values: More focused, deterministic sampling

**Example**: 0.9 to select from top 90% most probable tokens

# components.schemas.llmchatllmchatChatRequest.properties.tools.description
List of tools or function definitions available to the model. 

**Example**:
```json
{
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Retrieve current weather"
      }
    }
  ]
}
```

# components.schemas.llmchatllmchatChatRequest.properties.tool_choice.description
Specify how tools should be used in the completion. 

**Example values**:
- 'auto': Model decides when to use tools
- 'none': Disable tool usage
- Specific tool name to always use a particular tool

# components.schemas.llmchatllmchatChatRequest.properties.parallel_tool_calls.description
Allow the model to make multiple tool calls in parallel. 

**Example**: True to enable concurrent tool invocations

# components.schemas.llmchatllmchatChatRequest.properties.user.description
Optional identifier for the end-user to help track and monitor API usage. 

**Example**: 'user_123456'

# components.schemas.llmchatllmchatChatRequest.properties.function_call.description
Control how function calls are handled. 

**Example values**:
- 'auto': Default behavior
- 'none': Disable function calls
- Specific function name to force its execution

# components.schemas.llmchatllmchatChatRequest.properties.functions.description
List of function definitions available to the model. 

**Example**:
```json
{
  "functions": [
    {
      "name": "get_current_weather",
      "description": "Get the current weather for a location",
      "parameters": {...}
    }
  ]
}
```

# components.schemas.llmchatllmchatChatRequest.properties.thinking.description
Configuration for enabling Claude's extended thinking. When enabled, responses include thinking content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your max_tokens limit.

**Example**:
```json
{
  'thinking': {
    'type': 'enabled'
    'budget_tokens': '1024'  }
}
```

# components.schemas.llmchatllmchatChatRequest.properties.web_search_options.description
Options for web search integration. 
        **Example**:
        ```json
        web_search_options={
            "search_context_size": "medium"  # Options: "low", "medium", "high"
        }
        ```

# components.schemas.multimodalchatChatDataClass.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.multimodalchatChatRequest.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.multimodalchatChatRequest.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.multimodalchatChatRequest.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.multimodalchatChatRequest.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.multimodalchatChatRequest.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.multimodalchatChatRequest.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.multimodalchatChatRequest.properties.messages.description
A list containing all the conversations between the user and the assistant. Each item in the list should be a dictionary with two keys: 'role' and 'message'.

**role**: Specifies the role of the speaker and can have the values 'user' or 'assistant'.

**message**: A list of dictionaries. Each dictionary in the 'message' list must contain the keys 'type' and 'content'.

#### Structure
- **type**: Specifies the type of content and can be 'media_url', 'media_base64', or 'text'.
- **content**: A dictionary with the actual content based on the 'type':
  - If 'type' is 'media_url', 'content' must contain 'media_url' and must not contain 'media_base64' or 'text'.
  - If 'type' is 'media_base64', 'content' must contain 'media_base64' and must not contain 'media_url' or 'text'.
  - If 'type' is 'text', 'content' must contain 'text' and must not contain 'media_url' or 'media_base64'.

#### Example
```json
[
  {
    'role': 'user',
    'content': [
      {
        'type': 'text',
        'content': {'text': 'Describe this image'}
      },
      {
        'type': 'media_url',
        'content': {
           'media_url': 'https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg',
           'media_type': 'image/jpeg'}
      },
    ]
  }
]
```

# components.schemas.multimodalchatChatRequest.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.multimodalchatChatRequest.properties.temperature.description
Controls the creativity of the model's responses. Higher values (up to 2) make the output more random, while lower values make it more focused and deterministic. A value of 0 (argmax sampling) is useful for scenarios requiring precise answers.

# components.schemas.multimodalchatChatRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. This value, combined with the token count of your prompt, cannot exceed the model's context length.

# components.schemas.multimodalchatChatRequest.properties.stop_sequences.description
A list of sequences where the model will stop generating further tokens. Useful for controlling response length and format.

# components.schemas.multimodalchatChatRequest.properties.top_k.description
Limits the sampling pool to the top K options for each token. Setting this to a lower value can make the output more focused and deterministic.

# components.schemas.multimodalchatChatRequest.properties.top_p.description
Enables nucleus sampling, where the model considers the smallest number of tokens whose cumulative probability is at least top_p. This allows for a dynamic selection of tokens based on probability, offering a balance between focus and creativity.

# components.schemas.multimodalchatChatRequest.properties.reasoning_effort.description
Optional parameter to control the model's reasoning depth. 
Allows specifying the level of analytical effort in generating responses. 

**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.textchatChatRequest.properties.tool_choice.description
`auto`: the model will choose to use tools if needed, `required`: force model to use any of the available tools, `none`: force model to not select a tool

* `auto` - auto
* `required` - required
* `none` - none

# components.schemas.textchatChatRequest.properties.available_tools.description
A list of tools the model may generate the right arguments for.

# components.schemas.textchatChatRequest.properties.tool_results.description
List of results obtained from applying the tool_call arguments to your own tool.

# components.schemas.textchatChatRequest.properties.reasoning_effort.description
Optional parameter to control the model's reasoning depth. 
Allows specifying the level of analytical effort in generating responses. 

**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# 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.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.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.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.textchatChatStreamRequest.properties.tool_choice.description
`auto`: the model will choose to use tools if needed, `required`: force model to use any of the available tools, `none`: force model to not select a tool

* `auto` - auto
* `required` - required
* `none` - none

# components.schemas.textchatChatStreamRequest.properties.available_tools.description
A list of tools the model may generate the right arguments for.

# components.schemas.textchatChatStreamRequest.properties.tool_results.description
List of results obtained from applying the tool_call arguments to your own tool.

# components.schemas.textchatChatStreamRequest.properties.reasoning_effort.description
Optional parameter to control the model's reasoning depth. 
Allows specifying the level of analytical effort in generating responses. 

**Choices**:
- 'low': Minimal reasoning, quick responses
- 'medium': Balanced reasoning approach
- 'high': In-depth, comprehensive reasoning

**Example**: 'high' for complex problem-solving tasks

* `low` - low
* `medium` - medium
* `high` - high

# 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.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.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.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.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.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.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.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.textembeddingsEmbeddingsRequest.properties.dimensions.description
 parameter to control the size of the output embedding vecto

# 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.videodeepfake_detection_asyncDeepfakeDetectionAsyncDataClass.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.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.videogeneration_asyncGenerationAsyncDataClass.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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_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.videoquestion_answerQuestionAnswerRequest.properties.file_url.description
File **URL** to analyse to be used with with *content-type*: **application/json**.

# components.schemas.videoquestion_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.videoquestion_answerQuestionAnswerRequest.properties.max_tokens.description
The maximum number of tokens to generate in the completion. This value, combined with the token count of your prompt, cannot exceed the model's context length.

# components.schemas.videoquestion_answer_asyncQuestionAnswerAsyncDataClass.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.videoshot_change_detection_asyncShotChangeDetectionAsyncDataClas.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